Physical Therapy- Journal of the APTA, Volume 90, Issue 2 (February 2010)

February 2010  Volume 90  Number 2

Research Report 149

Sit-to-Stand Movement in Patients With Total Knee Arthroplasty

Jacquelin Perry Special Issue: Gait Rehab 157

Principles of Gait

177

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait

187

Split-Belt and Other Locomotor Adaptation Paradigms

224

Daily Stepping in Individuals With Motor Incomplete SCI

240

Mental Practice for Relearning Locomotor Skills

252

Cognitive Load and Dual-Task Performance During Locomotion Poststroke

261

A Treatment for Adults With Stiff Knee Gait

269

Strength Training and Gait Kinematics

196

Meaningful Gait Speed Improvement Poststroke

280

Evidence-Based Physical Therapy for Gait Disorders

209

Treadmill Training With Body-Weight Support After Stroke

289

Joint Kinematics and Muscle Demands in Elliptical Training and Walking

cover_02.10.indd 1

1/19/10 5:58 PM

Business Growing?

Referen

If your business is growing, you need a solid professional liability plan to help protect your interests so your business will flourish.

ce

A 3 D B A 2code PD

The Professional Liability Insurance program offered through HPSO gives you 24/7 protection for your business, assets, and employees against covered allegations of professional malpractice, including:

For Physical Therapy and Rehabilitation group Practices

 Professional Liability Insurance  Legal Defense for covered claims  License Protection  Deposition Representation  Defendant Expense Benefit for lost wages  And more

Optional Coverages:  General Liability  Additional Insured Endorsement  Medical Director Endorsement  Additional Office Locations

Start protecting your business interests first. To find out more about coverage offered through HPSO:

Call 1-888-288-3534 • www.hpso.com/ptj2

This program is underwritten by American Casualty Company of Reading, Pennsylvania, a CNA company, and is offered through the Heatlhcare Providers Service Organization Purchasing Group. Coverages, rates and limits may differ or may not be available in all states. All products and services are subject to change without notice. This material is for illustrative purposes only and is not a contract. It is intended to provide a general overview of the products and services offered. Only the policy can provide the actual terms, coverages, amounts, conditions and exclusions. CNA is a service mark and trade name registered with the U.S. Patent and Trademark Office. Healthcare Providers Service Organization is a division of Affinity Insurance Services, Inc.; in CA (License #0795465), MN & OK, AIS Affinity Insurance Agency, Inc.; and in NY, AIS Affinity Insurance Agency. ©2010 Affinity Insurance Services, Inc. PTJ0110

Ads_2.10.indd 2

1/13/10 3:42 PM

PT 2010 is the national event for physical therapy— featuring 14 tracks of advanced programming: • • • • • • • • • •

Hospital-based Pediatrics Manual Therapy Older Adults Postsurgical Rehabilitation Practice Management Sports and Fitness Tech Savvy Traumatic Brain Injury Ultrasound Imaging And More!

Plus, 3 brand NEW full-day Learning Labs focused on: • Manual Therapy • Postsurgical Rehabilitation • Ultrasound Imaging

www.apta.org/AnnualConference

Leadership [ Strength [ Coordination [ Momentum [ Balance [ Community Ads_2.10.indd 137

1/13/10 4:29 PM

Physical Therapy Journal of the American Physical Therapy Association

Editorial Office Managing Editor / Associate Director of Publications: Jan P. Reynolds, [email protected] PTJ Online Editor / Assistant Managing Editor: Steven Glaros Associate Editor: Stephen Brooks, ELS Production Manager: Liz Haberkorn

Editor in Chief Rebecca L. Craik, PT, PhD, FAPTA, Philadelphia, PA [email protected]

Deputy Editor in Chief Daniel L. Riddle, PT, PhD, FAPTA, Richmond, VA

Editor in Chief Emeritus Jules M. Rothstein, PT, PhD, FAPTA (1947–2005)

Steering Committee

Permissions / Reprint Coordinator: Michele Tillson

Anthony Delitto, PT, PhD, FAPTA (Chair), Pittsburgh, PA; J. Haxby Abbott, PhD, MScPT, DipGrad, FNZCP, Dunedin, New Zealand; Joanell Bohmert, PT, MS, Mahtomedi, MN; Alan M. Jette, PT, PhD, FAPTA, Boston, MA; Charles Magistro, PT, FAPTA, Claremont, CA; Ruth B. Purtilo, PT, PhD, FAPTA, Boston, MA; Julie Whitman, PT, DSc, OCS, Westminster, CO

Advertising Manager: Julie Hilgenberg

Editorial Board

Manuscripts Coordinator: Karen Darley

Director of Publications: Lois Douthitt

APTA Executive Staff Senior Vice President for Communications: Felicity Feather Clancy Chief Financial Officer: Rob Batarla Chief Executive Officer: John D. Barnes

Advertising Sales Ad Marketing Group, Inc 2200 Wilson Blvd, Suite 102-333 Arlington, VA 22201 703/243-9046, ext 102 President / Advertising Account Manager: Jane Dees Richardson

Board of Directors President: R. Scott Ward, PT, PhD Vice President: Paul A. Rockar Jr, PT, DPT, MS Secretary: Babette S. Sanders, PT, MS Treasurer: Connie D. Hauser, PT, DPT, ATC Speaker of the House: Shawne E. Soper, PT, DPT, MBA Vice Speaker of the House: Laurita M. Hack, PT, DPT, MBA, PhD, FAPTA Directors: Sharon L. Dunn, PT, PhD, OCS; Kevin L. Hulsey, PT, DPT, MA; Dianne V. Jewell, PT, DPT, PhD, CCS, FAACVPR; Aimee B. Klein, PT, DPT, DSc, OCS; Kathleen K. Mairella, PT, DPT, MA; Stephen C.F. McDavitt, PT, DPT, MS, FAAOMPT; Lisa K. Saladin, PT, PhD; Mary C. Sinnott, PT, DPT, MEd; Nicole L. Stout, PT, MPT, CLT-LANA

Rachelle Buchbinder, MBBS(Hons), MSc, PhD, FRACP, Malvern, Victoria, Australia; W. Todd Cade, PT, PhD, St. Louis, MO; James Carey, PT, PhD, Minneapolis, MN; John Childs, PT, PhD, Schertz, TX; Charles Ciccone, PT, PhD, FAPTA (Continuing Education), Ithaca, NY; Joshua Cleland, PT, DPT, PhD, OCS, FAAOMPT, Concord, NH; Janice J. Eng, PT/OT, PhD, Vancouver, BC, Canada; James C. (Cole) Galloway, PT, PhD, Newark, DE; Steven Z. George, PT, PhD, Gainesville, FL; Kathleen Gill-Body, PT, DPT, NCS, Boston, MA; Paul J.M. Helders, PT, PhD, PCS, Utrecht, The Netherlands; Maura D. Iversen, PT, ScD, MPH, Boston, MA; Diane U. Jette, PT, DSc, Burlington, VT; Christopher Maher, PT, PhD, Lidcombe, NSW, Australia; Christopher J. Main, PhD, FBPsS, Keele, United Kingdom; Kathleen Kline Mangione, PT, PhD, GCS, Philadelphia, PA; Patricia Ohtake, PT, PhD, Buffalo, NY; Carolynn Patten, PT, PhD, Gainesville, FL; Linda Resnik, PT, PhD, OCS, Providence, RI; Kathleen Sluka, PT, PhD, Iowa City, IA; Patty Solomon, PT, PhD, Hamilton, Ont, Canada

Statistical Consultants Steven E. Hanna, PhD, Hamilton, Ont, Canada; John E. Hewett, PhD, Columbia, MO; Hang Lee, PhD, Boston, MA; Xiangrong Kong, PhD, Baltimore, MD; Paul Stratford, PT, MSc, Hamilton, Ont, Canada; Samuel Wu, PhD, Gainesville, FL

Committee on Health Policy and Ethics Linda Resnik, PT, PhD, OCS (Chair), Providence, RI; Janet Freburger, PT, PhD, Chapel Hill, NC; Alan Jette, PT, PhD, FAPTA, Boston, MA; Michael Johnson, PT, PhD, OCS, Philadelphia, PA; Justin Moore, PT, DPT, Alexandria, VA; Ruth Purtilo, PT, PhD, FAPTA, Boston, MA

Linking Evidence And Practice Advisory Group Rachelle Buchbinder, MBBS(Hons), MSc, PhD, FRACP, Malvern, Victoria, Australia (Co-Chair); Diane U. Jette, PT, DSc, Burlington, VT (Co-Chair); W. Todd Cade, PT, PhD, St. Louis, MO; Christopher Maher, PT, PhD, Lidcombe, NSW, Australia; Kathleen Kline Mangione, PT, PhD, GCS, Philadelphia, PA; David Scalzitti, PT, DPT, PhD, Alexandria, VA

The Bottom Line Committee Eric Robertson, PT, DPT, OCS; Joanell Bohmert, PT, MS; Lara Boyd, PT, PhD; James Cavanaugh IV, PT, PhD, NCS; Todd Davenport, PT, DPT, OCS; Ann Dennison, PT, DPT, OCS; William Egan, PT, DPT, OCS; Helen Host, PT, PhD; Evan Johnson, PT, DPT, MS, OCS, MTC; M. Kathleen Kelly, PT, PhD; Catherine Lang, PT, PhD; Tara Jo Manal, PT, MPT, OCS, SCS; Kristin Parlman, PT, DPT, NCS; Susan Perry, PT, DPT, NCS; Maj Nicole H. Raney, PT, DSc, OCS, FAAOMPT; Rick Ritter, PT; Kathleen Rockefeller, PT, MPH, ScD; Michael Ross, PT, DHS, OCS; Katherine Sullivan, PT, PhD; Mary Thigpen, PT, PhD; Jamie Tomlinson, PT, MS; Brian Tovin, DPT, MMSc, SCS, ATC, FAAOMPT; Nancy White, PT, MS, OCS; Julie Whitman, PT, DSc, OCS

140 ■ Physical Therapy Volume 90 Number 2

Masthead_2.10.indd 140

February 2010

1/13/10 4:23 PM

Subscriptions

Physical Therapy (PTJ) (ISSN 00319023) is published monthly by the American Physical Therapy Association (APTA), 1111 North Fairfax Street, Alexandria, VA 22314-1488, at an annual subscription rate of $12 for members, included in dues. Nonmember rates are as follows: Individual (inside USA)— $99; individual (outside USA)—$119 surface mail, $179 air mail. Institutional (inside USA)—$129; institutional (outside USA)—$149 surface mail, $209 air mail. Periodical postage is paid at Alexandria, VA, and at additional mailing offices. Postmaster: Send address changes to Physical Therapy, 1111 North Fairfax Street, Alexandria, VA 22314-1488. Single copies: $15 USA, $15 outside USA; with the exception of January 2001: $50 USA, $70 outside USA. All orders payable in US currency. No replacements for nonreceipt after a 3-month period has elapsed. Canada Post International Publications Mail Product Sales Agreement No. 0055832.

Members and Subscribers Send changes of address to: APTA, Attn: Membership Dept, 1111 North Fairfax St, Alexandria, VA 22314-1488. Subscription inquiries: 703/684-2782, ext 3124. PTJ is available in a special format for readers who are visually impaired. For information, contact APTA’s Membership Department at 703/684-2782, ext 3124.

Mission Statement

Physical Therapy (PTJ) engages and inspires an international readership on topics related to physical therapy. As the leading international journal for research in physical therapy and related fields, PTJ publishes innovative and highly relevant content for both clinicians and scientists and uses a variety of interactive approaches to communicate that content, with the expressed purpose of improving patient care.

Readers are invited to submit manuscripts to PTJ. PTJ’s content—including editorials, commentaries, letters, and book reviews—represents the opinions of the authors and should not be attributed to PTJ or its Editorial Board. Content does not reflect the official policy of APTA or the institution with which the author is affiliated, unless expressly stated.

Masthead_2.10.indd 141

Full-text articles are available for free at ptjournal.apta.org 12 months after the publication date. Full text also is provided through DataStar, Dialog, EBSCOHost Academic Search, Factiva, InfoTrac, ProFound, and ProQuest.

Reprints

PTJ Online at ptjournal.apta.org PTJ Online is available via RSS feeds. PTJ posts articles ahead of print and rapid reader responses to articles. Articles, letters to the editor, and editorials are available in full text starting with Volume 79 (1999) and in searchable PDF format starting with Volume 60 (1980). Entire issues are available online beginning with Volume 86 (2006) and include additional data, video clips, and podcasts.

Indexing and Document Delivery

Readers should direct requests for reprints to the corresponding author of the article. Students and other academic customers may receive permission to reprint copyrighted material from this publication by contacting the Copyright Clearance Center Inc, 222 Rosewood Dr, Danvers, MA 01923. Authors who want reprints should contact June Billman, Cadmus Communications, at 800/4875625, or [email protected]. Nonacademic institutions needing reprint permission information should go to ptjournal.apta.org/misc/terms.dtl.

Advertising

PTJ is indexed and/or abstracted by Abridged Index Medicus, Abstracts of Health Care Management Studies, AgeLine, Allied and Complementary Medicine Database (AMED), Bibliography of Developmental Medicine and Child Neurology, Current Contents, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE/Exerpta Medica, Exceptional Child Education Resources, Focus on: Sports Science and Medicine, General Science Index (GSI), Health Index, Hospital and Health Administration Index, Index Medicus, Inpharma Weekly, International Nursing Index, ISR, Medical & Surgical Dermatology, MEDLINE, Neuroscience Citation Index, Personal Alert: Automatic Subject Citation Alert (ASCA), Pharmacoeconomics and Outcomes News, Physical Education Index, Reactions Weekly, RECAL Bibliographic Database, Science Citation Index (SCI), Social Sciences Citation Index (SSCI), and SportsS. Article abstracts are available online at ptjournal.apta.org (1980 through present) and via DataStar, Dialog, FirstSearch, Information Access, Ovid

February 2010

Technologies. Ingenta provides online document delivery for articles published since September 1988.

Advertisements are accepted by PTJ when they conform to the ethical standards of the American Physical Therapy Association. PTJ does not verify the accuracy of claims made in advertisements, and acceptance does not imply endorsement by PTJ or the Association. Acceptance of advertisements for professional development courses addressing advanced-level competencies in clinical specialty areas does not imply review or endorsement by the American Board of Physical Therapy Specialties.

Statement of Nondiscrimination APTA prohibits preferential or adverse discrimination on the basis of race, creed, color, gender, age, national or ethnic origin, sexual orientation, disability, or health status in all areas including, but not limited to, its qualifications for membership, rights of members, policies, programs, activities, and employment practices. APTA is committed to promoting cultural diversity throughout the profession.

Volume 90 Number 2 Physical Therapy ■ 141

1/13/10 4:08 PM

Editorial Jacquelin Perry, 9 Feet Tall

“C

ould PTJ be the vehicle for a Festschrift for Jacquelin Perry?” That’s the question that Sara Mulroy, PT, PhD, Director of the Pathokinesiology Laboratory at Rancho Los Amigos National Rehabilitation Center, asked me in 2008. Festschrift is a German term defined as a celebration publication or a celebratory piece of writing,1 usually honoring a respected academic. Of course I said yes. For Dr Perry— anything. Figure 1.

Dr Jacquelin Perry

Sara and Janice Eng, PT/OT, PhD, PTJ Editorial Board Member and Professor of Physical Therapy, University of British Columbia, enthusiastically took on the huge task of developing this special issue, titled “Jacquelin Perry Special Issue: Stepping Forward With Gait Rehabilitation,” as our Festschrift. I am very grateful to both Sara and Janice for developing the vision for this special issue, soliciting the manuscripts, and overseeing the peer review. Although we are 1 year too late to honor Dr Perry’s 90th birthday, we hope that she will forgive us and accept our thanks for her amazing contributions to the body of knowledge that underpins our understanding of human movement. For those who might not know about Dr Perry (Fig. 1), I can offer some facts. She was born in Denver, Colorado, in 1918. She started her professional life as a physical therapist, receiving her training at Walter Reed Army Hospital from 1940 to 1941 and practicing in the US Army for 5 years. In 1950, she graduated from the University of California, San Francisco, as a physician and became board certified as an orthopedic surgeon in 1958. At Ranchos Los Amigos, she was Chief of the Pathokinesiology Service for 30 years. I have reviewed many curriculum vitae, but none has humbled me more than Dr Perry’s— she has more than 400 publications, hundreds of invited presentations, and many, many awards. It might surprise you to know that this USC Professor of Orthopaedics also is a recipient of the American Physical Therapy Association’s (APTA) Golden Pen Award, Helen J. Hislop Award for Outstanding Contributions to Professional Literature, and honorary lifetime membership. She also received the Steven J. Rose Excellence in Research Award from APTA’s Orthopaedic Section. The name Perry and the word movement are almost synonymous—we hear “Perry,” and we think analysis of normal and abnormal movement of the trunk, upper extremity, and lower extremity and the restoration of movement through surgery, bracing, electrical stimulation, and exercise. Dr Perry has addressed movement disorders in patients with spinal cord injury, cerebral palsy, stroke, amputation, peripheral nerve injury, polio, limb fracture, arthritis—to name a few.

To comment, submit a Rapid Response to this editorial posted online at ptjournal.apta.org.

Dr Perry embraced the principles associated with the International Classification of Functioning, Disability and Health2 well before the World Health Organization articulated them so clearly. Her body of work certainly emphasizes the need to know which body structures are associated with disability and how body function is influenced by changes in the structures. She also highlighted the need to understand the relationship between a person’s activities and participation and limitations. As far as I know, every physical therapist student is still taught to observe human gait systematically using the observational gait analysis techniques that Dr Perry helped to develop. Her textbook, Gait Analysis: Normal and Pathological Function,3 and her innumerable publications convey the importance of measuring changes in body structures and function and the importance of contrasting the changes with a normative standard. She has helped us to understand

142 ■ Physical Therapy Volume 90 Number 2

editorial_Craik_2.10.indd 142

February 2010

1/19/10 6:02 PM

Editorial Figure 2. Infinite Dreams. Artist: Jose Robledo, Rancho Los Amigos National Rehabilitation Center. Courtesy of the Association of Mouth and Foot Painting Artists Worldwide (www.mfpausa.com). To view more art from the patients of Rancho Los Amigos, visit ptjournal.apta.org.

why we need to describe kinematics, kinetics, and bioenergetics as we work to discern the cause of movement disorders. Her work on classifying the walking handicap in people with stroke pushed us to understand the need to reach a certain quantifiable gait speed to achieve community ambulation.4 Children and adults have come from around the world to the gait analysis laboratory at Rancho Los Amigos (Fig. 2) for analysis that uses quantitative techniques. The “fancy tools” have always been coupled, however, with expertise and a driving passion to improve movement so that it enhances functional performance and societal participation. It’s not surprising that, among Dr Perry’s many accolades, there exists the Jacquelin Perry NeuroTrauma Institute and Rehabilitation Center at Rancho Los Amigos, dedicated in 1996 and one of the most advanced rehabilitation facilities in the United States. Nor is it surprising that Dr Perry received a lifetime achievement award from the Gait and Clinical Analysis Movement Society. On a personal level, when I think about Dr Perry, I think about professionalism and her commitment to the maturation of the physical therapy profession. I was employed at the Krusen Research Center at Moss Rehab Hospital in Philadelphia early in my career. This center, like Rancho Los Amigos, was funded federally as a bioengineering research center. The mission of these centers (which included Emory University, New York University, and Northwestern University, among others) was to develop engineering solutions to promote improved physical rehabilitation outcomes. Physicians, physical therapists, occupational therapists, psychologists, and speech pathologists worked with the engineers in these centers to develop innovations such as electromyography and force biofeedback devices, the early versions of functional electrical stimulation devices, improved motion analysis systems, and improved prosthetic and orthotic devices. The bioengineering research centers met annually to share their progress, and it was at one of these conferences that I met Jacquelin Perry in the late 1970s. Looking back, I could swear that she was at least 9 feet tall. She had such a commanding presence. She demanded justification from each presenter, and I thought she was most February 2010

editorial_Craik_2.10.indd 143

Volume 90 Number 2 Physical Therapy ■ 143

1/14/10 4:30 PM

Editorial tough on the physical therapists. She challenged our research results and probed our expertise. It was clear that she was a powerhouse who had earned the respect of her colleagues, and we were inspired to earn her respect. My path crossed hers many times during the ensuing years, but I have especially wonderful memories of my encounter with her in 2007. During a visit to Rancho Los Amigos, I sat in with Dr Perry as she examined a child with cerebral palsy who had an equinovarus deformity. Although Dr Perry was not 9 feet tall and had become very soft spoken, her presence was even more compelling to me than the first time I met her. She combined the data that Sara Mulroy had collected from the gait lab with her own physical examination and the social history provided by the child’s mother to make a decision that she deemed best based on her expertise and the child’s goals. The process appeared flawless, and the mutual respect and shared decision making between Dr Perry and Sara were palpable. Although I still quaked when she asked me to recall anatomy and facts about gait and when she asked my opinion about her decision, I was honored to be in the presence of the 89-year-old master clinician who was anxious to discuss her clinical reasoning. Later, during lunch, I gained insight into her younger, gruff, but extremely professional persona when she recounted tales of her career as a physical therapist in the Army, of being the only woman in her medical school class, and of being one of a very few women in orthopedic medicine. I also learned more about her commitment to the profession of physical therapy. I urge each of you to read her mid-1960s articles on professionalism5 and the contribution of the physical therapist to medicine,6 which will help you to understand the basis of my awe. Although some of the content is controversial in contemporary practice, much of what she said then is extremely relevant today. For example, in the article about professionalism, she wrote: Students must be challenged to think, to analyze and to solve problems. They must be encouraged to form opinions and to be able to support them when opposed. They must develop a sense of responsibility to create and organize —not just learn to do as told.5(p434)

When I look back on my encounters with Dr Perry, I recognize that she was always being a mentor, encouraging me—and thousands of others—to demonstrate our knowledge and skills, offer our opinions, and ensure that our opinions were developed from sound reasoning. Dr Perry, thank you for your numerous and sustained contributions to the science that underpins our understanding of human movement and for your advocacy for the profession of physical therapy. In your curriculum vitae, we recognize the names of numerous physical therapists, biomechanists, engineers, and physicians with whom you have worked. Many of your physical therapist coauthors have gone on to become outstanding scientists and master clinicians themselves, and you have played a vital role in their development. Your influence, however, goes well beyond those with whom you have worked and the people whom they, in turn, have influenced. You have taught thousands of physical therapist students through your systematic and comprehensive writings about diagnosis and intervention for movement disorders. And you have restored functional performance to patients around the world. I hope that “Stepping Forward With Gait Rehabilitation” inspires our readers to “step forward” in the same way that you inspired my colleagues and me to do. Rebecca L. Craik, PT, PhD, FAPTA Editor in Chief 144 ■ Physical Therapy Volume 90 Number 2

editorial_Craik_2.10.indd 144

February 2010

1/19/10 6:03 PM

Editorial

References 1 Festschrift [Wikipedia entry]. Available at: http://en.wikipedia.org/wiki/Festschrift. Accessed August 1, 2009. 2 World Health Organization, ICF Application Areas. Available at: http://www.who.int/classifications/icf/ appareas/en/index.html. Accessed January 11, 2010. 3 Perry J. Gait Analysis: Normal and Pathological Function. Thorofare, NJ: Slack Inc; 1992. 4 Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke. 1995;26:982–989. 5 Perry J. Professionalism in physical therapy. Phys Ther. 1964;44:429–434. 6 Perry J. The contribution of the physical therapist to medicine. Phys Ther. 1965;45:1033–1041. [DOI: 10.2522/ptj.2010.90.2.142]

Physical Therapy at CSM 2010 Reserve These Dates and Times for PTJ Sessions at CSM 2010 Stepping Forward With Gait Rehabilitation Friday, February 19

8:00–11:00 am

0.30 CEUs

Researchers who contributed to PTJ’s Jacquelin Perry Special Issue (February) share the highlights of their work and demonstrate cutting-edge and future directions in gait assessment and rehabilitation. Get a crash course in new knowledge related to theoretical frameworks; insights from a variety of gait paradigms; measurement strategies, such as accelerometry for measuring community ambulation in stroke and ambulatory self-efficacy in frail older adults; and gait applications such as virtual reality, mental practice, and body-weight–supported treadmill training. You’ll also identify exciting opportunities in both research and practice. This symposium honors Dr Jacquelin Perry and her many invaluable contributions to the field of gait rehabilitation over more than 40 years. Led by Special Issue Editors Janice Eng, PT/OT, PhD, and Sara Mulroy, PT, PhD. Speakers: Diane Damiano, PT, PhD; Arthur Kuo, PhD; Francine Malouin, PT, PhD.

How to Design and Conduct RCTs: Real-World Considerations Friday, February 19 4:00–6:45 pm

0.28 CEUs

Ever wish you could consult with a group of experienced investigators who have successfully conducted randomized trials and published the results? Wish granted! At this session, you have the undivided attention of an international panel of both physical therapist and non–physical therapist researchers who will share their strategies with you. Benefit from concrete examples and small-group discussion that will focus not only on design but on some of the key issues involved in actually conducting trials. Led by Editorial Board Members Rachelle Buchbinder, MBBS(Hons), MSc, PhD, FRACP, and Christopher G. Maher, PT, PhD.

PTJ Lunch for Authors and Reviewers Saturday, February 20

12:00–2:00 pm

0.25 CEUs

If you’re an author or a reviewer, you work hard! PTJ salutes you! Take advantage of the collective expertise of PTJ’s Editorial Board; come with your questions and your appetite. Authors want to “get it right,” reduce review time, and get published; reviewers want to enhance their evaluative skills, use their time efficiently, and build their scholarly contributions. Discuss the challenges, and learn new strategies. Led by Editor-in-Chief Rebecca Craik, PT, PhD, FAPTA; Editorial Board Member Patricia Ohtake, PT, PhD; and members of PTJ’s Editorial Board.

CSM 2010

SAN DIEGO

February 2010

editorial_Craik_2.10.indd 145

Feb ruary 17-20

American Physical Therapy Association’s Combined Sections Meeting

Volume 90 Number 2 Physical Therapy ■ 145

1/19/10 6:07 PM

Editorial Stepping Forward With Gait Rehabilitation

P

eople receiving rehabilitation often say that walking is their most important goal for recovery.1,2 With this in mind, it’s not surprising that physical therapists spend much time and effort assessing and retraining walking with their patients.3 Walking ability has major implications for health: poor walking performance is a predictor for heart disease,4 discharge to nursing homes,5 and osteoporosis6 and increases probability of death in older adults.4 Many health-related quality-of-life measures have a component of walking. We are delighted to showcase gait rehabilitation in this PTJ special issue. Gait assessment and treatment are a core competency for every physical therapist professional education program. This issue is dedicated to Dr Jacquelin Perry for her pioneering work in the field of gait analysis and gait rehabilitation, which has influenced the research and practice of so many physical therapists (see Dr Craik’s tribute to Dr Perry on page 142). The issue covers gait for several populations, including older adults and people with stroke, incomplete spinal cord injury, Parkinson disease, and cerebral palsy. All 12 articles highlight new advances and future directions in gait assessment and rehabilitation. We begin with a thought-provoking paper by Kuo and Donelan.7 They provide data that refute gait theories that have stood for more than 50 years—theories that many of us learned in school. The authors introduce us to the concept of dynamic gait models and the step-to-step transition cost of human walking. The testing and refining of these theories will serve to develop and advance the field of gait research and the clinical applications of that research. During the past decade, we have witnessed an explosive growth in health care technology. Mulroy et al8 demonstrate that a task-specific, lower-extremity training program that includes body-weight–supported treadmill training can improve walking speed and that these changes in walking speed are related to improvements in gait biomechanics. The data of Burnfield et al9 suggest that an elliptical trainer—technology that is available in your local community—might be a valid modality for training gait. Namdari et al10 use advanced surgical procedures involving a muscle transfer to improve stiff knee gait due to brain injury. They emphasize that appropriate surgical interventions rely on a comprehensive understanding of knee kinematics and kinetics during walking. Listen to the podcast of the “Stepping Forward With Gait Rehabilitation” Symposium recorded at APTA Combined Sections Meeting, San Diego.

To comment, submit a Rapid Response to this editorial posted online at ptjournal.apta.org.

Some technological advances in gait may seem futuristic, but, indeed, they are here today: • Imagine watching a child with cerebral palsy walk and then using a computer simulation to determine which intervention would improve the child’s walking. After inputting the data of a child with crouch gait, Damiano et al11 use a computer simulation to help explain how a strengthening program can improve walking by altering the gait dynamics. Musculoskeletal models and simulations of walking show potential in identifying which changes in impairment are critical for walking. • Imagine walking on a treadmill where the right and left legs move at different speeds. Reisman et al12 use split-belt treadmill technology to understand the extent to which the injured nervous system is capable of producing normal movement patterns.

146 ■ Physical Therapy Volume 90 Number 2

editorial_Eng_2.10.indd 146

February 2010

1/19/10 6:08 PM

Editorial • Imagine walking down a shopping aisle with a grocery cart, all within a typical rehabilitation clinic. This is what Kizony et al13 accomplish with their virtual reality task that allows them to assess the ability of individuals with stroke to complete a complex, practical task in a realistic setting. As with Kizony et al,13 there is a growing interest in developing gait outcomes and treatment paradigms that are realistic and meaningful for our patients. Tilson et al14 assess how much improvement in gait speed is necessary for patients with stroke to realize a change that is meaningful to them. Among individuals with incomplete spinal cord injury, Saraf et al15 assess the amount of daily stepping activity that reflects participation in the actual community environment. Walking in any realistic environment involves the motor system interacting with the cognitive system. Both Yogev-Seligmann et al16 and Kizony et al13 examine dual-task protocols that challenge walking and a cognitive task. Morris et al17 discuss how the visualization of walking can be helpful for improving gait in people with Parkinson disease. In their highly instructive article, Malouin and Richards18 address the specifics of prescribing mental practice to improve walking. This special issue highlights several new advances in gait assessment and treatment, with physical therapists playing a central role in many of these innovations. We urge you to “step forward” into this new decade and use this information to help you better assess and improve the walking ability of your patients. Janice J. Eng, PT, PhD Editorial Board Member and Guest Editor Sara J. Mulroy, PT, PhD Guest Editor References 1 Harris JE, Eng JJ. Goal priorities identified by individuals with chronic stroke: implications for rehabilitation professionals. Physiother Can. 2004;56:171–176. 2 Williams V, Bruton A, Ellis-Hill C, McPherson K. What really matters to patients living with chronic obstructive pulmonary disease? An exploratory study. Chron Respir Dis. 2007;4(2):77–85. 3 Latham NK, Jette DU, Slavin M, et al. Physical therapy during stroke rehabilitation for people with different walking abilities. Arch Phys Med Rehabil. 2005;86(12 suppl 2):S41–S50. 4 Newman AB, Simonsick EM, Naydeck BL, et al. Association of long-distance corridor walk performance with mortality, cardiovascular disease, mobility limitation, and disability. JAMA. 2006;295(17):2018–2026. 5 Foley S, Quinn S, Jones G. Pedometer determined ambulatory activity and bone mass: a population-based longitudinal study in older adults. Osteoporos Int. 2009 Dec 9. [Epub ahead of print] 6 Mahoney JE, Sager MA, Jalaluddin M. New walking dependence associated with hospitalization for acute medical illness: incidence and significance. J Gerontol A Biol Sci Med Sci. 1998;53(4):M307–M312. 7 Kuo AD, Donelan JM. Dynamic principles of gait and their clinical implications. Phys Ther. 2010;90:157–174. 8 Mulroy SJ, Klassen T, Gronley JK, et al. Gait parameters associated with responsiveness to treadmill training with body-weight support after stroke: an exploratory study. Phys Ther. 2010;90:209–223. 9 Burnfield JM, Shu Y, Buster T, Taylor A. Similarity of joint kinematics and muscle demands between elliptical training and walking: implications for practice. Phys Ther. 2010;90:289–305. 10 Namdari S, Pill SG, Makani A, Keenan MA. Rectus femoris to gracilis muscle transfer with fractional lengthening of the vastus muscles: a treatment for adults with stiff knee gait. Phys Ther. 2010;90:261–268. 11 Damiano DL, Arnold AS, Steele KM, Delp SL. Can strength training predictably improve gait kinematics? A pilot study on the effects of hip and knee extensor strengthening on lower-extremity alignment in cerebral palsy. Phys Ther. 2010;90:269–279. 12 Reisman DS, Bastian AJ, Morton SM. Neurophysiologic and rehabilitation insights from the split-belt and other locomotor adaptation paradigms. Phys Ther. 2010;90:187–195. 13 Kizony R, Levin MF, Hughey L, et al. Cognitive load and dual-task performance during locomotion poststroke: a feasibility study using a functional virtual environment. Phys Ther. 2010;90:252–260.

February 2010

editorial_Eng_2.10.indd 147

Volume 90 Number 2 Physical Therapy ■ 147

1/19/10 6:08 PM

Editorial 14 Tilson JK, Sullivan KJ, Cen SY, et al; Locomotor Experience Applied Post Stroke (LEAPS) Investigative Team. Meaningful gait speed improvement during the first 60 days poststroke: minimal clinically important difference. Phys Ther. 2010;90:196–208. 15 Saraf P, Rafferty MR, Moore JL, et al. Daily stepping in individuals with motor incomplete spinal cord injury. Phys Ther. 2010;90:224–235. 16 Yogev-Seligmann G, Rotem-Galili Y, Mirelman A, et al. How does explicit prioritization alter walking during dual-task performance? Effects of age and sex on gait speed and variability. Phys Ther. 2010;90:177–186. 17 Morris ME, Martin CL, Schenkman ML. Striding out with Parkinson disease: evidence-based physical therapy for gait disorders. Phys Ther. 2010;90:280–288. 18 Malouin F, Richards CL. Mental practice for relearning locomotor skills. Phys Ther. 2010;90:240–251.

Manuscript Reviewers for PTJ’s Perry Issue on Gait Rehabilitation Dr Rebecca Craik, Editor in Chief, and Dr Janice Eng and Dr Sara Mulroy, Guest Editors, gratefully acknowledge the manuscript reviewers who contributed their time, expertise, and constructive comments to this special issue: Gordon Alderink, PT, PhD

Therese Johnston, PT, PhD, MBA

Rosa Angulo-Barroso, PhD

Valerie Kelly, PT, PhD

M.J. Blaschak, PT, PhD, MSEE

Teresa Liu-Ambrose, PT, PhD

Jennifer Brach, PT, PhD, GCS

Trish Manns, PT, PhD

Jack Crosbie, PT, PhD

Karen McCulloch, PT, PhD

Vanina Dal Bello-Haas, PT, PhD

Jennifer McGinley, PT, PhD

Janis Daly, PhD

Jan Mehrholz, PhD

Judith Deutsch, PT, PhD

Patricia Pohl, PT, PhD

Nancy Devine, PT, DPT

Sandy Ross, MHS

Carol Giuliani, PT, PhD

Ann Spungen, EdD

Diana Glendinning, PT, PhD

Mary Thigpen, PT, MHS, NCS

Gammon Earhart, PT, PhD

A. Joseph Threlkeld, PT, PhD

Terry Ellis, PT, PhD

Carole Tucker, PT, PhD, PCS

Steven Hanna, PhD

Hidde van der Ploeg, PhD

Chris Hass, PhD

Jaynie Yang, PT, PhD

T. George Hornby, PhD [DOI: 10.2522/ptj.2010.90.2.146]

148 ■ Physical Therapy Volume 90 Number 2

editorial_Eng_2.10.indd 148

February 2010

1/19/10 6:09 PM

Research Report

Sit-to-Stand Movement as a Performance-Based Measure for Patients With Total Knee Arthroplasty Miranda C. Boonstra, Paul J.A. Schwering, Maarten C. De Waal Malefijt, Nico Verdonschot

Background. Functional recovery of patients after a total knee arthroplasty (TKA) usually is measured with questionnaires. However, these self-report measures assess the patient’s perspective on his or her ability to perform a task. Performancebased tests are needed to assess the patient’s actual ability to perform a task. Objective. The main purpose of this study was to quantify improvement in performance of the sit-to-stand movement of patients with a TKA.

Design and Methods. In this prospective study of 16 patients with end-stage knee osteoarthritis followed by a TKA, the maximal knee angular extension velocity and amount of unloading (shifting weight) of the affected leg during the sit-to-stand movement and the visual analog scale score for pain were assessed preoperatively and 6 months and 1 year postoperatively. These data were compared with data for a control group of individuals who were healthy (n⫽27).

Results. Before surgery, the participants in the TKA group unloaded their affected leg, but within 6 months after implantation, the affected leg was almost fully loaded again and comparable to the loading symmetry ratio of the control group. Furthermore, knee extension velocity also had increased, but remained lower than that of the control group. The changes in knee extension velocity took place during the first 6 months, after which a plateau was visible.

Limitations. A potential limitation of the study design was that the patients were not perfectly matched with the control subjects.

Conclusions. Implantation of a total knee prosthesis partly improved performance of the sit-to-stand movement. Participants in the TKA group could fully load their operated leg, but they could not generate enough knee angular velocity during rising compared with the control group.

M.C. Boonstra, PT, MSc, is a PhD student in the Orthopaedic Research Laboratory, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands. P.J.A. Schwering, MD, is Orthopaedic Surgeon, Department of Orthopaedics, Canisius-Wilhelmina Hospital, Nijmegen, the Netherlands. M.C. De Waal Malefijt, MD, PhD, is Orthopaedic Surgeon, Department of Orthopaedics, Radboud University Nijmegen Medical Centre. N. Verdonschot, PhD, is Professor and Head of the Biomechanics Department, Orthopaedic Research Laboratory, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, the Netherlands. Dr Verdonschot also is Professor, Laboratory for Biomechanical Engineering, University of Twente, Department CTW, Enschede, the Netherlands. Address all correspondence to Dr Verdonschot at: n.verdonschot@ orthop.umcn.nl. [Boonstra MC, Schwering PJA, De Waal Malefijt MC, Verdonschot N. Sit-to-stand movement as a performance-based measure for patients with total knee arthroplasty. Phys Ther. 2010;90:149 – 156.] © 2010 American Physical Therapy Association

Post a Rapid Response or find The Bottom Line: www.ptjournal.org February 2010

Volume 90

Number 2

Physical Therapy f

149

Sit-to-Stand Movement in Patients With Total Knee Arthroplasty

I

n general, pain and function are the predominant factors in the evaluation of recovery after total knee arthroplasty (TKA).1,2 Great reductions in pain are common3 and can easily be assessed with a visual analog scale (VAS).4 In addition to pain, functional outcome has become an increasingly important determinant of the success of a TKA due to an increasing number of young patients receiving a knee implant.5 Functional outcome can be scored by self-report measures (questionnaires)6 –10 or performance-based measures.11–15 Self-report measures assess the patient’s perspective on his or her ability to perform a task, whereas performance-based measures capture the patient’s ability to perform a certain task.16 Therefore, the 2 measures assess different aspects of function.17,18 For assessment of the function of patients with TKA, self-report measures have been used extensively.8,10,19 –21 In contrast, to our knowledge, only a few longitudinal performance-based follow-up studies of the recovery of patients with a TKA have been performed. Kennedy and colleagues13,17 assessed recovery of knee function with the Six-Minute Walk Test, the Timed “Up & Go” Test (TUG), and a timed stair test and found the greatest improvements in the first 9 Available With This Article at ptjournal.apta.org • Video: “The Sit-to-Stand (STS) Movement” • The Bottom Line clinical summary • The Bottom Line Podcast • Audio Abstracts Podcast This article was published ahead of print on December 10, 2009, at ptjournal.apta.org.

150

f

Physical Therapy

Volume 90

weeks after TKA. De Groot et al22 assessed the amount of physical activity and found only a minor increase after placement of a TKA. These 2 studies provide information about time-related variables and amount of activity. These parameters might be influenced by factors other than knee function, such as motivation, physical fitness, and age. In contrast, a biomechanical approach can provide more detailed information about a specific task and has implications for development of effective training techniques.23 For example, a patient with a severe limp may be able to move quickly, but the movement pattern still can be dysfunctional. A kinematic and kinetic analysis of the sit-to-stand (STS) movement can provide additional biomechanical information about the performance of this task. The STS movement is a challenging task for people with affected joints24,25 because it involves large movement amplitudes and the leg muscles have to generate sufficient power to lift the body mass.23,26 In several studies,24 –34 the STS test was found to be an adequate performance-based measure. In a previous study,35 we administered 2 patient-based measures (the Western Ontario and McMaster Universities Osteoarthritis Index and the Knee Society Score) and 3 performance-based measures (the STS movement, maximal isometric contraction, and the TUG) to assess which tests were adequate measures of knee function 1 year following a TKA. The tests were correlated to a VAS4 for pain in the affected knee. A high correlation with pain would indicate a large influence of pain and, therefore, lack of functional content validity.18 The 5 measures also were examined for discriminative capacity by assessing whether they could distinguish individuals who were healthy from patients with TKA. It appeared that, of these tests, the STS

Number 2

movement and the TUG had both functional content validity and discriminative capacity. A detailed analysis of various parameters during the STS movement showed that maximal knee angular extension velocity (kinematics) and the loading symmetry ratio (kinetics) met the functional content and discriminative capacity criteria. Time-to-stand, for example, was highly correlated with pain and, therefore, was not suitable as a parameter in a performance-based test. Therefore, in the current follow-up study, knee extension velocity and loading symmetry ratio were used for the quantification of the preoperative to postoperative biomechanical changes during the STS movement of patients with a TKA. The main purpose of this study was to quantify the biomechanical changes of patients with TKA during the STS movement 6 months and 1 year postoperatively compared with their preoperative status. We hypothesized that they would perform the STS movement with higher knee extension velocity and with more loading of the affected leg after placement of a TKA compared with the preoperative assessment. Furthermore, we theorized that the STS performance of the patients with TKA would remain affected compared with a control group.13,17,22

Method In the period from March 2004 to March 2006, 26 patients on a waiting list for unilateral TKA surgery with a diagnosis of primary or secondary osteoarthritis (OA) were included in the study. Exclusion criteria were contralateral knee or hip prosthesis, clear signs of contralateral OA, diabetes mellitus, rheumatoid arthritis, neurological disorders, and any other dysfunctions of the locomotor system that might influence performance during the STS activity. Four patients did not complete the February 2010

Sit-to-Stand Movement in Patients With Total Knee Arthroplasty follow-up period due to surgery of the contralateral leg, and 1 patient was not able to rise from the chair preoperatively. The data of these patients were excluded from the analysis post hoc, and 21 patients remained. However, 4 patients had surgery (3 patients received TKA in contralateral leg, and 1 patient had hernia surgery) within a year after the last measurement, and 1 patient was diagnosed with rheumatoid arthritis after the follow-up period. We decided post hoc to exclude these patients from the analysis because the results might have been confounded. In total, 16 patients remained (Tab. 1). The Press Fit Condylar Sigma system* with a posterior cruciate ligament retaining device was implanted in all cases, and surgery was performed by 2 orthopedic surgeons (P.S. and M.D.). Patients followed our routine postoperative rehabilitation program (Tab. 2). The time points for evaluation were preoperatively, 6 months postoperatively, and 1 year postoperatively (Tab. 1). A control group (n⫽27) of individuals without pain or dysfunctions of the locomotor system was selected in an attempt to match equivalent age, body mass index (BMI), and sex. We were not able to match the control group exactly to the TKA group for all 3 parameters, and thus we needed more control participants to be able to equalize the groups for age, BMI, and sex. The control participants were recruited from a senior tennis group, a senior running group, and a bridge club; they had no history of knee or leg injuries and had good general health. All participants provided written informed consent. Test Protocol Visual analog scale. In addition to the performance test, we adminis* DePuy International, St. Anthonys Road, Beeston, Leeds LS11 8DT, United Kingdom.

February 2010

Table 1. Mean (SD) for Age, Body Mass Index (BMI), and Months Preoperatively and Postoperatively for the Total Knee Arthroplasty (TKA) and Control Groupsa Sex (Male/Female)

Age (y)

BMI (kg/m2)

Pre (mo)

Post1 (mo)

Post2 (mo)

TKA (n⫽16)

5/11

65.4 (9.2)

30.2 (4.9)

0.8 (0.8)

6.5 (0.4)

13.2 (2.6)

Control (n⫽27)

8/19

66.1 (8.4)

28.9 (3.7)

Group

a

Pre⫽preoperatively, Post1⫽6 months postoperatively, Post2⫽1 year postoperatively.

Table 2. Postoperative Rehabilitation Program That Patients Generally Follow When Hospitalized in Our Hospital Postoperative Time

a

Rehabilitation Program During Hospital Stay

Day 0

Surgery

Day 1

Recovery in bed

Day 2

Flexion exercises sitting on a chair

Day 3

Flexion exercises are increased (Kinetec,a if necessary), and patient starts walking with crutches

Day 4

Increase of knee flexion angle and walking distance

Day 5

Discharge from hospital (90° of knee flexion)

Isokinetics Inc, PO Box 21 De Queen, AR 71832.

tered the VAS for pain.4 Reductions in pain have been found to be greater and faster to achieve compared with improvements in function.3 Therefore, we used the VAS score as an indicator of a patient’s perception of success of the TKA implant. The VAS was scored at the orthopedic outpatient center on the same day as the STS measurement. Each patient was instructed to point out his or her perception of the current pain in the affected knee on a 100-mm line, which was recorded by an independent researcher. Scores on the VAS range from 0 (no pain) to 100 (extreme pain). Sit-to-stand movement. The STS movement protocol has been used previously.35,36 See a video of the STS movement, available at ptjournal. apta.org. The participants were seated in a specially designed chair without armrests, which was adjustable in depth and height (Fig. 1). Their ankles were placed in a straight line directly under the knee,

and the chair height and depth were adjusted such that the knee angle in the sitting position was 90 degrees. The participants had their hands placed at their waist and were barefoot. They were not allowed to use their arms during rising. Maximal knee angular extension velocity (also referred to as “knee extension velocity”)35 and the loading symmetry ratio were the outcome measures for knee performance. The knee angle was measured using a combination of a biaxial accelerometer (ADXL202†) and a gyroscope‡ on both the lower and upper legs (Fig. 1),36 and knee extension velocity was the derivative of the measured knee angle. The combination of accelerometer and gyroscope signal to assess knee angle was validated against the Optotrack motion analysis system§ in an † Analog Devices Inc, 3 Technology Way, Norwood, MA 02062 ‡ Murata Manufacturing Co Ltd, 2–26 –10, Tenjin Nagaokakyo-shi, Kyoto 617-8555, Japan. § Optotrack Inc, PO Box 1242, Cary, NC 27512.

Volume 90

Number 2

Physical Therapy f

151

Sit-to-Stand Movement in Patients With Total Knee Arthroplasty ing symmetry ratio were means of 10 STS movements for each participant. Between the rising movements, the data had to be stored so that the participants had a rest of approximately 30 seconds between trials. In an unpublished pilot study, no effects of fatigue were observed; the last trial was not significantly different from the first. Matlab version 7.2.0㛳 was used for all signal processing.

Figure 1. Experimental setup for the sit-to-stand movement.

earlier study.36 In this earlier study, we calculated the optimal filter parameters necessary to process the accelerometer and gyroscope data.36 The sampling frequency was set at 128 Hz. The loading asymmetry ratio was measured with 2 forceplates (sampling frequency⫽1,000 Hz), which were placed lengthwise next to each other so that both feet were on separate forceplates (Fig. 1). The loading symmetry ratio was defined as maximal peak vertical ground reaction force in the affected leg divided by the maximal peak vertical ground reaction force in the contralateral leg for the TKA group: Peak force of affected leg Ratio ⫽ Peak force of contralateral leg

(1)

For the control group, loading asymmetry ratio was defined as:

The peak force generally is reached just after lift-off, when the vertical acceleration of the center of mass reaches its maximum. Therefore, the force curve is analyzed in the time frame between the moment of the maximal value of the derivative curve until the derivative of the force curve reaches zero. The peak force is defined as the maximal value of the force curve in this time frame. A loading symmetry ratio of 1 implies that a person rises with perfect symmetry in amount of force; both legs are equally loaded during the STS movement. The forceplate data were filtered with a third-order, onedimensional Butterworth filter with an 8-Hz cutoff frequency. The forceplate data were resampled from 1,000 to 128 Hz (by extracting every 1,000/128th point), to equal the sample frequency of the sensors. The recordings of the accelerometer, gyroscope, and forceplate were time synchronized.

Data Analysis We used SPSS version 16.0.01# for all statistical analyses. Based on knee extension velocity measurements in a previous study,35 a difference in group means of 22°/s, combined with an alpha level of .05 and a power of 0.80, required a sample size of 14 participants per group. Data were tested for normality with the one-sample Kolmogorov-Smirnov test. The data for VAS scores were not normally distributed for the control group (P⬍.001). These data, therefore, were nonparametrically tested, and results are shown as median (minimum-maximum). The Friedman test was used to assess the reduction in pain during the follow-up, and the Mann-Whitney U test was used to compare the 1-year postoperative pain level of the TKA group with that of the control group. The other data were normally distributed and shown as mean (SD). A repeated-measures analysis of variance (with time as the within-group factor) was used to assess the improvement of knee extension velocity and loading symmetry ratio, with Bonferroni adjustment as a post hoc test. The control participants were measured only once; therefore, differences between the TKA group and the control group were assessed 㛳

共2兲 Ratio ⫽

152

f

Peak force of left leg Peak force of right leg

Physical Therapy

Volume 90

In order to obtain consistent results, the knee extension velocity and load-

Number 2

The MathWorks Inc, 3 Apple Hill Dr, Natick, MA 01760-2098. # SPSS Inc, 233 S Wacker Dr, Chicago, IL, 60606.

February 2010

Sit-to-Stand Movement in Patients With Total Knee Arthroplasty Table 3. Mean (SD) [95% Confidence Interval] Maximal Knee Angle Extension Velocity and Median (Minimum–Maximum) Visual Analog Scale (VAS) Score for Pain for the Total Knee Arthroplasty (TKA) Group During the Follow-up Period and for the Control Group Maximal Knee Angle Extension Velocity (°/s) Group TKA (n⫽16) Control (n⫽27) a b c

VAS Score

Pre

Post1b

Post2

Pre

Post1b

Post2

91.4 (16.9) [82.3, 100.4]c

104.2 (15.7) [95.8, 112.5]c

100.4 (25.1) [87.0, 113.8]c

70.0 (45–100) [57.1, 77.9]c

10 (0–50) [5.0, 25.5]c

0 (0–75) [3.7, 36.3]c

126.7 (30.3) [114.8, 138.7]

0 (0–40) [⫺0.27, 7.7]

Pre⫽preoperatively, Post1⫽6 months postoperatively, Post2⫽1 year postoperatively. Significant improvement in the TKA group compared with preoperative measurement. Significant difference between TKA and control groups.

with the Student t test (age, BMI, knee extension velocity, and loading symmetry ratio). The chi-square test was used to test for differences in sex between the TKA and control groups. The level of significance was set at P⬍.025 (Bonferroni correction for 2 variables). Role of the Funding Source The study was sponsored by Johnson & Johnson. However, the company had no involvement in the study design; in collection, analysis, or interpretation of the data; or in writing the manuscript or the decision to publish the results.

patients with a TKA still showed lower knee extension velocity during rising (P⫽.005) compared with the control group, whereas the loading symmetry ratio was not different (P⫽.58).

Discussion In this study, we performed a 1-year follow-up study of patients with a TKA, using the STS movement as performance-based measure. Our main interest was to quantify the

performance-based changes during the STS activity after total knee replacement. Placement of a TKA showed an improvement of loading of the affected leg during the STS movement. One year postoperatively, the TKA group was not significantly different in loading symmetry from the control group. Earlier studies24,26 –30,32,33 measured various biomechanical parameters of the STS movement. How-

Results There were no significant differences in age (P⫽.78), BMI (P⫽.35), or sex (TKA group: 69% female, 31% male; control group: 70% female, 30% male; P⫽.59) between the TKA and control groups (Tab. 1). The VAS score for pain showed a reduction in pain (P⬍.001) (Tab. 3). However, the patients with a TKA did not achieve the low pain level of the control participants (P⫽.02) 1 year postoperatively. The statistical tests showed that the implantation of a TKA caused an increase in loading of the affected leg (Fig. 2), both during the first 6 months (P⫽.001) and between 6 months and 1 year (P⫽.004). Knee extension velocity (Tab. 3) increased during the first 6 months (P⫽.022), after which no further increase was measurable (P⫽.47). One year postoperatively, February 2010

Figure 2. Histogram of the loading symmetry ratio for the total knee arthroplasty (TKA) group preoperatively and postoperatively and for the control group. Pre⫽preoperatively, Post1⫽6 months postoperatively, Post2⫽1 year postoperatively. *Significant withingroup difference compared with previous measurement.

Volume 90

Number 2

Physical Therapy f

153

Sit-to-Stand Movement in Patients With Total Knee Arthroplasty ever, only 2 of these studies26,33 included the loading symmetry ratio during the STS movement. These 2 biomechanical studies showed that more load was applied on the nonoperated leg (3 months after implantation in the study by Mizner and Snyder-Mackler33 and 3 years after implantation in the study by Su et al26). The study by Mizner and Snyder-Mackler indicated that after 3 months, full recovery was not reached. Our first postoperative measurements were done after 6 months, at which point the patients with TKA were almost fully loading their affected leg, indicating recovery. The study by Su et al was conducted at various chair heights. Only at a very low seat height (65% of the lower leg) was the loading symmetry ratio still visible. This height was lower than the 90 degrees of knee flexion used in our study, and this difference might explain the persisting unloading of the operated leg in their study after 3 years of rehabilitation. Furthermore, asymmetric leg loading is an important issue, whereas excessive joint loading due to OA is a risk factor for evolving contralateral osteoarthritic progression.37 Therefore, it is important to measure the loading symmetry ratio frequently, starting when the patient has the first osteoarthritic symptoms in one leg. In case of unloading the affected leg, a physical therapy program can be implemented to reduce the risk of overloading the contralateral leg. In addition to the loading symmetry ratio, maximal knee angular velocity is an important measure for STS performance. The ability to generate vertical velocity is necessary to lift the body weight against gravity and is a good predictor of performance and function.38 In the study by Su et al,26 patients had decreased vertical center-of-mass velocity approximately 3 years after TKA implantation compared with a control group 154

f

Physical Therapy

Volume 90

of elderly subjects who were healthy. It seems that this decreased vertical center-of-mass velocity is partly caused by decreased knee extension angular velocity. The knee extension velocity during rising showed an increase after knee implantation compared with the preoperative situation, which plateaued after 6 months. Furthermore, patients with a TKA still rose with lower knee velocities compared with control subjects. It seems that implantation of a TKA does have a large positive effect on the kinetics (loading symmetry ratio) and a smaller effect on the kinematics (knee extension velocity) of the STS movement. Pain was measured with a VAS score. The patients with a TKA had a significant and clinically relevant reduction in pain in the affected leg, which has been a common finding in earlier studies.3 Next to improvement in function, reduction of pain is an indicator of the success of the TKA implant.1,2 Patients with a TKA still experienced more pain than control participants, as found in other studies.39,40 The influence of pain on performance has been studied previously. According to a study by Sharma et al,41 knee pain was not a risk factor for a poor STS outcome. In a previous study,35 we found that knee extension velocity and the loading symmetry ratio had a low correlation with pain. Therefore, we concluded that the STS task is a good performance-based measure and is minimally influenced by the remaining amount of postoperative pain. It appeared to be difficult to obtain a “healthy” TKA group, meaning that many patients could not be included due to the exclusion criterion of bilateral knee OA. A typical patient receiving a TKA is 60 years of age or older and is prone to various health risks. Because OA is rarely a unilateral problem, patients can develop contralateral knee problems during a

Number 2

1-year follow-up period. In a study by Spector et al,42 it appeared that 34% of middle-aged women with unilateral OA developed a bilateral knee disease within a period of 2 years. When examining other studies that focused on recovery, it seemed that most researchers had been able to find patient populations with no contralateral OA.10,20,32,43,44 Only Borden et al45 mentioned the hindrance of complications and revisions. We believe that it is important to carefully monitor potential contralateral knee degeneration, which may devaluate the validity of longitudinal studies. Although we did not perform a specific test-retest reliability trial for our method, we did perform calculations concerning validity and reliability. The use of the combination of biaxial accelerometer and gyroscope to measure the kinematics has been validated against the Optotrack motion analysis system.36 Five control participants were measured during the rising movement with both systems attached. They performed the rising movement 10 times (2 sets of 5 STS movements). The root mean square error (RMS) was 4.9°/s for leg angular velocity. This small RMS error makes the kinematic measurements a valid method that can easily be used in a clinical setting. Furthermore, each participant performed the STS movement 10 times, and we could calculate the individual intraclass correlation coefficients (ICCs). An ICC greater than .75 is regarded as excellent,46 and the ICCs for the knee and hip velocities and for the loading symmetry ratio were .76, .92, and .88, respectively. Combining these results, we believe that the method is valid and reliable. A potential limitation of this study design is that the patients with TKA were not perfectly matched with the control participants. Although no significant differences between February 2010

Sit-to-Stand Movement in Patients With Total Knee Arthroplasty groups were found for age, BMI, and sex, the groups were not equal. For example, the control group was classified as overweight (BMI ⬍30), whereas the TKA group was classified as obese (BMI ⬎30).47 This might have been a small bias in the study.

Posture and Gait Research; June 21–25, 2009; Bologna, Italy.

Conclusions

References

This study revealed that the STS movement can be used as a performance-based measure to assess functional changes after TKA implantation. Before surgery, the patients with TKA did not fully load their affected leg, as measured by the loading symmetry ratio; the load was unevenly distributed to the sound site. After 1 year of recovery, the patients loaded both legs evenly, comparable to the control participants. Furthermore, the patients increased knee extension velocity after TKA implantation, but remained impaired during the STS movement compared with the control group.

1 Kane RL, Saleh KJ, Wilt TJ, Bershadsky B. The functional outcomes of total knee arthroplasty. J Bone Joint Surg Am. 2005; 87:1719 –1724. 2 Davis AM, Agnidis Z, Badley E, et al. Predictors of functional outcome two years following revision hip arthroplasty. J Bone Joint Surg Am. 2006;88:685– 691. 3 Ethgen O, Bruyere O, Richy F, et al. Health-related quality of life in total hip and total knee arthroplasty: a qualitative and systematic review of the literature. J Bone Joint Surg Am. 2004;86:963–974. 4 Patrician PA. Single-item graphic representational scales. Nurs Res. 2004;53: 347–352. 5 Kurtz SM, Lau E, Ong K, et al. Future Young patient demand for primary and revision joint replacement: national projections from 2010 to 2030. Clin Orthop Relat Res. 2009;467(10):2606 –2612. 6 Bellamy N, Buchanan WW, Goldsmith CH, et al. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol. 1988;15:1833–1840. 7 Bachmeier CJ, March LM, Cross MJ, et al. A comparison of outcomes in osteoarthritis patients undergoing total hip and knee replacement surgery. Osteoarthritis Cartilage. 2001;9:137–146. 8 Jones CA, Voaklander DC, Suarez-Alma ME. Determinants of function after total knee arthroplasty. Phys Ther. 2003;83: 696 –706. 9 Insall JN, Dorr LD, Scott RD, Scott WN. Rationale of the Knee Society clinical rating system. Clin Orthop Relat Res. 1989; (248):13–14. 10 Diduch DR, Insall JN, Scott WN, et al. Total knee replacement in young, active patients: long-term follow-up and functional outcome. J Bone Joint Surg Am. 1997;79: 575–582. 11 Kennedy DM, Stratford PW, Pagura SM, et al. Comparison of gender and group differences in self-report and physical performance measures in total hip and knee arthroplasty candidates. J Arthroplasty. 2002;17:70 –77. 12 Stratford PW, Kennedy DM, Riddle DL. New study design evaluated the validity of measures to assess change after hip or knee arthroplasty. J Clin Epidemiol. 2009; 62:347–352.

Ms Boonstra, Dr De Waal Malefijt, and Dr Verdonschot provided concept/idea/research design. Ms Boonstra provided writing and data collection and analysis. Ms Boonstra and Dr Verdonschot provided project management. Dr De Waal Malefijt provided fund procurement. Dr Schwering and Dr De Waal Malefijt provided participants. Dr Schwering provided facilities/equipment. Dr Verdonschot provided consultation (including review of manuscript before submission). The authors thank the patients and control participants for their voluntary participation. This study would not have been possible without the assistance of students at the Orthopaedic Research Laboratory. The authors also thank Noe¨l Keijsers for his helpful comments about the manuscript. Furthermore, they thank Johnson & Johnson, Leeds, United Kingdom, for its funding. The study was approved by the Institutional Review Board of Commissie Mensgebonden Onderzoek Regio Arnheim-Nijmegen. Poster presentations of this research were given at the 54th Annual Meeting of the Orthopaedic Research Society; March 2–5, 2008; San Francisco, California, and the XIX Conference of the International Society for

February 2010

Clinical trial registration number: NCT00163228 (ClinicalTrials.gov). This article was received April 10, 2009, and was accepted September 6, 2009. DOI: 10.2522/ptj.20090119

13 Kennedy DM, Stratford PW, Hanna SE, et al. Modeling early recovery of physical function following hip and knee arthroplasty. BMC Musculoskelet Disord. 2006; 7:100. 14 Walsh M, Woodhouse LJ, Thomas SG, Finch E. Physical impairments and functional limitations: a comparison of individuals 1 year after total knee arthroplasty with control subjects. Phys Ther. 1998;78: 248 –258. 15 Bolton JW, Hornung CA, Olsen GN. Determinants of achievement in stair climbing as an exercise test. Mil Med. 1994;159: 644 – 646. 16 Piva SR, Fitzgerald GK, Irrgang JJ, et al. Get up and go test in patients with knee osteoarthritis. Arch Phys Med Rehabil. 2004; 85:284 –289. 17 Kennedy DM, Stratford PW, Riddle DL, et al. Assessing recovery and establishing prognosis following total knee arthroplasty. Phys Ther. 2008;88:22–32. 18 Terwee CB, van der Slikke RM, van Lummel RC, et al. Self-reported physical functioning was more influenced by pain than performance-based physical functioning in knee-osteoarthritis patients. J Clin Epidemiol. 2006;59:724 –731. 19 Fortin PR, Penrod JR, Clarke AE, et al. Timing of total joint replacement affects clinical outcomes among patients with osteoarthritis of the hip or knee. Arthritis Rheum. 2002;46:3327–3330. 20 Marx RG, Jones EC, Atwan NC, et al. Measuring improvement following total hip and knee arthroplasty using patient-based measures of outcome. J Bone Joint Surg Am. 2005;87:1999 –2005. 21 Martin SD, McManus JL, Scott RD, Thornhill TS. Press-fit condylar total knee arthroplasty: 5- to 9-year follow-up evaluation. J Arthroplasty. 1997;12:603– 614. 22 de Groot IB, Bussmann HJ, Stam HJ, Verhaar JA. Small increase of actual physical activity 6 months after total hip or knee arthroplasty. Clin Orthop Relat Res. 2008; 466(9):2201–2208. 23 Khemlani MM, Carr JH, Crosbie WJ. Muscle synergies and joint linkages in sitto-stand under two initial foot positions. Clin Biomech (Bristol, Avon). 1999;14: 236 –246. 24 Wretenberg P, Arborelius UP. Power and work produced in different leg muscle groups when rising from a chair. Eur J Appl Physiol Occup Physiol. 1994;68: 413– 417. 25 Itokazu M, Uemura S, Aoki T, Takatsu T. Analysis of rising from a chair after total knee arthroplasty. Bull Hosp Jt Dis. 1998; 57:88 –92. 26 Su FC, Lai KA, Hong WH. Rising from chair after total knee arthroplasty. Clin Biomech (Bristol, Avon). 1998;13:176 –181. 27 Ellis MI, Seedhom BB, Wright V. Forces in the knee joint whilst rising from a seated position. J Biomed Eng. 1984;6:113–120. 28 Arborelius UP, Wretenberg P, Lindberg F. The effects of armrests and high seat heights on lower-limb joint load and muscular activity during sitting and rising. Ergonomics. 1992;35:1377–1391.

Volume 90

Number 2

Physical Therapy f

155

Sit-to-Stand Movement in Patients With Total Knee Arthroplasty 29 Gross MM, Stevenson PJ, Charette SL, et al. Effect of muscle strength and movement speed on the biomechanics of rising from a chair in healthy elderly and young women. Gait Posture. 1998;8:175–185. 30 Lord SR, Murray SM, Chapman K, et al. Sit-to-stand performance depends on sensation, speed, balance, and psychological status in addition to strength in older people. J Gerontol A Biol Sci Med Sci. 2002; 57:M539 –M543. 31 Goulart FR, Valls-Sole J. Patterned electromyographic activity in the sit-to-stand movement. Clin Neurophysiol. 1999;110: 1634 –1640. 32 Saari T, Tranberg R, Zugner R, et al. The effect of tibial insert design on rising from a chair: motion analysis after total knee replacement. Clin Biomech (Bristol, Avon). 2004;19:951–956. 33 Mizner RL, Snyder-Mackler L. Altered loading during walking and sit-to-stand is affected by quadriceps weakness after total knee arthroplasty. J Orthop Res. 2005;23: 1083–1090. 34 Janssen WG, Bussmann HB, Stam HJ. Determinants of the sit-to-stand movement: a review. Phys Ther. 2002;82:866 – 879. 35 Boonstra MC, De Waal Malefijt MC, Verdonschot N. How to quantify knee function after total knee arthroplasty? Knee. 2008;15:390 –395.

156

f

Physical Therapy

Volume 90

36 Boonstra MC, van der Slikke RM, Keijsers NL, et al. The accuracy of measuring the kinematics of rising from a chair with accelerometers and gyroscopes. J Biomech. 2006;39:354 –358. 37 Shakoor N, Block JA, Shott S, Case JP. Nonrandom evolution of end-stage osteoarthritis of the lower limbs. Arthritis Rheum. 2002;46:318 –319. 38 Earles DR, Judge JO, Gunnarsson OT. Velocity training induces power-specific adaptations in highly functioning older adults. Arch Phys Med Rehabil. 2001;82: 872– 878. 39 van Essen GJ, Chipchase LS, O’Connor D, Krishnan J. Primary total knee replacement: short-term outcomes in an Australian population. J Qual Clin Pract. 1998; 18:135–142. 40 Kiebzak GM, Vain PA, Gregory AM, et al. SF-36 general health status survey to determine patient satisfaction at short-term follow-up after total hip and knee arthroplasty. J South Orthop Assoc. 1997;6: 169 –172. 41 Sharma L, Cahue S, Song J, et al. Physical functioning over three years in knee osteoarthritis: role of psychosocial, local mechanical, and neuromuscular factors. Arthritis Rheum. 2003;48(:3359 –3370.

Number 2

42 Spector TD, Hart DJ, Doyle DV. Incidence and progression of osteoarthritis in women with unilateral knee disease in the general population: the effect of obesity. Ann Rheum Dis. 1994;53:565–568. 43 Anderson JG, Wixson RL, Tsai D, et al. Functional outcome and patient satisfaction in total knee patients over the age of 75. J Arthroplasty. 1996;11:831– 840. 44 Laughman RK, Stauffer RN, Ilstrup DM, Chao EY. Functional evaluation of total knee replacement. J Orthop Res. 1984;2: 307–313. 45 Borden LS, Perry JE, Davis BL, et al. A biomechanical evaluation of one-stage vs twostage bilateral knee arthroplasty patients. Gait Posture. 1999;9:24 –30. 46 Fleiss JL. The Design and Analysis of Clinical Experiments. New York, NY; John Wiley & Sons Inc; 2009. 47 World Health Organization. Global database on body mass index. 2009. Available at: http://apps.who.int/bmi/index.jsp. Accessed September 30, 2009.

February 2010

Perry Issue: Gait Rehab

Dynamic Principles of Gait and Their Clinical Implications Arthur D. Kuo, J. Maxwell Donelan A healthy gait pattern depends on an array of biomechanical features, orchestrated by the central nervous system for economy and stability. Injuries and other pathologies can alter these features and result in substantial gait deficits, often with detrimental consequences for energy expenditure and balance. An understanding of the role of biomechanics in the generation of healthy gait, therefore, can provide insight into these deficits. This article examines the basic principles of gait from the standpoint of dynamic walking, an approach that combines an inverted pendulum model of the stance leg with a pendulum model of the swing leg and its impact with the ground. The heel-strike at the end of each step has dynamic effects that can contribute to a periodic gait and its passive stability. Biomechanics, therefore, can account for much of the gait pattern, with additional motor inputs that are important for improving economy and stability. The dynamic walking approach can predict the consequences of disruptions to normal biomechanics, and the associated observations can help explain some aspects of impaired gait. This article reviews the basic principles of dynamic walking and the associated experimental evidence for healthy gait and then considers how the principles may be applied to clinical gait pathologies.

A.D. Kuo, PhD, is Professor, Departments of Mechanical Engineering and Biomedical Engineering, University of Michigan, 2350 Hayward St, Ann Arbor, MI 48109-2125 (USA). Address all correspondence to Dr. Kuo at: [email protected]. J.M. Donelan, PhD, is Assistant Professor, Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada. [Kuo AD, Donelan JM. Dynamic principles of gait and their clinical implications. Phys Ther. 2010;90: 157–174.] © 2010 American Physical Therapy Association

Post a Rapid Response or find The Bottom Line: www.ptjournal.org February 2010

Volume 90

Number 2

Physical Therapy f

157

Principles of Gait

A

lthough walking poses little challenge to individuals who are healthy, those with gait pathologies such as hemiparesis, spinal cord injury, or amputation can find it tiring and difficult. Pathological gait, for example, can require twice the metabolic energy of healthy gait.1,2 It also can present neuromotor control challenges, such as to maintain balance or even to produce the gait pattern itself.3,4 These issues are addressed, in part, with gait rehabilitation, where the course of treatment relies heavily on the expertise of the individual caregiver. This experience may require long practice to gain and, once gained, may be difficult to disseminate.5 Consistency and quality of rehabilitation might be enhanced by the application of fundamental principles supported by evidence. Here we examine some basic principles underlying the mechanics and control of gait, along with potential clinical ramifications. A reasonable focus for gait rehabilitation—via therapy or assistive devices—is the recovery of mechanisms that reduce metabolic cost and increase stability. It is thus helpful to determine the mechanisms that underlie the metabolic cost and stabil-

Available With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

158

f

Physical Therapy

Volume 90

ity of healthy walking and to consider how they may be compromised by various pathologies. The purpose of this perspective is to review some of the important theoretical frameworks underlying healthy walking mechanics and how they might be applied to understand pathological gait. (To translate the principles presented here to walking in children with cerebral palsy, see Damiano et al6 in this issue.) We will focus on an approach termed dynamic walking, explaining some of its underlying principles, findings, and applications to clinical practice. (To compare and contrast dynamic walking with performance on elliptical training devices, refer to Burnfield et al.7) To put this approach in context, we begin by reviewing 2 historically important but contrasting paradigms— the “six determinants of gait” theory and the inverted pendulum model.

The “Six Determinants of Gait” Theory One of the most influential and longest-standing theories of gait was that proposed by Saunders et al,8 often referred to as the “six determinants of gait.” The six determinants are kinematic features—for example, the rotation of the pelvis and the pattern of stance-phase knee motion—thought to contribute to economical locomotion. Underlying the kinematics are 2 hypothesized goals, the first concerning minimization of metabolic energy expenditure: “Fundamentally, locomotion is the translation of the center of gravity through space along a pathway requiring the least expenditure of energy.”8(p558) The second concept is that “minimizing the amount that the body’s center of gravity is displaced from the line of progression is the major mechanism for reducing the muscular effort of walking, and consequently, saving energy.”9(p40) For more than 40 years, the “six determinants of gait” theory was ac-

Number 2

cepted nearly as fact.10 It was not until the late 1990s that researchers began to measure and quantify the kinematic features. The first kinematic feature to be examined was pelvic list—the frontal-plane tilt of the pelvis during single-limb support—which was found to have virtually no effect on the vertical excursion of the trunk.11 The same team later examined stance-phase knee flexion, which was found not to contribute significantly to reduction of vertical displacement. This was followed by measurement of pelvic rotation about the vertical axis, which was found to have little effect on smoothing the body center of mass (COM) trajectory.12 Reconsideration of all six determinants shows that some of the kinematic features do reduce COM displacement, but others appear to increase it.13 Although there may well be other aspects of the COM motion that are important to walking, there is little experimental evidence that humans seek to minimize its displacement. Other studies also have examined the hypothesized link between COM displacement and metabolic energy expenditure. Three studies showed that walking with voluntarily reduced displacement causes energy expenditure to increase.14 –16 They also showed that the greater the reduction in displacement, the greater the energetic cost, with maximum reduction in displacement leading to a doubling or more of energy expenditure. The experimental evidence suggests that it is energetically costly to minimize vertical COM displacement. The high cost of low COM displacement can be explained by several contributions. Many of the joints must undergo greater excursions during single-limb support if the COM is to be kept on a level path (Fig. 1). This is most evident at the knee, which must flex and extend February 2010

Principles of Gait substantially through each singlelimb–support phase, with the greatest flexion occurring at mid stance.17 One consequence of mid-stance knee flexion is the need for greater torque from knee extensors to support the body, with peaks more than 3 times higher than that of normal walking.16 Not only does this higher torque exact a high metabolic cost for producing muscle force, but it also acts through a greater angular displacement, resulting in 2 to 3 times as much work. This high cost of low COM displacement torque may explain why humans do not normally stand or walk with bent legs. Another disadvantage of bent-legged walking is that it requires greater angular excursions of the swing leg due to reduced ground clearance. Humans appear to compensate with a swing-leg trajectory with greater angular excursions, notably about the ankle and knee, and greater hip power during swing.16 The effects of reduced COM motion on additional stance and swing-leg motion and the torque required to support body weight, among other disadvantages, all appear to contribute to its high cost. Center-of-mass displacement also can be reduced simply by taking shorter but faster steps.17 Such a strategy makes it possible to avoid both bending the legs and reducing the height of the hip relative to normal, but human experiments show that there also are substantial disadvantages. For example, walking at normal speed but with steps 40% shorter than normal results in more than doubling of the energy expenditure,14 a penalty similar to that of bent-legged walking. This increase in energy expediture may be attributed to the higher step frequency required to maintain normal speed with shorter steps, which means that the legs must be moved faster than normal, requiring more joint work. It appears that regardless of how COM February 2010

Figure 1. The “six determinants of gait” theory2,8 proposes that walking economy is enhanced by reducing displacement of the body center of mass (COM). One drawback of walking with a level path for the COM, however, is that the joints must undergo large motions. The knee also must be flexed at mid stance, so that substantial extension torque is needed to support body weight. The high torque and large joint motion lead to a more than doubling of knee joint work and metabolic energy expenditure compared with normal walking.

displacement is reduced, there is a substantial energetic penalty. These experiments expose several flaws in the “six determinants of gait” as a scientific theory. The theory was based on the interpretation of a descriptive set of observations, from which purpose and consequence were inferred but not tested. To be scientifically useful, a theory must be prescriptive and make predictions that then should be tested. If the theory survives a series of falsifying tests, it may gain credence. Only recently, however, has the “six determinants of gait” theory been subjected to the scrutiny required of the scientific method. Upon receiving that scrutiny, it has not prevailed. One point of confusion stems from interpretation of the theory. Saunders et al claimed that energy is expended in the “elevation and depression of the center of gravity of the body”8(p553) and “an overall displacement of the center of gravity of the body through a sinusoidal path of low amplitude . . .

requires the minimal expenditure of energy.”8(p554) More than a dozen textbooks have consistently interpreted this claim to mean that minimization or reduction of COM displacement is desirable.18 –32 Although there is no experimental support for this interpretation, it could be proposed that COM displacement should be reduced below its theoretical maximum and, equally importantly, increased above its theoretical minimum. There are several conclusions to be drawn regarding the “six determinants of gait” theory. One conclusion is that they should more properly be called “kinematic features of gait.” Another conclusion is that they should have been proposed as a hypothesis rather than stated as fact. Finally, it would be preferable to make more specific, definitive, and quantitative predictions. For these and other reasons, Kirtley33 considered the “six determinants of gait” theory to be discredited. It remains for other textbooks to follow this lead.

Volume 90

Number 2

Physical Therapy f

159

Principles of Gait

Figure 2. The inverted pendulum analogy for the stance leg and its corollary for the swing leg. The stance leg appears to act like an inverted pendulum, which allows the body center of mass to move in an arc with conservation of mechanical energy. In principle, no mechanical work is needed to move or lift the body, and no knee torque is needed to support its weight. Longer and faster steps similarly require no effort. The swing leg also appears to move like a pendulum, whose ballistic motion theoretically requires no work. Mechanical energy conservation is unaffected by longer or faster steps.

The Inverted Pendulum Model Coexisting with the “six determinants of gait” theory is the inverted pendulum model of walking (Fig. 2). It states that the stance leg behaves like an inverted pendulum, allowing for economical gait. The advantage of a pendulum is that it conserves mechanical energy and thus requires no mechanical work to produce motion along an arc. Observations of mechanical energy exchange and leg-length change during single-limb support provide a strong indication of pendulum-like behavior.34,35 The 160

f

Physical Therapy

Volume 90

inverted pendulum model explains human gait far better than the “six determinants of gait” theory. The inverted pendulum predicts exchange of mechanical energy. If an inverted pendulum is acting conservatively, any change in kinetic energy will be offset by an opposing change in gravitational potential energy, with no need for mechanical work performed by muscle. Fluctuations in these 2 types of energy verify that they do act in opposition to each other, with relatively little mechanical work performed by the

Number 2

stance leg during single-limb support.34 Furthermore, the pattern of the energy exchange is very different during running, where the inverted pendulum mechanism is thought to be less dominant.34 A less appreciated advantage of the inverted pendulum comes from the straight leg. It is possible for the leg to act as an inverted pendulum without being kept straight by keeping a fixed distance between the ground contact point and the hip. For example, the knee could be kept at a fixed angle, and the flexed leg could still February 2010

Principles of Gait behave as an inverted pendulum and benefit from conservation of mechanical energy.17 However, humans evidently choose to keep the stance leg relatively straight, presumably because doing so reduces the moment of body weight (a vertical force with line of action through the COM) about the knee and thus also reduces the muscle force needed to support body weight. A person may walk with the knees kept at a flexed and fixed angle and find it nearly as exhausting as walking with the COM on a level path. The force savings of the straightened knee, therefore, may be just as significant as the work savings.

slower steps. Taken literally, pendulum mechanics predict that a step requires no work or force whatsoever.17 Once walking has commenced, there is no reason why work must be performed to maintain the conservative motion. The pendulum analogy also does not apply to double-limb support, where a pendulum (inverted or otherwise) clearly cannot swing. Although the pendulum analogy is important for understanding how walking can be economical, it does not explain why walking costs energy at all. In that respect, the inverted pendulum model is incomplete.

ficient to produce an arced trajectory for the hip, with the COM velocity approximately perpendicular to the stance leg. Although the original machines lacked knees, additional machines with knees39 have demonstrated that the principles of dynamic walking apply quite generally. Models with knees39 use a passive knee extension stop to prevent the stance knee from hyperextending and maintain the stance leg in full extension. Body weight thus is supported passively, and the entire single-limb–support phase can be produced with no need for active control and no need to actively lift the COM against gravity.

Dynamic Walking A corollary to the inverted pendulum is the pendulum-like motion of the swing leg. The same conservation of mechanical energy applies, so that little work is needed to move the swing leg. Given an appropriate initial speed and position, the entire single-limb–support phase can be produced largely through the ballistic motion of 2 coupled pendulums representing the stance and swing legs.36 This finding suggests that both the swing and stance legs may take advantage of pendulum dynamics during single-limb support. Other considerations, however, reveal limitations to the pendulum model. It successfully explains differences in energy exchange between walking and running, but it does not quantitatively explain how they should vary as a function of walking speed. For example, as speed increases, there are changes in the kinetic and gravitational potential energy exchange34 and the stance leg appears to behave less like a pendulum, but the pendulum analogy does not explain why this is the case. It gives no reason why longer and faster steps (up to the theoretical maximum walking speed) should require a different amount of mechanical work and force than shorter and February 2010

Building upon the pendulum analogy, the dynamic walking approach considers how passive dynamics alone can govern an entire gait cycle (Fig. 3). McGeer37 extended the ballistic model of stance and swing-leg motion to include the heel-strike collision between the leading leg and the ground. Properly configured, this collision can produce conditions for a periodic walking gait, with no need for active control and no need for energy input except for that gained by descending a shallow ramp. McGeer37 demonstrated both computational simulations and physical machines capable of passively stable locomotion. Subsequent robots have demonstrated walking on level ground,38 also governed primarily by passive dynamics but with minimal active energy input. We use the term dynamic walking for locomotion generated primarily by the passive dynamics of the legs, whether or not active powering or active control also is applied. The single-limb–support phase of dynamic walking resembles the inverted pendulum model (Fig. 3A). The original model of McGeer37 incorporated rigid stance and swing legs with human-like mass distribution. Their ballistic motion was suf-

A consequence of pendulum-like motion is that the transition to a new stance leg requires redirection of the COM velocity from one inverted pendulum arc to the next (Fig. 3B). This redirection occurs because the COM velocity is approximately perpendicular to the previous stance leg (trailing limb of double-limb support) and, therefore, directed forward and downward at the end of that step. The succeeding stance leg (leading limb of double-limb support) specifies a new arc for the COM, beginning with a forward and upward velocity. Although previous studies recognized that some form of redirection was necessary,40 the dynamic walking approach explicitly modeled the redirection37 as a collision and showed how it dissipates energy. The ground reaction force of the leading limb is oriented partially in opposition to the COM velocity and performs dissipative negative work on the COM as a result, which necessitates positive work to compensate for the dissipation. For a passive dynamic walking machine descending a ramp, the energy comes from gravitational potential energy. For level walking, energy may be supplied by pushing off at the ankle or powering the hip,41,42 as demonstrated by dynamic walking robots.38

Volume 90

Number 2

Physical Therapy f

161

Principles of Gait

Figure 3. Dynamic walking relies on passive leg dynamics to drive most or all of gait. (A) Dynamic walking uses the ballistic motions of the stance and swing legs behaving like pendulums, extended to a fully periodic motion. The collision of the leading leg with the ground redirects the body center of mass (COM) and initiates the subsequent step. (B) Dynamic walking also is thought to apply to humans, where passive dynamics allow much of gait to be produced with no work and body weight to be supported with little muscle force. Collision losses nevertheless must be offset by positive work, much of it at push-off.42

This redirection of COM velocity also appears to be relevant to human walking. As with the pendulum analogy, humans may benefit from keeping the stance leg relatively straight by supporting body weight with relatively little knee torque and allowing the single-limb–support motion 162

f

Physical Therapy

Volume 90

to occur with relatively little need for active muscle work.42 The negative work of the leading limb collision appears to be performed, in part, by active muscle, with an associated metabolic energy cost.43,44 The positive work to restore energy appears to be performed almost en-

Number 2

tirely by active muscle, with a higher metabolic cost. The combined costs of using both limbs to redirect the COM are referred to as the step-tostep transition cost of human walking.

February 2010

Principles of Gait

Figure 4. Alternative methods to perform positive work to offset collision losses. (A) Work can be performed during single-limb–support stance, for example, by leaning the torso forward. Hip extension torque then is needed to balance the torso, and this torque acts against the extending stance leg to perform positive work. (B) An alternative is to perform push-off about the ankle after heel-strike. Both (A) and (B) can restore work lost in collisions and successfully produce dynamic walking gaits, but higher economy can be achieved with another strategy. (C) Pre-emptive push-off refers to work commencing before the heel-strike collision. This work redirects the body center of mass velocity before the collision, so that the collision velocity (at mid-transition) is lower than in the other 2 cases. Pre-emptive push-off theoretically can reduce collision losses by three quarters and, therefore, reduce the amount of positive work needed to sustain steady gait.

Much of the work performed during walking can be understood in terms of reducing step-to-step transition costs.44 In principle, the positive work to restore collision losses can be performed at any time during a stride. For example, work can be performed during single-limb– support stance (Fig. 4A) by the hip muscles. As the hip extends, this torque will perform positive work that can compensate for the subsequent heel-strike collision. Another means of powering is to apply ankle push-off late in double-limb support (Fig. 4B), after the leading leg collision has occurred. A drawback of both of these methods, however, is that the heel-strike collision occurs at high velocity, resulting in a large amount of negative work. Negative work can be reduced, in an ideal dynamic walking model,41 by pushing off with the trailing limb in a brief pre-emptive impulse just before the leading-limb collision (Fig. 4C). Such a push-off reduces the collision velocity and, therefore, the amount of work that must be performed. If positive work is performed in anFebruary 2010

other manner—for example, with single-limb–support hip torque— up to 4 times the energy can be lost in redirecting the COM velocity.41 If push-off work does not adequately compensate for the collision, additional work must be performed elsewhere in the stride to maintain steady walking speed, resulting in greater work overall. Of course, humans cannot perform work with ideal impulses, but they may still benefit by pushing off in a relatively short burst of work beginning just prior to the leading-limb collision. Empirical observations have shown that people who are healthy perform step-to-step transitions in this manner.44 They perform positive mechanical work on the COM with the trailing leg, beginning just before and continuing throughout doublelimb support. This work largely compensates for the negative work performed by the leading leg at most walking speeds. Another prediction of dynamic walking models is that step-to-step transition costs should increase with ei-

ther longer or wider steps.42 Walking with longer steps while maintaining the same step frequency (Fig. 5A) requires the COM velocity to increase in magnitude and to undergo a greater directional change in the step-to-step transition.44 Both magnitude and direction contribute to redirection work, with the rate of work increasing with the fourth power of step length. Walking with wider steps of the same length and frequency causes the COM velocity to undergo greater directional changes, with much less effect on magnitude.43 The rate of work thus increases with the second power of step width. Experimental tests on humans support both of these predictions and show that the rate of metabolic energy expenditure increases with similar proportionalities, suggesting that the work performed to redirect COM velocity exacts a proportional metabolic cost. There also are complexities of the human step-to-step transition not captured by simple models.42 The models typically approximate the

Volume 90

Number 2

Physical Therapy f

163

Principles of Gait

Figure 5. Two competing costs to human walking. (A) Dynamic walking predicts that step-to-step transition work—performed to redirect the body center of mass (COM) velocity between steps—increases with step length. At the end of a step and before the step-to-step transition, the COM velocity is directed downward (see inset). It must be redirected upward by the end of the transition for the next pendulum-like step. Keeping step frequency fixed, the work rate is predicted to increase with the fourth power of step length.41 Experimental measurements show that humans walking at increasing step lengths perform more work and expend more energy, both at rates roughly proportional to the prediction.44 (B) Another possible contributor is the effort needed to move the legs back and forth relative to the body. Although pendulum-like motion requires no net work, both work and force may be used to induce faster leg motion. It may be economical to use energy to produce faster steps, if it reduces step-to-step transition costs.48 Independent measurements of swinging a leg at increasing frequency but fixed amplitude show metabolic rate increasing with the fourth power of frequency.46 The 2 competing costs of step-to-step transitions and forced leg motion appear to determine the preferred step length and frequency of normal human walking.48

COM redirection as an instantaneous collision, but humans perform the negative work over a collision phase beginning at heel-strike and typically extending slightly beyond doublelimb support. Joint power data show that much of the collision negative work may be attributed to the knee, which flexes while producing an ex164

f

Physical Therapy

Volume 90

tension torque. Some negative work also appears to be performed by other soft tissues, such as the heel pad, cartilage, viscera, and vertebral disks, but in an amount difficult to quantify. The collision is followed by a rebound phase, where the leg performs positive work to straighten the leg during the first half of single-limb

Number 2

support. The knee accounts for much of the rebound work, which may be due to both active muscle and elastic tendon. Toward the second half of single-limb support, the stance leg enters a second negative work region, referred to as the preload phase. This phase appears to be associated with storage of elastic enFebruary 2010

Principles of Gait ergy in the Achilles tendon, to be released during the subsequent push-off phase. Push-off work typically commences before and continues through double-limb support and may supplement elastic energy with active muscle fiber work. This work compensates for much of the energy dissipated in the collision phase. A second mechanism that people use to reduce step-to-step transition costs is to actively swing the leg back and forth (Fig. 5B). Dynamic walking models can walk faster simply by pushing off more, which results in longer steps. Greater push-off incurs a high cost for step-to-step transitions due to the step length dependence (discussed above). Walking speed, however, is the product of step length and step frequency, implying that another means to increase speed is to increase step frequency. Dynamic walking models show that elastic springs can passively speed leg swing, for example with biarticular springs about the hip and knee45 or a single torsional spring about the hip for rigid-legged models.41 Such springs theoretically can reduce collision losses to zero by swinging the legs so fast that one leg contacts the ground directly after the next, forming a nearly continuous line of contact with the ground, like a rolling wheel. Of course, humans do not walk with infinitely fast and short steps. Even if elastic tendons were to aid leg motion, the muscles attached to those tendons also must produce active force, incurring an energetic cost. Human subject experiments have shown that isolated leg swinging, performed at roughly the same frequency and torque amplitude as during walking, costs substantial energy. The rate of metabolic energy expenditure increases approximately with the fourth power of swing frequency, suggesting a similarly high cost for the forced leg motion of walking.46 This cost apFebruary 2010

pears to be associated not only with active muscle work, but also with production of active muscle force.47 The combined energetic costs of step-to-step transitions and forced leg motion appear to account for much of the cost of human walking. At a given speed, humans avoid long steps to reduce step-to-step transition costs, while also avoiding fast steps to reduce the cost of moving the legs back and forth. The trade-off between these 2 avoidance mechanisms appears to explain why humans walk faster by increasing step length and frequency in nearly equal proportion.48 At a comfortable walking speed, roughly two thirds of the net metabolic cost is attributable to step-to-step transitions, and up to one third is attributable to forced leg motion. A similar trade-off may explain the selection of preferred step width in healthy human walking. Wider steps incur step-to-step transition costs that increase with the square of step width, whereas narrower steps appear to require forced lateral motion of the swing leg to avoid interference with the stance leg. The sum of these 2 costs is minimized when step width is narrow, but not so narrow as to require substantial lateral forcing of the swing leg to clear the stance leg.43 A third means by which humans manage step-to-step transition costs is to use their feet like sections of wheels. In dynamic walking models, arc-shaped feet reduce negative collision work by reducing the directional change required of the COM. In theory, rigid feet with an arc radius equal to leg length will require no directional change and, therefore, no collision loss. Although such feet are impractical, arc-shaped feet with shorter radii and lengths still can reduce collision losses. Humans appear to take advantage of this feature: the human foot effectively rolls over the ground, with the center of

pressure progressing on the ground like a wheel. Although the actions of the ankle and foot are quite complex,9 their effect on the center of pressure is quite simple, so that its progression resembles that of a wheel with a radius equal to 30% of leg length.37,49,50 Experiments examining the effects of rigid foot curvature on human walking mechanics and energetics have demonstrated that a similar radius of curvature reduces COM redirection work and metabolic cost.51 The combined motion of the human ankle-foot system appears to benefit from effectively acting like a rigid, rolling foot. Aside from human subject tests, these principles also have been applied to the design of dynamic walking machines and robots. The sagittal-plane motions of the original passive dynamic walking machines have been extended to included lateral motion,52 and both ankle and hip actuation have been incorporated for walking on level ground.38 Other models and robots have been devised to include an upper body53,54 and actuation of multiple joints55,56 and have been found to display passive dynamic gaits with motion and stability similar to simpler models. These physical models have a variety of human-like and nonhuman features, but they all demonstrate that dynamic walking principles are based firmly in physics and have a degree of practical applicability.

Stability Dynamic walking models provide a means to examine walking stability. One of their most important features is passive dynamic stability, where gait is stabilized from step to step with no need for direct control over limb motions. The term limit cycle stability refers to the tendency for a small perturbation to the periodic gait to be dissipated over subsequent steps. This tendency may be quanti-

Volume 90

Number 2

Physical Therapy f

165

Principles of Gait fied with a small set of multipliers (also referred to as Floquet multipliers57) that characterize the step-bystep amplification or reduction of perturbations in particular directions. A multiplier with magnitude of less than 1 indicates that a particular perturbation will decrease over each step. An arbitrary perturbation may be composed from combinations of these directions, so that general stability may be inferred from combinations of multipliers. If all multipliers have a magnitude of less than unity, the gait is considered stable to small perturbations. Passive stability makes it possible to walk with little or no active control, highlighting the role of dynamics in generating gait. It is helpful, therefore, to consider the features of the modeled dynamics that produce this step-to-step stability. Passive dynamic stability typically is observed in the sagittal-plane motion of dynamic walking models. The most important feature contributing to stability is that collision losses increase with walking speed. As discussed previously, the loss per step increases in proportion to the square of walking speed and the square of step length.41 A feature of many dynamic walking gaits is that their step length is relatively insensitive to small fore-aft perturbations to the pelvis (Fig. 6A). This feature is indicated by the angle between the stance and swing legs, which increases slowly near the end of a step. The result is that a forward push will have little effect on step length, but will increase the COM velocity at the time of the next heel-strike. The velocity increase will cause the heelstrike collision to dissipate more than the nominal amount of energy, enough to counter the disturbance and stabilize the gait. This stability, however, is not retained for larger perturbations, which can cause substantially shorter steps (Fig. 6B). Shorter steps dissipate less energy, 166

f

Physical Therapy

Volume 90

so the heel-strike collision may not be sufficient to gain passive stability. Fortunately, it appears that the range of recoverable perturbations is sufficient to make passive dynamic walking machines practical.37 Another instability can occur in the lateral direction (Fig. 7). A dynamic walking model with a degree of freedom in the ankle eversion/inversion direction—roughly corresponding to the subtalar ankle joint—is laterally unstable.58 Its gait dynamics are similar to those of models constrained to the sagittal plane, including passive stability in that plane. However, even as perturbations have little effect on fore-aft motion, they cause the model to fall to the side relatively quickly. The amplification multiplier for this instability is great enough that even a very small perturbation can cause a fall within a few steps. The instability can be remedied somewhat in 3-dimensional models by removing the ankle degree of freedom and installing wide feet. Several dynamic walking robots produce stable gaits with such a configuration, but they are only able to reject small lateral direction perturbations. Humans bear closer resemblance to the models with unconstrained lateral motion and may have similar instability. The solution to lateral instability is active feedback control. There are several ways to stabilize the model’s gait, such as by moving the torso from side to side or producing active eversion/inversion torque at the ankle.58 A particularly simple solution is to make lateral adjustments to foot placement through active hip abduction and adduction. These adjustments require little effort because the swing leg must be moved only a small amount over the relatively long duration of the swing phase. The amount of adjustment must be determined from feedback, sensing the body state (the positions and veloci-

Number 2

ties of all body segments) at least once per step. Applied to humans, one possible implication of the hypothesized passive stability in the sagittal plane is that walking balance might be controlled largely through lateral foot placement, with little need for active fore-aft adjustments.58 This control, in turn, implies a greater dependence on sensory information, integrated by the central nervous system (CNS) from multiple different sensory pathways, to drive lateral foot placement. In contrast, the sagittal-plane motions might be stabilized by passive dynamics, with lesser need for sensing and control. Perhaps the CNS takes advantage of this fore-aft stability, thereby reducing sensorimotor and attentional demand for balance. Dynamic walking models thus predict that the degree of active control needed for balance during walking should be direction dependent. Direction dependence might be manifested in differences between fore-aft and lateral foot placement (Fig. 7). Whereas active control depends on sensory information that is necessarily imperfect, passive stability requires no control and no sensing. This difference leads to 2 predictions regarding human walking. First, the continual and imperfect adjustment of lateral foot placement would be expected to contribute to step width variability, whereas the hypothesized passive stability would be expected to cause step lengths to be less variable. Second, the dependence on sensory information implies that less information should render active control less precise, leading to greater step width variability without necessarily affecting step length variability. This hypothesis can be tested readily by removing vision. The greater uncertainty in sensing would be expected to result in greater step width variability, but not necessarily any change in step length variability. February 2010

Principles of Gait

Figure 6. Effect of (A) small and (B) larger perturbations on dynamic walking. A forward perturbation is applied at mid stance, causing the stance leg to move faster, resulting in a heel-strike collision that occurs both earlier and with higher speed. (A) Small perturbations have little effect on step length, so that the greater speed of collision results in more energy dissipation. The body center of mass (COM) velocity at the beginning of the next step is reduced and gradually returns to the nominal velocity over successive steps, contributing to passive dynamic stability. Inset shows the trajectory of stance leg angle (defined as positive in the clockwise direction relative to vertical) and inter-leg angle (defined as the angle between stance and swing legs) over a step. The inter-leg angle slows near end of swing, reducing sensitivity of step length to perturbations. (B) Larger perturbations cause a shortened step, which dissipates less energy. The COM velocity at the beginning of the next step is not reduced and increases with each step until the model falls. Inset shows how larger perturbations have relatively greater effect on step length, which is determined by the inter-leg angle at heel-strike.

These predictions are amenable to experimental testing. Overground59 and treadmill60 recordings of footsteps show that step width variability typically exceeds step length variability in normal walking, consistent with the hypothesized passive stabilFebruary 2010

ity properties. When humans walk with their eyes closed, their step width variability increases substantially, by more than twice as much as step length variability.59 The greater step width variability during normal walking and its increase with re-

moval or perturbation of vision61 are both consistent with the hypothesis that humans actively perform integrative control of a lateral instability. The relatively low variability of step length is consistent with the hypothesis that passive dynamics contribute

Volume 90

Number 2

Physical Therapy f

167

Principles of Gait

Figure 7. Dynamic stability and human gait variability. (A) Dynamic walking model is stable in the fore-aft direction, but unstable in the lateral direction. The instability can be controlled through active adjustments of lateral foot placement, using integrative sensing and control. (B) Measurements of normal human walking show more variability in step width than length. Individual footsteps for a representative person walking overground are shown as deviations from mean. Ellipses indicate ⫾1 standard deviation of variability across multiple subjects. (C) When humans walk with eyes closed, their step width variability increases substantially, with much less effect on step length variability. Humans appear to be more dependent on vision for lateral control of walking balance. Data are from Bauby and Kuo.59

to fore-aft stability, reducing the need for integrative balance control. Another experimental test is to manipulate lateral stability. Treadmill walking can be artificially stabilized through elastic cords extending laterally from the body and tensioned so that walking is essentially constrained to the sagittal plane. This external lateral stabilization causes adults who are healthy to walk with half the normal step width and about two thirds of the step width variability.62 These gait changes also are accompanied by a small (about 9%) but significant decrease in metabolic energy expenditure. Reductions in both step width and its variability may account for the reduced energy expenditure. The aggregate energy savings suggest that active control of balance requires some energy expenditure and that humans may benefit from passive dynamic stability in reducing the need for active control.

Clinical Implications Energetics Although humans who are healthy appear to accomplish economical step-to-step transitions across the full range of walking speeds, economy may be impaired in people with gait pathologies such as stroke, spinal cord injury, and amputation, due to 168

f

Physical Therapy

Volume 90

the reduction in strength (forcegenerating capacity) and coordination of the affected leg or legs. As discussed above, dynamic walking models gain economy by beginning push-off immediately before heelstrike, thereby reducing the collision velocity.41 Impairing or otherwise reducing push-off would be expected to result in an increased contralateral collision, which would require compensation through a greater amount of positive work. This work can be performed at a different phase of the gait cycle or a different joint, but regardless of how and where it is performed, the overall work requirements theoretically are increased. The increased work may explain part of the energetic penalty of many forms of pathological gait. In patients recovering from stroke, for example, mechanical work measurements suggest that the paretic leg performs much less push-off work and that both legs perform more total mechanical work than that performed by speed-matched individuals who are healthy.63– 65 This increase in work suggests that patients recovering from stroke experience an elevated metabolic cost because step-to-step transitions require more mechanical work and not because they perform work less effi-

Number 2

ciently. Indeed, it has been shown that the efficiency of step-to-step transition work in hemiparetic gait is equal to that of walkers who are healthy.66 This finding is consistent with the efficiency of paretic leg work production estimated during cycle ergometry. Although the paretic leg of people with hemiparesis produces only a fraction of the work of their nonparetic leg or the legs of people who are healthy, it performs work with the same efficiency (about 20%67), despite high levels of spasticity. As with patients with stroke, people with reduced ankle mobility expend more energy to walk at the same speed as individuals without gait pathologies.2 The energetic penalty increases with the degree of limb weakness, immobility, or loss. In some cases, such as ankle fusion, the energetic penalty is associated with reduction in ability to perform muscular work. The compensation for this loss apparently is less economical, or requires more mechanical work, than the unimpaired case. It is difficult to predict why an alternate recruitment pattern for the muscles might be less economical, but the theory of step-to-step transitions does explain why the amount of work might increase, because reFebruary 2010

Principles of Gait duced push-off would be expected to cause increased collision work on the contralateral side.

paretic leg extensor muscles but also their ability to perform mechanical power with the appropriate timing.

lums and conserve mechanical energy. In these and other cases, step-to-step transition costs may not apply.

Increased collision work theoretically can be countered by helping redirect the COM velocity with an appropriate foot bottom shape, which can greatly influence energy expenditure with a locked ankle. As mentioned above, shapes that are longer and have a higher radius of curvature can reduce the change in COM velocity for the step-to-step transition, thereby reducing work requirements.51 A walking boot with a fixed ankle and curved rocker bottom can have energy expenditure requirements essentially the same as for normal intact walking, when the weight of the boot is taken into account.68 Recently, prosthetic feet have been designed with a curved foot bottom profile,69 which may be energetically advantageous as well. Whether there is a net advantage depends on the decrease in collision due to foot bottom shape, relative to the possible increase in the contralateral collision due to reduced pushoff. Although these questions remain unanswered, the dynamic walking approach provides a scientific framework for considering gait with impaired ankle function.

The dynamic walking approach may provide insight regarding existing rehabilitation strategies. Partial bodyweight support, which provides an upward force to the body during treadmill training, is an effective tool for both spinal cord and stroke rehabilitation.72 It is unlikely that this device reduces the energetic cost of limb swing because it applies external forces only to the torso. Its effect on step-to-step transition work also is likely to be modest, as COM redirection depends on body mass (which is not changed by the upward force) rather than body weight (which is changed). The upward force will reduce the COM velocity only slightly prior to the transition and help redirect it toward the posttransition velocity. It follows that experiments on subjects who are healthy have demonstrated that an upward force of three quarters of body weight reduces energy expenditure by less than a quarter.73 Its main biomechanical effect may be to prevent the limbs from collapsing under body weight, while preserving the main contributions to the metabolic cost of walking.

There may be cases in which the legs move with a great deal of cocontraction. It is difficult to quantitatively predict the energetic cost of co-contraction, but the qualitative prediction is that energy expenditure can be high even if little net work is performed about the joints. A challenge for many conditions affecting gait is that the kinematics, motor commands, and degree of cocontraction are not predictable. Computational models can make few quantitative predictions for such cases.

The concept of step-to-step transitions may help guide the design of rehabilitation strategies, rehabilitation devices, and assistive devices aimed at lowering metabolic cost and increasing patient mobility and stability. For assistive devices, it may be effective to assist push-off by electrically stimulating the paretic leg extensors in a manner similar to that currently used to correct drop foot.70,71 The increased push-off may decrease the contralateral collision cost, decreasing the step-to-step transition cost. For rehabilitation strategies, the energetics of step-to-step transitions suggest that training should target not only the strength of

Dynamic walking is applicable mainly to cases where the legs behave like pendulums. Many conditions (eg, crouch gait, toe walking, severe hemiplegic gait) may limit the degree to which the pendulum model is relevant. Such conditions may cause the COM to move in a path that results in unusual step-tostep transitions, for which collision models do not apply. For example, there may be negligible step-to-step transition costs when the COM moves in a level path, whereas the effort to support body weight increases drastically.14 Spasticity (hypertonicity or abnormal reflex activity) also may affect how the legs can move like pendu-

February 2010

There are, nevertheless, general energetic principles that may apply to all pathological gait. Three general types of energetic cost appear to be relevant. The first is the step-to-step transition cost, which applies when the COM moves in an inverted pendulum arc. The combined costs of the negative collision work and the positive work to restore the energy dissipated may be substantial. Second is the cost of moving the legs back and forth relative to the body. When the legs are observed to swing like pendulums, there may be effort expended to force them to swing faster, with significant energetic cost. Finally, when body weight is supported on a bent leg, especially with knee flexion, substantial effort may be expended to counteract gravity. If the bent leg also undergoes flexion or extension motion, substantial work may be performed, at potentially very high energetic cost. Stability Balance is a limiting factor for the mobility of older adults, as well as those with vestibular, somatosensory, and other sensory deficits. The principles of dynamic walking may be helpful for addressing and understanding poor stability. If walking de-

Volume 90

Number 2

Physical Therapy f

169

Principles of Gait pends more critically on active control of lateral balance, but less for fore-aft control, there are several implications for impaired balance. One implication is that poorer sensing may adversely affect lateral balance, regardless of the modality. This adverse effect may result in increased step variability, perhaps accompanied by greater energy expenditure, which implies that the step width variability might serve as a useful quantifier of sensorimotor control of balance during walking. One model of balance deficits considers active sensorimotor control in terms of noise-like imprecision. It proposes that some forms of sensory loss result in unpredictable errors that affect the repeatability of sensory measurements, thereby affecting variability without necessarily introducing systematic (and, therefore, predictable) distortions. It also assumes that motor output is subject to unpredictable errors that affect the precision with which control adjustments are made. All of these effects can be modeled as noise, affecting both sensors and motor commands. Such models are helpful for modeling variability of postural stability74 and have been applied to stabilization of walking through lateral foot placement control.75 In the latter case, sensorimotor noise can reproduce age-related trends in step width variability, even without systematic changes in the body. Assuming that the lateral foot placement is fully intact but operating on noisy information, the model predicts greater step variability— especially in the lateral direction—as the amount of noise increases. There is some evidence to support these predictions. Older adults who are healthy walk with greater variability than younger adults, as assessed by a variety of measures, including step timing,76 length, and width.60 Among gait parameters, 170

f

Physical Therapy

Volume 90

however, step width variability is the strongest discriminating factor,77 agreeing well with the model prediction. Step width variability also appears to be related to variability of trunk motion in the frontal plane.78 Therefore, it appears that one of the age-related factors affecting gait may be decreased sensorimotor precision, resulting in greater step variability, especially in the lateral direction. Gait variability, however, is only one possible factor affecting mobility. Excessive step width variability has been linked to falling in older adults,79 perhaps due to the detrimental effects of poor sensorimotor precision. Unusually low step width variability also has been linked to falls,79,80 perhaps for different reasons. Dynamic walking models require that lateral foot placement be adjusted in proportion to errors in motion from the preceding step. A possible effect of poor sensory precision is to reduce the accuracy with which control adjustments are performed, also referred to as control sensitivity. This effect may be manifested in inaccurate foot placement adjustments, or even a failure to detect errors in motion altogether. Control sensitivity is a systematic rather than a random or unpredictable effect, and poor control sensitivity can lead to instability regardless of the noise-like effects. Computational models can hardly provide a full explanation for the cause of falls, but they do suggest how systematic and noise-like components both can be detrimental to walking balance. This approach to lateral stability may suggest potential rehabilitation aids. Individuals with poor balance often rely on handrails or other supports during walking. A goal of balance rehabilitation is to decrease reliance on such aids, but there are few automatic or systematic means to control

Number 2

or quantify such reliance during walking exercises. It is possible that systematic scaling of balance assistance could be afforded by external lateral stabilization during treadmill walking.62 This external stabilization could aid lateral balance, perhaps in concert with other aids such as partial body-weight support that reduce the physical load but do not specifically address balance. The amount of external stabilization could be adjusted according to the degree of unsteadiness and gradually reduced during a rehabilitation program. The stabilizing forces also could be measured to assess the amount of dependence on the assistance. Understanding of lateral stability might be helpful for the design of these and other aids. There are a few studies that demonstrate clinical applications of lateral stabilization. Older adults have been found to walk with reduced step width variability and energy expenditure when receiving external lateral stabilization,75 and energy expenditure can even be reduced to levels similar to those for young adults.81 Patients with myelomeningocele have sensory deficits and difficulty controlling walking. The application of external lateral stabilization to these patients has been found to reduce their step width and increase step length, with a onefourth decrease in heart rate.82 It is important to note that the stepto-step stability considered here is concerned primarily with small or local deviations from the nominal gait. In rehabilitation, the overriding concern typically is a more global definition of stability, such as whether patients are susceptible to falling. The issue of global stability is far more complex than that of local, step-to-step stability. It is possible for an individual to have good local stability but still be sensitive to large perturbations or otherwise susceptible to falling. It also is possible for an February 2010

Principles of Gait individual to have poor local stability and yet high robustness to perturbations. The clinical relevance of stepto-step stability, therefore, is limited. We nevertheless propose that stepto-step stability serves as one useful indicator of walking balance and that good local stability contributes to economical gait.

Energy Minimization and the Determination of Gait A number of studies assume that gait should minimize energy expenditure. As a general concept, it appears sensible for humans and other animals to conserve metabolic energy, historically a scarce resource. This is especially the case for locomotion, a costly and frequently performed task, as was stated explicitly as part of the “six determinants of gait” hypothesis as: “fundamentally locomotion is the translation of the center of gravity through space along a pathway requiring the least expenditure of energy.”8(p558) Although considerable experimental evidence would appear to support this statement, it is important to evaluate it with deeper consideration rather than treat it as scientific fact. Much of the experimental evidence to date supports the energy minimization hypothesis for normal gait. For example, the comfortable walking speed typically is close to optimal.83 At a given speed, humans are observed to walk at a combination of step length,84 frequency,85 and width43 that minimizes energy expenditure. Deviations from the preferred values for these parameters cause energy expenditure to increase. There is no limit to ways in which walking otherwise could be altered, but to our knowledge, there are no elective changes that a person can make to his or her walking pattern that will decrease energy expenditure relative to the norm for a given speed.

February 2010

Despite these observations, the minimization hypothesis (as stated by Saunders et al8) is incomplete as a scientific statement. Humans often locomote in a manner that does not minimize energy expenditure. They often walk very quickly or very slowly, and may occasionally even run—all examples of behaviors that expend more than a minimum of energy.85 Some pedestrians can be observed to climb stairs in preference to an escalator or elevator that would clearly save their own energy. More generally, pedestrians in large cities have been observed to prefer walking speeds as much as twice as fast as those of pedestrians in small towns86 and, therefore, to expend more energy to travel the same distance. They cannot all be using energy minimization as the only criterion for choosing their preferred walking pattern. It is apparent, therefore, that locomotion can have multiple goals, as is recognized in the International Classification of Functioning, Disability and Health.87 Even the speed of level walking is not necessarily energetically optimal because the need to travel within a specified amount of time may induce a person to hurry, even if that favors a fasterthan-optimal speed. Such goals often are not explicitly clear. The faster walking speeds in large cities illustrate how implicit social and cultural cues also may play an important role. Even the apparently straightforward task of walking “normally” across a laboratory floor may entail implicit goals. These implicit goals may include unspoken social cues conveyed by the experimenter that could affect the preferred speed, the interpretation of “normal,” and even the quality of gait. Clinicians have remarked that the gait demonstrated by a patient in the clinic often is not representative of daily living. Energy minimization, therefore, should be considered an important contribut-

ing goal that competes and sometimes even conflicts with a variety of other possible interests. When gait is treated as the result of competing goals, some of them inexplicable, there is little room for definitive statements regarding energy minimization. Although energy minimization may be one goal of gait, it remains to be determined how it applies to pathological gait. The hypothesis proposed by Saunders et al was that “pathological gait may be viewed as an attempt to preserve as low a level of energy consumption as possible by exaggerations of the motions at unaffected levels.”8(p558) Evidence is lacking to counter this proposal, but again implicit task goals may play a role. One possible implicit goal may be to appear healthy and avoid attention, for social and other reasons. For example, some patients may prefer to walk rather than to use a wheelchair, even if the wheelchair is more economical. Another issue in many clinical cases is symmetry. In the case where the body is symmetrical, it generally is expected that the dynamic walking gait, and perhaps the energetically optimal gait, also will be symmetrical. In pathological cases, however, there may be asymmetry in body biomechanics, such as differences in muscle strength, range of motion, or limb length, mass, or geometry. There also may be asymmetry in the neural command capacity, such as with the abnormal motor synergies that often accompany hemiplegia. In terms of passive dynamics, an asymmetric body generally will be expected to yield a passively asymmetric gait.37 Some form of active control, therefore, would be needed to reduce the asymmetry. There is little reason to expect forced symmetry to improve upon economy. The desire for symmetry might be cosmetic and, in some cases, even contrary to the goal of economy.

Volume 90

Number 2

Physical Therapy f

171

Principles of Gait Implicit task goals are difficult to identify, lessening the scientific utility of the energy minimization hypothesis. It nonetheless is instructive to consider an example where energy minimization may aid the design of experiments. Older adults have long been observed to expend more energy to walk than young adults moving at the same speed.2 One possibility is that neuromechanical differences between the 2 age groups are small, and older adults select their gait because of implicit task goals rather than an inability to produce the optimal gait. Perhaps they prefer a wide step to enhance stability, with long-term benefits—for example, fewer falls— sufficient to justify greater short-term energy expenditure. It then is relevant to test whether there is a gait pattern more economical than the preferred one. Another possibility is that gait patterns change to remain energetically optimal. This possibility implies age-related changes in neuromotor control abilities or biomechanical properties that make it more economical to adopt a gait pattern different from that of a younger person. Perhaps the tendons have become stiffer, body mass distribution has changed, or sensor precision has degraded. These and a myriad of other changes might be optimized by an altered gait pattern. Although many such changes certainly do occur with age, it is unclear whether they indeed explain the gait pattern. These possibilities show how the energy minimization hypothesis can drive scientific inquiry, without need to be taken as scientific fact. The concept of optimization and the competition of multiple goals help to clarify possible hypotheses, drive new experiments, and further our understanding of the fundamental principles underlying walking and their application to clinical practice.

172

f

Physical Therapy

Volume 90

Limitations We have reviewed how the principles of mechanics contribute to human walking. The dynamic behavior of the limbs is only one aspect of the overall walking pattern, and rehabilitation is necessarily concerned not with a single aspect but with the combined behavior of the CNS and the musculoskeletal system. It is challenging to incorporate other contributions into present quantitative models, because it is difficult to predict how neural control of muscular properties will adapt or compensate in response to injury or impairment. In some cases, the available theories are qualitative and difficult to make explicit and quantitative. In other cases, there may be quantitative theories that make predictions, but few methods of testing them experimentally. The intent of the present perspective is not to minimize the importance of neural control or musculoskeletal adaptations, but to highlight a few mechanical principles of walking that are amenable to experimental testing. A fortunate outcome of these experiments is the finding that the dynamics of the limbs play an important role in both determining the normal gait pattern, its overall energetic economy, and some aspects of stepto-step stability. We nevertheless consider the dynamic principles of gait not to offer a solution to rehabilitation, but rather to serve merely as a useful starting point for experimental inquiry.

Conclusion We have reviewed the principles of gait, as examined with the dynamic walking approach. This approach begins with the dynamics of the limbs and shows how they may be sufficient to generate an economical and stable gait on their own. This approach then considers how motor inputs can best provide power and contribute to improved stability. The approach has utility in the design of

Number 2

experimental tests for healthy gait and may have implications for clinical gait. It is important to note that dynamic walking is only one of many possible approaches. Pathological gait almost certainly is best studied with a multimodal approach, integrating simple principles, clinical observations, and controlled experiments conducted to explicate the deficits. The examples here show how the dynamic walking approach might apply to the integrative study of gait pathologies. These examples may be viewed as starting points for new investigations, wherein experimental testing will be indispensible. Simple principles cannot explain all of the complexities of gait, but they may help to pose useful questions that must be answered to understand those complexities. Both authors provided concept/idea/project design and writing. Dr Donelan provided data collection and analysis, project management, fund procurement, facilities/ equipment, institutional liaisons, clerical support, and consultation (including review of manuscript before submission). This work was supported, in part, by National Institutes of Health grants HD055706 and DC6466, the Michael Smith Foundation for Health Research, and the Canadian Institutes for Health Research. This article was received April 14, 2009, and was accepted October 25, 2009. DOI: 10.2522/ptj.20090125

References 1 Gonzalez EG, Corcoran PJ. Energy expenditure during ambulation. In: Downey JA, Myers SJ, Gonzalez EG, Lieberman JS, eds. The Physiological Basis of Rehabilitation Medicine. Boston, MA: Butterworth-Heinemann; 1994:413– 446. 2 Waters R, Mulroy S. The energy expenditure of normal and pathologic gait. Gait Posture. 1999;9:207–231. 3 Pardo RD, Deathe AB, Winter DA. Walker user risk index: a method for quantifying stability in walker users. Am J Phys Med Rehabil. 1993;72:301–305. 4 Said CM, Goldie PA, Patla AE, et al. Balance during obstacle crossing following stroke. Gait Posture. 2008;27:23–30.

February 2010

Principles of Gait 5 Bohannon RW, Andrews AW, Smith MB. Rehabilitation goals of patients with hemiplegia. Int J Rehabil Res. 1988;11;181–183. 6 Damiano DL, Arnold AS, Steele KM, Delp SL. Can strength training predictably improve gait kinematics? A pilot study on the effects of hip and knee extensor strengthening on lower-extremity alignment in cerebral palsy. Phys Ther. 2010;90:269 –279. 7 Burnfield JM, Shu Y, Buster T, Taylor A. Similarity of joint kinematics and muscle demands between elliptical training and walking: implications for practice. Phys Ther. 2010;90:289 –305. 8 Saunders JBD, Inman VT, Eberhart HD. The major determinants in normal and pathological gait. J Bone Joint Surg Am. 1953;35:543–558. 9 Perry J. Gait Analysis: Normal and Pathological Function. Thorofare, NJ: Slack Inc; 1992. 10 Gard SA, Childress DS. What determines the vertical displacement of the body during normal walking? J Prosthet Orthot. 2001;13:64 – 67. 11 Gard SA, Childress DS. The effect of pelvic list on the vertical displacement of the trunk during normal walking. Gait Posture. 1997;5:233–238. 12 Kerrigan DC, Riley PO, Lelas JL, Della Croce U. Quantification of pelvic rotation as a determinant of gait. Arch Phys Med Rehabil. 2001;82:217–220. 13 Della Croce U, Riley PO, Lelas JL, Kerrigan DC. A refined view of the determinants of gait. Gait Posture. 2001;14:79 – 84. 14 Gordon KE, Ferris DP, Kuo AD. Metabolic and mechanical energy costs of reducing vertical center of mass movement during gait. Arch Phys Med Rehabil. 2009; 90:136 –144. 15 Massaad F, Lejeune TM, Detrembleur C. The up and down bobbing of human walking: a compromise between muscle work and efficiency. J Physiol. 2007;582: 789 –799. 16 Ortega JD, Farley CT. Minimizing center of mass vertical movement increases metabolic cost in walking. J Appl Physiol. 2005; 99:2099 –2107. 17 Kuo AD. The six determinants of gait and the inverted pendulum analogy: a dynamic walking perspective. Hum Mov Sci. 2007; 26:617– 656. 18 Choi H. Physical Medicine and Rehabilitation Pocketpedia. Philadelphia, PA: Lippincott Williams & Wilkins; 2003:26. 19 Cuccurullo S. Physical Medicine and Rehabilitation Board Review. New York, NY: Demos Medical Publishing; 2004:411. 20 Gage JR. Gait Analysis in Cerebral Palsy. London, United Kingdom: Mac Keith; 1991:67. Clinics in Developmental Medicine series. 21 Gross J, Fetto J, Rosen E. Musculoskeletal Examination. Cambridge, MA: Blackwell Science; 1996:443. 22 Houglum PA. Therapeutic Exercise for Musculoskeletal Injuries. Champaign, IL: Human Kinetics Inc; 2005:359.

February 2010

23 Knudson DV, Morrison CS. Qualitative Analysis of Human Movement. Champaign, IL: Human Kinetics Inc; 2002:184. 24 Malanga G, Delisa JA. Clinical observation. In: Delisa JA, ed. Gait Analysis in the Science of Rehabilitation. Baltimore, MD: Diane Publishing; 1998:1–10. 25 Medved V. Measurement of Human Locomotion. Boca Raton, FL: CRC Press; 2001:63. 26 Pease WL, Bowyer BL, Kadyan V. Human walking. In: DeLisa JA, Gans BM, Walsh NE, et al, eds. Physical Medicine and Rehabilitation: Principles and Practice. Philadelphia, PA: Lippincott Williams & Wilkins; 2004:155–168. 27 Pedotti A, Ferrarin M. Restoration of Walking for Paraplegics: Recent Advancements and Trends. Amsterdam, the Netherlands: IOS Press; 1992:23. 28 Polak F. Gait analysis. In: Pitt-Brooke J, ed. Rehabilitation of Movement: Theoretical Basis of Clinical Practice. Oxford, United Kingdom: Elsevier Health Sciences; 1998: 282. 29 Seymour R. Prosthetics and Orthotics: Lower Limb and Spinal. Philadelphia, PA: Lippincott Williams & Wilkins; 2002:103 30 Spivack BS. Evaluation and Management of Gait Disorders. London, United Kingdom: Informa Healthcare; 1995:318. 31 Whittle MW. Gait Analysis: An Introduction. Boston, MA: Butterworth-Heinemann; 1996:97. 32 Wright V, Radin EL. Mechanics of Human Joints: Physiology, Pathophysiology, and Treatment. New York, NY: Marcel Dekker; 1993:96. 33 Kirtley C. Clinical Gait Analysis: Theory and Practice. Amsterdam, the Netherlands: Churchill Livingstone; 2006. 34 Cavagna GA, Heglund NC, Taylor CR. Mechanical work in terrestrial locomotion: two basic mechanisms for minimizing energy expenditure. Am J Physiol. 1977;233: R243–R261. 35 Lee CR, Farley CT. Determinants of the center of mass trajectory in human walking and running. J Exp Biol. 1998;201: 2935–2944. 36 Mochon S, McMahon TA. Ballistic walking. J Biomech. 1980;13:49 –57. 37 McGeer T. Passive dynamic walking. International Journal of Robotics Research. 1990;9;62– 82. 38 Collins SH, Ruina A, Tedrake R, Wisse M. Efficient bipedal robots based on passivedynamic walkers. Science. 2005;307: 1082–1085. 39 McGeer T. Passive walking with knees. In: Proceedings of the IEEE International Robotics & Automation Conference. Los Alamitos, CA: IEEE Computer Society; 1990:1640 –1645. 40 Margaria R. Biomechanics and Energetics of Muscular Exercise. London, United Kingdom: Oxford Press; 1976. 41 Kuo AD. Energetics of actively powered locomotion using the simplest walking model. J Biomech Eng. 2002;124: 113–120.

42 Kuo AD, Donelan JM, Ruina A. Energetic consequences of walking like an inverted pendulum: step-to-step transitions. Exerc Sport Sci Rev. 2005;33:88 –97. 43 Donelan JM, Kram R, Kuo AD. Mechanical and metabolic determinants of the preferred step width in human walking. Proc R Soc Lond B Biol Sci. 2001;268; 1985–1992. 44 Donelan JM, Kram R, Kuo AD. Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking. J Exp Biol. 2002;205: 3717–3727. 45 Dean JC, Kuo AD. Elastic coupling of limb joints enables faster bipedal walking. J R Soc Interface. 2009;6:561–573. 46 Doke J, Donelan JM, Kuo AD. Mechanics and energetics of swinging the human leg. J Exp Biol. 2005;208:439 – 445. 47 Doke J, Kuo AD. Energetic cost of producing muscle force, rather than work, to swing the human leg. J Exp Biol. 2007; 210:2390 –2398. 48 Kuo AD. A simple model of bipedal walking predicts the preferred speed-step length relationship. J Biomech Eng. 2001; 123:264 –269. 49 Hansen AH, Childress DS, Knox EH. Rollover shapes of human locomotor systems: effects of walking speed. Clin Biomech (Bristol, Avon). 2004;19:407– 414. 50 Hansen AH, Childress DS, Miff SC, et al. The human ankle during walking: implications for design of biomimetic ankle prostheses. J Biomech. 2004;37:1467–1474. 51 Adamczyk PG, Collins SH, Kuo AD. The advantages of a rolling foot in human walking. J Exp Biol. 2006;209:3953–3963. 52 Collins SH, Wisse M, Ruina A. A threedimensional passive-dynamic walking robot with two legs and knees. International Journal of Robotics Research. 2001;20:607– 615. 53 McGeer T. Dynamics and control of bipedal locomotion. J Theor Biol. 1993;163: 277–314. 54 Wisse M, Schwab AL, van der Helm FCT. Passive dynamic walking model with upper body. Robotica. 2004;22;681– 688. 55 Hobbelen DGE. Ankle actuation for limit cycle walkers. International Journal of Robotics Research. 2008;27:709 –735. 56 Wisse M, Feliksdal G, van Frankenhuyzen J, Moyer B. Passive-based robot Denise: a simple, efficient, and lightweight biped. IEEE Robotics and Automation Magazine. 2007;14:52– 62. 57 Garcia M, Chatterjee A, Ruina A, Coleman M. The simplest walking model: stability, complexity, and scaling. ASME Journal of Biomechanical Engineering. 1998;120: 281–288. 58 Kuo AD. Stabilization of lateral motion in passive dynamic walking. International Journal of Robotics Research. 1999; 18:917–930. 59 Bauby CE, Kuo AD. Active control of lateral balance in human walking. J Biomech. 2000;33:1433–1434.

Volume 90

Number 2

Physical Therapy f

173

Principles of Gait 60 Owings TM, Grabiner MD. Variability of step kinematics in young and older adults. Gait Posture. 2004;20:26 –29. 61 O’Connor SM, Kuo AD. Directiondependent control of balance during walking and standing. J Neurophysiol. 2009; 102:1411–1419. 62 Donelan JM, Shipman DW, Kram R, Kuo AD. Mechanical and metabolic requirements for active lateral stabilization in human walking. J Biomech. 2004;37: 827– 835. 63 Kim CM, Eng JJ. Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed. Gait Posture. 2004;20: 140 –146. 64 Olney SJ, Griffin MP, Monga TN, Mcbride ID. Work and power in gait of stroke patients. Arch Phys Med Rehabil. 1991;72: 309 –314. 65 Olney SJ, Richards C. Hemiparetic gait following stroke, part 1: characteristics. Gait Posture. 1996;4:136 –148. 66 Hewson D, Eng JJ, Christie A, Donelan JM. Efficiency of step-to-step transition work in hemiparetic gait. Presented at: North American Congress on Biomechanics; August 5–9, 2008; Ann Arbor, Michigan. 67 Stoquart GG, Detrembleur C, Nielens H, Lejeune TM. Efficiency of work production by spastic muscles. Gait Posture. 2005;22;331–337. 68 Vanderpool MT, Collins SH,Kuo AD. Ankle fixation need not increase the energetic cost of human walking. Gait Posture. 2008;28:27– 433. 69 Hansen AH, Meier MR, Sessoms PH, Childress DS. The effects of prosthetic foot roll-over shape arc length on the gait of trans-tibial prosthesis users. Prosthet Orthot Int. 2006;30:286 –299.

Invited Commentary

f

Physical Therapy

Volume 90

79 Brach JS, Berlin JE, VanSwearingen J.M, et al. Too much or too little step width variability is associated with a fall history in older persons who walk at or near normal gait speed. J Neuroeng Rehabil. 2005; 2:21. 80 Maki BE. Gait changes in older adults: predictors of falls or indicators of fear. J Am Geriatr Soc. 1997;45:313–320. 81 Ortega JD, Fehlman LA, Farley CT. Effects of aging and arm swing on the metabolic cost of stability in human walking. J Biomech. 2008;41:3303–3308. 82 Chang CL, Ulrich BD. Lateral stabilization improves walking in people with myelomeningocele. J Biomech. 2008;41: 1317–1323. 83 Ralston HJ. Energy-speed relation and optimal speed during level walking. Int Z Angew Physiol. 1958;17:277–283. 84 Elftman H. Biomechanics of muscle. J Bone Joint Surg Am. 1966;48:363–377. 85 Zarrugh MY, Todd FN, Ralston HJ. Optimization of energy expenditure during level walking. Eur J Appl Physiol. 1974;33:293– 306. 86 Bornstein MH, Bornstein HG. The pace of life. Nature. 1976;259:557–559. 87 International Classification of Functioning, Disability and Health: ICF. Geneva, Switzerland: World Health Organization; 2001.

Janice J. Eng

The article by Kuo and Donelan1 raises a number of essential issues about our understanding of gait. In the larger picture, the article reminds us of the importance of theory in research and practice. Theories underlying gait have an extensive history with anthropologists, who have long debated the evolutionary details of upright walking adopted by humans more than 4 million years ago.2 More recently in a historical review, Baker3 described how scientists made numerous observations of gait and developed theories of human movement using Newtonian

174

70 Hoffer JA, Stein RB, Haugland MK, et al. Neural signals for command control and feedback in functional neuromuscular stimulation: a review. J Rehabil Res Dev. 1996;33:145–157. 71 Weber DJ, Stein RB, Chan KM, et al. BIONic WalkAide for correcting foot drop. IEEE Trans Neural Syst Rehabil Eng. 2005;13:242–246. 72 Hesse S, Bertelt C, Jahnke MT, et al. Treadmill training with partial body weight support compared with physiotherapy in nonambulatory hemiparetic patients. Stroke. 1995;26:976 –981. 73 Grabowski A, Farley CT, Kram R. Independent metabolic costs of supporting body weight and accelerating body mass during walking. J Appl Physiol. 2005;98: 579 –583. 74 Kuo AD. An optimal state estimation model of sensory integration in human postural balance. J Neural Eng. 2005;2: S235–S249. 75 Dean JC, Alexander NB, Kuo AD. The effect of lateral stabilization on walking in young and old adults. IEEE Trans Biomed Eng. 2007;54:1919 –1926. 76 Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil. 2001;82:1050 – 1056. 77 Owings TM, Grabiner MD. Step width variability, but not step length variability or step time variability, discriminates gait of healthy young and older adults during treadmill locomotion. J Biomech. 2004;37: 935–938. 78 Woledge RC, Birtles DB, Newham DJ. The variable component of lateral body sway during walking in young and older humans. J Gerontol A Biol Sci Med Sci. 2005; 60:1463–1468.

mechanics in the 1700s and 1800s, but there was little experimental work to substantiate these theories. The Berkeley Biomechanics Group led by Verne Inman and Howard Eberhart contributed to the creation of modern-day gait analysis, and their group’s article, published by Saunders et al4 in 1953, was a major milestone toward the development of conceptual theories underlying gait. A position statement by the American Physical Therapy Association emphasized that, in research, theory can provide an understanding of ob-

Number 2

servable phenomena, yield testable predictions, and motivate new lines of investigation, including novel interventions.5 The theories presented by Saunders et al4 were based on observations and measurements of gait and used general principles such as Newton’s Laws. An important aspect of a theory is that it should generate testable hypotheses, which then may lead to refinement of existing theories or creation of new theories.5 Certainly, Saunders et al4 presented a number of hypotheses and predictions that were testable.

February 2010

Principles of Gait Despite the passing of more than 50 years since Saunders and colleagues’ article,4 Kuo and Donelan point out that there has been little validation of the theories presented in that article. One has to sympathize with the gait researchers of that time period because an analysis of a single stride required 14,000 numerical calculations done by hand, in addition to copious graphical plots.3 Nevertheless, it is somewhat surprising that such prominent theories of gait were not tested more extensively over the past 3 decades when computerized gait analysis came into existence. Thus, I applaud Kuo and Donelan’s work to test the hypotheses generated from Saunders et al4 and, in particular, the hypothesis that “fundamentally locomotion is the translation of the center of gravity through space along a pathway requiring the least expenditure of energy.” Kuo and Donelan present a very convincing and elegant example that contradicts this hypothesis on energy expenditure, that is, walking with shorter, but faster, steps minimizes center of mass displacement but results in greater energy expenditure. They also describe another example—walking with the legs bent (crouch gait)—that minimizes center of mass displacement but results in higher levels of energy expenditure. To be fair, it is difficult to imagine that Saunders et al4 meant for their hypothesis to be interpreted using this last example (an extreme walking pattern, albeit common in cerebral palsy). In fact, Saunders et al4 recognized that deviations away from the normal center of mass path caused inefficiencies. In a description of the gait of individuals with below-the-knee amputations, they stated, “Because of the minor changes in direction from the smooth sinusoidal pathway of the center of gravity, high accelerations are required which dissipate energy and make such deviations very costFebruary 2010

ly.”4 These examples serve as a reminder that a theory should permit generalization, but if the limits of generalizability are not defined, it leaves the theory open to debate and to potential misinterpretation. Kuo et al introduce the principle of the step-to-step transition cost of human walking, which is the combined costs of both limbs to redirect the center of mass. This principle can explain the trade-off between speed and step length not only in the anterior-posterior direction but, impressively, also in the medial-lateral direction (step width). Undoubtedly, we will see this model tested and refined to expand boundaries. There seem to be unlimited hypotheses in which the step-to-step transition cost of human walking can be tested in regard to aging and rehabilitation. For example, are the shorter steps observed in older adults a result of a need for less redirection of the center of mass velocity and potentially more stability? Dynamic walking models have been used to examine walking stability and balance deficits. From these models, Kuo et al suggest that external stabilization could be manipulated as part of a rehabilitation program. Clinicians would argue that such practice is already in place (manipulating the ground surface with foam, providing physical assistance from a therapist, and progressing to handrails and assistive devices). A useful application would be to use these dynamic models to inform clinicians of the calculated stability of one therapeutic practice (eg, walking using an overhead harness) versus another (eg, walking and grasping a rail). It also would be of interest to model the gains in walking stability derived from sensory information when simply touching a rail6 or when using a shoe with enhanced foot-sole properties,7 as these sen-

sory protocols have been shown to improve walking stability. In earlier work, Bauby and Kuo8 added “noise” to their dynamic walking models, which generated step variability, and thus simulated a model for balance deficits. Interestingly, step variability was shown to discriminate between “fallers” and “nonfallers” 3 decades ago,9 but there has been a recent surge in the use of this measure. Gait researchers have always known that step-to-step variability was greater in their pathological populations, with one practical solution being to collect more trials to reduce variability. It is heartening to know that individual trial data have value, rather than simply compressing all this information into a single “average” step profile. It also is promising to see psychometric properties being established for these variables, with a recent article on the clinical meaningful change of gait variability.10 Dynamic models have the potential to explore mechanisms underlying this measure, and Kuo’s group has attempted to understand the interactions of the active versus passive contributions underlying step-to-step variability. As with all models, the limits of generalizability must be defined. The dynamic walking models by Kuo and Donelan are applicable only where the limbs behave like pendulums. Many pathological gait patterns— such as genu recurvatum, crouch gait, and hip circumduction—that are common in neurological conditions might not emulate pendular activity, and thus the step-to-step transition cost might not be valid. In addition, dynamic models, especially of the ankle-foot complex, that utilize the nonsagittal planes are still in their infancy. Recently, there have been attempts to use multisegmented, multi-directional anklefoot models, which should provide new experimental data.11 As Kuo

Volume 90

Number 2

Physical Therapy f

175

Principles of Gait and Donelan highlight, computational models cannot be expected to make quantitative predictions about every case. We can expect that, together, computational and clinical researchers will generate new and pertinent theories about gait. The late Dr Jules Rothstein emphasized that theories must not become dogma seen as “truths.”12 The testing and refining of these theories will serve to develop and advance the field of gait research and its clinical applications. J.J. Eng, PhD, BSc(PT/OT), is Professor, Department of Physical Therapy, 212-2177 Wesbrook Mall, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z3, and Research Scientist, Rehab Research Lab, GF Strong Rehab Centre. Ad-

176

f

Physical Therapy

Volume 90

dress all correspondence to Dr Eng at: [email protected]. DOI: 10.2522/ptj.20090125.ic

References 1 Kuo AD, Donelan JM. Dynamic principles of gait and their clinical implications. Phys Ther. 2010;90:157–174. 2 Lovejoy CO. The Origin of Man. Science. 1981;211:341–350. 3 Baker R. The history of gait analysis before the advent of modern computers. Gait Posture. 2007;26:331– 42. 4 Saunders JB, Inman VT, Eberhart HD. The major determinants in normal and pathological gait. J Bone Joint Surg Am. 1953;35:543–558. 5 Position Statement: Role of Theory in Physical Therapy. BOD P11–99 –29 – 87 [Position]. Alexandria, VA: American Physical Therapy Association; 1987. 6 Boonsinsukh R, Panichareon L, PhansuwanPujito P. Light touch cue through a cane improves pelvic stability during walking in stroke. Arch Phys Med Rehabil. 2009; 90:919 –926.

Number 2

7 Menant JC, Perry SD, Steele JR, et al. Effects of shoe characteristics on dynamic stability when walking on even and uneven surfaces in young and older people. Arch Phys Med Rehabil. 2008;89:1970 –1976. 8 Bauby CE, Kuo AD. Active control of lateral balance in human walking. J Biomech. 2000;33:1433–1440. 9 Guimaraes RM, Isaacs B. Characteristics of the gait in old people who fall. Int Rehabil Med. 1980;2:177–180. 10 Brach JS, Perera S, Studenski S, et al. Meaningful change in measures of gait variability in older adults. Gait Posture. 2009 November 2. [Epub ahead of print] 11 Jenkyn TR, Shultz R, Giffin JR, Birmingham TB. A comparison of subtalar joint motion during anticipated medial cutting turns and level walking using a multi-segment foot model. Gait Posture. 2009 November 6. [Epub ahead of print] 12 Rothstein JM. The role of theory in research. Presented at: Combined Sections Meeting of the American Physical Therapy Association; February 4, 1999; Seattle, Washington.

February 2010

Principles of Gait 60 Owings TM, Grabiner MD. Variability of step kinematics in young and older adults. Gait Posture. 2004;20:26 –29. 61 O’Connor SM, Kuo AD. Directiondependent control of balance during walking and standing. J Neurophysiol. 2009; 102:1411–1419. 62 Donelan JM, Shipman DW, Kram R, Kuo AD. Mechanical and metabolic requirements for active lateral stabilization in human walking. J Biomech. 2004;37: 827– 835. 63 Kim CM, Eng JJ. Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed. Gait Posture. 2004;20: 140 –146. 64 Olney SJ, Griffin MP, Monga TN, Mcbride ID. Work and power in gait of stroke patients. Arch Phys Med Rehabil. 1991;72: 309 –314. 65 Olney SJ, Richards C. Hemiparetic gait following stroke, part 1: characteristics. Gait Posture. 1996;4:136 –148. 66 Hewson D, Eng JJ, Christie A, Donelan JM. Efficiency of step-to-step transition work in hemiparetic gait. Presented at: North American Congress on Biomechanics; August 5–9, 2008; Ann Arbor, Michigan. 67 Stoquart GG, Detrembleur C, Nielens H, Lejeune TM. Efficiency of work production by spastic muscles. Gait Posture. 2005;22;331–337. 68 Vanderpool MT, Collins SH,Kuo AD. Ankle fixation need not increase the energetic cost of human walking. Gait Posture. 2008;28:27– 433. 69 Hansen AH, Meier MR, Sessoms PH, Childress DS. The effects of prosthetic foot roll-over shape arc length on the gait of trans-tibial prosthesis users. Prosthet Orthot Int. 2006;30:286 –299.

Invited Commentary

f

Physical Therapy

Volume 90

79 Brach JS, Berlin JE, VanSwearingen J.M, et al. Too much or too little step width variability is associated with a fall history in older persons who walk at or near normal gait speed. J Neuroeng Rehabil. 2005; 2:21. 80 Maki BE. Gait changes in older adults: predictors of falls or indicators of fear. J Am Geriatr Soc. 1997;45:313–320. 81 Ortega JD, Fehlman LA, Farley CT. Effects of aging and arm swing on the metabolic cost of stability in human walking. J Biomech. 2008;41:3303–3308. 82 Chang CL, Ulrich BD. Lateral stabilization improves walking in people with myelomeningocele. J Biomech. 2008;41: 1317–1323. 83 Ralston HJ. Energy-speed relation and optimal speed during level walking. Int Z Angew Physiol. 1958;17:277–283. 84 Elftman H. Biomechanics of muscle. J Bone Joint Surg Am. 1966;48:363–377. 85 Zarrugh MY, Todd FN, Ralston HJ. Optimization of energy expenditure during level walking. Eur J Appl Physiol. 1974;33:293– 306. 86 Bornstein MH, Bornstein HG. The pace of life. Nature. 1976;259:557–559. 87 International Classification of Functioning, Disability and Health: ICF. Geneva, Switzerland: World Health Organization; 2001.

Janice J. Eng

The article by Kuo and Donelan1 raises a number of essential issues about our understanding of gait. In the larger picture, the article reminds us of the importance of theory in research and practice. Theories underlying gait have an extensive history with anthropologists, who have long debated the evolutionary details of upright walking adopted by humans more than 4 million years ago.2 More recently in a historical review, Baker3 described how scientists made numerous observations of gait and developed theories of human movement using Newtonian

174

70 Hoffer JA, Stein RB, Haugland MK, et al. Neural signals for command control and feedback in functional neuromuscular stimulation: a review. J Rehabil Res Dev. 1996;33:145–157. 71 Weber DJ, Stein RB, Chan KM, et al. BIONic WalkAide for correcting foot drop. IEEE Trans Neural Syst Rehabil Eng. 2005;13:242–246. 72 Hesse S, Bertelt C, Jahnke MT, et al. Treadmill training with partial body weight support compared with physiotherapy in nonambulatory hemiparetic patients. Stroke. 1995;26:976 –981. 73 Grabowski A, Farley CT, Kram R. Independent metabolic costs of supporting body weight and accelerating body mass during walking. J Appl Physiol. 2005;98: 579 –583. 74 Kuo AD. An optimal state estimation model of sensory integration in human postural balance. J Neural Eng. 2005;2: S235–S249. 75 Dean JC, Alexander NB, Kuo AD. The effect of lateral stabilization on walking in young and old adults. IEEE Trans Biomed Eng. 2007;54:1919 –1926. 76 Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil. 2001;82:1050 – 1056. 77 Owings TM, Grabiner MD. Step width variability, but not step length variability or step time variability, discriminates gait of healthy young and older adults during treadmill locomotion. J Biomech. 2004;37: 935–938. 78 Woledge RC, Birtles DB, Newham DJ. The variable component of lateral body sway during walking in young and older humans. J Gerontol A Biol Sci Med Sci. 2005; 60:1463–1468.

mechanics in the 1700s and 1800s, but there was little experimental work to substantiate these theories. The Berkeley Biomechanics Group led by Verne Inman and Howard Eberhart contributed to the creation of modern-day gait analysis, and their group’s article, published by Saunders et al4 in 1953, was a major milestone toward the development of conceptual theories underlying gait. A position statement by the American Physical Therapy Association emphasized that, in research, theory can provide an understanding of ob-

Number 2

servable phenomena, yield testable predictions, and motivate new lines of investigation, including novel interventions.5 The theories presented by Saunders et al4 were based on observations and measurements of gait and used general principles such as Newton’s Laws. An important aspect of a theory is that it should generate testable hypotheses, which then may lead to refinement of existing theories or creation of new theories.5 Certainly, Saunders et al4 presented a number of hypotheses and predictions that were testable.

February 2010

Principles of Gait Despite the passing of more than 50 years since Saunders and colleagues’ article,4 Kuo and Donelan point out that there has been little validation of the theories presented in that article. One has to sympathize with the gait researchers of that time period because an analysis of a single stride required 14,000 numerical calculations done by hand, in addition to copious graphical plots.3 Nevertheless, it is somewhat surprising that such prominent theories of gait were not tested more extensively over the past 3 decades when computerized gait analysis came into existence. Thus, I applaud Kuo and Donelan’s work to test the hypotheses generated from Saunders et al4 and, in particular, the hypothesis that “fundamentally locomotion is the translation of the center of gravity through space along a pathway requiring the least expenditure of energy.” Kuo and Donelan present a very convincing and elegant example that contradicts this hypothesis on energy expenditure, that is, walking with shorter, but faster, steps minimizes center of mass displacement but results in greater energy expenditure. They also describe another example—walking with the legs bent (crouch gait)—that minimizes center of mass displacement but results in higher levels of energy expenditure. To be fair, it is difficult to imagine that Saunders et al4 meant for their hypothesis to be interpreted using this last example (an extreme walking pattern, albeit common in cerebral palsy). In fact, Saunders et al4 recognized that deviations away from the normal center of mass path caused inefficiencies. In a description of the gait of individuals with below-the-knee amputations, they stated, “Because of the minor changes in direction from the smooth sinusoidal pathway of the center of gravity, high accelerations are required which dissipate energy and make such deviations very costFebruary 2010

ly.”4 These examples serve as a reminder that a theory should permit generalization, but if the limits of generalizability are not defined, it leaves the theory open to debate and to potential misinterpretation. Kuo et al introduce the principle of the step-to-step transition cost of human walking, which is the combined costs of both limbs to redirect the center of mass. This principle can explain the trade-off between speed and step length not only in the anterior-posterior direction but, impressively, also in the medial-lateral direction (step width). Undoubtedly, we will see this model tested and refined to expand boundaries. There seem to be unlimited hypotheses in which the step-to-step transition cost of human walking can be tested in regard to aging and rehabilitation. For example, are the shorter steps observed in older adults a result of a need for less redirection of the center of mass velocity and potentially more stability? Dynamic walking models have been used to examine walking stability and balance deficits. From these models, Kuo et al suggest that external stabilization could be manipulated as part of a rehabilitation program. Clinicians would argue that such practice is already in place (manipulating the ground surface with foam, providing physical assistance from a therapist, and progressing to handrails and assistive devices). A useful application would be to use these dynamic models to inform clinicians of the calculated stability of one therapeutic practice (eg, walking using an overhead harness) versus another (eg, walking and grasping a rail). It also would be of interest to model the gains in walking stability derived from sensory information when simply touching a rail6 or when using a shoe with enhanced foot-sole properties,7 as these sen-

sory protocols have been shown to improve walking stability. In earlier work, Bauby and Kuo8 added “noise” to their dynamic walking models, which generated step variability, and thus simulated a model for balance deficits. Interestingly, step variability was shown to discriminate between “fallers” and “nonfallers” 3 decades ago,9 but there has been a recent surge in the use of this measure. Gait researchers have always known that step-to-step variability was greater in their pathological populations, with one practical solution being to collect more trials to reduce variability. It is heartening to know that individual trial data have value, rather than simply compressing all this information into a single “average” step profile. It also is promising to see psychometric properties being established for these variables, with a recent article on the clinical meaningful change of gait variability.10 Dynamic models have the potential to explore mechanisms underlying this measure, and Kuo’s group has attempted to understand the interactions of the active versus passive contributions underlying step-to-step variability. As with all models, the limits of generalizability must be defined. The dynamic walking models by Kuo and Donelan are applicable only where the limbs behave like pendulums. Many pathological gait patterns— such as genu recurvatum, crouch gait, and hip circumduction—that are common in neurological conditions might not emulate pendular activity, and thus the step-to-step transition cost might not be valid. In addition, dynamic models, especially of the ankle-foot complex, that utilize the nonsagittal planes are still in their infancy. Recently, there have been attempts to use multisegmented, multi-directional anklefoot models, which should provide new experimental data.11 As Kuo

Volume 90

Number 2

Physical Therapy f

175

Principles of Gait and Donelan highlight, computational models cannot be expected to make quantitative predictions about every case. We can expect that, together, computational and clinical researchers will generate new and pertinent theories about gait. The late Dr Jules Rothstein emphasized that theories must not become dogma seen as “truths.”12 The testing and refining of these theories will serve to develop and advance the field of gait research and its clinical applications. J.J. Eng, PhD, BSc(PT/OT), is Professor, Department of Physical Therapy, 212-2177 Wesbrook Mall, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z3, and Research Scientist, Rehab Research Lab, GF Strong Rehab Centre. Ad-

176

f

Physical Therapy

Volume 90

dress all correspondence to Dr Eng at: [email protected]. DOI: 10.2522/ptj.20090125.ic

References 1 Kuo AD, Donelan JM. Dynamic principles of gait and their clinical implications. Phys Ther. 2010;90:157–174. 2 Lovejoy CO. The Origin of Man. Science. 1981;211:341–350. 3 Baker R. The history of gait analysis before the advent of modern computers. Gait Posture. 2007;26:331– 42. 4 Saunders JB, Inman VT, Eberhart HD. The major determinants in normal and pathological gait. J Bone Joint Surg Am. 1953;35:543–558. 5 Position Statement: Role of Theory in Physical Therapy. BOD P11–99 –29 – 87 [Position]. Alexandria, VA: American Physical Therapy Association; 1987. 6 Boonsinsukh R, Panichareon L, PhansuwanPujito P. Light touch cue through a cane improves pelvic stability during walking in stroke. Arch Phys Med Rehabil. 2009; 90:919 –926.

Number 2

7 Menant JC, Perry SD, Steele JR, et al. Effects of shoe characteristics on dynamic stability when walking on even and uneven surfaces in young and older people. Arch Phys Med Rehabil. 2008;89:1970 –1976. 8 Bauby CE, Kuo AD. Active control of lateral balance in human walking. J Biomech. 2000;33:1433–1440. 9 Guimaraes RM, Isaacs B. Characteristics of the gait in old people who fall. Int Rehabil Med. 1980;2:177–180. 10 Brach JS, Perera S, Studenski S, et al. Meaningful change in measures of gait variability in older adults. Gait Posture. 2009 November 2. [Epub ahead of print] 11 Jenkyn TR, Shultz R, Giffin JR, Birmingham TB. A comparison of subtalar joint motion during anticipated medial cutting turns and level walking using a multi-segment foot model. Gait Posture. 2009 November 6. [Epub ahead of print] 12 Rothstein JM. The role of theory in research. Presented at: Combined Sections Meeting of the American Physical Therapy Association; February 4, 1999; Seattle, Washington.

February 2010

Perry Issue: Gait Rehab G. Yogev-Seligmann, PT, MSc, is a PhD student at The Dr. Miriam and Sheldon G. Adelson Graduate School of Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel, and Research Associate, Movement Disorders Unit, Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.

How Does Explicit Prioritization Alter Walking During Dual-Task Performance? Effects of Age and Sex on Gait Speed and Variability Galit Yogev-Seligmann, Yael Rotem-Galili, Anat Mirelman, Ruth Dickstein, Nir Giladi, Jeffrey M. Hausdorff

Background. Previous studies have demonstrated that the performance of a secondary task during walking alters gait. Objective. This study investigated the effects of task prioritization on walking in young and older adults to evaluate the “default” prioritization scheme used, the flexibility to alter prioritization and cortical resources allocated to gait and a secondary cognitive task, and any age-associated changes in these abilities.

Design. A cross-sectional study that explicitly altered the focus of attention was used to investigate the effects of prioritization in young and older adults who were healthy.

Methods. Gait speed and gait variability were evaluated in young adults (n⫽40) and older adults (n⫽17) who were healthy, both during usual walking and under 3 dual-task conditions: (1) no specific prioritization instructions, (2) prioritization of gait, and (3) prioritization of the cognitive task.

Results. Young adults significantly increased gait speed in the gait prioritization condition compared with gait speed in the no-instruction condition; a similar tendency was seen in the older adults. Gait speed was reduced when priority was given to the cognitive task in both age groups; however, this effect was less dramatic in the older adults. In the young adults, prioritization of gait tended to have different effects on gait speed among both men and women. In the older adults, but not in the young adults, all dual-task conditions produced increased gait variability, whereas prioritization did not alter this gait feature.

Limitations. The sample size and the relative homogeneity of the older adults could be considered as possible limitations of the study.

Conclusions. Even among young adults, the effects of secondary, cognitive tasks on gait speed are strongly influenced by prioritization. This finding was less significant in the older adults, suggesting that there is an age-associated decline in the ability to flexibly allocate attention to gait. Somewhat surprisingly, when prioritization was not explicitly instructed, gait speed in both young and older adults most closely resembled that of the condition when they were instructed to focus attention on the cognitive task.

Y. Rotem-Galili is an MA student in the Department of Gerontology, Faculty of Social Welfare and Health Sciences, University of Haifa, Mount Carmel, Haifa, Israel, and Lecturer, School of Physical Therapy, Assaf Harofeh Medical Center, Israel. A. Mirelman PT, PhD, is Clinical Researcher, Movement Disorders Unit, Department of Neurology, Tel Aviv Sourasky Medical Center. R. Dickstein, PT, DSc, is Chair, Department of Physical Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa. N. Giladi, MD, is Chair, Department of Neurology, Tel Aviv Sourasky Medical Center, and Associate Professor, Department of Neurology, Sackler Faculty of Medicine, Tel Aviv University. J.M. Hausdorff, MSME, PhD, is Director, Laboratory for Gait Analysis & Neurodynamics, Movement Disorders Unit, Department of Neurology, Tel Aviv Sourasky Medical Center; Lecturer, Harvard Medical School, Boston, Massachusetts; and Associate Professor, Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University. Address all correspondence to Dr Hausdorff at: [email protected]. [Yogev-Seligmann G, Rotem-Galili Y, Mirelman A, et al. How does explicit prioritization alter walking during dual-task performance? Effects of age and sex on gait speed and variability. Phys Ther. 2010; 90:177–186.] © 2010 American Physical Therapy Association Post a Rapid Response or find The Bottom Line: www.ptjournal.org

February 2010

Volume 90

Number 2

Physical Therapy f

177

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait

I

n the classic publications on gait,1,2 cognitive function is hardly mentioned. It is only in the last decade or so that we have come to realize the impact of cognitive function on the walking pattern. Gait is no longer considered to be an automatic, biomechanical task; instead, the role of cognitive function is increasingly acknowledged.3–5 In particular, 2 closely related cognitive domains— executive function and attention— evidently influence gait. It has been demonstrated that even young adults who are healthy walk slower when they are required to walk while performing another task.4,6 –9 Older adults who are healthy and, to a greater extent, patients with neurological impairments (eg, Parkinson disease [PD], chronic stroke) not only slow down and increase double-limb support but also become less stable (ie, increasing gait variability).10 –15 Although the mechanisms underlying these reactions to a dual task are not fully understood,16 assessment of dual-task abilities may provide important information on gait, its automaticity, and the risk for falls that might not be apparent during a routine examination.17 The simultaneous performance of 2 or more tasks may create a conflict Available With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

178

f

Physical Therapy

Volume 90

and a need to determine which of the tasks receives priority, especially when information processing is limited.18,19 Bloem and colleagues10,20,21 reported that both young and older adults who are healthy spontaneously prioritize gait stability over success on the “secondary” cognitive task when no specific prioritization instruction or allocation of attention is given. This “posture-first” strategy, a concept originally introduced by Shumway-Cook et al in 1997,22 makes sense from an ecologic perspective, as it helps to prevent loss of balance. Interestingly, patients with PD apparently prioritize the cognitive task when they do not receive explicit instructions to prioritize gait, inadvertently increasing their risk for falling.21 A few studies have examined the effects of explicit instructions (also referred to as the “instructional set”) or prioritization on gait and the ability to allocate attention to either the cognitive task or the motor task.23–25 These initial reports raise interesting questions about the influence of attention on dual-task performance and the ability to successfully manipulate attentional demands and shift focus to and from gait. Several unanswered questions remain, including whether all aspects of gait respond similarly to changes in attention and prioritization. The main aims of the present study were: (1) to investigate the impact of explicit instructions of task prioritization on 2 different aspects of gait (ie, gait speed and gait variability); (2) to assess which prioritization condition (cognitive or gait) most closely resembles the default, nopriority instruction condition (ie, the spontaneous, self-selected strategy); (3) to evaluate the ability to change prioritization and allocation of cognitive resources during performance of a motor task (ie, gait) and a cognitive task (an ability likely related to executive function26,27 and flexibility

Number 2

in attention allocation); and (4) to determine the effects of aging on these abilities. In secondary analyses, we explored the possible role of sex and executive function abilities on dual-task performance and prioritization effects.

Method Participants Forty young adults who were healthy (20 women, 20 men; mean age [SD]⫽26.8 [1.6] years) and 17 older adults who were healthy (10 men, 7 women; mean age [SD]⫽72 [6.8] years) participated in this study. Young adults were included if they were between the ages of 20 and 30 years, were not taking any medications, and were free of acute or chronic disease. Elderly adults were recruited from the community and ongoing studies on older adults who are healthy. They were all between 60 and 90 years of age and able to walk independently without an assistive device. In addition, adults in both age groups were excluded if they had any orthopedic, neurological, or mental disturbances that might directly affect their gait or cognitive function. Procedure After providing written informed consent, the participants performed a verbal fluency (VF) task in the seated position (ie, as a single task). They were asked to recall as many words as possible beginning with a predefined letter during 1 minute. This task later served as the cognitive “dual task” in all of the walking conditions. For each condition, a different letter was given; each letter was used only once per participant, randomized across conditions. The VF task was used previously in several dual-task studies28 –31 and has been validated in Hebrew32 for equivalency of expected frequencies. Walking protocol. Participants were instructed to walk at their preFebruary 2010

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait ferred pace on level ground in a welllit, obstacle-free, 30-m-long corridor (turning at the end each time) for 1 minute under 4 conditions: (1) usual walking with no dual task, (2) while performing VF with no explicit instruction for prioritization of either task (no priority), (3) while instructed to prioritize the cognitive task (cognitive priority), and (4) while instructed to prioritize the gait task (gait priority). In the latter 2 conditions, participants were told to try to perform the prioritized task as if it were performed alone. Thus, in the gait priority condition, participants were asked to “concentrate mainly on the gait task” while performing the VF task and to walk as if they were not simultaneously performing a cognitive task (ie, usual walking). Similarly, during the cognitive priority condition, both tasks were performed, but participants were instructed to match their performance on the VF task to the sitting, single-task condition. Testing always started with a practice walk along the walking path, followed by the no-priority dual-task condition. The order of the other dualtask prioritization conditions was randomized. Gait assessment. An ambulatory recorder and footswitches, similar to those originally described by Perry,2 were used to quantify the temporal parameters of the gait cycle (ie, stride time and swing time). The system consisted of a pair of insoles* and a recording unit.† Each insole contained 4 footswitches that covered the surface of the sole and measured the vertical forces under the foot. The recording unit (11 ⫻ 7 ⫻ 3 cm, 230 gm) was carried on the waist. Measurements were sampled at a rate of 100 Hz, stored in a mem-

* B&L Engineering, 1901 Carnegie Ave, Suite Q, Santa Ana, CA 92705. † JAS Research Inc, 82 Horace Rd, Belmont, MA 02478.

February 2010

ory card during the walk, and later transferred to a personal computer for further analysis. The following gait parameters were determined using previously described methods33,34: average stride time, average swing time (%), stride time variability, and swing time variability. Variability measures were quantified using the coefficient of variation (eg, stride time variability⫽ 100 ⫻ [standard deviation/average stride time]). In addition, average gait speed was determined by measuring the average time it took a participant to walk the middle 10 m of the 30-m corridor. Cognitive tests. In addition to the VF test performed by all participants while seated, several other tests were administered only to the older adult group. The Montreal Cognitive Assessment was used to obtain a general measure of cognitive function. This test was designed as a rapid screening instrument for mild cognitive dysfunction and dementia.35 It provides a composite score based on assessment of several cognitive domains: attention and concentration, executive functions, memory, language, visuo-constructional skills, conceptual thinking, calculations, and orientation. The total possible score is 30 points; a score of 26 or above is considered normal. Because dual-task abilities have been related to executive function,36,37 several executive function tests were performed to assess their association with prioritization flexibility. The Frontal Assessment Battery (FAB)38 evaluates 6 aspects of executive function that have been related to frontal lobe function, using a simple battery: conceptualization, mental flexibility, motor programming, sensitivity to interference, inhibitory control, and environmental autonomy. The Trail Making Test (TMT)38 – 40 is a visuomotor timed

task used routinely in clinical evaluations that assesses the dimensions of cognitive flexibility and executive function. The test consists of 2 parts: TMT, part A (TMT-A) and TMT, part B (TMT-B). The TMT-A is a relatively simple visual-scanning task that requires a person to draw a line connecting consecutive numbers from 1 to 25. The TMT-B adds a dimension of cognitive flexibility by requiring a person to draw a line connecting numbers and letters in an alternating sequence. Delta TMT (TMT-B – TMT-A) was calculated as well; this parameter more specifically reflects executive skills, adjusted for performance on the TMT-A.39 Data Analysis To estimate the effect of the secondary tasks on gait, we applied the general linear model for repeated measures to assess main effects (ie, differences among the conditions) and interaction effects. We first compared the no-instruction dual-task condition with the baseline usual walking condition (within groups). We also estimated the effect of prioritization (ie, the instruction set) on gait to evaluate within-group differences among the 3 dual-task conditions (ie, no priority instructions, gait priority, cognitive priority). The dependent variable was the gait measure (eg, speed), and the independent variables were age group and the instructional set (ie, prioritization). If main effects were observed, post hoc analyses were performed using paired and unpaired t tests (with Bonferroni multiple testing correction adjustments to control for type I errors) to: (1) detect changes within the different conditions and (2) examine the role of age group and sex. Similar analyses were applied to examine the change in performance of the VF task as a function of condition and group. In addition, dual-task costs were calculated for the gait pri-

Volume 90

Number 2

Physical Therapy f

179

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait Table 1.

of the study participants. For the older adult group, scores on the cognitive and balance tests were consistent with those of previous studies of older adults who were healthy.16,37 For example, the mean score (SD) on the Timed “Up & Go” Test was 8.6 (1.2) seconds, far below the 13.5second threshold that indicates a high risk of falls.

Participant Characteristicsa Young Adults (nⴝ40)

Older Adults (nⴝ17)

Age (y)

26.8⫾1.6

72⫾6.8

Education (y)

14.7⫾1.5

13.5⫾3.9

Height (m)

1.70⫾0.1

1.67⫾0.08

Weight (kg)

64.9⫾11.3

67.5⫾10.8

Verbal fluency (no. of words generated while seated)

13.3⫾2.8

9.9⫾4.0

Variable

Frontal Assessment Battery

16.5⫾1.1

Montreal Cognitive Assessment

26.1⫾1.7

TMT-A (s)

64⫾20

TMT-B (s)

128⫾35

Delta TMT (TMT-B – TMT-A) (s)

76⫾51

Timed “Up & Go” Test (s)

8.6⫾1.2

a Values are mean⫾SD. TMT⫽Trail Making Test; TMT-A⫽Trail Making Test, part A; TMT-B⫽Trail Making Test, part B.

ority and cognitive priority conditions for each of the dependent variables. For each measure, cost was calculated as: 100 ⫻ (the priority condition ⫺ the no-instruction condition)/the no-instruction condition. The total prioritization effect was calculated as the difference between the 2 costs (ie, gait priority cost ⫺ cognitive priority cost). Repeated measures were applied to evaluate within- and between-group differences of the dual-task costs and the total prioritization effect. Pearson correlation coefficients were used to

test for associations between cognitive measures and these costs (deltas). The P values reported are based on 2-sided comparisons, with a value of Pⱕ.05 considered statistically significant. All statistical analyses were performed using SPSS software, version 15.0.‡

Results Table 1 summarizes the demographic and clinical characteristics ‡ SPSS Inc, 233 S Wacker Dr, Chicago, IL 60606.

Effects of a Dual Task and Instructions for Prioritization on Gait Speed A significant effect of performance of a dual task on gait speed in the no-priority condition was found in both age groups. Gait speed was lower in the no-priority condition compared with usual walking with no dual task in both the young and older adults (P⬍.001) (Tab. 2 and Fig. 1A). Prioritization effect. When asked to prioritize gait, the young adults significantly increased their gait speed (P⬍.001). When asked to prioritize the cognitive task, gait speed decreased (P⫽.051), although this reduction tended to be smaller than the increase of speed in the gait prioritization condition (Tab. 3 and Fig. 1A). The older adults had a similar pattern of prioritization, although the magnitude of the effect

Table 2. Effects of Dual-Task Performance on Gait and Performance of the Cognitive Taska

Usual Walking

Main Effect (P Value)

Young adults

1.45⫾0.14

1.28⫾0.16 (⬍.001)

⬍.001

.987

.042

Older adults

1.35⫾0.24

1.18⫾0.23 (⬍.001)

Young adults

1.59⫾0.57

1.74⫾0.56 (.141)

⬍.001

.012

.008

Older adults

1.73⫾0.48

2.46⫾1.12 (.01)

Young adults

1.92⫾0.56

2.00⫾0.60 (.40)

⬍.001

⬍.001

⬍.001

Older adults

2.42⫾0.41

3.46⫾1.12 (.001)

Young adults

While seated: 13.3⫾2.80

13.7⫾4.02 (.430)

.304

.002

Older adults

9.9⫾4.0

11.5⫾3.4 (.084)

Variable Gait speed (m/s)

Stride time variability (%)

Swing time variability (%)

No. of words generated

a

Interaction (Group ⴛ Priority Condition) (P Value)

No-Priority Condition

.06

Between-Group Effect (P Value)

Values are mean⫾SD. Post hoc P values for within-group comparisons are shown in parentheses.

180

f

Physical Therapy

Volume 90

Number 2

February 2010

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait tended to be smaller. Gait speed generally increased when the older adults were asked to prioritize gait (P⫽.052), and a nonsignificant decrease of speed was seen in the cognitive priority condition (P⫽.128). Sex differences among the young adults. Post hoc analysis revealed sex differences among the young adults for the prioritization effect (Fig. 2). Among the young women, gait speed while performing VF task in the cognitive priority condition was similar to that in the no-priority condition (P⫽.31). Conversely, when prioritizing gait, speed was significantly higher than in the nopriority or cognitive priority condition (P⬍.001) (Fig. 2). This effect was blunted in young men, whose gait speed in the gait priority condition was not significantly different from the no-priority condition. For men, gait speed tended to be lower in the cognitive priority condition than in the no-priority condition (P⫽.06). Nonetheless, the observed speed in the cognitive priority condition was significantly lower than the speed in the gait prioritization condition (P⫽.03) for both men and women. The interaction effect of sex ⫻ dual-task condition among the young adults was borderline significant (P⫽.057). Due to sample size limitations, we could not fully explore sex differences in the older adults. Effects of a Dual Task and Prioritization on Gait Variability In the young adults, performance of the VF task did not significantly affect stride time variability or swing time variability (Tab. 2 and Fig. 1B) in any of the conditions (P⬎.146). In contrast, in the older adults, VF task performance increased both measures of gait variability in all dual-task conditions (P⬍.01). Interestingly, this effect was not influenced by explicit instructions for gait or cognitive prioritization. February 2010

Figure 1. Effects of a dual task and prioritization on (A) gait speed and (B) stride time variability in young and older adults who were healthy. The no-priority dual-task condition is compared with usual walking (no dual task). The cognitive priority and gait priority conditions are compared with the no-priority condition.

Effect of Prioritization on VF Task Performance In both the young and older groups, VF task performance tended to change according to the prioritization instructions, as expected, but this change did not reach signifi-

cance (Tab. 3). On average, only one additional word was generated during the cognitive priority condition. Dual-task costs of prioritization conditions are summarized in Table 4. For gait speed, the gait priority effect

Volume 90

Number 2

Physical Therapy f

181

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait Table 3. Effects of Task Prioritization on Gait and Performance of the Cognitive Task During Dual-Task Performancea

Variable

No Priority

Gait Priority

Cognitive Priority

Main Effect

Interaction (Group ⴛ Priority Condition)

1.28⫾0.16

1.36⫾0.19

1.24⫾0.18 (⬍.001)

⬍.001

.282

.036

.442

.742

.001

.612

.543

⬍.001

.081

.662

.082

Gait speed (m/s)

Young adults Older adults

1.18⫾0.23

1.22⫾0.23

1.15⫾0.22 (.002)

Stride time variability (%)

Young adults

1.74⫾0.56

1.82⫾0.71

1.77⫾0.70

Older adults

2.46⫾1.12

2.64⫾1.02

2.69⫾1.66

Swing time variability (%)

Young adults

2.00⫾0.60

2.03⫾0.61

2.00⫾0.65

Older adults

3.46⫾1.12

3.26⫾0.79

3.29⫾1.22

No. of words generated

Young adults

13.7⫾4.0

12.9⫾3.2

14.1⫾3.4

Older adults

11.5⫾3.4

11.4⫾4.2

12.7⫾5.5

Between-Group Effect

a

Values are the mean⫾SD. Post hoc P values for within-group comparisons between the gait priority condition and the cognitive priority condition are shown in parentheses. Only those that were significant are shown.

tended to be 50% larger in the young adults compared with the older adults. For all measures, differences between the young and older adults were not statistically significant, perhaps due to the large within-group variability. Among the older adults, the cognitive measures assessed in the present study (ie, FAB, Montreal Cognitive Assessment, TMT-B, delta TMT, VF task) were not significantly correlated with the difference between gait speed in the no-priority condition and in the other 2 priority conditions (or the dual-task costs). That is, dual-task costs related to pri-

oritization were not associated with these tests of executive or cognitive function in the older adults.

Discussion Consistent with earlier studies,7,37,41 we observed that performance of a dual task reduced gait speed in both young and older adults who were healthy when explicit instructions regarding prioritization were not given, further supporting the idea that cortical function influences gait. Previous studies also have demonstrated that when older adults or patients with PD focus attention on a

Figure 2. Effects of dual-task instructional set on gait speed. Young men and women who were healthy apparently responded differently to prioritization.

182

f

Physical Therapy

Volume 90

Number 2

cognitive task, rather than on walking, gait speed and stride length are reduced.25,42 The results of the present study further extend these findings to young adults who are healthy and show the effects of task prioritization on gait speed and variability as a function of age and sex. Our findings suggest that task prioritization tends to alter gait speed more in young adults than in older adults, whereas gait variability is affected by performance of a dual task only in older adults. Young women, more than young men, change their gait speed when asked to prioritize either task. Thus, in some sense, the young men who are healthy behave more like older adults than do young women who are healthy. Interestingly, the present findings also suggest that for both age groups, gait speed in the default, no-priority instruction condition is similar to that seen in the cognitive priority condition. Thus, the “posture-first” concept apparently does not apply equally to all aspects of walking. In agreement with a previous study,23 our findings show that changes in gait speed in response to different instructions generally were smaller in the older adults, suggesting a reduced ability to prioritize and

February 2010

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait Table 4. Dual-Task Costs of Prioritizationa Variable Gait speed (m/s)

Young Adults Gait priority

6.5⫾9.1

Cognitive priority Total prioritization effect Stride time variability (%)

Swing time variability (%)

Gait priority

3.9⫾8.2

⫺3.2⫾10.8 (⬍.001)

⫺2.1⫾5.4 (.004)

Between-Group Effect (P Value) .31 .70

9.7⫾13.1

6.0⫾7.4

.28

12.2⫾58.1

11.9⫾22.6

.97

Cognitive priority

7.1⫾38.7 (.55)

8.9⫾27.4 (.71)

.86

Total prioritization effect

5.7⫾59.1

2.9⫾32.7

.85

Gait priority

6.0⫾36.0

⫺1.2⫾25.6

.45

Cognitive priority

3.2⫾28.4 (.66)

⫺3.0⫾20.2 (.74)

.42

Total prioritization effect No. of words generated

Older Adults

Gait priority Cognitive priority Total prioritization effect

2.4⫾34.9

1.7⫾22.5

.94

⫺1.6⫾27.7

0.56⫾29.2

.79

7.5⫾36.1 (.057)

12.4⫾44.2 (.30)

⫺9.1⫾29.4

⫺11.9⫾46.2

.66 .78

a Values are the % change with respect to the no-priority condition and the total effect, as indicated. Numbers in parentheses are the post hoc P values for within-group comparisons between gait priority cost and cognitive priority cost.

flexibly allocate attention among different tasks. An age-associated decline in mental flexibility39,43 could explain these findings. Somewhat surprisingly, however, changes in gait in response to the explicit instructions were not significantly related to any of the cognitive tests in the older adults. This finding is consistent with previously published results.23 Perhaps the small number of older adults or the nature of the group prevented us from observing the hypothesized associations. Disparate findings were obtained for gait variability. The young adults were not affected by performance of a dual task or by prioritization. This finding further supports the idea that young adults who are healthy have the cognitive capacity to handle even the most challenging dual-task conditions without altering gait variability (ie, during cognitive prioritization, where presumably fewer resources are allocated to gait). Moreover, young adults have the ability to preserve and maintain the “posture-first” strategy even when the focus of attention is directed toward cognitive tasks. In contrast, the February 2010

older adults significantly increased their gait variability in all dual-task conditions. However, prioritization did not have any specific effect on gait variability, which again might reflect a reduced flexibility to allocate attention to the prioritized task. A possible explanation for the observed response among the young adults is that gait stability (represented by gait variability) always receives unconscious priority, despite competition for information processes. Alternatively, for young adults who are healthy, regulation of gait variability may be largely automatic or subcortical, and thus it might not depend on attentional resources or prioritization. In contrast, our findings suggest that aging curtails the ability to maintain the automatic “posture-first” strategy. This suggestion is consistent with the results of a previous study that examined the general effects of dual-task performance (without manipulation of the instructional set).20 It is important, nonetheless, to keep in mind that no participants fell at any time under any dual-task conditions. Although the propensity and predispo-

sition to a fall apparently increased, as evidenced by the increased variability, all of the older adults maintained sufficient postural control to prevent a fall, perhaps by using compensatory mechanisms. Effect of Sex on Dual Task and Prioritization Compared with usual walking, all participants, men and women, reduced their gait speed when they performed a cognitive task (in the no-priority condition). Sex, however, apparently plays a role in task prioritization and its effect on gait speed in young adults who are healthy. It could be suggested that women are more flexible or that gait speed in men is less sensitive to the instructional set. Although sex effects have been reported during other dual-task activities, this is, to our knowledge, the first evidence of sex differences in dual-task effects on gait. Hancock et al44 compared the driving performance of men and women while responding to an invehicle phone. Women had significantly longer brake response times when distracted by the phone in comparison with men, and their

Volume 90

Number 2

Physical Therapy f

183

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait stopping accuracy was dramatically reduced when distracted. However, women also had a faster brake response time and higher accuracy compared with men when tested without the distraction. In the present study, we found that young men demonstrated less dramatic prioritization effects, especially in the gait prioritization condition, compared with young women. One possibility is that the young men were less adherent to the prioritization instructions (ie, less motivated) compared with the young women and, therefore, did not alter their gait speed. However, men (as well as women) showed a tendency of prioritization effects in the cognitive priority condition (P⫽.06), as well as changes in the performance of the cognitive task (see the VF task results in Tab. 2), so this motivation claim seems unlikely. Another possibility relates to dual-task abilities. In the gait priority condition, women could both increase their gait speed and perform the cognitive task on the same level as men, perhaps confirming the old myth that “women are better dual taskers.” A third possibility is that the VF task was easier for women, leaving them more cognitive resources to devote to the prioritization effects. Some cognitive tests have different sex biases.45 However, previous studies and the present results did not demonstrate sex effects on a VF task in the seated position, supporting the rejection of this explanation. Nonetheless, in the future, it might be helpful to compare the effects of prioritization across sexes while also taking into account possible sex effects on the cognitive task. For young adults who are healthy, prioritization probably does not have any clinical implications. We reason that the effect of prioritization is stronger in patient populations where gait or the cognitive task (or 184

f

Physical Therapy

Volume 90

both) demand greater attention. This might be especially true in conditions such as when individuals may pay more attention to “outside distractors” than to gait. Based on this assumption, it might be appropriate to instruct patients to always prioritize gait to ensure safety and minimize fall risk. Limitations and Further Research This study had a number of limitations. The number of older adults studied was relatively small, and this factor may have affected some of the findings. Further studies should investigate the sex effect and ageassociated changes in a larger and more heterogeneous sample, perhaps supplemented with more detailed cognitive testing. The manner in which instructions are phrased has an important role in the way individuals perceive the meaning of prioritization. The translation or understanding of instructions could be a determining factor in the performance of the task; women apparently translated the instructions into speed more than men did. Only one participant (male) asked for clarification of the instructions, mentioning that he never thinks about gait. Other studies phrased the instruction for prioritization slightly differently. Verghese et al,25 for example, asked the subjects to “pay attention to reciting alternate letters and not to concentrate on their walking.” Different instructions likely create different effects, but subtle effects of nuance need to be further evaluated. As suggested, further studies should test the prioritization effect in patient populations with cognitive or motor impairments and in older adults with a broader spectrum of abilities and evaluate the implications of prioritization on gait and fall risk. The trend of enhanced VF task performance while walking compared with performance of the task while

Number 2

seated also is of interest and raises questions for future investigations. There are at least 3 possible explanations for this trend: (1) a general practice effect, (2) a specific letter effect, and (3) a dual-task benefit. Regarding the practice effect, the literature generally suggests that use of alternate letters minimizes any practice effect.46 We examined this issue in another group of young subjects who performed a VF task several times with alternate letters. A practice effect was not observed. Still, we cannot completely rule out this possibility, because seated testing always came first. Regarding the letter effect, as mentioned above, the 3 letters used during walking were chosen based on a previous study that indicated that they have similar levels of difficulty.32 Another letter was used during sitting. Additional pilot data suggest that all of the letters used have the same difficulty level. Regarding the dual-task benefit, somewhat paradoxically, some studies have shown that performance of a dual task may enhance function, especially if the dual task is relatively easy and not demanding of much attention.47,48 In addition, exercise has been shown to improve cognitive function tasks under certain conditions.49 Thus, although unexpected, it is possible that the trend toward improved performance of the VF task was a result of the walking. In the present study, mean values of the VF task were similar across all walking conditions, supporting the idea that all letters had similar levels of difficulty and that there was no practice, letter, or dual-task performance effect with respect to prioritization. Nonetheless, the question of whether walking enhances VF remains to be studied more fully. We have learned many things about the relationship between cognitive function and gait since classic works on gait analysis were written in the 1990s. The present findings demonFebruary 2010

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait strate that the effects of prioritization and the instructional set on gait are feature specific (ie, variability differs from speed), that they may be sex-dependent, and that they are relatively preserved with healthy aging. We speculate that in certain patient groups (eg, poststroke or with other neurological conditions), the decreased ability to prioritize tasks during walking in response to the instruction set will be further impaired. (In this issue, Kizony et al50 begin to explore the relationship of dual-task performance and its effect on gait speed in people with stroke.) This lack of “mental flexibility” might have ramifications for the design of optimal rehabilitation programs, for the way in which therapists train patients, and perhaps for how we instruct patients to carry out their activities of daily living. However, many questions remain about these relationships and their clinical implications. We remain surprised and lacking a good explanation regarding the apparent increased sensitivity to prioritization instructions observed in the young women. We hope work over the next decades will give us new insight into these relationships and eventually lead to new ways of minimizing the effects of dual-task performance on gait and fall risk. Dr Yogev-Seligmann, Dr Mirelman, Dr Dickstein, Dr Giladi, and Dr Hausdorff provided concept/idea/research design. Dr YogevSeligmann, Ms Rotem-Galili, Dr Mirelman, and Dr Hausdorff provided writing. Dr Yogev-Seligmann, Ms Rotem-Galili, and Dr Mirelman provided data collection and analysis. Dr Yogev-Seligmann and Dr Hausdorff provided project management. Dr Giladi provided fund procurement and institutional liaisons. Ms Rotem-Galili and Dr Giladi provided participants. Dr Giladi and Dr Hausdorff provided facilities/equipment. Dr Mirelman, Dr Dickstein, Dr Giladi, and Dr Hausdorff provided consultation (including review of manuscript before submission). Portions of this work were presented at the International Congress of the International

February 2010

Society for Posture and Gait Research; June 21–25, 2009; Bologna, Italy. This work was supported, in part, by the National Institute on Aging (grant AG14100). This article was received February 12, 2009, and was accepted September 18, 2009. DOI: 10.2522/ptj.20090043

References 1 McMahon TA. Muscles, Reflexes, and Locomotion. Princeton, NJ: Princeton University Press, 1984. 2 Perry J. Gait Analysis: Normal and Pathological Function. Thorofare, NJ: Slack Inc; 1992. 3 Alexander NB, Hausdorff JM. Guest editorial: linking thinking, walking, and falling. J Gerontol A Biol Sci Med Sci. 2008;63: 1325–1328. 4 Woollacott M, Shumway-Cook A. Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture. 2002;16:1–14. 5 Yogev-Seligmann G, Hausdorff JM, Giladi N. The role of executive function and attention in gait. Mov Disord. 2008;23:329 – 342. 6 Abernethy B, Hanna A, Plooy A. The attentional demands of preferred and nonpreferred gait patterns. Gait Posture. 2002;15:25–265. 7 Beauchet O, Dubost V, Herrmann FR, Kressig RW. Stride-to-stride variability while backward counting among healthy young adults. J Neuroeng Rehabil. 2005; 2:26. 8 Ebersbach G, Dimitrijevic MR, Poewe W. Influence of concurrent tasks on gait: a dual-task approach. Percept Mot Skills. 1995;81:107–113. 9 Gage WH, Sleik RJ, Polych MA, et al. The allocation of attention during locomotion is altered by anxiety. Exp Brain Res. 2003; 150:385–394. 10 Bloem BR, Valkenburg VV, Slabbekoorn M, van Dijk JG. The multiple tasks test: strategies in Parkinson’s disease. Exp Brain Res. 2001;137:478 – 486. 11 Bond JM, Morris M. Goal-directed secondary motor tasks: their effects on gait in subjects with Parkinson disease. Arch Phys Med Rehabil. 2000;81:110 –116. 12 Hausdorff JM, Balash J, Giladi N. Effects of cognitive challenge on gait variability in patients with Parkinson’s disease. J Geriatr Psychiatry Neurol. 2003;16:53–58. 13 O’Shea S, Morris ME, Iansek R. Dual task interference during gait in people with Parkinson disease: effects of motor versus cognitive secondary tasks. Phys Ther. 2002;82:888 – 897. 14 Sheridan PL, Solomont J, Kowall N, Hausdorff JM. Influence of executive function on locomotor function: divided attention increases gait variability in Alzheimer’s disease. J Am Geriatr Soc. 2003;51: 1633–1637.

15 Yogev G, Giladi N, Peretz C, et al. Dual tasking, gait rhythmicity, and Parkinson’s disease: which aspects of gait are attention demanding? Eur J Neurosci. 2005; 22:1248 –1256. 16 Hausdorff JM, Schweiger A, Herman T, et al. Dual-task decrements in gait: contributing factors among healthy older adults. J Gerontol A Biol Sci Med Sci. 2008; 63:1335–1343. 17 Zijlstra A, Ufkes T, Skelton DA, et al. Do dual tasks have an added value over single tasks for balance assessment in fall prevention programs? A mini-review. Gerontology. 2008;54:40 – 49. 18 Pashler H. Dual-task interference in simple tasks: data and theory. Psychol Bull. 1994; 116:220 –244. 19 Tombu M, Jolicoeur P. A central capacity sharing model of dual-task performance. J Exp Psychol Hum Percept Perform. 2003; 29:3–18. 20 Bloem BR, Valkenburg VV, Slabbekoorn M, Willemsen MD. The Multiple Tasks Test: development and normal strategies. Gait Posture. 2001;14:191–202. 21 Bloem BR, Grimbergen YA, van Dijk JG, Munneke M. The “posture second” strategy: a review of wrong priorities in Parkinson’s disease. J Neurol Sci. 2006. 22 Shumway-Cook A, Woollacott M, Kerns KA, Baldwin M. The effects of two types of cognitive tasks on postural stability in older adults with and without a history of falls. J Gerontol A Biol Sci Med Sci. 1997; 52:M232–M240. 23 Siu KC, Chou LS, Mayr U, et al. Does inability to allocate attention contribute to balance constraints during gait in older adults? J Gerontol A Biol Sci Med Sci. 2008;63:1364 –1369. 24 Siu KC, Chou LS, Mayr U, et al. Attentional mechanisms contributing to balance constraints during gait: the effects of balance impairments. Brain Res. 2009; 1248:59 – 67. 25 Verghese J, Kuslansky G, Holtzer R, et al. Walking while talking: effect of task prioritization in the elderly. Arch Phys Med Rehabil. 2007;88:50 –53. 26 Ble A, Volpato S, Zuliani G, et al. Executive function correlates with walking speed in older persons: the InCHIANTI study. J Am Geriatr Soc. 2005;53:410 – 415. 27 Holtzer R, Verghese J, Xue X, Lipton RB. Cognitive processes related to gait velocity: results from the Einstein Aging Study. Neuropsychology. 2006;20:215–223. 28 Beauchet O, Dubost V, Aminian K, et al. Dual-task-related gait changes in the elderly: does the type of cognitive task matter? J Mot Behav. 2005;37:259 –264. 29 Bootsma-van der Wiel A, Gussekloo J, de Craen AJ, et al. Walking and talking as predictors of falls in the general population: the Leiden 85-Plus Study. J Am Geriatr Soc. 2003;51:1466 –1471. 30 Camicioli R, Oken BS, Sexton G, et al. Verbal fluency task affects gait in Parkinson’s disease with motor freezing. J Geriatr Psychiatry Neurol. 1998;11:181–185.

Volume 90

Number 2

Physical Therapy f

185

Effects of Dual Tasking, Prioritization, Age, and Sex on Gait 31 Dubost V, Kressig RW, Gonthier R, et al. Relationships between dual-task related changes in stride velocity and stride time variability in healthy older adults. Hum Mov Sci. 2006;25:372–382. 32 Kave G. Standardization and norms for a Hebrew naming test. Brain Lang. 2005; 92:204 –211. 33 Frenkel-Toledo S, Giladi N, Peretz C, et al. Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson’s disease. Mov Disord. 2005; 20:1109 –1114. 34 Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil. 2001;82:1050 – 1056. 35 Nasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53:695– 699. 36 Iersel MB, Kessels RP, Bloem BR, et al. Executive functions are associated with gait and balance in community-living elderly people. J Gerontol A Biol Sci Med Sci. 2008;63:1344 –1349. 37 Springer S, Giladi N, Peretz C, et al. Dualtasking effects on gait variability: the role of aging, falls, and executive function. Mov Disord. 2006;21:950 –957.

186

f

Physical Therapy

Volume 90

38 Dubois B, Slachevsky A, Litvan I, Pillon B. The FAB: a Frontal Assessment Battery at bedside. Neurology. 2000;55:1621–1626. 39 Lezak MD. Neuropsychological Assessment. New York, NY: Oxford University Press; 1995. 40 Stuss DT, Levine B. Adult clinical neuropsychology: lessons from studies of the frontal lobes. Annu Rev Psychol. 2002;53: 401– 433. 41 Hollman JH, Kovash FM, Kubik JJ, Linbo RA. Age-related differences in spatiotemporal markers of gait stability during dual task walking. Gait Posture. 2007;26:113– 119. 42 Canning CG. The effect of directing attention during walking under dual-task conditions in Parkinson’s disease. Parkinsonism Relat Disord. 2005;11:95–99. 43 Dorfman J. Problem solving, inhibition and frontal lobe function. In: Raz N, ed. The Other Side of the Error Term: Aging and Development as Model Systems in Cognitive Neuroscience. Amsterdam, the Netherlands: Elsevier; 1998:395– 407. 44 Hancock PA, Lesch M, Simmons L. The distraction effects of phone use during a crucial driving maneuver. Accid Anal Prev. 2003;35:501–514.

Number 2

45 Weiss EM. Sex differences in cognitive functions. Pers Individ Dif. 2003;35:863– 875. 46 Strauss E. A Compendium of Neuropsychological Tests. New York, NY: Oxford University Press; 2006. 47 Leitner Y, Barak R, Giladi N, et al. Gait in attention deficit hyperactivity disorder: effects of methylphenidate and dual tasking. J Neurol. 2007;254:1330 –1338. 48 Shumway-Cook A. Attentional mechanisms in balance control. In: Proceedings of the International Congress of the 18th International Society for Posture and Gait Research; July 14 –18, 2007; Burlington, Vermont. 2007:22. 49 Davranche K, McMorris T. Specific effects of acute moderate exercise on cognitive control. Brain Cogn. 2009;69:565–570. 50 Kizony R, Levin MF, Hughey L, et al. Cognitive load and dual-task performance during locomotion poststroke: a feasibility study using a functional virtual environment. Phys Ther. 2010;90:252–260.

February 2010

Perry Issue: Gait Rehab

Neurophysiologic and Rehabilitation Insights From the Split-Belt and Other Locomotor Adaptation Paradigms Darcy S. Reisman, Amy J. Bastian, Susanne M. Morton Locomotion is incredibly flexible. Humans are able to stay upright and navigate long distances in the face of ever-changing environments and varied task demands, such as walking while carrying a heavy object or in thick mud. The focus of this review is a behavior that is critical for this flexibility: motor adaptation. Adaptation is defined here as the process of adjusting a movement to new demands through trial-and-error practice. A key feature of adaptation is that more practice without the new demand is required to return the movement to its original state. Thus, motor adaptation is a short-term motor learning process. Several studies have been undertaken to determine how humans adapt walking to novel circumstances. Many of these studies have examined locomotor adaptation using a split-belt treadmill. The results of these studies of people who were healthy and people with neurologic damage suggest that the cerebellum is required for normal adaptation of walking and that the role of cerebral structures may be less critical. They also suggest that intersegmental and interlimb coordination is critical but readily adaptable to accommodate changes in the environment. Locomotor adaptation also can be used to determine the walking potential of people with specific neurologic deficits. For instance, split-belt and limb-weighting locomotor adaptation studies show that adults with chronic stroke are capable of improving weight-bearing and spatiotemporal symmetry, at least temporarily. Our challenge as rehabilitation specialists is to intervene in ways that maximize this capacity.

D.S. Reisman, PT, PhD, is Assistant Professor, Department of Physical Therapy, University of Delaware, Newark, Delaware. A.J. Bastian, PT, PhD, is Associate Professor, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD 21205 (USA), and Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland. Address all correspondence to Dr Bastian at: [email protected]. S.M. Morton, PT, PhD, is Assistant Professor, Graduate Program in Physical Therapy and Rehabilitation Science, University of Iowa Carver College of Medicine, Iowa City, Iowa. [Reisman DS, Bastian AJ, Morton SM. Neurophysiologic and rehabilitation insights from the splitbelt and other locomotor adaptation paradigms. Phys Ther. 2010;90:187–195.] © 2010 American Physical Therapy Association

Post a Rapid Response or find The Bottom Line: www.ptjournal.org February 2010

Volume 90

Number 2

Physical Therapy f

187

Split-Belt and Other Locomotor Adaptation Paradigms

H

umans must precisely coordinate the activities of many muscles to control everyday movements such as walking. This is done in the face of a constantly changing environment—we hold full coffee cups, walk on icy surfaces, and wear high heels (sometimes all at the same time!). Locomotor commands, therefore, must be constantly updated or recalibrated in order to correctly predict what is required in each situation. Despite this complex problem, humans walk with relative ease— our locomotor patterns are smooth and accurate and can even be graceful. What are the specific neural mechanisms that produce this flexibility? What happens when there is damage to the nervous system or the motor apparatus? Can this flexibility be utilized to promote recovery of movement after injury? The focus of this review is a behavior that is critical for motor flexibility: motor adaptation. First, motor adaptation will be defined and compared with more-permanent motor skill learning. Second, central nervous system (CNS) mechanisms of mammalian locomotion will be reviewed briefly, as an introduction to mechanisms of locomotor adaptation in hu-

Available With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

188

f

Physical Therapy

Volume 90

mans and animals. Next, recent human locomotor adaptation studies will be evaluated in terms of the response of specific gait parameters to adaptive stimuli and how this response informs mechanisms of locomotor control. Finally, clinical implications to walking rehabilitation will be drawn for a variety of neurologic conditions. The split-belt treadmill locomotor adaptation paradigm will be highlighted throughout the article because studies using this approach have revealed interesting and novel concepts about the relevance of motor adaptation to walking rehabilitation.

Motor Adaptation and Its Relationship to Motor Skill Learning Motor learning forms a foundation for rehabilitation interventions used to treat patients with neurological disease or injury. It is important to distinguish motor adaptation from motor learning. Motor adaptation is defined here, using the terminology of Martin et al,1 as the process of modifying or adjusting an already well-learned movement or motor skill that occurs over a period of trialand-error practice when exposing the movement to a novel, perturbing context or environment. Initially, the CNS does not correctly predict the new demands, and, as a result, significant movement errors occur. After minutes to hours of practice, the movements become more and more accurate as the CNS makes the necessary adjustments to the feedforward motor plan. Once the adaptation is complete, if the new demand is removed, movements once again are erroneous, this time in the opposite manner, because the CNS adjustments remain. These initial oppositely directed errors are termed negative aftereffects. The presence of negative aftereffects demonstrates that the adaptation has been stored by the CNS.2,3 Thus, hallmarks of mo-

Number 2

tor adaptation are that it is acquired relatively quickly, occurs only with motor practice, and requires active de-adaptation.1,4 That is, further practice without the new demand is required to return the movement to its original state (Figure). Using this definition, therefore, motor adaptation can be said to be one specific component of true motor skill learning. Motor learning has been defined as a set of processes associated with practice or experience leading to relatively permanent changes in skilled behavior.5 Thus, to fully learn (ie, retain permanently) a novel motor skill requires much longer time periods and is influenced not only by adaptive mechanisms but also by offline learning, consolidation, and long-term storage.6,7 Learning a novel motor skill also is affected by other non–motor processes such as attention, decisionmaking factors, and reward mechanisms.8 –10 Thus, there are many mechanisms for learning a new movement, but the one of interest here is tied to the motor adaptation process. Why is adaptation important, given that it is a short-term learning process? First, locomotor adaptation allows for flexibility of walking patterns. Quickly learning and storing a new modification of the walking pattern is important to help walking remain relatively “automatic,” allowing attention to be focused on potentially more-important information. In many circumstances, the adapted pattern is needed only temporarily, so it is advantageous to be able to unlearn it fairly rapidly (ie, in minutes rather than days). Second, adaptation allows for the study of long-term learning in a more controlled and timely way in the laboratory, providing a means to determine factors that drive adaptation and how damage to different brain February 2010

Split-Belt and Other Locomotor Adaptation Paradigms regions affects this process. Assessment of motor adaptive capabilities may be useful for ascertaining whether people with certain types of brain damage have the capacity to generate a more normal motor pattern.11,12 Third, repeated adaptations may result in learning a more permanent motor pattern. For example, if subjects adapt and de-adapt certain movement patterns repeatedly over days or weeks, they can develop a new learned calibration for the context that initially drove adaptation.1 That is, they no longer have to adapt from one behavior to the other but instead have 2 learned behaviors that they can switch between, without practice, immediately upon introduction of the different context. This concept is illustrated in the Figure. This method of learning may be most ideal for situations in which a person can already make a movement that approximates the new movement to be learned. Although the transition from adaptation to learning is not fully understood yet, it is thought to be an important process for motor learning and rehabilitation.

CNS Regions Involved in Locomotion Walking is a relatively unique motor behavior because, unlike other voluntary movements, spinal central pattern generators (CPGs) are thought to produce much of the basic locomotor synergy, at least in lower mammals. Central pattern generators have been demonstrated in cats13–17 and other simpler vertebrates and, by definition, control movement without supraspinal influences or afferent feedback. When sensory information is available, spinal structures also provide some degree of flexibility to the basic locomotor pattern, even when separated from the brain.18,19 Yet, in the intact animal, supraspinal structures, inFebruary 2010

Figure. (a) The process of motor adaptation. The typical walking pattern (A) is adapted through practice to accommodate a change in task demands (eg, walking on a split-belt treadmill with one leg moving twice as fast as the other), and this adaptation results in a modified pattern (A’). After the new demands are removed (eg, the belt speeds are returned to normal), the adapted pattern continues, and practice under the original task demands is required in order to return to the typical walking pattern (A). (b) The transition from short-term adaptation to longer-term learning. After days to weeks of practicing both the original pattern (A) and the adapted pattern (A’), people may be able to produce 2 patterns (A and B) that they can switch between, given the appropriate task demands.

cluding the brain stem, cerebellum, and even the motor cortex, are thought to play a substantial role in locomotion. The brain stem houses several locomotor regions (eg, mesencephalic lo-

comotor region, subthalamic locomotor region), which when stimulated electrically or chemically, generate a variety of locomotor patterns in cats.20 Brain-stem reticular and vestibular nuclei have projections to the spinal cord and assist

Volume 90

Number 2

Physical Therapy f

189

Split-Belt and Other Locomotor Adaptation Paradigms with maintaining postural tone and facilitating the reciprocal pattern of flexor and extensor muscle activations required during walking.21,22 The cerebellum also plays a role in locomotion, although its connections to spinal locomotor neurons are less direct.23 Lesioning a portion or the entire cerebellum can result in profound locomotor impairments, ranging from the inability to control upright postural supporting reactions and maintaining dynamic balance during walking24,25 to disrupted precision of limb placement and poor regulation of agonist-antagonist muscle pairs during stance.26 –28 In general, medial cerebellar regions are more involved in balance, muscle tone, and modulating the reciprocal patterns of leg muscle activation,21,26 and the lateral regions are more involved in precision limb placement, particularly when visual guidance is required or when adjusting locomotor patterns in more-novel or morecomplex environments.25,29 –30 The motor cortex also contributes to locomotor control, especially when adaptability and precision are required.31–33 For example, cats with discrete lesions to the corticospinal and rubrospinal tracts but intact vestibulospinal and reticulospinal pathways have very few, if any, long-term gross deficits during simple treadmill walking.34,35 However, if these same animals are made to place their legs more precisely during walking (eg, walking on rungs of a horizontal ladder) or to walk in a more challenging environment (eg, stepping over obstacles), clear deficits are easily apparent.36 –39 Importantly, the role of supraspinal structures such as the cerebellum and motor cortex is presumed to be even more substantial in humans because compared with quadrupeds, bipeds have a smaller base of support, a higher center of mass, and only half the number of contact points with the ground, all of which 190

f

Physical Therapy

Volume 90

makes human walking a much more complicated skill. Indeed, cerebellar or cerebral brain injury typically results in devastating locomotor impairments in humans.40 – 46

CNS Regions Involved in Locomotor Adaptation From the previous section, it is clear that the spinal cord, brain stem, cerebellum, and cortex are all involved in human locomotor control to varying degrees. Yet, this does not mean that all are also involved in adaptation of locomotion. Converging evidence from the fields of neuroimaging, electrophysiology, and computational modeling and from behavioral studies in humans with lesions now strongly suggests the cerebellum is a critical structure for predictive feedforward adaptations of a wide variety of arm and eye movements.2,47–52 It stands to reason, therefore, that the cerebellum also is involved in walking adaptation, although this has not been studied nearly as thoroughly. One tool that has been used recently to test locomotor adaptation in humans and animals is the split-belt treadmill. With a split-belt treadmill, the speeds of the right and left legs can be controlled independently. When the legs are forced to walk at 2 different speeds, both rapid and longer-duration (adaptive) changes to the gait pattern take place in adult humans who are healthy.53,54 In decerebrate cats, the firing rates of complex spikes in cerebellar Purkinje cells increase dramatically during the initial period of split-belt treadmill walking, suggesting that the cerebellum has at least some involvement in this form of adaptation.55 In addition, nitric oxide deprivation, which is thought to prevent cerebellar long-term depression (a principal mechanism proposed for the cerebellar plasticity required for learning), abolishes the adaptive walking behavior altogether in these

Number 2

cats.56 The few studies that have examined true motor adaptations of walking in humans with cerebellar damage seem to be in agreement with the physiological studies in cats. Individuals with cerebellar damage show significant impairments of acquisition and storage of novel walking adaptations.11,57,58 Together, these data strongly suggest that the cerebellum is a necessary component for this type of locomotor adaptation. Because decerebrate cats maintain the ability to walk on a split-belt treadmill,59 it appears that cerebral structures are not essential for walking adaptations of this kind, at least in the cat. Recently, this question was addressed in a study of humans with chronic cerebral stroke and hemiparesis.12 These individuals were found to adapt similarly to healthy controls, suggesting that unilateral cerebral damage does not affect the ability to acquire a novel locomotor adaptation, despite the presence of significant paresis and somatosensory loss. In contrast, a number of children who have had a hemispherectomy (surgical hemidecortication as treatment for intractable seizures, removing all cortical gray matter unilaterally and sometimes portions of the basal ganglia or thalamus but sparing underlying white matter) show partial disruption of the split-belt treadmill adaptation.60 Interestingly, the adaptive deficits in this group appear to be specific to the temporal domain; adaptation of spatial walking parameters (eg, step length) is not impaired in children with hemispherectomy. Further studies are needed to determine exactly why adults with cerebral stroke show better adaptive capabilities than children with hemispherectomy, but it might relate to the relatively larger lesion size in the case of hemispherectomy.

February 2010

Split-Belt and Other Locomotor Adaptation Paradigms

Control of Specific Walking Parameters: Insights From Adaptation Studies As previously discussed, the control of walking in humans is complex, involving both spinal and supraspinal structures. Furthermore, it must be flexible and adaptable to accommodate an ever-changing environment. Results from a variety of locomotor adaptation studies provide insight into this complex, yet flexible, control. In this section, we discuss what has been learned about the control of walking from human locomotor adaptation studies. Recall that according to the definition provided earlier, motor adaptation is characterized by an altered movement pattern that is generated within seconds to minutes following exposure to a novel perturbing context and results in negative aftereffects when the perturbation is removed. Many treadmill training studies have successfully produced walking pattern changes in patients with neurological deficits.61,62 However, to induce these gait pattern changes (typically using treadmill speed changes or body-weight–support systems) requires training (ie, repeated exposure to the new condition occurring over many— often 10 to 12—sessions) and cannot be induced within minutes. Nor do these training sessions produce negative aftereffects. Thus, based on the criteria provided here for motor adaptation,1 these studies provide information more in line with the concepts of motor learning than motor adaptation. Therefore, discussion of conventional treadmill training paradigms is not included here. Humans are remarkably adept at maintaining appropriate spatial orientation and navigating a straight path during walking. Insight into how this is accomplished has been gained from a series of studies that examined locomotor adaptation dur-

February 2010

ing stepping on a circular treadmill. In these studies, participants adapted to stepping on a rotating circular disk for a brief period of time and then either walked, stepped, or hopped overground while blindfolded.63– 66 People who are healthy consistently walk or hop overground in a curved trajectory following circular treadmill stepping and are unaware of the curvature.63,64 This negative aftereffect is called a “podokinetic” after-rotation (PKAR), and it is thought to be due to a recalibration of the proprioceptive relationship between the trunk and stance limb that occurs when stepping on the circular treadmill. Interestingly, the PKAR effect is observed when either forward or backward walking or hopping is undertaken following circular treadmill stepping65,66 and appears to be influenced by vestibular input, despite the fact that individuals do not perceive moving in a curved trajectory.67,68 Thus, information from studies of PKAR indicate that vestibular input and information about trunk rotation relative to the feet is integral to the control of locomotor trajectory and spatial orientation,64 – 68 regardless of the direction (forward or backward) or form (walking or hopping) of locomotion. Notably, if participants are not blindfolded, there is no PKAR. This is evidence of the strong reliance on visual guidance humans have during walking and demonstrates our ability to override other conflicting sensory information when vision is available. Strict coordination within a limb and between limbs is a requirement for normal human walking. A variety of locomotor adaptation studies have provided insight into the flexibility and control of intralimb and interlimb coordination during walking.11,12,53,60,69 –72 One group of studies has examined locomotor adaptation through weighting of a leg or applying resistance (through a motorized robotic device) to a leg

during walking.69 –71 Robot-applied, velocity-dependent resistance against hip and knee movements of one leg during the swing phase of walking results in decreased hip and knee flexion when the resistance is initially applied. As people walk with the resistance, they gradually adapt by increasing flexor muscle activity, and hip and knee flexion values return to normal. When the resistance is removed, they continue to produce the adapted pattern of increased flexor activity, which then results in increased hip and knee flexion for approximately 20 steps after removal of the resistance (the negative aftereffect).71 Similar adaptations of hip and knee flexion are observed when a weight is applied to one leg during walking.69 Application of a weight to one leg also results in adaptation of interlimb coordination. For example, the initial weighting causes a decrease in single-limb support time and step length on the weighted side and an increase in single-limb support time and step length on the unweighted side. Over a period of minutes walking with the weight, people gradually adapt such that single-limb support time and step length values return to their original values bilaterally. They continue to produce the adapted pattern when the weight is removed, resulting in increased single-limb support time and step length on the previously weighted side and vice versa on the previously unweighted side for a short period of walking after weight removal69,70 (negative aftereffect). These studies illustrate that human locomotor intralimb and interlimb coordination is quite plastic and adaptable. Furthermore, both intralimb and interlimb coordination is adapted when a unilateral perturbation is applied during walking. This bilateral response to a unilateral perturbation supports the suggestion that there is a strong neural coupling between the legs during walking.73–75

Volume 90

Number 2

Physical Therapy f

191

Split-Belt and Other Locomotor Adaptation Paradigms Locomotor adaptations that occur when walking on a split-belt treadmill also provide insight into the control of intralimb and interlimb coordination during walking. When people walk on a split-belt treadmill, where each leg moves at a different speed, there are 2 types of changes that occur in the walking pattern. The first change in the walking pattern is an immediate reaction that is necessary to accommodate the differing belt speeds and results in the slower leg spending more time in stance and the faster leg spending less time in stance.11,12,53,54 This reaction persists throughout split-belt walking and then immediately reverses when the belts are returned to normal treadmill conditions (ie, the belts tied at the same speed). Because there is no gradual adaptation of these parameters, nor are there any aftereffects, this is an example of a reactive, or feedback, type of adjustment.11 The second change in the walking pattern that occurs during split-belt walking is adaptive and feedforward in nature. During splitbelt walking, step length, double support time, and interlimb phasing are asymmetric initially, but people slowly adjust the coordination between their legs to reduce the initial asymmetry created by split belts. When the belts are returned to the same speed, they continue to produce this new adjusted pattern. In people who are healthy, these aftereffects result in step length, interlimb phasing, and double support asymmetries that are in the opposite direction from the asymmetries observed during early adaptation.11,12,53 In contrast, there is essentially no change in intralimb joint timing during or after split-belt walking.53 These results show that interlimb coordination (eg, step length) can be independently controlled and modified without necessarily altering many aspects of intralimb coordination (eg, stance time, intralimb joint timing), which suggests that the neu192

f

Physical Therapy

Volume 90

ral elements that control interlimb coordination are dissociable from those that control intralimb coordination during human bipedal walking. This suggestion has been supported by the results of a study where individuals who were healthy walked on a split-belt treadmill in a backward configuration (with the belts moving backward) or in a hybrid configuration with the right belt moving forward while the left belt was moving backward.72 The results from this study show that adaptation to forward and backward walking is independent in that there is no transfer between directions, nor any interference with one another. Thus, each leg can be adapted separately from its contralateral counterpart, and the effects from adaptation are stored individually for each leg. These findings support the suggestion that there are independently adaptable locomotor networks for each leg in humans. This type of specialized locomotor adaptability is functionally useful because it means the intact adult human locomotor system can learn new patterns without compromising other related patterns.72 In summary, studies of human locomotor adaptation in adults who are healthy illustrate that the control of walking is extremely flexible, allowing the system to readily adapt coordination between the limbs and trunk to accommodate changes in the environment. This flexibility is critical for the wide-ranging functional capacity of normal human locomotion.

Clinical Implications The information gained from studying locomotor adaptation has a number of clinical implications. First, motor adaptation can help us begin to assess the motor learning capabilities of an individual. Both motor adaptation and motor learning require trial-

Number 2

and-error practice,1,5 and motor adaptation may be an initial component in the process of motor learning.4 Thus, studying motor adaptation allows us to begin to assess whether and to what extent the capacity for motor learning may be intact in an individual. This information is critical for appropriate planning in rehabilitation. For example, as discussed previously, chronic damage to the cerebellum appears to reduce adaptive walking capabilities in humans, but this does not seem to be the case for adults with cerebral damage due to stroke. Thus, it might be reasonable to expect some improvement in the walking pattern of an adult with cerebral stroke with an intervention that focuses on trialand-error learning, yet this expectation may be less realistic for a person with chronic cerebellar damage or cerebellar degeneration. Studying motor adaptation in people with neurologic damage also allows us to assess whether and to what extent the injured nervous system is capable of producing normal movement patterns.4 For instance, the locomotor adaptation that occurs with split-belt walking results in asymmetric step length and double support time in humans who are healthy during the aftereffect period. However, in adults with unilateral stroke who show step length or double support asymmetry during unperturbed walking (as a consequence of their neurological injury), the aftereffects can result in improved symmetry.12,76 This improvement occurs because the initial asymmetry is exaggerated during split-belt walking. The nervous system adjusts the interlimb coordination to correct for the exaggerated asymmetry, and when the belts are returned to the same speed, this corrected pattern persists and results in improved asymmetry compared with baseline. This result demonstrates that the compromised nervous system of an adult with February 2010

Split-Belt and Other Locomotor Adaptation Paradigms stroke is still capable of producing a more-normal spatiotemporal walking pattern. Regnaux and colleagues77 weighted the nonparetic leg (2 kg for women, 4 kg for men) of adults with stroke during 20 minutes of treadmill walking. After removal of the load, weight bearing on the paretic leg was increased, leading to improved short-term weight-bearing symmetry during overground walking. This result demonstrates that despite nervous system damage, an adult with stroke is still capable of producing more-normal weight-bearing symmetry during walking. Together, these adaptation studies show that adults with stroke are capable, at least in the short-term, of walking with a more-normal pattern (in terms of spatiotemporal and weight-bearing symmetry). This is a critical finding, given that previous studies have shown that changes in coordination and symmetry during locomotor activities can be difficult to achieve in adults with stroke, even with training.78 – 80 Similarly, it has been shown that people with Parkinson disease (PD) retain the ability to adapt the locomotor trajectory in response to podokinetic stimulation.81 Just as has been found in individuals who were neurologically intact, when people with PD step on a rotating treadmill for a period of time and then step overground while blindfolded, they consistently step with a trunk rotational velocity as would be observed when walking in a curved trajectory.81 This adaptation study shows that adults with PD are capable, at least in the short-term, of producing trunk rotation required for turning during walking, which typically is impaired in people with PD. Future studies are needed to determine whether the rotating platform could be used in rehabilitation to remedi-

February 2010

ate turning difficulties in people with PD. Utilizing locomotor adaptation as an intervention has appeal because these paradigms result in short-term improvements in many of the most pervasive gait deficits observed in people with neurologic damage or disease.12,70,76,77 For example, as discussed previously, a split-belt treadmill walking adaptation poststroke can lead to short-term improvements in step length and double support asymmetry.12,76 Two critical questions must be addressed in order to better understand the direct utility of locomotor adaptation paradigms as rehabilitation interventions. First, do the effects observed during treadmill walking or rotating platform stepping transfer to overground walking? Second, can the short-term improvements, through repeated practice of the adapted pattern, produce longterm changes in the walking pattern of those with gait deficits? There is evidence to support the hypothesis that short-term improvements in the walking pattern of adults with stroke following locomotor adaptation are not just observed on the treadmill, but transfer to overground walking.76,77 Whether the short-term changes observed can result in longterm improvements is an open question. Studies investigating long-term changes in spatiotemporal asymmetry following multiple sessions of split-belt treadmill training are under way in adults with cerebral damage due to stroke and children who have undergone a hemispherectomy. An important concept when considering the use of a locomotor adaptation paradigm as a rehabilitation intervention for people with gait deficits is the direction the perturbation is applied during adaptation. The studies in stroke described above have all shown that adapting walking so that the movement deficits (asymmetry) are initially wors-

ened is what leads the adaptation to result in aftereffects that improve symmetry. That is, the nervous system tries to correct the exaggerated asymmetry, and this correction results in aftereffects of improved symmetry.4 Selecting the correct perturbation direction is critical, although perhaps not intuitive. For example, an adult with stroke who has a baseline asymmetry of longer step length on the paretic side compared with the nonparetic side would need to train on the split-belt treadmill such that this asymmetry is initially exaggerated during the early adaptation period (that is, the paretic leg should be placed on the slower belt because the leg on the slower belt will initially produce a longer step length in response to the asymmetric belt speeds). Over the period of adaptation, the initially exaggerated asymmetry will be restored to nearbaseline levels, therefore, the negative aftereffect will temporarily establish a new symmetric walking pattern. If the same person with stroke is trained with the paretic leg on the faster belt during the split-belt period, his or her asymmetry will be temporarily worsened following the adaptation.12 Similarly, in a limb loading paradigm, the paretic leg of the adult with stroke must be weighted during adaptation in order to observe increased single-limb support time on the paretic leg after the weight is removed.70 This is an interesting concept for rehabilitation: movement error enhancement may cue the nervous system to attempt to make a movement correction. This could be particularly important for people with chronic gait deviations, where the deviation may no longer be perceived by the nervous system as a movement error that requires correction. Currently, there are few studies of locomotor adaptation in humans.

Volume 90

Number 2

Physical Therapy f

193

Split-Belt and Other Locomotor Adaptation Paradigms However, these studies have provided critical information about the walking capacity of many types of patients frequently seen in rehabilitation. Specifically, adaptation studies have revealed that many people with neurological impairments appear to retain the capacity to produce a more-normal walking pattern. Our challenge as rehabilitation specialists is to intervene in ways that maximize this capacity. All authors provided concept/idea/project design and writing. Dr Bastian provided data collection and analysis and fund procurement. Dr Reisman provided data collection and analysis, project management, fund procurement, participants, and facilities/equipment for studies reviewed in which she was an investigator. This work was supported by the following grants: NIH K01 HD050582 (NCMRR) and 1 S10 RR022396-01 (Dr Reisman); NIH R01 HD048740 (NCMRR) and R01 HD40289 (NCMRR) (Dr Bastian); and NIH K01 HD050369 (NCMRR) and a Foundation for Physical Therapy Research Grant (Dr Morton). This article was received March 2, 2009, and was accepted July 29, 2009. DOI: 10.2522/ptj.20090073

References 1 Martin TA, Keating JG, Goodkin HP, et al. Throwing while looking through prisms, II: specificity and storage of multiple gazethrow calibrations. Brain. 1996;119: 1199 –1211. 2 Weiner MJ, Hallett M, Funkenstein HH. Adaptation to lateral displacement of vision in patients with lesions of the central nervous system. Neurology. 1983;33: 766 –772. 3 Shadmehr R, Mussa-Ivaldi FA. Adaptive representation of dynamics during learning of a motor task. J Neurosci. 1994;14(5 pt 2):3208 –3224. 4 Bastian AJ. Understanding sensorimotor adaptation and learning for rehabilitation. Curr Opin Neurol. 2008;21:628 – 633. 5 Schmidt RA. Motor Control and Learning: A Behavioral Emphasis. 2nd ed. Champaign, IL: Human Kinetics Publishers; 1988. 6 Krakauer JW, Shadmehr R. Consolidation of motor memory. Trends Neurosci. 2006; 29:58 – 64.

194

f

Physical Therapy

Volume 90

7 Ghilardi MF, Moisello C, Silvestri G, et al. Learning of a sequential motor skill comprises explicit and implicit components that consolidate differently. J Neurophysiol. 2009;101:2218 –2229. 8 Krakauer JW, Shadmehr R. Towards a computational neuropsychology of action. Prog Brain Res. 2007;165:383–394. 9 Dehaene S, Changeux JP. Rewarddependent learning in neuronal networks for planning and decision making. Prog Brain Res. 2000;126:217–229. 10 Hikosaka O, Nakamura K, Sakai K, Nakahara H. Central mechanisms of motor skill learning. Curr Opin Neurobiol. 2002;12: 217–222. 11 Morton SM, Bastian AJ. Cerebellar contributions to locomotor adaptations during splitbelt treadmill walking. J Neurosci. 2006;26:9107–9116. 12 Reisman DS, Wityk R, Silver K, Bastian AJ. Locomotor adaptation on a split-belt treadmill can improve walking symmetry poststroke. Brain. 2007;130:1861–1872. 13 Brown TG. The intrinsic factors in the act of progression in the mammal. Proc R Soc Lond. 1911;84:308 –319. 14 Forssberg H, Grillner S. The locomotion of the acute spinal cat injected with clonidine i.v. Brain Res. 1973;50: 184 –186. 15 Grillner S. Locomotion in vertebrates: central mechanisms and reflex interaction. Physiol Rev. 1975;55:247–304. 16 Grillner S, Zangger P. On the central generation of locomotion in the low spinal cat. Exp Brain Res. 1979;34:241–261. 17 Rossignol S, Drew T, Brustein E, Jiang W. Locomotor performance and adaptation after partial or complete spinal cord lesions in the cat. Prog Brain Res. 1999;123: 349 –365. 18 Forssberg H, Grillner S, Rossignol S. Phase dependent reflex reversal during walking in chronic spinal cats. Brain Res. 1975;85: 103–107. 19 Pearson KG. Proprioceptive regulation of locomotion. Curr Opin Neurobiol. 1995; 5:786 –791. 20 Shik ML, Severin FV, Orlovskiıˇ GN. Control of walking and running by means of electric stimulation of the midbrain. Biofizika. 1966;11:659 – 666. 21 Orlovsky GN. Activity of vestibulospinal neurons during locomotion. Brain Res. 1972;46:85–98. 22 Orlovsky GN. The effect of different descending systems on flexor and extensor activity during locomotion. Brain Res. 1972;40:359 –371. 23 Asanuma C, Thach WT, Jones EG. Brainstem and spinal projections of the deep cerebellar nuclei in the monkey, with observations on the brainstem projections of the dorsal column nuclei. Brain Res Rev. 1983;5:299 –322. 24 Sprauge JM, Chambers WW. Regulation of posture in intact and decerebrate cat, I: cerebellum, reticular formation, vestibular nuclei. J Neurophysiol. 1953; 16:451– 463.

Number 2

25 Thach WT, Goodkin HP, Keating JG. The cerebellum and the adaptive coordination of movement. Annu Rev Neurosci. 1992; 15:403– 442. 26 Udo M, Oda Y, Tanaka K, Horikawa J. Cerebellar control of locomotion investigated in cats: discharges from Deiters’ neurones, EMG and limb movements during local cooling of the cerebellar cortex. Prog Brain Res. 1976;44:445– 459. 27 Yu J, Eidelberg E. Recovery of locomotor function in cats after localized cerebellar lesions. Brain Res. 1983;273:121–131. 28 Orlovsky GN. Activity of rubrospinal neurons during locomotion. Brain Res. 1972; 46:99 –112. 29 Marple-Horvat DE, Criado JM. Rhythmic neuronal activity in the lateral cerebellum of the cat during visually guided stepping. J Physiol. 1999;518:595– 603. 30 Marple-Horvat DE, Criado JM, Armstrong DM. Neuronal activity in the lateral cerebellum of the cat related to visual stimuli at rest, visually guided step modification, and saccadic eye movements. J Physiol. 1998;506:489 –514. 31 Armstrong DM, Drew T. Discharges of pyramidal tract and other motor cortical neurons during locomotion in the cat. J Physiol. 1984;346:471– 495. 32 Drew T. Motor cortical cell discharge during voluntary gait modification. Brain Res. 1988;457:181–187. 33 Beloozerova IN, Sirota MG. The role of the motor cortex in the control of accuracy of locomotor movements in the cat. J Physiol. 1993;461:1–25. 34 Armstrong DM. Supraspinal contributions to the initiation and control of locomotion in the cat. Prog Neurobiol. 1986;26:273– 361. 35 Drew T, Jiang W, Widajewicz W. Contributions of the motor cortex to the control of the hindlimbs during locomotion in the cat. Brain Res Rev. 2002;40:178 –191. 36 Jiang W, Drew T. Effects of bilateral lesions of the dorsolateral funiculi and dorsal columns at the level of the low thoracic spinal cord on the control of locomotion in the adult cat, I: treadmill walking. J Neurophysiol. 1996;76:849 – 866. 37 Drew T, Jiang W, Kably B, Lavoie S. Role of the motor cortex in the control of visually triggered gait modifications. Can J Physiol Pharmacol. 1996;74:426 – 442. 38 Friel KM, Drew T, Martin JH. Differential activity-dependent development of corticospinal control of movement and final limb position during visually guided locomotion. J Neurophysiol. 2007;97:3396 – 3406. 39 Drew T, Andujar JE, Lajoie K, Yakovenko S. Cortical mechanisms involved in visuomotor coordination during precision walking. Brain Res Rev. 2008;57:199 –211. 40 Hallett M, Massaquoi SG. Physiologic studies of dysmetria in patients with cerebellar deficits. Can J Neurol Sci. 1993;20(suppl 3):S83–S92.

February 2010

Split-Belt and Other Locomotor Adaptation Paradigms 41 Palliyath S, Hallett M, Thomas SL, Lebiedowska MK. Gait in patients with cerebellar ataxia. Mov Disord. 1998;13:958 – 964. 42 Morton SM, Bastian AJ. Relative contributions of balance and voluntary leg coordination deficits to cerebellar gait ataxia. J Neurophysiol. 2003;89:1844 –1856. 43 Wall JC, Turnbull GI. Gait asymmetries in residual hemiplegia. Arch Phys Med Rehabil. 1986;67:550 –553. 44 Richards CL, Malouin F, Dean C. Gait in stroke: assessment and rehabilitation. Clin Geriatr Med. 1999;15:833– 855. 45 Yelnik A, Albert T, Bonan I, Laffont I. A clinical guide to assess the role of lower limb extensor overactivity in hemiplegic gait disorders. Stroke. 1999;30:580 –585. 46 Wade DT, Wood VA, Heller A, et al. Walking after stroke: measurement and recovery over the first 3 months. Scand J Rehabil Med. 1987;19:25–30. 47 Imamizu H, Miyauchi S, Tamada T, Sasaki Y, et al. Human cerebellar activity reflecting an acquired internal model of a new tool. Nature. 2000;403:192–195. 48 Ohyama T, Medina JF, Nores WL, Mauk MD. Trying to understand the cerebellum well enough to build one. Ann N Y Acad Sci. 2002;978:425– 438. 49 Mauk MD, Ohyama T. Extinction as new learning versus unlearning: considerations from a computer simulation of the cerebellum. Learn Mem. 2004;11:566 –571. 50 Gerwig M, Kolb FP, Timmann D. The involvement of the human cerebellum in eyeblink conditioning. Cerebellum. 2007; 6:38 –57. 51 Martin TA, Keating JG, Goodkin HP, et al. Throwing while looking through prisms, I: focal olivocerebellar lesions impair adaptation. Brain. 1996;119:1183–1198. 52 Baizer JS, Kralj-Hans I, Glickstein M. Cerebellar lesions and prism adaptation in macaque monkeys. J Neurophysiol. 1999;81: 1960 –1965. 53 Reisman DS, Block HJ, Bastian AJ. Interlimb coordination during locomotion: what can be adapted and stored? J Neurophysiol. 2005;94:2403–2415. 54 Dietz V, Zijlstra W, Duysens J. Human neuronal interlimb coordination during splitbelt locomotion. Exp Brain Res. 1994;101:513–520. 55 Yanagihara D, Udo M. Climbing fiber responses in cerebellar vermal Purkinje cells during perturbed locomotion in decerebrate cats. Neurosci Res. 1994;19: 245–248. 56 Yanigahara D, Kondo I. Nitric oxide plays a key role in adaptive control of locomotion in cat. Proc Natl Acad Sci USA. 1996; 93:13292–13297.

February 2010

57 Earhart GM, Fletcher WA, Horak FB, et al. Does the cerebellum play a role in podokinetic adaptation? Exp Brain Res. 2002; 146:538 –542. 58 Morton SM, Bastian AJ. Prism adaptation during walking generalizes to reaching and requires the cerebellum. J Neurophysiol. 2004;92:2497–2509. 59 Yanagihara D, Udo M, Kondo I, Yoshida T. A new learning paradigm: adaptive changes in interlimb coordination during perturbed locomotion in decerebrate cats. Neurosci Res. 1993;18:241–224. 60 Choi JT, Vining EP, Reisman DS, Bastian AJ. Walking flexibility after hemispherectomy: split-belt treadmill adaptation and feedback control. Brain. 2009;132: 722–733. 61 Sullivan KJ, Knowlton BJ, Dobkin BH. Step training with body weight support: effect of treadmill speed and practice paradigms on poststroke locomotor recovery. Arch Phys Med Rehabil. 2002;83:683– 691. 62 Pohl M, Mehrholz J, Ritschel C, Ruckriem S. Speed-dependent treadmill training in ambulatory hemiparetic stroke patients: a randomized controlled trial. Stroke. 2002; 33:553–558. 63 Gordon CR, Fletcher WA, Melvill Jones G, Block EW. Adaptive plasticity in the control of locomotor trajectory. Exp Brain Res. 1995;102:540 –545. 64 Weber KD, Fletcher WA, Gordon CR, et al. Motor learning in the “podokinetic” system and its role in spatial orientation during locomotion. Exp Brain Res. 1998;120: 377–385. 65 Earhart GM, Melvill Jones G, Horak FB, et al. Forward versus backward walking: transfer of podokinetic adaptation. J Neurophysiol. 2001;86:1666 –1670. 66 Earhart GM, Melvill Jones G, Horak FB, et al. Transfer of podokinetic adaptation from stepping to hopping. J Neurophysiol. 2002;87:1142–1144. 67 Earhart GM, Sibley KM, Horak FB. Effects of bilateral vestibular loss on podokinetic after-rotation. Exp Brain Res. 2004;155: 251–256. 68 Melvill Jones G, Fletcher WA, Weber KD, Block EW. Vestibular-podokinetic interaction without vestibular perception. Exp Brain Res. 2005;167:649 – 653. 69 Noble JW, Prentice SD. Adaptation to unilateral change in lower limb mechanical properties during human walking. Exp Brain Res. 2006;169:482– 495. 70 Savin DN, Tseng SC, Morton SM. Interlimb coupling during adaptation to a unilateral leg perturbation while walking: results in healthy adults and individuals with poststroke hemiparesis. Presented at: Abstract Viewer/Itinerary Planner Society for Neuroscience Annual Meeting; November 15– 19, 2008; Washington, DC.

71 Lam T, Anderschitz M, Dietz V. Contribution of feedback and feedforward strategies to locomotor adaptations. J Neurophysiol. 2006;95:766 –773. 72 Choi JT, Bastian AJ. Adaptation reveals independent control networks for human walking. Nature Neuroscience. 2007;10: 1055–1062. 73 Ting LH, Raasch CC, Brown DA, et al. Sensorimotor state of the contralateral leg affects ipsilateral muscle coordination of pedaling. J Neurophysiol. 1998;80:1341– 1351. 74 Ting LH, Kautz SA, Brown DA, Zajac FE. Contralateral movement and extensor force generation alter flexion phase muscle coordination in pedaling. J Neurophysiol. 2000;83:3351–3365. 75 Verschueren SM, Swinnen SP, Desloovere K, Duysens J. Effects of tendon vibration on the spatiotemporal characteristics of human locomotion. Exp Brain Res. 2002; 143:231–223. 76 Reisman DS, Wityk R, Silver K, Bastian AJ. Split-belt treadmill adaptation transfers to over ground walking in persons poststroke, Neurorehabil Neural Repair. 2009 Mar 23 [Epub ahead of print]. 77 Regnaux JP, Pradon D, Roche N, et al. Effects of loading the unaffected limb for one session of locomotor training on laboratory measures of gait in stroke. Clin Biomech. 2008;23:762–768. 78 Den Otter AR, Geurts AC, Mulder T, Duysens J. Gait recovery is not associated with changes in the temporal patterning of muscle activity during treadmill walking in patients with post-stroke hemiparesis. Clin Neurophysiol. 2006;117:4 –15. 79 Kautz SA, Duncan PW, Perera S, et al. Coordination of hemiparetic locomotion after stroke rehabilitation. Neurorehabil Neural Repair. 2005;19:250 –258. 80 Silver KH, Macko RF, Forrester LW, et al. Effects of aerobic treadmill training on gait velocity, cadence, and gait symmetry in chronic hemiparetic stroke: a preliminary report. Neurorehabil Neural Repair. 2000;14:65–71. 81 Hong M, Perlmutter JS, Earhart GM. Podokinetic after-rotation in Parkinson disease. Brain Res. 2007;1128:99 –106.

Volume 90

Number 2

Physical Therapy f

195

Perry Issue: Gait Rehab Meaningful Gait Speed Improvement During the First 60 Days Poststroke: Minimal Clinically Important Difference J.K. Tilson, PT, DPT, NCS, is Assistant Professor of Research Physical Therapy, Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 E Alcazar St, CHP 155, Los Angeles, CA 90089 (USA). Address all correspondence to Dr Tilson at: [email protected]. K.J. Sullivan, PT, PhD, FAHA, is Associate Professor of Clinical Physical Therapy, Division of Biokinesiology and Physical Therapy, and Associate Chair and Director, Doctor of Physical Therapy Program, University of Southern California. S.Y. Cen, PhD, is Assistant Professor of Research, Division of Biokinesiology and Physical Therapy, University of Southern California. D.K. Rose, PT, PhD, is Research Assistant Professor, Department of Physical Therapy, University of Florida, Gainesville, Florida. C.H. Koradia, PT, is Physical Therapist, First Class Physical Therapy, Brooklyn, NY. At the time of the study, she was an MS student in the Graduate Program in Biokinesiology and Physical Therapy, University of Southern California. S.P. Azen, PhD, is Professor and Co-Director of Biostatistics, Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California. Author information continues on next page.

Julie K. Tilson, Katherine J. Sullivan, Steven Y. Cen, Dorian K. Rose, Cherisha H. Koradia, Stanley P. Azen, Pamela W. Duncan; for the Locomotor Experience Applied Post Stroke (LEAPS) Investigative Team

Background. When people with stroke recover gait speed, they report improved function and reduced disability. However, the minimal amount of change in gait speed that is clinically meaningful and associated with an important difference in function for people poststroke has not been determined. Objective. The purpose of this study was to determine the minimal clinically important difference (MCID) for comfortable gait speed (CGS) associated with an improvement in the modified Rankin Scale (mRS) score for people between 20 to 60 days poststroke.

Design. This was a prospective, longitudinal, cohort study. Methods. The participants in this study were 283 people with first-time stroke prospectively enrolled in the ongoing Locomotor Experience Applied Post Stroke (LEAPS) multi-site randomized clinical trial. Comfortable gait speed was measured and mRS scores were obtained at 20 and 60 days poststroke. Improvement of ⱖ1 on the mRS was used to detect meaningful change in disability level.

Results. Mean (SD) CGS was 0.18 (0.16) m/s at 20 days and 0.39 (0.22) m/s at 60 days poststroke. Among all participants, 47.3% experienced an improvement in disability level ⱖ1. The MCID was estimated as an improvement in CGS of 0.16 m/s anchored to the mRS.

Limitations. Because the mRS is not a gait-specific measure of disability, the estimated MCID for CGS was only 73.9% sensitive and 57.0% specific for detecting improvement in mRS scores. Conclusions. We estimate that the MCID for gait speed among patients with subacute stroke and severe gait speed impairments is 0.16 m/s. Patients with subacute stroke who increase gait speed ⱖ0.16 m/s are more likely to experience a meaningful improvement in disability level than those who do not. Clinicians can use this reference value to develop goals and interpret progress in patients with subacute stroke.

Post a Rapid Response or find The Bottom Line: www.ptjournal.org 196

f

Physical Therapy

Volume 90

Number 2

February 2010

Meaningful Gait Speed Improvement Poststroke

R

ecovery of walking ability is the most frequently stated goal for patients after stroke.1 In the first week poststroke, 63% of patients are unable to walk independently and 50% cannot walk even with assistance.2 Patients and therapists naturally focus on improved walking function as a primary goal in acute and subacute stroke rehabilitation.

Gait speed has been shown to be sensitive to change over time3,4 and significantly correlated with level of disability in people with stroke.5,6 Perry et al5 identified gait speed categories that correlated with progressive levels of functional walking and disability. People walking at speeds of ⬍0.4 m/s were household ambulators, people walking at speeds of ⱖ0.4 m/s but ⬍0.8 m/s were limited community ambulators, and people walking at speeds of ⱖ0.8 m/s were able to walk in the community without substantial limitations. As people with stroke recover gait speed and transition between these categories, they experience substantially better function and quality of life.6

Minimal Clinically Important Difference Gait speed of an individual poststroke can be referenced as a percentage of age- and sex-matched normative values.7 However, reference values that define clinically meaningful changes in gait speed are lacking. Thus, clinicians lack the reference values needed to answer questions such as, “How much improvement in gait speed is necessary for my patient to achieve a meaningful improvement in level of disability?” The minimal clinically important difference (MCID) is a reference value that addresses this clinical question. The MCID represents the smallest change of score in an outcome measure that a patient would perceive as beneficial.8 Clinicians can use the MCID to interpret the clinical relevance of February 2010

changes observed in an individual poststroke. Researchers can use the MCID to determine the magnitude of difference between groups needed to identify an important benefit of one intervention over another. For people with stroke, the MCID has been estimated for the Functional Independence Measure,9 the Barthel Index,10 and several upper-extremity measures.11 Perera et al12 estimated small meaningful change for gait speed in a cohort of 692 older adults, including 100 people with stroke; however, their study focused on meaningful changes for decline in function. The MCID for gait speed also has been estimated for people with hip fracture.13 To our knowledge, the MCID has not been estimated for changes in gait speed associated with improved function among people with stroke.

P.W. Duncan, PT, PhD, FAHA, FAPTA, is Professor and Bette Busch Maniscalco Research Fellow, Division of Doctor of Physical Therapy, Department of Community and Family Medicine; Professor, School of Nursing; and Senior Fellow, Center for the Study of Aging and Human Development, Duke University, Durham, North Carolina. Locomotor Experience Applied Post Stroke (LEAPS) Investigative Team (see list of investigators on page 206). [Tilson JK, Sullivan KJ, Cen SY, et al; Locomotor Experience Applied Post Stroke (LEAPS) Investigative Team. Meaningful gait speed improvement during the first 60 days poststroke: minimal clinically important difference. Phys Ther. 2010;90:196 –208.] © 2010 American Physical Therapy Association

Minimal Detectible Change Minimal detectible change (MDC) is another commonly reported reference value for interpretation of clinical outcome measures. Whereas the MCID indicates clinically meaningful change, the MDC indicates the amount of change required to exceed measurement variability.14,15 That is, the MDC represents the smallest change on an outcome measure that would be considered “real.” The MDC is derived using the distribution, variability, and reliability of an outcome measure when it is studied in a stable population at 2 time points.* Thus, for the clinician, knowing the MDC would indicate whether a difference observed between 2 measurements on the same patient represents a true difference in performance or whether the difference could be expected due to in* MDC⫽SEM(1.96)公2, where SEM⫽standard error of the measure, 1.96 represents the z score for a 95% confidence interval, and the 公2 accounts for the difference of 2 variances used to derive SEM. SEM⫽SD(公1⫺r), where SD⫽standard deviation of within-subject testretest differences and r⫽measure of reliability (test-retest reliability or Cronbach alpha).16

Available With This Article at ptjournal.apta.org • eAppendix: Locomotor Experience Applied Post Stroke (LEAPS) Procedures for 10-Meter Walk Test and Modified Rankin Scale • Video: “Demonstration of a Standardized Procedure for Conducting the 10-Meter Walk Test to Assess Comfortable Gait Speed in a Hospital Setting.” • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

Volume 90

Number 2

Physical Therapy f

197

Meaningful Gait Speed Improvement Poststroke trinsic variability associated with the outcome measure. Because changes smaller than the MDC are likely to be due to measurement variability rather than real change, it is important that the estimated MCID be larger than the MDC for any given outcome measure.

Estimating MCID Whereas the MDC is the value that exceeds the expected internal variability of a measure, the MCID addresses a more complex concept; it is the magnitude of change in an outcome measure that represents a meaningful change to the patient. Because individuals interpret “meaningful change” differently, depending on a multitude of factors (eg, prior level of function, severity of disability, age, physical environment, time since last measurement), the MCID is a dynamic and contextspecific concept. Thus, derivations of the MCID only estimate the minimum value likely to represent meaningful change for a specific population at a particular stage of recovery.11,15 To gain a clear picture of the MCID for different stroke outcome measures, the MCID will need to be estimated for different stages of recovery and levels of severity (eg, ambulators and nonambulators, chronic and acute). Because estimation of the MCID is an iterative process (ie, evolves from multiple perspectives), it is important to begin to estimate the MCID for key clinical outcome measures such as gait speed among people with stroke. Numerous methods have been described for deriving the MCID.8,15,17–20 Traditionally, analysis methods have been divided into 2 broad categories: distribution based and anchor based.17 Anchor-based analyses are considered a more robust method for estimating clinically meaningful change because the measure of interest, in this case gait speed, is compared with an established measure 198

f

Physical Therapy

Volume 90

of meaningful change.14,15,21 By comparing the measure of interest with a gold standard measure with established clinical relevance and responsiveness to change, a reliable estimate of meaningful change can be determined.15,17 In this study, the MCID was estimated for comfortable gait speed (CGS) of people in the first 60 days poststroke using an anchor-based analysis. Because previous work5,6 has identified that improved gait speed is associated with reduced disability, we chose an anchor that could detect change in level of disability. The gold standard anchor used to identify minimal clinically important improvement in disability was the modified Rankin Scale (mRS).22 The mRS is a global index of disability broadly used as an outcome measure in pharmaceutical, epidemiologic, and behavioral studies of stroke recovery.23–28 Although the literature reflects the impact of gait speed on recovery after stroke and its relationship to community ambulation (ie, participation) and level of disability (ie, participation restrictions), there are no studies that have reported the minimal amount of change in gait speed that is expected to improve level of disability for an individual with stroke. Therefore, the purpose of this study was to estimate the MCID of gait speed for ambulatory individuals with subacute stroke using the mRS, an established measure of disability.

Method Participants Participants (N⫽283) in this study were prospectively enrolled in the multi-site Locomotor Experience Applied Post Stroke (LEAPS) randomized clinical trial between May 2005 and August 2008.27,† Participants were † http://www.clinicaltrials.gov/ct2/show/NCT 00243919.

Number 2

recruited from inpatient rehabilitation hospitals and the surrounding community in 5 different geographic locations across the United States. Inclusion criteria included: (1) age ⱖ18 years, (2) stroke within 45 days, (3) residual paresis in the lower extremity, (4) ability to walk at least 3 m with maximum assist of one person, (5) ability to follow a 3-step command, (6) CGS of ⬍0.80 m/s, (7) expected to be discharged to home, and (8) ability to travel to the intervention site 3 times per week. Exclusion criteria included living in a nursing home prior to stroke, inability to walk at least 30 m (100 ft) prior to stroke, and medical conditions that contraindicate moderateintensity exercise. A full list of inclusion and exclusion criteria for the LEAPS study has been published previously.27 All participants provided written informed consent to participate, as approved by each site’s institutional review board. Assessment Protocol and Outcome Measures As part of the LEAPS trial, standardized assessments were conducted by trained assessors at approximately 20 days poststroke (T20)‡ and at approximately 60 days poststroke (T60). Between T20 and T60, participants did not participate in a research intervention but were engaged in usual care rehabilitation activities in their community. The assessment protocols and methods used to train assessors were published previously.27 For this study, stroke impairment severity was characterized using the National Institutes of Health Stroke Scale (NIHSS)29 and the Fugl-Meyer Sensorimotor Assessment upper-extremity (FM-UE), lower-extremity (FM-LE), and sensory (FM-S) domains.30 Com‡ Participants were assessed initially between the 5th and 30th days poststroke; however, the protocol included tolerance for initial assessment up to 45 days poststroke.

February 2010

Meaningful Gait Speed Improvement Poststroke fortable gait speed and mRS score were used for the MCID analysis. Comfortable gait speed. Trained assessors, all licensed physical therapists, measured CGS using a standardized procedure for the 10-Meter Walk Test (10mWT) (see the eAppendix and video at ptjournal. apta.org) previously described in a poststroke walking intervention study.31 High interrater and intrarater reliability have been established for timed walking tests, including the 10mWT.32–34 The walking course consisted of a total of 14 m in a hallway: a 2-m warm-up, 10 m used for the speed measurement, and 2 m for slowing down to a stop. Instructions were provided to the participant to “walk at a comfortable pace.” Participants were provided up to maximum assist by one person for balance and stability (but not for paretic-limb advancement). Participants used the assistive device (eg, cane, walker) or orthotic device (eg, ankle-foot orthosis) that they used “most often” (if any) at each time point. Two trials were conducted in succession, with a brief seated or standing rest as needed by the participant between trials. Modified Rankin Scale. Modified Rankin Scale scores range from 0 (no symptoms at all) to 5 (severe disability) (Tab. 1). When administered without a structured interview, the mRS has high intrarater reliability (weighted kappa⫽.94 –.99)35 and moderate to high interrater reliability (weighted kappa⫽.71–.91).35,36 Numerous studies have established mRS content and convergent validity.37 Sensitivity to clinically meaningful change has been established for shifts of mRS scores of ⱖ1 in large prospective studies.38,39 A standardized procedure was used to determine the mRS score to optimize interrater reliability. Participant mRS scores were determined by the same assessor who conducted the February 2010

Table 1. Modified Rankin Scale22 Score

Description

0

No symptoms at all

1

No significant disability: despite symptoms, able to carry out all usual duties and activities

2

Slight disability: unable to carry out all previous activities but able to look after own affairs without assistance

3

Moderate disability: requiring some help but able to walk without assistance

4

Moderately severe disability: unable to walk without assistance and unable to attend to own bodily needs without assistance

5

Severe disability: bedridden, incontinent, and requiring constant nursing care and attention

10mWT.35 The mRS score was assigned at the conclusion of a standardized 3- to 4-hour assessment. The assessment included measures of impairment (physical and cognitive), functional activities (physical and cognitive), and life participation, all of which affect the mRS score. Additional information required to accurately determine a participant’s score was obtained from the participant or caregivers at the assessor’s discretion (see the eAppendix at ptjournal.apta.org). Data Analysis Descriptive statistics were used to characterize cohort demographics, stroke characteristics, days poststroke, severity of stroke impairment, CGS, and mRS scores. The Student paired t test and the Bowker test40 were used to identify statistically significant differences between continuous and ordinal measures, respectively. Estimation of MCID Improvement in mRS score (shift of ⱖ1) between T20 and T60 served as the gold standard anchor for detecting minimal clinically important change in gait speed. Anchor-based MCID studies can be designed for analysis of data at an individual or group level.15 Individual-level analyses use statistical tests commonly reported in studies of dichotomous diagnostic tests (eg, receiver operating

characteristic [ROC] curve, sensitivity, specificity, likelihood ratios).15,17,41 In this study, these methods were used in conjunction with a Classification and Regression Tree (CART) analysis in a 2-step process. Step 1: receiver operating characteristic curve. To estimate MCID, the sample population was divided into participants who did or did not experience a “true” change in disability (improvement of ⱖ1 in mRS score). Individual cutoff scores for change in CGS ranging from 0.01 to 0.78 m/s then were tested to determine their sensitivity and specificity for detecting participants who did or did not experience a true change in disability. Sensitivity represents the percentage of participants who experienced an improvement of ⱖ1 on the mRS and met or exceeded the estimated MCID for CGS. Specificity represents the percentage of participants who did not experience an improvement of ⱖ1 on the mRS and failed to meet or exceed the estimated MCID for CGS. Figure 1a and the second column of Figure 1c summarize the formulas for the proposed analyses. A ROC curve was generated by plotting sensitivity against 1 – specificity for each potential cutoff score. The area under the curve (AUC) and 95% confidence interval (CI) were calcu-

Volume 90

Number 2

Physical Therapy f

199

Meaningful Gait Speed Improvement Poststroke

Figure 1. (a) A 2 ⫻ 2 table traditionally used to calculate sensitivity and specificity. The sample population is divided into 4 groups (cells A, B, C, and D). Cell A represents the number of participants who had a positive result on both the gold standard test and the new test (true positives). Cell B represents the number of participants who had a positive result on the gold standard test but a negative result on the new test (false negatives). Cell C represents the number of participants who had a negative result on the gold standard test but a positive result on the new test (false positives). Cell D represents the number of participants who had a negative result on both the gold standard test and the new test (true negatives). (b) A 2 ⫻ 2 table of actual data from this study for the gold standard anchor (the modified Rankin Scale) and the minimal clinically important difference (MCID) of 0.16 m/s for comfortable gait speed. (c) Formulas used to calculate sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio and actual data used to calculate values for the MCID of 0.16 m/s for comfortable gait speed.

lated using the SAS (version 9.1)§ %ROC macro described by Delong et al42 to determine the presence of a relationship between change in CGS and shift of mRS score sufficient to estimate MCID.43 If the lower limit of the AUC 95% CI was ⬎0.5, the relationship between change in CGS and mRS was considered sufficient to estimate the MCID for CGS. Identifying a sufficient relationship between the §

SAS Institute Inc, PO Box 8000, Cary, NC 27513.

200

f

Physical Therapy

Volume 90

2 variables was the primary purpose of the ROC curve. Traditionally, if a sufficient relationship existed; the ROC curve could be used to qualitatively estimate MCID by visually determining the point on the curve closest to the upper left-hand corner of the graph, which represents the point of optimal sensitivity and specificity.9,43,44 In this study, however, the second step of the analysis provided a more-quantitative method for estimating MCID.

Number 2

Step 2: Classification and Regression Tree. The second step of the analysis used CART analysis (version 6)㛳,45 to provide a more quantitative estimate of the best cutoff score to estimate MCID. For this analysis, potential cutoff scores were defined by the minimum and maximum values for change in CGS between T20 and T60 in our sample population. 㛳

Salford Systems, 4740 Murphy Canyon Rd, Suite 200, San Diego, CA 92123 (http://www. salfordsystems.com/112.php).

February 2010

Meaningful Gait Speed Improvement Poststroke Within that range, cutoff scores were tested at 0.01-m/s increments. Each cutoff score served as a metric for splitting the data into 2 groups: participants whose change in CGS exceeded the cutoff score and participants whose change in CGS did not exceed the cutoff score. A heterogeneity value (ie, impurity) associated with each cutoff score was computed. The cutoff score with the largest heterogeneity represented the score with the best discrimination of the data (ie, the best MCID candidate). Thus, the cutoff score with the highest heterogeneity value was identified as the estimated MCID. Next, tenfold cross validation was used to substantiate this estimate. This process involved development of an ancillary cross-validation learning tree (ie, computational modeling) using a randomly selected 90% of our data set. The remaining 10% of the data served as a pseudoindependent data set that was used to validate the estimate generated from the initial 90% (by calculation of a classification error). This procedure was repeated 10 times. The results of the 10 cross-validation procedures were combined to compute a statistical score for determining the significance of the estimated MCID. The CART program will provide a result only if it is statistically significant. Thus, the CART analysis produces a more-quantitative result than the ROC curve analysis. CART analysis is not probabilistic and, therefore, provides a point estimate but not a CI. Results from the CART analysis were compared with the ROC curve to ensure that the computer-generated cutoff score corresponded to a visual representation of the data. If the 2 models were in general agreement, the cutoff score identified by CART would be considered the preferred method to estimate MCID.

February 2010

Likelihood Ratios Finally, to facilitate clinical interpretation and utilization of the MCID value, positive and negative likelihood ratios (LR⫹, LR–) were calculated to characterize the value of the MCID for identifying a meaningful change in level of disability for individual patients. Likelihood ratios combine sensitivity and specificity and were used to determine the likelihood, based on change in CGS, that an individual would experience a meaningful change in level of disability.44 Specifically, LR⫹ was used to estimate the likelihood that a participant who met or exceeded the estimated MCID would actually experience a meaningful improvement in level of disability, and LR– was used to estimate the likelihood that a participant who did not exceed the estimated MCID would experience a meaningful improvement in level of disability. Formulas used to calculate LR⫹ and LR– are illustrated in the second column of Figure 1c. Once likelihood ratios were calculated, a likelihood ratio nomogram46 was used to determine the probability that an individual similar to the participants of our cohort would experience an improvement in level of disability based upon whether he or she did or did not achieve the estimated MCID for CGS. A nomogram, in this case a likelihood ratio nomogram, is a graphical calculating device.# Whenever possible, 95% CIs were calculated to demonstrate the precision of statistical analyses (AUC,42 sensitivity and specificity,47 likelihood ratios48).

# The likelihood ratio nomogram is used traditionally to determine the probability that someone has (or does not have) a condition based upon certain baseline characteristics and the likelihood ratio of a diagnostic test.46 In this case, the tool was used to determine the probability of improved level of disability (improvement of ⱖ1 in mRS score) based upon likelihood ratios calculated for the estimated CGS MCID.

Table 2. Baseline Demographics, Stroke Characteristics, and Impairmenta Demographics (nⴝ283) Age at stroke onset (y)

Value 63.5 (12.5)

Sex Female

136 (48.1%)

Male

147 (51.9%)

Stroke type Ischemic

229 (80.9%)

Hemorrhagic

49 (17.3%)

Uncertain

5 (1.8%)

Stroke location Right hemisphere

134 (47.3%)

Left hemisphere

109 (38.5%)

Brain stem

36 (12.7%)

Bilateral

4 (1.4%)

Stroke impairment NIHSS (0⫽no deficits, 42⫽maximum deficits)

7.5 (4.0)

a

Mean (SD) for continuous data; frequency (%) for categorical data. NIHSS⫽National Institutes of Health Stroke Scale.

Role of the Funding Source This work was supported by funding from National Institute of Neurological Disorders and Stroke and the National Center for Medical Rehabilitation Research (RO1 NS050506). The funding source had no role in the design, conduct, or reporting of this study.

Results Participants A total of 283 participants (age: mean [SD]⫽63.5 [12.5] years, range⫽29 –98 years) completed assessments T20 and T60. Scores for stroke impairment severity at T20, based on the NIHSS, ranged from 0 to 20 out of 42 (mean [SD]⫽7.5 [4.0]) (Tab. 2). Significant increases in CGS, FM-UE, FM-LE, FM-S, and mRS scores (P⬍.001) associated with natural recovery and participation in therapeutic rehabilitation programs were evident (Tab. 3).

Volume 90

Number 2

Physical Therapy f

201

Meaningful Gait Speed Improvement Poststroke Table 3. Stroke Severity, Gait Speed, and Disability Level at 20 Days and 60 Days Poststrokea Measures Conducted

20 Days

Change

P

Days poststroke for 10-Meter Walk Test

21.9 (10.7)

63.2 (8.6)

41.3 (12.7)

⬍.001b

FM-UE motor score (maximum⫽66)

30.4 (19.9)

36.5 (20.7)

6.1 (9.1)

⬍.001b

FM-LE motor score (maximum⫽34)

21.4 (6.6)

25.1 (6.3)

3.8 (4.4)

⬍.001b

FM-S score (maximum⫽24)d

18.3 (6.7)

19.3 (6.1)

1.2 (3.9)

⬍.001b

0.21 (0.17)

⬍.001b

Stroke sensorimotor severity

60 Days

c

Gait Comfortable gait speed (m/s)

0.18 (0.16)

0.39 (0.22)

Level of disability Modified Rankin Scale score 0: no symptoms at all

0

⬍.0001e

0

1: no significant disability

1 (0.4%)

2 (0.7%)

2: slight disability

8 (2.8%)

43 (15.2%)

3: moderate disability 4: moderately severe disability 5: severe disability

47 (16.6%)

124 (43.8%)

225 (79.5%)

114 (40.3%)

2 (0.7%)

0 (0.0%)

a

Mean (SD) for continuous data; frequency (%) for categorical data. b Student paired t test was used to determine statistical significance for continuous data. c FM-UE⫽Fugl-Meyer Sensorimotor Assessment– upper extremity, FM-LE⫽Fugl-Meyer Sensorimotor Assessment–lower extremity, FM-S⫽Fugl-Meyer Sensorimotor Assessment–sensory. d A small number of participants were unable to participate in the FM-S assessment due aphasia (T20, n⫽272; T60, n⫽270; change, n⫽270). e The Bowker test was used to determine statistical significance for ordinal data.

Table 4. Stroke Outcome Characterized by Modified Rankin Scale (mRS) Score at Assessment 1 (T20) and Assessment 2 (T60)a mRS Score at T20

mRS Score at T60 0

1

2

Total

3

4

3

5

0 1

1

2

3

2

3 4

1

14

31

2

47

2

25

91

107

225

2

2

2

43

124

114

283

5 Total

8

a

Blue boxes represent participants who had no change in mRS score from T20 to T60 (n⫽141 [49.8%]). Participants represented above blue boxes experienced increased disability from T20 to T60 (n⫽8 [2.8%]). Participants represented below blue boxes experienced reduced disability from T20 to T60 (n⫽134 [47.3%]).

Comfortable Gait Speed Individual gait speeds at T20 and T60 met the LEAPS inclusion criterion of a CGS of ⬍0.80 m/s, ranging from 0.00 to 0.79 m/s. At T20 and T60, 67 and 7 participants, respectively, met the inclusion criterion of ability to walk 3 m but were unable to walk 202

f

Physical Therapy

Volume 90

10 m due to fatigue and, therefore, were considered to have a gait speed of 0.0 m/s. For the purposes of this study, these cases were not considered missing data. At T20, only 14% of the participants (n⫽41) were classified5 as limited community ambulators (ⱖ0.4 to ⬍0.8 m/s), whereas the

Number 2

remainder (n⫽242, 86%) were classified as household ambulators (⬍0.4 m/s). At T60, the proportion of limited community ambulators increased to 49% (n⫽139). Mean (SD) CGS increased 0.21 (0.17) m/s, from 0.18 (0.16) m/s at T20 to 0.39 (0.22) m/s at T60 (P⬍.001); change in gait speed ranged from ⫺0.21 m/s to 0.75 m/s. Between assessments, 15 participants (5%) experienced a decline in CGS (median⫽⫺0.04 m/s). Modified Rankin Scale Table 4 shows frequency counts of mRS scores at T20 and T60 and illustrates the degree of change experienced by individual participants between the 2 time points. Participants represented in the blue boxes (n⫽141, 49.8%) did not experience a shift in mRS scores. Participants represented in cells above (and to the right of) the blue boxes (n⫽8, 2.8%) experienced a worsening of disability level. Conversely, participants

February 2010

Meaningful Gait Speed Improvement Poststroke

Figure 2. Receiver operating characteristic (ROC) curve for the ability of change in gait speed to detect a change in modified Rankin Scale (mRS) scores. The ROC curve provides a visual depiction of the sensitivity (y-axis) and specificity (x-axis) of gait speed cutoff scores for detecting ⱖ1 level of improvement in mRS scores. Each point along the curve represents a change in gait speed for which sensitivity and specificity were calculated.

represented in cells below (and to the left of) the blue boxes (n⫽134, 47.3%) experienced a clinically meaningful improvement in function (ⱖ1 improvement of mRS scores) between T20 and T60. There was a statistically significant shift of mRS category from T20 to T60 (P⬍.001). MCID–ROC Curve To derive the CGS MCID, the best match between a change in CGS and a change in the gold standard anchor (mRS) was identified. The ROC curve is illustrated in Figure 2, and the AUC was calculated as 0.69 (95% CI⫽0.63– 0.75). The shape of the ROC curve is relatively smooth and difficult to interpret with regard to cutoff scores. Having established a substantial relationship between CGS and mRS scores (AUC ⬎0.5), we proceeded to CART analysis to derive a specific cutoff score.

February 2010

MCID–CART Analysis The CART analysis showed that a CGS of ⱖ0.16 m/s produced the optimal combination of sensitivity (73.9%, 95% CI⫽65.9%– 80.6%) and specificity (57.0%, CI⫽49.0%– 64.7%) for detecting improvement in mRS scores among our participants (Fig. 1b and columns 3 and 4 of Fig. 1c). This cutoff score produced the strongest association to the anchor compared with all other potential cutoff scores. This finding is consistent with the trend presented in the ROC curve.

disability (47.3%) served as the estimated pretest probability that a participant would experience a meaningful change in disability. Using a likelihood ratio nomogram, we determined that a participant who met or exceeded a 0.16-m/s change in CGS had a posttest probability of 60% for experiencing a meaningful change in disability (Fig. 3). In contrast, those who did not meet or exceed a 0.16-m/s CGS change had a posttest probability of only 29% for experiencing a meaningful change in disability.

Likelihood Ratios The LR⫹ for the CGS MCID of 0.16 m/s was 1.72 (95% CI⫽1.39 –2.12), and the LR– was 0.46 (0.33– 0.63). Overall, 47.3% of the participants in the cohort experienced a meaningful improvement in disability level. The overall prevalence rate of improved

Discussion For people between 20 and 60 days after first-time stroke who are ambulatory but have severe gait impairment (eg, mean gait speed⫽0.18 m/s), we estimate the MCID for gait speed to be 0.16 m/s. We anchored our MCID analysis to the mRS, an

Volume 90

Number 2

Physical Therapy f

203

Meaningful Gait Speed Improvement Poststroke

Figure 3. Nomogram graphical representation of the probability that an individual with stroke will experience a meaningful change in disability level. The green line plots the pretest probability estimated at 47% (based on the overall percentage of participants in this study with improved disability level) and the positive likelihood ratio (LR⫹) used when an individual meets or exceeds the minimal clinically important difference (MCID) of 0.16 m/s for comfortable gait speed to determine the posttest probability that the individual has a 60% probability of experiencing an improvement in disability level. The red line plots the pretest probability and the negative likelihood ratio (LR–) used when an individual does not meet the MCID of 0.16 m/s for comfortable gait speed to determine the posttest probability that the individual has only a 29% probability of experiencing an improvement in disability level. Likelihood ratio nomogram adapted and reprinted with permission from the Centre for Evidence-Based Medicine, Oxford, United Kingdom.61

accepted and reliable indicator of disability level. From the patient perspective, walking at faster speeds results in higher levels of participation such as going out of the home for family, recreational, or occupational outings.6 For the clinician, this reference for meaningful change in gait speed can be used to interpret clinical outcomes, particularly the effectiveness of walking rehabilitation programs. The mRS is a robust measure of global disability that has convergent validity with the Barthel Index (BI), 204

f

Physical Therapy

Volume 90

another common measure of disability poststroke, while avoiding the ceiling effect observed in the BI.39,49 Additionally, the mRS is more sensitive than the BI for distinguishing between mild and moderate disability.39 A recent survival analysis by Huybrechts et al50 demonstrated the importance of a 1-point shift on the mRS. They found that mRS scores at 3 months poststroke are not only predictive of long-term functional independence but also highly predictive of mortality. For every 1-point improvement on the mRS, participants’ life expectancy was statisti-

Number 2

cally significantly longer. Further support that the relationship between CGS and mRS scores is justified and sound is provided by the ROC curve and the associated AUC, which was substantial (⬎0.50). A requirement of valid, longitudinal, anchor-based MCID studies is that time between assessments is sufficient for individuals in the study cohort to experience a clinically meaningful change.15 We chose to evaluate the time points of 20 and 60 days poststroke because this is a critical time of change when most indiFebruary 2010

Meaningful Gait Speed Improvement Poststroke viduals are involved in some form of rehabilitation (ie, inpatient rehabilitation, home health care, outpatient therapy). Indeed, across the 20- to 60-day period poststroke, the participants in our study had a mean improvement in gait speed of 0.21 (0.17) m/s. This represents greater than 100% improvement in mean gait speed from T20 to T60. However, not all participants experienced an improvement in speed or disability level, providing sufficient diversity within the population for a difference to be detected between those who experienced at least a minimal clinically important improvement and those who did not. Clinical Interpretation of MCID By defining the threshold for clinically important change, we improve our ability to interpret the value of rehabilitation efforts in clinical settings and randomized clinical trials of intervention effectiveness. Thus, an MCID reference value of 0.16 m/s for gait speed could serve as an explicit therapeutic goal for rehabilitation interventions aimed at improving participation levels for individuals poststroke. Not only are higher gait speeds associated with improved function poststroke,6 but gait speed also is associated with reduced mortality in older adults.51 Improvement in usual gait speed has been shown to predict a substantial reduction in mortality, whereas a decline in gait speed predicts increased risk for hospitalization and onset of disability among older adults.52,53 Another important aspect of maintaining gait speed and high levels of participation is the established benefits of physical activity to reduce stroke risk.54 Clearly, people with stroke who are at risk for secondary stroke need to be involved in physical activities such as walking to maintain health and wellness. A valid MCID for gait speed improves not only the clinical interpretation of individual rehabilitation programs but also the February 2010

clinical significance of intervention studies that may find statistical improvements in gait speed but may not achieve a threshold that is clinically meaningful.

be confusing for the clinician to use and interpret. This confusion is further confounded by studies that do not adhere to optimal design methods for derivation of MDC.14,16

Likelihood ratios incorporate sensitivity and specificity and typically are used to describe diagnostic tests.46 However, in our study, they provided valuable insight into the interpretation of our results by estimating how likely an individual with subacute stroke is to experience an improvement in level of disability based solely on change in gait speed. Of all participants in our study, 47.3% experienced an improvement in level of disability. For an individual with stroke who has characteristics similar to those of our cohort and achieves an improvement in CGS of ⱖ0.16 m/s, a nomogram and the LR⫹ of 1.72 estimate that this individual has a 60% probability of experiencing a meaningful change in disability level (Fig. 3). Conversely, an individual who fails to meet or exceed a 0.16-m/s change in CGS has only a 29% probability of experiencing a meaningful change in disability level. Thus, although our MCID estimate for CGS is not a perfect indicator of meaningful change, it provides a valuable reference for identifying meaningful change in clinical and research settings.

For example, the MDC for CGS among patients with stroke was reported in 3 studies, all during the inpatient subacute phase of recovery (number of subjects ranged from 24 to 35 individuals poststroke). The MDC was reported as 0.12 m/s,55 0.17 m/s,56 and 0.30 m/s.57 The limitation of these studies is that the test-retest coefficient, an integral component of MDC derivation, was derived during a time of rapid recovery. Deviation of test-retest reliability in a changing population potentially overestimates variability, causing inflation of the MDC. The smallest real difference (SRD) is considered the same construct as the MDC.16 Perera et al12 estimated SRD to be 0.05 m/s for decline in gait speed among older adults, 14% of whom were people with chronic stroke. Similarly, among individuals following a hip fracture, the MDC for gait speed was determined to be 0.08 m/s. Thus, it appears that the MDC for gait speed in older adults and most likely in people with stroke is more in the range of 0.05 to 0.08 m/s, rather than the reported range of 0.12 to 0.30 m/s. Additional study of MDC in people with stroke who are not expected to make gains through natural recovery and are not participating in a rehabilitation program is needed.

What Is the Difference Between MCID and MDC? Both MCID and MDC provide reference values for interpreting magnitude of change on an outcome measure. The MCID indicates the magnitude of change required to achieve a clinically meaningful change. The MDC indicates the magnitude of change required to exceed test-retest reliability. As mentioned previously, it is important to interpret estimates of MCID in light of random measurement error represented by the MDC. Unfortunately, these 2 measurement constructs may

Magnitude of change for MCID is driven by characteristics of the population studied. The MCID of 0.16 m/s found in our study is similar to, although slightly larger than, findings in other populations. Palombaro et al13 estimated an MCID of 0.10 m/s for habitual gait speed among elderly people after hip fracture with a mean (SD) initial CGS of 0.66 (0.28) m/s (range⫽0.14 –1.33 m/s). Perera et al12 estimated “substantial mean-

Volume 90

Number 2

Physical Therapy f

205

Meaningful Gait Speed Improvement Poststroke ingful change” for decline in gait speed among older adults to be 0.10 m/s in a population with initial mean (SD) gait speeds ranging from 0.65 (0.28) m/s to 0.88 (0.24) m/s. Participants in the present study, with a mean (SD) CGS of 0.18 (0.16) m/s at T20, had relatively severe initial gait speed impairments. It may be that a larger magnitude of change in speed is required to produce meaningful change in people with more-severe deficits. Future analyses of patients with mild impairment secondary to stroke are needed and may produce smaller values for MCID. Strengths and Limitations This study had several strengths. We were able to prospectively follow a large cohort of participants recruited from 5 distinct geographic locations during a time of rapid change in walking recovery poststroke.2,58 Data for outcome measures were collected using a standardized protocol by therapists who had completed rigorous training and competency testing. Our analysis included the traditional method of ROC curve analysis combined with a quantitative CART analysis. Finally, the mRS is a robust measure that captures small, but clearly important, changes in global disability. By using the mRS, we are able to understand the smallest magnitude of CGS improvement likely to contribute a meaningful change in disability level for individual patients during the subacute phase poststroke. Our MCID estimate was 73.9% sensitive and 57.0% specific to improvement in mRS scores. The lack of precision (sensitivity and specificity) of our MCID estimate may be considered a limitation. However, the mRS does not directly correlate with gait speed because it is a global measure of disability. Disability from the individual perspective is a complex and multivariate phenomenon that encompasses more than gait speed. 206

f

Physical Therapy

Volume 90

Thus, gait speed, an activity-level measure of mobility, is one of many variables (eg, arm and hand function, cognition level, emotional impairment, bowel and bladder control, pain) that contribute to mRS score.59 We consider improvement on the mRS to be a robust anchor for determining CGS MCID because it reflects change on a participation level that is important from the individual perspective. Another possible limitation is that participants were allowed to use different assistive devices at the 2 time points. Due to the acuity of our participants (mean⫽21.9 days poststroke at T20), we expect that spontaneous neurologic recovery and response to therapeutic interventions are occurring simultaneously. Thus, an ecologically valid (ie, reallife) MCID for CGS would reflect both the expected changes associated with time poststroke (ie, acute, subacute, chronic)60 and the beneficial effects expected of therapy. That is, we are interested in the real-life change in gait speed regardless of assistive device. Additional studies are needed to expand our understanding of MCID for gait speed among individuals with stroke. Other anchors also should be used to develop additional estimates of MCID, including measures that directly assess patients’ perspective of change. Ideally, over time a relatively narrow range of MCID estimates will emerge that clinicians can use to more definitively understand the minimal amount of change in gait speed likely to represent clinically meaningful change for individual patients. Other subsets of people with stroke also should be studied. For example, in this study, there were insufficient participants with moderate gait speed deficits (ⱖ0.4 to 0.8 m/s) at initial evaluation to support subanalysis by gait speed severity. Likewise, people who were able to

Number 2

walk at speeds of ⬎0.8 m/s were excluded from the study. The MCID needs to be determined for people with stroke across various time frames and levels of severity.

Conclusion We estimate that the MCID for gait speed among patients with subacute stroke and severe gait speed impairments is 0.16 m/s. Thus, patients with similar characteristics who increase their gait speed ⱖ0.16 m/s are more likely to experience a meaningful improvement in disability level than those who do not. This reference value can be used by clinicians to develop goals and interpret progress in patients with subacute stroke. The MCID estimate also is useful for interpretation of walking intervention effectiveness studies. Dr Tilson, Dr Sullivan, Dr Cen, Ms Koradia, Dr Azen, and Dr Duncan provided concept/ idea/research design. Dr Tilson, Dr Sullivan, Dr Cen, Ms Koradia, and Dr Duncan provided writing. Dr Tilson, Dr Cen, Dr Rose, Ms Koradia, and Dr Azen provided data collection. Dr Tilson, Dr Cen, Ms Koradia, and Dr Duncan provided data analysis. Dr Tilson, Dr Sullivan, Dr Cen, Dr Rose, and Dr Duncan provided project management. Dr Sullivan and Dr Duncan provided fund procurement and institutional liaisons. Dr Sullivan provided facilities/equipment. Dr Tilson, Dr Cen, Dr Rose, and Dr Azen provided consultation (including review of manuscript before submission). The authors acknowledge the participants who dedicated their time to this study and the contributions of the following members of the LEAPS investigative team: Brooks Rehabilitation Hospital, Jacksonville, Florida: Trevor Paris, MD, Deborah Stewart, MD, and Joann Gallichio, PT; Florida Hospital Rehabilitation and Sports Medicine, Orlando, Florida: Mitchell Freed, MD, Michelle Dolske, PhD, Craig Moore, PT, and Bettina Brutsch, PT; Long Beach Memorial Medical Center, Long Beach, California: H. Richard Adams, MD, Diemha Hoang, MD, and Anita Correa, PT; Sharp Memorial Rehabilitation Center, San Diego, California: Jerome Stenehjem, MD, Roxanne Hon, MD, and Molly McLeod, PT; USC PT Associates, Los Angeles, California: David Alexander, MD, Julie Hershberg, PT, DPT, and Samneang Ith-Chang, PT, DPT. Locomotor Experience Applied Post-Stroke

February 2010

Meaningful Gait Speed Improvement Poststroke (LEAPS) Principal Investigator is Pamela W. Duncan, PT, PhD, FAHA, FAPTA (Duke University, Durham, North Carolina). Co-Principal Investigators are: Andrea L. Behrman, PT, PhD, FAPTA (University of Florida, Gainesville, Florida) and Katherine J. Sullivan, PT, PhD, FAHA (University of Southern California, Los Angeles, California). Members of the Steering Committee include: Stanley P. Azen, PhD (University of Southern California, Los Angeles, California), Samuel S. Wu, PhD (University of Florida, Gainesville, Florida), Bruce H. Dobkin, MD (University of California–Los Angeles, Los Angeles, California), and Stephen E. Nadeau, MD (University of Florida, Gainesville, Florida). The Data Management and Analysis Center (DMAC) is located at the University of Southern California and is directed by Stanley P. Azen, PhD. Samuel S. Wu, PhD, serves as the trial’s Lead Statistician, and Steven Cen, PhD (University of Southern California, Los Angeles, California) co-directs the DMAC. Dorian K. Rose, PT, PhD (University of Florida, Gainesville, Florida) and Julie K. Tilson, PT, DPT (University of Southern California, Los Angeles, California) are the Clinical Research Coordinators for the LEAPS trial. Sarah Hayden (Duke University, Durham, North Carolina) is the Project Manager. The 4 members of the Data Safety and Monitoring Committee are: Bruce M. Coull, MD, Chair (University of Arizona, Tucson, Arizona), Elizabeth A. Noser, MD (University of Texas Medical School, Houston, Texas), Michael K. Parides, PhD (Columbia University, New York, New York), and Steven L. Wolf, PT, PhD, FAPTA (Emory University, Atlanta, Georgia). Abstracts of the data were presented at the Combined Sections Meeting of the American Physical Therapy Association; February 9 –12, 2009; Las Vegas, Nevada, and at the Annual Meeting of the California Chapter of the American Physical Therapy Association Annual Meeting; October 2–3, 2009; Pasadena, California. This work was supported by funding from National Institute of Neurological Disorders and Stroke and the National Center for Medical Rehabilitation Research (RO1 NS050506). The funding source had no role in the design, conduct, or reporting of this study. This article was received March 8, 2009, and was accepted July 20, 2009. DOI: 10.2522/ptj.20090079

References 1 Bohannon RW, Andrews AW, Smith MB. Rehabilitation goals of patients with hemiplegia. Int J Rehabil Res. 1988;11:181–183.

February 2010

2 Jorgensen HS, Nakayama H, Raaschou HO, et al. Outcome and time-course of recovery in stroke, 2: time-course of recovery. The Copenhagen Stroke Study. Arch Phys Med Rehabil. 1995;76:406 – 412. 3 Salbach NM, Mayo NE, Higgins J, et al. Responsiveness and predictability of gait speed and other disability measures in acute stroke. Arch Phys Med Rehabil. 2001;82:1204 –1212. 4 Goldie PA, Matyas TA, Evans OM. Deficit and change in gait velocity during rehabilitation after stroke. Arch Phys Med Rehabil. 1996;77:1074 –1082. 5 Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke. 1995;26: 982–989. 6 Schmid A, Duncan PW, Studenski SA, et al. Improvements in speed-based gait classifications are meaningful. Stroke. 2007;38: 2096 –2100. 7 Perry J. Gait Analysis: Normal and Pathological Function. Thorofare, NJ: Slack Inc; 1992. 8 Jaeschke R, Singer J, Guyatt GH. Measurement of health status: ascertaining the minimal clinically important difference. Control Clin Trials. 1989;10:407– 415. 9 Beninato M, Gill-Body KM, Salles S, et al. Determination of the minimal clinically important difference in the FIM instrument in patients with stroke. Arch Phys Med Rehabil. 2006;87:32–39. 10 Hsieh YW, Wang CH, Wu SC, et al. Establishing the minimal clinically important difference of the Barthel Index in stroke patients. Neurorehabil Neural Repair. 2007;21:233–238. 11 Lang CE, Edwards DF, Birkenmeier RL, Dromerick AW. Estimating minimal clinically important differences of upperextremity measures early after stroke. Arch Phys Med Rehabil. 2008;89:1693– 1700. 12 Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54:743–749. 13 Palombaro KM, Craik RL, Mangione KK, Tomlinson JD. Determining meaningful changes in gait speed after hip fracture. Phys Ther. 2006;86:809 – 816. 14 de Vet HC, Terwee CB, Ostelo RW, et al. Minimal changes in health status questionnaires: distinction between minimally detectable change and minimally important change. Health Qual Life Outcomes. 2006;4:54. 15 Beaton DE, Bombardier C, Katz JN, et al. Looking for important change/differences in studies of responsiveness. J Rheumatol. 2001;28:400 – 405. 16 Beckerman H, Roebroeck M, Lankhorst G, et al. Smallest real difference, a link between reproducibility and responsiveness. Qual Life Res. 2001;10:571–578. 17 Crosby R, Kolotkin R, Williams G. Defining clinically meaningful change in healthrelated quality of life. J Clin Epidemiol. 2003;56:395– 407.

18 Crosby R, Kolotkin R, Williams G. An integrated method to determine meaningful changes in health-related quality of life. J Clin Epidemiol. 2004;57:1153–1160. 19 Jacobson N, Follette W, Revenstorf D. Toward a standard definition of clinically significant change. Behav Ther. 1986;17: 308 –311. 20 Jacobson N, Truax P. Clinical significance: a statistical approach to defining meaningful change in psychotherapy research. J Consult Clin Psychol. 1991;59:12–19. 21 Haley SM, Fragala-Pinkham MA. Interpreting change scores of tests and measures used in physical therapy. Phys.Ther. 2006; 86:735–743. 22 Bonita R, Beaglehole R. Recovery of motor function after stroke. Stroke. 1988;19: 1497–1500. 23 Vanswieten JC, Koudstaal PJ, Visser MC, et al. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19:604 – 607. 24 Sulter G, Steen C, De Keyser J. Use of the Barthel Index and modified Rankin Scale in acute stroke trials. Stroke. 1999;30: 1538 –1541. 25 Duncan PW, Jorgensen HS, Wade DT. Outcome measures in acute stroke trials: a systematic review and some recommendations to improve practice. Stroke. 2000; 31:1429 –1438. 26 Huybrechts KF, Caro JJ. The Barthel Index and modified Rankin Scale as prognostic tools for long-term outcomes after stroke: a qualitative review of the literature. Curr Med Res Opin. 2007;23:1627–1636. 27 Duncan PW, Sullivan KJ, Behrman AL, et al. Protocol for the Locomotor Experience Applied Post-stroke (LEAPS) trial: a randomized controlled trial. BMC Neurol. 2007;7:39. 28 Bernhardt J, Dewey H, Thrift A, et al. A very early rehabilitation trial for stroke (AVERT) phase II safety and feasibility. Stroke. 2008;39:390 –396. 29 Brott T, Adams HP, Olinger CP, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. 1989; 20:864 – 870. 30 Gladstone DJ, Danells CJ, Armesto A, et al. Physiotherapy coupled with dextroamphetamine for rehabilitation after hemiparetic stroke: a randomized, double-blind, placebo-controlled trial. Stroke. 2006;37: 179 –185. 31 Sullivan KJ, Brown DA, Klassen T, et al. Effects of task-specific locomotor and strength training in adults who were ambulatory after stroke: results of the STEPS randomized clinical trial. Phys.Ther. 2007; 87:1580 –1602. 32 Rossier P, Wade DT. Validity and reliability comparison of 4 mobility measures in patients presenting with neurologic impairment. Arch Phys Med Rehabil. 2001;82: 9 –13. 33 Wolf SL, Catlin PA, Gage K, et al. Establishing the reliability and validity of measurements of walking time using the emory functional ambulation profile. Phys Ther. 1999;79:1122–1133.

Volume 90

Number 2

Physical Therapy f

207

Meaningful Gait Speed Improvement Poststroke 34 Flansbjer UB, Holmback AM, Downham D, et al. Reliability of gait performance tests in men and women with hemiparesis after stroke. J Rehabil Med. 2005;37:75– 82. 35 Wilson JTL, Hareendran A, Hendry A, et al. Reliability of the modified Rankin Scale across multiple raters: benefits of a structured interview. Stroke. 2005;36:777–781. 36 Wilson JTL, Hareendran A, Grant M, et al. Improving the assessment of outcomes in stroke: use of a structured interview to assign grades on the modified Rankin Scale. Stroke. 2002;33:2243–2246. 37 Banks JL, Marotta CA. Outcomes validity and reliability of the modified Rankin Scale: implications for stroke clinical trials—a literature review and synthesis. Stroke. 2007;38:1091–1096. 38 Lai SM, Duncan PW. Stroke recovery profile and the modified Rankin assessment. Neuroepidemiology. 2001;20:26 –30. 39 Weimar C, Kurth T, Kraywinkel K, et al. Assessment of functioning and disability after ischemic stroke. Stroke. 2002;33: 2053–2059. 40 Krampe A, Kuhnt S. Bowker’s test for symmetry and modifications within the algebraic framework. Computational Statistics and Data Analysis. 2007;51: 4124 – 4142. 41 Deyo R, Centor R. Assessing the responsiveness of functional scales to clinical change : an analogy to diagnostic test performance. J Chronic Dis. 1986;39:897–906. 42 Delong ER, Delong DM, Clarkepearson DI. Comparing the areas under 2 or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837– 845. 43 Ward MM, Marx AS, Barry NN. Identification of clinically important changes in health status using receiver operating characteristic curves. J Clin Epidemiol. 2000;53:279 –284.

208

f

Physical Therapy

Volume 90

44 Stratford PW, Binkley JM, Riddle DL, Guyatt GH. Sensitivity to change of the Roland-Morris Back Pain Questionnaire: part 1. Phys Ther. 1998;78:1186 –1196. 45 Breiman I. Classification and Regression Trees. Belmont, CA: Wadsworth International Group; 1984. 46 Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice. 3rd ed. Upper Saddle River, NJ: Pearson/Prentice Hall; 2009. 47 Newcombe R. Interval estimation for the difference between independent proportions: comparison of eleven methods. Stat Med. 1998;17:873– 890. 48 Simel D, Samsa G, Martchar D. Likelihood ratios with confidence: sample size estimation for diagnostic test studies. J Clin Epidemiol. 1991;44:763–770. 49 Kwon S, Hartzema AG, Duncan PW, Lai SM. Disability measures in stroke: relationship among the Barthel Index, the Functional Independence Measure, and the Modified Rankin Scale. Stroke. 2004;35: 918 –923. 50 Huybrechts KF, Caro JJ, Xenakis JJ, Vemmos KN. The prognostic value of the modified Rankin Scale score for long-term survival after first-ever stroke. Cerebrovasc Dis. 2008;26:381–387. 51 Hardy SE, Perera S, Roumani YF, et al. Improvement in usual gait speed predicts better survival in older adults. J Am Geriatr Soc. 2007;55:1727–1734. 52 Studenski SA, Perera S, Wallace D, et al. Physical performance measures in the clinical setting. J Am Geriatr Soc. 2003;51: 314 –322. 53 Guralnik JM, Ferrucci L, Pieper CF, et al. Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci. 2000;55:M221–M231.

Number 2

54 Lloyd-Jones D, Adams R, Carnethon M, et al. Heart disease and stroke statistics— 2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2009;119:e21– e181. 55 Stephens JM, Goldie PA. Walking speed on parquetry and carpet after stroke: effect of surface and retest reliability. Clin Rehabil. 1999;13:171–181. 56 Evans MD, Goldie PA, Hill KD. Systematic and random error in repeated measurements of temporal and distance parameters of gait after stroke. Arch Phys Med Rehabil. 1997;78:725–729. 57 Fulk GD, Echternach JL. Test-retest reliability and minimal detectable change of gait speed in individuals undergoing rehabilitation after stroke. J Neurol Phys Ther. 2008;32:8 –13. 58 Jorgensen HS, Nakayama H, Raaschou HO, et al. Outcome and time-course of recovery in stroke, 1: outcome. The Copenhagen Stroke Study. Arch Phys Med Rehabil. 1995;76:399 – 405. 59 Kasner SE. Clinical interpretation and use of stroke scales. Lancet Neurology. 2006; 5:603– 612. 60 Sullivan KJ. Letter to the editor on “Modified constraint-induced therapy in patients with chronic stroke exhibiting minimal movement ability in the affected arm.” Phys Ther. 2007;87:1560. 61 Centre for Evidence-Based Medicine Web site. Available at: http://www.cebm.net/ index.aspx?o⫽1043. Accessed June 20, 2009.

February 2010

Perry Issue: Gait Rehab Gait Parameters Associated With Responsiveness to Treadmill Training With Body-Weight Support After Stroke: An Exploratory Study Sara J. Mulroy, Tara Klassen, JoAnne K. Gronley, Valerie J. Eberly, David A. Brown, Katherine J. Sullivan

Background. Task-specific training programs after stroke improve walking function, but it is not clear which biomechanical parameters of gait are most associated with improved walking speed.

Objective. The purpose of this study was to identify gait parameters associated with improved walking speed after a locomotor training program that included body-weight–supported treadmill training (BWSTT).

Design. A prospective, between-subjects design was used. Methods. Fifteen people, ranging from approximately 9 months to 5 years after stroke, completed 1 of 3 different 6-week training regimens. These regimens consisted of 12 sessions of BWSTT alternated with 12 sessions of: lower-extremity resistive cycling; lower-extremity progressive, resistive strengthening; or a sham condition of arm ergometry. Gait analysis was conducted before and after the 6-week intervention program. Kinematics, kinetics, and electromyographic (EMG) activity were recorded from the hemiparetic lower extremity while participants walked at a self-selected pace. Changes in gait parameters were compared in participants who showed an increase in self-selected walking speed of greater than 0.08 m/s (highresponse group) and in those with less improvement (low-response group).

Results. Compared with participants in the low-response group, those in the high-response group displayed greater increases in terminal stance hip extension angle and hip flexion power (product of net joint moment and angular velocity) after the intervention. The intensity of soleus muscle EMG activity during walking also was significantly higher in participants in the high-response group after the intervention.

Limitations. Only sagittal-plane parameters were assessed, and the sample size was small.

Conclusions. Task-specific locomotor training alternated with strength training resulted in kinematic, kinetic, and muscle activation adaptations that were strongly associated with improved walking speed. Changes in both hip and ankle biomechanics during late stance were associated with greater increases in gait speed.

S.J. Mulroy, PT, PhD, is Director, Pathokinesiology Laboratory, Rancho Los Amigos National Rehabilitation Center, 7601 E Imperial Hwy, Bldg 800, Room 33, Downey, CA 90242 (USA). Address all correspondence to Dr Mulroy at: [email protected]. gov. T. Klassen, MS, PT, NCS, is Clinical Instructor, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada. J.K. Gronley, PT, DPT, is Associate Director of Clinical Research, Pathokinesiology Laboratory, Rancho Los Amigos National Rehabilitation Center. V.J. Eberly, PT, NCS, is Research Physical Therapist, Pathokinesiology Laboratory, Rancho Los Amigos National Rehabilitation Center. D.A. Brown, PT, PhD, is Associate Professor and Associate Chair for Post-Professional Education, Department of Physical Therapy and Human Movement Sciences; Associate Professor, Department of Physical Medicine and Rehabilitation; and Adjunct Faculty, Department of Biomedical Engineering, Northwestern University, Chicago, Illinois. K.J. Sullivan, PT, PhD, is Associate Chair and Associate Professor of Clinical Physical Therapy, Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, California. [Mulroy SJ, Klassen T, Gronley JK, et al. Gait parameters associated with responsiveness to treadmill training with body-weight support after stroke: an exploratory study. Phys Ther. 2010;90:209 – 223.] © 2010 American Physical Therapy Association Post a Rapid Response or find The Bottom Line: www.ptjournal.org

February 2010

Volume 90

Number 2

Physical Therapy f

209

Treadmill Training With Body-Weight Support After Stroke

S

troke affects almost 1 person every 40 seconds and is the leading cause of serious, longterm disability in the United States.1 Although physical therapists intervene with many facets of physical function, particular attention is focused on improving walking ability because this is one of the most common goals stated by people recovering from a stroke.2 For people with mild to moderate motor impairment, eventual independent walking ability is likely; nevertheless, 60% of those who achieve physical independence in walking will be limited in community ambulation.3 Evidence is building that for walking rehabilitation after stroke, innovations such as body-weight–supported treadmill training (BWSTT) (ie, task-specific locomotor training) are more effective than approaches based on neurofacilitation or inhibition of muscle activity, which were used by physical therapists in the 1980s and 1990s.4 – 6 Task-specific locomotor training has been associated with increases in strength (force-generating capacity), endurance, and walking speed.7–9 These global outcomes indicate functional changes but do not provide insights about the underlying neuromuscular

Available With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

210

f

Physical Therapy

Volume 90

or biomechanical contributors to the therapeutic improvements. With further insights into underlying contributors, interventions can be targeted to specific impairments with the expectation of improved outcomes at greater efficiency. Although many studies have demonstrated improved walking function after BWSTT for people with stroke,7–9 the biomechanical parameters of gait underlying the long-term improvements seen in overground walking as a result of BWSTT have not been identified. Both hip flexion power (product of net joint moment and angular velocity) and ankle plantar-flexion power during late stance are critical determinants of improvements seen in gait speed as a result of other interventions after stroke.10 –12 However, it is not known whether BWSTT targets biomechanical functions that are uniquely affected by treadmill training and body-weight support. There is limited evidence that walking on a treadmill with body-weight support (compared with overground ambulation) results in immediate, short-term changes, including increased stance-swing symmetry,13,14 increased hip extension during single-limb stance, and decreased gastrocnemius muscle activity.15 Walking at higher speeds on the treadmill increases the activation of stance-phase muscles, including the gastrocnemius, vastus lateralis, biceps femoris, and gluteus medius muscles.16 To our knowledge, no studies have included instrumented gait analysis before and after a program of BWSTT to determine the biomechanical parameters underlying the long-term improvements seen in overground walking. Understanding the gait mechanics and muscle activity patterns in people who respond well and in those who do not may suggest a different physiological basis for those with the best recovery

Number 2

versus those with persistent walking dysfunction. The primary purpose of this study was to identify the biomechanical gait parameters associated with responsiveness to a task-specific intervention that included BWSTT and that was designed to improve locomotor recovery after stroke. (In this issue, Kuo and Donelan17 review the determinants of dynamic walking.) The secondary objective was to identify the baseline participant characteristics and changes in lowerextremity maximal isometric torque and maximal muscle activation that were associated with responsiveness to the intervention. We hypothesized that, compared with people who showed little or no improvement in walking speed after the BWSTT interventions, people who responded to the BWSTT interventions (ie, people with significant postintervention increases in selfselected overground walking speed) would show improvements in kinematic and kinetic parameters at the end of stance and at the stance-swing interface in the paretic hip and ankle joints; increased intensity of electromyographic (EMG) activity of the paretic ankle plantar-flexor, hip extensor, and hip flexor muscles during walking; and increased maximal isometric torque of the hip flexor and ankle plantar-flexor muscle groups in the paretic leg.

Method Study Design The participants in the present study were a subset of those in a larger randomized clinical trial, the Strength Training Effectiveness Post-Stroke (STEPS) trial.18 In the STEPS study, participants were randomly assigned to 1 of 4 intervention groups: task-specific BWSTT and upper-extremity ergometry (UE Ex), locomotor strength training (Cycle) and UE Ex, BWSTT and Cycle, and BWSTT and muscleFebruary 2010

Treadmill Training With Body-Weight Support After Stroke

Figure 1. Outline of Strength Training Effectiveness Post-Stroke (STEPS) study. BWSTT⫽body-weight–supported treadmill training, Cycle⫽locomotor strength training, LE⫽muscle-specific lower-extremity strength training, UE Ex⫽upper-extremity ergometry.

specific lower-extremity strength training (LE Ex). An exploratory instrumented gait analysis to examine the biomechanical mechanisms associated with the STEPS interventions was conducted at Rancho Los Amigos National Rehabilitation Center. The first 5 participants from each exercise group (n⫽20) enrolled at the University of Southern California or Rancho Los Amigos National Rehabilitation Center underwent the instrumented gait analysis at baseline (after group randomization but before intervention) and after the 6-week BWSTT period. February 2010

All subjects read and signed an informed consent form that described the STEPS protocol approved by the institutional review board of each institution; subjects who participated in the gait analysis also signed an additional consent form specifically related to the gait analysis. The results of the primary STEPS study indicated that self-selected walking speed increased significantly and similarly after each of the 3 BWSTT interventions but not after the Cycle–UE Ex intervention. Therefore, in the present study, we evaluated only data from the 15 partici-

pants assigned to the 3 BWSTTrelated groups (ie, BWSTT–UE Ex, BWSTT–Cycle, and BWSTT–LE Ex). The STEPS study design and the additional allocation specifically related to the gait analysis are shown in Figure 1. Participants The 15 participants included in the present study met the inclusion and exclusion criteria for the STEPS trial.18 In summary, the participants were 18 years of age or older; approximately 9 months to 5 years after the initial onset of an ischemic or hemorrhagic cerebrovascular ac-

Volume 90

Number 2

Physical Therapy f

211

Treadmill Training With Body-Weight Support After Stroke cident; able to ambulate, at a selfselected walking speed of less than or equal to 1.0 m/s, at least 14 m with an assistive device, lowerextremity orthosis, or both and with the assistance of 1 person; and free from any serious medical, orthopedic, or premorbid condition that would physically or cognitively limit participation in the study. Intervention The 15 participants received BWSTT–UE Ex, BWSTT–Cycle, or BWSTT–LE Ex. In brief, the UE Ex component consisted of lowresistance upper-extremity ergometry. The Cycle intervention was a program of progressive, resistive lower-extremity cycling on a Biodex semirecumbent cycle* that required resistance during the down stroke of the cycle (extension) to maintain the position of the seat in the target zone. Finally, the LE Ex component was a progressive, resistive exercise program for specific lower-extremity muscle groups of the hemiparetic leg (ie, hip extensor, knee extensor, plantar-flexor, hip flexor, knee flexor, and dorsiflexor muscles). Participants in all 3 intervention groups received the BWSTT interventions twice per week. This intervention included 20 minutes (in 4to 5-minute bouts) of stepping on a treadmill with body-weight support at a treadmill speed of approximately 3.2 km/h (2.0 mph). A complete description of the exercise protocols is provided in the report of the primary STEPS study.18 The BWSTT and strengthening exercises were alternated over 4 days per week (excluding weekends) for 6 weeks (for a total of 24 sessions). Each exercise session was 1 hour in duration and was conducted by a licensed physical therapist.

Outcome Measures Specific demographic and clinical data obtained from the baseline evaluation of the STEPS study also were used to characterize the participants in the present study.18 These data included participant demographics, stroke characteristics (including onset), and lower-extremity Fugl-Meyer motor scale score. The instrumented gait analysis was conducted within 1 week before and within 1 week after the 24-session exercise intervention. Participants walked in shoes without any lowerextremity orthoses but were permitted to use their customary assistive devices. Participants performed several practice walking trials to improve the likelihood of stepping with the tested foot landing entirely on the forceplate. Simultaneous recordings of foot-floor contacts, lower-extremity kinematics, and EMG activity were made as participants traversed a 10-m walkway at a self-selected speed; the middle 6 m of the walkway was delineated for data collection by photoelectric beams. Walking was repeated until 2 successful trials with the participant’s foot landing completely on the forceplate were recorded. Any trial that resulted in only part of the foot landing on the forceplate was discarded. Assistive devices were not permitted to contact the forceplate. Foot-floor contact patterns were recorded by use of a Stride Analyzer System† with compression-closing footswitches taped to the bottom of the participant’s shoes. The 3dimensional kinematics of the participant’s hemiplegic lower extremity were documented by use of a Vicon Motion Analysis System.‡ Six infrared, 50-Hz cameras recorded the lo-

212

f

Physical Therapy

Volume 90

Intramuscular EMG recording was accomplished with indwelling, finewire electrodes inserted into the belly of each of 8 lower-extremity muscles (gluteus maximus, gluteus medius, semimembranosus, adductor longus, rectus femoris, vastus intermedius, soleus, and anterior tibialis muscles) using the technique of Basmajian and Stecko.19 Electrode placement was confirmed by palpating tension in the tendon or muscle belly during mild electrical stimulation through the inserted wires. Electromyographic signals were transmitted by FM-FM telemetry (model 2600 apparatus),# filtered through an analog band-pass filter (150 –1,000 Hz), and sampled and digitized at 2,500 Hz. The overall signal gain was 1,000. Before the walking trials, EMG recording was performed to determine the baseline threshold of myoelectric activity for each muscle at rest and during a 5-second resisted isometric maximal voluntary contraction for normalization. Participants performed a practice maximal contraction for each muscle before data collection. §

†

* Biodex Medical Systems Inc, 20 Ramsay Rd, PO Box 702, Shirley, NY 11967.

cations of 14 retroreflective markers taped to the skin overlying the bony landmarks, including the midline sacrum at the level of the posterior iliac spines, anterior superior iliac spines (bilaterally), greater trochanter, anterior thigh, medial and lateral femoral condyles, anterior tibia, medial and lateral malleoli, dorsum of the foot, first and fifth metatarsal heads, and posterior heel. Motion data were acquired by use of a DEC PDP 11/23 computer.§ The ground reaction forces of the hemiparetic lower extremity were sampled at 2,500 Hz by use of a Kistler forceplate储 embedded in the walkway.

B & L Engineering, 3002 Dow Ave, Ste 416, Tustin, CA 92780. ‡ Vicon Motion Systems, 14 Minns Business Pk, West Way, Oxford OX2 0JB, United Kingdom.

Number 2

Digital Equipment Corp, 1 Kendall Sq, Cambridge, MA 02139 储 Kistler Instrument Corp, 75 John Glenn Dr, Amherst, NY 14228-2171. # Biosentry Telemetry Inc, 207–20G Earl St, Torrance, CA 90503.

February 2010

Treadmill Training With Body-Weight Support After Stroke Maximal isometric torque was recorded with a Biodex dynamometer* for ankle dorsiflexion, ankle plantar flexion, and flexion and extension of both the hip and the knee. Participants performed a practice submaximal contraction and then 3 maximaleffort trials. Testing of the nonparetic extremity preceded that of the paretic extremity for each muscle group. The average peak torque from the 3 trials was recorded. Isometric torque at the ankle was measured with the participant in a long sitting position with the seat back reclined slightly (85°) and the knee supported in 20 to 30 degrees of flexion. The ankle was positioned in 5 degrees of plantar flexion for recording isometric ankle plantarflexion torque and in 15 degrees of plantar flexion for the ankle dorsiflexion test. Knee torque testing was performed while the participant was sitting with the seat back reclined to 85 degrees. Torque for both isometric knee extension and knee flexion was measured in 45 degrees of knee flexion. Torque for hip flexion and hip extension was recorded with the participant in the supine position and the cuff attached just proximal to the popliteal fossa. Hip extension torque was measured with the hip flexed to 90 degrees, and hip flexion torque was measured with the hip flexed to 60 degrees. Data Management Footswitch data were used to calculate walking speed, cadence, and stride length and to identify gait cycle timing. Each stride was time normalized with initial contact defined as 0% of the gait cycle, the end of stance defined as 65%, and the end of swing defined as 100% to allow for comparison across participants. Ground reaction forces and segment kinematic data were filtered with a fourth-order, zero-lag, low-pass digital Butterworth filter (20- and 4-Hz February 2010

cutoff frequencies, respectively). Kinematic data were processed with Adtech Motion Analysis Software** to produce 3-dimensional trajectories for each marker. The position and orientation of each lowerextremity segment were obtained, and lower-extremity joint angles for each percentage of the gait cycle were determined by use of computer algorithms with Euler embedded coordinates. An ensemble average for all complete strides (typically 4 – 6) was determined for the sagittal plane joint motions of each participant.

however, if it was less than 122 mV, then the normalization value for the walking trials was set at 122. The use of this minimum normalization value, which was approximately 20% of a full interference pattern, prevented inflation of EMG signals during walking in muscles in which a participant lacked sufficient volitional control to produce a significant signal during manual muscle testing.22 The intensity of EMG activity was expressed as a percentage of the maximal voluntary contraction.

The magnitude, orientation, and point of application of the resultant ground reaction forces were determined from the forceplate data. Measured body segment parameters were used in conjunction with empirical relationships, derived from cadaver studies, to estimate the mass, center of mass, and moments of inertia of body segments.20 Joint and body segment kinematic data were combined with kinetic data to calculate the joint forces and moments by use of the inverse dynamics approach.21 Joint moments were normalized to body weight and leg length. Joint power for the hip, knee, and ankle was calculated as the product of the joint moment and the angular velocity.

Phasing of EMG activity during walking was determined with EMG Analyzer Software.†,23 The EMG Analyzer identified the onset and cessation times (as a percentage of the gait cycle) for each packet of muscle activity that had an intensity of at least 5% of the maximal voluntary contraction and a duration of at least 5% of the gait cycle. With the minimum normalization value of 122 mV, the threshold for 5% of the maximal voluntary contraction for significant EMG activity would correspond to 6 mV over a 0.02second interval. Any signal lower than this value was not considered functionally significant. Packets of EMG activity separated by quiescent intervals of less than 5% of the gait cycle were combined. The average intensity of activation between onset and cessation was calculated for each muscle.23

Electromyographic signals were subjected to full-wave rectification and integrated over intervals of 0.01 second. A moving window was used to identify the highest EMG signal recorded in a 1-second interval during the 5-second maximal muscle contraction, and this value was used to calculate the average EMG signal in a 0.02-second interval. If the latter value was at least 122 mV, then it served as the normalization value for the EMG signals recorded during walking22; ** Adtech Inc, 3465 Waialae Ave, Ste 200, Honolulu, HI 96816.

Data Analysis Participants were stratified into either a high-response group or a lowresponse group on the basis of the magnitude of the change in selfselected walking speed between the baseline and postintervention sessions. Participants in the highresponse group showed increases in self-selected walking speed of greater than or equal to 0.08 m/s, whereas the low-response group comprised participants with walking

Volume 90

Number 2

Physical Therapy f

213

Treadmill Training With Body-Weight Support After Stroke

Figure 2. Change in walking speed (y-axis) versus baseline walking speed (A) and lower-extremity (LE) Fugl-Meyer motor scale score (B) for high-response and low-response groups. The bold horizontal line represents the minimum detectable change (MDC) threshold for walking speed (0.08 m/s). Participants above the walking speed MDC threshold were categorized as showing a high response; participants below this threshold were categorized as showing a low response. Two participants with high baseline walking speeds but relatively low baseline LE Fugl-Meyer motor scale scores are indicated with circles.

speed changes of less than 0.08 m/s. The minimum detectable change in customary walking speed for older adults with stroke has been reported in most studies to range from 0.05 to 0.08 m/s; thus, we selected the higher range value of 0.08 m/s as the threshold for improvement in our analysis.24 –27 We tested the specific hypotheses that participants who showed greater improvements in selfselected walking speed (highresponse group) after completing a 24-session program of task-specific locomotor training and strength training designed to improve walking recovery would show the following biomechanical changes in the hemiparetic lower extremity (compared with participants who showed minimal or no improvements in walking speed [lowresponse group]): increased hip extension angle, hip flexion moment, and hip flexion power at terminal stance–pre-swing; increased 214

f

Physical Therapy

Volume 90

plantar-flexion angle and plantarflexion power at terminal stance– pre-swing; increased intensity of EMG activity of the ankle plantarflexor, hip extensor, and hip flexor muscles (soleus, gluteus maximus, semimembranosus, and adductor longus muscles) during walking; and increased isometric torque of the hip flexor and ankle plantarflexor muscles. Two-way repeated-measures analysisof-variance models were used to determine the interaction effects of group (high-response group and low-response group) and time (before intervention and after intervention) for spatiotemporal characteristics; peak values for paretic lowerextremity joint motion, moment, and power; and average intensities of EMG activity of paretic lowerextremity muscles during walking. The main effect of time was evaluated only when the interaction was not statistically significant. Similar analyses were conducted for the

Number 2

maximal isometric lower-extremity torque and the maximal EMG signal elicited during manual muscle testing of each of the 8 muscles at the baseline and postintervention tests. The baseline clinical characteristics of the 2 response groups were compared by use of an independent t test or a chi-square test for categorical data. A P value of .05 was set as the criterion for statistical significance. The analyses were conducted by use of BMDP statistical software.††

Results Participant Characteristics Seven of the 15 participants showed improvements in self-selected walking speed of greater than 0.08 m/s (high-response group) after the 24 exercise sessions, and 8 participants showed improvements of less than 0.08 m/s (low-response group) (Fig. 2A). There were no sig††

Statistical Solutions, Stonehill Corporate Center, 999 Broadway, Ste 104, Saugus, MA 01906.

February 2010

Treadmill Training With Body-Weight Support After Stroke Table 1. Participant Characteristics Participants Characteristic

All (Nⴝ15)

High-Response Group (nⴝ7)

Low-Response Group (nⴝ8)

Age (y) X (SD)

58.47 (14.86)

58.25 (13.00)

58.71 (17.83)

Range

35–80

35–76

37–80

Effect Size

.90

0.03

0.04

Sex

7 women, 8 men

3 women, 4 men

4 women, 4 men

.78

Treatment group (no. of participants)

BWSTT–UE Ex (5)

BWSTT–UE Ex (2)

BWSTT–UE Ex (3)

.77

BWSTT–Cycle (5)

BWSTT–Cycle (3)

BWSTT–Cycle (2)

BWSTT–LE Ex (5)

BWSTT–LE Ex (2)

BWSTT–LE Ex (3)

X (SD)

0.50 (0.24)

0.58 (0.18)

0.43 (0.27)

Range

0.11–0.93

0.40–0.90

0.11–0.93

Baseline evaluation self-selected speed (m/s)

Mo since stroke X (SD)

25.43 (15.46)

25.57 (14.92)

23.23 (16.79)

Range

9.26–57.20

12.85–57.20

9.26–55.39

Baseline lower-extremity Fugl-Meyer motor scale score X (SD)

25.87 (4.69)

Range Assistive device (no. of participants using the indicated device)

28.71 (3.60)

.20

0.65

.72

0.15

.02a

1.35

.21

0.91

23.50 (4.11)

17–34

25–34

16–30

None (6)

None (4)

None (2)

Single cane (3)

Single cane (1)

Single cane (2)

Quad cane (5)

Quad cane (2)

Quad cane (3)

Single crutch (1) a

P

Single crutch (1)

Statistically significant.

nificant differences between the high-response group and the lowresponse group with respect to age, sex, time since stroke, intervention group, baseline self-selected walking speed, or assistive device use (Tab. 1). The baseline lowerextremity Fugl-Meyer motor scale score was significantly higher in the high-response group (mean⫽28.7, SD⫽3.6) than in the low-response group (mean⫽23.5, SD⫽4.1) (P⫽ .02) (Fig. 2B). Spatiotemporal Characteristics The increase in average walking speed in participants in the highresponse group after the intervention was 0.153 m/s (SD⫽0.056); the increase in participants in the lowresponse group was 0.017 m/s (SD⫽0.034) (Tab. 2). Both cadence February 2010

Table 2. Baseline Values and Changes in Spatiotemporal Characteristics of Walking Participants High-Response Group (nⴝ7)a

Low-Response Group (nⴝ8)a

Baseline

0.580 (0.177)

0.426 (0.275)

Change

⫹0.153 (0.056)

⫹0.017 (0.034)

Characteristic Speed (m/s)

Cadence (steps/min) Baseline

76.70 (13.72)

63.69 (18.44)

Change

⫹7.77 (4.88)

⫹1.51 (4.37)

Stride length (m)

a

Baseline

0.891 (0.126)

0.744 (0.311)

Change

⫹0.129 (0.048)

⫹0.022 (0.074)

P

Effect Size

.001

2.94

.02

1.35

.01

1.72

Values are reported as mean (SD).

Volume 90

Number 2

Physical Therapy f

215

Treadmill Training With Body-Weight Support After Stroke Table 3. Baseline Values and Changes in Peak Hip Joint Angles, Moments, and Power During Walking Participants High-Response Group (nⴝ7)a

Measurement

LowResponse Group (nⴝ8)a

Hip flexion angle during loading (°) Baseline

29.6 (8.21)

26.09 (8.49)

Change

⫺2.16 (5.84)

⫹2.73 (5.66)

Hip extension angle during stance (°) Baseline

⫺1.99 (3.95)

⫺3.68 (10.70)

Change

⫹6.79 (5.66)

⫹0.63 (6.41)

Baseline

⫺15.06 (6.16)

⫺9.43 (6.17)

Change

⫹2.10 (3.98)

⫹2.53 (4.26)

Thigh extension angle during stance (°)

Hip flexion angle during swing (°) Baseline

31.05 (6.08)

25.99 (8.01)

Change

⫺1.59 (6.57)

⫹4.47 (5.54)

Baseline

17.86 (5.89)

20.58 (7.90)

Change

⫹2.98 (4.94)

⫹2.52 (3.41)

Thigh flexion angle during swing (°)

Medial (internal) hip extension moment during loading (N䡠m/kg䡠m) Baseline

0.340 (0.211)

0.363 (0.292)

Change

⫹0.109 (0.236)

⫺0.050 (0.218)

Baseline

⫺0.364 (0.120)

⫺0.347 (0.252)

Change

⫺0.144 (0.230)

⫹0.028 (0.128)

Medial (internal) hip flexion moment during stance (N䡠m/kg䡠m)

Hip extension power generation during loading (W/kg䡠m) Baseline

0.309 (0.302)

0.221 (0.309)

Change

⫹0.116 (0.392)

⫹0.043 (0.144)

Baseline

⫺0.167 (0.167)

⫺0.196 (0.268)

Change

⫺0.201 (0.325)

⫺0.045 (0.139)

Hip flexion power absorption during stance (W/kg䡠m)

Hip flexion power generation during pre-swing (W/kg䡠m) Baseline

0.268 (0.231) ⫹0.195 (0.18)

Change a

P

Effect Size

.12

0.85

.04

1.23

.85

0.10

.07

1.00

.83

0.11

.20

0.70

.09

0.92

.64

0.25

.24

0.62

.02

1.34

0.168 (0.160) ⫹0.004 (0.09)

Values are reported as mean (SD).

and stride length also improved to a greater extent in the high-response group than in the low-response group (P⫽.02 and P⫽.01, respectively). Kinematics and Kinetics Compared with participants in the low-response group, participants in the high-response group displayed 216

f

Physical Therapy

Volume 90

greater increases in the peak hip extension angle (⫹6.8° [SD⫽5.7] versus ⫺0.6° [SD⫽6.4]) (Tab. 3, Fig. 3A) and in hip flexor muscle power during stance (⫹0.195 W/kg䡠m [SD⫽0.18) versus ⫹0.004 W/kg䡠m [SD⫽0.09]) (Fig. 3B); these differences were statistically significant (P⫽.02). In contrast, the increases in the peak thigh extension angle (rel-

Number 2

ative to laboratory vertical) in both groups were nearly identical (⫹2.1° versus ⫹2.5°), indicating that the differences in the hip extension angle between the groups reflected decreased anterior pelvic tilt in the high-response group and increased anterior pelvic tilt in the lowresponse group.

February 2010

Treadmill Training With Body-Weight Support After Stroke

Figure 3. Mean curves for high-response (HIGH) and low-response (LOW) groups at preintervention (PRE) and postintervention (POST) assessments for hip motion (A) and power (B) and ankle motion (C) and power (D). Curves for participants in the high-response group are depicted with black lines, and curves for participants in the low-response group are depicted with blue lines. Preintervention data are represented by dashed lines, and postintervention data are represented by solid lines. The vertical lines indicate the end of stance and the beginning of swing. Changes in hip extension motion and hip flexor muscle power generation during terminal stance–pre-swing were significantly greater in the high-response group than in the low-response group. Changes in ankle plantarflexion motion and power generation also tended to be greater in the high-response group. Asterisk indicates significant at P⬍.05.

The peak ankle plantar-flexion angle during initial double-limb support (loading) increased more in participants in the high-response group (⫹2.8° [SD⫽2.7]) than in participants in the low-response group (⫺1.6° [SD⫽3.9]) (P⫽.03) (Tab. 4, Fig. 3C). In terminal double-limb support (pre-swing), the increases in the peak ankle plantar flexion angle (⫹4.2° [SD⫽3.8] versus ⫹0.04° [SD⫽4.9]) and peak ankle plantarflexion power (⫹0.219 W/kg䡠m [SD⫽0.236] versus ⫹0.026 W/kg䡠m [SD⫽0.146]) (Fig. 3D) also were greater in the high-response group than in the low-response group; however, these differences did not February 2010

reach statistical significance (P⫽.09 and P⫽.08, respectively). The effect sizes for both of these comparisons exceeded 0.9. EMG Activity During Walking and Manual Muscle Testing A difference in the intensity of EMG activity between participants in the high-response group and participants in the low-response group was observed only for the soleus muscle. The increase in the average intensity of soleus muscle EMG activity during walking was significantly greater in the high-response group after the intervention (12.7% maximal [SD⫽ 10.8] versus 1.2% maximal [SD⫽

8.5]) (P⫽.05, effect size⫽1.18) (Fig. 4A). Changes in maximal muscle activation during manual muscle testing from the preintervention gait analysis to the postintervention gait analysis were not significantly different (no significant interaction between time and group) between the high-response group and the lowresponse group for any muscle tested. However, the main effects of time on maximal activation of the semimembranosus muscle during manual muscle testing and on the intensity of EMG activity during walking (Fig. 4B and 4D) were statistically significant. For both groups, the intensity of semimembranosus

Volume 90

Number 2

Physical Therapy f

217

Treadmill Training With Body-Weight Support After Stroke Table 4. Baseline Values and Changes in Peak Ankle Joint Angles, Moments, and Power During Walking Participants High-Response Group (nⴝ7)

Measurement

Low-Response Group (nⴝ8)

Plantar-flexion angle during loading (°) Baseline

11.21 (3.91)

11.00 (5.59)

Change

⫹2.83 (2.72)

⫺1.60 (3.90)

Baseline

9.67 (3.56)

7.66 (6.70)

Change

⫺2.10 (2.05)

⫺0.31 (3.56)

Dorsiflexion angle during stance (°)

Plantar-flexion angle during pre-swing (°) Baseline

4.14 (3.78)

1.56 (3.93)

Change

⫹4.23 (3.75)

⫹0.04 (4.85)

Baseline

⫺0.84 (3.76)

⫺0.08 (4.28)

Change

⫺2.58 (2.06)

⫹1.64 (5.12)

Dorsiflexion angle during swing (°)

Medial (internal) dorsiflexion moment during loading (N䡠m/kg䡠m) Baseline

⫺0.041 (0.057)

⫺0.027 (0.039)

Change

⫺0.021 (0.047)

⫺0.004 (0.040)

Baseline

0.753 (0.150)

0.512 (0.194)

Change

⫹0.032 (0.111)

⫹0.114 (0.103)

Medial (internal) plantar flexion moment during stance (N䡠m/kg䡠m)

Dorsiflexion power absorption during loading (W/kg䡠m) Baseline

⫺0.181 (0.055)

⫺0.121 (0.047)

Change

⫺0.071 (0.167)

⫺0.070 (0.070)

Baseline

⫺0.458 (0.183)

⫺0.326 (0.242)

Change

⫺0.075 (0.191)

⫺0.029 (0.158)

Plantar-flexion power absorption during stance (W/kg䡠m)

Plantar-flexion power generation during pre-swing (W/kg䡠m)

a

Baseline

0.504 (0.432)

0.249 (0.219)

Change

⫹0.219 (0.236)

⫹0.026 (0.146)

P

Effect Size

.03

1.32

.26

0.62

.09

0.97

.06

1.08

.45

0.39

.16

0.77

.99

0.01

.62

0.26

.08

0.98

Values are reported as mean (SD).

muscle EMG activity after the intervention was significantly higher than that before the intervention. Maximal activation during manual muscle testing before the intervention was 68.8 mV (SD⫽15.6), and that after the intervention was 131.0 mV (SD⫽51.5) (P⬍.01, effect size⫽1.63). Average EMG intensity of semimembranosus muscle activity during gait before the intervention was 13.3% maximal (SD⫽7.5), and that after the intervention was 22.1% maximal (SD⫽8.2) (P⫽.05, effect size⫽1.12). 218

f

Physical Therapy

Volume 90

Maximal Isometric Torque For most muscle groups, maximal isometric torque was not improved in either participant group. Only the knee flexion torque of the paretic limb showed a significantly greater change in participants in the highresponse group than in participants in the low-response group (⫹9.0 N䡠m [SD⫽12.4] versus ⫺7.1 [SD⫽9.1]) (P⫽.02, effect size⫽1.43).

Number 2

Discussion Kinetic and Kinematic Changes After Intervention After a task-specific intervention that included BWSTT, participants who exhibited a clear increase in selfselected overground walking speed (ie, higher than 0.08 m/s) showed greater and more consistent changes in the kinematics and kinetics of the hip than of the ankle during late stance, providing partial support for our hypotheses. The increase in the maximal hip extension angle during February 2010

Treadmill Training With Body-Weight Support After Stroke

Figure 4. Electromyographic (EMG) activity during walking at baseline (pre-exercise) and postintervention (post-exercise) assessments for the soleus muscle (A and C) and the semimembranosus muscle (SMEMB) (B and D) in the high-response group (A and B) and the low-response group (C and D). At the postintervention assessment, participants in the high-response group walked with a significantly higher intensity of soleus muscle activation during mid stance and terminal stance (A) and a higher intensity of activation of the semimembranosus muscle during terminal swing and early loading, as expected with more typical gait activation (B). MMT⫽manual muscle testing.

late stance in participants in the high-response group was attributable to a combination of increased thigh extension and decreased anterior pelvic tilt. In contrast, participants in the low-response group showed increased anterior pelvic tilt. Participants in the high-response group exhibited a tendency toward greater increases in ankle plantarflexion angle and power generation during pre-swing than participants in the low-response group. The large effect sizes for these data (0.92 and 0.98) (Tabs. 3 and 4) indicated that these differences likely would have been statistically significant with a larger sample size. Increases in ankle plantar-flexion power and hip flexion power also were identified as the February 2010

mechanisms used to increase walking speed in both people who were able-bodied28 and people with stroke after traditional interventions.29,30 Also in agreement with the results of the present study, Jonsdottir and colleagues31 reported that after stroke, most people increased walking speeds from preferred to high speeds by preferentially increasing work production at the hip to a greater extent than at the ankle; these findings suggested that after stroke, the capacity to increase work production at the ankle may be limited. Increased joint power generation during walking implies an increase in the intensity of muscle activation,

force generated for a given activation level (hypertrophy or improved length–tension relationship), or an improved moment arm.32 Only the soleus muscle showed a greater increase in activation during walking in participants in the high-response group than in participants in the lowresponse group. Although the soleus muscle is a uniarticular muscle crossing only the ankle joint, musculoskeletal models have determined that its activity, in addition to providing ankle plantar flexion and forward propulsion of the trunk,33 also accelerates both the hip and the knee into extension during the second half of stance.34 This description is consistent with the improved mechanics

Volume 90

Number 2

Physical Therapy f

219

Treadmill Training With Body-Weight Support After Stroke seen at both the hip and the ankle in the high-response group. The lack of change in ankle angle or power in participants in the lowresponse group could be explained by neural factors, such as the size and location of the stroke lesion. Corroborating evidence for this explanation was documented in a previous case study of a 38-year-old woman from the low-response group who had severe stroke-related impairment (lower-extremity FuglMeyer motor scale score⫽24/34) and severe walking limitation (initial gait speed⫽0.33 m/s).35 Her minimal improvements in walking speed after BWSTT were associated with increased motion at the hip but little change at the ankle.35 Magnetic resonance imaging after the stroke revealed extensive white matter tract damage to the internal capsule, which could indicate limited distal recovery potential. Muscle Activation and Torque Changes All participants, regardless of the extent of improvements in walking speed, showed increases in activation of the semimembranosus muscle during both manual muscle testing and walking. A closer examination of the EMG profiles of participants in the high-response and lowresponse groups before and after the intervention indicated that participants in the high-response group showed increased intensity of semimembranosus muscle EMG activity during the period of normal phasing, from mid swing through loading, whereas those in the low-response group exhibited increased intensity more diffusely throughout the gait cycle (Fig. 4B). Increased semimembranosus muscle activation in early swing actually would inhibit swing limb advancement by resisting thigh flexion.36

220

f

Physical Therapy

Volume 90

The hamstring muscles are biarticular hip extensor and knee flexor muscles during isolated voluntary contractions. During walking, the hamstring muscles function primarily as hip extensor muscles, acting to decelerate the flexing hip from mid swing to initial contact as well as to extend the hip during the first half of stance.33,34,37 The proximal attachment of the hamstring muscles on the ischial tuberosity also results in posterior tilting of the pelvis, particularly during stance, when its distal attachment is relatively fixed.38 Thus, the kinematic changes seen in participants in the high-response group (increased hip extension and decreased anterior pelvic tilt) are consistent with the function of the semimembranosus muscle during walking. Maximal isometric knee flexion torque was increased after the intervention only for participants in the high-response group. However, hip extension torque was not significantly improved in participants in either group. Hip extension torque likely was more reflective of torque generation of the uniarticular hip extensor muscles (gluteus maximus, gluteus medius, and adductor magnus muscles) because the resistance cuff was placed proximal to the knee and the knee was flexed with minimal support of the lower leg. In contrast, the hamstring muscles are the primary contributors to isolated knee flexion torque.32 Thus, it is likely that although all of the participants showed increased activation of the semimembranosus muscle during walking, increased strength in this muscle group was seen only in participants with a greater increase in walking speed. We did not find evidence of increased EMG intensity of either of the hip flexor muscles studied (adductor longus and rectus femoris muscles). We did not expect that in-

Number 2

creased rectus femoris muscle activation would correspond to increased walking speed because of its role in knee extension, which would inhibit knee flexion during swing. In contrast to the findings of the present study, increased activation of the adductor longus and soleus muscles was strongly associated with improved walking speeds over the first 6 months after stroke in 2 other studies.22,39 It is possible that other hip flexor muscles, including the iliopsoas, sartorius, and gracilis muscles, contributed to the increased hip flexor muscle power generation seen in participants in the highresponse group, but we did not record data from these muscles. The increased hip flexor muscle power generation also might have resulted from the increased angular velocity over the greater arc of flexion created by the increased hip extension angle during late stance. The hip adductor muscles, which function as hip flexor muscles during gait, would have a greater moment arm for hip flexion at angles of greater extension and could generate a larger moment with the same amount of force.32 In addition, greater hip extension would increase the elastic energy storage and release of the passive joint structures of the hip, reducing the amount of work required of the hip flexor muscles to accelerate the leg into swing.33 Contrary to our hypothesis, maximal isometric torque of the ankle plantar flexor and hip flexor muscle groups was not increased in participants in the high-response group. This finding is consistent with the overall results of the STEPS trial.18 Among the 3 interventions that included BWSTT in the STEPS trial, increases in maximal torque were seen only for the combined flexor muscles of the paretic limb and the combined extensor muscles of the nonparetic limb and only in the BWSTT–UE Ex February 2010

Treadmill Training With Body-Weight Support After Stroke group. Lower-extremity torque was not increased in the other 2 BWSTTrelated groups, although walking speed was increased to similar degrees in all 3 BWSTT-related groups. Strength gains in the knee flexor muscle group for the other 2 BWSTTrelated groups in the STEPS trial might have been masked by combining all of the flexor torque values into 1 variable. Taken together, the results of these studies indicated that the observed improvements in walking speed were not dependent on the strength gains for most of the muscle groups. Instead, the observed improvements in walking speed and muscle activation in the present study are more consistent with neural adaptation. Several studies have provided evidence of neural plasticity in people with stroke after BWSTT, including increased corticomotor excitability and activation40,41 and increased activation of cortical and subcortical networks.42,43 Our study is the first to identify the specific long-term changes in muscle activation that accompany improved biomechanics of overground walking after BWSTT. Implications for Clinical Practice On the basis of the results of the present study, we recommend emphasizing hip extension in late stance during BWSTT and training at increased walking speeds to facilitate more rapid and appropriately phased muscle activation. Hornby and colleagues9 showed that BWSTT with manual facilitation produced greater improvements in walking function than robot-supported treadmill training after stroke. The ability to facilitate specific components of walking mechanics, such as increased hip extension with decreased anterior pelvic tilt, likely is more feasible with manual guidance than with mechanical support.

February 2010

The lower-extremity Fugl-Meyer motor scale score was the baseline characteristic that best differentiated participants in the high-response group from those in the low-response group. Participants in the highresponse group had greater selective motor control at baseline. The extent of walking speed improvements after the intervention also tended to correspond to a higher baseline speed and no assistive device. These factors likely would have reached statistical significance with a larger sample size. Two participants in the low-response group had baseline walking speeds of greater than 0.7 m/s but showed no increases in walking speed after the intervention (Fig. 2A). These 2 participants had baseline Fugl-Meyer motor scale scores of 23 and 25, suggesting that they had achieved relatively high baseline walking speeds through compensatory strategies44,45 but might have had limited capacity for further improvement. Norton and Gorassini46 also found that the response to BWSTT in people with incomplete spinal cord injury was related to the amount of preserved corticospinal drive. However, lowerextremity Fugl-Meyer motor scale scores would not have been sufficiently discriminating to predict individual responses because the scores of both groups overlapped considerably (Fig. 2B). Limitations This exploratory study had several limitations. The small sample size increased the possibility of a type II statistical error limiting the ability to detect true changes. Analysis of effect sizes could identify comparisons that likely would have reached statistical significance with a larger sample. Moreover, conducting multiple comparisons increased the probability of a type I statistical error; consequently, the results must be viewed with caution. However, the inclusion of variables from multiple do-

mains (kinematic, kinetic, and muscle activation) provided evidence about the gait parameters that were associated with improved walking speeds as well as an indication about how the changes occurred. Because of the low statistical power, correcting for the number of comparisons would have been overly conservative and likely would have eliminated many valid results along with any type I errors. A comparison of the changes in gait parameters between participants in the highresponse group and participants in the low-response group controlled for variability and learning associated with repeated testing. The differences in joint angle changes between the groups were modest (6.8° at the hip and 4.2° at the ankle) but exceeded the average error associated with repeated testing of sagittalplane motion during walking (2°–3° at the ankle and 2°–5° at the hip).47 Measurement error would be expected to vary equally in either direction and irrespective of group membership. Only the paretic leg was evaluated, and only kinematic and kinetic variables in the sagittal plane were examined. However, gluteus medius muscle activation was studied, and this muscle, with primarily frontalplane function, did not show a change in the intensity of activation in either group. Moreover, all gait trials were conducted without the use of any lower-extremity orthosis. The BWSTT interventions also were conducted without bracing, but 6 participants (3 in the low-response group and 3 in the high-response group) customarily wore an anklefoot orthosis during community ambulation. We based our decision to record gait biomechanics without the orthosis to avoid the potential for masking any distal changes, particularly activation of the anterior tibialis muscle. However, the participants who customarily walked with the an-

Volume 90

Number 2

Physical Therapy f

221

Treadmill Training With Body-Weight Support After Stroke kle orthosis might have exhibited greater increases in walking speed after the intervention with the distal stabilization of the brace. Finally, we recorded maximal torque only with isometric contractions in isolated positions. Changes in muscle strength at higher speeds or in synergy patterns might not have been reflected in the isometric tests.

Conclusion Participants who responded to a 6-week (24-session) intervention including BWSTT not only showed increases in walking speed but also showed improvements in gait biomechanics and muscle activation consistent with improved forward propulsion during walking. Participants who exhibited clear increases in walking speed after the intervention did so with increased activation of both the soleus muscle and the semimembranosus muscle during walking that was sufficient to reduce the anterior tilt of the pelvis and extend the thigh during terminal stance and that tended to increase plantar flexion during pre-swing. These kinematic changes resulted in increased hip flexion power generation and a tendency toward increased plantarflexion power generation. Thus, stabilization of the limb during stance was increased both distally and proximally. [Readers may want to compare the results of this intervention, which Reisman et al48 in this issue discuss as “motor learning,” to the results from intervention using the split-belt treadmill (“motor adaptation”).] Of all of the baseline participant characteristics, only the lowerextremity Fugl-Meyer motor scale score was significantly higher in participants with a positive response to the intervention, suggesting that significant improvements after the intervention were dependent on a threshold capacity for selective motor control. The present study pro222

f

Physical Therapy

Volume 90

vided preliminary evidence that a task-specific lower-extremity training program that includes BWSTT can promote improved gait biomechanics and neural adaptation in people who have stroke but who have sufficient hemiparetic lowerextremity motor control. Dr Mulroy, Dr Gronley, Dr Brown, and Dr Sullivan provided concept/idea/research design. Dr Mulroy, Ms Klassen, Dr Brown, and Dr Sullivan provided writing and project management. Ms Klassen and Ms Eberly provided data collection. All authors provided data analysis. Dr Brown and Dr Sullivan provided fund procurement. Ms Klassen provided participants. Dr Mulroy provided facilities/equipment. Dr Sullivan provided institutional liaisons. Ms Klassen, Dr Gronley, Ms Eberly, Dr Brown, and Dr Sullivan provided consultation (including review of manuscript before submission). The authors acknowledge the STEPS Research Team: University of Southern California—Robbin Howard, PT, DPT, NCS, Didi Matthews, PT, DPT, NCS, Bernadette Currier, PT, DPT, NCS, Arlene Yang, PT, MSPT, NCS, Barbara Lopetinsky, PT, BS, and Maria Caro, PT, DPT; Northwestern University— Nicole Furno, PT, BS, Nicole Korda, PT, BS, Carolina Carmona, PT, BS, Allie Hyngstrom, PT, MSPT, Sheila Schindler-Ivens, PT, PhD, and Lynn Rogers, MS; and Rancho Los Amigos National Rehabilitation Center—Craig Newsam, PT, DPT, Valerie J. Eberly, PT, NCS, JoAnne K. Gronley, PT, DPT, Jennifer Whitney, PT, MPT, Betsy King, PT, DPT, and Louis Ibarra, PTA. The authors acknowledge the Foundation for Physical Therapy for funding the Physical Therapy Clinical Research Network (PTClinResNet). The PTClinResNet Network Principal Investigator is Carolee J. Winstein, PT, PhD, FAPTA, and the Co-Principal Investigator is James Gordon, PT, EdD, FAPTA (both at University of Southern California, Los Angeles, California). Project Principal and Co-Principal Investigators include David A. Brown, PT, PhD (Northwestern University, Chicago, Illinois); Sara J. Mulroy, PT, PhD, and Bryan Kemp, PhD (Rancho Los Amigos National Rehabilitation Center, Downey, California); Loretta M. Knutson, PT, PhD, PCS (Missouri State University, Springfield, Missouri); Eileen G. Fowler, PT, PhD (University of California, Los Angeles, Los Angeles, California); and Sharon K. DeMuth, PT, DPT, Kornelia Kulig, PT, PhD, and Katherine J. Sullivan, PT, PhD (Uni-

Number 2

versity of Southern California, Los Angeles, California). The Data Management Center is located at the University of Southern California and is directed by Stanley P. Azen, PhD. The members of the Data Safety and Monitoring Committee are Nancy Byl, PT, PhD, FAPTA, Chair (University of California, San Francisco, San Francisco, California); Hugh G. Watts, MD (Shriners’ Hospital for Children–LA Unit, Los Angeles, California); June Isaacson Kailes, MSW (Western University of Health Sciences, Pomona, California); and Anny Xiang, PhD (University of Southern California, Los Angeles, California). The authors acknowledge Biodex Medical Systems Inc, which donated 3 Cyclocentric semirecumbent ergometers used in the Strength Training Effectiveness Post-Stroke (STEPS) study. This research study was approved by the Institutional Review Board of Los Amigos Research and Education Institute. Parts of the data were presented as a poster at the Combined Sections Meeting of the American Physical Therapy Association; January 31–February 4, 2006; San Diego, California; and as part of an accepted symposium at the Combined Sections Meeting of the American Physical Therapy Association; February 14 –18, 2007; Boston, Massachusetts. A case study of 1 of the participants was given as a platform presentation at the III STEP Conference: Linking Movement Science and Intervention; July 15–21, 2005; Salt Lake City, Utah. Data in these presentations were from all 4 of the STEPS intervention groups; data in this article were from those participants in 1 of the 3 interventions that included body-weight–supported treadmill training. This article was received May 1, 2009, and was accepted August 13, 2009. DOI: 10.2522/ptj.20090141

References 1 Heart Disease and Stroke Statistics— 2009 Update. Dallas, TX: American Heart Association; 2009:1–36. 2 Harris JE, Eng JJ. Goal priorities identified by individuals with chronic stroke: implications for rehabilitation professionals. Physiother Can. 2004;56:171–176. 3 Jorgensen HS, Nakayama H, Raaschou HO, Olsen TS. Recovery of walking function in stroke patients: The Copenhagen Stroke Study. Arch Phys Med Rehabil. 1995;76: 27–32. 4 Laufer Y, Dickstein R, Chefez Y, Marcovitz E. The effect of treadmill training on the ambulation of stroke survivors in the early stages of rehabilitation: a randomized study. J Rehabil Res Dev. 2001;38:69 –78.

February 2010

Treadmill Training With Body-Weight Support After Stroke 5 Barbeau H, Visintin M. Optimal outcomes with body-weight support combined with treadmill training in stroke subjects. Arch Phys Med Rehabil. 2003;84:1458 –1465. 6 Ada L, Dean C, Hall JM, et al. A treadmill training and overground walking program improves walking in persons residing in the community after stroke: a placebocontrolled randomized trial. Arch Phys Med Rehabil. 2003;84:1486 –1491. 7 Macko RF, Ivey FM, Forrester LW. Taskoriented aerobic exercise in chronic hemiparetic stroke: training protocols and treatment effects. Top Stroke Rehab. 2005;12:45–57. 8 Sullivan KJ, Knowlton BJ, Dobkin BH. Step training with body weight support: effect of treadmill speed and practice paradigms on poststroke locomotor recovery. Arch Phys Med Rehabil. 2002;83: 683– 691. 9 Hornby TG, Campbell DD, Kahn JH, et al. Enhanced gait-related improvements after therapist- versus robotic-assisted locomotor training in subjects with chronic stroke: a randomized controlled study. Stroke. 2008;39:1786 –1792. 10 Nadeau S, Gravel D, Arsenault AB, Bourbonnais D. Plantarflexor weakness as a limiting factor of gait speed in stroke subjects and the compensating role of hip flexors. Clin Biomech. 1999;14:125–135. 11 Olney SJ, Griffin MP, McBride ID. Temporal, kinematic, and kinetic variables related to gait speed in subjects with hemiplegia: a regression approach. Phys Ther. 1994; 74:872– 885. 12 Richards CL, Malouin F, Bravo G, et al. The role of technology in task-oriented training in persons with subacute stroke: a randomized controlled trial. Neurorehabil Neural Repair. 2004;18:199 –211. 13 Silver KH, Macko RF, Forrester LW, et al. Effects of aerobic treadmill training on gait velocity, cadence, and gait symmetry in chronic hemiparetic stroke: a preliminary report. Neurorehabil Neural Repair. 2000;14:65–71. 14 Hesse S, Konrad M, Uhlenbrock D. Treadmill walking with partial body weight support versus floor walking in hemiparetic subjects. Arch Phys Med Rehabil. 1999; 80:421– 427. 15 Hesse S, Uhlenbrock D, Sarkodie-Gyan T. Gait pattern of severely disabled hemiparetic subjects on a new controlled gait trainer as compared to assisted treadmill walking with partial body weight support. Clin Rehabil. 1999;13:401– 410. 16 Hesse S, Werner C, Paul T, et al. Influence of walking speed on lower limb muscle activity and energy consumption during treadmill walking of hemiparetic patients. Arch Phys Med Rehabil. 2001;82: 1547–1550. 17 Kuo AD, Donelan JM. Dynamic principles of gait and their clinical implications. Phys Ther. 2010;90:157–174. 18 Sullivan KJ, Brown DA, Klassen T, et al. Effects of task-specific locomotor and strength training in adults who were ambulatory after stroke: results of the STEPS randomized clinical trial. Phys Ther. 2007; 87:1580 –1602.

February 2010

19 Basmajian JV, Stecko GA. A new bipolar indwelling electrode for electromyography. J Appl Physiol. 1962;17:849. 20 Yeadon MR, Morlock M. The appropriate use of regression equations for the estimation of segmental inertia parameters. J Biomech. 1989;22:683– 689. 21 Meglan DW, Todd F. Kinetics of human locomotion. In: Rose J, Gamble JG, eds. Human Walking. Baltimore, MD: Williams & Wilkins; 1994:75–99. 22 Mulroy SJ, Gronley JK, Weiss W, et al. Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke. Gait Posture. 2003;18:114 –125. 23 Bogey RA, Barnes LA, Perry J. Computer algorithms to characterize individual subject EMG profiles during gait. Arch Phys Med Rehabil. 1992;73:835– 841. 24 Cunha-Filho IT, Henson H, Wankadia S, Protas EJ. Reliability of measures of gait performance and oxygen consumption with stroke survivors. J Rehabil Res Dev. 2003;40:19 –26. 25 Stephens JM, Goldie PA. Walking speed on parquetry and carpet after stroke: effect of surface and test reliability. Clin Rehabil. 1999;13:171–181. 26 Perera S, Mody SH, Woodman RC, Studenski S. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54:743–749. 27 Tilson JK, Sullivan KJ, Cen SY, et al; Locomotor Experience Applied Post Stroke (LEAPS) Investigative Team. Meaningful gait speed improvement during the first 60 days poststroke: minimal clinically important difference. Phys Ther. 2010;90: 196 –208. 28 Jonkers I, Delp S, Patten C. Capacity to increase walking speed is limited by impaired hip and ankle power generation in lower functioning persons post-stroke. Gait Posture. 2009;29:129 –137. 29 Parvataneni K, Olney SJ, Brouwer B. Changes in muscle group work associated with changes in gait speed of persons with stroke. Clin Biomech. 2007;22:813– 820. 30 Teixeira-Salmela LF, Nadeau S, McBride I, Olney SJ. Effects of muscle strengthening and physical conditioning training on temporal, kinematic and kinetic variables during gait in chronic stroke survivors. J Rehabil Med. 2001;33:53– 60. 31 Jonsdottir J, Recalcati M, Rabuffetti M, et al. Functional resources to increase gait speed in people with stroke: strategies adopted compared to healthy controls. Gait Posture. 2009;29:355–359. 32 Hoy MG, Zajac FE, Gordon ME. A musculoskeletal model of the human lower extremity: the effect of muscle, tendon, and moment arm on the moment-angle relationship of musculotendon actuators at the hip, knee, and ankle. J Biomech. 1990; 23:157–169. 33 Neptune RR, Sasaki K, Kautz SA. The effect of walking speed on muscle function and mechanical energetics. Gait Posture. 2008;28:135–143.

34 Arnold AS, Anderson FC, Pandy MG, Delp SL. Muscular contributions to hip and knee extension during the single limb stance phase of normal gait: a framework for investigating the causes of crouch gait. J Biomech. 2005;38:2181–2189. 35 Sullivan KJ, Klassen TD, Mulroy SJ. Combined task-specific training and strengthening effects on locomotor recovery poststroke: a case study. J Neurol Phys Ther. 2006;30:130 –141. 36 Kerrigan DC, Gronley JK, Perry J. Stifflegged gait in spastic paralysis: a study of quadriceps and hamstring activity. Am J Phys Med. 1991;70:294 –300. 37 Perry J. Gait Analysis: Normal and Pathological Function. Thorofare, NJ: Slack Inc; 1992. 38 Stewart C, Postans N, Schwartz MH, et al. An investigation of the action of the hamstring muscles during standing in crouch using functional electrical stimulation (FES). Gait Posture. 2008;28:372–377. 39 Sullivan KJ, Mulroy SJ, Kautz SA. Walking recovery and rehabilitation after stroke. In: Stein J, Harvey RL, Macko RF, et al, eds. Stroke Recovery and Rehabilitation. New York, NY: Demos Medical Publishing, LLC; 2009:323–342. 40 Yen CL, Wang RY, Liao KK, et al. Gait training induced change in corticomotor excitability in patients with chronic stroke. Neurorehabil Neural Repair. 2008;22:22–30. 41 Dobkin BH, Firestine A, West M, et al. Ankle dorsiflexion as an fMRI paradigm to assay motor control for walking during rehabilitation. Neuroimage. 2004;23: 370 –381. 42 Luft AR, Macko RF, Forrester LW, et al. Treadmill exercise activates subcortical neural networks and improves walking after stroke. Stroke. 2008;39:3341–3350. 43 Enzinger C, Dawes H, Johansen-Berg H, et al. Brain activity changes associated with treadmill training after stroke. Stroke. 2009;40:2460 –2467. 44 Bowden MG, Balasubramanian CK, Neptune RR, Kautz SA. Anterior-posterior ground reaction forces as a measure of paretic leg contribution in hemiparetic walking. Stroke. 2006;37:872– 876. 45 Kim CM, Eng JJ. Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed. Gait Posture. 2004;20:140 – 146. 46 Norton JA, Gorassini MA. Changes in cortically related intermuscular coherence accompanying improvements in locomotor skills in incomplete spinal cord injury. J Neurophys. 2006;95:2580 –2589. 47 McGinley JL, Baker R, Wolfe R, Morris ME. The reliability of three-dimensional kinematic gait measurements: a systematic review. Gait Posture. 2009;29:360 –369. 48 Reisman DS, Bastian AJ, Morton SM. Neurophysiologic and rehabilitation insights from the split-belt and other locomotor adaptation paradigms. Phys Ther. 2010;90:187–195.

Volume 90

Number 2

Physical Therapy f

223

Perry Issue: Gait Rehab

Daily Stepping in Individuals With Motor Incomplete Spinal Cord Injury Poonam Saraf, Miriam R. Rafferty, Jennifer L. Moore, Jennifer H. Kahn, Kathryn Hendron, Kristan Leech, T. George Hornby P. Saraf, PT, MSPT, is Physical Therapist, St. Francis Hospital, Evanston, Illinois. M.R. Rafferty, PT, DPT, is Research and Staff Physical Therapist, Rehabilitation Institute of Chicago, Chicago, Illinois. J.L. Moore, PT, MPT, NCS, is Research Physical Therapist, Rehabilitation Institute of Chicago. J.H. Kahn, PT, DPT, NCS, is Research Physical Therapist, Rehabilitation Institute of Chicago. K. Hendron is a student physical therapist at the University of Illinois at Chicago, Chicago, Illinois. K. Leech is a student physical therapist at the University of Illinois at Chicago. T.G. Hornby, PT, MPT, PhD, is Assistant Professor, Department of Physical Therapy, University of Illinois at Chicago, 1919 W Taylor St, Room 413, Chicago, IL 60612 (USA), and Research Scientist, Rehabilitation Institute of Chicago. Address all correspondence to Dr Hornby at: [email protected]. [Saraf P, Rafferty MR, Moore JL, et al. Daily stepping in individuals with motor incomplete spinal cord injury. Phys Ther. 2010;90: 224 –235.] © 2010 American Physical Therapy Association

Background. In individuals with motor incomplete spinal cord injury (SCI), ambulatory function determined in the clinical setting is related to specific measures of body structure and function and activity limitations, although few studies have quantified the relationship of these variables with daily stepping (steps/day).

Objective. The aim of this study was to quantify daily stepping in ambulatory individuals with SCI and its relationship with clinical walking performance measures and specific demographics, impairments, and activity limitations.

Design. A cross-sectional study was performed to estimate relationships among clinical variables to daily stepping in self-identified community versus non– community (household) walkers. Methods. Average daily stepping was determined in 50 people with chronic, motor incomplete SCI. Data for clinical and self-report measures of walking performance also were collected, and their associations with daily stepping were analyzed using correlation and receiver operating characteristic (ROC) analyses. Relationships between daily stepping and the measures of demographics, impairments, and activity limitations were identified using correlation and regression analyses.

Results. The ROC analyses revealed a significant discriminative ability between self-reported community and non– community walkers using clinical gait measures and daily stepping. Stepping activity generally was low throughout the sample tested, however, with an average of approximately 2,600 steps/day. Knee extension strength (force-generating capacity) and static balance were the primary variables related to daily stepping, with metabolic efficiency and capacity and balance confidence contributing to a lesser extent. Limitations. The small sample size and use of specific impairment-related measures were potential limitations of the study. Conclusions. Daily stepping is extremely limited in individuals with incomplete SCI, with a potentially substantial contribution of impairments in knee extension strength and balance.

Post a Rapid Response or find The Bottom Line: www.ptjournal.org 224

f

Physical Therapy

Volume 90

Number 2

February 2010

Daily Stepping in Individuals With Motor Incomplete SCI

S

pinal cord injury (SCI) is a common cause of disability, with an incidence rate of more than 12,000 cases per year in the United States alone.1 The incidence of incomplete lesions, indicating partial preservation of sensorimotor function below the level of spinal cord lesion,2 has increased steadily in recent decades. Individuals with sparing of motor function (ie, motor incomplete SCI) may have the potential to recover some degree of functional ambulation, which often is a primary goal during rehabilitation.3 Although many individuals with motor incomplete SCI regain some capacity to walk overground, functional ambulation in the home and community may be slow and physically demanding. Impaired walking performance may be the result of multiple impairments in body function and activity limitations and is thought to be a major factor contributing to many secondary medical complications associated with SCI (for a review, see Bauman and Spungen4). The contributions of specific body impairments and activity limitations to gait dysfunction following incomplete SCI have been a source of controversy. Previous reports5,6 have reinforced the long-standing notion that spasticity (hypertonicity) negatively influences activities of daily living, including walking ability. In contrast, other studies7,8 indicate that lower-extremity strength (forcegenerating capacity) is the significant determinant of independent ambulation. Specifically, knee extensor and hip flexor strength are thought to be primary predictors of walking performance, as determined by clinical measures of gait speed or selfreport determination of successful return to community ambulation.7 Impaired postural control and decreased cardiopulmonary or metabolic capacity and efficiency also are thought to influence ambulatory acFebruary 2010

tivity in people with neurological injury, including incomplete SCI.6,9 Additional demographic (age,6 duration of injury10) and self-report (balance confidence,11 depression12) variables may contribute to impaired ambulation in other neurological disorders, although their relative contribution to incomplete SCI is unknown. These data provide insight into potential contributions to impaired ambulation, typically evaluated using clinical gait assessments over short or long distances. Such measures are thought to be strong predictors of self-reported walking activity in the home and community, particularly in ambulatory survivors of stroke.13 Using the modified Hoffer scale, Perry et al13 found that subjects who identified themselves as community walkers had gait speeds greater than 0.8 m/s, whereas limited community walkers ambulated at 0.4 to 0.8 m/s. In contrast, individuals who walked only about the home (ie, household, or non– community, walkers) ambulated at gait speeds of less of than 0.4 m/s.7,13 Despite recent challenges to these values,14 improvements in gait speed that surpass these criteria are associated with greater perception of improved mobility.15 Unfortunately, there are limited data describing the applicability of these criteria across diagnoses, including incomplete SCI. Another potential limitation to studies using only clinical or self-report measures of walking performance is the lack of quantitative assessment of daily stepping. Namely, the number of actual steps taken each day in the home and community and its relationship to self-reported or clinical measures, have not been determined in ambulatory individuals with motor incomplete SCI. Total daily stepping activity in the general population is considered a primary indicator of daily physical activity, with sedentary lifestyles (ie, stepping activity

⬍5,000 steps/day16) thought to be a major risk factor for obesity, atherosclerosis, and diabetes mellitus, which are well-established secondary complications of SCI.4 Although many people with incomplete SCI can ambulate, it remains unclear how much these individuals walk and whether actual stepping activity is sufficient to ameliorate the risks of secondary complications associated with physical inactivity. Techniques used to quantify daily stepping in patients with neurological injury have improved substantially through development of lightweight, microprocessor-based accelerometers worn throughout the day.17 Average daily stepping (steps/day) has been determined in individuals poststroke, revealing markedly reduced physical activity compared with that of age-matched control subjects18 and a level of stepping activity far below the published recommendation of 10,000 steps/ day.16 Whether this recommendation is applicable to ambulatory individuals with motor incomplete SCI is unclear, although few studies provide quantitative estimates of daily stepping in this population.19,20 Accordingly, the purpose of this study was twofold. First, we wanted Available With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

Volume 90

Number 2

Physical Therapy f

225

Daily Stepping in Individuals With Motor Incomplete SCI to quantify daily stepping in a sample of people with incomplete SCI. Quantitative measures of daily stepping in the home or community were compared with clinical and self-report measures of walking performance. A second goal was to identify associations between impairments in body structure or function and activity limitations, specifically those variables that have been shown to predict ambulatory activity versus daily stepping.13 Understanding the relative contributions of specific body impairments and activity limitations to daily stepping may help guide treatment strategies for patients with incomplete SCI.

Method Participants Ambulatory individuals with motor incomplete SCI, classified by the American Spinal Injury Association (ASIA) Impairment Scale (AIS) as C or D,21 were recruited from a non– public registry of the Rehabilitation Institute of Chicago. Inclusion criteria consisted of the following: age from 18 to 85 years; history of nonprogressive lesion of spinal cord (between C1–T10 levels; indicating upper motoneuron injury); duration post-SCI ⬎6 months; and patient report of walking ability. To provide standardized definitions of community and non– community walkers, we used the modified Hoffer scale13; categories 1 to 3 denote household ambulators (non– community walkers), and categories 4 to 6 denote community walkers. A goal of 50 participants was targeted, similar to previous reports evaluating walking ability in individuals with neurological impairments.22 Ethics approval was obtained from the necessary institutional review boards, and all participants gave their written informed consent to participate. Participants were excluded if they had any other significant concurrent medical conditions 226

f

Physical Therapy

Volume 90

that might limit their ambulatory capacity, including the presence of uncontrolled cardiopulmonary or orthopedic diseases. Testing Procedures and Measurements Standardized measures of body function or structure and activity limitations of specific factors that were determined previously as predictors of walking function were completed in two 2-hour testing sessions. Spastic motor behaviors. Spasticity of the quadriceps femoris and hamstring muscles was quantified using the Modified Ashworth Scale (MAS),23 with raw scores converted to an ordinal scale and summed bilaterally. The magnitude or duration of flexor spasms, extensor spasms, and clonus was quantified using the Spinal Cord Assessment Tool for Spastic Reflexes (SCATS),24 with scores summed to obtain a composite score. Lower-extremity strength. Strength was measured using 2 methods. First, clinical assessment was performed using the ASIA Lower Extremity Motor Score (LEMS).25 Second, quantitative assessment of maximal isometric volitional torque was performed as a more-sensitive measure of strength in this population. Muscle groups tested were those identified previously as primary contributors to walking ability (knee extensors26 and hip flexors7) using a 6-degree-offreedom load cell* attached to a Biodex Testing and Rehabilitation System.† The knee extensors were tested with the knee in 90 degrees of flexion during upright sitting (hip flexion between 80° and 90°), and the hip flexors were tested with the participant positioned supine and

* ATI Industrial Automation, 1031 Goodworth Dr, Apex, NC 27539. † Biodex Medical Equipment, 20 Ramsay Rd, Shirley, NY 11967.

Number 2

the hip flexed to 30 degrees. Moment arm lengths were maintained at 75% of the respective leg segment lengths. Participants exerted maximal-effort contractions for 3 to 5 seconds until the visual determination of a decline in torque was made by the tester. Torque signals were low-pass filtered at 200 Hz and acquired at 1,000 Hz using custommade LabView software.‡ Torque signals were smoothed (10 Hz), with the maximum torque identified for each trial, and the average of 3 maximal efforts was calculated and normalized to body weight (N䡠m/kg). Balance/postural stability. Balance was evaluated using the Berg Balance Scale (BBS), which assesses postural stability during 14 tasks of increasing difficulty.27 Metabolic parameters. Cardiorespiratory or metabolic capacity was assessed using measures of peak ox˙ O2peak; mL/kg/min) ygen uptake (V during graded treadmill testing and gait efficiency or oxygen cost28 during overground walking. For both ˙ O2) was meatests, oxygen uptake (V sured using a K4b2 portable metabolic system.§ Baseline measure˙ O2 were obtained with ments of V participants sitting comfortably for a minimum of 2 minutes. Metabolic data were obtained during performance of the Six-Minute Walk Test, with participants instructed to walk at their normal, comfortable pace. ˙ O2 Baseline measurements of sitting V were subtracted from measurements ˙ O2, and oxygen cost was of walking V calculated as the oxygen uptake per meter walked (mL/kg/m). If participants rested during the Six-Minute Walk Test, gait efficiency was calculated until the rest break.

‡

NI Corp, 11500 Mopac Expressway, Austin, TX 78759. § CosMed USA Inc, 2211 N Elston Ave, Chicago, IL 60614.

February 2010

Daily Stepping in Individuals With Motor Incomplete SCI Following more than 10 minutes of ˙ O2peak was assessed during a rest, V graded exercise test, during which participants walked on a treadmill at 0.5 kmph, with speed increased every 3 minutes by 0.5 kmph until volitional fatigue, gait instability, or termination of graded exercise testing according to ACSM guidelines29 (rating of perceived exertion⫽20, heart rate within 10 beats of predicted maximum, respiratory exchange ratio ⬎1.15). All participants were secured on the treadmill using an overhead safety harness that did not restrict movement and did not provide body-weight support. Clinical walking measures. Walking assessments were conducted with participants allowed to use their customary assistive device and braces as needed. The GaitMatII㛳,30 was used to reliably assess walking speed over short distances. Participants were asked to walk along the walking platform 3 times at their selfselected speed and 3 times at their fastest possible speed, and averages were calculated. Walking performance over longer distances was assessed during the Six-Minute Walk Test for distance (6MWD) as described above. Participants were asked to continue stepping for the duration of the test but were allowed rest breaks as needed, with the total distance covered over the entire 6-minute period recorded. Daily stepping. Daily stepping (steps/day) was measured using the Step Activity Monitor (SAM).# Participants were asked to wear the SAM at their ankle during all waking hours except during bathing for more than 6 days. Data were accepted only if participants wore the SAM for at least 90% of their waking hours. 㛳

EQ Inc, PO Box 16, Chalfont, PA 189140016. # Cyma, 6405 218th St, #100, Mountlake Terrace, WA 98043.

February 2010

Average daily steps were calculated and compared with criteria determined previously (sedentary⫽ ⬍5,000 steps/day, low active⫽ 5,000 –7,499 steps/day, somewhat active⫽7,500 –9,999 steps/day, and active to highly active⫽⬎10,000 steps/day).16 Self-reported impairments and demographic measures. Measurements for additional demographic and self-report variables associated with walking ability were collected. Balance confidence was determined using the Activitiesspecific Balance Confidence (ABC) Scale.11 Symptoms of depression were evaluated using the Center for Epidemiological Studies-Depression Scale (CES-D).12 Age6 and duration of injury (DOI)10 were used as potential predictors of ambulatory function. Data Analysis Demographic and clinical characteristics. Descriptive statistics are provided as mean values (SD) and were calculated for all variables for simplicity of data presentation. Participants were grouped into community versus non– community walkers, and their demographics and measures of body function or structure and activity limitations compared using unpaired t tests (age), Mann-Whitney U tests (duration of injury), and chi-square analyses (sex, neurological level of injury, AIS classification, and use of antispasticity medications). Association between clinical measures of walking performance and daily stepping. Quantitative walking assessments were compared using simple correlation (Pearson) analyses. In addition, receiver operating characteristic (ROC) analyses were performed to estimate the ability of clinical walking measures or daily stepping to discriminate among categories of self-reported estimates of walking activity.31 Receiver oper-

ating characteristic curves were generated by plotting sensitivity (true positive rate) versus 1 ⫺ specificity (false positive rate) values of specific walking measures at specified intervals for the dichotomous variable of community versus non–community walkers. Sensitivity and specificity values were calculated at specific intervals of the quantitative walking measures (every 0.05 m/s for selfselected speed and fastest possible speed, 10 m for 6MWD, and 200 steps/day for daily stepping). The area under the curve (AUC) and 95% confidence interval (CIs) were determined for each ROC analysis, which provide common metrics for comparison between clinical walking tests. Scores closer to 1.0 indicate improved prediction of community versus non– community walkers; a score of 0.5 indicates no predictive value better than chance. The ROC analyses also aided in the selection of cutoff values of gait performance measures that provide a desired trade-off in true positive and false positive rates to estimate self-report ambulatory function. Cutoff values were determined as the maximum value of the sum of the sensitivity and specificity measures for each of the walking variables and expressed as Youden Index (ie, sensitivity ⫹ specificity ⫺ 1).32 Association between daily stepping and measures of demographics, impairments, and activity limitations. Correlation and regression analyses were performed to identify associations between daily stepping and measures of body structure or function, activity limitations, and demographic characteristics associated with self-reported or clinical measures of walking performance. Analyses were performed for all participants combined and separately for community versus noncommunity walkers. Daily stepping and parametric independent variables (knee extensor and hip flexor

Volume 90

Number 2

Physical Therapy f

227

Daily Stepping in Individuals With Motor Incomplete SCI Table 1. Demographic Characteristics of the Participants, With Differences Tested Using Unpaired t Tests (Age, Duration of Spinal Cord Injury) or Chi-Square Tests (All Other Variables)a Variable

Community Walkers

Non–community Walkers

41 (16)

50 (13)*

18/7

18/7

79 (68)

91 (101)

8 high tetraplegia

6 high tetraplegia

13 low tetraplegia

10 low tetraplegia

4 paraplegia

9 paraplegia

Age (y), X (SD) Sex (male/female) Duration of spinal cord injury (mo), X (SD) Neurological level of injury

AIS classification

1 ASIA C

14 ASIA C

24 ASIA D

11 ASIA D**

n⫽1

n⫽7*

Antispasticity medications a

Significant differences noted at P⬍.05 (*) and P⬍.01 (**). AIS⫽American Spinal Injury Association (ASIA) Impairment Scale.

˙ O2peak, age, strength, oxygen cost, V duration of injury) were compared using Pearson correlations, with Spearman coefficients used to determine associations with nonparametric variables. Stepwise multiple linear regression models were used to estimate relative associations of independent variables with daily stepping (average steps/day) in all participants and in specific subgroups. To limit the number of independent predictors, we chose a single variable from each domain (spasticity, strength, balance, metabolic parameters, selfreport assessments) with the strongest correlations with daily stepping across the entire group and the subgroups. Normally distributed data were evaluated using KolmogorovSmirnov tests for combined data and separately for each subgroup. Linearity of the independent predictors and daily stepping was determined by investigating individual scatter plots, with data transformation performed as necessary (power transformation for left-skewed data, square root transformation for right-skewed data). Analysis was performed separately with raw and transformed predictors, and standardized predictor 228

f

Physical Therapy

Volume 90

and residual values were checked to ensure values were less than 3 standard deviations from the mean. Collinearity diagnostics were monitored, with variance inflation factors less than 5.0 considered acceptable.33 All analyses were performed using SPSS version 15 software,** with the alpha level set at .05. Role of the Funding Source The study was funded by the Department of Education, National Institute of Disability and Rehabilitation Research, Model Systems for Spinal Cord Injury, grant H133N060014.

Results Demographic and Clinical Characteristics Table 1 presents the demographic and clinical characteristics of the 50 participants recruited for this study, with equal numbers of self-reported community and non– community walkers.13 Nearly all community walkers were classified as ASIA D, with only 5/25 reporting themselves as categories 4 or 5 on the modified Hoffer scale (ie, limited community walkers).13 Most non– community ** SPSS Inc, 233 S Wacker Dr, Chicago, IL 60606.

Number 2

walkers were classified as ASIA C and were significantly older than the community walkers, with increased use of antispasticity medications. Other clinical demographics were not different between groups. Association Between Clinical Measures of Walking Performance and Daily Stepping Across all participants, daily stepping averaged 2,658 steps/day (SD⫽2,745, range⫽0 –11,660), with expected differences between groups. Table 2 summarizes the descriptive statistics of all independent and dependent variables for both groups, with a fourfold difference in stepping between community and non– community walkers. For non– community walkers, stepping averaged approximately 1,000 steps/day, with no participants stepping more than 4,000 steps/day. Individuals identified as community walkers ambulated approximately 4,300 steps/ day. Only 2/25 community walkers surpassed the 10,000 steps/day threshold for “active” adults, whereas 17/25 community walkers performed fewer than 5,000 steps/ day (ie, threshold for “sedentary” individuals). Differences in clinical measures of walking performance also were observed between the community and non– community walkers, with correlation coefficients between clinical measures and daily stepping much higher for the community walkers. Specifically, correlation coefficients were high for daily stepping versus self-selected speed, fastest possible speed, and 6MWD (all r⫽.74, P⬍.01), whereas moderate to low correlations were found for the noncommunity walkers (self-selected speed: r⫽.55, P⬍.01; fastest possible speed: r⫽.62, P⬍.01; 6MWD: r⫽.46, P⬍.05). Figure 1 provides an example of these differences using 6MWD versus daily stepping.

February 2010

Daily Stepping in Individuals With Motor Incomplete SCI Receiver operating characteristic analyses were used to determine the ability of clinical gait measures and daily stepping to discriminate between community and noncommunity walkers. All 4 quantitative measures of walking performance were nearly identical in their discriminative abilities, as determined by the AUC (all 0.88 – 0.90; Tab. 3). Calculation of 95% CIs indicated no difference among variables, but improved prediction as compared with 0.50 (ie, random chance). The Youden Index (ie, value with maximum combination of sensitivity and specificity; Tab. 3) facilitated determination of cutoff scores for each walking measure, with 0.35 m/s identified as the cutoff for selfselected speed and 140 m identified as the cutoff for 6MWD (average of 0.38 m/s over 6 minutes). These speeds approximate estimates of gait speed thresholds used to discriminate between household and community ambulation in survivors of stroke.13 In addition, a cutoff score of 2,200 steps/day for daily stepping was found to best discriminate between community and non– community walkers. Association of Daily Stepping and Measures of Body Structure and Function and Activity Limitations Table 2 also presents differences in clinical characteristics that may contribute to reduced ambulation in community and non– community walkers. Community walkers demonstrated significantly greater balance, balance confidence, strength, ˙ O2peak, and gait efficiency (ie, V lower oxygen cost) compared with non– community walkers. There were, however, no differences in spasticity or spasms or in depressive symptoms between groups. Table 4 details the correlation coefficients between potential contributors to impaired walking versus daily February 2010

Table 2. Measured Clinical Variables for Community and Non–Community Walkers Presented as Mean (SD), With Differences Calculated Using Unpaired Parametric (Walking Assessments, Metabolic Parameters, Hip Flexor [HF] Strength, and Knee Extensor [KE] Strength) or Nonparametric Comparisons (All Other Variables)a Variable

Community Walkers

Non–Community Walkers

Walking assessments Daily stepping (steps/day) Self-selected speed (m/s) Fastest possible speed (m/s) 6MWD (m)

4,310 (2,921)

1,006 (1,082)**

0.69 (0.37) 1.0 (0.48)

0.25 (0.17)** 0.37 (0.27)**

236 (123)

84 (55)**

Spasticity/spasms MAS

5.9 (4.6)

4.9 (4.4)

SCATS

6.5 (3.9)

6.1 (3.4)

Lower-extremity strength LEMS

43 (7.8)

31 (9.2)**

HF (N䡠m/kg)

0.83 (0.39)

0.42 (0.29)**

KE (N䡠m/kg)

1.1 (0.74)

0.63 (0.38)**

Balance BBS

44 (12)

23 (13)**

22 (8.7)

13 (8.0)**

Metabolic parameters ˙ O2peak (mL/kg/min) V Oxygen cost (mL/kg/m)

0.37 (0.29)

1.04 (1.35)*

Self-report measures ABC CES-D

68 (19)

42 (22)**

8.4 (6.7)

10 (9.4)

a

Significant differences noted at P⬍.05 (*) and P⬍.01 (**). 6MWD⫽Six-Minute Walking Test distance, MAS⫽Modified Ashworth Scale, SCATS⫽Spinal Cord Assessment Tool for Spastic Reflexes, ˙ 2peak⫽peak oxygen uptake, LEMS⫽Lower Extremity Motor Score, BBS⫽Berg Balance Scale, VO ABC⫽Activities-specific Balance Confidence Scale, CES-D⫽Center for Epidemiological Studies–Depression Scale.

stepping for all participants and separately for community versus noncommunity walkers. For all participants, the strongest correlations were observed for the BBS and strength measures, with lower but significant correlations for metabolic measures and ABC scores. There were no significant relationships between stepping activity and the measures of spasticity or spasms, depression, age, or duration of injury. Further analysis revealed substantial differences between community and non– community walkers, with the data from the highest coefficients determined with all participants (ie, BBS scores and knee extensor

strength) plotted in Figure 2. As shown in Figure 2, correlation coefficients of BBS scores for daily stepping in both community and noncommunity walkers were nearly equivalent, despite a potential ceiling effect observed in the community walkers (4/25 participants with BBS scores⫽56/56). In contrast, a strong association between daily stepping and knee extensor strength was observed in the community walkers (r⫽.80), with very little association in the non– community walkers (r⫽.18). Stepwise multiple linear regression analyses were performed to determine the potential contributions of

Volume 90

Number 2

Physical Therapy f

229

Daily Stepping in Individuals With Motor Incomplete SCI

Figure 1. Associations between Six-Minute Walk Test distance (6MWD) and daily stepping in community and non– community walkers. Correlation coefficients and regression equations are provided.

the measures of body structure or function and activity limitations to daily stepping. To limit the number of independent predictors, we chose the variable from each domain with the largest correlation coefficients across combined and separated data. Independent variables chosen were SCATS scores, knee extensor strength, BBS scores, oxygen cost, and ABC scores. Deviations from normality were observed for oxygen cost (P⬍.05), and data were normalized using a square root transformation. Despite normal distribution of BBS data in all subgroups, investiga-

tion of the scatter plots revealed potential nonlinear relationships between BBS and daily stepping (Fig. 2). Accordingly, a power (square) transformation was applied to the BBS data and improved the linear relationship between the combined data (raw r2⫽.53, transformed r2⫽.61) and community walkers (raw r2⫽.38, transformed r2⫽.43), but not non– community walkers (raw r2⫽.40, transformed r2⫽.38). Regression models were calculated for the entire sample and each subgroup, using raw and transformed data separately.

Table 5 demonstrates the primary contributors to the regression models. Analysis of the raw data for the entire sample revealed the strongest association of daily stepping, with knee extensor strength accounting for approximately 54% of the variance and with a significant secondary contribution of BBS scores (9.3% of the variance) independent of the knee extensor strength. For transformed data, however, BBS scores were the primary predictor for daily stepping, explaining 59% of the variance in daily stepping, with a secondary contribution of knee extensor strength (6.5% of the variance). When data were separated into subgroups, knee extensor strength was the primary determinant of stepping activity. For community walkers, BBS scores were the primary determinant of daily stepping in noncommunity ambulators, with very small differences in model fit using either raw or transformed data. Variance inflation factors were all less than 3.0, verifying the lack of collinearity between predictors, and standardized residuals and predictors were within 3 standard deviations of the mean, supporting the use of linear regression analysis.

Discussion The present study examined daily stepping in ambulatory individuals with incomplete SCI and its association with self-reported ambulation, clinical walking assessments, and other measures of body function or structure and activity limitations po-

Table 3. Receiver Operating Characteristic Analyses to Discriminate Between Community and Non–Community Walkers Using Clinical Gait Measures and Daily Steppinga Variable

a

AUC (95% CI)

Youden Index (Sensitivity, Specificity)

Cutoff Point

Self-selected speed

0.88 (0.78–0.98)

0.68 (0.84, 0.84)

0.35 m/s

Fastest possible speed

0.88 (0.79–0.98)

0.68 (0.88, 0.80)

0.55 m/s

6MWD

0.89 (0.81–0.97)

0.64 (0.72, 0.92)

140 m

Daily stepping

0.90 (0.82–0.98)

0.64 (0.76, 0.88)

2,200 steps/day

AUC⫽area under the curve, CI⫽confidence interval, 6MWD⫽Six-Minute Walk Test distance. Positive value⫽community walker.

230

f

Physical Therapy

Volume 90

Number 2

February 2010

Daily Stepping in Individuals With Motor Incomplete SCI Table 4. Correlation Coefficients and Confidence Intervals (in Parentheses) Between Daily Stepping and Measures of Body Structure or Function, Activity Limitations, and Demographic Characteristics in All Participants Combined and Separately for Community and Non–Community Walkersa Variable

All Participants

Community Walkers

Non–Community Walkers

Spasticity/spasms MAS

0.03 (⫺0.36 to 0.42)

⫺0.25 (⫺0.59 to 0.16)

⫺0.01 (⫺0.40 to 0.39)

SCATS

0.01 (⫺0.38 to 0.40)

⫺0.39 (⫺0.69 to 0.00)

0.23 (⫺0.18 to 0.57)

LEMS

0.73** (0.57 to 0.84)

0.45* (0.07 to 0.72)

0.49* (0.12 to 0.74)

KE

0.74** (0.58 to 0.86)

0.80** (0.59 to 0.91)

0.18 (⫺0.23 to 0.54)

HF

0.62** (0.46 to 0.76)

0.54** (0.19 to 0.77)

0.18 (⫺0.23 to 0.54)

0.82** (0.70 to 0.89)

0.71** (0.44 to 0.86)

0.63** (0.31 to 0.82)

Strength

Balance BBS Metabolic parameters ˙ O2peak V Oxygen cost

0.43** (0.17 to 0.63) ⫺0.40** (0.13 to 0.61)

0.17 (⫺0.24 to 0.53)

0.40* (0.06 to 0.69)

⫺0.64** (⫺0.83 to ⫺0.32)

⫺0.39 (⫺0.68 to 0.01)

Sef-report measures 0.12 (⫺0.29 to 0.49)

⫺0.02 (⫺0.41 to 0.38)

0.04 (⫺0.24 to 0.31)

⫺0.01 (⫺0.40 to 0.40)

0.22 (⫺0.19 to 0.57)

Age

⫺0.28 (⫺0.52 to 0.00)

⫺0.15 (⫺0.51 to 0.26)

⫺0.03 (⫺0.42 to 0.37)

DOI

⫺0.20 (⫺0.45 to 0.08)

⫺0.38 (⫺0.67 to 0.03)

⫺0.02 (⫺0.41 to 0.38)

ABC

0.36* (0.09 to 0.58)

CES-D Demographics

a

Significant differences noted at P⬍.05 (*) and P⬍.01 (**). MAS⫽Modified Ashworth Scale, SCATS⫽Spinal Cord Assessment Tool for Spastic Reflexes, ˙ O2peak⫽peak oxygen uptake, ABC⫽Activities-specific LEMS⫽Lower Extremity Motor Score, KE⫽knee extensors, HF⫽hip flexors, BBS⫽Berg Balance Scale, V Balance Confidence Scale, CES-D⫽Center for Epidemiological Studies-Depression Scale, DOI⫽duration of injury.

tentially related to walking ability. As suggested previously,34 use of outcome measures that describe selfselected behavior at the participation (ie, home and community) level provides knowledge beyond that obtained from laboratory and clinical assessments. Measurement tools that can accurately quantify walking performance in a real-world setting may be important to estimate current physical activity levels and elucidate the effects of rehabilitation. Association of Daily Stepping With Clinical and Self-Report Measures of Ambulatory Function Daily stepping measured across the sample averaged approximately 2,600 steps/day and varied from no steps taken to more than 10,000 February 2010

steps/day. When data were separated by community versus non– community walkers, the strength (coefficients) of the relationships between clinical walking measures and daily stepping suggested reasonable prediction of average walking from these clinical measures (also see Busse et al35), whereas prediction in non– community walkers may prove less accurate. Nonetheless, ROC analyses revealed high to very high AUC values for discriminating between community and non– community walkers using clinical walking measures. In addition, cutoff values for self-selected speed (0.35 m/s) and 6MWD (140 m/s, or an average of 0.38 m/s over 6 minutes), which best discriminated community versus non– community walkers, approximate cutoff points used by

other researchers15 to discriminate walking function in individuals poststroke (0.4 m/s), suggesting that similar criteria also may be valid in individuals with incomplete SCI. Our analyses further provided an estimate of the cutoff point of daily stepping between community and non– community walkers at approximately 2,200 steps/day. Although the applicability of this finding to other neurological diagnoses warrants further testing, the current study presents an initial attempt to quantify how much daily stepping a community or non– community walker actually performs, at least in individuals with incomplete SCI with use of the modified Hoffer scale.

Volume 90

Number 2

Physical Therapy f

231

Daily Stepping in Individuals With Motor Incomplete SCI across the entire sample and in non– community walkers alone using raw or transformed data is consistent with data from previous studies in subjects with SCI6 and other diagnoses.36 Importantly, however, the lack of sensitivity and ceiling effects of the BBS scores in community walkers may limit its predictive value in individuals who are higher functioning, thus necessitating data transformation procedures for regression analyses. Despite this limitation, impaired postural stability appears to limit daily stepping in ambulatory patients with SCI, possibly secondary to reducing fall risk.

Figure 2. Association between (A) Berg Balance Scale scores and daily stepping and (B) knee extensor strength and daily stepping in community and non– community walkers. Correlation coefficients are provided.

Association of Daily Stepping and Measures of Impairments in Body Function and Activity Limitations The current data support the hypothesis that lower-extremity strength and balance are the primary determi232

f

Physical Therapy

Volume 90

nants of daily stepping in individuals with incomplete SCI, with lower, but significant, correlations for metabolic measures and balance confidence versus daily stepping. The finding that BBS scores were a primary contributor to daily stepping

Number 2

A strong association of muscle strength, particularly of the knee extensors, with walking function was evident across the entire sample, and particularly in community walkers. Early recovery of lower-extremity strength after SCI, specifically of the knee extensors,26 has been shown to be an indicator of self-reports of community ambulation. However, subsequent studies either did not test for specific muscle group contributions6 or revealed different muscles may contribute to a greater extent.7 Potential explanations for the strong association of knee extensor strength to daily stepping may be the increased role of the quadriceps femoris muscle during walking, particularly in early stance,37 at speeds required to safely ambulate in the community.9 Stepping tasks encountered in the community,38 including negotiating uneven or compliant terrains,39 inclines,40 or stairs,41 also are associated with increased knee extensor activity compared with level walking, which may account for its strong contribution to the regression models. Importantly, many nonwalking behaviors, such sit-to-stand transfers or static stance, are important for initiation and termination of stepping activity and require sufficient volitional February 2010

Daily Stepping in Individuals With Motor Incomplete SCI strength across many lowerextremity muscles. However, the above-mentioned tasks also are specific tests for the BBS. Indeed, there is a strong link between balance and lower-extremity strength, and both variables have been shown to contribute to ambulatory function.42,43 The present data also confirm with regression analysis that both BBS scores and knee extensor strength provide independent contributions to daily stepping, although the relative contributions are not entirely clear with the aforementioned limitations in the BBS scores. Significant but lower correlations between metabolic capacity and efficiency with daily stepping were found. Walking is a physiologically demanding task for patients with SCI,9 with a higher energetic cost of walking compared with individuals who are neurologically intact. In people with stroke, peak metabolic capacity has been correlated with clinical gait measures,36 but also was not the primary variable associated with daily stepping.22 Elevated oxygen cost may be partly due to increased upperextremity use44 secondary to impaired balance or lower-extremity strength,45 further emphasizing the associations between these dependent measures. Future work should be directed at determining the relative contributions of these selected neuromuscular impairments on postural stability and impaired ambulatory function. Other potential contributors, including age, duration of injury, depression, and spasticity or spasms, were not significantly related with daily stepping. This finding contrasts with recent reports of significant associations of spastic motor behaviors to walking performance in the laboratory setting6 and traditional theories regarding motor dysfunction in people with neurological injury.46,47 Our findings, however, are consistent February 2010

Table 5. Stepwise Multiple Linear Regression for Stepping Activity in All Participants, Community Walkers, and Non–Community Walkersa Variable

a

r2

P

.54

⬍.01

All participants

Model 1: KE Model 2: KE BBS

.63

⬍.01

All participants transformed

Model 1: BBS2

.59

⬍.01

Model 2: BBS2 KE

.65

⬍.01

Community walkers

Model 1: KE

.64

⬍.01

Non–community walkers

Model 1: BBS

.36

⬍.01

KE⫽knee extensor strength, BBS⫽Berg Balance Scale scores, BBS2⫽squared BBS data.

with data indicating little functional improvement following pharmacological spasticity interventions in ambulatory individuals with SCI.48 Physical interventions directed toward suppression of spastic reflexes may minimize the time and resources that could be better utilized by using strategies to facilitate improvements in the primary determinants of stepping (ie, strength, postural stability). Limitations The current data should be interpreted with caution due to specific limitations, including small sample sizes of the separated subgroups of participants. In addition, the separation of participants was performed using the modified Hoffer scale, which has not been validated for use in individuals with SCI. Despite this, our use of the scale was designed to provide clear definitions for participants as to their perceived walking activity, although further research should validate the use of this scale in other neurological populations. Another primary limitation was the use of specific assessments of body structure or function and activity limitations that may contribute to daily stepping. We specifically chose tests across multiple domains that could be completed within two 2-hour experimental sessions. Accordingly, our assessments may have been limited in certain patient sub-

groups, as evident with the use of the BBS in community walkers. Many participants who were higher functioning achieved near-maximal scores on the BBS, and the lack of sensitivity or a potential ceiling effect likely contributed to the potential nonlinear relationship of BBS scores with daily stepping. Data transformation improved the linear relationship among variables, although more-sensitive clinical or quantitative measures may be more appropriate for this population. Regardless, a clear relationship between balance and daily stepping was observed, although the precise magnitude of this relationship is uncertain. Similarly, use of the LEMS in the community walker population may be limited secondary to the decreased sensitivity and ceiling effect of manual muscle scores in patients who are higher functioning.49 Accordingly, quantitative strength measures of specific muscle groups were used to obviate this concern. The validity of MAS scores also has come into question,50 and the MAS may not be the best measure to quantify spastic reflex activity in people with SCI, despite its widespread use. Given these limitations, we also used the SCATS, which assesses both single and multijoint spastic reflexes characteristic of people with SCI. Regardless, neither measure was significantly associated with daily stepping.

Volume 90

Number 2

Physical Therapy f

233

Daily Stepping in Individuals With Motor Incomplete SCI Clinical Implications Although reduced daily stepping is expected in individuals identified as non– community walkers, an important finding is the limited total daily stepping in those identified as community ambulators, which was, on average, below recommended levels of daily walking typical of sedentary individuals.16 Limited daily stepping well below this threshold of 5,000 steps/day in both groups emphasizes the need for individuals with incomplete SCI to enhance their physical activity. Specific recommendations have been articulated for this population,51,52 although much of the research has been directed toward patients with complete SCI. In ambulatory individuals with incomplete SCI, increasing daily stepping provides an additional mechanism to augment total physical activity and energy expenditure to minimize secondary complications associated with SCI and sedentary lifestyles. Interventions such as strength or balance training or, more directly, intensive walking training can increase energy expenditure and facilitate improvements in neuromuscular and cardiovascular function related to walking performance. Whether such interventions contribute to increased daily stepping or reduce risks for secondary medical complications associated with SCI is uncertain and warrants further investigation. Additional articles in this issue explore relationships between a limitation in body structure (ie, muscle strength) and functional motor performance. Boonstra et al53 report on the retention of diminished ability to perform sit-to-stand movement 1 year after total knee arthroplasty in some patients. Damiano et al54 report that muscle strength training resulted in improved walking performance in some children with cerebral palsy but not in others.

234

f

Physical Therapy

Volume 90

Ms Saraf, Ms Moore, and Dr Hornby provided concept/idea/research design. Ms Saraf and Dr Hornby provided writing. Ms Saraf, Dr Rafferty, Ms Moore, Dr Kahn, Ms Hendron, and Dr Hornby provided data collection. Ms Saraf, Dr Rafferty, Ms Hendron, Ms Leech, and Dr Hornby provided data analysis. Ms Saraf, Dr Rafferty, and Dr Hornby provided project management. Dr Hornby provided fund procurement, facilities/equipment, and institutional liaisons. Dr Rafferty, Ms Moore, Dr Kahn, and Ms Leech provided participants. Dr Rafferty, Ms Moore, Dr Kahn, and Ms Hendron provided clerical support. Dr Rafferty, Ms Moore, Dr Kahn, Ms Hendron, Ms Leech, and Dr Hornby provided consultation (including review of manuscript before submission). A poster presentation of these results was given at the Combined Sections Meeting of the American Physical Therapy Association; February 9 –12, 2009; Las Vegas, Nevada. This study was funded by the Department of Education, National Institute of Disability and Rehabilitation Research, Model Systems for Spinal Cord Injury, grant H133N060014. This article was received February 24, 2009, and was accepted June 29, 2009. DOI: 10.2522/ptj.20090064

References 1 2004 Annual Statistical Report for the Model Spinal Cord Injury Care Systems. Birmingham, AL: National Spinal Cord Injury Statistical Center; 2004. 2 Annual Report for the Model Spinal Cord Injury Care Systems. Birmingham, AL: National Spinal Cord Injury Statistical Center; 2008. 3 Ditunno PL, Patrick M, Stineman M, et al. Cross-cultural differences in preference for recovery of mobility among spinal cord injury rehabilitation professionals. Spinal Cord. 2006;44:567–575. 4 Bauman WA, Spungen AM. Coronary heart disease in individuals with spinal cord injury: assessment of risk factors. Spinal Cord. 2008;46:466 – 476. 5 Adams MM, Hicks AL. Spasticity after spinal cord injury. Spinal Cord. 2005;43: 577–586. 6 Scivoletto G, Romanelli A, Mariotti A, et al. Clinical factors that affect walking level and performance in chronic spinal cord lesion patients. Spine. 2008;33:259 –264. 7 Kim CM, Eng JJ, Whittaker MW. Level walking and ambulatory capacity in persons with incomplete spinal cord injury: relationship with muscle strength. Spinal Cord. 2004;42:156 –162. 8 Waters RL, Adkins RH, Yakura JS, Sie I. Motor and sensory recovery following incomplete paraplegia. Arch Phys Med Rehabil. 1994;75:67–72.

Number 2

9 Lapointe R, Lajoie Y, Serresse O, Barbeau H. Functional community ambulation requirements in incomplete spinal cord injured subjects. Spinal Cord. 2001;39: 327–335. 10 Dobkin B, Barbeau H, Deforge D, et al. The evolution of walking-related outcomes over the first 12 weeks of rehabilitation for incomplete traumatic spinal cord injury: the multicenter randomized Spinal Cord Injury Locomotor Trial. Neurorehabil Neural Repair. 2007;21:25–35. 11 Pang MY, Eng JJ, Miller WC. Determinants of satisfaction with community reintegration in older adults with chronic stroke: role of balance self-efficacy. Phys Ther. 2007;87:282–291. 12 van de Port IG, Kwakkel G, Bruin M, Lindeman E. Determinants of depression in chronic stroke: a prospective cohort study. Disabil Rehabil. 2007;29:353–358. 13 Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke. 1995;26: 982–989. 14 Lord SE, McPherson K, McNaughton HK, et al. Community ambulation after stroke: how important and obtainable is it and what measures appear predictive? Arch Phys Med Rehabil. 2004;85:234 –239. 15 Schmid A, Duncan PW, Studenski S, et al. Improvements in speed-based gait classifications are meaningful. Stroke. 2007;38: 2096 –2100. 16 Tudor-Locke C, Bassett DR Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34:1– 8. 17 Berlin JE, Storti KL, Brach JS. Using activity monitors to measure physical activity in free-living conditions. Phys Ther. 2006;86: 1137–1145. 18 Macko RF, Haeuber E, Shaughnessy M, et al. Microprocessor-based ambulatory activity monitoring in stroke patients. Med Sci Sports Exerc. 2002;34:394 –399. 19 Behrman AK, Lawless-Dixon AR, Davis SB, et al. Locomotor training progression and outcomes after incomplete spinal cord injury. Phys Ther. 2005;85:1356 –1371. 20 Bowden MG, Hannold EM, Nair PM, et al. Beyond gait speed: a case report of a multidimensional approach to locomotor rehabilitation outcomes in incomplete spinal cord injury. J Neurol Phys Ther. 2008; 32:129 –138. 21 International Standards for Neurological and Functional Classification of Spinal Cord Injury. Chicago, IL: American Spinal Injury Association; 1994. 22 Michael KM, Allen JK, Macko RF. Reduced ambulatory activity after stroke: the role of balance, gait, and cardiovascular fitness. Arch Phys Med Rehabil. 2005;86:1552– 1556. 23 Bohannon RW, Smith MB. Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys Ther. 1987;67: 206 –207. 24 Benz E, Hornby TG, Bode RK, et al. A physiologically based clinical measure for spastic reflexes in spinal cord injury. Arch Phys Med Rehabil. 2005;86:52–59.

February 2010

Daily Stepping in Individuals With Motor Incomplete SCI 25 Maynard FM, Reynolds GG, Fountain S, et al. Neurological prognosis after traumatic quadriplegia: three-year experience of the California Regional Spinal Cord Injury Care System. J Neurosurg. 1979;50: 611– 616. 26 Crozier KS, Cheng LL, Graziani V, et al. Spinal cord injury: prognosis for ambulation based on quadriceps recovery. Paraplegia. 1992;30:762–767. 27 Berg K, Wood-Dauphine´e S, Williams JI. The Balance Scale: reliability assessment with elderly residents and patients with an acute stroke. Scand J Rehabil Med. 1995; 27:27–36. 28 Waters RL, Yakura JS, Adkins RH. Gait performance after spinal cord injury. Clin Orthop Relat Res. 1993;(288):87–96. 29 ACSM’s Guidelines for Exercise Testing and Prescription. 6th ed. Philadelphia, PA: Lippincott, Williams & Wilkins; 2000. 30 Barker S, Craik R, Freedman W, et al. Accuracy, reliability, and validity of a spatiotemporal gait analysis system. Med Eng Phys. 2006;28:460 – 467. 31 Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiveroperating characteristic analysis for diagnostic tests. Prev Vet Med. 2000;45:23– 41. 32 Godoy DA, Pinero G, Di Napoli M. Predicting mortality in spontaneous intracerebral hemorrhage: can modification to original score improve the prediction? Stroke. 2006;37:1038 –1044. 33 Kleinbaum DG, Kupper LL, Muller KE, Niziam A. Applied Regression Analysis and Other Multivariable Methods. 3rd ed. Pacific Grove, CA: Duxbury Press; 1997. 34 Bowden MG, Behrman AL. Step Activity Monitor: accuracy and test-retest reliability in persons with incomplete spinal cord injury. J Rehabil Res Dev. 2007;44: 355–362.

Invited Commentary Saraf et al1 address a critical need in locomotor rehabilitation, the exploration of participation-level measurement in the form of daily step counts and the impairment-based contributions to daily step activity after spinal cord injury (SCI). Although a patient’s actual home and community ambulatory behavior often is unknown to clinicians and researchers, in reality optimization of home and community ambulation is the ultimate clinical goal. In the use of receiver operating characteristic curves,

February 2010

35 Busse ME, Wiles CM, van Deursen RW. Community walking activity in neurological disorders with leg weakness. J Neurol Neurosurg Psychiatry. 2006;77:359 –362. 36 Patterson SL, Forrester LW, Rodgers MM, et al. Determinants of walking function after stroke: differences by deficit severity. Arch Phys Med Rehabil. 2007;88: 115–119. 37 Liu MQ, Anderson FC, Schwartz MH, Delp SL. Muscle contributions to support and progression over a range of walking speeds. J Biomech. 2008;41:3243–3252. 38 Musselman KE, Yang JF. Walking tasks encountered by urban-dwelling adults and persons with incomplete spinal cord injuries. J Rehabil Med. 2007;39:567–574. 39 Marigold DS, Patla AE. Adapting locomotion to different surface compliances: neuromuscular responses and changes in movement dynamics. J Neurophysiol. 2005;94:1733–1750. 40 Leroux A, Fung J, Barbeau H. Adaptation of the walking pattern to uphill walking in normal and spinal-cord injured subjects. Exp Brain Res. 1999;126:359 –368. 41 Ploutz-Snyder LL, Manini T, Ploutz-Snyder RJ, Wolf DA. Functionally relevant thresholds of quadriceps femoris strength. J Gerontol A Biol Sci Med Sci. 2002;57:B144 – B152. 42 Gerrits KH, Beltman MJ, Koppe PA, et al. Isometric muscle function of knee extensors and the relation with functional performance in patients with stroke. Arch Phys Med Rehabil. 2009;90:480 – 487. 43 Kluding P, Gajewski B. Lower-extremity strength differences predict activity limitations in people with chronic stroke. Phys Ther. 2009;89:73– 81. 44 Ulkar B, Yavuzer G, Guner R, Ergin S. Energy expenditure of the paraplegic gait: comparison between different walking aids and normal subjects. Int J Rehabil Res. 2003;26:213–217.

45 Waters RL, Yakura JS, Adkins R, Barnes G. Determinants of gait performance following spinal cord injury. Arch Phys Med Rehabil. 1989;70:811– 818. 46 Bobath B. Adult Hemiplegia: Evaluation and Treatment. 3rd ed. Oxford, United Kingdom: Butterworth-Heinemann; 1990. 47 Bobath Centre. Available at: http://www. bobath.org.uk/contact.html. 2009. Accessed June 10, 2009. 48 Taricco M, Pagliacci MC, Telaro E, Adone R. Pharmacological interventions for spasticity following spinal cord injury: results of a Cochrane systematic review. Eura Medicophys. 2006;42:5–15. 49 Bohannon RW, Corrigan D. A broad range of forces is encompassed by the maximum manual muscle test grade of five. Percept Mot Skills. 2000;90(3 pt 1):747–750. 50 Pandyan AD, Johnson GR, Price CI, et al. A review of the properties and limitations of the Ashworth and modified Ashworth scales as measures of spasticity. Clin Rehabil. 1999;13:373–383. 51 Myslinski MJ. Evidence-based exercise prescription for individuals with spinal cord injury. J Neurol Phys Ther. 2005;29: 104 –106. 52 Nash MS. Exercise as a health-promoting activity following spinal cord injury. J Neurol Phys Ther. 2005;29:87–103, 106. 53 Boonstra MC, Schwering PJA, De Waal Malefijt MC, Verdonschot N. Sit-to-stand movement as a performance-based measure for patients with total knee arthroplasty. Phys Ther. 2010;90:149 –156. 54 Damiano DL, Arnold AS, Steele KM, Delp SL. Can strength training predictably improve gait kinematics? A pilot study on the effects of hip and knee extensor strengthening on lower-extremity alignment in cerebral palsy. Phys Ther. 2010;90:269 –279.

Mark G. Bowden, Andrea L. Behrman

the authors have provided a threshold for differentiating between community and household ambulators using quantitative measures such as daily step counts that previously had been used only in case report format.2– 4 Similarly, in a recently published article specific to the stroke population, we found that stepping activity was 1 of only 2 factors that differentiated groups into household, limited community, and community ambulators.5 We concluded that this was a logical relationship, as both

walking speed and step activity represent complex behaviors with multiple modes of input into the observed outcome. The article by Saraf et al expands this concept by examining a battery of impairment-level measurements in order to delineate the multiple contributors to stepping activity after SCI. Although we agree with the necessity of understanding the mechanistic contributions to impaired community mobility, we raise the question of the utility of our current clinical mea-

Volume 90

Number 2

Physical Therapy f

235

Daily Stepping in Individuals With Motor Incomplete SCI 25 Maynard FM, Reynolds GG, Fountain S, et al. Neurological prognosis after traumatic quadriplegia: three-year experience of the California Regional Spinal Cord Injury Care System. J Neurosurg. 1979;50: 611– 616. 26 Crozier KS, Cheng LL, Graziani V, et al. Spinal cord injury: prognosis for ambulation based on quadriceps recovery. Paraplegia. 1992;30:762–767. 27 Berg K, Wood-Dauphine´e S, Williams JI. The Balance Scale: reliability assessment with elderly residents and patients with an acute stroke. Scand J Rehabil Med. 1995; 27:27–36. 28 Waters RL, Yakura JS, Adkins RH. Gait performance after spinal cord injury. Clin Orthop Relat Res. 1993;(288):87–96. 29 ACSM’s Guidelines for Exercise Testing and Prescription. 6th ed. Philadelphia, PA: Lippincott, Williams & Wilkins; 2000. 30 Barker S, Craik R, Freedman W, et al. Accuracy, reliability, and validity of a spatiotemporal gait analysis system. Med Eng Phys. 2006;28:460 – 467. 31 Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiveroperating characteristic analysis for diagnostic tests. Prev Vet Med. 2000;45:23– 41. 32 Godoy DA, Pinero G, Di Napoli M. Predicting mortality in spontaneous intracerebral hemorrhage: can modification to original score improve the prediction? Stroke. 2006;37:1038 –1044. 33 Kleinbaum DG, Kupper LL, Muller KE, Niziam A. Applied Regression Analysis and Other Multivariable Methods. 3rd ed. Pacific Grove, CA: Duxbury Press; 1997. 34 Bowden MG, Behrman AL. Step Activity Monitor: accuracy and test-retest reliability in persons with incomplete spinal cord injury. J Rehabil Res Dev. 2007;44: 355–362.

Invited Commentary Saraf et al1 address a critical need in locomotor rehabilitation, the exploration of participation-level measurement in the form of daily step counts and the impairment-based contributions to daily step activity after spinal cord injury (SCI). Although a patient’s actual home and community ambulatory behavior often is unknown to clinicians and researchers, in reality optimization of home and community ambulation is the ultimate clinical goal. In the use of receiver operating characteristic curves,

February 2010

35 Busse ME, Wiles CM, van Deursen RW. Community walking activity in neurological disorders with leg weakness. J Neurol Neurosurg Psychiatry. 2006;77:359 –362. 36 Patterson SL, Forrester LW, Rodgers MM, et al. Determinants of walking function after stroke: differences by deficit severity. Arch Phys Med Rehabil. 2007;88: 115–119. 37 Liu MQ, Anderson FC, Schwartz MH, Delp SL. Muscle contributions to support and progression over a range of walking speeds. J Biomech. 2008;41:3243–3252. 38 Musselman KE, Yang JF. Walking tasks encountered by urban-dwelling adults and persons with incomplete spinal cord injuries. J Rehabil Med. 2007;39:567–574. 39 Marigold DS, Patla AE. Adapting locomotion to different surface compliances: neuromuscular responses and changes in movement dynamics. J Neurophysiol. 2005;94:1733–1750. 40 Leroux A, Fung J, Barbeau H. Adaptation of the walking pattern to uphill walking in normal and spinal-cord injured subjects. Exp Brain Res. 1999;126:359 –368. 41 Ploutz-Snyder LL, Manini T, Ploutz-Snyder RJ, Wolf DA. Functionally relevant thresholds of quadriceps femoris strength. J Gerontol A Biol Sci Med Sci. 2002;57:B144 – B152. 42 Gerrits KH, Beltman MJ, Koppe PA, et al. Isometric muscle function of knee extensors and the relation with functional performance in patients with stroke. Arch Phys Med Rehabil. 2009;90:480 – 487. 43 Kluding P, Gajewski B. Lower-extremity strength differences predict activity limitations in people with chronic stroke. Phys Ther. 2009;89:73– 81. 44 Ulkar B, Yavuzer G, Guner R, Ergin S. Energy expenditure of the paraplegic gait: comparison between different walking aids and normal subjects. Int J Rehabil Res. 2003;26:213–217.

45 Waters RL, Yakura JS, Adkins R, Barnes G. Determinants of gait performance following spinal cord injury. Arch Phys Med Rehabil. 1989;70:811– 818. 46 Bobath B. Adult Hemiplegia: Evaluation and Treatment. 3rd ed. Oxford, United Kingdom: Butterworth-Heinemann; 1990. 47 Bobath Centre. Available at: http://www. bobath.org.uk/contact.html. 2009. Accessed June 10, 2009. 48 Taricco M, Pagliacci MC, Telaro E, Adone R. Pharmacological interventions for spasticity following spinal cord injury: results of a Cochrane systematic review. Eura Medicophys. 2006;42:5–15. 49 Bohannon RW, Corrigan D. A broad range of forces is encompassed by the maximum manual muscle test grade of five. Percept Mot Skills. 2000;90(3 pt 1):747–750. 50 Pandyan AD, Johnson GR, Price CI, et al. A review of the properties and limitations of the Ashworth and modified Ashworth scales as measures of spasticity. Clin Rehabil. 1999;13:373–383. 51 Myslinski MJ. Evidence-based exercise prescription for individuals with spinal cord injury. J Neurol Phys Ther. 2005;29: 104 –106. 52 Nash MS. Exercise as a health-promoting activity following spinal cord injury. J Neurol Phys Ther. 2005;29:87–103, 106. 53 Boonstra MC, Schwering PJA, De Waal Malefijt MC, Verdonschot N. Sit-to-stand movement as a performance-based measure for patients with total knee arthroplasty. Phys Ther. 2010;90:149 –156. 54 Damiano DL, Arnold AS, Steele KM, Delp SL. Can strength training predictably improve gait kinematics? A pilot study on the effects of hip and knee extensor strengthening on lower-extremity alignment in cerebral palsy. Phys Ther. 2010;90:269 –279.

Mark G. Bowden, Andrea L. Behrman

the authors have provided a threshold for differentiating between community and household ambulators using quantitative measures such as daily step counts that previously had been used only in case report format.2– 4 Similarly, in a recently published article specific to the stroke population, we found that stepping activity was 1 of only 2 factors that differentiated groups into household, limited community, and community ambulators.5 We concluded that this was a logical relationship, as both

walking speed and step activity represent complex behaviors with multiple modes of input into the observed outcome. The article by Saraf et al expands this concept by examining a battery of impairment-level measurements in order to delineate the multiple contributors to stepping activity after SCI. Although we agree with the necessity of understanding the mechanistic contributions to impaired community mobility, we raise the question of the utility of our current clinical mea-

Volume 90

Number 2

Physical Therapy f

235

Daily Stepping in Individuals With Motor Incomplete SCI surements and the need for the development of more task-specific measurement tools. In order to examine the contribution of quantitative muscle function to step activity, Saraf et al chose to examine isometric capacity of the hip flexors and knee extensors. Although these are important muscles in functional walking, the plantar flexors also are critical in walking performance, as evidenced by both experimental6 and simulation7 studies. Olney et al8 demonstrated that ankle joint power was barely evident in a group of 10 household walkers, suggesting that maximal walking speed was limited by plantar-flexor dysfunction. In addition, power profiles resulting from a locomotor rehabilitation intervention poststroke demonstrate that H1 (hip extension during initial stance), H3 (hip flexion during early swing), and A2 (ankle plantar flexion during late stance and pre-swing) all increased after therapy, and the A2 and H3 bursts were significantly correlated with walking speed.9 Furthermore, the A2 peak burst improvement was responsible for 25% of the gain in walking speed. Saraf et al chose to examine only isometric muscle force generation, through clinical examination of the American Spinal Injury Association (ASIA) Impairment Scale (AIS) assessment or quantified on the Biodex. Jonkers et al10 demonstrated in people poststroke that improved ankle and hip power are related to gains in walking speed and that power has a stronger relationship than strength (force-generating capacity) to walking speed, steps per day, and total distance walked in older adults.11 Examination of muscle torque at the velocity at which it is needed during the gait cycle may be the most effective way to capture the requirements of the muscle during the task. For example, Gregory et al12 reported in a case series of individ236

f

Physical Therapy

Volume 90

uals with incomplete SCI undergoing high-velocity resistance training that increases in gait speed were accompanied by increases in ankle, knee, and hip power. Furthermore, activity-dependent locomotor rehabilitation yielded not only improvements in walking performance, but also improvements in the average rate of torque development in a case series of individuals with incomplete SCI.13 Thus, appropriately timed velocitydependent force production14 may be equally important in training walking and in its examination. The idea of task-specific assessment is of importance in the discussion of balance control as well, which perhaps suggests that the Berg Balance Scale (BBS) (a measure of “static balance”) chosen by Saraf et al may not have captured all of the important elements of dynamic balance during walking. The BBS has excellent psychometric properties15,16 and has predictive validity for falls risk, with a score of less than 45 out of a possible score of 56.17 However, should a measure of primarily static, or at least non– gait-related, tasks theoretically be sufficient to predict balance control during a dynamic activity such as walking? If we are attempting to relate dynamic postural control to walking performance, should our outcome measures not attempt to measure the balance control requirements during walking?18 Although it is not yet validated for people with SCI, the Dynamic Gait Index (DGI)19 evaluates the ability to adapt to changes in task demands during walking, including changing speed, stepping over obstacles, and stopping and turning. One limitation of the DGI, however, is the inclusion of customary assistive devices. Their inclusion eliminates the potential of assessing an individual’s locomotor adaptive capacity versus a reliance on compensatory strategies. Dynamic balance control remains an area that almost all agree is critical to successful ambulation, but a

Number 2

uniform standardized assessment currently does not exist to completely assess its task-specific nature. The use of the Step Activity Monitor (SAM) is an exciting avenue for clinicians and researchers to pursue investigations of self-elected walking behavior. Researchers recently have examined more detailed information from walking patterns that may illuminate home and community walking behavior.20,21 Cavanaugh et al20 examined not only total daily step counts but also how the patterns may have been attained in order to investigate the physiological capacity of home and community ambulation. In doing so, they were able to ask additional questions regarding selfelected walking beyond step counts: Is walking always at the same cadence? Are there distinct bouts of walking? Does the pattern of activity change throughout the day? Thus, 2 individuals may each achieve 6,000 steps per day, but do so via a very different pattern. By assessing variables such as variability of steps and patterns of steps, we can begin to understand the multitude of factors that interact in achieving home and community walking. In conclusion, Saraf et al have taken the available impairment-based measures and have advanced our knowledge of how these scores relate to the participation-level measure of daily step counts. Given our current understanding of activity-based therapies and task-specific motor control, however, whether this is the direction in which we should proceed in the future remains an open question. An alternate perspective we support would be to develop improved task-specific assessments— particularly during walking, as walking is at least partially controlled by subcortical influences and afferent feedback, making non–walkingspecific clinical examinations less effective.22 Doing so would make February 2010

Daily Stepping in Individuals With Motor Incomplete SCI more direct linkages between taskspecific activity and participation measures and more accurately account for the impact of environmental and personal influences on community walking ability. M.G. Bowden, PT, PhD, is Research Physical Therapist, Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida. A.L. Behrman, PT, PhD, FAPTA, is Associate Professor, Department of Physical Therapy, College of Public Health and Health Professions, University of Florida, PO Box 100154, Gainesville, FL 32610-0154 (USA), and Research Scientist, Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida. Address all correspondence to Dr Behrman at: [email protected]. DOI: 10.2522/ptj.20090064.ic

References 1 Saraf P, Rafferty MR, Moore JL, et al. Daily stepping in individuals with motor incomplete spinal cord injury. Phys Ther. 2010;90:224 –235. 2 Behrman AL, Lawless-Dixon AR, Davis SB, et al. Locomotor training progression and outcomes after incomplete spinal cord injury. Phys Ther. 2005;85:1356 –1371. 3 Behrman AL, Nair PM, Bowden MG, et al. Locomotor training restores walking in a nonambulatory child with chronic, severe, incomplete cervical spinal cord injury. Phys Ther. 2008;88:580 –590.

Author Response We thank Bowden and Behrman for their commentary1 on our findings,2 which helps further convey an important point regarding what we as therapists often strive to achieve when we work with our patients— although clinical and laboratorybased measures can quantify many aspects of locomotor ability in those following neurological injury, we have continued to rely on selfreported assessments of walking

February 2010

4 Bowden MG, Hannold EM, Nair PM, et al. Beyond gait speed: a case report of a multidimensional approach to locomotor rehabilitation outcomes in incomplete spinal cord injury. J Neurol Phys Ther. 2008;32:129 –138. 5 Bowden MG, Balasubramanian CK, Behrman AL, Kautz SA. Validation of a speedbased classification system using quantitative measures of walking performance poststroke. Neurorehabil Neural Repair. 2008;22:672– 675. 6 Nadeau S, Gravel D, Arsenault AB, Bourbonnais D. Plantarflexor weakness as a limiting factor of gait speed in stroke subjects and the compensating role of hip flexors. Clin Biomech (Bristol, Avon). 1999;14:125–135. 7 Neptune RR, Kautz SA, Zajac FE. Contributions of the individual ankle plantar flexors to support, forward progression and swing initiation during walking. J Biomech. 2001;34:1387–1398. 8 Olney SJ, Griffin MP, Monga TN, McBride ID. Work and power in gait of stroke patients. Arch Phys Med Rehabil. 1991; 72:309 –314. 9 Richards CL, Malouin F, Bravo G, et al. The role of technology in task-oriented training in persons with subacute stroke: a randomized controlled trial. Neurorehabil Neural Repair. 2004;18:199 –211. 10 Jonkers I, Delp S, Patten C. Capacity to increase walking speed is limited by impaired hip and ankle power generation in lower functioning persons post-stroke. Gait Posture. 2009;29:129 –137. 11 Puthoff ML, Janz KF, Nielson D. The relationship between lower extremity strength and power to everyday walking behaviors in older adults with functional limitations. J Geriatr Phys Ther. 2008; 31:24 –31. 12 Gregory CM, Bowden MG, Jayaraman A, et al. Resistance training and locomotor recovery after incomplete spinal cord injury: a case series. Spinal Cord. 2007;45: 522–530.

13 Jayaraman A, Shah P, Gregory C, et al. Locomotor training and muscle function after incomplete spinal cord injury: case series. J Spinal Cord Med. 2008;31:185–193. 14 Pepin A, Norman KE, Barbeau H. Treadmill walking in incomplete spinal-cordinjured subjects, 1: adaptation to changes in speed. Spinal Cord. 2003;41:257–270. 15 Berg KO, Maki BE, Williams JI, et al. Clinical and laboratory measures of postural balance in an elderly population. Arch Phys Med Rehabil. 1992;73:1073–1080. 16 Berg KO, Wood-Dauphine´e SL, Williams JI, Maki B. Measuring balance in the elderly: validation of an instrument. Can J Public Health. 1992;83(suppl 2):S7–S11. 17 Thorbahn LD, Newton RA. Use of the Berg Balance Test to predict falls in elderly persons. Phys Ther. 1996;76:576 –583; discussion 584 –585. 18 Kang HG, Dingwell JB. A direct comparison of local dynamic stability during unperturbed standing and walking. Exp Brain Res. 2006;172:35– 48. 19 Shumway-Cook A, Baldwin M, Polissar NL, Gruber W. Predicting the probability for falls in community-dwelling older adults. Phys Ther. 1997;77:812– 819. 20 Cavanaugh JT, Coleman KL, Gaines JM, et al. Using step activity monitoring to characterize ambulatory activity in communitydwelling older adults. J Am Geriatr Soc. 2007;55:120 –124. 21 Orendurff MS, Schoen JA, Bernatz GC, et al. How humans walk: bout duration, steps per bout, and rest duration. J Rehabil Res Dev. 2008;45:1077–1090. 22 Maegele M, Muller S, Wernig A, et al. Recruitment of spinal motor pools during voluntary movements versus stepping after human spinal cord injury. J Neurotrauma. 2002;19:1217–1229.

Poonam Saraf, Miriam R. Rafferty, Jennifer L. Moore, Jennifer H. Kahn, Kathryn Hendron, Kristan Leech, T. George Hornby

function in the home and community setting.3 The development of wearable accelerometers has allowed more accurate quantification of stepping activity performed outside the laboratory and has revealed markedly reduced stepping activity in individuals with locomotor dysfunction associated with neurological injury.4,5 The present study adds to the data, indicating that daily stepping, unfortunately, is quite limited

in ambulatory individuals with motor incomplete spinal cord injury (SCI), rarely reaching levels above that considered “sedentary,”6 even in those individuals who consider themselves community walkers. The second goal of the article was to identify some of the major impairments in body function or activity limitations that contribute to reduced stepping activity in the home

Volume 90

Number 2

Physical Therapy f

237

Daily Stepping in Individuals With Motor Incomplete SCI more direct linkages between taskspecific activity and participation measures and more accurately account for the impact of environmental and personal influences on community walking ability. M.G. Bowden, PT, PhD, is Research Physical Therapist, Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida. A.L. Behrman, PT, PhD, FAPTA, is Associate Professor, Department of Physical Therapy, College of Public Health and Health Professions, University of Florida, PO Box 100154, Gainesville, FL 32610-0154 (USA), and Research Scientist, Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida. Address all correspondence to Dr Behrman at: [email protected]. DOI: 10.2522/ptj.20090064.ic

References 1 Saraf P, Rafferty MR, Moore JL, et al. Daily stepping in individuals with motor incomplete spinal cord injury. Phys Ther. 2010;90:224 –235. 2 Behrman AL, Lawless-Dixon AR, Davis SB, et al. Locomotor training progression and outcomes after incomplete spinal cord injury. Phys Ther. 2005;85:1356 –1371. 3 Behrman AL, Nair PM, Bowden MG, et al. Locomotor training restores walking in a nonambulatory child with chronic, severe, incomplete cervical spinal cord injury. Phys Ther. 2008;88:580 –590.

Author Response We thank Bowden and Behrman for their commentary1 on our findings,2 which helps further convey an important point regarding what we as therapists often strive to achieve when we work with our patients— although clinical and laboratorybased measures can quantify many aspects of locomotor ability in those following neurological injury, we have continued to rely on selfreported assessments of walking

February 2010

4 Bowden MG, Hannold EM, Nair PM, et al. Beyond gait speed: a case report of a multidimensional approach to locomotor rehabilitation outcomes in incomplete spinal cord injury. J Neurol Phys Ther. 2008;32:129 –138. 5 Bowden MG, Balasubramanian CK, Behrman AL, Kautz SA. Validation of a speedbased classification system using quantitative measures of walking performance poststroke. Neurorehabil Neural Repair. 2008;22:672– 675. 6 Nadeau S, Gravel D, Arsenault AB, Bourbonnais D. Plantarflexor weakness as a limiting factor of gait speed in stroke subjects and the compensating role of hip flexors. Clin Biomech (Bristol, Avon). 1999;14:125–135. 7 Neptune RR, Kautz SA, Zajac FE. Contributions of the individual ankle plantar flexors to support, forward progression and swing initiation during walking. J Biomech. 2001;34:1387–1398. 8 Olney SJ, Griffin MP, Monga TN, McBride ID. Work and power in gait of stroke patients. Arch Phys Med Rehabil. 1991; 72:309 –314. 9 Richards CL, Malouin F, Bravo G, et al. The role of technology in task-oriented training in persons with subacute stroke: a randomized controlled trial. Neurorehabil Neural Repair. 2004;18:199 –211. 10 Jonkers I, Delp S, Patten C. Capacity to increase walking speed is limited by impaired hip and ankle power generation in lower functioning persons post-stroke. Gait Posture. 2009;29:129 –137. 11 Puthoff ML, Janz KF, Nielson D. The relationship between lower extremity strength and power to everyday walking behaviors in older adults with functional limitations. J Geriatr Phys Ther. 2008; 31:24 –31. 12 Gregory CM, Bowden MG, Jayaraman A, et al. Resistance training and locomotor recovery after incomplete spinal cord injury: a case series. Spinal Cord. 2007;45: 522–530.

13 Jayaraman A, Shah P, Gregory C, et al. Locomotor training and muscle function after incomplete spinal cord injury: case series. J Spinal Cord Med. 2008;31:185–193. 14 Pepin A, Norman KE, Barbeau H. Treadmill walking in incomplete spinal-cordinjured subjects, 1: adaptation to changes in speed. Spinal Cord. 2003;41:257–270. 15 Berg KO, Maki BE, Williams JI, et al. Clinical and laboratory measures of postural balance in an elderly population. Arch Phys Med Rehabil. 1992;73:1073–1080. 16 Berg KO, Wood-Dauphine´e SL, Williams JI, Maki B. Measuring balance in the elderly: validation of an instrument. Can J Public Health. 1992;83(suppl 2):S7–S11. 17 Thorbahn LD, Newton RA. Use of the Berg Balance Test to predict falls in elderly persons. Phys Ther. 1996;76:576 –583; discussion 584 –585. 18 Kang HG, Dingwell JB. A direct comparison of local dynamic stability during unperturbed standing and walking. Exp Brain Res. 2006;172:35– 48. 19 Shumway-Cook A, Baldwin M, Polissar NL, Gruber W. Predicting the probability for falls in community-dwelling older adults. Phys Ther. 1997;77:812– 819. 20 Cavanaugh JT, Coleman KL, Gaines JM, et al. Using step activity monitoring to characterize ambulatory activity in communitydwelling older adults. J Am Geriatr Soc. 2007;55:120 –124. 21 Orendurff MS, Schoen JA, Bernatz GC, et al. How humans walk: bout duration, steps per bout, and rest duration. J Rehabil Res Dev. 2008;45:1077–1090. 22 Maegele M, Muller S, Wernig A, et al. Recruitment of spinal motor pools during voluntary movements versus stepping after human spinal cord injury. J Neurotrauma. 2002;19:1217–1229.

Poonam Saraf, Miriam R. Rafferty, Jennifer L. Moore, Jennifer H. Kahn, Kathryn Hendron, Kristan Leech, T. George Hornby

function in the home and community setting.3 The development of wearable accelerometers has allowed more accurate quantification of stepping activity performed outside the laboratory and has revealed markedly reduced stepping activity in individuals with locomotor dysfunction associated with neurological injury.4,5 The present study adds to the data, indicating that daily stepping, unfortunately, is quite limited

in ambulatory individuals with motor incomplete spinal cord injury (SCI), rarely reaching levels above that considered “sedentary,”6 even in those individuals who consider themselves community walkers. The second goal of the article was to identify some of the major impairments in body function or activity limitations that contribute to reduced stepping activity in the home

Volume 90

Number 2

Physical Therapy f

237

Daily Stepping in Individuals With Motor Incomplete SCI and community, determined using clinical and additional quantitative measures. Our findings suggest that the major determinants of stepping activity were balance, measured with the Berg Balance Scale (BBS), and strength, particularly of the knee extensors, during assessment of peak isometric volitional torque. As Behrman and Bowden can attest, identifying all of the potential contributors to locomotor performance, whether evaluated in the clinic, laboratory, or home and community settings, is a daunting, if not impossible, task. By necessity, investigators limit their analysis to what they believe are the specific constructs primarily related to the dependent variable of interest. Accordingly, our outcomes excluded some specific measures on which Bowden and Behrman have commented. With regard to the assessment of the contributions of strength (forcegenerating capacity), we generally agree with the position taken by Drs Bowden and Behrman that the assessment of additional muscles and the use of more task-specific measures may better estimate the role of muscle strength and power to walking ability. As indicated in their commentary, other muscle groups (including the plantar flexors7 and hip extensors8) contribute substantially during walking but were not evaluated here. Based on preliminary data in elderly subjects,9 measurement of joint powers during non-walking conditions also may better identify the contribution of specific muscle groups to daily stepping activity. However, our initial strategy was to ascertain the contributions of selected muscle groups that have been shown to be primary contributors to walking function using a wellestablished method. In this case, knee extensors and hip flexors often have been shown to contribute substantially to walking ability after SCI or stroke. Indeed, hip flexor activity 238

f

Physical Therapy

Volume 90

is thought to compensate for reduced plantar-flexor strength in people poststroke10 and was a factor in its selection to our analysis. Further work can be directed to identifying the specific contributions of additional muscle groups and how specific measures of muscle function in conditions more directly related to the motor tasks performed (ie, isometric torque versus power) may contribute to walking ability in people with incomplete SCI. Another construct that garnered discussion was the use of the BBS to assess postural stability and upright balance. Bowden and Behrman suggest that the use of a more gaitoriented test, such as the Dynamic Gait Index (DGI), could better evaluate postural stability during primarily stepping tasks. We again agree that such dynamic measures should be used, although they probably will be useful primarily for individuals who are high-functioning (community) walkers. Considering the complexity of many of the tasks in these measures and the level of impairment and activity limitations in non– community walkers, we would anticipate a floor effect for the DGI for much of the studied population. Conversely, our data revealed a potential ceiling effect for the BBS, which limited our ability to more accurately assess the contribution of postural stability in the individuals who were highest functioning. Regardless, postural stability is still a primary predictor from the independent variables studies, and further assessment using clinical or quantitative measures of balance function may be warranted. The commentary also discusses how daily stepping activity in the home and community might be better characterized by evaluating patterns of stepping activity rather than simply total amount of stepping. Stepping variability, evaluated primarily

Number 2

as the variations between individual steps, has been shown to be an indicator of limb coordination11,12 or fall risk.13 Alternatively, Cavanaugh and colleagues14 evaluated the variability within bouts of stepping activity between elderly individuals with and without self-identified gait dysfunction and younger adults who were healthy. Although there were some selected differences between groups, including differences in measures of variability between the elderly subjects with gait dysfunction and the younger subjects who were healthy, the meaning of these findings remains unclear. Indeed, those authors indicate that the “patterns of activity . . . could represent an important aspect of ambulatory function,” although the relative importance, at this point, remains speculative. Somewhat lost in this discussion, however, is the lack of associations of specific physical impairments to daily stepping in individuals with incomplete SCI. More directly, the only measures of physical impairments or activity limitations that were not significantly related to walking function were assessments of spastic motor activity. Spasticity or spasms traditionally have been thought of as the primary motor impairment in individuals with supraspinal or incomplete spinal lesions. However, the present data also add to accumulating evidence suggesting that spastic motor behaviors contribute minimally to functional measures of walking ability,15–18 particularly compared with muscle strength and postural control. Various factors may contribute to this lack of association, including limited validity and reliability of a single assessment to estimate spastic motor activity19 or the lack of measures of abnormal reflex excitability during walking tasks.20 However, various pharmacological and physical interventions directed toward the February 2010

Daily Stepping in Individuals With Motor Incomplete SCI treatment of spastic motor behaviors can elicit substantial changes in clinical measures of spasticity or spasms, with little change in walking function.21,22 Such changes can be explained by simultaneous depression of neural circuitry or muscular activity underlying spastic and volitional motor activity during functional tasks, which would be consistent with the prevailing theory underlying mechanisms of spastic motor behaviors23 and available treatment options.24 Alternatively, the contribution of spastic motor activity to impaired ambulatory function in people with incomplete SCI may be irrelevant. Considering the available data, continued emphasis on physical or pharmacological interventions targeting spastic motor behaviors to improve ambulatory function seems misdirected. In summary, we agree with Bowden and Behrman that more task-specific measures of body function and activity related to walking might better explain the variance in daily stepping. Whether additional assessments of muscle function and balance serve as better predictors of stepping activity is unclear, but this question certainly warrants further study. Regardless of the specific contribution of each variable, interventions directed toward mitigating impairments and limitations that are associated with walking (eg, muscle strength, postural stability, metabolic capacity and efficiency), as opposed to those that are not (eg, spasticity), are advised. Alternatively, the interventions could focus on walking.

References 1 Bowden MG, Behrman AL. Invited commentary on: “Daily stepping in individuals with motor incomplete spinal cord injury.” Phys Ther. 2010;90:235–237. 2 Saraf P, Rafferty MR, Moore JL, et al. Daily stepping in individuals with motor incomplete spinal cord injury. Phys Ther. 2010;90:224 –235. 3 Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke. 1995;26: 982–989. 4 Michael KM, Allen JK, Macko RF. Reduced ambulatory activity after stroke: the role of balance, gait, and cardiovascular fitness. Arch Phys Med Rehabil. 2005;86: 1552–1556. 5 Shaughnessy M, Michael KM, Sorkin JD, Macko RF. Steps after stroke: capturing ambulatory recovery. Stroke. 2005;36: 1305–1307. 6 Tudor-Locke C, Bassett DR Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34:1– 8. 7 Gregory CM, Bowden MG, Jayaraman A, et al. Resistance training and locomotor recovery after incomplete spinal cord injury: a case series. Spinal Cord. 2007;45: 522–530. 8 Kim CM, Eng JJ, Whittaker MW. Level walking and ambulatory capacity in persons with incomplete spinal cord injury: relationship with muscle strength. Spinal Cord. 2004;42:156 –162. 9 Puthoff ML, Janz KF, Nielson D. The relationship between lower extremity strength and power to everyday walking behaviors in older adults with functional limitations. J Geriatr Phys Ther. 2008;31: 24 –31. 10 Nadeau S, Gravel D, Arsenault AB, Bourbonnais D. Plantarflexor weakness as a limiting factor of gait speed in stroke subjects and the compensating role of hip flexors. Clin Biomech (Bristol, Avon). 1999;14:125–135. 11 Daly JJ, Roenigk KL, Butler KM, et al. Response of sagittal plane gait kinematics to weight-supported treadmill training and functional neuromuscular stimulation following stroke. J Rehabil Res Dev. 2004; 41:807– 820. 12 Field-Fote EC, Tepavac D. Improved intralimb coordination in people with incomplete spinal cord injury following training with body weight support and electrical stimulation. Phys Ther. 2002;82:707–715.

13 Brach JS, Studenski S, Perera S, et al. Stance time and step width variability have unique contributing impairments in older persons. Gait Posture. 2008;27:431– 439. 14 Cavanaugh JT, Coleman KL, Gaines JM, et al. Using step activity monitoring to characterize ambulatory activity in community-dwelling older adults. J Am Geriatr Soc. 2007;55:120 –124. 15 Ada L, Vattanasilp W, O’Dwyer NJ, Crosbie J. Does spasticity contribute to walking dysfunction after stroke? J Neurol Neurosurg Psychiatry. 1998;64:628 – 635. 16 Ng SS, Hui-Chan CW. The Timed Up & Go Test: Its reliability and association with lower-limb impairments and locomotor capacities in people with chronic stroke. Arch Phys Med Rehabil. 2005;86: 1641–1647. 17 Pang MY, Eng JJ, Dawson AS. Relationship between ambulatory capacity and cardiorespiratory fitness in chronic stroke: Influence of stroke-specific impairments. Chest. 2005;127:495–501. 18 Ross SA, Engsberg JR. Relationships between spasticity, strength, gait, and the GMFM-66 in persons with spastic diplegia cerebral palsy. Arch Phys Med Rehabil. 2007;88:1114 –1120. 19 Hsieh JT, Wolfe DL, Miller WC, Curt A. Spasticity outcome measures in spinal cord injury: psychometric properties and clinical utility. Spinal Cord. 2008;46: 86 –95. 20 Mazzaro N, Nielsen JF, Grey MJ, Sinkjaer T. Decreased contribution from afferent feedback to the soleus muscle during walking in patients with spastic stroke. J Stroke Cerebrovasc Dis. 2007;16: 135–144. 21 Kofler M, Quirbach E, Schauer R, et al. Limitations of intrathecal baclofen for spastic hemiparesis following stroke. Neurorehabil Neural Repair. 2009;23:26 –31. 22 Taricco M, Pagliacci MC, Telaro E, Adone R. Pharmacological interventions for spasticity following spinal cord injury: results of a Cochrane systematic review. Eura Medicophys. 2006;42:5–15. 23 Heckmann CJ, Gorassini MA, Bennett DJ. Persistent inward currents in motoneuron dendrites: Implications for motor output. Muscle Nerve. 2005;31:135–156. 24 Gallichio JE. Pharmacologic management of spasticity following stroke. Phys Ther. 2004;84:973–981.

DOI: 10.2522/ptj.20090064.ar

February 2010

Volume 90

Number 2

Physical Therapy f

239

Perry Issue: Gait Rehab

Mental Practice for Relearning Locomotor Skills Francine Malouin, Carol L. Richards F. Malouin, PhD, is Professor Emeritus, Department of Rehabilitation, Faculty of Medicine, Laval University, and Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS), Rehabilitation Institute of Quebec, 525 Blvd Wilfrid-Hamel Est, Quebec City, Quebec, Canada G1M 2S8. Address all correspondence to Dr Malouin at: Francine. [email protected]. C.L. Richards, PT, PhD, DU, FCAHS, is Professor and Holder of the Laval University Research Chair in Cerebral Palsy, Department of Rehabilitation, Faculty of Medicine, Laval University, and Director, Centre for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS), Rehabilitation Institute of Quebec.

Over the past 2 decades, much work has been carried out on the use of mental practice through motor imagery for optimizing the retraining of motor function in people with physical disabilities. Although much of the clinical work with mental practice has focused on the retraining of upper-extremity tasks, this article reviews the evidence supporting the potential of motor imagery for retraining gait and tasks involving coordinated lower-limb and body movements. First, motor imagery and mental practice are defined, and evidence from physiological and behavioral studies in healthy individuals supporting the capacity to imagine walking activities through motor imagery is examined. Then the effects of stroke, spinal cord injury, lower-limb amputation, and immobilization on motor imagery ability are discussed. Evidence of brain reorganization in healthy individuals following motor imagery training of dancing and of a foot movement sequence is reviewed, and the effects of mental practice on gait and other tasks involving coordinated lower-limb and body movements in people with stroke and in people with Parkinson disease are examined. Lastly, questions pertaining to clinical assessment of motor imagery ability and training strategies are discussed.

[Malouin F, Richards CL. Mental practice for relearning locomotor skills. Phys Ther. 2010;90:240–251.] © 2010 American Physical Therapy Association

Post a Rapid Response or find The Bottom Line: www.ptjournal.org 240

f

Physical Therapy

Volume 90

Number 2

February 2010

Mental Practice for Relearning Locomotor Skills

O

ver the past 2 decades, much work has been carried out on the use of mental practice through motor imagery for optimizing the retraining of motor function in people with physical disabilities. Although much of the clinical work with mental practice has focused on the retraining of upper-extremity tasks, in this article we will review the evidence supporting the potential of motor imagery for retraining gait and tasks involving coordinated lower-limb and body movements. First, we will define motor imagery and mental practice and examine evidence from physiological and behavioral studies in healthy individuals supporting the capacity to imagine walking activities through motor imagery. Then the effects of stroke, spinal cord injury (SCI), lower-limb amputation, and immobilization on motor imagery ability will be discussed. Evidence of brain reorganization in healthy individuals following motor imagery training of dancing and of a foot movement sequence will be reviewed, and then the effects of mental practice on gait and other tasks involving coordinated lower-limb and body movements in people with stroke and in people with Parkinson disease will be examined. Lastly, questions pertaining to clinical assessment of motor imagery ability and training strategies will be discussed.

Defining Mental Practice and Motor Imagery Motor imagery is the imagining of an action without its physical execution; it is an active process during which the representation of an action is internally reproduced within working memory without any overt output.1 Mental practice or motor imagery practice, on the other hand, is the repetition or rehearsing of imagined motor acts with the intention of improving their physical execution.2 Mental practice of locomotor skills thus requires the ability to February 2010

form internal representations of locomotor activities. Movement representations can be made from 2 perspectives: (1) from the third-person perspective (or external imagery), as spectator, when imagining another person walking or (2) from the firstperson perspective (or internal imagery), from the inside as if the actor, when imagining oneself walking.2– 4 Each perspective has different properties. The external perspective implies primarily a visual representation of the motor task, whereas the internal perspective entails, in addition to the visual representation, the kinesthetic sensations associated with the simulated movements, thus both visual and kinesthetic cues.

What Evidence Do We Have That Locomotor Activities Can Be Imagined Through Motor Imagery? Experimental studies have used different approaches to examine the mental representation of locomotor activities in individuals without disabilities. Our understanding of motor imagery of walking comes from neurophysiological and cerebral imaging studies examining the similarities between real and simulated locomotor activities. These studies have shown that locomotor activities, either performed physically or imagined, are subject to common laws and principles. For instance, autonomic studies that monitored changes in heart and respiration rates while healthy individuals imagined walking on a treadmill at different speeds showed speed-related increases during the imagination of walking.5–7 Mental chronometric studies comparing movement times in people walking or imagining walking to targets placed at different distances showed the duration of walking to be similar in both conditions, thus indicating a temporal coupling between the duration of real and imagined walking conditions.3,8 –12 Moreover, when

people walk or imagine themselves walking on narrow beams,12 through gates11 or along paths of different widths,3 uphill or downhill,9 and at different speeds,9 both the actual and imagined walking times increase as a function of the difficulty of the task. The latter findings thus indicate that Fitts’ law,13 which states that more-difficult movements take more time to produce physically than easier movements, also applies to motor imagery of walking. Further confirmation of functional similarity between real walking and imagined walking comes from functional brain imaging studies. Direct comparison of cortical activity evoked during actual gait and the imagination of gait with near-infrared spectroscopy14 have shown that actual and simulated walking increase brain activity bilaterally in the medial primary sensorimotor cortices and the supplementary motor area (SMA). These findings have been confirmed with positron emission tomography (PET)15 and functional magnetic resonance imaging (fMRI).16,17 Further demonstration that cortical brain areas are engaged during locomotor activities comes from PET and fMRI studies that examined brain activation patterns during the imagining

Available With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

Volume 90

Number 2

Physical Therapy f

241

Mental Practice for Relearning Locomotor Skills of standing,15,18 initiating gait,15,19 normal walking,15–17 walking with obstacles,15 precision gait,16 walking along a curved path,19 and running20,21 and during the imagining of other complex movements involving the whole body (eg, swimming, dancing; lifting a heavy box).21 Altogether, the findings from the functional brain imaging studies confirm that the simulation of locomotor activities and complex whole-body tasks result in the activation of cortical networks similar to those found during motor imagery of simple movements, thus suggesting that the overlapping among neural substrates during real and imagined movements also applies to complex body movements.21 In summary, mentally simulated and physically executed locomotor activities share similar autonomic responses and temporal organization and activate neural networks that greatly overlap. Consequently, it has been suggested that the benefits of mental practice training are linked to the activation of cerebral networks that are comparable to those activated during physical execution.2,21

Is Motor Imagery Ability Affected by Central and Peripheral Lesions of the Nervous System? Because the ability to form internal representations of motor acts is necessary for training with mental practice, there is a need to determine whether this ability is specifically affected following central nervous system (CNS) or peripheral nervous system (PNS) lesions. Because of its concealed nature, however, motor imagery is difficult to assess.22–24 Three main approaches have been used to assess motor imagery ability in clinical settings: mental rotation, mental chronometry, and questionnaires. Mental rotation is used to measure the accuracy of motor rep-

242

f

Physical Therapy

Volume 90

resentations. In this approach, people are asked to verbally judge the laterality of hands or feet from pictures in different postural conditions. Behavioral and functional neuroanatomy studies have demonstrated that mental rotation of body parts is carried out through a sort of inner motor simulation.25,26 Mental chronometry involves the comparison of movement times during the simulation and execution of a motor task in various conditions (fast and slow paces or over short and long time periods); it is used to examine temporal organization of simulated actions.10,27–30 Lastly, the clarity and details of the images and the intensity of the sensations (vividness) perceived during movement simulation are assessed with motor imagery questionnaires.24,31 After Stroke Motor imagery ability has been studied extensively in people with cerebral lesions. The findings indicate that the representation of movement remains possible after stroke,24 –26,28 even in people with chronic or severe motor impairments,26 suggesting that the mental representation of movement is not dependent on motor activity following CNS injury. To date, only a few patients with focalized lesions in the superior region of the parietal cortex29 or the frontal cortex25 have shown motor imagery impairment. Recently, findings in patients with stroke and age-matched healthy subjects who were assessed with the Kinesthetic and Visual Imagery Questionnaire (KVIQ)31 revealed that the level of motor imagery vividness following stroke was similar to that of healthy subjects, with good and bad imagers in both groups24 (Fig. 1). Likewise, using a left- or right-hand judgment task that implicitly requires motor imagery (mental rotation), Johnson and colleagues26 found that people with stroke and healthy subjects had a range of accuracy scores that was

Number 2

similar in both groups, with very accurate subjects (above 90%) and lessaccurate subjects (above 65%) in each group.26 This wide range of scores in accuracy and vividness of motor imagery indicates that motor imagery ability is not an all-or-none phenomenon; rather, there is a continuum in the level of performance, and in some cases the difficulty in forming internal representations of movement can be a premorbid trait unrelated to cerebral damage.24 Although these clinical studies focused mainly on the representation of simple limb movements, findings from a chronometric study in a group of people with stroke and a control group who were asked to physically execute and imagine the Timed “Up & Go” Test (TUG) indicate that the temporal representation of this complex locomotor task is retained following stroke.32 The variability in the movement times during imagination and execution conditions in the group with stroke was very similar to that observed in the control group (Fig. 2: upper graphs), and there was no significant difference between the mean imagination times and execution times for each of the subtasks in both groups. Moreover, the relative percentage of time dedicated to each subtask was similar in both groups (Fig. 2: lower graphs). These results imply that both the temporal coupling and the temporal structure of the TUG are retained after stroke. Such findings are notable because they indicate that the ability to rehearse mentally complex motor tasks is preserved after stroke.32 After Complete Spinal Cord Injury Findings from behavioral10 and fMRI studies33–35 indicate that the representation of foot movements is retained in people with complete SCI. Indeed, scores of motor imagery vividness after complete SCI are simFebruary 2010

Mental Practice for Relearning Locomotor Skills ilar to those of control subjects, and the extent of brain activation during imagery of foot movements correlates with the vividness of their imagery.33–35 The persistence of motor representations in the disconnected limbs after a complete SCI is further evidence that motor imagery is maintained even when voluntary movements are not possible. After Limb Amputation and Limb Immobilization Although motor imagery ability following cerebral lesions24 –26,28 and spinal lesions33–35 is retained even when physical movements are impaired or impossible, findings indicate that motor imagery ability is diminished by the lack of movement after the loss of a limb36,37 or temporary disuse following limb immobilization.37 More specifically, although the representation of movements is still present after upper-limb amputation, it has been shown to be less accurate during a left- or right-hand judgment task in people with an upper-limb amputation, suggesting that the absence of a limb does not prevent motor imagery, but makes it more difficult.36 Similarly, motor imagery vividness after lower-limb amputation is significantly decreased for foot movements of the missing limb,37 further demonstrating that although it is still possible to generate a mental representation of movement, the vividness of these images is weaker after the loss of a limb. Similar changes of motor imagery vividness were found in subjects who had an ankle immobilized in a cast for 2 to 4 weeks without weight bearing.37 A significant decrease in motor imagery vividness for movements of the foot (the distal segment that was immobilized) also was observed. Such findings are remarkable, given the short period of immobilization, and imply that changes in motor imagery vividness with limb disuse can take place relatively quickly. Most interesting is the sigFebruary 2010

Figure 1. Individual visual (A) and kinesthetic (B) scores of the Kinesthetic and Visual Imagery Questionnaire for people with a left hemispheric lesion (LHL) (n⫽13), a right hemispheric lesion (RHL) (n⫽19), and age-matched healthy individuals (CTL) (n⫽32). The horizontal lines indicate the 2-sided 95% confidence interval (CI). Scores above the CI lines⫽very good imagery ability, scores between the CI lines⫽good imagery ability, and scores below the CI lines⫽poor imagery ability. Adapted and reprinted with permission of the American Society of Neurorehabilitation from: Malouin F, Richards CL, Durand A, Doyon J. Clinical assessment of motor imagery after stroke. Neurorehabil Neural Repair. 2008;22:330 –340.

nificant positive correlation (r⬎.79) found between the onset of walking with a prosthesis and scores of motor imagery vividness on the amputated side, which suggests that prosthesis use helps in maintaining the mental representation of the missing limb.37 In contrast, after the immobilization of one lower limb, the positive correlation (r⬎.90) between motor imagery vividness and the duration of immobilization found in the intact limb suggests that the augmented use of the intact limb, due to longer periods of immobilization, promoted imagery vividness on that side. Overall, these findings indicate

that after limb amputation and disuse, the mental representation of actions is retained but weaker and highly modulated by sensorimotor inputs.37

What Evidence Do We Have That Mental Practice Training of Tasks Involving Coordinated Lower-Limb and Body Movements Induces Brain Reorganization? To date, 2 studies have investigated changes in brain activation patterns in people who used mental practice

Volume 90

Number 2

Physical Therapy f

243

Mental Practice for Relearning Locomotor Skills

Does Mental Practice Training Improve the Performance of Gait and Other Tasks Involving Coordinated Lower-Limb and Body Movements in People With Stroke and in People With Parkinson Disease?

Figure 2. Individual imagination and execution times of the Timed “Up & Go” Test (TUG) in a group of people with stroke (CVA: top left panel) and an age-matched group of healthy individuals (CTL: top right panel). Relative percentage of time dedicated during the imagination and execution of each subtask (lower panels). Adapted and reprinted with permission of Oxford University Press from: Malouin F, Richards CL, Jackson PL, Doyon J. Motor imagery for optimizing the reacquisition of locomotor skills after cerebral damage. In: Guillot A, Collet C, eds. The Neurophysiological Foundations of Mental and Motor Imagery, Part 3: Motor Imagery in Rehabilitation. London, United Kingdom: Oxford University Press. In press.

to learn sequences of leg movements38 and a foot movement sequence.39 Sacco and colleagues38 found that, in contrast to subjects who only physically practiced sequences of leg movements through tango lessons (45 minutes a day for 5 days), those who rehearsed the sequences mentally (15 minutes a day for 5 days) in addition to physical practice showed an expansion of the bilateral motor areas. In addition, there was a reduction of the visuospatial activation in the posterior cortex, suggesting that focusing a subject’s attention on the foot movements involved in dancing decreases the role of visual imagery processes in favor of motor-kinesthetic processes.38 Likewise, changes in brain activity found in the medial aspect of the orbitofrontal cortex (increase) and cerebellum (decrease) reported after intense mental practice (300 244

f

Physical Therapy

Volume 90

repetitions a day for 5 days) of a sequence of foot movements further support the notion that mental practice through motor imagery initially improves performance by acting on motor preparation and planning.38,40 Although foot movements seem remote from the more-complex limb and body movements in walking, a recent transcranial magnetic stimulation (TMS) study41 that examined how corticospinal excitability was affected by motor imagery of foot dorsiflexion and motor imagery of gait showed a close relationship in the control of the tibialis anterior muscle during motor imagery of simple foot dorsiflexion and gait. The findings of that study indicate that corticospinal effects of a simple motor imagery task can predict corticospinal effects of a more-complex motor imagery task involving the same muscle.

Number 2

Gait Mental practice with motor imagery provides an opportunity to improve locomotor skills through safe and self-paced locomotor training in people with severe disability that renders walking practice difficult and limited in time, especially in the early phase of rehabilitation.2,42,43 Yet, the potential use of mental practice for optimizing the relearning of activities such as walking43– 45 and rising from a chair and sitting,46,47 as well as sequential foot movements,48 has been examined mainly in exploratory studies and case reports with small sample sizes. Dickstein and colleagues43– 45 have developed a motor imagery training program for gait rehabilitation poststroke. The effects of this training program were first described in a case report of a 69-year-old man with left hemiparesis,43 and later the feasibility of using this motor imagery training at home was examined in 4 case studies.44 Dunsky and colleagues45 recently investigated the effects of this homebased motor imagery program in a group of 17 people poststroke to confirm and extend previous findings. Motor imagery training in the gait rehabilitation program consisted of 15- to 20-minute sessions, 3 times a week for 6 weeks, without any physical intervention. Both internal and external perspectives of motor imagery were used. The main objectives of training were to facilitate movements of the affected lower February 2010

Mental Practice for Relearning Locomotor Skills limb and improve posture by focusing on specific problems (eg, forefoot initial contact, push-off) and to promote functional walking in the patient’s own environment. The complexity of the task during motor imagery was increased progressively from familiarization with motor imagery of walking in an isolated place, on flat terrain and without disturbance (week 1), to more-complex situations such as imagining walking toward meaningful targets in the patient’s home and outdoors to increase gait speed and symmetry (weeks 5 and 6). Spatiotemporal parameters (gait speed, step length, single-leg support) and some kinematic variables (knee extension at initial foot contact) served as outcome measures. Most patients increased their gait speed, with gains ranging from about 10% to 80%, for a mean increase of 15 cm/s.45 The effect size was 0.64, corresponding to a moderate treatment effect. In addition to a gain in gait speed, stride length increased by 18% and singleleg stance time increased by 13%, indicating an improvement in mobility and dynamic balance.45 Most of the gains were retained at follow-up (3 weeks after the end of training). Such a substantial improvement raises much interest because it supports the idea that walking skills can be enhanced by mental practice. However, because it has been shown that mental practice of upper-limb movements led to an increase in the physical use of the trained extremity,49,50 an increase in real walking over the 6-week training program also is likely. Thus, as the amount of real walking over the 6-week training period was not monitored in the study by Dunsky and colleagues,45 the extent of motor improvement attributed to mental practice must be interpreted with caution because the gains, in part, could be due to more walking. Further studies controlling for walking activities during February 2010

the training program are needed to validate the contribution of mental practice to the extent of the gains reported. In a recent controlled study of people with Parkinson disease, evidence that mental practice could help in reducing bradykinesia during the TUG task was provided when the group of patients who combined physical and mental practice over a 12-week period showed faster performance than the group who trained physically only.51 An important limitation of this study and of many others is the lack of information about treatment adherence over the 12 week-program and the amount of physical locomotor training received by patients in each group. Coordinated Lower-Limb and Body Movements Beneficial effects of one session of mental practice in 12 people with chronic stroke were found in a study that examined the effects of mental practice in combination with a small amount of physical practice (7 series, each consisting of 1 physical repetition and 5 mental repetitions) to improve the amount of loading on the affected leg during rising from a chair and sitting down.46,47 The loading on the affected leg after training significantly improved by 17.9% and 16.2%, respectively, when rising from the chair and sitting down. Gains were still significant 24 hours later during rising (12.8%) and sitting down (11.2%), indicating that learning had occurred. Patients with deficits in at least 2 domains of working memory had a smaller improvement (27% versus 72%) and showed no retention at follow-up, suggesting that learning effects are strongly related to working memory abilities.46 In contrast, the duration of the task did not change with training. The latter findings suggest that, in the

early stage of learning a complex motor task, changes in motor strategies predominate over changes in speed of execution and that, at this stage, vertical forces (limb loading) represent a more-sensitive measure of performance than movement time. Although, this study had no control group and did not tease out the specific effects of mental practice, the gains achieved with a relatively small amount of physical practice (7 physical repetitions and 35 mental repetitions) had a magnitude similar to those measured after 3 weeks of regular physical training.52 One reason for retention of gains with such little physical practice is the combination of physical practice with mental practice that requires the person to mentally and explicitly rehearse the sequence of movements associated with the mobility task. Such rehearsal makes the person focus each time on the preparation and planning of the proper strategy, thus increasing his or her awareness of the required movements. Such an interpretation is in line with the results of Pascual-Leone and colleagues,40 who, using TMS, demonstrated that mental practice has preparatory effects and increases the efficiency of subsequent physical training. Another possible explanation for retention of the gains after only one mental training session could be the consolidating effect of sleep on motor learning, which was reported recently following mental practice in individuals who were healthy.53 Additional support for the priming or added effects of motor imagery on motor performance comes from the findings of a pilot study54 showing that a small number of physical repetitions alone (total of 120 repetitions over a 4-week period) did not enhance the motor performance (limb loading of the affected leg during rising-up and sitting-down tasks).

Volume 90

Number 2

Physical Therapy f

245

Mental Practice for Relearning Locomotor Skills When these relatively few physical repetitions were combined with a large number of mental repetitions (total of 1,100 repetitions), however, the loading on the affected leg was significantly increased and was retained 3 weeks after training. Another example pertaining to the effect of combining physical practice and mental practice on lowerlimb function is a case study that investigated the effect of mental practice on the learning of a foot movement sequence task in a 38year-old man with a left hemorrhagic subcortical stroke.48 During the first 2 weeks, the patient physically practiced a serial response time task with the lower limb. The next week mental practice was combined with physical practice, and then the patient practiced only mentally for 2 weeks at home. The patient’s average response time improved significantly during the first 5 days of physical practice (26%), but then failed to show improvement. The combination of mental practice and physical practice during the third week yielded an additional improvement (10.3%), and the following 2 weeks of mental practice resulted in a marginal increase in performance (2.2%). These findings indicate that the addition of mental practice when the performance has reached a plateau can lead to further improvement in the performance of a sequential motor skill.48 Moreover, the retention of the motor skill with motor imagery practice alone at home suggests that mental practice can play a role in the retention of newly acquired abilities.48 In summary, although results from these clinical studies suggest that mental practice can lead to improvements in gait and other tasks involving coordinated lower-limb and body movements after stroke, randomized clinical trials with larger samples are needed to confirm and generalize 246

f

Physical Therapy

Volume 90

findings about the effects reported so far in a small number of subjects. Yet, despite shortcomings of their designs, these case reports and feasibility and exploratory studies have provided useful data about the patients’ ability to adhere to diverse training approaches, the sensitivity of outcome measures, and the amount of training required to obtain significant clinical improvements.

What Do We Know About the Clinical Assessment of Motor Imagery Ability and Motor Imagery Training Strategies? Clinical Assessment The inclusion of mental practice in rehabilitation training strategies is still relatively new. Consequently, strategies and guidelines for its use as an adjunct therapy to promote the relearning of functional activities such as walking are under development. Because mental practice through motor imagery requires the representation of an action that is internally reproduced within working memory,1 good cognitive function and communicative skills are necessary. Clinical studies have used different approaches to test cognition, but a common test is the Modified Mini-Mental State Exam, with an inclusion score of 24/30 or more.45 A normal working memory in 2 domains (eg, visuospatial, verbal, kinesthetic) also has been suggested.46 Patients with severe communication problems were excluded from most studies because of their difficulty in understanding the verbal instructions and in expressing themselves in order to actively participate in the assessment of motor imagery and in the learning of imagery.2,39,47 The next step is to assess motor imagery ability because it may be deficient as a result of the nature of the

Number 2

injury or simply as a premorbid trait.24 Because of its complex nature, however, more than one assessment tool should be used to determine whether a person is able to engage in motor imagery.22–24 Recently, the combination of the Time Dependent Motor Imagery (TDMI) screening test55 and the KVIQ,24,31 2 measures that are simple and easy to use in a clinical setting, has been proposed as a clinical assessment procedure.24 The TDMI is a chronometric screening test wherein the examiner records the number of movements imagined (eg, stepping movement) over 3 time periods (15, 25, and 45 seconds); it assumes that individuals who report an increase in the number of movements imagined with increasing time are able to simulate movements and likely engage in motor imagery24,37,55 (Fig. 3). Similar chronometric tests can be applied to locomotor tasks. For instance, whether patients are really imagining walking can be verified by asking them to imagine themselves walking along a short versus a longer path or along a wide versus a narrow path. If the patients really engage in motor imagery, movement times while imagining walking are expected to increase with increasing distance3,8,12 and decrease with increasing pathway width.3,11 Likewise, autonomic responses (heart or respiratory rates) also could be monitored during the imagination of walking at slow and fast speeds; cardiorespiratory responses are expected to increase at faster walking speeds.5–7 Lastly, when scheduling patients for assessing motor imagery, care should be taken to keep track of the time of day because, contrary to real walking, the duration of the imagination of walking is influenced by the time of day.56 The KVIQ is a motor imagery questionnaire developed for people with physical disabilities that assesses the vividness of motor imagery from a first-person perspective31 and uses a February 2010

Mental Practice for Relearning Locomotor Skills 5-point scale to rate the clarity of the image (visual subscale) and the intensity of the sensations (kinesthetic subscale). It consists of 20 items (10 movements in each subscale) representing gestures with different body parts (head, shoulders, trunk, upper and lower limbs), and all movements are performed from a sitting position. Both the TDMI and the KVIQ have been standardized, and their test-retest reliability in people with stroke has been confirmed.31,55 Of a sample of 37 patients with chronic stroke who underwent these evaluation procedures, only 2 patients failed the chronometric screening test, and the KVIQ revealed that 3 patients who had passed the screening tests had difficulty in forming mental images of movement.24 Thus, although patients may pass the chronometric test, they may have difficulty in generating vivid internal representations of movements. Failure to adhere to the KVIQ also has been observed occasionally in people who were healthy.24 The assessment procedures provide not only an idea on the motor imagery ability of the patient but also a good introduction to the notion of motor imagery that prepares the patient for mental practice training. Validity of Motor Imagery Questionnaires The validity of imagery questionnaires for assessing motor imagery ability has been questioned,23,57 given the subjective nature of selfreported ratings. However, over the past few years, several studies examining brain activation patterns33–35 or motor cortex excitability58-60 during the imagination of movements have shown strong relationships between imagery vividness scores and the level of brain activations, suggesting that ratings from imagery questionnaires provide a good indication of the ability to generate vivid mental images of movement. Recently, in an fMRI study of subjects allocated February 2010

Figure 3. Number of stepping movements imagined over 3 time periods (15, 25, and 45 seconds) in the Time Dependent Motor Imagery screening test in a group of people with stroke (CVA) and an age-matched control group (CTL). Note the increase in the number of movements imagined with longer time periods. Adapted and reprinted with permission of the American Society of Neurorehabilitation from: Malouin F, Richards CL, Durand A, Doyon J. Clinical assessment of motor imagery after stroke. Neurorehabil Neural Repair. 2008;22:330 –340.

to bad and good imager groups based on combined scores from a motor imagery questionnaire, mental chronometry and electrodermal responses—2 distinct functional neuroanatomical networks, each specific to either the good or bad imagers— were described.61 The latter findings provide further evidence of a link between behavioral outcome measures and functional neuroanatomical networks. Finally, in a recent study in people with lower-limb amputation,37 the agreement between KVIQ scores, which are based on an explicit imagery paradigm (the individ-

ual is asked to imagine a movement), and scores obtained by Nico and colleagues,36 who used an implicit imagery paradigm (ie, mental rotation, in which the individual is asked to verbally judge the laterality of hands and feet portrayed in pictures in different postural conditions), provides additional support for the validity of motor imagery questionnaires. Training Strategies Results from behavioral, psychophysical, and brain imaging studies in athletes and healthy individuals

Volume 90

Number 2

Physical Therapy f

247

Mental Practice for Relearning Locomotor Skills provide some guidelines for training strategies in rehabilitation.

Are All Tasks Amenable to Mental Practice? A large body of knowledge supporting the use of mental practice to enhance skill acquisition comes from studies conducted in athletes and in healthy individuals.62– 67 It is generally recognized that for tasks with a large cognitive component (eg, pegboard, card sorting), mental practice yields stronger effects (effect size⫽ 1.44) compared with motor tasks with an effect size of 0.43.62 The difficulty, however, is to determine the size of the cognitive component in any motor task.63 Moreover, the cognitive dimension of a task changes as the skill level of the performer changes; a novice may be thinking about how to do the skills, whereas an expert is concentrating on the strategy and tactics related to the performance of that skill.63 It also is believed that other factors such as the level of familiarity and task complexity interact to determine effects.64 For example, the use of motor imagery to train isometric voluntary contractions, which induced a substantial increase in maximal torque with the untrained (weak) abductor muscle of the fifth digit,65 had no beneficial effects on the strong elbow flexor muscles.66 Such findings indicate that even a task with a low cognitive component (eg, learning to contract a muscle maximally) can benefit from mental imagery training in the early stage of strength training that involves a neural component (eg, learning to contract a muscle maximally requires spatial recruitment of existing motor units).67 Thus, benefits are to be expected with a motor task when mental practice targets motor planning and preparation components9,40 (eg, sequencing of complex foot movements, coordination of movements from different body parts). 248

f

Physical Therapy

Volume 90

Interestingly, meta-analyses revealed that positive effects for highly cognitive tasks (eg, card sorting, pegboard) were associated with very few trials and minutes per session, whereas for motor tasks, positive effects were obtained with longer sessions and more repetitions per session.62 What Imagery Perspective Should Be Used? One issue in mental practice is the selection of perspective: should an internal perspective or an external perspective be used? Because the terminology with visual (external) and kinesthetic (internal) imagery versus the type of perspective sometimes is confusing,2– 4,16,23,42 in the present article external perspective refers to a perspective that involves primarily a visual representation (third person) of the motor task, whereas internal perspective entails, in addition to the kinesthetic sensations, a visual representation (from the inside: first person) of the simulated movements, thus both visual and kinesthetic cues. Therefore, should the patient be instructed to imagine another person walking or to imagine himself or herself walking from the inside? Behavioral, neurophysiological, and brain imaging studies have shown that, compared with the third-person perspective, the first-person perspective shares more physiological characteristics with those observed during the execution of movement, and thus movement imagery in the first-person perspective is closer to the real execution of movement.2,16,61,68 –72 For that reason, the challenge with motor imagery training of gait is to ascertain that the patient really is imagining the task in the first-person perspective and focusing his or her attention on both visual and kinesthetic cues to promote activation of neural networks associated with motor imagery of gait. Consequently, instructions should direct

Number 2

the patient to focus on both visual and kinesthetic components seen and felt from the inside. Because the vividness of visual imagery usually is better than that of kinesthetic imagery,24,37,73 concentrating on visual cues may be easier initially, but both should be encouraged. For instance, patients should envision walking within an environment (eg, imagine a path’s width, the size or the position of the obstacles) and the displacement of their limbs (eg, see the top of the feet, the inside of the swinging arms) and re-create the sensations associated with the task (eg, feeling the push-off, the effort to increase the step height or length). These cues should be introduced gradually and progress according to each individual’s needs and ability. In addition, therapists should inquire about what patients see and feel during the imagination phase to ascertain that they are engaged in first-person motor imagery. Checking periodically with mental chronometry or autonomic responses is another way to monitor whether the patient really is engaged in imagining a given task. These procedures should not only assist in the development of vivid images but also help control the mental representation of the motor tasks throughout the training session.63 How Should We Position the Patient During Imagery Training? Another consideration during motor imagery training is the patient’s posture because internal representation of a movement implies a motor plan based on a body-centered frame of reference, which depends on visuokinesthetic inputs. Results from fMRI and TMS studies have shown that when the position of the imagined hand is congruent with the actual hand position, higher levels of cortical facilitation and brain activations are recorded,69,72,74 implying that motor imagery generates motor plans that depend on the current configuration of the limbs. However, a recent study that examined the effects February 2010

Mental Practice for Relearning Locomotor Skills of hand posture on mental rotation of hands and feet showed that mental rotation of hands but not of feet was influenced by changes in hand posture.75 Their findings suggest that postural information coming from the body may influence mental rotation of body parts according to specific somatotopic rules.75 To date, no study has investigated whether body orientation (sitting or standing versus lying supine) influences cortical activation during the imagining of hand or foot movements. However, during imagery training in a clinical setting, there are advantages to placing the patient in a position similar to that used during physical execution of the task. For instance, for a task such as rising to a standing position or reaching forward, a sitting position will provide the visual and kinesthetic cues that will help develop a mental representation of the task from a first-person perspective.47,48 Moreover, assuming the same position during both conditions (simulation and execution) is more practical when series of mental repetitions are combined with physical repetitions.47,48 Lastly, to promote the concentration and relaxation that facilitate motor imagery vividness,63 care should be taken to provide seated individuals with back and head support during the simulation condition.

Is It Important to Combine Mental Practice and Physical Practice, and, If So, How Should It Be Done? Another question concerns optimal conditions for motor imagery training. Although mental rehearsal alone can promote brain reorganization38,39,40 and have a priming effect on subsequent physical training,40 the number of sessions and repetitions required to observe motor improvement may depend on the type and complexity of the task.39,40,76,77 Evidence of improved motor performance was observed after 1,500 mental rehearsals of

February 2010

a foot movement sequence over a 5-day training period.39 In another study, young individuals who were healthy needed 120 mental repetitions over a single training session before a significant improvement of a complex manipulative skill was observed.76 A third study involving complex finger movement sequences showed that subjects who only trained mentally for 2 hours a day had reached after 5 days the level of performance attained after 3 days of physical practice.40 Most interesting is the fact that on the fifth day, the group who had trained mentally needed only 2 hours of physical practice to reach a level of performance similar to that of the subjects who had trained physically 2 hours a day for 5 days.40 If findings can be extended to locomotor tasks in people with physical disabilities, it could mean that patients who mentally rehearse walking before they actually can stand and walk would show improvement faster once they start walking than those who did not rehearse mentally. Although motor improvement can be obtained with motor imagery alone, better results are obtained when combining physical and mental rehearsals of a task.2,42,62 We also must keep in mind that mental practice is an adjunct to habitual therapy and that mental rehearsal of a task does not replace physical practice of the same task.2,62,63 Therefore, the choice of strategy for combining mental and physical repetitions is paramount. We know that the temporal features of imagined walking are less variable from trial to trial when each mental rehearsal is separated by a physical rehearsal, suggesting that the afferent information is helpful for consistent reproduction of the next imagined movement.9 Only a few clinical studies46 – 48,54 have provided details about the training procedures and controlled for the number of physical and mental rehearsals.

To date in the clinical setting, good adherence and learning effects46 – 48,54 have been reported with training paradigms combining physical execution trials and mental rehearsal trials in proportions ranging from 1 physical execution and 5 mental rehearsals to 1 physical execution and 10 mental rehearsals for retraining rising and sitting down in people following stroke. Because mental rehearsals at the outset of training demand much attention and concentration, it is suggested to gradually increase the number of mental repetitions46,47,54 between physical repetitions. Including one physical execution between bouts of mental repetitions has been found to help in maintaining the kinesthetic sensations of the task.46,47,62

Teaching Motor Imagery Findings from studies carried out in athletes62– 64,78 can provide some guidelines for teaching imagery in clinical settings. Because motor imagery is a complex, multidimensional process, it is important to provide imagery instructions with sufficient details to ensure that the individual is imagining the task in the desired manner (eg, with vividness and perspective). It must be clear whether the entire task is to be imagined or just specific parts, and for more-complex tasks, the proper sequence of movements should be taught. Thus, verbal instructions are very important, and it is essential to establish a dialogue between the teacher and the learner, especially in the beginning, to make sure that the instructions are well understood. It also is suggested that people who have low imagery ability or who are not familiar with motor imagery start by imagining skill tasks that they already do well.62 Best outcomes are expected when the imagery includes a positive or successful performance.78

Conclusions Several lines of evidence point to the beneficial effects of mental prac-

Volume 90

Number 2

Physical Therapy f

249

Mental Practice for Relearning Locomotor Skills tice for retraining locomotor skills. However, further clinical studies with strong designs and larger groups are needed to confirm and generalize the positive findings reported so far. Clinicians must be aware that good and bad imagers coexist after stroke,24 and thus it is imperative to evaluate motor imagery ability before introducing mental practice. Based on recent findings that mental representation of actions is highly modulated by imagery practice,37 patients who initially demonstrate difficulty in generating mental representation of movements eventually may improve their motor imagery ability with repeated exposures. Virtual environments79,80 and observation of gait17 may provide the visual reinforcements necessary to promote the generation of proper mental images in poor imagers of walking or those who cannot yet walk. The use of mental practice as an adjunct to physical practice in neurorehabilitation still is relatively new, and many questions remain regarding the optimization of training strategies. Should patients be lying down and relaxing while listening to prerecorded instruction tapes, or, in contrast, should they be more actively engaged in their training? Should they learn to self-monitor their training and solve problems along the way46,47,54,81,82 so that motor imagery eventually can be practiced alone48 or at home without supervision?82 Given the potential benefits of mental practice in rehabilitation and increasing clinician interest in its use, more information on strategies and clinical guidelines that address the questions raised in this article should be available in the near future. Both authors provided concept/idea/project design, writing, fund procurement, institutional liaisons, and consultation (including review of manuscript before submission). Dr Malouin provided data analysis, project management, and clerical support. The au-

250

f

Physical Therapy

Volume 90

thors acknowledge the assistance of Daniel Tardif in the preparation of the figures. This article was received January 28, 2009, and was accepted May 28, 2009. DOI: 10.2522/ptj.20090029

References 1 Decety J, Gre`zes J. Neural mechanisms subserving the perception of human actions. Trends in Cog Sci. 1999:3:172–178. 2 Jackson PL, Lafleur MF, Malouin F, et al. Potential role of mental practice using motor imagery in neurological rehabilitation. Arch Phys Med Rehabil. 2001;82: 1133–1141. 3 Bakker M, de Lange FP, Stevens JA. Motor imagery of gait: a quantitative approach. Exp Brain Res. 2007;179;497–504. 4 Solodkin A, Hlustik P, Chen EE. Small SI. Fine modulation in network activation during motor execution and motor imagery. Cerebral Cortex. 2004;14:1246 –1255. 5 Decety J, Jeannerod M, Germain M, Pastene J. Vegetative responses during imagined movement is proportional to mental effort. Behav Brain Res. 1991:42:1–5. 6 Wuyam B, Moosavi SH, Decety J, et al. Imagination of dynamic exercise produced ventilatory responses which were more apparent in competitive sportsmen. J Physiol. 1995:482(pt 3):713–724. 7 Fusi S, Cutuli D, Valente MR, et al. Cardioventilatory responses during real or imagined walking at low speed. Arch Ital Biol. 2005;143:223–228. 8 Bakker M, Verstappen CCP, Bloem B R, Toni I. Recent advances in functional neuroimaging of gait. J Neural Transm. 2007; 114:1323–1331. 9 Courtine G, Papaxanthis C, Gentili R, Pozzo T. Gait-dependent motor memory facilitation in covert movement execution. Cog Brain Res. 2004;22:67–75. 10 Decety J, Boisson D. Effect of brain and spinal cord injuries on motor imagery. Eur Arch Psychiatr Clin Sci.1990;240:39 – 43. 11 Decety J, Jeannerod M. Mentally simulated movements in virtual reality: does Fitts’ law hold in motor imagery? Behav Brain Res.1996;72:127–134. 12 Decety J, Jeannerod M, Prablanc C. The timing of mentally represented action. Behav Brain Res. 1989;34:35– 42. 13 Fitts PM. The information capacity of the human motor system in controlling the amplitude of movement. J Exp Psychol. 1954;47:381–391. 14 Miyai I, Tanabe HC, Sase I, et al. Cortical mapping of gait in humans: a near-infrared spectroscopic topography study. Neuroimage. 2001;14:1186 –1192. 15 Malouin F, Richards CL, Jackson PL, et al. Brain activations during motor imagery of locomotor-related tasks: a PET study. Hum Brain Mapp. 2003;19:47– 62. 16 Bakker M, de Lange FP, Helmich RC, et al. Cerebral correlates of motor imagery of normal and precision gait. Neuroimage. 2008;41:998 –1010.

Number 2

17 Iseki K, Hanakawa T, Shinozaki J, et al. Neural mechanisms involved in mental imagery and observation of gait. Neuroimage. 2008;41:1021–1031. 18 Ouchi Y, Okada H, Yoshikawa E, et al. Brain activation during maintenance of standing postures in humans. Brain. 1999; 22(pt 2):329 –338. 19 Wagner J, Stephan T, Kalla R, et al. Mind the bend: cerebral activations associated with mental imagery of walking along a curved path. Exp Brain Res. 2008;191: 247–255. 20 Jahn K, Deutschlander A, Stephan T, et al. Brain activation patterns during imagined stance and locomotion in functional magnetic resonance imaging. Neuroimage. 2004;22:1722–1731. 21 Szameitat AJ, Shen S, Sterr A. Motor imagery of complex everyday movements: an fMRI study. Neuroimage. 2007;34:702–713. 22 Guillot A, Collet C. Contribution from neurophysiological and psychological methods to the study of motor imagery. Brain Res Rev. 2005;15:287–297. 23 Sharma N, Pomeroy VM, Baron JC. Motor imagery: a backdoor to the motor system after stroke? Stroke. 2006;37:1941–1952. 24 Malouin F, Richards CL, Durand A, Doyon J. Clinical assessment of motor imagery after stroke. Neurorehabil Neural Repair. 2008;22:330 –340. 25 Johnson SH. Imagining the impossible: intact motor representations in hemiplegics. Neuroreport. 2000;11:729 –732. 26 Johnson SH, Sprehn G, Saykin AJ. Intact motor imagery in chronic upper limb hemiplegics: evidence for activity-independent action representations. J Cog Neurosc. 2002; 14:841– 852. 27 Malouin F, Richards CL, Desrosiers J, Doyon J. Bilateral slowing of mentally simulated actions after stroke. Neuroreport. 2004;7:1349 –1353. 28 Sirigu A, Cohen L, Duhamel JR, et al. Congruent unilateral impairments for real and imagined hand movements. Neuroreport. 1995,6:997–1001. 29 Sirigu A, Duhamel JR, Cohen L, et al. The mental representation of hand movements after parietal cortex damage. Science. 1996; 273:1564 –1568. 30 Stinear CM, Fleming MK, Barber PA, Byblow WD. Lateralization of motor imagery following stroke. Clin Neurophysiol. 2007; 118:1794 –1801. 31 Malouin F, Richards CL, Jackson PL, et al. The Kinesthetic and Visual Imagery Questionnaire (KVIQ) for assessing motor imagery in persons with physical disabilities: a reliability and construct validity study. J Neurol Phys Ther. 2007;31:20 –29. 32 Malouin F, Richards CL, Jackson PL, Doyon J. Motor imagery for optimizing the reacquisition of locomotor skills after cerebral damage. In: Guillot A, Collet C, eds. The Neurophysiological Foundations of Mental and Motor Imagery, Part 3: Motor Imagery in Rehabilitation. London, United Kingdom: Oxford University Press. In press.

February 2010

Mental Practice for Relearning Locomotor Skills 33 Alkadhi H, Brugger P, Boendermaker SH, et al. What disconnection tells about motor imagery: evidence from paraplegic patients. Cereb Cortex. 2005;15:131–140. 34 Cramer SC, Orr ELR, Cohen MJ, Lacourse MG. Effects of motor imagery training after chronic, complete spinal cord injury. Exp Brain Res. 2007;177:233–242. 35 Hotz-Boendermaker S, Funk M, Summers P, et al. Preservation of motor programs in paraplegics as demonstrated by attempted and imagined foot movements. Neuroimage. 2008;39:383–394. 36 Nico D, Daprati E, Rigal F, et al. Left and right hand recognition in upper limb amputees. Brain. 2003;27:120 –132. 37 Malouin F, Richards CL, Durand A, et al. Effects of practice, visual loss, limb amputation and disuse on motor imagery vividness. Neurorehabil Neural Repair. 2009; 23:449 – 463. 38 Sacco K, Cauda F, Cerliani L, et al. Motor imagery of walking following training in locomotor attention: the effect of “the tango lesson.” Neuroimage. 2006;32:1441–1449. 39 Jackson PL, Lafleur MF, Malouin F, et al. Functional cerebral reorganization following motor sequence learning through mental practice with motor imagery. Neuroimage. 2003;20:1171–1180. 40 Pascual-Leone A, Nguyet D, Cohen LG, et al. Modulation of muscle responses evoked by transcranial magnetic stimulation during the acquisition of new fine motor skills. J Neurophysiol. 1995;74: 1037–1045. 41 Bakker M, Overeem S, Snijders AH, et al. Motor imagery of foot dorsiflexion and gait: effects on corticospinal excitability. Clin Neurophysiol. 2008;119:2519 –2527. 42 Dickstein R, Deutsch JE. Motor imagery in physical therapist practice. Phys Ther. 2007;87:942–953. 43 Dickstein R, Dunsky A, Marcovitz E. Motor imagery for gait rehabilitation in post-stroke hemiparesis. Phys Ther. 2004:84:1167–1177. 44 Dunsky A, Dickstein R, Ariav C, et al. Motor imagery practice in gait rehabilitation of chronic post-stroke hemiparesis: four case studies. Int J Rehabil Res. 2006;29: 351–356. 45 Dunsky A, Dickstein R, Marcovitz,E, et al. Home-based motor imagery training for gait rehabilitation of people with chronic poststroke hemiparesis. Arch Phys Med Rehabil. 2008;89:1580 –1588. 46 Malouin F, Belleville S, Desrosiers J, et al. Working memory and mental practice after stroke. Arch Phys Med Rehabil. 2004; 85:177–183. 47 Malouin F, Richards CL, Belleville S, et al. Training mobility tasks after stroke with combined mental and physical practice: a feasibility study. Neurorehabil Neural Repair. 2004;18:66 –75. 48 Jackson PL, Doyon J, Richards CL, Malouin F. The efficacy of combined physical and mental practice in learning of a footsequence task after stroke: a case study. Neurorehabil Neural Repair. 2004:18: 106 –111.

February 2010

49 Page SJ, Levine P, Leonard A. Mental practice in chronic stroke: results of a randomized, placebo-controlled trial. Stroke. 2007; 38:1293–1297. 50 Page SJ, Levine P, Leonard AC. Effects of mental practice on affected limb use and function in chronic stroke. Arch Phys Med Rehabil. 2005;86:399 – 402. 51 Tamir R, Dickstein R, Huberman M. Integration of motor imagery and physical practice in group treatment applied to subjects with Parkinson’s disease. Neurorehabil Neural Repair. 2007;21:68 –75. 52 Engardt M, Ribbe T, Olsson E. Vertical ground reaction force feedback to enhance stroke patients’ symmetrical body-weight distribution while rising/sitting down. Scand J Rehabil Med. 1993;25:41– 48. 53 Debarnot U, Creveaux T, Collet C, et al. Sleep-related improvements in motor learning following mental practice. Brain Cog. 2009;69:398 – 405. 54 Malouin F, Richard CL, Durand A, Doyon J. Added value of mental practice combined with a small amount of physical practice on the relearning of rising and sitting post-stroke: a pilot study. J Neurol Phys Ther. In press. 55 Malouin F, Richards CL, Durand A, Doyon J. Reliability of mental chronometry for assessing motor imagery ability after stroke. Arch Phys Med Rehabil. 2008;89:311–319. 56 Gueugneau N, Mauvieux B, Papaxanthis C. Circadian modulation of mentally simulated motor actions: implications for the potential use of motor imagery in rehabilitation. Neurorehabil Neural Repair. 2009; 23:237–245. 57 Lotze M, Halsband U. Motor imagery. J Physiol (Paris). 2006;99:386 –395. 58 Lotze M. Scheler G, Tan H-RM, et al. The musician’s brain: functional imaging of amateurs and professionals during performance and imagery. Neuroimage. 2003; 20:1817–1829. 59 Lotze M, Flor H, Grodd W, et al. Phantom movements and pain: a fMRI study in upper limb amputees. Brain. 2001;124: 2268 –2277. 60 Fourkas AD, Bonavolonta` V, Avenanti A, Aglioti SM. Kinesthetic imagery and toolspecific modulation of corticospinal representations in expert tennis players. Cereb Cortex. 2008;18:2382–2390. 61 Guillot A, Collet C, Nguyen VA, et al. Functional neuroanatomical networks associated with expertise in motor imagery. Neuroimage. 2008;41:1471–1483. 62 Feltz DL, Landers DM. The effects of mental practice on motor skill learning and performance: a meta-analysis. J Sports Psychol. 1982;5:25–57. 63 Hall C, Schmidt D, Durand MC, Buckolz E. Imagery and motor skill acquisition. In: Sheikh AA, Korn ER, eds. Imagery in Sports and Physical Performance. Imagery and Human Development Series. New York, NY: Baywood Publishing Co Inc; 1994:121–134. 64 Corbin CB. Mental practice. In: Morgan WP, ed. Ergogenic Aids and Muscular Performance. New York, NY: Academic Press; 1972:94 –118.

65 Yue G, Cole KJ. Strength increases from the motor program: comparison of training with maximal voluntary and imagined muscle contractions. J Neurophysiol. 1992; 67:1114 –1123. 66 Herbert RD, Dean C, Gandevia SC. Effects of real and imagined training on voluntary muscle activation during maximal isometric contractions. Acta Physiol Scand. 1998;163:361–368. 67 Sale DG. Neural adaptation to resistance training. Med Sci Sports Exerc. 1988;20: S135–S145. 68 Fourkas AD, Avenanti A, Urgesi C, Aglioti SM. Corticospinal facilitation during first and third person imagery. Exp Brain Res. 2006;168:143–151. 69 de Lange FP, Helmich RC, Toni I. Posture influences motor imagery: an fMRI study. Neuroimage. 2006;33:609 – 617. 70 Guillot A, Collet C, Nguyen VA, et al. Brain activity during visual versus kinesthetic imagery: an fMRI study. Hum Brain Mapp. 2009;30:2157–2172. 71 Stinear CM, Byblow WD, Steyvers M, et al. Kinesthetic, but not visual, motor imagery modulates corticomotor excitability. Exp Brain Res. 2006;168:157–164. 72 Vargas CD, Olivier E, Craighero L, et al. The influence of hand posture on corticospinal excitability during motor imagery: a transcranial magnetic stimulation study. Cereb Cortex. 2004;14:1200 –1206. 73 Hall CR, Pongrac J, Buckolz E. The measurement of imagery ability. Hum Mov Sci. 1985;4:107–118. 74 Fourkas AD, Ionta S, Aglioti SM. Influence of imagined posture and imagery modality on corticospinal excitability. Behav Brain Res. 2006;168:190 –196. 75 Ionta S, Fourkas AD, Fiorio M, Aglioti SM. The influence of hands posture on mental rotation of hands and feet. Exp Brain Res. 2007;183:1–7. 76 Allami N, Paulignan Y, Brovelli A. Boussaoud D. Visuo-motor learning with combination of different rates of motor imagery and physical practice. Exp Brain Res. 2008;184:105–113. 77 Louis M, Guillot A, Maton S, et al. Effect of imagined movement speed on subsequent motor performance. J Mot Behav. 2008; 40:117–132. 78 Woolfolk RL, Parrish MW, Murphy M. The effects of positive and negative imagery on motor skill performance. Cogn Ther Res. 1985;9:335–341. 79 Fung J, Malouin F, McFadyen BJ, et al. Locomotor rehabilitation in a complex virtual environment. Conf Proc IEEE Eng Med Biol Soc. 2004;7:4859 – 4861. 80 Kizony R, Levin MF, Hughey L, et al. Cognitive load and dual-task performance during locomotion poststroke: a feasibility study using a functional virtual environment. Phys Ther. 2010;90:252–260. 81 Liu KP, Chan CC, Lee TM, Hui-Chan CW. Mental imagery for promoting relearning for people after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2004; 85:1403–1408. 82 Braun S, Kleynen M, Schols J, et al. Using mental practice in stroke rehabilitation: a framework. Clin Rehabil. 2008;22:579 –591.

Volume 90

Number 2

Physical Therapy f

251

Perry Issue: Gait Rehab Cognitive Load and Dual-Task Performance During Locomotion Poststroke: A Feasibility Study Using a Functional Virtual Environment Rachel Kizony, Mindy F. Levin, Lucinda Hughey, Claire Perez, Joyce Fung R. Kizony, OT, PhD, is Lecturer, Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Mount Carmel, Haifa 31905 Israel; Lecturer, Department of Occupational Therapy, Ono Academic College, Kiryat Ono, Israel; and Occupational Therapist, Sheba Medical Center, Tel Hashomer, Israel. Address all correspondence to Dr Kizony at: [email protected]. M.F. Levin, PT, PhD, is Professor, School of Physical and Occupational Therapy, McGill University, Montreal, Quebec, Canada. L. Hughey, PhD, is Research Associate, Jewish Rehabilitation Hospital (site of the Center for Interdisciplinary Research in Rehabilitation), Laval, Quebec, Canada. C. Perez, PT, BSc, is MSc candidate, School of Physical and Occupational Therapy, McGill University, and Physiotherapist and Research Assistant, Jewish Rehabilitation Hospital. J. Fung, PT, PhD, is Associate Professor and William Dawson Scholar, School of Physical and Occupational Therapy, McGill University, and Director of Research, Hospital Feil and Oberfeld Research Centre, Jewish Rehabilitation Hospital. [Kizony R, Levin MF, Hughey L, et al. Cognitive load and dual-task performance during locomotion poststroke: a feasibility study using a functional virtual environment. Phys Ther. 2010;90:252–260.] © 2010 American Physical Therapy Association

Background. Gait and cognitive functions can deteriorate during dual tasking, especially in people with neurological deficits. Most studies examining the simultaneous effects of dual tasking on motor and cognitive aspects were not performed in ecological environments. Using virtual reality technology, functional environments can be simulated to study dual tasking. Objectives. The aims of this study were to test the feasibility of using a virtual functional environment for the examination of dual tasking and to determine the effects of dual tasking on gait parameters in people with stroke and age-matched controls who were healthy.

Design. This was a cross-sectional observational study. Methods. Twelve community-dwelling older adults with stroke and 10 agematched older adults who were healthy participated in the study. Participants walked on a self-paced treadmill while viewing a virtual grocery aisle projected onto a screen placed in front of them. They were asked to walk through the aisle (single task) or to walk and select (“shop for”) items according to instructions delivered before or during walking (dual tasking).

Results. Overall, the stroke group walked slower than the control group in both conditions, whereas both groups walked faster overground than on the treadmill. The stroke group also showed larger variability in gait speed and shorter stride length than the control group. There was a general tendency to increase gait speed and stride length during dual-task conditions; however, a significant effect of dual tasking was found only in one dual-task condition for gait speed and stride duration variability. All participants were able to complete the task with minimal mistakes.

Limitations. The small size and heterogeneity of the sample were limitations of the study.

Conclusions. It is feasible to use a functional virtual environment for investigation of dual tasking. Different gait strategies, including an increase or decrease in gait speed, can be used to cope with the increase in cognitive demands required for dual tasking.

Post a Rapid Response or find The Bottom Line: www.ptjournal.org 252

f

Physical Therapy

Volume 90

Number 2

February 2010

Cognitive Load and Dual-Task Performance During Locomotion Poststroke

A

chieving an optimal level of participation in community activities is a main goal of rehabilitation. A common daily activity such as shopping requires the ability to perform 2 or more cognitive and motor activities simultaneously (ie, dual tasking) and to adapt the performance even when unexpected events occur. The paradigm of dual tasking and the effect of a secondary task on balance, gait, and cognitive performance have been examined in healthy and clinical populations in order to understand the role of attention on the maintenance of postural stability and walking. In this issue, for example, Yogev-Seligmann et al explore the influence on walking performance of manipulating attentional demands under dual-task conditions.1 In particular, studies have investigated the “cost” of dual-task performance, usually measured by performance changes in one or both tasks when carried out simultaneously.2 Gait and cognitive performance can deteriorate during dual-task performance, especially in people with neurological deficits. Several studies have examined the change in gait or balance parameters while performing a secondary cognitive task3–5 or the reaction to postural perturbations during performance of another task.6,7 Dual tasking was found to increase the risk of falling among frail elderly people8 and, thus, can be used to predict future falls in older adults.9 Dual tasking decreases gait speed and stride length during overground walking in people who are survivors of stroke.5,10 Plummer-D’Amato et al11 found that in communitydwelling adults who were survivors of stroke, the largest decrease in gait speed occurred during a spontaneous speech task compared with auditory one-back (working memory) and auditory clock (visuospatial) February 2010

tasks. Canning et al12 also found that walking performance in survivors of stroke can deteriorate (reduced gait speed, stride length, step length, and cadence) under dual- and triple-task conditions, similar to that observed in elderly people. Lord et al13 examined the effect of constraints in the physical environment (clinic, shopping mall, suburban street) and task (no task, stepping over an obstacle, and identifying even and odd numbers) on gait parameters in a cohort of patients with chronic stroke. A significant effect due to environmental context was found on gait speed (eg, patients walked slower within the shopping mall), but there were no significant main effects due to task or interaction effects between task and environment on gait parameters. Although the approach used in that study was novel, the observations should be interpreted with caution because results were reported for only 3 subjects in each of 9 conditions. Controlled studies are needed to determine how the environmental context affects dual tasking, and virtual reality (VR) technology can be used to create simulated functional environments that can be manipulated by the researcher. Virtual reality refers to the use of interactive simulations created with computer hardware and software to introduce users to opportunities to interact in environments that seem and feel similar to the real world. Users interact, move, and manipulate virtual objects in a way that attempts to “immerse” them within the virtual environment (VE), thereby producing a feeling of “presence” in the virtual world.14 The rationale for using VR for rehabilitation is based on a number of unique features of this technology.15,16 One important feature is the ability to manipulate and grade stimulus delivery while measuring changes in performance

within the VE. In addition, behavioral changes can be measured by adding other types of technologies such as motion analysis systems. Virtual reality hardware, composed of several types of technologies, facilitates the input and output of information and, when used in combination with programmed VEs, can provide the necessary tools for designing a variety of environments and complex tasks. These tools can enable researchers to analyze task performance in ecologically valid situations similar to real life, yet under experimentally controlled conditions.16 In the past decade, studies have demonstrated the potential of using VR of various levels of complexity and ecologically valid VEs to study a range of motor and cognitive behaviors following stroke or brain injury.17–22 Virtual reality also has been used to assess multitasking in people who were healthy23 and in people with brain injury24 and stroke.25 Most studies that examined dual-task performance have limited ecological validity because the tasks (eg, walking within the laboratory and memorizing a shopping list, walking and counting backward) were not performed within a functional environment or context. Those studies

Available With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

Volume 90

Number 2

Physical Therapy f

253

Cognitive Load and Dual-Task Performance During Locomotion Poststroke that were done within functional physical environments or VEs focused mainly on cognitive performance and did not examine the performance of an accompanying motor activity. Therefore, the objectives of this study were to test the feasibility of using a virtual functional environment for the examination of dual tasking and to determine the effect of dual tasking within a functional context on gait parameters in people with stroke in comparison with agematched controls who were healthy. We hypothesized that gait parameters (ie, speed, stride length, and duration and variability of these parameters) would change during dual tasking performed in a functional context. Moreover, we hypothesized that these changes would be greater in people with stroke compared with age-matched controls.

Method Participants A convenience sample of 7 men and 5 women who had had a stroke (mean [⫾SD] age⫽68.7⫾6.9 years) were recruited for the study. Five individuals had a left hemispheric stroke, and 7 individuals had a right hemispheric stroke. Participants were included if they were community dwelling, at least 3 months poststroke, able to walk on a self-paced treadmill, and scored at least 25 on the Mini Mental State Examination.26 Their mean (⫾SD) overground gait speed during performance of the 10-m walk test was 0.74⫾0.42 m/s. Slow walkers were defined as those individuals in the lower quartile, with an overground gait speed of less than 0.54 m/s. In addition, 4 men and 6 women who were healthy (mean [⫾SD] age⫽69.7⫾7.1 years) were recruited to participate as a control group. Their mean (⫾SD) overground gait speed during performance of the 10-m walk test was 1.26⫾0.20 m/s (available for only 8 participants in whom subsequent analyses were done). All par254

f

Physical Therapy

Volume 90

ticipants signed an informed consent form prior to the study. Instrumentation and Measurement Virtual reality instrumentation. The instrumentation has been documented previously,27 where VR technology was used in combination with a self-paced treadmill mounted on a motion platform and a real-time motion tracking system. In that study, the feasibility of using the combined technologies was demonstrated for gait training poststroke, as 2 individuals with chronic stroke were able to adapt and control their gait speed to overcome physical changes in the terrain and in the VE while walking on the treadmill. In the current study, participants stood or walked on a self-paced, motorized treadmill mounted on a 6degree-of-freedom motion platform. The VE was rear projected on a 2.44⫻ 3.05-m screen mounted 1.5 m in front of the end of the treadmill. The treadmill (0.6 ⫻ 1.5 m) was custombuilt and incorporated a PID servocontrolled motor driven by an algorithm that included the real-time distance acquired by a potentiometer attached to the walking individual, as well as the instantaneous velocity. Thus, the speed of the treadmill was adjusted at will by the moving individual. The participant held with both hands on to a bar that was mounted with linear-bearing sliders on 2 handrails over the treadmill. The handle bar could be pushed up to a predefined point to simulate walking while pushing a shopping cart. A functional VE of a grocery aisle, 16-m long, was created and controlled within the CAREN (Computer Assisted Rehabilitation Environment) system*,28 (Fig. 1). This system synchronized the instantaneous treadmill speed and scene progres* MOTEK Medical BV, Keienbergweg 77, 1101GE Amsterdam, the Netherlands.

Number 2

sion such that the participant had control of his movement within the VE. In addition, motion of the body was captured in real-time with a 6-camera Vicon motion analysis system† at 100 Hz. Participants walked through the grocery store aisle and selected or “shopped for” items that were placed at the rear of the aisle in front of them, according to the auditory instructions (with different levels of complexity) delivered prior to or after gait initiation. “Shopping for” an item consisted of deciding which object to select and then touching it with the hand. There were 4 experimental dual-task conditions: (1) condition 1—shop for one item only (instruction was delivered after gait initiation); (2) condition 2—shop for one item, which was changed to another item after 6 seconds; (3) condition 3—shop for 2 items (instruction was delivered after gait initiation); and (4) condition 4 —memorize and shop for a list of 5 items provided prior to gait initiation. The combination of items for which the participants were asked to shop was randomly changed between repetitions. Two baseline walking and standing conditions (single tasks) were included in which the participant walked through the grocery aisle without instructions (repeated 4 –5 times) or shopped for items while standing (repeated 4 times with a different number of items), respectively. Experimental conditions (dual tasks) were grouped into blocks of 4 trials containing one trial of each condition, randomly ordered within each block. Data from 2 to 5 blocks were collected. Data analysis focused on the third walking baseline trial and the second experimental block in order to control for fatigue and learning effects, as well as for adapting to the dual task. Data analysis also focused on “steady-state lo† Vicon–UK, 14 Minns Business Park, West Way, Oxford OX2 0JB, United Kingdom.

February 2010

Cognitive Load and Dual-Task Performance During Locomotion Poststroke

Figure 1. (A) The virtual reality setup viewed from behind the participant. (B) The virtual grocery aisle.

comotion” in the middle (60%) of each trial based on the number of strides. The number of strides for analysis varied among the participants depending on their stride length and gait speed and ranged between 6 and 32. Measurement of gait parameters. For the analysis of gait parameters, a special algorithm (written in MATLAB‡) was used to detect critical gait cycle events, based on the foot trajectory in the sagittal plane (using 2 markers on each foot). The algorithm took into account different foot-fall patterns (heel-strike or toe contact) in accurately detecting initial contact (beginning of stance) or toe-off (beginning of swing). The following gait parameters were measured: stride length, stride duration, and cadence. Gait speed was derived from the treadmill motor output acquired by the CAREN system. In addition, the variability of each parameter across multiple strides was measured by the coefficient of variation (CV), defined as a percentage of the standard deviation over ‡ The MathWorks Inc, 3 Apple Hill Dr, Natick, MA 01760-2098.

February 2010

the mean. Task completion was measured as the number and type of mistakes that occurred. Mistakes were defined as forgetting an item, selecting the wrong item, or selecting extra items. Data Analysis Descriptive statistics were used to describe the performance of the participants for each of the gait parameters. In order to examine how well the self-paced treadmill simulated natural walking, a mixed-model, 2way, repeated-measures analysis of variance (ANOVA) was used to compare overground and baseline treadmill gait speed. The independent variables were group (between-subject factor: control versus stroke) and condition (within-subject factor: overground versus baseline). To compare gait parameters between baseline and experimental (dual-task) conditions, taking into account the different locomotor abilities of the participants, mixedmodel, repeated-measures analyses of covariance (ANCOVA) were used for each gait outcome measure with the same independent variables (the

conditions here were single and dual tasks), but using overground gait speed as a covariate. If there was an interaction between the covariate and group, further analyses were done to compare the different outcomes based on different locomotor abilities (gait speed). Post hoc comparisons were used to investigate differences between conditions and groups (Bonferroni correction ⫺.005 for condition). Statistical analyses were performed using SPSS version 15§ and SAS version 9.1.3㛳 software. Role of Funding Source Dr Kizony was funded by a fellowship from the Bernice Kaufman Foundation through the Jewish Rehabilitation Hospital Foundation and by a postdoctoral fellowship from the Quebec Provincial Research Network of Rehabilitation (REPAR). This project was funded through an infrastructure grant from the Canada Foundation for Innovation and a team grant (Multidisciplinary Loco-

§

SAS Institute Inc, PO Box 8000, Cary, NC 27513. 㛳 SPSS Inc, 233 S Wacker Dr, Chicago, IL 60606.

Volume 90

Number 2

Physical Therapy f

255

Cognitive Load and Dual-Task Performance During Locomotion Poststroke motor Rehabilitation) from the Canadian Institutes of Health Research.

Table. Gait Parameters at Baseline for the Stroke Group and the Control Group Stroke Group (nⴝ12) Parameter Gait speed (m/s)

X

SD

0.51

0.23

Range 0.31–0.92

Control Group (nⴝ10) X 0.87

SD 0.12

Range 0.68–1.06

Lega Stride length (mm)

622

Stride duration (s) Cadence (steps/min)

232

1.46 42.3

324–1,042

0.27

1.13–1.89

7.5

31.8–52.9

1,050 1.33 46.0

125

841–1,230

0.18

0.98–1.54

7.1

38.9–61.0

Legb Stride length (mm)

619

Stride duration (s) Cadence (steps/min) a b

236

1.46 42.3

250–1,159

0.28

1.14–1.89

7.5

31.8–52.7

1,049 1.33 45.9

121

841–1,211

0.18

0.98–1.54

7.1

39.0–61.2

Nonparetic leg in stroke group, right leg in control group. Paretic leg in stroke group, left leg in control group.

Results Participants from both groups were able to walk on the self-paced treadmill and interact within the VE. For 3 participants (2 in the stroke group and 1 in the control group), data from one condition were lost for current analysis due to technical problems, such as a marker falling off while walking. Descriptive statistics for gait parameters at baseline for both groups are presented in the Table. Gait Speed In both groups, participants walked significantly faster overground (0.74⫾ 0.42 m/s for the stroke group versus 1.26⫾0.20 m/s for the control group) than on the treadmill (0.51⫾0.23 for the stroke group versus 0.87⫾0.13 m/s for the control group; F1,18⫽ 35.25, P⫽.0001). The control group walked significantly faster than the stroke group in both conditions (F1,18⫽13.47, P⫽.002).

Figure 2. Means and standard deviations of gait speed in baseline and experimental conditions for the stroke group and the control group. Condition 1⫽shop for one item only (instruction was delivered after gait initiation); condition 2⫽shop for one item, which was changed to another item after 6 seconds; condition 3⫽shop for 2 items (instruction was delivered after gait initiation); and condition 4⫽memorize and shop for a list of 5 items provided prior to gait initiation.

Figure 3. Means and standard deviations of stride length of the stroke group and the control group across baseline and dual-task conditions (paretic leg for stroke group, left leg for control group). Condition 1⫽shop for one item only (instruction was delivered after gait initiation); condition 2⫽shop for one item, which was changed to another item after 6 seconds; condition 3⫽shop for 2 items (instruction was delivered after gait initiation); and condition 4⫽memorize and shop for a list of 5 items provided prior to gait initiation.

Analyzing the differences in gait speed between baseline (single task) and experimental conditions (dual tasks) revealed that the direction of change was not consistent, although an overall tendency to increase gait speed during the dual-task conditions was seen. For gait speed, a main effect due to task conditions was found (Fig. 2; F4,70⫽3.83, P⫽ .007). Post hoc comparisons showed that participants walked slower at baseline than in condition 1 (t⫽3.64, P⫽.0005, 95% confidence interval [CI]⫽0.05 to 0.18). A similar but nonsignificant change in gait speed was observed between baseline and condition 2 (t⫽2.57, P⫽.012, 95% CI⫽0.02 to 0.15) and between baseline and condition 3 (t⫽2.76, P⫽ .007, 95% CI⫽0.03 to 0.15). The stroke group showed greater variability in gait speed compared

256

f

Physical Therapy

Volume 90

Number 2

February 2010

Cognitive Load and Dual-Task Performance During Locomotion Poststroke

1.20

Stride Length in Condition 3 (m)

Stride Length at Baseline (m)

1.20

1.00

0.80

0.60

0.40

Stride Length at Baseline (m)= 0.82×Gait Speed at Baseline+0.34 R 2=.72

0.40

0.60

0.80

1.00

Gait Speed at Baseline (m/s)

1.20

1.00

0.80

0.60

0.40

Stride Length in Condition 3 (m)= 0.57×Gait Speed in Condition 3+0.55 R 2=.56

0.40

0.60

0.80

1.00

1.20

Gait Speed in Condition 3 (m/s)

Figure 4. Illustration of the correlations between stride length and gait speed at baseline and in condition 3 (shop for 2 items; instruction was delivered after gait initiation) in the control group.

with the control group (F1,16⫽7.61, P⫽.014). The group difference was due mainly to the lower functioning of the participants in the stroke group, who were slow walkers overground (t⫽⫺2.63, P⫽.018, 95% CI⫽ ⫺32.8 to ⫺3.5). Gait speed variability ranged from 20.7% at baseline to 24.3% in condition 2 for the stroke group, as compared with the control group (ranging from 10.1% in condition 3 to 14.7% in condition 1). Stride Length and Duration Overall, there was large variability within each group for stride length and duration during single or dual tasking. For stride length (paretic leg of the stroke group and left leg of the control group), a main effect due to group was found (Fig. 3; F1,17⫽5.74, P⫽.028). The same was found for stride length of the other leg (nonparetic leg of the study group and right leg of the control group) (F1,17⫽5.59, P⫽.03). The control group had significantly longer stride lengths across all conditions. There was an overall tendency to decrease stride duration during dualtask conditions; however, there was February 2010

a main effect only for stride duration variability in the nonparetic leg of the stroke group and right leg of the control group (F4,70⫽2.57, P⫽ .045). Post hoc comparisons almost reached significance, showing that stride duration variability tended to be smaller at baseline (4.72%⫾2.12) compared with condition 1 (6.79%⫾ 5.23) (t⫽2.61, P⫽.011, 95% CI⫽0.49 to 3.67). In addition, stride duration variability tended to be greater in condition 1 than in conditions 2, 3, and 4 (t⫽2.42, P⫽.018; 95% CI⫽0.34 to 3.55; t⫽2.76, P⫽.007, 95% CI⫽0.61 to 3.79; and t⫽1.99, P⫽.05, 95% CI⫽0.0001 to 3.21, respectively). Cadence An overall tendency to increase cadence during dual-task conditions was seen; however, none of the differences reached significance in either group. In addition, no significant differences were found between groups. Additional Analysis In order to better understand the participants’ performance during the various dual-task conditions, an ANCOVA was done with performance at base-

line as the covariate. An interaction effect between group and stride length at baseline was found for the bilateral stride lengths (left/paretic leg: F1,56⫽8.52, P⫽.005; right/nonparetic leg: F1,57⫽8.66, P⫽.005), with greater differences in stride length during dual-task conditions occurring in participants who had shorter strides at baseline (left/paretic leg: t⫽2.94, P⫽.008, 95% CI⫽98.93 to 588.75; right/nonparetic leg: t⫽2.92, P⫽.005, 95% CI⫽93.86 to 575.01). A main effect due to group was found for the left/paretic leg stride length variability (F1,19⫽4.73, P⫽.042). An interaction effect between group and stride duration at baseline was found for the bilateral stride durations (left/paretic leg: F1,56⫽9.99, P⫽ .003; right/nonparetic leg: F1,57⫽ 10.30, P⫽.002). However, post hoc analyses revealed no differences based on short or long stride duration. In addition, for the purpose of explaining the increase in gait speed, Spearman correlations were performed between stride length and duration, cadence, and gait speed. In the stroke group, high correlations (r⫽.85–.96) were found only be-

Volume 90

Number 2

Physical Therapy f

257

Cognitive Load and Dual-Task Performance During Locomotion Poststroke tween gait speed and stride length of both legs. The correlations did not change between baseline and dualtask conditions. In contrast, as expected in the control group, moderate to high correlations were found between all gait parameters and gait speed. Interestingly, the correlation coefficients between stride length and gait speed decreased from baseline (r⫽.86 for left leg and .83 for right leg) to dual-task conditions (range between r⫽.57 to r⫽.71 in both legs) (Fig. 4). Completion of Task The ability to complete the task was determined in comparison with baseline performance (where participants were asked to shop without walking). Overall, the participants in both groups were able to complete the task with only minor mistakes. In the stroke group, 3 participants selected the wrong item once, 1 participant selected an extra item, and 1 participant forgot to select an item. In the control group, 3 participants selected the wrong item once, 2 participants selected an extra item, and 1 participant forgot to select an item.

Discussion and Conclusions This study showed the potential of using a functional VE to examine dual-task performance during locomotion. The use of the VR setup involving tasks that were contextdependent made it possible to examine performance of dual tasking in an ecologically valid setting. The participants, even those who were lower functioning or slow walkers, were able to walk on the self-paced treadmill and interact with the VE. However, it is important to note that the participants’ overground gait speed measured in the physical environment was significantly faster than in the baseline (single-task) condition measured in the VE, in both groups. The decrease in gait speed may have been partly due to having to walk on the treadmill while holding on to 258

f

Physical Therapy

Volume 90

the handle of a simulated shopping cart or due to the visual processing required to see the virtual aisle. In addition, the overground and VE conditions may not be strictly comparable because we did not test participants walking overground in a similar aisle pushing a shopping cart. Mean overground gait speed of the stroke group in the current study was similar to that reported for single tasks in previous studies.11–13,29 There are a number of potential reasons why there were no significant differences in most gait variables between single- and dual-task conditions in the current study. The results of this study showed large between- and within-subjects’ variability in direction and amount of change of gait parameters between single- and dual-task conditions. Although the differences between the conditions in either direction were not always statistically significant, when we examined the relative percentage of change between baseline and experimental conditions, we found that some participants had decreased gait speed during dualtask conditions, whereas other participants had increased gait speed. Thus, the same individual could cope with the increased cognitive load by decreasing gait speed in one condition while increasing it in another. However, despite the large variability, a significant increase in gait speed was found between the single task and one of the dual-task conditions (condition 1—shopping for one item), and a similar trend was found in other dual-task conditions except one (condition 4 —memorizing and shopping for a list of 5 items). These results are in contrast to the findings of other studies that examined the performance of dual tasking in people who had a stroke5,10,11 or in elderly individuals.12,30 Those studies showed consistent directions

Number 2

of change in all participants that mostly resulted in decreased gait speed and stride length during dual tasking. In addition, a decrease in gait speed was found in other populations, such as in people with Parkinson disease (PD),31 in young subjects who were healthy, and in older individuals.32 The differences may be explained by the fact that the task in the current study was different from that used in other studies; participants were asked to perform a functional task of shopping within the relevant VE of a grocery aisle, while walking on a self-paced treadmill. On one hand, this task was a familiar everyday activity for all of the participants, who probably have developed their own habits and routines.33 On the other hand, walking on a self-paced treadmill could be perceived by most participants as a novel task in itself, which was reflected by a decrease in gait speed compared with overground walking. These characteristics of the task might have led the participants to use different strategies during dualtask performance, which might explain the inconsistent changes found within- and between-subjects. Withinsubject variability was reported by Bock,34 who examined different dualtask conditions and showed that young and older individuals who were healthy decreased gait speed in a task that required visual processing while walking but not in another task that required memorizing details from a picture. Bock suggested that the visual demand of the secondary task might have affected the cost of dual tasking. In the current study, the visual demands of the secondary task during walking were small, which might explain the lack of a main effect for condition in most of the variables tested. Two explanations can be found for the strategy of increasing gait speed and stride length during dual-task conditions. Canning35 found that February 2010

Cognitive Load and Dual-Task Performance During Locomotion Poststroke when subjects with PD were given the instruction to focus on walking and not on a secondary motor task (carrying a tray with glasses), they walked at a speed similar to the single-task condition, and this had no impact on the secondary task. It might be that the participants in the current study, although they were not asked to, prioritized the more novel walking task over the routine shopping task, with the latter task being perceived as easier. Prioritization of gait, especially in novel situations, is considered to be an appropriate strategy.2 An alternative explanation can be derived from the motor learning literature. As mentioned previously, the VE treadmill walking task in the current study was new to the majority of the participants. Despite the practice and habituation that were done prior to the beginning of the study and the fact that analysis was performed on the second block of trials, it might be that the participants were in the process of learning this new walking task. One of the principles of motor learning stipulates that an external focus of attention (ie, focusing on the result of the action or on the object) enhances motor learning and performance more than an internal focus of attention (ie, focusing on the movement itself ) in adults who are healthy36 as well as in people with PD.37 It is possible that some of the participants focused on the shopping task, trying not to forget the items (which were projected on the screen in front of them) they needed to “buy,” knowing that at the end of the aisle, they would need to select the requested items. Therefore, they paid less attention to walking, which became more automatic and thus faster and closer to their overground speed. Verghese et al38 reported that when older adults were asked to prioritize a secondary talking task while walking, February 2010

they decreased their gait speed. Because the paradigm of the current study did not address either of these proposed explanations, future studies should examine the effect of task prioritization as well as the role of motor learning theories in dual tasking. In addition, future studies should investigate whether the increase in gait speed and stride length is an efficient and safe strategy, especially for people who have had a stroke. It might be that, in the event of an unexpected external perturbation, the person who speeds up will not be able to maintain balance, resulting in a fall. As suggested by Kelly et al,39 the usual finding of a decrease in gait speed during dual tasking might be a mechanism that helps to maintain stability during walking and not necessarily a sign of impaired locomotor control. These authors found that adding a cognitive load to narrowbased walking in elderly people who were healthy resulted in decreased gait speed but did not affect frontalplane stability. Overall gait variability did not worsen during dual-task conditions in either group, which may suggest that the participants generally were able to maintain gait stability during dual tasking. Changes in gait parameters and stability often are seen when the walking task and the secondary task are complex and challenging.2 In the current study, because the feasibility of the setup was being explored, there were no perturbations of the surface or manipulation of the VE, which could add to the complexity of the tasks. This might explain the lack of interference with the cognitive task or absence of interaction effects. The absence of an interaction effect on gait is consistent with previous findings in survivors of stroke who were asked to memorize a shopping list as a secondary task,5 as well as with similar outcomes when comparing elderly people who were healthy with people with stroke using dual

and triple tasks.12 Our findings, however, were different from those reported by Yang et al,10 who found greater changes in gait during dualtask conditions that involved a motor task in survivors of stroke, especially those who were least-limited community ambulators, than in elderly individuals who were healthy. Moreover, the interaction found between groups and performance at baseline in our study suggests that the differences were mainly between those participants with stroke who had poorer locomotor abilities at baseline and the control participants. Finally, all analyses were done using overground gait speed as a covariate. Simple analyses that were done without this variable as covariate did show more significant results. The heterogeneity of this variable in our sample may have underpowered the study, leading to nonsignificant findings. It should be noted that many of the comparisons were significant at a level of P⬍.05, although not significant after applying the strict criterion of P⬍.005 with Bonferroni correction. In conclusion, the results of the current study showed the potential of using a functional VE for investigating dual-task performance. In addition, the different coping strategies adopted by each individual should be investigated further. However, the results of this study should be interpreted with caution due to the small size and heterogeneity of the study sample, as well as the lack of a more-complex secondary task. Dr Kizony, Dr Levin, and Dr Fung provided concept/idea/research design. Dr Kizony, Dr Levin, Ms Perez, and Dr Fung provided writing. Dr Kizony, Dr Hughey, Ms Perez, and Dr Fung provided data collection. Dr Kizony, Dr Hughey, and Dr Fung provided data analysis. Dr Kizony and Dr Fung provided project management. Dr Fung provided fund procurement and participants. Dr Levin and Dr Fung provided facilities/equipment and in-

Volume 90

Number 2

Physical Therapy f

259

Cognitive Load and Dual-Task Performance During Locomotion Poststroke stitutional liaisons. Dr Levin provided clerical support. Dr Levin and Dr Hughey provided consultation (including review of manuscript before submission). The authors acknowledge Christian Beaudoin and Valeri Goussev for providing programming and Eric Johnstone for technical support. This study was approved by the Review Ethics Board of the Center for Interdisciplinary Research in Rehabilitation. Part of this work (initial results of the participants who were healthy) was presented at the Society for Neuroscience annual meeting; November 15–19, 2008; Washington, DC. Another part of this work (initial results of 4 participants with stroke) was presented at Virtual Rehabilitation 2008; August 25– 27, 2008; Vancouver, British Columbia, Canada. Dr Kizony was funded by a fellowship from the Bernice Kaufman Foundation through the Jewish Rehabilitation Hospital Foundation and by a postdoctoral fellowship from the Quebec Provincial Research Network of Rehabilitation (REPAR). This project was funded through an infrastructure grant from the Canada Foundation for Innovation and a team grant (Multidisciplinary Locomotor Rehabilitation) from the Canadian Institutes of Health Research. This article was received February 22, 2009, and was accepted June 29, 2009. DOI: 10.2522/ptj.20090061

References 1 Yogev-Seligmann G, Rotem-Galili Y, Mirelman A, et al. How does explicit prioritization alter walking during dual-task performance? Effects of age and sex on gait speed and variability. Phys Ther. 2010; 90:177–186. 2 Yogev-Seligmann G, Hausdorff JM, Giladi N. The role of executive function and attention in gait. Mov Disord. 2008;23: 329 –342. 3 Hauer K, Pfisterer M, Weber C, et al. Cognitive impairment decreases postural control during dual tasks in geriatric patients with a history of severe falls. J Am Soc Geriatr. 2003;51:1638 –1644. 4 Yogev G, Giladi N, Peretz C, et al. Dual tasking, gait rhythmicity, and Parkinson’s disease: which aspects of gait are attention demanding. Eur J Neurosci. 2005;22: 1248 –1256. 5 Hyndman D, Ashburn A, Yardley L, Stack A. Interference between balance, gait and cognitive task performance among people with stroke living in the community. Disabil Rehabil. 2006;28:849 – 856. 6 Paquette C, Fung J. Temporal facilitation of gaze in the presence of postural reactions triggered by sudden surface perturbations. Neuroscience. 2007;145:505–519.

260

f

Physical Therapy

Volume 90

7 Brauer SG, Woollacott M, Shumway-Cook A. The interacting effects of cognitive demand and recovery of postural stability in balance-impaired elderly persons. J Gerontol A Biol Sci Med Sci. 2001;56:M489 –M496. 8 Springer S, Giladi N, Peretz C, et al. Dualtasking effects on gait variability: the role of aging, falls, and executive function. Mov Disord. 2006;2:950 –957. 9 Lundin-Olsson L, Nyberg L, Gustafson Y. “Stops walking when talking” as a predictor of falls in elderly people. Lancet. 1997; 349:617. 10 Yang YR, Chen YC, Lee CS, et al. Dual-taskrelated gait changes in individuals with stroke. Gait Posture. 2007;25:185–190. 11 Plummer-D’Amato P, Altmann LJP, Saracino D, et al. Interactions between cognitive tasks and gait after stroke: a dual task study. Gait Posture. 2008;27:683– 688. 12 Canning CG, Ada L, Paul SS. Is automaticity of walking regained after stroke? Disabil Rehabil. 2006;28:97–102. 13 Lord SE, Rochester L, Weatherall M, et al. The effect of environment and task on gait parameters after stroke: a randomised comparison of measurement conditions. Arch Phys Med Rehabil. 2006;87:967–973. 14 Weiss PL, Kizony R, Feintuch U, Katz N. Virtual reality in neurorehabilitation. In: Selzer ME, Cohen L, Gage FH, et al, eds. Textbook of Neural Repair and Neurorehabilitation. New York, NY: Cambridge University Press; 2006:182–197. 15 Riva G, Rizzo A, Alpini D, et al. Virtual environments in the diagnosis, prevention, and intervention of age-related diseases: s review of VR scenarios proposed in the EC VETERAN Project. Cyber Psychol Behav. 1999;2:577–591. 16 Rizzo AA, Schultheis MT, Kerns K, Mateer C. Analysis of assets for virtual reality in neuropsychology. Neuropsychol Rehabil. 2004;14:207–239. 17 Katz N, Ring H, Naveh Y, et al. Interactive virtual environment training for safe street crossing of right hemisphere stroke patients with unilateral spatial neglect. Disabil Rehabil. 2005;27:1235–1243. 18 Matheis RJ, Schultheis MT, Tiersky LA, et al. Is learning and memory different in a virtual environment? Clin Neuropsychol. 2007;21:146 –161. 19 Piron L, Cenni F, Tonin P, Dam M. Virtual reality as an assessment tool for arm motor deficits after brain lesions. Stud Health Technol Inform. 2001;81:386 –392. 20 You SH, Jang SH, Kim YH, et al. Virtual reality-induced cortical organization and associated locomotor recovery in chronic stroke. Stroke. 2005;36:1166 –1171. 21 Viau A, Feldman AG, McFadyen BJ, Levin MF. Reaching in reality and virtual reality: a comparison of movement kinematics in healthy subjects and in adults with hemiparesis. J Neuroeng Rehabil. 2004;1:11. 22 Subramanian S, Knaut LA, Beaudoin C, et al. Virtual reality environments for poststroke arm rehabilitation. J Neuroeng Rehabil. 2007;4:20. 23 Law AS, Logie RH, Pearson DG. The impact of secondary tasks on multitasking in a virtual environment. Acta Psychol. 2006; 122:27– 44.

Number 2

24 McGeorge P, Phillips LH, Crawford JR, et al. Using virtual environments in the assessment of executive dysfunction. Presence. 2001;10:375–383. 25 Rand D, Abu-Rukun S, Weiss PL, Katz N. Validation of the VMall as an assessment tool for executive functions. Neuropsychol Rehabil. 2008 December 5 [Epub ahead of print]. 26 Folstein MF, Folstein SE, Mchugh PR. Mini mental state: a practical method of grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189 –198. 27 Fung F, Richards CL, Malouin F, et al. A treadmill and motion coupled virtual reality system for gait training post-stroke. Cyberpsychol Behav. 2006;9:157–162. 28 CAREN (Computer Assisted Rehabilitation Environment) system. Available at: http:// www.e-motek.com/medical/index.htm. 29 Bowen A, Wenman R, Mickelborough J, et al. Dual-task effects of talking while walking on velocity and balance following a stroke. Age Ageing. 2001;30:319 –323. 30 Hausdorff JM, Schweiger A, Herman T, et al. Dual-task decrements in gait: contributing factors among healthy older adults. J Gerontol A Biol Sci Med Sci. 2008;63: 1335–1343. 31 Yogev G, Giladi N, Peretz C, et al. Dual tasking, gait rhythmicity, and Parkinson’s disease: which aspects of gait are attention demanding? Eur J Neurosci. 2005;22: 1248 –1256. 32 Priest AW, Salamon KB, Hollman JH. Agerelated differences in dual task walking: a cross sectional study. J Neuroeng Rehabil. 2008;5:29. 33 Roley SS, Delany JV, Barrows CJ, et al; American Occupational Therapy Association Commission on Practice. Occupational therapy practice framework: domain and process, 2nd ed. Am J Occup Ther. 2008;62:625– 683. 34 Bock O. Dual-task costs while walking increase in old age for some, but not for other tasks: an experimental study of healthy young and elderly persons. J Neuroeng Rehabil. 2008;5:27. 35 Canning CG. The effect of directing attention during walking under dual-task conditions in Parkinson’s disease. Parkinsonism Relat Disord. 2005;11:95–99. 36 Emanuel M, Jarus T, Bart O. Effect of focus of attention and age on motor acquisition, retention, and transfer: a randomized trial. Phys Ther. 2008;88:251–260. 37 Wulf G, Landers M, Lewthwaite R, To ¨ llner T. External focus instructions reduce postural instability in individuals with Parkinson disease. Phys Ther. 2009;89:162–168. 38 Verghese J, Kuslansky G, Holtzer R, et al. Walking while talking: effect of task prioritization in the elderly. Arch Phys Med Rehabil. 2007;88:50 –53. 39 Kelly VE, Schrager MA, Price R, et al. Ageassociated effects of a concurrent cognitive task on gait speed and stability during narrow-base walking. J Gerontol A Biol Sci Med Sci. 2008;63:1329 –1334.

February 2010

Perry Issue: Gait Rehab Rectus Femoris to Gracilis Muscle Transfer With Fractional Lengthening of the Vastus Muscles: A Treatment for Adults With Stiff Knee Gait Surena Namdari, Stephan G. Pill, Amun Makani, Mary Ann Keenan

Background. Stiff knee gait, which may be seen in patients with upper motor neuron injury, describes a gait pattern with a relative loss of sagittal knee motion. It interferes with foot clearance during swing, often leading to inefficient compensatory mechanisms and ambulatory dysfunction. Distal rectus femoris muscle transfers and fractional lengthening of the vastus muscles have been performed in adult patients. Objective. The purpose of this study was to describe a unique surgical technique and report on initial outcomes.

Design. A retrospective case-series study design was used. Methods. The patients were adults with stiff knee gait due to stroke or traumatic brain injury who underwent distal rectus femoris muscle transfer with fractional lengthening of the vastus muscles. The patients (19 men and 18 women) had an average age of 51 years at the time of surgery. Lower-extremity examinations, clinical gait analyses, and satisfaction levels were recorded preoperatively and postoperatively.

Results. At a mean follow-up time of 10 months, 36 (97%) of the 37 patients were satisfied with their clinical and functional results, and the average Viosca score improved from 3.1 to 3.5.

Limitations. Limitations of the study include use of a retrospective design, lack of a control group, and limited quantitative measures of gait.

Conclusion. Distal rectus femoris muscle transfer and fractional lengthening of the vastus muscles were found to be a possible treatment for adults with stiff-knee gait caused by stroke or traumatic brain injury.

S. Namdari, MD, is Orthopaedic Surgery Resident, Department of Orthopaedic Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania. S.G. Pill, PT, MD, MSPT, is Orthopaedic Surgery Resident, Department of Orthopaedic Surgery, Hospital of the University of Pennsylvania. A. Makani, MD, is Orthopaedic Surgery Resident, Department of Orthopaedic Surgery, Hospital of the University of Pennsylvania. M.A. Keenan, MD, is Chief, Neuro-Orthopaedics Program, and Professor and Vice Chair for Graduate Medical Education, Department of Orthopaedic Surgery, Hospital of the University of Pennsylvania, 3400 Spruce St, 2 Silverstein, Philadelphia, PA 19104 (USA). Address all correspondence to Dr Keenan at: maryann.keenan@ uphs.upenn.edu. [Namdari S, Pill SG, Makani A, Keenan MA. Rectus femoris to gracilis muscle transfer with fractional lengthening of the vastus muscles: a treatment for adults with stiff knee gait. Phys Ther. 2010;90: 261–268.] © 2010 American Physical Therapy Association

Post a Rapid Response or find The Bottom Line: www.ptjournal.org February 2010

Volume 90

Number 2

Physical Therapy f

261

A Treatment for Adults With Stiff Knee Gait

S

tiff knee gait describes a gait pattern with a relative loss of sagittal-plane knee motion, which interferes with foot clearance during swing.1 It may be seen in patients with upper motor neuron injury, such as stroke or traumatic brain injury (TBI), and is commonly seen in children with cerebral palsy after hamstring muscle lengthening surgery. One proposed etiology of stiff knee gait is abnormal timing of the rectus femoris muscle. Instead of its normal brief action from terminal swing into mid stance and again in pre-swing, the rectus femoris muscle in patients with stiff knee gait has prolonged activity in the swing phase or is active throughout the entire gait cycle.2 This muscle activity leads to inadequate knee flexion during swing and poor foot clearance. Consequently, these patients often compensate with hip circumduction, pelvic elevation, and vaulting of the contralateral lower extremity. These compensatory mechanisms are inefficient and ineffective in these patients due to poor selective muscle control and co-spasticity of the hamstring and quadriceps muscles at mid-swing.3 Increased vastus muscle activity and decreased iliopsoas muscle activity

Available With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

262

f

Physical Therapy

Volume 90

also have been proposed as potential causes of stiff knee gait. Perry4 demonstrated that the rectus femoris muscle often assumes the role of a primary hip flexor in these patients. In addition, there is inadequate action of the sartorius and gracilis muscles and the short head of the biceps muscle during the swing phase.4 Surgery is considered if a patient with stiff knee gait has a reasonable ambulatory speed, because the amount of knee flexion during swing is directly related to walking speed. The patient also should have good strength (force-generating capacity) of the hip flexors, as forward momentum of the leg normally provides the inertial force to flex the knee. Dynamic electromyography (EMG) often demonstrates that the rectus femoris muscle displays dyssynergistic activity from pre-swing through terminal swing, and surgical intervention is more likely to be effective if a block of the rectus femoris or vastus intermedius muscle improves knee flexion. Numerous surgical procedures have been proposed to manage stiff knee gait, including proximal release of the rectus femoris muscle5; fractional lengthening of the hamstring muscles6; release of the distal rectus femoris muscle, with or without release of the vastus intermedius muscle3; and distal rectus muscle transfer.7 Perry7 proposed transferring the distal rectus femoris muscle posterior to the knee axis in conjunction with hamstring muscle lengthening for treating children with cerebral palsy. This procedure allowed knee extension in stance, while augmenting knee flexion in swing for foot clearance.7 We have been performing distal rectus muscle transfers in conjunction with vastus muscle lengthening in adult patients with stiff knee gait following stroke or TBI. Our study is unique in that previous studies on

Number 2

rectus muscle transfers have centered on a pediatric population with cerebral palsy. In addition, we include vastus muscle lengthening at the time of rectus muscle transfer, which, to our knowledge, has not been previously described. The purpose of this study was to determine whether rectus muscle transfer combined with vastus muscle lengthening improves ambulation.

Method Study Sample Patients who underwent rectus femoris muscle transfer and fractional lengthening of the vastus muscles for stiff knee gait related to stroke or a TBI from January 2003 to August 2008 were included in the study. All procedures were conducted by the senior surgeon (M.A.K.). Patients with cerebral palsy, spinal cord injury, heterotopic ossification, an equinovarus foot deformity, or less than 6 months of follow-up were excluded. Patients who were nonambulatory and those who had multiple simultaneous surgical procedures also were excluded. Stiff knee gait was defined as delayed and decreased peak knee flexion in the swing phase and diminished knee range of motion throughout the gait cycle. Dynamic EMG was used preoperatively to assist in selecting surgical candidates. Continuous or prolonged activity of the rectus femoris muscle during the swing phase was considered a positive result. Patients with a positive dynamic EMG test, a positive Duncan-Ely test, and reduced knee flexion during the swing phase were considered surgical candidates. All methods were approved by the Institutional Review Board (IRB) of the Hospital of the University of Pennsylvania. This study was found to be IRB exempt, and a waiver of consent was obtained. Surgical Technique Each patient was positioned supine on an operating table, and a pneuFebruary 2010

A Treatment for Adults With Stiff Knee Gait

Figure 1. Surgical technique showing first incision. The patient is positioned supine on the operating table, and a pneumatic tourniquet is applied. A longitudinal incision measuring approximately 10 cm is made on the distal anterior thigh over the distal rectus femoris (Fig. 1A). The distal quadriceps muscle is exposed medially and laterally. The rectus femoris muscle is carefully dissected free from the other quadriceps muscles distally to the level of the mid-patella (Fig. 1B). A locking stitch (Krakow) of heavy, nonabsorbable suture is placed in the distal free end of rectus tendon. The rectus muscle is freed proximally to isolate it from the vastus muscles (Fig. 1C). The vastus intermedius muscle is identified beneath the reflected rectus femoris muscle. The tendon fibers over its muscle belly are incised. The myotendinous junctions on the undersurface of the vastus medialis and vastus lateralis muscles then are dissected. These muscles are lengthened by transecting the tendon fibers over the muscle belly (Fig. 1D). Photographs may not be used or reproduced without written permission from Dr Keenan.

matic tourniquet was applied. A longitudinal incision measuring approximately 10 cm was made on the distal anterior thigh over the distal rectus femoris muscle (Fig. 1A). The distal quadriceps muscle was exposed medially and laterally. The rectus femoris muscle was carefully dissected free from the other quadriceps muscles distally to the level of the mid patella (Fig. 1B). A locking stitch (Krakow) of heavy, nonabsorbable suture was placed in the distal free February 2010

end of the rectus tendon. The rectus muscle was freed proximally to isolate it from the vastus muscles (Fig. 1C). The vastus intermedius muscle was identified beneath the reflected rectus femoris muscle. The tendon fibers over its muscle belly were incised. The myotendinous junctions on the undersurface of the vastus medialis and vastus lateralis muscles then were dissected. These muscles

were lengthened by transecting the tendon fibers over the muscle belly (Fig. 1D). A second incision was made on the posteromedial distal thigh (Fig. 2A). The gracilis muscle and tendon were identified and isolated. The gracilis tendon was released proximally from the muscle belly, but was left attached distally (Fig. 2B). A subcutaneous tunnel was created between the anterior and medial thigh inci-

Volume 90

Number 2

Physical Therapy f

263

A Treatment for Adults With Stiff Knee Gait

Figure 2. Surgical technique showing second incision. A second incision is made on the posteromedial distal thigh (Fig. 2A). The gracilis muscle and tendon are identified and isolated. The gracilis tendon is released proximally from the muscle belly, but is left attached distally (Fig. 2B). A subcutaneous tunnel is created between the anterior and medial thigh incisions. The medial intramuscular septum is sharply divided for a distance of approximately 5 cm (Fig. 2C). The distal end of the rectus femoris tendon is passed subcutaneously through the tunnel to the posteromedial thigh incision. The rectus femoris and gracilis tendons are interwoven using a Pulvertaft technique and secured with multiple sutures of heavy, nonabsorbable suture (Fig. 2D). Photographs may not be used or reproduced without written permission from Dr Keenan.

sions. The medial intramuscular septum was sharply divided for a distance of approximately 5 cm (Fig. 2C). The distal end of the rectus femoris tendon was passed subcutaneously through the tunnel to the posteromedial thigh incision. The rectus femoris and gracilis tendons were interwoven using a Pulvertaft technique and secured with multiple sutures of heavy, nonabsorbable suture (Fig. 2D). The tourniquet was released, and the incisions were irrigated. The wounds were closed in routine fashion. 264

f

Physical Therapy

Volume 90

Postoperative Protocol A knee immobilizer was used postoperatively to allow healing of the tendon transfer. A continuous passive motion machine was used on the first day postoperatively, and physical therapy was instituted for gait training and knee passive range of motion exercises. Over the course of 1 to 2 weeks, patients were allowed to start full weight-bearing ambulation training, and they were weaned off of the knee immobilizer. Gait training emphasized hip and knee flexion during the swing phase.

Number 2

Marching exercises are helpful and were encouraged. Active knee extension exercises without ankle weights were started immediately to strengthen the quadriceps muscle. Hip flexor strengthening was started immediately after surgery. Electrical stimulation of the hip flexor or knee extensor muscles may be used 3 weeks after surgery if deemed appropriate by the treating therapist, physiatrist, or neurologist. Biofeedback techniques may be used immediately after surgery.

February 2010

A Treatment for Adults With Stiff Knee Gait Table.

Data Acquisition/Analysis Patient charts, including preoperative and postoperative office notes and surgical reports, were reviewed and data parameters were obtained. All patients were evaluated before and after surgery using a standard, detailed format. Data parameters included age, sex, duration from TBI or stroke to surgery, assistive device use, and ambulatory status using the Viosca score.8 The Viosca score represented the primary outcome measure in this study. The Viosca score is a validated instrument used to evaluate functional ambulatory capacity, which places patients into 1 of 6 categories: (0) unable to ambulate; (1) nonfunctional ambulation, (2) household ambulation, (3) neighborhood ambulation, (4) independent community ambulation, and (5) normal ambulation. The Viosca score was calculated retrospectively, based on descriptions of patients’ ambulatory status from detailed histories. As noted by Viosca et al,8 a patient is assigned to one of the functional walking levels after his or her gait is examined, and certain data are obtained by questioning the patient. These important data parameters include the degree of walking independence, agility, and safety, as well as information obtained directly from both patients and their relatives. Special attention is given to the patient’s ability to deal with different surroundings.8 Viosca et al8 reported good interrater reliability of this instrument.

ation of ambulation. During the swing phase, knee flexion and knee range of motion were recorded as kinematic variables. Postoperative complications also were recorded. Statistical analysis was conducted using the Student t test for independent samples to compare preoperative and postoperative swing-phase knee flexion and Viosca functional ambulation level.

Secondary outcome measures included patient satisfaction, physical examination parameters, and observational gait analysis. Patient satisfaction was specifically addressed at each office visit by the senior author (M.A.K.) and was specifically documented in each office note. All physical examinations were conducted by the senior author. Gait analysis was a clinical measure by the senior author based on observational evalu-

The average Viosca score was 3.1 preoperatively and 3.5 postoperatively. This improvement was statistically significant (P⫽.001). Thirteen patients experienced an improvement in Viosca score, and 24 patients experienced no change in Viosca score. No patients had a decrease in Viosca score. Twenty-one patients used an orthosis preoperatively, which decreased to 13 patients at the time of latest follow-up. Simi-

February 2010

Patient Characteristics Characteristics

Values

N (study sample)

37

Male

19

Female

18

Mean age (range)

51 y (24–76)

No. poststroke

29

No. post-traumatic brain injurya

8

Mean duration between stroke/traumatic brain injury and surgery (range)

8 y (1.75–14.0)

Mean follow-up (range)

10 mo (6–31)

Results Thirty-seven patients (19 men and 18 women) underwent rectus muscle transfer with vastus muscle lengthening during the study period. Their average age was 51 years at the time of surgery. Twenty-nine patients had a previous stroke, and 8 patients had a TBI. There was an average delay of 8 years between the stroke or traumatic injury and date of surgery. The mean follow-up was 10 months. Patient characteristics are shown in the Table.

larly, 20 patients used an assistive device for ambulation preoperatively, which decreased to 15 patients at most recent follow-up. Thirty-six (97%) of the 37 patients were satisfied with their clinical and functional results. The patient who was dissatisfied had recurrence of deformity to near preoperative levels. Of the 21 patients with documented swing-phase knee range of motion, the average knee flexion during swing increased from 8 degrees (range⫽0°–15°) preoperatively to 33 degrees (range⫽20°– 50°) at the most recent follow-up (P⫽.12). Clinical gait analysis revealed an improvement in compensatory foot clearance strategies during gait, such as reduced hip hiking, vaulting, and circumduction; however, these variables were not consistently documented and were not formally evaluated. There were no reported deleterious effects on knee flexion during the stance phase, as no patients had knee instability or buckling during the stance phase. There were 4 complications (1 deep venous thrombosis, 1 superficial wound infection, and 2 hematomas).

Discussion To our knowledge, this was the first study to evaluate the effects of distal rectus muscle transfer in an adult population with stroke or TBI and

Volume 90

Number 2

Physical Therapy f

265

A Treatment for Adults With Stiff Knee Gait stiff knee gait. Simultaneous fractional lengthening of the quadriceps muscle also had not been described previously. Our results suggest that the combination of distal rectus muscle transfer with fractional lengthening of the vastus muscles improves knee flexion during the swing phase while not compromising knee stability during the stance phase. More importantly, patients reported excellent satisfaction postoperatively and an increased Viosca functional ambulation level. These outcomes may lead to fewer falls in this at-risk patient population. We believe that this procedure improves knee extension in stance while augmenting knee flexion in swing for foot clearance. Although a Viosca score increase from 3.1 to 3.5 may appear to lack clinical relevance, we believe that it demonstrates a promising trend toward improved ambulation after surgery. There is no minimal clinically important difference reported for the Viosca score; however, a linear correlation has been found between Viosca score and walking speed.8 Unfortunately, the Viosca score may not be a sensitive enough instrument to adequately assess walking efficiency, which is one of the primary goals of rectus muscle transfer surgery. In order to offer appropriate surgical intervention for stiff knee gait, a clear understanding of knee kinetics and kinematics is essential. Although the knee has many functions during the gait cycle, its primary roles are to provide foot clearance during the swing phase and shock absorption during the loading response. The knee normally begins the stance phase in near full extension, flexes approximately 15 degrees during loading, and then gradually extends toward terminal stance. The vastus muscles counteract the ground reaction flexion moment during the loading response. After mid stance, knee extension is maintained by a plantar 266

f

Physical Therapy

Volume 90

flexion and knee extension couple under control of the triceps surae muscle.1 The knee rapidly flexes approximately 40 degrees at pre-swing, which increases to approximately 60 degrees at initial swing. The rectus femoris muscle is active in pre-swing and initial swing and again from terminal swing to mid stance.4 It functions to accelerate the thigh and lower leg while restraining excessive knee flexion.1 Swing-phase sagittal knee motion is mostly passive at a normal walking cadence but requires increasing muscle activity as cadence increases. In stiff knee gait, the rectus femoris muscle has prolonged activity during the swing phase, thus disturbing the precise balance of knee motor activity. In addition to abnormal activity during the swing phase, Goldberg et al9 found that many patients with stiff knee gait have abnormally large knee extension moments during double support, which correlated with low knee flexion velocity at toe-off. A subsequent study by Reinbolt et al10 confirmed that rectus femoris muscle activity during pre-swing is equally as important as early swing activity and may be more responsible for the loss of knee motion during the swing phase in some patients. The evolution of surgical management for stiff knee gait started with proximal release of the rectus femoris muscle.11,12 In a series of 8 patients with cerebral palsy and spastic gait, Sutherland et al5 found that proximal tenotomy of the rectus femoris muscle improved gait in patients with enough rectus muscle spasticity (reflex activity) to interfere with initiation of the swing phase and in patients with decreased knee flexion. However, they did not find any benefits from proximal tenotomy for improving hip or pelvic biomechanics. Perry7 proposed that a rectus muscle transfer would pro-

Number 2

vide preservation of hip flexion while increasing knee flexion during swing. Rectus femoris muscle transfer has a greater influence on increasing knee flexion and improving postoperative knee range of motion compared with rectus femoris muscle release.13 Gage et al1 proposed transferring the rectus femoris muscle to the sartorius muscle in children with cerebral palsy and inadequate knee flexion during swing and an internal foot progression angle in stance. Similarly, they proposed rectus femoris muscle transfer to the posterior iliotibial band if there is inadequate knee flexion during swing and an external foot progression angle. Although they did not find a significant change in foot progression angle postoperatively, they observed an improvement in knee flexion during the swing phase in both groups. They concluded that the sartorius muscle is not an ideal recipient for the transfer because it inserts anteriorly on the tibia and thus has less rotation force. They also found the sartorius muscle to be of poor structural integrity, which may lead to transfer failure.1 Gage14 later emphasized the importance of a posterior transfer to better augment knee flexion and described transferring the distal rectus femoris muscle to either the semitendinosus muscle or the posterior iliotibial band. Other authors have demonstrated similar success of the distal rectus femoris muscle transfer in children with cerebral palsy and a stiff knee gait. It is capable of developing a knee flexion moment during swing while preserving rectus femoris muscle activity at the hip, which may further contribute to knee flexion via dynamic coupling of a flexion moment about the hip. Sutherland et al15 reported that knee flexion during the swing phase increased an average of 16 degrees after transfer of February 2010

A Treatment for Adults With Stiff Knee Gait the rectus femoris muscle. Transferring the distal rectus femoris muscle also prevents it from reattaching to the patella, which has been observed in rectus femoris muscle release. Ounpuu et al16 investigated the effect of transfer location site (sartorius, gracilis, semitendinosus, iliotibial band) on the outcome of the rectus femoris muscle transfer surgery and found no statistically significant difference among transfer sites in postoperative knee range of motion. Similar to previous studies, we found the sartorius muscle to have inferior anatomic and biomechanical characteristics. The thin overlying fascia of the sartorius muscle often is too fragile to accept a transfer. In comparison, the gracilis tendon is stout and can withstand adequate tensioning. The gracilis tendon also is more posterior to the knee axis, thus providing an improved mechanical advantage for knee flexion. Fractional lengthening often is done concurrently by the senior author, which has shown promising early results. Knee flexion depends on sufficient extensibility of the antagonists. Fractional lengthening of the quadriceps muscle improves knee flexibility without compromising its strength. Weakening of the quadriceps muscle also is a potential risk associated with a distal rectus muscle transfer because the rectus muscle comprises approximately 12% of the total quadriceps muscle mass.15 The combination of quadriceps muscle fractional lengthening with distal rectus muscle transfer theoretically may cause increased knee flexion during the stance phase, especially at weight acceptance, leading to either knee instability or crouch gait. However, this has not been observed in any of our patients. No patients reported knee instability or buckling postoperatively, which was confirmed with postoperative clinical February 2010

gait analysis. It is likely that the vastus muscles retain sufficient strength to compensate for the loss of rectus muscle activity. Limitations of the study include a retrospective study design without a control group. However, given that the patients had surgery at a mean of 8 years after TBI or stroke and that all patients had unsuccessful outcomes following nonsurgical interventions, we do not believe that these limitations detract considerably from our conclusions. Unfortunately, we were unable to determine adherence to physical therapy protocols postoperatively and could not separately analyze the interplay between surgery and therapy. However, we believe them to be intimately involved in providing a successful outcome. Additional weaknesses include the difficulty in conducting outcome evaluation in gait-related problems in patients with stiff knee gait. For example, we have noticed improved limb advancement and decreased foot dragging during follow-up gait analyses, but these variables are difficult to record and compare without expensive technology. We have noticed gait to be smoother as a result of the improved knee flexion, which has correlated positively with patient satisfaction levels. However, dynamic gains are difficult to measure and may not be necessary to reach a conclusion about the efficacy of the procedure. This was a retrospective review of patient data documented by the surgeon in an unblinded manner and thus is prone to both internal bias and confounding. Despite these limitations, we believe this study provides insight into the potential benefits of this type of surgery for stiff knee gait after TBI or stroke and develops a foundation for future, more rigorous studies. Future directions include determining whether dynamic EMG results correlate with our clinical findings.

As Perry7 demonstrated that the rectus femoris muscle has inconsistent action among patients with stiff knee gait, we agree that dynamic EMG testing is needed prior to surgery. However, we cannot justify the utility of routine postoperative dynamic EMG testing. Ongoing evaluation also is needed to determine whether any differences in muscle activity exist among patients with stiff knee gait due to stroke and TBI.

Conclusions We have found that adults with stroke or TBI who develop stiff knee gait due abnormal rectus femoris muscle activity benefit from a distal rectus femoris muscle transfer with fractional lengthening of the vastus muscles. The results closely resemble outcomes following distal rectus muscle transfer for treating stiff knee gait in children with cerebral palsy. The gracilis tendon offers many anatomic and biomechanic advantages and is our preferred recipient tendon for the transfer. Simultaneous fractional lengthening of the quadriceps muscle offers improved knee flexibility without compromising knee stability. All authors provided concept/idea/research design and data collection and analysis. Dr Namdari, Dr Pill, and Dr Keenan provided writing and consultation (including review of manuscript before submission). Dr Keenan provided project management, patients, and facilities/equipment. This article was received May 8, 2009, and was accepted October 12, 2009. DOI: 10.2522/ptj.20090151

References 1 Gage JR, Perry J, Hicks RR, et al. Rectus femoris transfer to improve knee function of children with cerebral palsy. Dev Med Child Neurol. 1987;29:159 –166. 2 Chambers H, Lauer A, Kaufman K, et al. Prediction of outcome after rectus femoris surgery in cerebral palsy: the role of cocontraction of the rectus femoris and vastus lateralis. J Pediatr Orthop. 1998;18: 703–711.

Volume 90

Number 2

Physical Therapy f

267

A Treatment for Adults With Stiff Knee Gait 3 Waters RL, Garland DE, Perry J, et al. Stifflegged gait in hemiplegia: surgical correction. J Bone Joint Surg Am. 1979;61:927– 933. 4 Perry J. Normal and pathologic gait. In: Atlas of Orthotics: Biomechanical Principles and Application. St Louis, MO: CV Mosby Co; 1985:223–235. 5 Sutherland DH, Larsen LJ, Mann R. Rectus femoris release in selected patients with cerebral palsy: a preliminary report. Dev Med Child Neurol. 1975;17:26 –34. 6 Baumann JU, Ruetsch H, Schurmann K. Distal hamstring lengthening in cerebral palsy: an evaluation by gait analysis. Int Orthop. 1980;3:305–309. 7 Perry J. Distal rectus femoris transfer. Dev Med Child Neurol. 1987;29:153–158. 8 Viosca E, Martinez JL, Almagro PL, et al. Proposal and validation of a new functional ambulation classification scale for clinical use. Arch Phys Med Rehabil. 2005; 86:1234 –1238.

268

f

Physical Therapy

Volume 90

9 Goldberg SR, Ounpuu S, Arnold AS, et al. Kinematic and kinetic factors that correlate with improved knee flexion following treatment for stiff-knee gait. J Biomech. 2006;39:689 – 698. 10 Reinbolt JA, Fox MD, Arnold AS, et al. Importance of preswing rectus femoris activity in stiff-knee gait. J Biomech. 2008;41: 2362–2369. 11 Duncan W. Release of the rectus femoris in spastic paralysis. In: Proceedings of the American Academy of Orthopaedic Surgery. J Bone Joint Surg Am. 1955;37:634. 12 Cotrell G. Role of RF in spastic children. In: Proceedings of the American Academy for Cerebral Palsy. J Bone Joint Surg. 1963; 45:1556. 13 Ounpuu S, Muik E, Davis RB III, et al. Rectus femoris surgery in children with cerebral palsy, part II: a comparison between the effect of transfer and release of the distal rectus femoris on knee motion. J Pediatr Orthop. 1993;13:331–335.

Number 2

14 Gage JR. Surgical treatment of knee dysfunction in cerebral palsy. Clin Orthop Relat Res. 1990;253:45–54. 15 Sutherland DH, Santi M, Abel MF. Treatment of stiff-knee gait in cerebral palsy: a comparison by gait analysis of distal rectus femoris transfer versus proximal rectus release. J Pediatr Orthop. 1990;10:433– 441. 16 Ounpuu S, Muik E, Davis RB III, et al. Rectus femoris surgery in children with cerebral palsy, part I: the effect of rectus femoris transfer location on knee motion. J Pediatr Orthop. 1993;13:325–330.

February 2010

Perry Issue: Gait Rehab Can Strength Training Predictably Improve Gait Kinematics? A Pilot Study on the Effects of Hip and Knee Extensor Strengthening on LowerExtremity Alignment in Cerebral Palsy Diane L. Damiano, Allison S. Arnold, Katherine M. Steele, Scott L. Delp

Background. Computer simulations have demonstrated that excessive hip and knee flexion during gait, as frequently seen in ambulatory children with cerebral palsy (CP), can reduce the ability of muscles to provide antigravity support and increase the tendency of hip muscles to internally rotate the thigh. These findings suggest that therapies for improving upright posture during gait also may reduce excessive internal rotation. Objective. The goal of this study was to determine whether strength training can diminish the degree of crouched, internally rotated gait in children with spastic diplegic CP.

Design. This was a pilot prospective clinical trial. Methods. Eight children with CP participated in an 8-week progressive resistance exercise program, with 3-dimensional gait analysis and isokinetic testing performed before and after the program. Secondary measures included passive range of motion, the Ashworth Scale, and the PedsQL CP Module. To identify factors that may have influenced outcome, individual and subgroup data were examined for patterns of change within and across variables.

Results. Strength (force-generating capacity) increased significantly in the left hip extensors, with smaller, nonsignificant mean increases in the other 3 extensor muscle groups, yet kinematic and functional outcomes were inconsistent. The first reported subject-specific computer simulations of crouch gait were created for one child who showed substantial benefit to examine the factors that may have contributed to this outcome.

Limitations. The sample was small, with wide variability in outcomes. Conclusions. Strength training may improve walking function and alignment in

D.L. Damiano, PT, PhD, is Chief, Functional and Applied Biomechanics Section, Rehabilitation Medicine Department, NIH Clinical Center, Bldg 10, Room 1–1469, Bethesda, MD 20892 (USA). Address all correspondence to Dr Damiano at: damianod@ cc.nih.gov. A.S. Arnold, PhD, is Research Associate, Concord Field Station, Harvard University, Bedford, Massachusetts. K.M. Steele, MS, is a doctoral student in the Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, California. S.L. Delp, PhD, is Professor of Bioengineering and Mechanical Engineering, Stanford University Schools of Engineering and Medicine. [Damiano DL, Arnold AS, Steele KM, Delp SL. Can strength training predictably improve gait kinematics? A pilot study on the effects of hip and knee extensor strengthening on lower-extremity alignment in cerebral palsy. Phys Ther. 2010;90:269 –279.] © 2010 American Physical Therapy Association

some patients for whom weakness is a major contributor to their gait deficits. However, in other patients, it may produce no change or even undesired outcomes. Given the variability of outcomes in this and other strengthening studies in CP, analytical approaches to determine the sources of variability are needed to better identify those individuals who are most likely to benefit from strengthening.

Post a Rapid Response or find The Bottom Line: www.ptjournal.org February 2010

Volume 90

Number 2

Physical Therapy f

269

Strength Training and Gait Kinematics

T

he long-term goal of this research collaboration is to determine whether targeted strengthening of the hip and knee extensors, alone or in combination with other treatments, can effectively diminish the excessive knee flexion, hip flexion, and hip internal rotation commonly observed in children with spastic diplegia who walk with a crouched gait pattern. Cerebral palsy (CP) is the most prevalent physical disability originating in childhood, with the largest proportion of this patient population having spastic diplegia,1 characterized by involvement primarily in the lower extremities. Nearly all children with spastic diplegia will ambulate, although often at a later age and with greater hip and knee flexion than children without neuromotor disabilities.2 Much therapeutic effort is directed at promoting and maintaining upright ambulation in these chil-

Available With This Article at ptjournal.apta.org • eTable 1: Participants Ordered by Decreasing Change in Knee Extensor Torque • eTable 2: Mean Results and P Values for Selected Gait Measures Before and After Strengthening • eTable 3: Mean Changes in PedsQL Cerebral Palsy Module Child Questionnaire Scores Before and After Strengthening • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

270

f

Physical Therapy

Volume 90

dren, including regular physical therapy and bracing, as well as more invasive methods of spasticity reduction (eg, intramuscular injections, orthopedic surgery). Unfortunately, the outcomes of strength training and of many of the other interventions for improving walking in these individuals remain inconsistent.

tributing to excessive hip internal rotation.9 For example, the capacity of the gluteus maximus muscle to generate external rotation of the hip is compromised when the hip is flexed. This finding suggests that improvement of excessive hip flexion in people with crouch gait might improve hip rotation.

There are several reasons to hypothesize that strengthening the hip and knee extensors might improve the gait kinematics of children with spastic diplegia. Perry’s pioneering work demonstrated electromyographic (EMG) activity of the hip and knee extensors during the early stance phase of normal gait,3 and computerbased simulations of walking dynamics have revealed that the gluteus maximus and vastus muscles play an important role in early stance by supporting body weight and controlling hip and knee extension.4 (For a review of principles associated with human locomotion, refer to Kuo and Donelan in this issue.5) Simulations analyzing the roles of these muscles during crouch gait have revealed that the capacity of gluteus maximus and other muscles to extend the hip and knee may be diminished substantially by hip and knee flexion.6 The presence of weakness in children with spastic diplegia is now well documented, with even the most functional individuals demonstrating substantial generalized muscle weakness.7,8 Together, these observations suggest that the crouched postures of some children with CP may be exacerbated by lowerextremity extensor weakness. Musculoskeletal modeling and anatomical studies also have shown that excessive hip flexion alters the balance of the muscles that rotate the hip. With excessive hip flexion, the moment arms (ie, the lever arms or mechanical advantage) of some hip external rotators are diminished and the moment arms of hip internal rotators are increased, potentially con-

Despite this rationale, the potential for strength training to improve the gait mechanics of people with spastic diplegia remains unclear. Strength training was briefly a part of early physical therapy management of CP and has experienced a resurgence over the past 10 to 15 years. This resurgence was preceded by decades during which resistance training was contraindicated for people with CP because of clinical concerns that it would exacerbate spasticity. Some studies have demonstrated that short-term progressive resistance exercise programs can safely increase strength (force-generating capacity) in targeted muscles in people with CP without increasing spasticity.10,11 Positive effects on gait and gross motor function also have been reported,12 although less consistently than gains in strength.13 A recent meta-analysis, however, failed to provide evidence that strength training is effective in improving strength, increasing gait speed, or producing a clinically meaningful change in gross motor function in people with CP.14 The potential for strength training to improve the gait kinematics of people with CP has been evaluated in only a few studies, and findings have been equivocal. Most of these studies averaged data over a small number of subjects (ie, 11–21 subjects) and revealed only modest mean changes in the subjects’ joint angles during walking.15–19

Number 2

The variability in outcomes reported in previous studies may be due to a range of factors, including methodological factors that may have limFebruary 2010

Strength Training and Gait Kinematics ited the effectiveness of the training (eg, insufficient muscle loads, insufficient training durations) or, in some cases, impairments other than weakness that may have limited the subjects’ functional gains following strengthening (eg, difficulties with balance). We believe new approaches are needed to rigorously evaluate the biomechanical effects of resistance exercise programs, to help explain the reasons for inconsistent outcomes across subjects, and to identify individuals who are most likely to benefit from strengthening. This article presents the results from a pilot strength training study in which we used a combination of physical examination, gait analysis, and state-of-the-art computer simulation to evaluate the effects of an 8-week progressive resistance exercise program on participants’ hip and knee angles (kinematics) during walking. We hypothesized that strengthening the hip and knee extensor muscles would decrease the exaggerated hip and knee flexion and hip internal rotation of children with spastic diplegia during the stance phase of gait. Secondary hypotheses were that strengthening would improve temporal spatial gait parameters, such as walking speed and stride length, and lead to improved physical functioning and related quality of life. Our main objective was to assess whether strength training could improve lowerextremity alignment during walking and, if so, use simulation to provide biomechanical insights into the reasons for these improvements.

Method Participants The goal of this pilot study was to enroll 10 children with spastic diplegia, based on a power analysis using data from 2 previous studies,15,17 within the age range of 5 to 17 years and diagnosed at Gross Motor FuncFebruary 2010

tional Classification System (GMFCS) levels I to III. The children had to be more than 1 year postsurgery and 6 months post–botulinum toxin injections in the lower extremity. Additional inclusion criteria, based on physical examination, included bilateral passive hip extension to neutral with the other hip flexed to 90 degrees, passive knee extension within 5 degrees of full extension while positioned supine, and passive hip external rotation of at least 20 degrees as assessed in a prone position with hip extended and knees flexed to 90 degrees. These criteria were chosen in an effort to exclude children whose gait deviations were predominantly constrained by musculoskeletal contracture. Previous orthopedic surgery or neurosurgery were not considered a reason for exclusion, with the exception of previous rotational osteotomies. Children were screened visually to determine whether they exhibited a crouched internal rotation gait pattern, and these kinematic criteria were confirmed by gait analysis. In particular, children had to satisfy the following requirements bilaterally prior to participating in the strengthening portion of the study: (1) hip and knee flexion greater than 1 standard deviation above the mean normative value at initial contact; (2) excessive adduction and internal rotation of the hip at mid stance; and (3) less than 20 degrees of ankle dorsiflexion at mid stance, because excessive weakness of the plantar flexors or overlengthening of the calf muscles alone may contribute to crouch gait. Participants were recruited from the neurology and neurosurgery cerebral palsy clinics and the physical therapy clinic at St. Louis Children’s Hospital associated with Washington University. Informed consent and pediatric assent were obtained from all

participants prior to the initial assessment. Eight children (3 male, 5 female) met the inclusion criteria and completed the 8-week strengthening program during the study period (eTab. 1 available at ptjournal.apta.org). The children ranged in age from 5.5 to 13.4 years. Five children were classified at GMFCS level III and used an assistive device to walk, 2 children were classified at GMFCS level II, and 1 child was classified at GMFCS level I. Strength Training Program Each child participated one-on-one with a therapist in a communitybased physical therapy program, attending three 1-hour sessions per week for 8 weeks. Each therapistguided session consisted of a progressive resistance exercise program using free weights or weight machines that targeted the gluteus maximus and quadriceps muscles bilaterally, with at least 1 day of rest between sessions (examples shown in Fig. 1). The program used a combination of open- and closed-chain exercises for maximum transfer to both the stance and swing phases of gait, and these exercises were designed to work the muscles in the most extended portion of the range. Examples include the use of cuff weights attached to the distal thigh during prone hip extension exercises with the knee kept flexed throughout the motion, the use of a resisted leg press, and the use of a weight machine that resisted knee extension in a reclined sitting position. The intensity and difficulty of the program were adjusted individually. The amount of resistance applied was based on the number of repetitions each child could perform before fatiguing; the target was 8 to 10 repetitions, with the total number of repetitions consistent across participants (30 repetitions per muscle

Volume 90

Number 2

Physical Therapy f

271

Strength Training and Gait Kinematics

Figure 1. Two examples of hip extensor strengthening exercises performed during training using weight machines. Other exercises also were performed (see text).

group). Passive stretching exercises at the hip and knee and a 5-minute walk at a relaxed pace on a treadmill or indoor track (depending on the child’s ability and preference) were performed for warm-up before and cool-down after the strengthening exercises. Functional Assessment Each child received an assessment consisting of the following: (1) a physical examination; (2) a 3dimensional (3-D) gait analysis; (3) an isokinetic strength assessment, as measured by maximum hip and knee extensor concentric torque at 30°/s; and (4) a validated self-report measure of physical functioning and related quality of life, as quantified by the parent-proxy version of the PedsQL 3.0 Cerebral Palsy Module.20 The exercise program was started within a week after the initial assessment, and an identical assessment was conducted within a week of completing the program. The physical examination was performed to assess range of motion at the hip, knee, and ankle (to ensure that the child met the inclusion and exclusion criteria) and to assess spasticity in the hamstring and quadriceps muscles. Spasticity was measured using the Ashworth Scale (ranging from 0 to 4, with a score of 1 indi272

f

Physical Therapy

Volume 90

cating normal tone [resistance to passive stretch] and a score of 0 indicating less-than-normal tone). The 3-D gait analysis was performed to determine the child’s lowerextremity joint angles during walking, with data from 5 trials collected at both freely selected and “as fast as possible without running” speeds. Fifteen retroreflective markers were placed on the skin overlying specific anatomic locations on the pelvis and bilateral lower extremities. The 3-D locations of these markers were tracked using an 8-camera Vicon system,* and the data were processed using Plug-In-Gait.† The isokinetic strength assessment enabled comparison of each child’s isokinetic peak torque before and after strengthening. The peak torque data were expressed as the actual value and also were divided by body weight and multiplied by 100 to facilitate comparisons across children of varying sizes. Paired t tests (P⬍.05) were used to evaluate changes in selected assessment measures for the children before and after strengthening. Due to the small sample and the

* Vicon, 7388 S Revere Pkwy, Suite 901, Centennial, CO 80112. † Oxford Metrics, 14 Minns Business Park, West Way, Oxford OX2 0JB, United Kingdom.

Number 2

pilot nature of this study, no correction for multiple tests was applied. Subject-Specific Simulations of Crouch Gait Additional data were collected on children who were independent ambulators with the aim of creating subject-specific computer simulations of each child’s crouched gait. To enable dynamic simulations, these data must include “clean” consecutive forceplate strikes during the gait analysis. These data were available, from both prestrengthening and poststrengthening training assessments, for one individual in this sample. In this case, the gait assessments included full-body 3-D kinematics, ground reaction forces and moments from forceplates, and surface EMG recordings from the tibialis anterior, gastrocnemius, rectus femoris, medial hamstring, adductor longus, vastus medialis, gluteus medius, and gluteus maximus muscles. Computer simulations of the stance phase were generated that reproduced this child’s 3-D gait kinematics and kinetics before and after strengthening. This process provided one of the first subject-specific simulations of an individual with a crouched, internal rotation gait pattern and is the first such model to evaluate changes in the February 2010

Strength Training and Gait Kinematics actions of muscles as a result of strength training. To generate the simulations, we first created a computer model of the child that included 3-D representations of the bones, joints, and muscles scaled to the child’s anthropometric dimensions. Next, we used OpenSim biomechanics software21 to estimate the muscle activation patterns and muscle forces that, when applied to the model, produced joint angles and ground reaction forces that corresponded closely to the experimental measurements of the child’s gait kinematics and kinetics. This software uses an optimization algorithm, called Computed Muscle Control,22 to determine the excitation patterns for each of the 92 muscle compartments in the model. We used the child’s measured EMG data to verify that the simulated muscle excitations reflected the child’s activation patterns. We generated 2 simulations: one that reproduced the child’s gait dynamics before strengthening and another that reproduced the gait dynamics after strengthening. Lastly, we analyzed the simulations to identify factors that enabled this individual to walk more upright after strengthening. In particular, we performed a perturbation analysis4 to assess the capacity of the gluteus maximus, vastus, and other muscles to support the body and extend the hip and knee during the stance phase. This analysis makes small adjustments (ie, perturbations) to the force in each muscle and measures the resulting changes in the hip and knee angles to determine the role that each muscle plays in extending or flexing the joints throughout the movement.

February 2010

Figure 2. Change in isokinetic peak torque divided by body weight and multiplied by 100 for each participant for the (A) right and left hip extensors and (B) right and left knee extensors. A positive change indicates an increase in strength. (C) Change in minimum knee flexion angle during stance. A positive change indicates an increase in knee flexion (ie, greater crouch).

Volume 90

Number 2

Physical Therapy f

273

Strength Training and Gait Kinematics Table. Mean (SD) Before and After Strengthening, Mean Difference and Standard Error (SE) of the Difference, 95% Confidence Interval (CI) of the Difference, Percent Change, and P Values for Isokinetic Strength Tests (N⫽8)a Before Strengthening Mean (SD)

After Strengthening Mean (SD)

Mean Difference (SE)

Right hip extensors

12.7 (6.9)

17.2 (7.0)

4.5 (2.8)

Left hip extensors

10.7 (4.9)

19.2 (8.6)

8.5 (2.5)

Right knee extensors

13.9 (5.5)

16.4 (7.0)

2.6 (1.2)

⫺0.20 to 5.34

Left knee extensors

13.1 (6.3)

16.8 (9.3)

3.7 (1.6)

⫺0.22 to 7.56

95% CI of the Difference

Percent Change

P

35.4

.16

79.4

.01

18.0

.06

28.2

.06

Peak concentric torque at 30°/s (ft-lb)

a

⫺2.21 to 11.0 2.70 to 14.4

Values significant at P⬍.05 shown in bold.

Results Effects of Strength Training on Peak Isokinetic Torque Most of the participants’ hip and knee extensor muscles were stronger after training, as measured by the change in isokinetic peak torque. The mean percentage gains in strength (ie, change in peak torque divided by initial peak torque value) ranged from 18.0% for the right knee extensors to 79.4% for the left hip extensors (Table); however, the percentage gains varied widely across participants. The magnitude and consistency of the strength changes generally were greater at the hip than at the knee (eTab. 2 available at ptjournal.apta.org). Four children achieved appreciable increases in peak torque in at least 3 of the 4 muscle groups (Fig. 2). The other 4

children responded less dramatically to the strengthening program, showing either a negligible change or a small decrease in peak torque in one or both limbs and joints. Effects of Strength Training on Gait Kinematics Some, but not all, of the children walked with improved hip and knee extension during stance following the exercise program (Fig. 2). The changes in hip extension generally were correlated to the changes in knee extension (r⬎.75, P⬍.05). Several of the children walked with improved hip rotation; however, we did not detect a correlation between the changes in hip rotation and the changes in either hip or knee extension.

Figure 3. Scatterplot of change in minimum knee flexion angle (average for right and left legs) against (A) Ashworth Scale score and (B) change in knee extensor strength. Positive correlation between change in minimum knee flexion angle during stance and Ashworth Scale score of the hamstring muscles. Note that the Ashworth Scale score was not collected for one participant.

274

f

Physical Therapy

Volume 90

Number 2

The 2 children who achieved the greatest gains in knee extensor strength (participant 1, classified at GMFCS level I, and participant 2, classified at GMFCS level III) also showed the greatest improvements in knee extension during the stance phase. However, for the other 6 children, knee flexion appeared to worsen with increasing strength gains, so no general conclusions could be made (Fig. 3). Effects of Strength Training on Gait Temporal-Spatial Parameters Gait speed, stride length, and cadence were not significantly changed, on average, in either the self-selected or fast speed conditions following the strengthening program (eTab. 2 available at ptjournal. apta.org). However, changes in all 3 measures varied across subjects; for example, self-selected walking speed improved 18% for participant 7, but diminished 25% for participant 5. No significant correlations were found between change in strength and change in these temporal-spatial parameters. Effects of Strength Training on Perceived Physical Function and Related Quality of Life A secondary outcome measure was the parent-proxy version of the PedsQL 3.0 Cerebral Palsy Module.

February 2010

Strength Training and Gait Kinematics

Figure 4. Joint angles (with standard deviation bands), obtained from gait analysis, from the left side of one participant who responded well to the strengthening program (one for whom computer simulation was created). Note that the participant’s excessive knee flexion, hip flexion, and internal hip rotation were diminished after strengthening. Pre⫽before strengthening, Post⫽after strengthening, Flex⫽flexion, Ext⫽extension, Dorsi⫽dorsiflexion, Plantar⫽plantar flexion.

These data showed trends toward a small amount of improvement (eTab. 3 available at ptjournal.apta.org), but none of the changes were statistically significant when averaged across the participants. Secondary Measures of Impairment Mean passive knee extension was ⫺2 degrees at the start of the program. A small, but significant, decrease (increased tightness) in passive knee extension was found as a result of the training (P⬍.05 for both right and left sides), with a mean February 2010

change of about 3 degrees. The popliteal angle also worsened slightly, but the change was not significant on either side. No significant correlation was found between changes in passive and active knee extension. Spasticity did not change significantly before and after the training, but the degree of spasticity was directly related to the change in knee position (r⫽.55, P⬍.05). Figure 3 shows the initial Ashworth Scale score for each leg plotted against the change in minimum knee flexion, indicating that the greater the spastic-

ity, the worse the response to strengthening. Analysis of the Subject-Specific Simulations Participant 1 responded well to the strengthening program, demonstrating notable improvements in knee extension, hip extension, and hip rotation throughout the gait cycle (Fig. 4). Analysis of the subject-specific computer simulations and the associated clinical data provided insights into why this child’s gait improved.

Volume 90

Number 2

Physical Therapy f

275

Strength Training and Gait Kinematics

Figure 5. (Left) The 3-dimensional musculoskeletal model in poses corresponding to key events in the gait cycle. The musculoskeletal model used to create a simulation of one of the participants has 10 body segments and 92 muscle compartments. (Right) Capacity of the gluteus maximus and vastus muscles to accelerate the hip and knee into extension before (Pre) and after (Post) strengthening for the participant on whom the simulation was performed.

Prior to the strengthening program, participant 1 had lower than normal strength of the hip and knee extensors, with the left side slightly weaker than the right side, and he walked in a moderate crouch gait pattern. As reported in previous studies, this posture puts greater than normal demands on the vastus muscles to support the body weight and diminishes the potential of the gluteus maximus and vastus muscles to extend the hip and knee.4,6,23 Although this child walked with excessive knee flexion, analysis of the computer simulation revealed that his hamstring muscles were operating at muscle-tendon lengths sufficient for normal walking,24 so a surgical lengthening of the hamstring muscles was not needed to release excessively tight muscles. He had normal range of motion at the hip and knee, with popliteal angles of 126 and 132 degrees on the right and left sides, respectively. He had undergone a selective dorsal rhizotomy at age 5 years and exhibited good selective control, as demonstrated by the ability to dorsiflex bilaterally 276

f

Physical Therapy

Volume 90

without evidence of hip flexion. Thus, for this participant, an increase in strength was sufficient to improve his gait, and the resulting functional gains were not limited by other confounding impairments. Our analysis of the child’s gait dynamics supports this explanation. After the strengthening program, he demonstrated a substantial increase (from 20% to 85% of peak isometric torque) in the strength of his hip and knee extensors, with the left extremity values now exceeding those of the right extremity. The simulations suggested that the force generated by the left gluteus maximus muscle during early stance was increased, restoring an important mechanism that promotes hip and knee extension in normal gait. Furthermore, the capacity for the gluteus maximus and vastus muscles to accelerate the hip and knee into extension was increased (ie, angular acceleration per unit muscle force) (Fig. 5). Thus, after strengthening, the child’s excessive knee flexion was diminished, which reduced the demands on the

Number 2

vastus muscles and increased the capacity of the gluteus maximus, vastus, and other muscles to support the body weight and to produce hip and knee extension. The decrease in hip flexion in stance also shifted the moment arms of the gluteus maximus, gluteus medius, and other hip muscles toward external rotation; this may have contributed to this individual’s small improvement in hip rotation.

Discussion The goal of our pilot strengthening program, guided by previous studies and insights from musculoskeletal models, was to improve the children’s lower-extremity alignment during walking. Unfortunately, the program did not produce systematic decreases in excessive hip flexion or knee flexion during the stance phase. Furthermore, because stancephase hip extension was improved in only a few children, we were unable to test our hypothesis that improving hip extension would decrease hip internal rotation. We made an effort to exclude children February 2010

Strength Training and Gait Kinematics whose gait deviations may have been caused primarily by other factors, such as weak or over-lengthened plantar flexors, muscle contractures, or bone deformities. Nonetheless, responses to the strength training were variable, similar to previous studies.15,16,18 The large variability in response, combined with the small sample size, likely affected our ability to detect significant mean group responses to strengthening. Although the conclusions we can draw from analysis of only 8 participants are limited, examination of these results is informative for clinical practice and for guiding future research. Consistent with previous studies, we showed that gains in hip and knee extensor strength are possible in ambulatory children with CP, although some children achieved larger gains, and responded differently to those gains, than others. It is difficult to explain why some children did not make appreciable strength gains, given the fairly long (8-week) duration and the design of this program, whereby a therapist closely monitored each individual and progressively increased the amount of resistance used during training. Possible explanations include neurological factors, such as a primary agonist insufficiency that was not amenable to training, or pre-existing muscle adaptations that may have limited the capacity of some muscles to change in response to loading. At present, these explanations remain hypothetical and warrant further investigation. The children’s strength changes were less consistent and smaller at the knee than at the hip, which may be related to increases in hamstring muscle strength. Greater strength in the hamstring muscles would contribute to increased extension torque at the hip, but could be a potential source of greater antagonist restraint when producing extension torque at the knee.

February 2010

Previous studies have shown that resistance training, in some cases, can produce positive effects on gross motor functional abilities, including self-selected and maximum walking speed.10,13 In the present study, although there was a nonsignificant trend toward improvement on the questionnaire that measured the children’s perception of physical performance and related quality of life, we did not detect a systematic improvement in walking speed or other gait parameters. This finding is not surprising, given the limited response to strength training for some of the children, as we suspect that improvements in strength may be needed to stimulate changes in these functional measures.

ger and colleagues16 evaluated the effects of an 8-week, generalized lower-extremity strengthening program for 21 adolescents with CP and reported a significant improvement in their gait kinematics of 5.1 degrees at mid stance only by summing up changes at the hip, knee, and ankle. Eek and colleagues18 trained the 4 weakest muscles per subject in 16 independently ambulatory children with CP and observed improvements in stride length and hip and ankle kinetics, but no systematic change in the gait kinematics. Lee and colleagues19 conducted a generalized functional lower-extremity training program in 16 children, ages 4 to 12 years, and found no changes in the gait kinematics.

Only a few studies have evaluated the effects of increased strength on gait kinematics in people with CP, and findings have been equivocal. For example, Damiano and colleagues15 examined the effectiveness of a 6-week isotonic quadriceps muscle strengthening program for improving crouch gait in 14 children with spastic diplegia. Knee extensor strength improved significantly, as did stride length at free and fast speeds. However, the only significant change in the gait kinematics was a more extended knee position, on average, at initial contact. Six of the children also achieved greater knee extension at mid stance, but 2 children developed a slightly worsened crouch gait (ie, knee flexion increased approximately 5°) (unpublished observation).

Previous studies10,12,15–19 generally have reported mean changes in gait measures and have provided little insight into subject-specific factors that may have influenced the functional or kinematic outcomes. One potential precipitating factor often mentioned clinically is growth. As children grow, their strength must keep pace, or else their posture is compromised, as often is apparent in normal adolescence, suggesting that crouch in individuals with CP may be exacerbated by growth. However, Wren and colleagues25 recently evaluated the influence of age, among other factors, on different types of malalignment in nearly 1,000 patients with CP and found that rotational malalignment was associated with advancing age, but interestingly, crouch did not emerge as being significantly affected by age. It is likely that the natural history of crouch is altered by surgical procedures or botulinum toxin injections on the hamstrings and other muscles, which may have affected their analyses.

In a subsequent strength study, Damiano and Abel17 targeted the 2 weakest muscles per individual and found significant changes in walking speed and function in a sample of 11 children with hemiplegia or diplegia, but no consistent improvement in gait kinematics, which was not surprising given the variability in the target muscles across subjects. Un-

Given the variability of responses observed here, the reasons why some children in the present study walked

Volume 90

Number 2

Physical Therapy f

277

Strength Training and Gait Kinematics with exaggerated knee flexion or internal rotation following the program remain unclear. In the subgroup analysis comparing independent ambulators with those who used an assistive device, the children who were more functional showed a small improvement in comparison with an appreciable worsening in the children who required an assistive device to walk; however, this difference did not reach significance, and the subgroup sample sizes were small. We did find decreased passive knee extension as a result of the program, as well as a relationship between greater hamstring muscle spasticity and poorer kinematic outcomes. We believe that unavoidable strengthening of the hamstring muscles during the hip extensor training may be responsible for the decrease in active and passive knee extension, despite the greater extensor strength, with those children with greater hamstring muscle spasticity at greater risk. More stringent muscle length or spasticity criteria, as well as closer monitoring of hamstring muscle length during the intervention, may be warranted in future studies. Interestingly, the child who had the most positive outcome was an independent ambulator and had undergone a selective dorsal rhizotomy several years prior to the study. In their review of the role of strength training for improving gait in ambulatory children and adolescents with CP, Mockford and Caulton13 noted that the wide range of gait-related outcomes within and across studies occurs because individuals have shown such a wide variety of abnormalities and compensations; therefore, they concluded that no general conclusions can be drawn. Indeed, the heterogeneity of the motor disorder in people with CP makes it difficult to predict outcomes of any intervention in this population. One study in the adult orthopedic literature supports the premise that an 278

f

Physical Therapy

Volume 90

8-week strengthening program—in this case, targeting the hip abductors and lateral rotators in 15 female runners who were healthy— can alter kinematics in a clinically significant and predictable way.26 Based on our own observations and data from the literature, we remain cautiously optimistic about the potential for strength training to improve walking in some patients. One hypothesis that emerges from these results is that strengthening may be more beneficial for children with milder involvement, which seems reasonable because those patients are likely to have less spasticity and fewer motor control deficits that could limit their response to strengthening. It also is possible that programs need to be of longer duration or coupled with other interventions to address all of the factors that potentially contribute to excessive hip flexion, knee flexion, and hip internal rotation during walking.

they hold promise for identifying the impairments that impede locomotor function, thereby refining and improving treatment approaches in CP. Dr Damiano was the principal investigator and project director of the clinical trial. Dr Arnold and Dr Delp provided concept/idea/ research design and project management. Dr Arnold, Ms Steele, and Dr Delp provided writing and data analysis. Ms Steele provided consultation (including review of manuscript before submission). The authors acknowledge the United Cerebral Palsy Research & Education Foundation for funding this project. This research also was supported, in part, by the intramural program at the National Institutes of Health. The authors thank Julie Anderson for her assistance in all aspects of the study and Tom Nuzum and Dave Reddy for conducting the training sessions for all participants. Human subjects approval was obtained from the Washington University Human Studies Committee prior to study initiation. This article was received February 23, 2009, and was accepted September 21, 2009. DOI: 10.2522/ptj.20090062

Conclusion The effect of strength training on gait kinematics in people with CP remains unpredictable at the level of the individual patient, and the current approach of mean group analysis of heterogeneous samples will not resolve this dilemma. We contend that studies with larger samples that can more adequately examine patient and intervention factors that could influence outcome, large-scale regression analyses, or subjectspecific computer simulations of walking are needed to help explain the variability in outcomes and to identify individuals who are most likely to benefit from strengthening. More research also is needed to determine how strength training might complement or interact with other treatments, especially those interventions that tend to further weaken patients. Subject-specific musculoskeletal models and simulations of walking are currently labor- and technology-intensive efforts, but

Number 2

References 1 Hirtz D, Thurman DJ, Gwinn-Hardy K, et al. How common are the “common” neurologic disorders? Neurology. 2007; 68:326 –337. 2 Sussman M, ed. The Diplegic Child Evaluation and Management. Rosemont, IL: American Academy of Orthopaedic Surgeons; 1992. 3 Perry J Determinants of muscle function in the spastic lower extremity. Clin Orthop Relat Res. 1993;(288):10 –26. 4 Arnold AS, Anderson FC, Pandy MG, Delp SL. Muscular contributions to hip and knee extension during the single limb stance phase of normal gait: a framework for investigating the causes of crouch gait. J Biomech. 2005;38:2181–2189. 5 Kuo AD, Donelan JM. Dynamic principles of gait and their clinical implications. Phys Ther. 2010;90:157–174. 6 Hicks JL, Schwartz MH, Arnold AS, Delp SL. Crouched postures reduce the capacity of muscles to extend the hip and knee during the single-limb stance phase of gait. J Biomech. 2008;41:960 –967. 7 Damiano DL, Vaughan, CL, Abel MF. Muscle response to heavy resistance exercise in spastic cerebral palsy. Dev Med Child Neurol. 1995;37:731–739. 8 Wiley ME, Damiano DL. Lower extremity strength profiles in spastic cerebral palsy. Dev Med Child Neurol. 1998;40:100 –107.

February 2010

Strength Training and Gait Kinematics 9 Delp SL, Hess WE, Hungerford DS, Jones LC. Variation of rotation moment arms with hip flexion. J Biomech. 1999;32: 493–501. 10 Andersson C, Grooten W, Hellsten M, et al. Adults with cerebral palsy: walking ability after progressive strength training. Dev Med Child Neurol. 2003;45:220 –228. 11 Fowler EG, Ho TW, Nwigwe AI, Dorey FJ. The effect of quadriceps femoris muscle strengthening exercises on spasticity in children with cerebral palsy. Phys Ther. 2001;81:1215–1223. 12 Dodd KJ, Taylor NF, Damiano DL. A systematic review of the effectiveness of strength-training programs for people with cerebral palsy. Arch Phys Med Rehabil. 2002;83:1157–1164. 13 Mockford M, Caulton JM. Systematic review of progressive strength training in children and adolescents with cerebral palsy who are ambulatory. Pediatr Phys Ther. 2008;20:318 –333. 14 Scianni A, Butler JM, Ada L, TeixeiraSalmela LF. Muscle strengthening is not effective in children and adolescents with cerebral palsy: a systematic review. Aust J Physiother. 2009;55:81– 87.

February 2010

15 Damiano DL, Kelly LE, Vaughn CL. Effects of quadriceps femoris muscle strengthening on crouch gait in children with spastic diplegia. Phys Ther. 1995;75:658 – 667. 16 Unger M, Faure M, Frieg A. Strength training in adolescent learners with cerebral palsy. Clin Rehabil. 2006;20:469 – 477. 17 Damiano DL, Abel MF. Functional outcomes of strength training in spastic cerebral palsy. Arch Phys Med Rehabil. 1998; 79:119 –125. 18 Eek MN, Tranberg R, Zu ¨ gner R, et al. Muscle strength training to improve gait function in children with cerebral palsy. Dev Med Child Neurol. 2008;50:759 –764. 19 Lee JH, Sung IY, Yoo JY. Therapeutic effects of strengthening on gait function in cerebral palsy. Disabil Rehab. 2008;30: 1439 –1444. 20 Varni JW, Burwinkle TM, Berrin SJ, et al. The PedsQL in pediatric cerebral palsy: reliability, validity, and sensitivity of the Generic Core Scales and Cerebral Palsy Module. Dev Med Child Neurol. 2006;48: 442– 449. 21 Delp SL, Anderson FC, Arnold AS, et al. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng. 2007;55: 1940 –1950.

22 Thelen DG, Anderson FC, Delp SL. Generating dynamic simulations of movement using computed muscle control. J Biomech. 2003;36:321–328. 23 Liu MQ, Anderson FC, Pandy MG, Delp SL. Muscles that support the body also modulate forward progression during walking. J Biomech. 2006;39:2623–2630. 24 Arnold AS, Asakawa DJ, Delp SL. Do the hamstrings and the adductors contribute to excessive internal rotation of the hip in persons with cerebral palsy? Gait Posture. 2000;11:181–190. 25 Wren TA, Rethlefsen S, Kay RM. Prevalence of specific gait abnormalities in children with cerebral palsy: influence of cerebral palsy subtype, age, and previous surgery. J Pediatr Orthop. 2005;25: 79 – 83. 26 Snyder KR, Earl JE, O’Connor KM, Ebersole KT. Resistance training is accompanied by increases in hip strength and changes in lower extremity biomechanics during running. Clin Biomech (Bristol, Avon). 2009;24:26 –34.

Volume 90

Number 2

Physical Therapy f

279

Perry Issue: Gait Rehab

Striding Out With Parkinson Disease: Evidence-Based Physical Therapy for Gait Disorders Meg E. Morris, Clarissa L. Martin, Margaret L. Schenkman M.E. Morris, PT, PhD, is Professor and Head, Melbourne School of Health Sciences, The University of Melbourne, Victoria, Melbourne 3010, Australia. Address all correspondence to Dr Morris at: [email protected]. C.L. Martin, PT, PhD, is Senior Research Fellow, Centre for Health, Exercise and Sports Medicine, The University of Melbourne. M.L. Schenkman, PT, PhD, FAPTA, is Professor and Director, Physical Therapy Program, Department of Physical Medicine and Rehabilitation, and Associate Dean of Physical Therapy Education, School of Medicine, University of Colorado Denver, Aurora, Colorado.

Although Parkinson disease (PD) is common throughout the world, the evidence for physical therapy interventions that enable long-term improvement in walking is still emerging. This article critiques the major physical therapy approaches related to gait rehabilitation in people with PD: compensatory strategies, motor skill learning, management of secondary sequelae, and education to optimize physical activity and reduce falls. The emphasis of this review is on gait specifically, although balance and falls are of direct importance to gait and are addressed in that context. Although the researchers who have provided the evidence for these approaches grounded their studies on different theoretical paradigms, each approach is argued to have a valid place in the comprehensive management of PD generally and of gait in particular. The optimal mix of interventions for each individual varies according to the stage of disease progression and the patient’s preferred form of exercise, capacity for learning, and age.

[Morris ME, Martin CL, Schenkman ML. Striding out with Parkinson disease: evidence-based physical therapy for gait disorders. Phys Ther. 2010;90:280 –288.] © 2010 American Physical Therapy Association

Post a Rapid Response or find The Bottom Line: www.ptjournal.org 280

f

Physical Therapy

Volume 90

Number 2

February 2010

Evidence-Based Physical Therapy for Gait Disorders

P

arkinson disease (PD) is a common disorder, especially among older adults. Based on an incidence rate of 16 to 19 per 100,000 per year, it is estimated that more than 2 million Americans, and 6 million people worldwide, are currently living with this progressive neurological condition.1 Movement disorders, and in particular gait disorders, are a hallmark of PD.2,3 The slow, shortstepped, shuffling, forward-stooped gait with asymmetrical arm swing is quickly recognizable to clinicians and varies according to the timing of assessment in the PD medication cycle.2– 4 In addition to experiencing difficulties with the performance of well-learned movement sequences such as walking, turning, writing, and transfers, some people with PD report falls, cognitive impairment, and autonomic disturbances.5 Together, these problems can can affect quality of life and participation in societal roles.6 Physical therapist management of gait disorders in people with PD has 3 key elements. The first element is teaching the person how to move more easily and to maintain postural stability by using cognitive strategies. This is known as “strategy training” and targets the primary motor control deficit in the basal ganglia, brain stem, and motor cortex. There are 2 forms of strategy training: (1) compensatory strategies to bypass the defective basal ganglia and (2) learning strategies to improve performance through practice. The second element of physical therapy is the management of secondary sequelae affecting the musculoskeletal and cardiorespiratory systems that occur as a result of deconditioning, reduced physical activity, advanced age, and comorbid conditions. The third element is the promotion of physical activities that assist the person in making lifelong changes in exercise and physical activity habits as well as preventing falls. The reFebruary 2010

searchers who have provided the evidence for these approaches have grounded their studies on different theoretical paradigms and studied targeted questions based on those paradigms. Nevertheless, each element has a valid place in the management of PD. In the clinical setting, physical therapists draw from each approach to provide comprehensive, client-centered care. Measurement of gait-related outcomes includes a variety of perspectives. Included are the assessment of kinematics of gait (eg, stepping rate, stride length), assessment of functional factors (eg, 2- or 6-minute walk distance, ability to climb stairs), and assessment of factors associated with postural control that are closely related to gait (eg, incidence of falls, measures of balance control such as functional reach). These different outcomes have been used by researchers to better understand the effects of a variety of physical intervention approaches related to gait of people in early and middle stages of PD.

Strategy Training Morris and colleagues2– 4,7 provided some of the first evidence that movement strategies can assist people with PD to move, walk, and balance more easily. Their laboratory trials showed that external cues, such as white lines on the floor or a rhythmical beat provided by a metronome or music, enabled elderly people with moderate to severe PD to walk with longer steps and at a more normal stepping rate.4,8,9 These strategies assisted people to walk faster by compensating for the most common movement disorder: hypokinesia. Hypokinesia refers to reduced movement amplitude and speed and is seen as reduced step length and, in some individuals, alterations in the rate and timing of footsteps (cadence). Morris and colleagues3,7 demonstrated that many people with

PD who are cognitively intact and do not have marked postural instability can immediately walk with long, fast steps simply by focusing their attention on walking with long steps, even when floor markers are absent. Through bypassing the defective basal ganglia and instead using the frontal cortex to regulate movement size or timing by consciously thinking about the desired movement, people with PD arguably compensate for the neurotransmitter imbalance in the basal ganglia. Other strategies include visualizing walking with long steps, mentally rehearsing the desired movement pattern before the action is performed, breaking down long or complex motor sequences into parts and focusing on the performance of each individual segment (segmentation), avoiding dual task performance, reading instructions on a cue card, and verbally reciting phrases such as “think big” or “long steps.”2,3,9 –13 This model is based on the theory that the ability to move normally is not lost in PD.2,3 Instead, there is an activation problem that can be overcome through targeted physical therapy together with optimal pharmacotherapy.14 Strategy training can be used either to compensate for movement disorAvailable With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

Volume 90

Number 2

Physical Therapy f

281

Evidence-Based Physical Therapy for Gait Disorders ders as just described or to assist people to learn methods to move more easily through carefully structured practice. So far, we have discussed how intact regions of the central nervous system have the potential to compensate for the defective basal ganglia via attention strategies or external cues. The randomized controlled trial by Morris et al7 showed that compensatory techniques can be beneficial, yet are sometimes associated with relatively short-term effects. They reported that a 2-week inpatient hospital program of twice-daily therapy of up to 45 minutes per session was effective in reducing disability and improving walking speed and balance, although gains were not fully maintained at a 3-month follow-up. One possible reason for the reversion toward baseline was the relatively small amount of practice. In addition, cognitively driven strategy training can require a lot of mental effort and can be fatiguing for some people. For this reason, therapists usually train people to use the cognitive strategies selectively, when they are needed for key functional tasks, rather than using them continuously throughout the day.2 There is growing evidence that, in the early stages of PD, there remains the capacity to learn new motor skills.15,16 For example, Behrman et al16 reported that the capacity to learn new upper-limb movement sequences was retained in people in the early to middle stages of PD. Subjects with PD and aged-matched comparison subjects repeatedly practiced a series of rapid armreaching tasks with different levels of movement complexity over several days.16 Fast performance of sequential targeting tasks improved with practice in both groups and was retained over 48 hours. Similarly, Canning et al15 showed that a multiple-task gait training program, combining walking and cognitive and manual activity practice, re282

f

Physical Therapy

Volume 90

sulted in increased multiple-task walking speed in people with mild PD. This improvement in walking performance, achieved within only three 30-minute training sessions, was maintained over a 3-week retention period, suggesting that people with mild PD may have the capacity to learn how to walk under multipletask conditions. The message from these lines of evidence is that physical therapists should consider taking different approaches to strategy training according to disease severity. For newly diagnosed individuals and those with mild to moderate disease, it is recommended that therapists provide highintensity, variable, distributed practice regimens with regular booster sessions over the longer term, with the aim of maximizing motor skill learning. How intensive and sustained the practice needs to be will vary according to the type and severity of movement disorders, the capacity of the person for learning, and whether there are coexisting conditions that limit the ability to practice. As a guide, physical therapy for people with mild to moderate disease could incorporate practice daily up to 3 times per week, for periods of 6 to 8 weeks, until the motor skill is acquired. Bursts of therapy then could be provided 2 to 3 times per year to promote retention of training. For people who are more severely affected or those with cognitive impairment, very advanced age, or comorbidities that compromise skill acquisition, compensatory strategies are recommended. These strategies typically include repetition and drill practice of a given movement or action sequence, avoidance of multitasking, use of external cues and reminders, and segmentation of actions into simple components.2– 4,7,17 The incidence of cognitive impairment is high in people whose disease severity is moderate

Number 2

to severe,18 adding weight to the notion that compensatory methods are likely to be more effective than motor skill learning approaches in this group.

Management of Musculoskeletal Sequelae Schenkman and Butler19 were among the first investigators to propose that physical therapy interventions targeting sequelae such as weakness, loss of range, and reduced aerobic capacity could assist some people with PD to improved balance, gait, and function. This concept recognizes that people with PD can develop sequelae to the disorder that might contribute substantially to their difficulty with activities and participation in societal roles. By using physical therapy interventions to reduce the sequelae, it should be possible to improve function despite the primary central nervous system disorder affecting the basal ganglia. Schenkman and colleagues20 –22 have conducted a number of laboratory experiments designed to test whether improved flexibility, muscle strength (force-generating capacity), and cardiovascular condition can improve task performance, including gait, postural control, and overall function. Not all of these studies focused on gait specifically. We contend that the findings are of importance because these factors are intimately related to gait. Studies are under way to measure outcomes of gait more specifically.23 It is well recognized that people with PD may have altered posture, including forward trunk flexion and lack of spinal extension range of motion.22,24 Schenkman and Butler19 proposed that loss of range of motion of axial structures (axial mobility) might contribute to loss of postural control, gait impairment, and decline in overall function. Laboratory experiments confirmed that trunk flexibility was associated with February 2010

Evidence-Based Physical Therapy for Gait Disorders balance (measured by functional reach) and task performance.20 –22 Exercises designed to improve axial range of motion were shown to improve functional reach distance.25 Furthermore, a secondary analysis of the data demonstrated nearly significant improvements in 6-minute walk distance for participants in the exercise program.25 Although axial flexibility alone might improve balance and gait to some degree, exercises that target balance, gait, and overall function could maximize the functional benefits from improved axial mobility. Another line of work has focused on the benefits of strengthening exercises for people with PD. The degree to which people with PD are weak is under debate. Furthermore, it is unclear to what extent weakness is a consequence of deconditioning associated with living with a chronic progressive disease, coupled with aging, and how much of the weakness is related to altered central drive to muscles.26 What is well known is that loss of lower-extremity strength contributes to problems with balance, falls, and functional decline in older people.26,27 Several investigators have begun to explore the benefits of strength training. Hirsch et al28 carried out one of the first laboratory experiments, demonstrating improvements in lower-extremity strength and response to external perturbations. Dibble et al29 showed that a high-intensity eccentric quadriceps muscle strengthening program resulted in increased quadriceps muscle volume, improved 6-minute walk distance, and improved stair descent time. Dibble and colleagues30 also demonstrated the safety of the program. Although it is not clear whether the loss of strength is a sequel to PD, or associated with disuse or aging, it is apparent that lower-extremity strengthening exercise can benefit some people with PD, both in terms of better February 2010

lower-extremity strength among exercisers compared with controls and in terms of balance and function.26 A third line of inquiry has focused on the cardiovascular system. As with loss of strength, cardiovascular decline can contribute to functional loss in older adults.31 Protas and colleagues32 measured cardiovascular function in people with PD, noting that 8 people in the early to middle stages were able to achieve maximum oxygen consumption comparable to that of 7 older adults without PD. The adults with PD used as much as 20% more oxygen to perform bicycling tasks than did the people without PD, indicating a reduced economy (or efficiency) of movement. These findings have been replicated in nearly 150 people with a walking task, showing that people with PD consume more oxygen than people without PD at every walking speed from 1 to 4 mph.33 Importantly, Schenkman and colleagues34 and other researchers35,36 have demonstrated that aerobic conditioning programs can lead to improvements in maximum oxygen consumption, economy of movement, 6-minute walk distance, and kinematics of gait, as well as overall function. The message from all of these studies is that some people with mild to moderately severe PD can benefit from interventions that target flexibility, lower-extremity strength, and cardiovascular conditioning. These interventions may improve aspects of balance, gait, and overall functional ability, although further studies are needed to fully define the scope of benefits from each approach. Such exercise is important for people in early stages of PD to prevent sequelae that can interfere with function, to reverse sequelae early in the disease, and to prevent or reverse declines associated with disuse and aging itself. Nevertheless,

it is neither realistic nor appropriate to think we can always reverse sequelae in later stages (eg, Hoehn and Yahr stage 3 and onward). As a guide, individuals should exercise daily to 3 times per week, for periods of 6 to 12 weeks, to improve spinal flexibility, depending on the extent of loss of flexibility.25 They should exercise at least 3 times per week for 4 months to improve cardiovascular fitness.33 In order to achieve muscle strength gains, it is recommended that individuals train 2 or 3 days per week, completing 1 to 3 sets of each exercise using resistance loads equivalent to 8- to 12repetition maximum, for a minimum of 6 weeks.37 Whichever approach to exercise is used, to sustain benefits, individuals should continue exercising at least a few times per week as part of their daily routine. They should be reassessed by a physical therapist at least annually in the early stages of the disease and more often in later stages of the disease to progress their exercise program.

Promoting Physical Activity and Preventing Falls Because PD is a chronic progressive disorder, it is probable that sustained exercise is necessary to maintain benefits. Indeed, follow-up data from a number of human exercise interventions have demonstrated a gradual return to baseline abilities after the supervised intervention is finished.7,25,38 Results from several sources (including both animal models and information from humans) support the importance of vigorous exercise for people with PD and raise the question of whether such exercise might play a neuroprotective role. For example, Tillerson et al39 showed that motorized treadmill running twice daily for 10 days enhanced motor

Volume 90

Number 2

Physical Therapy f

283

Evidence-Based Physical Therapy for Gait Disorders performance and brain neurochemistry in 2 different rat models of PD. Likewise, Dobrossy and Dunnett40 reported that rats that received motor training after striatal lesions or striatal grafts showed some recovery in spontaneous movements and skilled motor performance. Data from Fisher and colleagues41 indicate central effects can occur with exercise for people in the early stages of PD. Finally, Thacker and colleagues42 examined the impact of recreational physical activity on future risk of developing PD. Data were examined from 143,325 people who were followed for 8 years. The authors identified a reduced relative risk of developing the disease for those individuals who had reported moderate to vigorous activity at baseline. Although it is not yet clear whether exercise has a neuroprotective effect for people with PD, at a minimum, exercise does assist people to maintain functional ability. Taking all of the results together, we advocate the importance of enabling people with PD to make long-term adaptations to integrate physical activity into their daily lives. Furthermore, we advocate that vigorous exercise begin immediately on diagnosis, if possible, and continue throughout the course of the disease for as long as the individual is able to exercise. Because weekly intervention with a physical therapist, throughout the entire course of PD, is neither realistic nor desirable, patients need to take responsibility for their physical activity and exercises. Methods have been developed, based on theories of behavior, for improving exercise habits. Strategies include exploration of the patient’s beliefs about exercise and barriers to regular exercise and discussing the possibility of looking at things differently to change beliefs and overcome barriers.43– 45 Together, the clinician and patient then establish reasonable goals that the patient thinks are attainable; they 284

f

Physical Therapy

Volume 90

build on those goals as exercise habits improve. Regular follow-up appointments also are important (eg, monthly, quarterly, annually) to monitor progression and provide support to the patient. Additionally, to assist patients to achieve a regular exercise regimen, we recommend determining how they prefer to exercise (eg, alone, in a group), where they prefer to exercise (eg, at home, in a community setting), and what type of exercise they prefer (eg, dancing; regular brisk walking, biking). Especially in the earlier stages of PD, any form of activity may be useful in maintaining gains made with specific, targeted, supervised exercise. For example, regular dancing is an attractive strategy for some people. Hackney and colleagues46,47 have shown improvements in balance with both Argentine tango and American ballroom dancing. Adherence to a regular exercise regimen may be the most difficult challenge for the physical therapist and the patient. The physical therapist needs to shift roles from being the “doer” and “coach” (both of which may be necessary during the supervised interventions) to a role of “consultant” as the patient takes on the responsibility of maintaining activity. When patients embrace the importance of regular exercise, develop the necessary habits, and accept personal responsibility, optimal outcomes are more likely to occur. There is considerable information regarding exercise psychology for older adults.48,49 However, the area of exercise psychology in PD has not been studied extensively, and it cannot be assumed that all of the research in older adults is directly transferable to the PD population. Particularly troubling in this regard is the impact of non-motor signs of PD, such as depression, apathy, and lack of initiative, in many patients.50 In-

Number 2

vestigations are needed to establish the consequences of these impairments with regard to the patient’s ability to adopt and adhere to a regular exercise regimen. We recommend that clinicians be alert to the possibility of such non-motor symptoms with their patients and take appropriate steps when possible to facilitate exercise habits even in the face of apathy. Clearly, this is an important area needing research. As the disease progresses, falls frequently accompany gait disorders in PD. Thus, minimization of falls is a key goal of physical therapy for patients with locomotor dysfunction.2,3 Between 50% and 70% people with PD experience one or more falls over a 12-month period, which is much higher than the 30% fall rate reported for community-dwelling older people.51,52 Self-reporting is known to markedly underestimate the true fall rate in a range of conditions; thus, there is a need for therapists to look for other indicators of falls such as gait deviations, injuries, increased use of professional services, hospitalization, and reduced participation in societal roles. Many of the falls in PD occur when people attempt to perform multiple tasks or long or complex movement sequences.2,3,8,9,53 Turning during walking is particularly problematic, as is carrying trays and other objects or walking at the same time as talking. Moreover, evidence is emerging that patients who experience freezing (an episodic inability to initiate or continue stepping) are particularly at risk of falls.14 This group is susceptible to multiple falls, with some individuals falling many times each week despite the best attempts to optimize medication.14 Health promotion activities that educate patients and their families about fall risk factors and how to prevent slips, trips, and falls, therefore, are integral to comprehensive physical therapy February 2010

February 2010

27 E: 13 C: 14

33 E: 13 C: 13 W:7

Ebersbach et al66 (2008)

Hackney and Earhart56 (2008)

Volume 90

Number 2

Physical Therapy f

28 E: 14 C: 14

II–III

I–II

I.5-III

Not described

II–III

I–III

Hoehn and Yahr Stage Age (y), XⴞSD

E: 68.0 C: 66.0

Group 1: 63.1⫾11.5 Group 2: 61.5⫾9.8 Group 3: 64.0⫾14.5

E: 64.9⫾8.3 C: 62.6⫾10.2

E: 72.5⫾6.0 C: 75.0⫾6.8

71.8⫾6.4

62.1⫾9.1

Intervention

E: movement strategy training in addition to usual care (inpatient rehabilitation setting) C: musculoskeletal exercises in addition to usual care, as above

Group 1: “zero-intensity” exercise (education and information sessions) Group 2: “low-intensity” exercise based on “traditional” physical therapy for people with PD (including balance, gait, strategy training, and musculoskeletal exercises) Group 3: “high-intensity” exercises (BWSTT with progression of speed and intensity of training)

E: structured Tai Chi lessons from an experienced instructor C: no intervention

E: WBV in addition to usual care (inpatient rehabilitation setting) C: “conventional” balance exercises in addition to usual care, as above

E: “exercise,” including speed-dependent treadmill training ⫹ stretches C: not described

GT under 3 conditions ⫹ 1 control condition: GT1: speed-dependent treadmill training GT2: limited progressive treadmill training GT3: “conventional” GT (based on principles of PNF) C: rest, no therapy

Outcomes

Movement strategy training produced a significant reduction in disability (measured on the UPDRS) and significant improvements in 10-m walk time, 2-minute walk distance, and quality of life (measured on the PDQ39); the control group demonstrated a significant improvement in quality of life, with no significant change in disability or gait performance.

All groups demonstrated some reduction in disability (measured on the UPDRS) and some improvements in gait performance including selfselected speed and stride length (note: statistical analyses were not reported). Group 3 (BWSTT) demonstrated improvements in gait performance of a greater magnitude than the other 2 groups.

Tai Chi group demonstrated a significant improvement in balance (measured on the BBS and timed tandem stance); modest (nonsignificant) improvements in gait performance on the Six-Minute Walk Test and for backward walking velocity also were reported in the Tai Chi group. No significant changes were detected in the control group.

Both groups demonstrated significant improvements in balance performance (measured on the Tinetti Balance Score and pull test) and gait performance (measured on the 10-m walk test and Timed “Up & Go” Test). There was no evidence to suggest that WBV therapy was more effective than a conventional inpatient rehabilitation program for people with PD.

Experimental group demonstrated significant improvements in balance performance (measured on the BBS) and gait performance (measured on the DGI, maximum treadmill walking speed and distance). No significant changes were noted in the control group.

GT1 and GT2 conditions both resulted in significant improvements in temporal-spatial gait parameters measured at self-selected walking speed. No significant changes were detected for the GT3 and control conditions. There was no significant difference in outcome between the GT1 and GT2 conditions.

a GT⫽gait training, PNF⫽proprioceptive neuromuscular facilitation, E⫽experimental group, C⫽control group, W⫽withdrawals, BBS⫽Berg Balance Scale, DGI⫽Dynamic Gait Index, WBV⫽whole-body vibration, UPDRS⫽Unified Parkinson’s Disease Rating Scale, BWSTT⫽body-weight–supported treadmill training, PDQ39⫽39-item Parkinson’s Disease Questionnaire. b Randomized, cross-over trial.

Morris et al7 (2009)

30 (10 per group)

54 E: 21 C: 10 W: 23

Cakit et al65 (2007)

Fisher et al41 (2008)

17

No. of Participants

Pohl et al67 (2003)b

Study

Overview of Recent Randomized Controlled Trials Investigating Physical Therapy Interventions for Gait Disorders in People With Parkinson Disease (PD)a

Table.

Evidence-Based Physical Therapy for Gait Disorders

285

Evidence-Based Physical Therapy for Gait Disorders management of people with PD. Again, long-term adaptations by the patient are needed to integrate fall prevention strategies into their daily lives. It is important for the clinician to know when, where, and during what tasks the falls occur, as well as the direction. Although population-based data provide a good guide, the physical therapist still needs to know the specific experience for each individual. Dibble et al54 conducted a systematic review of exercise-based interventions to improve balance in PD and determined that although there is moderate evidence to support the efficacy of exercise in improving postural instability and balance task performance, it remains unclear which specific types and dosages of exercise are optimal for the management of balance disorders in people with PD. However, with exercise interventions as diverse as treadmill training,38 spinal flexibility training,25 Qigong,36 muscle strengthening,55 Tai Chi,56 and tango dancing,46 each proving to be of some benefit to people with PD, it appears that an exercise program tailored to the individual’s balance impairment, fall history, lifestyle, and personal interests may be preferable to a “one size fits all” approach.

Evidence Supporting the Efficacy of Physical Therapy for Gait Disorders A previous systematic review of therapies for PD57 has been published, and Kwakkel et al58 published a subsequent critical review of the literature on physical therapy for PD. The systematic review produced equivocal results, having been performed at a time when few controlled trials of physical therapy for PD had been published. The review by Kwakkel et al58 identified 23 randomized controlled trials investigating the effects of physical therapy on function in

286

f

Physical Therapy

Volume 90

people with PD. Only 3 of these studies targeted gait disorders.10,59,60 An additional 6 studies measured gait- and mobility-related outcomes from programs directed toward improving posture and balance.25,55,61– 64 These studies were of moderate methodological quality and demonstrated some benefits of physical therapy for gait and mobility. The interventions tested and outcome measures used varied markedly, making between-study comparisons difficult. Interventions included cueing, mental rehearsal, exercises, and cycling. As suggested by Kwakkel et al,58 the quality of physical therapy research in PD has improved in the last decade, yet gaps in the evidence base for specific interventions remain. More recently, a number of alternative treatment modalities, such as Tai Chi, treadmill training, and wholebody vibration have been proposed for the management of gait disorders in people with PD.41,56,65– 67 These treatment modalities were identified when we conducted a systematic search of the physical therapy literature, which identified an additional 6 randomized controlled trials (Table) published since Kwakkel and colleagues’ review. Three of these studies investigated the effects of treadmill training, including high-intensity body-weight–supported training,41 and speed-dependent treadmill training, demonstrating positive results in the small samples tested.65,67 Similarly, whole-body vibration therapy66 and Tai Chi training56 have been reported to produce modest improvements in the gait and balance performance of people with PD. Although all of these studies included a comparison group, none provided control interventions founded on evidence-based “best practice” such as strategy training, cueing, or the management of musculoskeletal sequelae.

Number 2

Finally, assistive devices sometimes are prescribed for people with PD to improve gait or reduce falling. A small number of trials have shown up-turned walking sticks to be a useful visual cue for some people (see Morris and colleagues2,3,17 for summaries). In addition, wheeled walking frames sometimes can be of benefit for people with balance impairment and a high risk for falls, provided that the secondary task of maneuvering the frame does not compromise movement. Specific investigations are needed to measure the degree to which assistive devices optimize movement in people with PD, particularly in the more advanced stages of disease progression.

Conclusion Comprehensive, client-centered physical therapy for people with PD is based on compensatory strategies to bypass the defective basal ganglia, strategies to improve motor learning and performance through practice, management of secondary sequelae affecting the musculoskeletal and cardiorespiratory systems, and fall education, as well as on assisting people to make lifelong changes in physical activity habits. The extent to which strategies, exercises, and health education are used varies according to individual needs and changes over time as the person ages and the disease progresses. Overall, the aim is to enable the person with PD to live well by providing effective physical therapy interventions at optimal times to promote health and well-being and by educating the individual regarding long-term selfmanagement strategies. Other articles in this issue relate to the promotion of movement in people with PD. For example, Kuo and Donelan68 discuss the principles of dynamic walking; Reisman et al69 distinguish between motor adaptation and motor learning, which is relevant to the interventions reviewed February 2010

Evidence-Based Physical Therapy for Gait Disorders here; and Yogev-Seligmann et al70 and Kizony et al71 explore dual-task methods that are relevant for people with PD who have compromised cognitive performance and are trying to walk. All authors provided concept/idea/project design, writing, and consultation (including review of manuscript before submission). Dr Morris and Dr Martin provided data collection and analysis and clerical support. Dr Morris provided project management, fund procurement, facilities/equipment, and institutional liaisons. The authors gratefully acknowledge the support of the Michael J. Fox Foundation, the National Health and Medical Research Council of Australia, The Clinical Centre for Research Excellence in Gait Analysis and Gait Rehabilitation, and the National Institutes of Health (R01 HD043770 – 04). This article was received March 19, 2009, and was accepted September 12, 2009. DOI: 10.2522/ptj.20090091

References 1 Twelves D, Perkins KSM, Counsell C. Systematic review of incidence studies of Parkinson’s disease. Mov Disord. 2003; 18:19 –31. 2 Morris ME. Movement disorders in people with Parkinson disease: a model for physical therapy. Phys Ther. 2000;80:578 –597. 3 Morris ME. Locomotor training in people with Parkinson disease. Phys Ther. 2006; 86:1426 –1435. 4 Morris ME, Iansek R. Characteristics of motor disturbance in Parkinson’s disease and strategies for movement rehabilitation. Hum Mov Sci. 1996;15:649 – 669. 5 Simuni T, Sethi K. Nonmotor manifestations of Parkinson’s disease. Ann Neurol. 2008;64(suppl 2):S65–S80. 6 Visser M, Verbaan D, van Rooden S, et al. A longitudinal evaluation of health-related quality of life of patients with Parkinson’s disease. Value Health, 2009;12:392–396. 7 Morris ME, Iansek R, Kirkwood B. A randomized controlled trial of movement strategies compared with exercise for people with Parkinson’s disease. Mov Disord. 2009;24:64 –71. 8 Morris ME, Iansek R, Matyas T, Summers JL. The pathogenesis of gait hypokinesia in Parkinson’s disease. Brain. 1994;117: 1169 –1181. 9 Morris ME, Iansek R, Matyas TA, Summers JJ. Stride length regulation in Parkinson’s disease: normalization strategies and underlying mechanisms. Brain. 1996;119: 551–568.

February 2010

10 Nieuwboer A, Kwakkel G, Rochester L, et al. Cueing training in the home improves gait-related mobility in Parkinson’s disease: the RESCUE trial. J Neurol Neurosurg Psychiatry. 2007;78:134 –137. 11 Rochester L, Hetherington V, Jones D, et al. The effect of external rhythmic cues (auditory and visual) on walking during a functional task in homes of people with Parkinson’s disease. Arch Phys Med Rehabil. 2005;86:999 –1006. 12 Farley B, Koshland G. Training BIG to move faster: the application of the speedamplitude relation as a rehabilitation strategy for people with Parkinson’s disease. Exp Brain Res. 2005;167:462– 467. 13 Lehman D. Training with verbal instructional cues results in near-term improvement of gait in people with Parkinson disease. J Neurol Phys Ther. 2005;29:2– 8. 14 Morris ME, Huxham F, Menz HB, et al. Optimizing movement and preventing falls in Parkinson’s disease: strategies for patients and caregivers. In: Trail M, Protas EJ, Lai EC, eds. Neurorehabilitation in Parkinson’s Disease: An Evidence-Based Treatment Model. Thorofare, NJ: Slack Inc; 2008:177–187. 15 Canning CG, Ada L, Woodhouse E. Multiple-task walking training in people with mild to moderate Parkinson’s disease: a pilot study. Clin Rehabil. 2008;22: 226 –233. 16 Behrman AL, Cauraugh JH, Light KE. Practice as an intervention to improve speeded motor performance and motor learning in Parkinson’s disease. J Neurol Sci. 2000; 174:127–136. 17 Morris ME, Iansek R, Galna B. Gait festination and freezing in Parkinson’s disease: pathogenesis and rehabilitation. Mov Disord. 2008;23(suppl 2):S451-S460. 18 Dubois B, Pillon B. Cognitive deficits in Parkinson’s disease. J Neurol. 1997;244: 2– 8. 19 Schenkman ML, Butler RB. A model for multisystem evaluation treatment of individuals with Parkinson’s disease. Phys. Ther. 1989;69:932–944. 20 Schenkman ML, Morey M, Kuchibhatla M. Spinal flexibility and balance control among community-dwelling adults with and without Parkinson’s disease. J Gerontol A Biol Sci Med Sci. 2000;55:M441–M445. 21 Schenkman ML, Shipp KM, Chandler J, et al. Relationships between mobility of axial structures and physical performance. Phys Ther. 1996;76:276 –285. 22 Schenkman ML, Clark K, Xie T, et al. Spinal movement and performance of a standing reach task in participants with and without Parkinson disease. Phys Ther. 2001;81:400 –1411. 23 Keus SJ, Munneke M, Nijkrake MJ, et al. Physical therapy in Parkinson’s disease: evolution and future challenges. Mov Disord. 2009;24:1–14. 24 Bloem BR, Beckley DJ, van Dijk JG. Are automatic postural responses in patients with Parkinson’s disease abnormal due to their stooped posture? Exp Brain Res. 1999;124:481– 488.

25 Schenkman ML, Cutson TM, Kuchibhatla M, et al. Exercise to improve spinal flexibility and function for people with Parkinson’s disease: a randomized, controlled trial. J Am Geriatr Soc. 1998;46:1207–1216. 26 Falvo MJ, Schilling BK, Earhart GM. Parkinson’s disease and resistive exercise: rationale, review, and recommendations. Mov Disord. 2008;23:1–11. 27 Chandler J, Duncan PW, Kochersberger G, Studenski S. Is lower extremity strength gain associated with improvement in physical performance and disability in frail, community-dwelling elders? Arch Phys Med Rehabil. 1998;79:24 –30. 28 Hirsch MA, Toole T, Maitland CG, Rider RA. The effects of balance training and highintensity resistance training on persons with idiopathic Parkinson’s disease. Arch Phys Med Rehabil. 2003;84:1109 –1117. 29 Dibble LE, Hale TF, Marcus RL, et al. Highintensity resistance training amplifies muscle hypertrophy and functional gains in persons with Parkinson’s disease. Mov Disord. 2006;21:1444 –1452. 30 Dibble LE, Hale T, Marcus RL, et al. The safety and feasibility of high-force eccentric resistance exercise in persons with Parkinson’s disease. Arch Phys Med Rehabil. 2006:87:1280 –1282. 31 Morey MC, Pieper CF, Cornoni-Huntley J. Physical fitness and functional limitations in community dwelling older adults. Med Sci Sports Exerc. 1998;30:715–723. 32 Protas EJ, Stanley RK, Jankovic J, MacNeill B. Cardiovascular and metabolic responses to upper- and lower-extremity exercise in men with idiopathic Parkinson’s disease. Phys Ther. 1996;76:34 – 40. 33 Christiansen CL, Schenkman ML, Kohrt WM, et al. Energy expenditure in people with Parkinson’s disease during treadmill walking. Presented at: Combined Sections Meeting of the American Physical Therapy Association; February 9 –12, 2009; Las Vegas, Nevada. 34 Schenkman ML, Hall D, Kumar R, Kohrt WM. Endurance exercise training to improve economy of movement of people with Parkinson disease: three case reports. Phys Ther. 2008;88:63–76. 35 Bergen JL, Toole T, Elliott RG, et al. Aerobic exercise intervention improves aerobic capacity and movement initiation in Parkinson’s disease patients. NeuroRehabilitation. 2002;17:1621–1628. 36 Burini D, Farabollini B, Iacucci S. A randomized controlled cross-over trial of aerobic training versus qigong in advanced Parkinson’s disease. Europa Medicophysica. 2006;4:231–238. 37 Taylor NF, Dodd KJ, Damiano DL. Progressive resistance exercise in physical therapy: a summary of systematic reviews. Phys Ther. 2005;85:1208 –1223. 38 Ellis T, Goede CJ, Feldman R, et al. Efficacy of a physical therapy program in patients with Parkinson’s disease: a randomised control trial. Arch Phys Med Rehabil. 2005;4:626 – 632.

Volume 90

Number 2

Physical Therapy f

287

Evidence-Based Physical Therapy for Gait Disorders 39 Tillerson JL, Caudle WM, Reveron ME, Miller GW. Exercise induces behavioral recovery and attenuates neurochemical deficits in rodent models of Parkinson’s disease. Neurosciences. 2003;119:899 –911. 40 Dobrossy MD, Dunnett SB. Motor training effects on recovery of function after striatal lesions and striatal grafts. Exp Neurol. 2003;184:274 –284. 41 Fisher BE, Wu AD, Salem GJ, et al. The effect of exercise training in improving motor performance and corticomotor excitability in people with early Parkinson’s disease. Arch Phys Med Rehabil. 2008;89: 1221–1229. 42 Thacker EL, Chen H, Patel AV, et al. Recreational physical activity and risk of Parkinson’s disease. Mov Disord. 2008;23: 69 –74. 43 Dalton CC. The concept of readiness to change. J Adv Nurs. 2003;42:108 –117. 44 Jette AM, Lachman M, Giorgetti MM, et al. Exercise: it’s never too late: the Strong-forLife Program. Am J Public Health. 1999; 89:66 –72. 45 Perri MG, Anton SD, Durning PE, et al. Adherence to exercise prescriptions: effects of prescribing moderate versus higher levels of intensity and frequency. Health Psychol. 2002;21:452– 458. 46 Hackney ME, Kantorovich S, Levin R, Earhart GM. Effects of tango on functional mobility in Parkinson’s disease: a preliminary study. J Neurol Phys Ther. 2007;31: 173–179. 47 Hackney ME, Earhart G. Effects of dance movement control in Parkinson’s disease: a comparison of Argentine tango and American ballroom. J Rehabil Med. 2009; 41:475– 481. 48 McAuley E, Jerome GJ, Marquez DX, et al. Exercise self-efficacy in older adults: social, affective, and behavioral influences. Ann Behav Med. 2003;25:1–7. 49 Rhodes RE, Martin AD, Taunton JE, et al. Factors associated with exercise adherence among older adults: an individual perspective. Sports Med. 1999;28:397– 411. 50 Smith A, Nutt JG, Ransom BR. Parkinson’s disease. Lancet. 2004;363(9423):1783– 1793.

288

f

Physical Therapy

Volume 90

51 Bloem BR, Grimbergen YAM, Cramer M, et al. Prospective assessment of falls in Parkinson’s disease. J Neurol. 2001;248: 950 –958. 52 Wood BH, Bilclough JA, Bowron A, Walker RW. Incidence and prediction of falls in Parkinson’s disease: a prospective multidisciplinary study. J Neurol Neurosurg Psychiatry. 2002;72:721–725. 53 Morris ME, Huxham F, McGinley J, et al. The biomechanics and motor control of gait in Parkinson’s disease. Clin Geriatr Med. 1996;12:825– 845. 54 Dibble LE, Addison O, Papa E. The effects of exercise on balance in persons with Parkinson’s disease: a systematc review across the disability spectrum. J Neurol Phys Ther. 2009;33:14 –26. 55 Ashburn A, Fazakarley L, Ballinger C, et al. A randomised controlled trial of a home based exercise programme to reduce the risk of falling among people with Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2007;78:678 – 684. 56 Hackney ME, Earhart GM. Tai Chi improves balance and mobility in people with Parkinson disease. Gait Posture. 2008;28:456 – 460. 57 Deane KHO, Ellis-Hill C, Jones D, et al. Systematic review of paramedical therapies for Parkinson’s disease. Mov Disord. 2002;17:984 –991. 58 Kwakkel G, de Goede CJT, van Wegen EEH. Impact of physical therapy for Parkinson’s disease: a critical review of the literature. Parkinsonism Relat Disord. 2007;13(suppl 3):S478 –S487. 59 Miyai I, Fujimoto Y, Yamamoto H, et al. Long-term effect of body weightsupported treadmill training in Parkinson’s disease: a randomized controlled trial. Arch Phys Med Rehabil. 2002;83: 1370 –1373. 60 Thaut MH, McIntosh GC, Rice RR, et al. Rhythmic auditory stimulation in gait training for Parkinson’s disease patients. Mov Disord. 1996;11:193–200. 61 Comella CL, Stebbins GT, Brown-Toms N, Goetz CG. Physical therapy and Parkinson’s disease: a controlled clinical trial. Neurology. 1994;44(3 pt 1):376 –378.

Number 2

62 Marchese R, Diverio M, Zucchi F, et al. The role of sensory cues in the rehabilitation of parkinsonian patients: a comparison of two physical therapy protocols. Mov Disord. 2000;15:879 – 883. 63 Mu ¨ ller V, Mohr B, Rosin R, et al. Shortterm effects of behavioral treatment on movement initiation and postural control in Parkinson’s disease: a controlled clinical study. Mov Disord. 1997;12:306 –314. 64 Protas EJ, Mitchell K, Williams A, et al. Gait and step training to reduce falls in Parkinson’s disease. NeuroRehabilitation. 2005; 20:183–190. 65 Cakit BD, Saracoglu M, Genc H, et al. The effects of incremental speed-dependent treadmill training on postural instability and fear of falling in Parkinson’s disease. Clin Rehabil. 2007;21:698 –705. 66 Ebersbach G, Edler D, Kaufhold O, Wissel J. Whole body vibration versus conventional physiotherapy toimprove balance and gait in Parkinson’s disease. Arch Phys Med Rehabil. 2008;89:399 – 403. 67 Pohl M, Rockstroh G, Ru ¨ ckriem S, et al. Immediate effects of speed-dependent treadmill training on gait parameters in early Parkinson’s disease. Arch Phys Med Rehabil. 2003;84:1760 –1766. 68 Kuo AD, Donelan JM. Dynamic principles of gait and their clinical implications. Phys Ther. 2010;90:157–174. 69 Reisman DS, Bastian AJ, Morton SM. Neurophysiologic and rehabilitation insights from the split-belt and other locomotor adaptation paradigms. Phys Ther. 2010;90:187–195. 70 Yogev-Seligmann G, Rotem-Galili Y, Mirelman A, et al. How does explicit prioritization alter walking during dual-task performance? Effects of age and sex on gait speed and variability. Phys Ther. 2010;90:177–186. 71 Kizony R, Levin MF, Hughey L, et al. Cognitive load and dual-task performance during locomotion poststroke: a feasibility study using a functional virtual environment. Phys Ther. 2010;90:252–260.

February 2010

Perry Issue: Gait Rehab Similarity of Joint Kinematics and Muscle Demands Between Elliptical Training and Walking: Implications for Practice Judith M. Burnfield, Yu Shu, Thad Buster, Adam Taylor

Background. People with physical disabilities often face barriers to regaining walking ability and fitness after discharge from rehabilitation. Physical therapists are uniquely positioned to teach clients the knowledge and skills needed to exercise on functionally relevant equipment available in the community, such as elliptical trainers. However, therapeutic use is hindered by a lack of empirical information. Objective. The purpose of this study was to examine joint kinematics and muscle activation recorded during walking and elliptical training to provide evidence-based data to guide clinical decision making.

Design. This was a prospective, controlled laboratory study using a repeatedmeasures design.

Methods. Twenty adults free from impairments that might hinder gait participated. After familiarization procedures, subjects walked and trained on 4 elliptical devices while kinematic, electromyographic (EMG), and stride characteristic data were recorded.

Results. Movement similarities between elliptical training and walking were supported by the documentation of relatively high coefficients of multiple correlation for the hip (.85–.89), thigh (.92–.94), knee (.87–.89) and, to a lesser extent, the ankle (.57–.71). Significantly greater flexion was documented at the trunk, pelvis, hip, and knee during elliptical training than during walking. One of the elliptical trainers most closely simulated sagittal-plane walking kinematics, as determined from an assessment of key variables. During elliptical training, gluteus maximus and vastus lateralis muscle activation were increased; medial hamstring, gastrocnemius, soleus, and tibialis anterior muscle activation were decreased; and gluteus medius and lateral hamstring muscle activation were relatively unchanged compared with muscle activation of those muscles in walking. On the basis of EMG findings, no elliptical trainer clearly emerged as the best for simulating gait.

Limitations. To date, only 4 elliptical trainers have been studied, and the contributions of the upper extremities to movement have not been quantified.

J.M. Burnfield, PT, PhD, is Director, Institute for Rehabilitation Science and Engineering, Director, Movement and Neurosciences Center, and Clifton Chair in Physical Therapy and Movement Science, Institute for Rehabilitation Science and Engineering, Madonna Rehabilitation Hospital, 5401 South St, Lincoln, NE 68506 (USA). Address all correspondence to Dr Burnfield at: jburnfield@ madonna.org. Y. Shu, PhD, is Postdoctoral Researcher, Movement and Neurosciences Center, Institute for Rehabilitation Science and Engineering, Madonna Rehabilitation Hospital. T. Buster, BS, is Chief Research Analyst, Movement and Neurosciences Center, Institute for Rehabilitation Science and Engineering, Madonna Rehabilitation Hospital. A. Taylor, BS, is Research Analyst, Movement and Neurosciences Center, Institute for Rehabilitation Science and Engineering, Madonna Rehabilitation Hospital. [Burnfield JM, Shu Y, Buster T, Taylor A. Similarity of joint kinematics and muscle demands between elliptical training and walking: implications for practice. Phys Ther. 2010;90:289 –305.] © 2010 American Physical Therapy Association

Conclusions. Although one of the elliptical trainers best simulated sagittal-plane walking kinematics, EMG analysis failed to identify one clearly superior device. This research provides evidence-based data to help guide clinical decision making related to the use of elliptical trainers across the health care continuum and into the community.

February 2010

Volume 90

Number 2

Post a Rapid Response or find The Bottom Line: www.ptjournal.org Physical Therapy f

289

Joint Kinematics and Muscle Demands in Elliptical Training and Walking

W

alking and remaining physically active often are central goals for people with physical disabilities and chronic conditions, yet many people face barriers to pursuing these objectives when formal rehabilitation ends. Equipment that may be available in the therapy setting, such as treadmills with partial body-weight support1– 4 and mechanized gait retraining devices (eg, the Lokomat*),5,6 are rarely available for use after discharge. Additionally, despite the availability of numerous health and fitness centers in most cities, many people with activity limitations are unable to use these facilities because of unfamiliar or inaccessible equipment and lack of staff expertise in how to safely develop and implement fitness programs for people with special needs.7–11 The challenges of trying to exercise in the community are unfortunate because involvement in moderate levels of sustained exercise helps prevent or delay the onset of other chronic conditions8,12–19 and reduces functional declines associated with disuse and inactivity.8,12–17,19 –23

* Hocoma Inc, 100 Reservoir Park Dr, Rockland, MA 02370.

Available With This Article at ptjournal.apta.org • Video: In honor of Dr Jacquelin Perry, view art by patients from Rancho Los Amigos National Rehabilitation Center. • Podcast: “Stepping Forward With Gait Rehabilitation” symposium recorded at APTA Combined Sections Meeting, San Diego. • Audio Abstracts Podcast This article was published ahead of print on December 18, 2009, at ptjournal.apta.org.

290

f

Physical Therapy

Volume 90

The expansion of commercial fitness equipment into rehabilitation clinics has created opportunities for improving cardiovascular fitness and refining gait mechanics on alternative exercise devices (eg, elliptical trainers) that are readily available in many community and home settings. Because of their clinical expertise and professional training, physical therapists are uniquely positioned to teach clients with physical disabilities and chronic conditions how to safely and effectively use fitness equipment to achieve functional and cardiovascular goals. These educational interventions could not only influence the formal rehabilitation period but also prepare patients with the knowledge and skills necessary for adopting an independent, physically active lifestyle in their homes and communities.

pedic conditions (eg, a heel spur). Additionally, bilateral, moveable hand grips that are mechanically linked to the foot pedals could enable people to use their upper extremities to augment the movement of weakened lower limbs.

Selecting the ideal exercise device to help a person regain gait skills requires clinicians not only to apply research findings from the sciences underpinning physical therapy but also to consider the unique needs of each client. For example, principles arising from neuroscience emphasize the importance of interventions that are task specific.24,25 Thus, elliptical trainers, which have been marketed as equipment that promotes a movement pattern similar to that of walking (Fig. 1), may be appealing therapeutic training tools to help people relearn walking. Such devices are relatively affordable (ranging from $3,000 to $8,000) and widely available in many homes, senior centers, fitness facilities, and medical environments. The ability of people to maintain constant contact of both feet with the support surface throughout the movement cycle could be beneficial for those with balance deficits. Sharing of the load between the 2 limbs also has been shown to reduce heel plantar pressures,26 a factor that could be beneficial for clients with certain ortho-

Our purpose in this study was to compare joint kinematics and muscle activation patterns recorded during walking with those occurring during exercise on 4 elliptical trainers to determine similarities and differences between gait and elliptical training. (For a review of the principles that underpin human locomotion, refer to Kuo and Donelan27 in this special issue.) We hypothesized that movement patterns of the legs (joint kinematics) and muscle demands on the legs (electromyography [EMG]) during exercise on some elliptical trainers would show more similarities to walking than would movement patterns and muscle demands on other trainers. We expect that the findings from this research will provide physical therapists and other health care professionals with information critical to guiding therapeutic exercise interventions for people seeking to regain or retain walking function.

Number 2

However, selection of the most appropriate device is often confounded by a lack of equipment-specific data to guide informed clinical decision making. For example, manufacturers claim that elliptical training and walking are similar, yet a search of published literature reveals no study that has systematically compared the extent to which different elliptical trainers emulate the normal motions and muscle demands of walking. Thus, clinicians lack key information that could guide elliptical trainer selection based on task specificity.

February 2010

Joint Kinematics and Muscle Demands in Elliptical Training and Walking

Figure 1. Phases of the normal walking cycle (top row) and corresponding elliptical training cycle (bottom row).

Method Participants Twenty adults (10 men and 10 women) who were healthy volunteered. Their mean age was 48.5 years (SD⫽23.2, range⫽9 –75), their mean body mass was 73.3 kg (SD⫽17.7, range⫽54.5–121.0), and their mean height was 172.8 cm (SD⫽8.4, range⫽160 –190.5). The participants were recruited from the staff at Madonna Rehabilitation Hospital (Lincoln, Nebraska) and the surrounding community. All were free from any musculoskeletal, neurological, or cardiovascular impairment that might affect their ability to walk or exercise. Instrumentation The variable-stride-length elliptical devices selected for the study were the SportsArt Fitness E870† (manufacturer’s reported stride length, 43–74 cm), the Life Fitness X7‡ (46 – 61 cm), the Octane Fitness Pro4500§ (46 –58 cm), and the True † SportsArt Fitness, 19510 144th Ave NE, Suite A-1, Woodinville, WA 98072. ‡ Life Fitness Corp, 5100 N River Rd, Schiller Park, IL 60176. § Octane Fitness, 9200 Wyoming Ave North, Suite 380, Brooklyn Park, MN 55445.

February 2010

Fitness Technology TSXa㛳 (43– 66 cm) (respectively referred to hereafter as SportsArt, Life Fitness, Octane, and True). Each is available for use in either commercial or home settings and promotes a movement pattern similar to gait on the basis of observational analysis. All of the devices have both static and dynamic (movable) handles. As in gait, the dynamic handles move reciprocally with leg motion (ie, if the right foot is posterior, then the right hand is more anterior). Although both types of handles were available, all of the subjects in the present study elected to use the movable handles. The moveable handles in the SportsArt contain heart rate sensors. In contrast, the remaining devices have sensors only in the stationary handles. The manufacturers’ suggested retail prices vary by approximately $1,500 across devices (SportsArt, $5,499; Life Fitness, $4,299; Octane, $5,599; and True, $4,099). Different space requirements (footprints) across devices may have implica㛳

True Fitness Technology, 865 Hoff Rd, St Louis, MO 63366.

tions for incorporation into a clinic or home setting, as evidenced by their various widths and lengths (SportsArt, 71.1 ⫻ 213.4 cm; Life Fitness, 63.5 ⫻ 213.4 cm; Octane, 78.7 ⫻ 180.3 cm; and True, 81 ⫻ 166 cm). The SportsArt and Life Fitness required entry or mounting from the side, whereas the Octane and True allowed participants to step on and off from behind the devices (Tab. 1 shows photographs of the devices). Comprehensive gait analysis for each participant was conducted along a 10-m walkway; the middle 6 m was designated for data collection to eliminate the effects of acceleration and deceleration. Initiation and termination of data collection were triggered with E3G-MR19-US photoelectric sensors.# An MA-300 –10 EMG system** and footswitch insoles†† recorded foot-floor contact patterns. The footswitch data were recorded # Omron Inc, 1920 N Thoreau Dr, Suite 165, Schaumburg, IL 60173. ** Motion Lab Systems Inc, 15045 Old Hammond Hwy, Baton Rouge, LA 70816. †† B & L Engineering, 1901 Carnegie Ave, Suite Q, Santa Ana, CA 92705.

Volume 90

Number 2

Physical Therapy f

291

Joint Kinematics and Muscle Demands in Elliptical Training and Walking Table 1. Selected Movement Cycle Characteristics of Walking and Elliptical Training (n⫽20) X (SD) Speed (m/min)

Displayed Elliptical Speed

Stride Length (m)

Cadence (Steps/min)

78.0 (10.8)

Not applicable

1.4 (0.1)

108.7 (10.3)

1.1 (0.2)a

102.5 (13.4)

1.0 (0.1)a

110.1 (10.8)

56.7 (14.1)a

57.4 (8.4)a

47.3 (6.6) rpm

123.4 (18.8) m/min

53.9 (9.0)a

49.0 (6.0) rpm

1.1 (0.1)a

102.3 (12.4)a

57.3 (11.8)a

52.7 (7.3) rpm

1.1 (0.1)a

107.6 (14.2)

a Determined to be significantly different from the value for walking on the basis of a significance level after Bonferroni adjustment of P⬍.016 (0.05/3). Displayed elliptical speed was not compared.

292

f

Physical Therapy

Volume 90

Number 2

February 2010

Joint Kinematics and Muscle Demands in Elliptical Training and Walking at 1,200 Hz and transmitted to a computer over a coaxial cable.**

The foot used to kick the ball was deemed to be dominant.

A Qualisys Motion Analysis System‡‡ and Qualisys Track Manager software‡‡ defined the 3-dimensional motion of the trunk, pelvis, and lower limbs. The system included 12 Oqus Series-3 cameras, retroreflective markers†† (12.5-mm diameter), and 12 marker clusters. Motion data were sampled at 120 Hz and recorded on a computer.

Next, participants walked at a selfselected, comfortable speed across the walkway. Gait trials were repeated until 10 were recorded within ⫾5% of the mean speed (calculated from the total time required to traverse the middle 6 m of the walkway). On average, the participants performed a total of 11 walking trials (range⫽10 –13) to achieve 10 trials of a similar speed.

The timing and intensity of muscle activity were recorded with the MA300 –10 EMG system and MA-411 surface electrodes.†† Signals were filtered with a low-pass filter (500 Hz), transmitted to a computer over a coaxial cable, and digitally recorded (1,200 Hz per channel). Subsequent signal processing was performed with Visual 3D software.§§ Procedure All testing occurred in the Movement and Neurosciences Center located in the Institute for Rehabilitation Science and Engineering at Madonna Rehabilitation Hospital. After signing an informed-consent form approved by the Institutional Review Board of Madonna Rehabilitation Hospital, each volunteer participated in 2 familiarization sessions and 1 biomechanical assessment. Each session was separated by at least 24 hours. Participants were instructed to “wear workout clothing.” No specific instructions were provided about the type of footwear to be used. Screening and familiarization. During the first session, basic anthropometric data, age, sex, and dominant lower limb were recorded. For the determination of limb dominance, participants kicked a ball.

Next, participants exercised on each elliptical device for a minimum of 3 minutes to ensure familiarity and to determine self-selected settings (eg, stride length and cadence). Participants were instructed to train at a comfortable pace and stride length “similar to walking.” Although discussion of the findings is beyond the scope of this article, additional conditions were included to examine the impact of varying stride length on kinematic and EMG variables. In particular, participants also trained at a pace matched to their walking cadence by using the longest (SportsArt, 74 cm; Life Fitness, 61 cm; Octane, 58 cm; and True, 66 cm), selfselected, and shortest (SportsArt, 43 cm; Life Fitness, 46 cm; Octane, 46 cm; and True, 43 cm) stride lengths available on the devices. Elliptical order was randomized, as was the order of activities on each elliptical device, by using a computer program written in MATLAB.㛳㛳 During the second session, familiarization procedures were repeated. Biomechanical testing. Biomechanical assessment occurred during session 3. For quantifying muscular responses during activities, surface electrodes were taped over the muscle bellies of the gluteus maximus, gluteus medius, vastus lateralis, me-

‡‡

Qualisys AB, Packhusgatan 6, S-411 13 Gothenburg, Sweden. §§ C-Motion Inc, 20030 Century Blvd, Suite 104, Germantown, MD 20874.

February 2010

㛳㛳 The MathWorks Inc, 3 Apple Hill Dr, Natick, MA 01760-2098.

dial hamstring, lateral hamstring, tibialis anterior, medial gastrocnemius, and soleus muscles of the dominant limb by using standard techniques.28 Electrodes were secured to the skin by using Hypafix medical tape## and Coban wrap.*** Three 5-second EMG signals were recorded during maximum isometric manual muscle tests (MMTs) of each muscle by using standard testing positions for all muscles, with the exception of the calf muscles.29 Because the calf MMT includes multiple unilateral heel raises to assess endurance, the test was modified to include only one unilateral heel raise. In addition, the examiner applied downward pressure to the bilateral shoulders to further challenge the calf muscles. Placement of the electrodes and MMTs were validated by inspecting a realtime display of the EMG signals arising from specific resisted movements (DI-720 A/D Board††† and WinDaq Pro v2.72 software†††). After MMTs, two 5-second resting baseline EMG trials were recorded. Next, footswitch insoles were inserted into participants’ shoes to determine stride characteristics and identify gait cycle phasing. For the determination of sagittal-plane trunk, pelvis, and dominant-limb lowerextremity kinematics, reflective markers were placed on the skin over selected anatomic locations by using Visual 3D software–recommended marker set guidelines.30 In brief, markers were placed over the acromion (bilaterally), iliac crest (bilaterally), posterior superior iliac spine (bilaterally), anterior superior iliac spine (bilaterally), greater trochanter, medial and lateral femoral condyles, medial and lateral malleoli, posterior heel, and medial first metaBSN Medical GmbH, Quickbornstra␤e 24, 20253 Hamburg, Germany. *** 3M Corp, 3M Center, St Paul, MN 551441000. ††† DATAQ Instruments Inc, 241 Springside Dr, Akron, OH 44333. ##

Volume 90

Number 2

Physical Therapy f

293

Joint Kinematics and Muscle Demands in Elliptical Training and Walking tarsal; between the distal second and third metatarsals; and over the distal lateral fifth metatarsal and the lateral border of the midfoot. Additional tracking marker clusters were secured on the trunk, thigh, and lower leg. After marker placement, a static calibration trial was recorded. Next, the participants walked and used the elliptical trainers according to the procedures described for the familiarization sessions, and stride characteristic, kinematic, and EMG data were recorded simultaneously. Participants were not required to perform any warm-up exercises or stretching before walking or elliptical training. However, those who wanted to were not prevented from engaging in such activities. During elliptical trials, participants exercised for 2 minutes on each device once the preferred stride length and cadence were achieved. Data were recorded during the final 30 seconds. For reducing the potential impact of fatigue on exercise performance, the participants rested for up to 5 minutes between each elliptical training activity and walking activity performed within the session. Data Analysis Data recorded during walking at a self-selected speed and during elliptical training at a self-selected stride length and cadence were analyzed. For each participant, at least 7 but no more than 10 dominant-limb strides per condition were used to calculate stride characteristics, kinematics, and EMG variables. Specifically, for the kinematic data, 96.5% of trials incorporated 10 strides, 3.0% had 9 strides, and 0.5% had 7 strides. For the EMG data, 96.6% of trials had 10 strides, 1.7% had 9 strides, 0.6% had 8 strides, and 1.1% had 7 strides. Stride characteristics. Footswitch data defined stride characteristics recorded during gait trials. Each stride 294

f

Physical Therapy

Volume 90

was time normalized, with initial contact being defined as 0% of the gait cycle and swing termination being defined as 100% of the gait cycle.31,32 Stance, defined as the period when the reference limb was in contact with the ground, was subdivided into 5 phases based on bilateral footswitch patterns.33 Swing, defined as the period when the limb was not in contact with the ground, was divided into 3 equal intervals.33 Average speed (meters per minute), stride length (meters), and cadence (steps per minute) were determined for each stride with Visual 3D algorithms. During elliptical training, both feet remained in contact with the footplates throughout the cycle (Fig. 1). Analysis of the movement cycle on the basis of ground (pedal) reaction forces identified periods of cyclic weight acceptance and weight removal, with vertical ground reaction forces peaking at approximately 94% of body weight (compared with 103% during walking) at a time similar to the early stance peak that occurs during gait.34 The force reaches a minimum (⬃10% of body weight) at approximately 60% of the elliptical trainer movement cycle, similar to what is observed during normal gait.34 Because the foot pedals in the present study were not instrumented with force transducers, reflective markers on each machine’s footplates assisted in defining movement cycle timing. In addition, temporal events calculated from each participant’s walking trials were used to further subdivide the elliptical movement cycle into the corresponding phases of gait (Fig. 1).33 We elected to adopt terminology commonly applied to gait for describing elliptical training because of the notable observational and kinematic similarities between the 2 activities. Specifically, each cycle was time normalized, with “initial con-

Number 2

tact” being defined as the moment when the reference limb’s footplate marker was most anterior. A full movement cycle was demarcated as the period from the most anterior location of the reference limb’s footplate marker to the next ipsilateral most anterior location of the marker. Each participant’s initial double-limb support period during gait was calculated as a percentage of the gait cycle, and this value was used to identify the “weight acceptance” phases (initial contact and “loading response”) for elliptical training. Similarly, the duration of “pre-swing” for elliptical training was determined from the percentage of time spent by each participant in terminal doublelimb support during walking; the onset of pre-swing for elliptical training was defined as the moment when the elliptical trainer marker reached its most posterior location (corresponding to 50% of a full movement cycle). The remainder of stance was divided into 2 equal intervals: “mid stance” and “terminal stance.” Notable observational similarities between elliptical training and walking movement patterns during this period supported the use of this terminology for comparative purposes. In particular, during both activities, the thigh achieved a trailing-limb posture (thigh extension relative to vertical), the knee moved toward extension, and the ankle showed progressively greater dorsiflexion. “Swing” was divided into 3 equal intervals: “initial swing,” “mid swing,” and “terminal swing.” Again, the observation of similar movement patterns, including rapid hip and knee flexion during the early swing phases and then progressive extension in the later swing phases, supported the use of this comparative terminology. For each elliptical device, average speed was calculated by multiplying each participant’s self-selected step length (based on values provided by February 2010

Joint Kinematics and Muscle Demands in Elliptical Training and Walking the display panel of the elliptical device) by cadence (calculated from footplate markers). In addition, visual display values were recorded when participants achieved their self-selected stride length and cadence. Specifically, revolutions per minute were displayed for the SportsArt, Octane, and True, and speed was provided for the Life Fitness. Kinematics. Motion data were processed with Visual 3D software to produce 3-dimensional trajectories for each marker. Data were filtered with a 6-Hz Butterworth lowpass digital filter. The position and orientation of the trunk, pelvis, thigh, lower leg, and foot segments in the laboratory coordinate system were obtained, and Visual 3D algorithms with a Cardan sequence of rotations were used to determine trunk, pelvis, and lower-extremity joint angles for each percentage of the gait cycle. Sagittal-plane trunk, pelvis, and thigh orientations were expressed relative to vertical. For each participant, condition, and stride, the magnitude and timing of the peak joint angles were calculated, and the values were averaged for each phase of the gait or elliptical training cycle. Separate ensemble averaged profiles (plots) were created for each participant and condition. These data then were used to create 5 group ensemble averaged profiles (1 walking and 4 elliptical) that combined the data for the 20 participants. Electromyography. After EMG data screening, data for 1 subject were excluded from the subsequent data analysis because of a configuration error in the recording software. After adjustment for direct current bias and baseline noise, the digitally acquired EMG data were digitally filtered (60-Hz notch; 10-Hz high-pass filter and 350-Hz Butterworth lowpass filter). Filtered signals were fullwave rectified and integrated over 0.01-second intervals. The EMG timFebruary 2010

ing, duration, and amplitude were assessed with Visual 3D software. Intensities were normalized and reported as a percentage of the maximum isometric MMT EMG signal (% MMT) to allow for comparisons of intensities between muscles. Onset and cessation were determined for all envelopes of EMG data that exceeded an amplitude of 5% of the MMT (ie, ⬎5% MMT).35,36 Envelopes of EMG data separated by shortduration gaps (⬍5% of the gait cycle) were combined into larger packets for analysis and for the calculation of duration (expressed as a percentage of the gait cycle).35,36 The average peak and mean EMG activity levels across strides were calculated and expressed as a percentage of the maximum isometric MMT EMG signal (% MMT). When a participant’s EMG activity levels did not achieve the 5% threshold for any stride, that participant’s peak and mean values were excluded from the calculations of overall group mean and peak EMG values. Thus, the peak, mean, and duration of EMG activity reported in Table 2 represent data from participants with significant EMG activity levels only. A time-normalized mean profile for each participant and muscle was created for each condition, and mean onset and cessation times were calculated and expressed as a percentage of the gait (movement) cycle. Finally, ensemble averaged profiles containing EMG data for participants with significant EMG activity levels were created for each muscle and condition. These profiles were used to identify the phase of peak EMG activation for each muscle and to describe the variability in peak timing. Statistical analysis. Descriptive statistics were obtained for key variables with SigmaPlot 11.0 software‡‡‡ and Excel.§§§ Separate one‡‡‡

Systat Software Inc, 225 W Washington St, Suite 425, Chicago, IL 60606.

way analyses of variance with repeated measures (5 ⫻ 1) identified significant differences in selected stride characteristics (speed, stride length, and cadence), kinematics (mean trunk and pelvic tilt and critical events, as defined by Perry,33 at the hip, thigh, knee, and ankle),31 and EMG variables (peak, mean, and duration) between each elliptical trainer and gait. Assumptions of normality were first examined with SigmaPlot using the KolmogorovSmirnov test (with Lilliefors correction); if the assumptions were violated, then the Friedman analysis of variance with ranks was used to identify significant differences. We performed Bonferroni adjustments to account for multiple comparisons within a priori selected stride characteristic (n⫽3; P⬍.016), kinematic (n⫽14; P⬍.003), and EMG (n⫽24; P⬍.002) data. Statistical significance was defined as P⬍.05/n, where n represented the number of variables analyzed within each type of data. In addition, for each participant, the ensemble hip, thigh, knee, and ankle movement patterns occurring during elliptical training on each device were compared with the waveforms occurring during walking by calculating the coefficient of multiple correlations37 (CMC) with Excel. The CMC is a statistic that can be used to describe similarities or differences between 2 or more waveforms (eg, the sagittal-plane knee movement pattern occurring during walking versus elliptical training on the SportsArt). A CMC that approximates 1 indicates a high level of similarity between the waveforms being compared, whereas a value closer to zero reflects highly dissimilar patterns. In the present study, the goal was to quantify (for a given joint) the similarity of the movement pattern occurring on a specific elliptical trainer §§§ Microsoft Corp, One Microsoft Way, Redmond, WA 98052-6399.

Volume 90

Number 2

Physical Therapy f

295

Joint Kinematics and Muscle Demands in Elliptical Training and Walking Table 2. Peak, Mean, and Duration of Electromyography (EMG) Activity Recorded During Walking and Elliptical Training (n⫽19) X (SD) forb: Muscle Gluteus maximus

Gluteus medius

Lateral hamstring

Medial hamstring

Vastus lateralis

Gastrocnemius

Variablea Duration

Tibialis anterior

15 (10)

SportsArt

Life Fitness

Octane

True

36 (7)

1

34 (10)

1

35 (13)

1

35 (11)

1 1

Peak

26 (25)

42 (36)

1

42 (45)

1

41 (45)

1

38 (33)

Mean

13 (7)

20 (15)

1

20 (19)

1

19 (18)

1

18 (14)

Duration

34 (19)

36 (16)

40 (24)

39 (23)

39 (25)

Peak

38 (28)

35 (31)

41 (41)

41 (41)

38 (35) 18 (15)

Mean

16 (11)

18 (14)

20 (21)

20 (19)

Duration

34 (18)

40 (20)

35 (21)

39 (22)c

35 (25)

c

21 (17)

Peak

33 (16)

31 (20)

27 (21)

27 (23)

Mean

16 (6)

15 (10)

15 (11)

14 (12)c

c

Duration

34 (16)

Peak

35 (22)c c

Mean

16 (8)

Duration

31 (12)

c

15 (11)c

21 (16)

8 (5)

c

2

18 (15)

d

2

13 (10)d d

2

11 (8)

2

2

16 (15)

e

2

15 (15)

2

12 (11)e

2

10 (9)f

2

f

2

e

f

2

2

7 (5)

2

7 (6)

2

6 (5)

74 (19)

1

72 (19)

1

73 (18)

1

76 (18)

1

Peak

31 (16)

55 (27)

1

55 (27)

1

57 (28)

1

59 (28)

1

Mean

15 (5)

26 (11)

1

26 (11)

1

27 (12)

1

26 (11)

1

54 (13)

34 (22)

2

46 (24)

108 (74)

63 (42)

2

63 (30)

2

56 (27)

2

2

25 (11)

2

22 (9)

2

Duration Peak

Soleus

Walking

Mean

41 (25)

24 (13)

Duration

62 (13)

62 (19)

43 (20)

71 (19)

52 (18)

71 (17)

63 (36)

2

22 (9)

2

75 (17)

1 2

Peak

113 (43)

69 (42)

2

68 (34)

2

69 (33)

2

69 (26)

Mean

41 (14)

25 (12)

2

28 (12)

2

27 (11)

2

31 (11)

Duration

70 (19)

37 (30)

24 (20)d

2

21 (21)f

2

19 (19)d

2

25 (20)

d

2

f

2

20 (16)d

2

Peak Mean

58 (17) 19 (5)

11 (7)

2

21 (19)

2

d

10 (9)

2

20 (17) 10 (9)

f

2

11 (9)

d

2

a

Duration is reported as a percentage of the gait/elliptical cycle. Peak and mean EMG amplitude are reported as a percentage of the maximum isometric manual muscle test (% MMT). b An “up” arrow indicates that the EMG value was significantly greater than that for walking; the significance level after Bonferroni adjustment was P⬍.002 (0.05/24). A “down” arrow indicates that the EMG value was significantly less than that for walking; the significance level after Bonferroni adjustment was P⬍.002 (0.05/24). c n⫽18 (1 participant lacked significant EMG activity for the muscle and training condition). d n⫽17 (2 participants lacked significant EMG activity for the muscle and training condition). e n⫽16 (3 participants lacked significant EMG activity for the muscle and training condition). f n⫽15 (4 participants lacked significant EMG activity for the muscle and training condition).

to the waveform recorded during walking (ie, the criterion of interest). Mean and standard deviation CMCs for the hip, thigh, knee, and ankle for the 20 subjects are shown in Figure 2. Values for the trunk and pelvis were not calculated because of the strong influence of an overall small arc of total motion.37

(H133G070209) from the Department of Education, National Institute on Disability and Rehabilitation Research. However, the contents do not necessarily represent the policy of the Department of Education, and endorsement by the federal government should not be assumed.

Results Role of the Funding Source The contents of this research report were developed under a grant 296

f

Physical Therapy

Volume 90

Stride Characteristics The self-selected elliptical speeds were approximately 30% lower than

Number 2

walking speed mainly because of reductions in self-selected stride length during elliptical training (P⬍.001) (Tab. 1). The maximum stride length of each device was selected infrequently. In particular, none of the subjects used the longest stride length of the SportsArt (74 cm), only 1 used the maximum of the True (66 cm), 3 selected the maximum of the Life Fitness (61 cm), and 5 elected to use the longest stride length of the Octane (58 cm). Although the caFebruary 2010

Joint Kinematics and Muscle Demands in Elliptical Training and Walking

Figure 2. Sagittal-plane mean joint motion (degrees) of the trunk (A), pelvis (B), hip (C), thigh (D), knee (E), and ankle (F) during walking (black line⫽mean; gray band⫽SD) and elliptical training on the SportsArt (SA; pink line), Life Fitness (LF; green line), Octane (OC; orange line), and True (TR; blue line). Movement reversals at the trunk and pelvis were consistent across activities. Coefficients of multiple correlation (CMC), presented as mean (standard deviation), are shown for the hip, thigh, knee, and ankle. High CMCs at the hip, thigh, and knee reflect a strong similarity between walking and elliptical training movements; however, the curves indicate more flexion in posture during elliptical training. Characteristic dorsiflexion–plantar flexion reversals were lacking at the ankle during elliptical training, so that CMCs were lower. IC⫽initial contact, ISw⫽initial swing, LR⫽loading response, MSt⫽mid stance, MSw⫽mid swing, PSw⫽pre-swing, TSt⫽terminal stance, TSw⫽terminal swing.

dence recorded for the Life Fitness and True closely approximated that of walking, the cadence recorded for the Octane was lower (P⫽.013). The cadence recorded for the SportsArt (102.5) also was notably lower than that of walking; however, this difference did not reach the level of statistical significance. During walking, stance occupied 62% of the gait cycle (SD⫽2%), swing occupied 38% of February 2010

the gait cycle (SD⫽2%), initial double-limb support occupied 12% of the gait cycle (SD⫽2%), and terminal double-limb support occupied 13% of the gait cycle (SD⫽1%). Kinematics Trunk, pelvis, hip, thigh, and knee movements revealed notable similarities between elliptical training and walking; however, participants gen-

erally showed more flexion during elliptical training (Tab. 3, Fig. 2A, 2B, 2C, 2D, and 2E). During elliptical training, the ankle lacked the plantarflexion and dorsiflexion reversals characteristic of walking (Fig. 2F). The shallow sinusoidal waves (⬍3°) of decreasing and increasing flexion (forward lean) of the trunk during elliptical training closely mimicked

Volume 90

Number 2

Physical Therapy f

297

Joint Kinematics and Muscle Demands in Elliptical Training and Walking Table 3. Critical-Event Joint Angles Recorded During Walking and Elliptical Training (n⫽20) X (SD) Angles (°) fora: Joint Hip

Thigh

Phase

SportsArt

Life Fitness

IC

Walking 31.5 (7.2)

43.8 (5.7)b

46.1 (4.9)b

44.7 (5.4)b

45.4 (5.1)b

TSt peak ext

⫺7.3 (7.6)

4.3 (7.3)b

6.7 (8.3)b

6.6 (7.7)b

5.4 (7.3)b

MSw peak flex

34.4 (4.9)

54.1 (6.7)

b

b

b

56.5 (5.4)b

IC

23.3 (4.2)

32.2 (4.3)b

31.9 (3.3)b

30.6 (3.0)b

31.4 (3.1)b

⫺9.5 (5.0)

⫺8.2 (4.7)

⫺8.9 (4.4)

TSt peak ext MSw peak flex Knee

b

True

57.6 (6.3)

b

⫺10.0 (5.0)b

26.3 (6.4)

40.7 (4.0)b

41.9 (2.9)b

43.2 (3.1)b

41.8 (3.0)b

3.7 (5.6)

34.1 (5.6)

b

b

b

36.2 (5.4)b

IC LR final position

Ankle

⫺14.7 (4.4)

b

56.9 (5.6)

Octane

38.7 (5.0)

36.1 (5.4)

19.3 (6.8)

21.2 (5.6)

23.2 (5.9)

23.0 (6.2)

21.0 (6.2)

TSt peak ext

6.2 (5.6)

16.2 (5.4)b

17.9 (4.6)b

18.5 (5.3)b

17.8 (5.8)b

ISw peak flex

66.8 (7.1)

72.4 (5.3)b

78.2 (5.4)b

80.4 (5.9)b

82.0 (5.7)b

3.0 (3.7)

⫺4.7 (3.4)

LR peak PF

⫺2.9 (3.1)

⫺4.8 (3.8)

4.3 (3.9)

TSt peak DF

14.8 (3.2)

16.6 (5.6)

18.2 (5.5)

IC

MSw final position

3.4 (2.3)

1.3 (3.7)

b

5.3 (3.7)

11.5 (4.3)

b

b

0.8 (4.3)

b

5.0 (3.4)

0.7 (4.4)

b

2.9 (3.8)b

16.4 (5.2)

16.9 (4.2)

7.1 (4.7)

11.8 (4.3)b

a

Positive values indicate flexion of hip, thigh, and knee and dorsiflexion of ankle. Negative values indicate extension of hip, thigh, and knee and plantar flexion of ankle. IC⫽initial contact, TSt⫽terminal stance, ext⫽extension, MSw⫽mid swing, flex⫽flexion, LR⫽loading response, ISw⫽initial swing, PF⫽plantar flexion, DF⫽dorsiflexion. b The value was significantly different from that for walking; the significance level after Bonferroni adjustment was P⬍.003 (0.05/14).

those of walking (Fig. 2A). The mean trunk flexion was greatest on the True (9.9°; SD⫽5.3°), followed by the Octane (9.5°; SD⫽6.0°) and the Life Fitness (9.4°; SD⫽5.3°), significantly exceeding that recorded during walking (3.0°; SD⫽4.0°; P⬍.001). Only SportsArt (6.0°; SD⫽4.8°) did not differ significantly from walking for trunk flexion. During elliptical training, the pattern of pelvic tilts was comparable to that during walking; however, the pelvis was more anteriorly tilted (Fig. 2B). Compared with the mean gait posture (7.2°), the twofold-greater anterior tilt recorded for the Life Fitness (14.6°; SD⫽4.9°), True (14.3°; SD⫽4.1°), and Octane (14.3°; SD⫽4.7°) differed significantly (P⬍.001). Anterior tilt recorded for the SportsArt (12.7°) was lower than that recorded for the other elliptical trainers but still exceeded that of walking (P⬍.001). 298

f

Physical Therapy

Volume 90

The movement of the hip (thigh relative to pelvis) during elliptical training showed a moderately strong similarity to that during walking, as evidenced by the CMCs (ⱖ.85) (Fig. 2C). The CMCs ranged from 0 (no similarity) to 1 (highest level of similarity).37 The hip showed more flexion (⬃12°–14°) at the beginning of the elliptical cycle than during gait (Tab. 3). During the loading response, hip flexion decreased during elliptical training; in contrast, a plateau in hip flexion was observed during walking. Mid stance hip motion was characterized by progressive hip extension across all elliptical training and walking activities. In late stance, the hip failed to achieve full extension during elliptical training, in contrast to the posture during walking (4.3°– 6.7° of flexion across elliptical training devices versus 7.3° of extension during walking). Additionally, there was an earlier reversal toward flexion during elliptical training (on

Number 2

average, 41% of the elliptical cycle versus 51% of the walking cycle). In mid swing, the hip achieved significantly more flexion during elliptical training than during walking (54.1°– 57.6° of flexion across elliptical training devices versus 34.4° during walking). Similar to the movement of the hip, the movement of the thigh (relative to vertical) during elliptical training showed a strong similarity to that during walking (CMCs of ⱖ.92) (Fig. 2D). The thigh was more flexed at the initiation of the elliptical cycle than during walking (Tab. 3). In the loading response, the immediate and steady decrease in thigh flexion during elliptical training contrasted notably with the plateau characteristic of walking. Mid stance was exemplified by progressive thigh extension across all activities. In the latter half of stance, a trailing-limb posture (thigh extension relative to vertical) February 2010

Joint Kinematics and Muscle Demands in Elliptical Training and Walking Table 4. Timing of Peak Muscle Activation for Participants With Significant Electromyographic (EMG) Activity and Number of Participants With Timing Within ⫾10% of That Value Muscle

Variablea

Walking

SportsArt

Life Fitness

Octane

True

4 (2)

8 (4)

6 (4)

3 (6)

5 (4)

18/19

19/19

18/19

16/19

18/19

7 (6)

8 (5)

9 (5)

9 (6)

8 (6)

No. of subjects within ⫾10%

18/19

17/19

18/19

18/19

17/19

Timing of peak, X (SD)

94 (4)

Gluteus maximus

Timing of peak, X (SD)

Gluteus medius

Timing of peak, X (SD)

Lateral hamstring

No. of subjects within ⫾10%

19/19

15/19

Medial hamstring

Timing of peak, X (SD)

93 (6)

98 (12)

No. of subjects within ⫾10%

17/18

12/18

Vastus lateralis

Timing of peak, X (SD) No. of subjects within ⫾10%

19/19

12/19

14/19

11/19

13/19

Gastrocnemius

Timing of peak, X (SD)

40 (4)

18 (18)

23 (3)

18 (11)

23 (4)

No. of subjects within ⫾10%

6 (3)

7 (10)

5 (11) 13/19 5 (13)

1 (12)

2 (11) 9/18 1 (14)

4 (10) 14/19 4 (16)

9/17

9/16

8/15

1 (9)

98 (13)

100 (15)

No. of subjects within ⫾10%

18/19

17/19

19/19

18/19

19/19

Soleus

Timing of peak, X (SD)

44 (3)

17 (18)

15 (15)

20 (4)

18 (4)

No. of subjects within ⫾10%

19/19

15/19

Tibialis anterior, first peak

Timing of peak, X (SD)

Tibialis anterior, second peak

1 (2)

16/19

18/19

18/19

6 (12)

N/Ab

N/Ab

N/Ab

No. of subjects within ⫾10%

19/19c

12/19d

N/Ab

N/Ab

N/Ab

Timing of peak, X (SD)

69 (6)

49 (11)

45 (16)

46 (25)

47 (11)

12/17

10/15

14/17

No. of subjects within ⫾10%

16/19

c

14/19

d

a

Timing of peak muscle activation is reported as a percentage of the gait/elliptical cycle. b N/A⫽no significant EMG activity was available for identifying a primary peak. c Sixteen participants had both peaks. d Nine participants had both peaks.

was achieved during elliptical training and walking. However, the reversal occurred earlier during elliptical training (on average, 41% of the elliptical cycle versus 52% of the walking cycle), and peak extension was significantly lower (8.2°–10.0° across elliptical devices versus 14.7° during walking). Peak thigh flexion during mid swing was significantly accentuated during elliptical training compared with walking. The movement of the knee during elliptical training showed a moderate similarity to that during walking, with CMCs ranging from .87 to .89 (Fig. 2E). Like the thigh, the knee was significantly more flexed at initial contact during elliptical training (range⫽34.1°–38.7°) than during walking (3.7°) (Tab. 3). The loading response was accompanied by a proFebruary 2010

gressive reduction in elliptical training knee flexion; in contrast, an increase was registered during gait. By the end of the loading response, the posture of the knee was similar across activities (range⫽19.3°–23.3°). In mid stance, the knee showed progressive extension, and the extension occurred more rapidly during gait than during elliptical training. Terminal stance peak extension was notably lower during elliptical training (16.2°–18.5° of flexion) than during walking (6.2° of flexion), and the reversal occurred earlier (on average, 36% of the elliptical cycle versus 41% of the walking cycle). Although initial swing peak flexion was excessive on all elliptical devices compared with that during walking, the SportsArt was most similar to gait.

Elliptical and walking movements were least similar at the ankle (CMCs of .57–.71) (Fig. 2F). At initial contact, the positions of the ankle on the True (5.0° of dorsiflexion) and Life Fitness (5.3° of dorsiflexion) were similar to that during walking (3.0° of dorsiflexion), but the SportsArt differed notably (4.7° of plantar flexion) (Tab. 3). Although terminal stance peak dorsiflexion did not vary significantly across activities, the continued increase in elliptical training dorsiflexion during pre-swing and initial swing contrasted sharply with the normal rapid reversal into plantar flexion during walking. Mid swing and terminal swing were characterized by a progressive decrease in dorsiflexion during elliptical training, unlike the relatively stable dorsiflexion (3°–5°) recorded during gait.

Volume 90

Number 2

Physical Therapy f

299

Joint Kinematics and Muscle Demands in Elliptical Training and Walking

Figure 3. Mean rectified and integrated electromyography (EMG) profiles (expressed as a percentage of the maximum isometric manual muscle test [% MMT]) for the gluteus maximus (A), gluteus medius (B), lateral hamstring (C), medial hamstring (D), vastus lateralis (E), gastrocnemius (F), tibialis anterior (G), and soleus (H) muscles during walking (black line⫽mean; gray band⫽SD) and elliptical training on the SportsArt (pink line), Life Fitness (green line), Octane (orange line), and True (blue line). Note the similarities in the timing of peak EMG activation between elliptical training and gait for the gluteus maximus (loading response [LR]) and gluteus medius (LR) muscles. The vastus lateralis muscle EMG activation peaked between late swing and early stance across conditions. The gastrocnemius and soleus muscle EMG activation peaked earlier during elliptical training (mid stance [MSt]) than during gait (terminal stance [TSt]), and hamstring muscle activation was delayed (LR vs terminal swing [TSw]). The activation of the gluteus maximus and vastus lateralis muscles was higher during elliptical training than during gait, whereas the activation of the medial hamstring, soleus, gastrocnemius, and tibialis anterior muscles was lower. IC⫽initial contact, ISw⫽initial swing, MSw⫽mid swing, PSw⫽pre-swing.

300

f

Physical Therapy

Volume 90

Number 2

February 2010

Joint Kinematics and Muscle Demands in Elliptical Training and Walking Electromyography Although ensemble averaged walking and elliptical muscle activation profiles revealed many similarities to walking, statistical analyses identified differences (Tabs. 2 and 4, Fig. 3). Additionally, a few participants did not meet the 5% amplitude and duration threshold35,36 required for identifying significant EMG activity during a given task. In particular, one participant did not have significant lateral hamstring muscle EMG activity when using the Octane. Significant medial hamstring muscle activity also was lacking for some participants during walking (n⫽1) and elliptical training on the SportsArt (n⫽1), Life Fitness (n⫽2), Octane (n⫽3), and True (n⫽4). No significant tibialis anterior muscle activity was detected for a subset of participants using the Life Fitness (n⫽2), Octane (n⫽4), and True (n⫽2). Peak activation timing. The most consistent pattern for peak muscle activation was noted for the gluteus maximus and gluteus medius muscles (Tab. 4). During walking, the average time at which peak gluteus maximus muscle activity occurred was 4% of the cycle (ie, in the loading response). This time was comparable to the average time at which the peak occurred on each elliptical trainer (3%– 8% of the movement cycle). For more than 84% of participants (16/19), peak activation occurred within a range from 10% before this point in the movement cycle to 10% after this point (ie, within ⫾10% of the movement cycle). Peak gluteus medius muscle activity also occurred, on average, in the loading response for all conditions, with peak activation occurring within ⫾10% of the mean peak time for more than 89% of participants. Although the average time at which peak activation of both the medial and the lateral hamstring muscles occurred during walking was the terminal swing, peak activation during February 2010

elliptical training occurred in the subsequent loading response for all conditions except for the medial hamstring muscle on the SportsArt. The range of times at which peak hamstring muscle activity occurred was most consistent during walking (⬎94% within ⫾10% of the movement cycle), whereas only 50% to 78% of values occurred within ⫾10% of the movement cycle during elliptical training. Peak vastus lateralis muscle activity occurred, on average, in the loading response during walking and training on the SportsArt and Life Fitness, whereas it occurred slightly earlier, in the preceding phase (ie, in initial contact), on the Octane and True. The time at which peak calf muscle (gastrocnemius and soleus) activity occurred was notably later during walking (terminal stance) than during elliptical training (mid stance). Peak calf muscle activity occurred within ⫾10% of the mean peak time for more than 94% of participants, with only 3 exceptions (gastrocnemius muscle during training on the SportsArt and soleus muscle during training on the SportsArt and Life Fitness). Finally, although a biphasic pattern of tibialis anterior muscle activity was identified during walking and training on the SportsArt, a monophasic pattern was documented for the remaining elliptical conditions. Duration. The duration of activity of both the gluteus maximus and the vastus lateralis muscles was longer during elliptical training than during walking because of both earlier onset (P⬍.001) and later cessation (Pⱕ .002). Gluteus medius muscle activity duration did not vary significantly between conditions despite an earlier onset across all elliptical conditions than during walking (P⬍.001). Although cessation appeared earlier, this finding was not statistically significant (P⫽.266). Although the lat-

eral hamstring muscle activity duration did not vary significantly among conditions, the medial hamstring muscle activity duration was significantly curtailed during elliptical training, partly because of the delayed onset of functionally significant EMG (ie, ⬎5% MMT). The plantar flexor muscles displayed 2 patterns. Soleus muscle activity duration was prolonged only on the True compared with walking. The significantly earlier soleus muscle activity onset (initial swing) (P⬍.001) registered on each elliptical device was offset by the subsequent early cessation in terminal stance (P⬍.001) for all but the True. Gastrocnemius muscle activity duration was shortened only on the SportsArt compared with walking. Although not reaching the level of statistical significance, the later onset of gastrocnemius muscle activity on the SportsArt, combined with the early cessation of gastrocnemius muscle activity across all elliptical conditions (P⬍.001), led to the shortened duration on the SportsArt. Tibialis anterior muscle activity duration was shorter on all elliptical devices except for the SportsArt compared with walking. The extended period of quiescence during mid swing contributed to this reduction because onset and cessation times did not vary significantly between elliptical training and walking. Amplitude. In addition to temporal EMG variations between elliptical training and walking, differences in relative intensities (peak and mean) were registered. Only the gluteus medius muscle showed no significant differences in EMG activation across conditions. Compared with walking, elliptical training led to significantly higher intensities of gluteus maximus muscle peak (46%– 62% higher) and mean (38%–54% higher) activation levels in all but one instance (mean intensity on the True). The differences in intensities

Volume 90

Number 2

Physical Therapy f

301

Joint Kinematics and Muscle Demands in Elliptical Training and Walking between elliptical training and walking were even more substantial for vastus lateralis muscle peak (77%– 90% higher) and mean (73%– 80% higher) activation levels. The remaining muscles showed reduced peak and mean activation levels of various magnitudes for elliptical training compared with walking. The medial hamstring muscle showed decreases in the intensities of peak (57%–71%) and mean (50%– 63%) activation levels across all elliptical conditions compared with walking. In contrast, the lateral hamstring muscle showed decreased intensity (36% for peak and 31% for mean) only on the True. Ankle muscle recordings documented significantly reduced intensities of peak activation for the gastrocnemius (42%– 48%), soleus (39%– 40%), and tibialis anterior (57%– 66%) muscles across all elliptical conditions compared with walking. The intensities of mean activation levels were similarly reduced for the gastrocnemius (39%– 46%), soleus (24%–39%, except with the True), and tibialis anterior (42%– 47%) muscles.

Discussion The need for effective walking and exercise interventions after discharge from therapy is clear. Approximately 53 million people living in the United States have a chronic condition or disability,38,39 and an estimated 15 million adults in that population experience difficulty with walking.40 People with disabilities and chronic medical conditions are at greater risk for developing medical problems than people without disabilities, partly because of an inability to exercise at sufficiently challenging levels.14,18,20,41– 43 Physical therapists are uniquely positioned to teach clients the knowledge and skills needed to exercise on the variety of functionally relevant cardiovascular equipment available in the community. This research provides 302

f

Physical Therapy

Volume 90

data to help guide clinical decision making related to elliptical trainers. Kinematic analysis revealed many similarities as well as distinct differences between walking and elliptical training. The trunk and pelvis moved similarly during both activities; however, subjects showed greater forward trunk lean and anterior pelvic tilt when using the elliptical devices because of the anterior location of the elliptical handles. Similarity of movement between elliptical training and walking was supported by the relatively high CMCs documented for the hip (.85–.89), thigh (.92–.94), and knee (.87–.89); however, the CMCs for the ankle were notably lower (.57–.71). The values fell short of previously reported within-day CMCs calculated for the hip, knee, and ankle during gait (⬎.97).37 Reduced CMCs arose partly from the greater flexion occurring during elliptical training than during walking. One design feature inherent in the elliptical devices studied appears to be relevant. The arc of motion on each device elevated the leg higher (13–28 cm) than the 1 to 2 cm typically occurring during the swing period of walking (Fig. 1).44 The finding that participants achieved a trailing-limb posture (thigh extension relative to vertical) during the terminal stance of elliptical training may be clinically important, even though this extension was less marked than that during walking. People with hip flexor muscle weakness may be able to capitalize on gravity to allow the femur to passively fall forward during limb advancement. Additionally, previous research demonstrated that hip extension provides an important afferent input that subsequently assists the motor system in advancing the limb during swing.45– 49 In the present study, terminal stance hip extension (ie, thigh relative to pel-

Number 2

vis) was lower during elliptical training (4.3°– 6.7° flexion) than during walking (7.3° extension). The finding in a participant with a complete spinal cord injury that the iliopsoas muscle EMG amplitude increased sharply despite the trailing limb achieving only approximately 6° of hip flexion during walking (50 m/min)45 suggests that full extension to neutral may not be required for limb advancement during walking. However, additional research is required to determine the demands of elliptical training on key hip flexor muscles and the impact of the trailing-limb posture on hip flexor muscle activation. Research is under way in our laboratory to determine the impact on hip and thigh kinematics of varying the stride length of each elliptical trainer. Of the elliptical trainers evaluated, the SportsArt best simulated sagittalplane gait kinematics, as indicated by 3 evidence sources. First, the trunk and pelvis posture on the SportsArt most closely approximated those in gait. Second, the highest CMCs arose from a comparison of SportsArt and gait kinematic profiles. Finally, although critical-event joint postures frequently varied between elliptical training and walking, the fewest significant differences occurred when comparing training on the SportsArt with walking. The EMG profiles obtained during elliptical training and walking highlighted the impact of posture and functional demands on muscle activation. For example, the finding of greater gluteus maximus and vastus lateralis muscle EMG activity during elliptical training was not unexpected because the lower extremity was in greater flexion during elliptical training than during gait. Increased extensor muscle activity stabilized the leg, preventing hip and knee collapse. During initial swing and mid swing, the vastus lateralis February 2010

Joint Kinematics and Muscle Demands in Elliptical Training and Walking muscle activity that occurred was necessary to initiate and sustain the forward push of the elliptical pedal. The timing and intensity of the lateral and medial hamstring muscle activities were similar to each other during gait; however, their elliptical profiles showed 2 patterns. Medial hamstring muscle activity was reduced during elliptical training, particularly during terminal swing, partly reflecting the reduced need for limb deceleration. The mechanical constraints of the elliptical path limited forward advancement of the limb during this period. The relatively higher amplitude of the lateral than of the medial hamstring muscle, particularly during terminal swing and early stance, suggests a possible role for the lateral hamstring muscle in controlling transverse limb rotation on an elliptical device. Lu et al34 identified subtle differences in knee rotation (1°–2°) during late swing and early stance in their study of 15 male subjects walking and training on a single elliptical device; however, no EMG data were recorded for comparison. We are currently exploring the impact of varying the stride length on frontal- and transverse-plane kinematic and EMG demands to better understand the possible implications. During walking, the tibialis anterior muscle is primarily responsible for foot elevation during swing, which is followed by controlled lowering during weight acceptance. Tibialis anterior muscle activity was reduced on the elliptical trainer because the elliptical pedal provided continuous external support for the foot. Peak EMG activation of the tibialis anterior muscle in the present study occurred just before (⬃45%– 49% of the elliptical cycle) the period (identified by Lu et al34) during which the vertical reaction forces under the foot pedal of the reference limb were at a minimum (⬃10% of body February 2010

weight). Collectively, these data suggest that the tibialis anterior muscle may play a role in reducing the load between the foot and the pedal during upward movement of the pedal in the late stance phase.

vestigation. It is interesting that despite differences in muscle activation patterns between Lokomat training and treadmill walking,53 Lokomat training has led to improved walking in people with spinal cord injuries.5,6

The early onset of soleus but not gastrocnemius muscle activity during initial swing highlights selective muscle activation. The soleus muscle was optimal for pushing the pedal downward because of its role as a plantar flexor muscle. However, because of its dual role as a knee flexor muscle and an ankle plantar flexor muscle, the gastrocnemius muscle would have contributed an undesirable knee flexor muscle force at a time when the vastus lateralis muscle was rapidly increasing amplitude to support the flexed knee. During the remainder of the elliptical cycle, gastrocnemius and soleus muscle activities were reduced and peaked earlier (mid stance instead of the terminal stance for gait). The lack of a single-limb support period during elliptical training eliminated the need to support the full body weight on one leg. The rapid increase in calf muscle activity between the loading response and mid stance helped push the elliptical pedal downward and posteriorly. During gait, adequate loading of the calf muscles during the latter half of stance has been shown to promote the requisite activity for stabilizing the limb during that period.46,50 –52 Whether the early and reduced calf muscle activity documented in the present study decreases the reflex activation of leg stabilizers warrants further investigation.

The variations documented between elliptical training and walking in the present study elucidate several potential therapeutic applications. The relatively high gluteus maximus and vastus lateralis muscle activation levels during elliptical training suggest that elliptical devices could serve as a tool for helping people increase strength (force-generating capacity) and endurance in the key hip and knee stabilizers required for gait. The reduced ankle plantar-flexor and dorsiflexor muscle activity during elliptical training indicate that if reducing demand on these muscles is a therapeutic necessity, such as when poliomyelitis has weakened the ankle muscles, elliptical training may provide a mechanism for minimizing demand distally while concurrently strengthening proximally.

On the basis of the EMG variables assessed, no elliptical trainer emerged as the best for simulating the patterns recorded during gait. The extent to which EMG differences documented between elliptical training and walking influence therapeutic outcomes warrants in-

An additional consideration is that during elliptical training, mechanical linkages between the foot pedals and the upper-extremity handles allow forces produced by the contralateral lower extremity and both upper extremities to be transferred to the reference lower extremity. As a result, users can apply multiple strategies to generate reference limb movement. If the legs contribute symmetrically to the movement, kinematic and EMG patterns for the contralateral limb would be expected to be similar to those for the reference limb but offset by 50% of the movement cycle. If a client had unilateral or bilateral lower-extremity weakness that prevented the generation of sufficient force to move the pedals through the elliptical path, one compensation could be moving the handles more forcefully, thus reducing demand on the weakened legs. In

Volume 90

Number 2

Physical Therapy f

303

Joint Kinematics and Muscle Demands in Elliptical Training and Walking the present study, we included people who were healthy and assessed only the dominant lower limb. Future research exploring the role of the upper extremities in elliptical training as well as the impact of particular areas of weakness on the demands of elliptical training appears to be warranted. To our knowledge, the present study is the first to address similarities and differences in EMG and kinematic demands between walking and elliptical training. We elected to include participants who varied widely in age (range⫽19 –75 years) and height (range⫽160 –191 cm) because such variability is representative of the population expected to use elliptical trainers. A potential limitation of this approach is that the variability, combined with a relatively small number of subjects (n⫽20), may have contributed to difficulties in detecting significant differences. However, this foundational work is expected to provide a basis for future research focusing on specific populations, elliptical trainers, or anthropometric characteristics. In the present report, only sagittal-plane kinematics have been discussed. Meaningful variations may exist across elliptical trainers in the frontal and transverse planes. Future work will explore variations in non–sagittal-plane joint motions and potential implications for practice.

Conclusion This work expands the understanding of the extent to which elliptical trainers emulate gait and provides empirical data to guide therapeutic use across the health care continuum and into the community. The SportsArt emerged as the elliptical device that most closely simulated sagittal-plane walking kinematics. Electromyographic analysis did not identify one clearly superior device. Research with clients who have physical disabilities should provide 304

f

Physical Therapy

Volume 90

valuable insight into the potential therapeutic benefits and challenges of elliptical training. Dr Burnfield and Mr Buster provided concept/idea/research design and project management. All authors provided writing and data analysis. Mr Buster and Mr Taylor provided data collection. Dr Burnfield provided fund procurement. The authors acknowledge Aaron Ankeny for his contributions to data collection and processing and Nicholas Doher, Tyler Scherr, Brad Balogh, and Ryan Roemmich for their contributions to data processing. This study was approved by the Institutional Review Board of Madonna Rehabilitation Hospital. The contents of this research report were developed under a grant (H133G070209) from the Department of Education, National Institute on Disability and Rehabilitation Research. However, the contents do not necessarily represent the policy of the Department of Education, and endorsement by the federal government should not be assumed. This article was received February 3, 2009, and was accepted June 21, 2009. DOI: 10.2522/ptj.20090033

References 1 Da Cunha I, Lim P, Qureshy H. A comparison of regular rehabilitation with supported treadmill ambulation training for acute stroke subjects. J Rehabil Res Dev. 2001;38:245–255. 2 Hesse S, Bertelt C, Schaffrin A. Restoration of gait in nonambulatory hemiparetic patients by treadmill training with partial body-weight support. Arch Phys Med Rehabil. 1994;75:1087–1093. 3 Visintin M, Barbeau H, Korner-Bitensky N. A new approach to retrain gait in stroke patients through body weight support and treadmill stimulation. Stroke. 1998;29: 1122–1128. 4 Laufer Y, Dickstein R, Chefez Y. The effect of treadmill training on the ambulation of stroke survivors in the early stages of rehabilitation: a randomized study. J Rehabil Res Dev. 2001;38:69 –78. 5 Winchester P, McColl R, Querry R. Changes in supraspinal activation patterns following robotic locomotor therapy in motor-incomplete spinal cord injury. Neurorehabil Neural Repair. 2005;19: 313–324. 6 Wirz M, Zemon DH, Rupp R. Effectiveness of automated locomotor training in patients with chronic incomplete spinal cord injury: a multicenter trial. Arch Phys Med Rehabil. 2005;86:672– 680.

Number 2

7 Meyers AR, Anderson JJ, Miller DR. Barriers, facilitators, and access for wheelchair users: substantive and methodologic lessons from a pilot study of environmental effects. Soc Sci Med. 2002;55:1435–1446. 8 Durstine JL, Painter P, Franklin BA. Physical activity for the chronically ill and disabled. Sports Med. 2000;30:207–219. 9 Kinne S, Patrick DL, Maher EJ. Correlates of exercise maintenance among people with mobility impairments. Disabil Rehabil. 1999;21:15–22. 10 Rimmer JH, Riley B, Wang E. Physical activity participation among persons with disabilities: barriers and facilitators. Am J Prev Med. 2004;26:419 – 425. 11 Finch C, Owen N, Price R. Current injury or disability as a barrier to being more physically active. Med Sci Sports Exerc. 2001;33:778 –782. 12 Kohrt WM, Bloomfield SA, Little KD. American College of Sports Medicine position stand: physical activity and bone health. Med Sci Sports Exerc. 2004;36: 1985–1996. 13 Albright A, Franz M, Hornsby G. American College of Sports Medicine position stand: exercise and type 2 diabetes. Med Sci Sports Exerc. 2000;32:1345–1360. 14 Eng JJ, Chu KS, Kim CM. A communitybased group exercise program for persons with chronic stroke. Med Sci Sports Exerc. 2003;35:1271–1278. 15 Gill TM, Baker DI, Gottschalk M. A prehabilitation program for the prevention of functional decline: effect on higher-level physical function. Arch Phys Med Rehabil. 2004;85:1043–1049. 16 Strawbridge WJ, Deleger S, Roberts RE. Physical activity reduces the risk of subsequent depression for older adults. Am J Epidemiol. 2002;156:328 –334. 17 Weil E, Wachterman M, McCarthy EP. Obesity among adults with disabling conditions. J Am Med Assoc. 2002;288: 1265–1268. 18 Penninx BWJH, Messier SP, Rejeski WJ. Physical exercise and the prevention of disability in activities of daily living in older persons with osteoarthritis. Arch Intern Med. 2001;161:2309 –2316. 19 US Department of Health and Human Services. Healthy People 2010. 2nd ed. With Understanding and Improving Health and Objectives for Improving Health. 2 vols. Focus Area 22: Physical Activity and Fitness. Washington, DC: US Government Printing Office; 2000:57. 20 Hirvensalo M, Rantanen T, Heikkinen E. Mobility difficulties and physical activity as predictors of mortality and loss of independence in the community-living older population. J Am Geriatr Soc. 2000;48: 493– 498. 21 Ada L, Dean CM, Hall JM. A treadmill and overground walking program improves walking in persons residing in the community after stroke: a placebo-controlled, randomized trial. Arch Phys Med Rehabil. 2003;84:1486 –1491.

February 2010

Joint Kinematics and Muscle Demands in Elliptical Training and Walking 22 Pescatello LS, Franklin BA, Fagard R. American College of Sports Medicine position stand: exercise and hypertension. Med Sci Sports Exerc. 2004;36:533–553. 23 Reuter I, Engelhardt M, Stecker K. Therapeutic value of exercise training in Parkinson’s disease. Med Sci Sports Exerc. 1999; 31:1544 –1549. 24 Richards C, Malouin F, Wood-Dauphine´e S. Task-specific physical therapy for optimization of gait recovery in acute stroke patients. Arch Phys Med Rehabil. 1993; 74:612– 620. 25 Carr J, Shepherd R. Neurological Rehabilitation. Oxford, UK: Butterworth & Heinemann; 1998. 26 Burnfield J, Jorde A, Augustin T. Variations in plantar pressure variables across five cardiovascular exercises. Med Sci Sports Exerc. 2007;39:2012–2020. 27 Kuo AD, Donelan JM. Dynamic principles of gait and their clinical implications. Phys Ther. 2010;90:157–174. 28 Cram JR, Kasman GS, Holtz J. Introduction to Surface Electromyography. Gaithersburg, MD: Aspen Publishers, Inc; 1998. 29 Hislop HJ, Montgomery J. Daniels and Worthingham’s Muscle Testing: Techniques of Manual Examination. 8th ed. Philadelphia, PA: WB Saunders Co; 2007. 30 Cappozzo A, Cappello A, Croce UG. Surface-marker cluster design criteria for 3-D bone movement reconstruction. IEEE Trans Biomed Eng. 1997;44:1165–1174. 31 Pathokinesiology Service and Physical Therapy Department. Observational Gait Analysis. 4th ed. Downey, CA: Los Amigos Research and Education Institute Inc, Rancho Los Amigos National Rehabilitation Center; 2001. 32 Bekey GA, Kim JJ, Gronley JK. GAIT-ERAID: an expert system for diagnosis of human gait. Artif Intell Med. 1992;4: 293–308.

February 2010

33 Perry J. Gait Analysis: Normal and Pathological Function. Thorofare, NJ: Charles B Slack; 1992. 34 Lu T, Chien H, Chen H. Joint loading in lower extremities during elliptical exercise. Med Sci Sports Exerc. 2007;39: 1651–1658. 35 Bogey RA, Barnes LA, Perry J. Computer algorithms to characterize individual subject EMG profiles during gait. Arch Phys Med Rehabil. 1992;73:835– 841. 36 Perry J, Bontrager EL, Bogey RA. The Rancho EMG Analyzer: a computerized system for gait analysis. J Biomed Eng. 1993;15: 487– 496. 37 Kadaba MP, Ramakrishnan HK, Wootten ME. Repeatability of kinematic, kinetic and electromyographic data in normal adult gait. J Orthop Res. 1989;7:849 – 860. 38 Waldrop J, Stern SM. Census 2000 Brief C2KBR-17: Disability Status: 2000. Washington, DC: US Census Bureau; 2003. 39 McNeil J. Census Bureau Report P70 –73: Americans With Disabilities Household Economic Studies. Washington, DC: US Census Bureau; 2001. 40 Lethbridge-C ¸ ejku M, Rose D, Vickerie J. Summary health statistics for US adults: National Health Interview Survey, 2004. Vital Health Stat 10. 2006;(228):51. 41 Bean JF, Vora A, Frontera WR. Benefits of exercise for community-dwelling older adults. Arch Phys Med Rehabil. 2004; 85(suppl):S31–S42. 42 American College of Sports Medicine. American College of Sports Medicine position stand: physical activity, physical fitness, and hypertension. Med Sci Sports Exerc. 1993;25:i–x. 43 American Diabetes Association/American College of Sports Medicine. Joint position statement: diabetes mellitus and exercise. Med Sci Sports Exerc. 1997;29:1– 6.

44 Murray MP, Clarkson BH. The vertical pathways of the foot during level walking. II. Clinical examples of distorted pathways. Phys Ther. 1966;46:590 –599. 45 Harkema SJ. Neural plasticity after human spinal cord injury: application of locomotor training to the rehabilitation of walking. Neuroscientist. 2001;7:455– 468. 46 Behrman AL, Bowden MG, Nair PM. Neuroplasticity after spinal cord injury and training: an emerging paradigm shift in rehabilitation and walking recovery. Phys Ther. 2006;86:1406 –1425. 47 Dietz V, Colombo G. Recovery from spinal cord injury: underlying mechanisms and efficacy of rehabilitation. Acta Neurochir Suppl. 2004;89:95–100. 48 Pang M, Yang J. The initiation of the swing phase in human infant stepping: importance of hip position and leg loading. J Physiol. 2000;528:389 – 404. 49 Calancie B, Needham-Shropshire B, Jacobs P, et al. Involuntary stepping after chronic spinal cord injury: evidence for a central rhythm generator for locomotion in man. Brain. 1994;117:1143–1159. 50 Harkema SJ, Hurley SL, Patel UK. Human lumbosacral spinal cord interprets loading during stepping. J Neurophysiol. 1997;77: 797– 811. 51 Nielsen JB, Sinkjaer T. Afferent feedback in the control of human gait. J Electromyogr Kinesiol. 2002;12:213–217. 52 Sinkjaer T, Andersen JB, Ladouceur M, et al. Major role for sensory feedback in soleus EMG activity in the stance phase of walking in man. J Physiol. 2000;523: 817– 827. 53 Hidler JM, Wall AE. Alterations in muscle activation patterns during robotic-assisted walking. Clin Biomech (Bristol, Avon). 2005;20:184 –193.

Volume 90

Number 2

Physical Therapy f

305

Letters to the Editor On “Motor control exercise for chronic low back pain…” Costa LOP, Maher CG, Latimer J, et al. Phys Ther. 2009;89:1275– 1286. In reading this article,1 I questioned whether the small responses seen in the treatment group were universal, or whether there were some patients who benefited more than others from motor control training. If some patients benefited more than others, are there characteristics that would lead to identifying a subgroup of patients who were more likely to respond? A weakness in this study, I believe, is that the intervention does not accurately represent what typically occurs in physical therapist management of patients with chronic low back pain. Current evaluation schemes look to classify patients with nonspecific low back pain to determine which treatment they would likely benefit most from. Paul C. Weiss P.C. Weiss, PT, DipMDT, is Physical Therapist and sole proprietor of Cedar Hill Physical Therapy, Greensboro, NC 27410. This letter was posted as a Rapid Response on December 4, 2009, at ptjournal.apta.org.

Reference 1 Costa LOP, Maher CG, Latimer J, et al. Motor control exercise for chronic low back pain: a randomized placebo-controlled trial. Phys Ther. 2009;89:1275–1286. [DOI: 10.2522/ptj.2010.90.2.307.1]

Author Response We appreciate Weiss’ comments1 about our recently published randomized controlled trial.2 He commented on the possibility of identifying characteristics of patients who would benefit more from the February 2010

Letter 2.10.indd 307

motor control intervention and that the intervention “does not accurately represent what typically occurs in physical therapist management of patients with chronic low back pain.” Although the effects of exercise for the management of chronic low back pain reported in trials usually are classified as small or moderate at best,3 there is always the argument that different patients will respond differently to the same intervention, and, therefore, subgroups should be considered. Despite what people may write or say, there is no evaluation scheme that allows a physical therapist to determine which treatment a patient with back pain would most likely benefit from. We can understand why people would wish for such evaluation schemes, but, unfortunately, they do not yet exist. Although subgrouping is a common (and controversial) topic of discussion, few studies have been undertaken to identify patients who may respond best to motor control exercise,4,5 and, therefore, well-designed studies about classification, subgrouping, and clinical prediction rules are needed. We caution that identification of subgroups is difficult6 and that studies aiming to select the best therapies for specific groups of patients are still in the early stages of development7 or still in data collection stages.6 We agree that there may be a subgroup of patients who respond more favorably to motor control exercise, and we have a number of research projects under way that may identify the variables that define such a group. However, we reiterate that identification of treatment subgroups is deceptively complex.5

We believe that our trial does represent contemporary best practice in the application of motor control exercise. Our treatment protocol was developed by Professor Paul Hodges, who is one of the world leaders in motor control training in patients with low back pain. In addition, our therapists were well trained and had access to rehabilitative ultrasound for assessment and biofeedback. We emphasize that the treatment was not applied in a generic fashion to anyone with low back pain. We included only patients deemed suitable by their therapist for motor control exercise, and the program was individualized to each patient’s presentation.8 Leonardo O.P. Costa, Christopher G. Maher, Jane Latimer, Paul W. Hodges, Robert D. Herbert, Kathryn M. Refshauge, James H. McAuley, Matthew D. Jennings L.O.P. Costa, PT, PhD, is Research Fellow, Musculoskeletal Division, The George Institute for International Health, PO Box M201, Missenden Rd, Sydney, New South Wales 2050, Australia. Address all correspondence to Mr Costa at: [email protected]. C.G. Maher, PT, PhD, is Director, Musculoskeletal Division, The George Institute for International Health, and Professor, Sydney Medical School, The University of Sydney, Sydney, Australia. J. Latimer, PT, PhD, is Senior Research Fellow, Musculoskeletal Division, The George Institute for International Health, and Associate Professor, Sydney Medical School, The University of Sydney. P.W. Hodges, PhD, BPhty(Hons), is Professor and NHMRC Senior Research Fellow/Professorial Research Fellow, Division of Physiotherapy, NHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury and Health, School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Queensland, Australia. R.D. Herbert, PT, PhD, is Senior Research Fellow, Musculoskeletal Division, The George Institute for International Health, and Associate Professor, Sydney Medical School, The University of Sydney.

Volume 90 Number 2 Physical Therapy ■ 307

1/13/10 3:55 PM

Letters to the Editor On “Motor control exercise for chronic low back pain…” Costa LOP, Maher CG, Latimer J, et al. Phys Ther. 2009;89:1275– 1286. In reading this article,1 I questioned whether the small responses seen in the treatment group were universal, or whether there were some patients who benefited more than others from motor control training. If some patients benefited more than others, are there characteristics that would lead to identifying a subgroup of patients who were more likely to respond? A weakness in this study, I believe, is that the intervention does not accurately represent what typically occurs in physical therapist management of patients with chronic low back pain. Current evaluation schemes look to classify patients with nonspecific low back pain to determine which treatment they would likely benefit most from. Paul C. Weiss P.C. Weiss, PT, DipMDT, is Physical Therapist and sole proprietor of Cedar Hill Physical Therapy, Greensboro, NC 27410. This letter was posted as a Rapid Response on December 4, 2009, at ptjournal.apta.org.

Reference 1 Costa LOP, Maher CG, Latimer J, et al. Motor control exercise for chronic low back pain: a randomized placebo-controlled trial. Phys Ther. 2009;89:1275–1286. [DOI: 10.2522/ptj.2010.90.2.307.1]

Author Response We appreciate Weiss’ comments1 about our recently published randomized controlled trial.2 He commented on the possibility of identifying characteristics of patients who would benefit more from the February 2010

Letter 2.10.indd 307

motor control intervention and that the intervention “does not accurately represent what typically occurs in physical therapist management of patients with chronic low back pain.” Although the effects of exercise for the management of chronic low back pain reported in trials usually are classified as small or moderate at best,3 there is always the argument that different patients will respond differently to the same intervention, and, therefore, subgroups should be considered. Despite what people may write or say, there is no evaluation scheme that allows a physical therapist to determine which treatment a patient with back pain would most likely benefit from. We can understand why people would wish for such evaluation schemes, but, unfortunately, they do not yet exist. Although subgrouping is a common (and controversial) topic of discussion, few studies have been undertaken to identify patients who may respond best to motor control exercise,4,5 and, therefore, well-designed studies about classification, subgrouping, and clinical prediction rules are needed. We caution that identification of subgroups is difficult6 and that studies aiming to select the best therapies for specific groups of patients are still in the early stages of development7 or still in data collection stages.6 We agree that there may be a subgroup of patients who respond more favorably to motor control exercise, and we have a number of research projects under way that may identify the variables that define such a group. However, we reiterate that identification of treatment subgroups is deceptively complex.5

We believe that our trial does represent contemporary best practice in the application of motor control exercise. Our treatment protocol was developed by Professor Paul Hodges, who is one of the world leaders in motor control training in patients with low back pain. In addition, our therapists were well trained and had access to rehabilitative ultrasound for assessment and biofeedback. We emphasize that the treatment was not applied in a generic fashion to anyone with low back pain. We included only patients deemed suitable by their therapist for motor control exercise, and the program was individualized to each patient’s presentation.8 Leonardo O.P. Costa, Christopher G. Maher, Jane Latimer, Paul W. Hodges, Robert D. Herbert, Kathryn M. Refshauge, James H. McAuley, Matthew D. Jennings L.O.P. Costa, PT, PhD, is Research Fellow, Musculoskeletal Division, The George Institute for International Health, PO Box M201, Missenden Rd, Sydney, New South Wales 2050, Australia. Address all correspondence to Mr Costa at: [email protected]. C.G. Maher, PT, PhD, is Director, Musculoskeletal Division, The George Institute for International Health, and Professor, Sydney Medical School, The University of Sydney, Sydney, Australia. J. Latimer, PT, PhD, is Senior Research Fellow, Musculoskeletal Division, The George Institute for International Health, and Associate Professor, Sydney Medical School, The University of Sydney. P.W. Hodges, PhD, BPhty(Hons), is Professor and NHMRC Senior Research Fellow/Professorial Research Fellow, Division of Physiotherapy, NHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury and Health, School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Queensland, Australia. R.D. Herbert, PT, PhD, is Senior Research Fellow, Musculoskeletal Division, The George Institute for International Health, and Associate Professor, Sydney Medical School, The University of Sydney.

Volume 90 Number 2 Physical Therapy ■ 307

1/13/10 3:55 PM

Letters to the Editor K.M. Refshauge, DipPhty, GradDipManipTher, MBiomedE, PhD, is Director, Research & Innovation, and Deputy Dean, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia. J.H. McAuley, PhD, is Research Manager, Musculoskeletal Division, The George Institute for International Health. M.D. Jennings, PT(Hons), is Deputy Director, Physiotherapy Department, Liverpool Hospital, Sydney South West and Western Sydney Area Health Services, Sydney, New South Wales, Australia. This letter was posted as a Rapid Response on December 11, 2009, at ptjournal.apta.org.

References 1 Weiss PC. Letter to the editor on ”Motor control exercise for chronic low back pain: a randomized placebo-controlled trial.” Phys Ther. 2010;90:307. 2 Costa LOP, Maher CG, Latimer J, et al. Motor control exercise for chronic low back pain: a randomized placebo-controlled trial. Phys Ther. 2009;89:1275–1286. 3 Airaksinen O, Brox JI, Cedraschi C, et al; COST B13 working group. European guidelines for the management of chronic non-specific low back pain. Eur Spine J. 2006;15:S192–S300. 4 Macedo LG, Latimer J, Maher CG, et al. Motor control or graded activity exercises for chronic low back pain? A randomised controlled trial. BMC Musculoskelet Disord. 2008;9:65. 5 Hancock M, Herbert RD, Maher CG. A guide to interpretation of studies investigating subgroups of responders to physical therapy interventions. Phys Ther. 2009;89:698–704. 6 Fritz JM, Brennan GP, Clifford SN, et al. An examination of the reliability of a classification algorithm for subgrouping patients with low back pain. Spine (Phila Pa 1976). 2006;31:77–82. 7 Childs JD, Fritz JM, Flynn TW, et al. A clinical prediction rule to identify patients with low back pain most likely to benefit from spinal manipulation: a validation study. Ann Intern Med. 2004;141:920– 928. 8 Maher CG, Latimer J, Hodges PW, et al. The effect of motor control exercises versus placebo in patients with chronic low back pain. BMC Musculoskelet Disord. 2005;6:54. [DOI: 10.2522/ptj.2010.90.2.307.2]

On “Motor control exercise for chronic low back pain…” Costa LOP, Maher CG, Latimer J, et al. Phys Ther. 2009;89:1275– 1286. In the interest of full disclosure, Dr Dennis Hart is a director of Focus on Therapeutic Outcomes, Inc (FOTO), which develops outcome measures and services for physical therapy and rehabilitation.

The work by Costa et al1 represents a step forward in the assessment of treatments that are helpful for patients with lumbar spine impairments. The strength of their study lies in their design, which has been nicely reviewed by Fritz.2 However, we would like to direct attention to a psychometric matter that appears to have been overlooked by Fritz, the editors and reviewers, and the authors. Costa et al used the Patient-Specific Functional Scale (PSFS)3,4 to assess change in “activity” in their sample. Using aggregated PSFS scores to assess change in groups of patients represents a major conceptual and psychometric error, which makes interpretation of results difficult. In the present study, the authors also used the RolandMorris Disability Questionnaire5 to assess “activity,” which provided results similar to those obtained using the aggregated PSFS scores, so our discussion is entirely related to the psychometric appropriateness of using aggregated PSFS scores to assess change in function in groups of patients, not the ultimate results of the study. If one starts with the assumption that the reported purpose of the PSFS is to assess functional ability and its change in individual patients, not to assess change in groups of patients,3,4,6,7 then there are 2 major hurdles a researcher must clear before anyone can aggregate PSFS scores in conceptually and mathe-

308 ■ Physical Therapy Volume 90 Number 2

Letter 2.10.indd 308

matically meaningful ways. Conceptually, diverse functional tasks (1, 2, or 3 per patient) need to provide one clinically meaningful scale (content validity) from which the researcher can estimate a measure of function that can be compared among patients. Psychometrically, the diverse items assessed from different patients, which must by definition represent parallel forms of the same test,8 produce measures that can be equated on the same metric,9 the items of which form a unidimensional scale10 that meets the mathematical assumptions necessary for parametric statistics.11–15 The authors did not clear either hurdle. Conceptually, the authors did not describe or justify the clinical logic of grouping diverse functional tasks into one scale. No tasks were reported, so the reader cannot judge the variability or conceptual strength of the functional activities that the patients selected as troublesome. In their discussion of the PSFS, the authors made no attempt to determine whether the items should or could be grouped in a clinically logical way. No attempt was made to assess the content validity of the items. As questioned by Hart and Werneke,16 is it clinically logical to group PSFS items such as cross-country running over hilly ground, vacuuming the living room, and lifting the 40 pounds required to return to work? How diverse were the items in the current study? Is it clinically logical to include the items selected by the patients into a single scale or item bank? The authors provided no clinically logical justification for comparing any of the PSFS scores. What if a patient selected “difficulty lifting 50 pounds from the floor” and another patient selected “difficulty rolling over in bed,” 2 common complaints for patients with lumbar impairments? February 2010

1/13/10 4:03 PM

Letters to the Editor K.M. Refshauge, DipPhty, GradDipManipTher, MBiomedE, PhD, is Director, Research & Innovation, and Deputy Dean, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia. J.H. McAuley, PhD, is Research Manager, Musculoskeletal Division, The George Institute for International Health. M.D. Jennings, PT(Hons), is Deputy Director, Physiotherapy Department, Liverpool Hospital, Sydney South West and Western Sydney Area Health Services, Sydney, New South Wales, Australia. This letter was posted as a Rapid Response on December 11, 2009, at ptjournal.apta.org.

References 1 Weiss PC. Letter to the editor on ”Motor control exercise for chronic low back pain: a randomized placebo-controlled trial.” Phys Ther. 2010;90:307. 2 Costa LOP, Maher CG, Latimer J, et al. Motor control exercise for chronic low back pain: a randomized placebo-controlled trial. Phys Ther. 2009;89:1275–1286. 3 Airaksinen O, Brox JI, Cedraschi C, et al; COST B13 working group. European guidelines for the management of chronic non-specific low back pain. Eur Spine J. 2006;15:S192–S300. 4 Macedo LG, Latimer J, Maher CG, et al. Motor control or graded activity exercises for chronic low back pain? A randomised controlled trial. BMC Musculoskelet Disord. 2008;9:65. 5 Hancock M, Herbert RD, Maher CG. A guide to interpretation of studies investigating subgroups of responders to physical therapy interventions. Phys Ther. 2009;89:698–704. 6 Fritz JM, Brennan GP, Clifford SN, et al. An examination of the reliability of a classification algorithm for subgrouping patients with low back pain. Spine (Phila Pa 1976). 2006;31:77–82. 7 Childs JD, Fritz JM, Flynn TW, et al. A clinical prediction rule to identify patients with low back pain most likely to benefit from spinal manipulation: a validation study. Ann Intern Med. 2004;141:920– 928. 8 Maher CG, Latimer J, Hodges PW, et al. The effect of motor control exercises versus placebo in patients with chronic low back pain. BMC Musculoskelet Disord. 2005;6:54. [DOI: 10.2522/ptj.2010.90.2.307.2]

On “Motor control exercise for chronic low back pain…” Costa LOP, Maher CG, Latimer J, et al. Phys Ther. 2009;89:1275– 1286. In the interest of full disclosure, Dr Dennis Hart is a director of Focus on Therapeutic Outcomes, Inc (FOTO), which develops outcome measures and services for physical therapy and rehabilitation.

The work by Costa et al1 represents a step forward in the assessment of treatments that are helpful for patients with lumbar spine impairments. The strength of their study lies in their design, which has been nicely reviewed by Fritz.2 However, we would like to direct attention to a psychometric matter that appears to have been overlooked by Fritz, the editors and reviewers, and the authors. Costa et al used the Patient-Specific Functional Scale (PSFS)3,4 to assess change in “activity” in their sample. Using aggregated PSFS scores to assess change in groups of patients represents a major conceptual and psychometric error, which makes interpretation of results difficult. In the present study, the authors also used the RolandMorris Disability Questionnaire5 to assess “activity,” which provided results similar to those obtained using the aggregated PSFS scores, so our discussion is entirely related to the psychometric appropriateness of using aggregated PSFS scores to assess change in function in groups of patients, not the ultimate results of the study. If one starts with the assumption that the reported purpose of the PSFS is to assess functional ability and its change in individual patients, not to assess change in groups of patients,3,4,6,7 then there are 2 major hurdles a researcher must clear before anyone can aggregate PSFS scores in conceptually and mathe-

308 ■ Physical Therapy Volume 90 Number 2

Letter 2.10.indd 308

matically meaningful ways. Conceptually, diverse functional tasks (1, 2, or 3 per patient) need to provide one clinically meaningful scale (content validity) from which the researcher can estimate a measure of function that can be compared among patients. Psychometrically, the diverse items assessed from different patients, which must by definition represent parallel forms of the same test,8 produce measures that can be equated on the same metric,9 the items of which form a unidimensional scale10 that meets the mathematical assumptions necessary for parametric statistics.11–15 The authors did not clear either hurdle. Conceptually, the authors did not describe or justify the clinical logic of grouping diverse functional tasks into one scale. No tasks were reported, so the reader cannot judge the variability or conceptual strength of the functional activities that the patients selected as troublesome. In their discussion of the PSFS, the authors made no attempt to determine whether the items should or could be grouped in a clinically logical way. No attempt was made to assess the content validity of the items. As questioned by Hart and Werneke,16 is it clinically logical to group PSFS items such as cross-country running over hilly ground, vacuuming the living room, and lifting the 40 pounds required to return to work? How diverse were the items in the current study? Is it clinically logical to include the items selected by the patients into a single scale or item bank? The authors provided no clinically logical justification for comparing any of the PSFS scores. What if a patient selected “difficulty lifting 50 pounds from the floor” and another patient selected “difficulty rolling over in bed,” 2 common complaints for patients with lumbar impairments? February 2010

1/13/10 4:03 PM

Letters to the Editor Is it clinically logical to compare the responses to these 2 activities and say the comparison is clinically meaningful? Without such justification, comparing measures from such diverse PSFS items becomes invalid. The psychometric concerns are more concerning and were not approached by the authors. Aggregating estimates of functional status quantified from 1, 2, and 3 items that are different among diverse patients requires the estimates be equated mathematically. The only way we are aware of doing such equating is to start with a set of items (such as the PSFS items) and test the items for unidimensionality,17–19 local independence,20 and differential item functioning for important variables21,22 so that the items can be mathematically placed onto one unidimensional scale.23 Then, selection of 1, 2, or 3 items from the item bank can be used to assess one construct, such as functional ability, regardless of whether the measures are estimated using 1, 2, or 3 items for one patient or a group of patients. Of course, that process produces a single scale that defeats the purpose of the PSFS, but without such a process of scale development, you do not provide the methods needed to validate aggregation of scores such as PSFS scores, which leaves the researcher back where MacKenzie et al6 in 1986, Tugwell et al7 in 1987, and Stratford et al3 in 1995 started: PSFS scores are meant for intra-patient comparisons, not group comparisons. Westaway et al4 confirmed this conclusion by suggesting that the fact that each patient reports unique functional activities makes comparisons of disability among patients less meaningful. Hart and Werneke16 took Westaway and col-

February 2010

Letter 2.10.indd 309

leagues’ conclusion further, noting that no one has demonstrated psychometrically that change in different functional activities assessed in different patients can be tracked over time, aggregated, and compared among patients or groups of patients in a valid manner. Costa et al provided no evidence to contradict these conclusions. Therefore, we see no evidence supporting the validity of aggregating and comparing PFSF scores among patients.

adjusted for baseline PSFS scores) associated with assessing different PSFS scores. Without satisfactory mathematical answers to these concerns, interpretation of the methods and results from aggregated PSFS scores probably represent psychometrically insurmountable hurdles.

We would like to offer some food for thought moving forward if authors persist in using aggregated PSFS scores to assess change in function in groups of patients. Provide the mathematics that support the contention that aggregating content-diverse PSFS scores from heterogeneous patients is psychometrically sound and represents strong content and construct validity so that clinically interpretable scores can be produced. Demonstrate that a PSFS score from one patient was equivalent mathematically to a PSFS score from another patient who selected a different functional task to quantify. Demonstrate that the change in one patient’s PSFS scores were mathematically equivalent to the change in PSFS scores of another patient who selected different functional activities. Demonstrate that the differences in PSFS measures—for example, between scores of 2 and 7 for lifting compared with a difference between scores of 2 and 7 for rolling in bed—are linear. Demonstrate that there was no effect of different content from different PSFS measures on the PSFS change scores. Because measurement error of single-item scales such as the PSFS is quite large, report the measurement error (possibly minimal detectable change

M.W. Werneke, PT, MS, DipMDT, is Physical Therapist, CentraState Medical Center Rehabilitation and Spine Center, Freehold, New Jersey.

Dennis L. Hart, Mark W. Werneke D.L. Hart, PT, PhD, is Director of Consulting and Research, Focus On Therapeutic Outcomes, Inc, White Stone, Virginia.

This letter was posted as a Rapid Response on December 11, 2009, at ptjournal.apta.org.

References 1 Costa LOP, Maher CG, Latimer J, et al. Motor control exercise for chronic low back pain: a randomized placebo-controlled trial. Phys Ther. 2009;89:1275–1286. 2 Fritz JM. Invited commentary on “Motor control exercise for chronic low back pain: a randomized placebo-controlled trial.” Phys Ther. 2009;89:1287–1289. 3 Stratford PW, Gill C, Westaway MD, Binkley JM. Assessing disability and change on individual patients: a report of a patient specific measure. Physiother Can. 1995;47:258–263. 4 Westaway MD, Stratford PW, Binkley JM. The Patient-Specific Functional Scale: validation of its use in persons with neck dysfunction. J Orthop Sports Phys Ther. 1998;27:331–338. 5 Roland M, Morris R. A study of the natural history of low-back pain, part II: development of guidelines for trials of treatment in primary care. Spine (Phila Pa 1976). 1983;8:145–150. 6 MacKenzie CR, Charlson ME, DiGioia D, Kelley K. A patient-specific measure of change in maximal function. Arch Intern Med. 1986;146:1325–1329. 7 Tugwell P, Bombardier C, Buchanan WW, et al. The MACTAR Patient Preference Disability Questionnaire: an individualized functional priority approach for assessing improvement in physical disability in clinical trials in rheumatoid arthritis. J Rheumatol. 1987;14:446–451. 8 Lord FM, Novick MR. Statistical Theories of Mental Test Scores. Reading, MA: Addison-Wesley; 1968.

Volume 90 Number 2 Physical Therapy ■ 309

1/19/10 6:09 PM

Letters to the Editor 9 Dorans N. Scaling and equating. In: Wainer H, ed. Computerized Adaptive Testing: A Primer. 2nd ed. Mahwah, NJ: Lawrence Erlbaum Associates; 2000:135–158. 10 Hambleton RK, Swaminathan H, Rogers HJ. Fundamentals of Item Response Theory. Newbury Park, CA: Sage; 1991. 11 Bjorner JB, Kosinski M, Ware JE Jr. Using item response theory to calibrate the Headache Impact Test (HIT) to the metric of traditional headache scales. Qual Life Res. 2003;12:981–1002. 12 Bjorner JB, Kosinski M, Ware JE Jr. Calibration of an item pool for assessing the burden of headaches: an application of item response theory to the Headache Impact Test (HIT). Qual Life Res. 2003;12:913–933. 13 Bjorner JB, Kosinski M, Ware JE Jr. The feasibility of applying item response theory to measures of migraine impact: a re-analysis of three clinical studies. Qual Life Res. 2003;12:887–902. 14 Wright BD, Linacre JM. Observations are always ordinal; measurements, however, must be interval. Arch Phys Med Rehabil. 1989;70:857–860. 15 Wright BD, Masters GN. Rating Scale Analyses. Chicago, IL: MESA Press; 1982. 16 Hart DL, Werneke MW. Comment on “Responsiveness of pain, disability, and physical impairment outcomes in patients with low back pain.” Spine (Phila Pa 1976). 2004;29:2475–2476. 17 Hart DL, Mioduski JE, Werneke MW, Stratford PW. Simulated computerized adaptive test for patients with lumbar spine impairments was efficient and produced valid measures of function. J Clin Epidemiol. 2006;59:947–956. 18 Lord FM. Applications of Item Response Theory to Practical Testing Problems. Hillsdale, NJ: Lawrence Erlbaum Associates; 1980. 19 Rose M, Bjorner JB, Becker J, et al. Evaluation of a preliminary physical function item bank supported the expected advantages of the Patient-Reported Outcomes Measurement Information System (PROMIS). J Clin Epidemiol. 2008;61: 17–33. 20 Fliege H, Becker J, Walter OB, et al. Development of a computer-adaptive test for depression (D-CAT). Qual Life Res. 2005;14:2277–2291. 21 Crane PK, Gibbons LE, Jolley L, van Belle G. Differential item functioning analysis with ordinal logistic regression techniques: DIFdetect and difwithpar. Med Care. 2006;44:S115–S123. 22 Millsap RE, Everson HT. Methodology review: statistical approaches for assessing measurement bias. Appl Psychol Meas. 1993;17:287–334. 23 Hays RD, Morales LS, Reise SP. Item response theory and health outcomes measurement in the 21st century. Med Care. 2000;38:II28–II42. [DOI: 10.2522/ptj.2010.90.2.308]

Author Response Hart and Werneke argue in their letter1 that the use of aggregate scores of the Patient-Specific Functional Scale (PSFS)2 in our study3 is psychometrically inadequate. We do not understand why anyone would list all the potential reasons why the PSFS should not perform well when it does perform well. If the PSFS provided nothing more than meaningless noise, as Hart and Werneke’s letter would lead us to believe, we would not see the pattern of results seen time and time again in randomized trials4–6 and in other studies on measurement properties.2,7–14 A quick look at our trial results shows that the PSFS data are not simply noise. We see Hart and Werneke’s letter as reminiscent of the tale of the scientist who argued that, according to the known laws of physics, the bumblebee should not be able to fly. But, of course, bumblebees do fly, and the PSFS measures. Let’s move on. We appreciate that Hart and Werneke disclosed financial interests that were relevant to their comments, in accordance with International Committee on Medical Journal Editors guidelines. We disclose that we have no financial interests in the PSFS or competing outcome measures. Leonardo O.P. Costa, Christopher G. Maher, Jane Latimer, Paul W. Hodges, Robert D. Herbert, Kathryn M. Refshauge, James H. McAuley, Matthew D. Jennings L.O.P. Costa, PT, PhD, is Research Fellow, Musculoskeletal Division, The George Institute for International Health, PO Box M201, Missenden Rd, Sydney, New South Wales 2050, Australia. Address all correspondence to Mr Costa at: [email protected]. C.G. Maher, PT, PhD, is Director, Musculoskeletal Division, The George Institute for International Health, and Professor, Sydney Medical School, The University of Sydney, Sydney, Australia.

310 ■ Physical Therapy Volume 90 Number 2

Letter 2.10.indd 310

J. Latimer, PT, PhD, is Senior Research Fellow, Musculoskeletal Division, The George Institute for International Health, and Associate Professor, Sydney Medical School, The University of Sydney. P.W. Hodges, PhD, BPhty(Hons), is Professor and NHMRC Senior Research Fellow/Professorial Research Fellow, Division of Physiotherapy, NHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury and Health, School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Queensland, Australia. R.D. Herbert, PT, PhD, is Senior Research Fellow, Musculoskeletal Division, The George Institute for International Health, and Associate Professor, Sydney Medical School, The University of Sydney. K.M. Refshauge, DipPhty, GradDipManipTher, MBiomedE, PhD, is Director, Research & Innovation, and Deputy Dean, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia. J.H. McAuley, PhD, is Research Manager, Musculoskeletal Division, The George Institute for International Health. M.D. Jennings, PT(Hons), is Deputy Director, Physiotherapy Department, Liverpool Hospital, Sydney South West and Western Sydney Area Health Services, Sydney, New South Wales, Australia. This letter was posted as a Rapid Response on December 18, 2009, at ptjournal.apta.org.

References 1 Hart DL, Werneke MW. Letter to the editor on “Motor control exercise for chronic low back pain: a randomized placebocontrolled trial.” Phys Ther. 2010;90:308– 309. 2 Stratford PW, Gill C, Westaway M, Binkley JM. Assessing disability and change on individual patients: a report of a patient-specific measure. Physiother Can. 1995;47:258–263. 3 Costa LOP, Maher CG, Latimer J, et al. Motor control exercise for chronic low back pain: a randomized placebo-controlled trial. Phys Ther. 2009;89:1275–1286. 4 Ferreira ML, Ferreira PH, Latimer J, et al. Comparison of general exercise, motor control exercise and spinal manipulative therapy for chronic low back pain: a randomized trial. Pain. 2007;131:31–37. 5 Hancock MJ, Maher CG, Latimer J, et al. Assessment of diclofenac or spinal manipulative therapy, or both, in addition to recommended first-line treatment for acute low back pain: a randomised controlled trial. Lancet. 2007;370:1638–1643.

February 2010

1/19/10 6:10 PM

Letters to the Editor 9 Dorans N. Scaling and equating. In: Wainer H, ed. Computerized Adaptive Testing: A Primer. 2nd ed. Mahwah, NJ: Lawrence Erlbaum Associates; 2000:135–158. 10 Hambleton RK, Swaminathan H, Rogers HJ. Fundamentals of Item Response Theory. Newbury Park, CA: Sage; 1991. 11 Bjorner JB, Kosinski M, Ware JE Jr. Using item response theory to calibrate the Headache Impact Test (HIT) to the metric of traditional headache scales. Qual Life Res. 2003;12:981–1002. 12 Bjorner JB, Kosinski M, Ware JE Jr. Calibration of an item pool for assessing the burden of headaches: an application of item response theory to the Headache Impact Test (HIT). Qual Life Res. 2003;12:913–933. 13 Bjorner JB, Kosinski M, Ware JE Jr. The feasibility of applying item response theory to measures of migraine impact: a re-analysis of three clinical studies. Qual Life Res. 2003;12:887–902. 14 Wright BD, Linacre JM. Observations are always ordinal; measurements, however, must be interval. Arch Phys Med Rehabil. 1989;70:857–860. 15 Wright BD, Masters GN. Rating Scale Analyses. Chicago, IL: MESA Press; 1982. 16 Hart DL, Werneke MW. Comment on “Responsiveness of pain, disability, and physical impairment outcomes in patients with low back pain.” Spine (Phila Pa 1976). 2004;29:2475–2476. 17 Hart DL, Mioduski JE, Werneke MW, Stratford PW. Simulated computerized adaptive test for patients with lumbar spine impairments was efficient and produced valid measures of function. J Clin Epidemiol. 2006;59:947–956. 18 Lord FM. Applications of Item Response Theory to Practical Testing Problems. Hillsdale, NJ: Lawrence Erlbaum Associates; 1980. 19 Rose M, Bjorner JB, Becker J, et al. Evaluation of a preliminary physical function item bank supported the expected advantages of the Patient-Reported Outcomes Measurement Information System (PROMIS). J Clin Epidemiol. 2008;61: 17–33. 20 Fliege H, Becker J, Walter OB, et al. Development of a computer-adaptive test for depression (D-CAT). Qual Life Res. 2005;14:2277–2291. 21 Crane PK, Gibbons LE, Jolley L, van Belle G. Differential item functioning analysis with ordinal logistic regression techniques: DIFdetect and difwithpar. Med Care. 2006;44:S115–S123. 22 Millsap RE, Everson HT. Methodology review: statistical approaches for assessing measurement bias. Appl Psychol Meas. 1993;17:287–334. 23 Hays RD, Morales LS, Reise SP. Item response theory and health outcomes measurement in the 21st century. Med Care. 2000;38:II28–II42. [DOI: 10.2522/ptj.2010.90.2.308]

Author Response Hart and Werneke argue in their letter1 that the use of aggregate scores of the Patient-Specific Functional Scale (PSFS)2 in our study3 is psychometrically inadequate. We do not understand why anyone would list all the potential reasons why the PSFS should not perform well when it does perform well. If the PSFS provided nothing more than meaningless noise, as Hart and Werneke’s letter would lead us to believe, we would not see the pattern of results seen time and time again in randomized trials4–6 and in other studies on measurement properties.2,7–14 A quick look at our trial results shows that the PSFS data are not simply noise. We see Hart and Werneke’s letter as reminiscent of the tale of the scientist who argued that, according to the known laws of physics, the bumblebee should not be able to fly. But, of course, bumblebees do fly, and the PSFS measures. Let’s move on. We appreciate that Hart and Werneke disclosed financial interests that were relevant to their comments, in accordance with International Committee on Medical Journal Editors guidelines. We disclose that we have no financial interests in the PSFS or competing outcome measures. Leonardo O.P. Costa, Christopher G. Maher, Jane Latimer, Paul W. Hodges, Robert D. Herbert, Kathryn M. Refshauge, James H. McAuley, Matthew D. Jennings L.O.P. Costa, PT, PhD, is Research Fellow, Musculoskeletal Division, The George Institute for International Health, PO Box M201, Missenden Rd, Sydney, New South Wales 2050, Australia. Address all correspondence to Mr Costa at: [email protected]. C.G. Maher, PT, PhD, is Director, Musculoskeletal Division, The George Institute for International Health, and Professor, Sydney Medical School, The University of Sydney, Sydney, Australia.

310 ■ Physical Therapy Volume 90 Number 2

Letter 2.10.indd 310

J. Latimer, PT, PhD, is Senior Research Fellow, Musculoskeletal Division, The George Institute for International Health, and Associate Professor, Sydney Medical School, The University of Sydney. P.W. Hodges, PhD, BPhty(Hons), is Professor and NHMRC Senior Research Fellow/Professorial Research Fellow, Division of Physiotherapy, NHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury and Health, School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Queensland, Australia. R.D. Herbert, PT, PhD, is Senior Research Fellow, Musculoskeletal Division, The George Institute for International Health, and Associate Professor, Sydney Medical School, The University of Sydney. K.M. Refshauge, DipPhty, GradDipManipTher, MBiomedE, PhD, is Director, Research & Innovation, and Deputy Dean, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia. J.H. McAuley, PhD, is Research Manager, Musculoskeletal Division, The George Institute for International Health. M.D. Jennings, PT(Hons), is Deputy Director, Physiotherapy Department, Liverpool Hospital, Sydney South West and Western Sydney Area Health Services, Sydney, New South Wales, Australia. This letter was posted as a Rapid Response on December 18, 2009, at ptjournal.apta.org.

References 1 Hart DL, Werneke MW. Letter to the editor on “Motor control exercise for chronic low back pain: a randomized placebocontrolled trial.” Phys Ther. 2010;90:308– 309. 2 Stratford PW, Gill C, Westaway M, Binkley JM. Assessing disability and change on individual patients: a report of a patient-specific measure. Physiother Can. 1995;47:258–263. 3 Costa LOP, Maher CG, Latimer J, et al. Motor control exercise for chronic low back pain: a randomized placebo-controlled trial. Phys Ther. 2009;89:1275–1286. 4 Ferreira ML, Ferreira PH, Latimer J, et al. Comparison of general exercise, motor control exercise and spinal manipulative therapy for chronic low back pain: a randomized trial. Pain. 2007;131:31–37. 5 Hancock MJ, Maher CG, Latimer J, et al. Assessment of diclofenac or spinal manipulative therapy, or both, in addition to recommended first-line treatment for acute low back pain: a randomised controlled trial. Lancet. 2007;370:1638–1643.

February 2010

1/19/10 6:10 PM

Letters to the Editor 6 Pengel LHM, Refshauge KM, Maher CG, et al. Physiotherapist-directed exercise, advice or both for subacute low back pain: a randomized trial. Ann Intern Med. 2007;146:787–796.

10 Beurskens AJ, de Vet HC, Köke AJ, et al. A patient-specific approach for measuring functional status in low back pain. J Manipulative Physiol Ther. 1999;22: 144–148.

7 Costa LOP, Maher CG, Latimer J, et al. Clinimetric testing of three self-report outcome measures for low back pain patients in Brazil: which one is the best? Spine (Phila Pa 1976). 2008;33:2459– 2463.

11 Frost H, Lamb SE, Stewart-Brown S. Responsiveness of a patient specific outcome measure compared with the Oswestry Disability Index v2.1 and Roland and Morris Disability Questionnaire for patients with subacute and chronic low back pain. Spine (Phila Pa 1976). 2008;33:2450–2457.

8 Pengel LHM, Refshauge KM, Maher CG. Responsiveness of pain, disability and physical impairment outcomes in patients with low back pain. Spine. 2004;29: 879–883. 9 Beusrkens AJHM, de Vet HCW, Köke AJA. Responsiveness of functional status in low back pain: a comparison of different instruments. Pain. 1996;65:71–76.

February 2010

Letter 2.10.indd 311

13 Cleland JA, Fritz JM, Whitman JM, et al. The reliability and construct validity of the Neck Disability Index and patient-specific functional scale in patients with cervical radiculopathy. Spine (Phila Pa 1976). 2006;31:598–602. 14 Westaway MD, Stratford PW, Binkley JM. The Patient-Specific Functional Scale: validation of its use in persons with neck dysfunction. J Orthop Sports Phys Ther. 1998;27:331–338. [DOI: 10.2522/ptj.2010.90.2.310]

12 Chatman AB, Hyams SP, Neel JM, et al. The Patient-Specific Functional Scale: measurement properties in patients with knee dysfunction. Phys Ther. 1997;77:820–829.

Volume 90 Number 2 Physical Therapy ■ 311

1/13/10 4:03 PM

Scholarships, Fellowships, and Grants News from the Foundation for Physical Therapy Recent Publications by Foundation-Funded Researchers “Novel Patterns of Functional Electrical Stimulation Have an Immediate Effect on Dorsiflexor Muscle Function During Gait for People Poststroke,” by Kesar TM, Perumal R, Jancosko A, Reisman DS, Rudolph KS, Higginson JS, and Binder-Macleod SA, was published online ahead of print in Physical Therapy on November 19, 2009. Darcy S. Reisman, PT, PhD, is a 1999 Mary McMillan Doctoral Scholarship recipient. Katherine S. Rudolph, PT, PhD, received a Doctoral Research Award in 1997 and currently serves as chair of the Foundation’s Scientific Review Committee (SRC). Stuart BinderMacleod, PT, PhD, FAPTA, received a Research Grant from the Foundation in 1989. “Hallux Valgus and the First Metatarsal Arch Segment: A Theoreti-

cal Biomechanical Perspective,” by Glasoe WM, Nuckley DJ, and Ludewig PM, was published online ahead of print in Physical Therapy on November 19, 2009. Ward M. Glasoe, PT, MA, ATC, received a 2006 Florence P. Kendall Scholarship and Paula M. Ludewig, PT, PhD, received a Foundation Doctoral Award in 1996. “Denuded Subchondral Bone and Knee Pain in Persons with Knee Osteoarthritis,” by Moisio K, Eckstein F, Chmiel JS, Guermazi A, Prasad P, Almagor O, Song J, Dunlop D, Hudelmaier M, Kothari A, and Sharma L, was published ahead of print in Arthritis & Rheumatism on November 30, 2009. Kirsten Moisio, PT, PhD, received a Promotion of Doctoral Studies (PODS) II scholarship in 2001. The editorial, “CARE V Series: Integrating Patient Viewpoints Into Health Care Practice and Research,” by Maura D. Iversen, PT,

The Foundation at CSM 2010 • The Foundation for Physical Therapy will be at CSM 2010 in San Diego! Please stop by, and meet the Foundation staff. Learn more about programs offered by the Foundation and how to help further their mission! • Join your friends and colleagues for a fun-filled evening at the Sports Physical Therapy Section (SPTS) Beach Party Redux. The evening will include music and dancing to the Beach Toys, a Beach Boys tribute band, as well as the ever-popular Silent Auction, featuring sports memorabilia, gifts, clothing, and equipment. Tickets are $25 for

members ($10 for students). Purchase tickets by calling APTA Member Services 800/9992782, ext 3395, or online at the Foundation’s Web site. • Catch the Buzz at the Home Health Section Coffee to benefit the Foundation. Grab a cup of Starbucks coffee at this event where Foundation-funded researchers will be our special guests. Gentiva Health Service, a Foundation Partner in Research, is sponsoring this event. Tickets are $15 ($5 for students) and can be purchased through APTA Member Services or on the Foundation’s Web site.

312 ■ Physical Therapy Volume 90 Number 2

Foundation 02.10.indd 312

DPT, ScD, MPH, appeared in Physical Therapy (2009;89[12]:1266– 1268). Iversen was awarded a Research Grant in 2000. “Choosing Among 3 Ankle-Foot Orthoses for a Patient with Stage II Posterior Tibial Tendon Dysfunction,” by Neville CG and Houck JR, was published in the Journal of Orthopaedic & Sports Physical Therapy (2009;89[11]:816–824). Christopher Neville, PT, PhD, received a 2004 McMillan Doctoral Scholarship and his co-author, Jeff Houck, PT, PhD, currently serves as a member of the Foundation’s SRC.

2010 Miami–Marquette Challenge Raises Funds for Research This year, students participating in the 2010 Miami–Marquette Challenge pledged to raise $200,000 in support of the Foundation. The student-led fundraiser supports research grants and PODS I and II scholarships for emerging physical therapist researchers. Students hope to meet this year’s goal and expand their role by helping to fund the Clagett Family Research Grant. Donations must be received by the challenge deadline of May 5, 2010 to count for this year’s fundraiser. For a list of participating schools and ways to get involved, visit the Foundation’s Web site at www.FoundationforPhysical Therapy.org, or contact Barbara Malm at 800/875-1378, ext 8502, for more information.

February 2010

1/13/10 3:58 PM

Scholarships, Fellowships, and Grants

Foundation Currently Accepting Clagett Family Research Grant Applications The Foundation for Physical Therapy is currently accepting applications for the Clagett Family Research Grant. This opportunity will provide $300,000 over the course of 2 years to explore exercise interventions in an older adult population living with multiple chronic conditions. Multidisciplinary teams of investigators are encouraged to apply; however, at least 1 member of the investigatory team must be a licensed physical therapist. A Letter of Intent must be submitted in

order to apply for this grant. Please visit the Foundation’s Web site for important information including guidelines, eligibility requirements, instructions, and pertinent deadlines.

one of nine $2,000 prizes or the $10,000 grand prize. Contact Barbara Malm for more information on how to participate, or visit the Foundation’s Web site for complete rules. [DOI: 10.2522/ptj.2010.90.2.312]

22nd Annual Foundation Split Raffle The Foundation’s Annual Split Raffle fundraiser supports doctoral scholarships for emerging physical therapist researchers. The purchase of a split raffle ticket serves as an investment in the strength and future of the physical therapy profession with a chance to win

Tired of wasting money?

$99

Subscription to Physical Therapy

$89

Subscription to PT In Motion

$300

Online advertising

$85

Articles from research databases

$480

Continuing education

Get it free. The Dollars & Sense of APTA Membership

February 2010

Foundation 02.10.indd 313

Become an APTA member.

Not a Member Yet? Visit www.apta.org/join or call 800/999-2782, ext 3395, to join.

Volume 90 Number 2 Physical Therapy ■ 313

1/13/10 3:58 PM