sports medicine Volume 38 Issue 12 December 2008

Sports Med 2008 2008; 38 (12): 971-973 0112-1642/08/0012-0971/$48.00/0

ACKNOWLEDGEMENT

ª 2008 Adis Data Information BV. All rights reserved.

Dear Reader, As we reach the final issue of the year for Sports Medicine, we hope that you have found the articles published throughout 2008 to be both interesting and informative. The editors and publishing staff have appreciated the high quality of content contributed to the journal this year and look forward to keeping you up to date with topical issues in the fields of sports science and medicine in 2009. We are also pleased to advise you of a number of important developments to affect the Wolters Kluwer Health | Adis journals portfolio in 2008. Pediatric Drugs was accepted as the official journal of the International Alliance for Better Medicines for Children. The journal has made great strides in meeting the information needs of paediatricians, paediatric clinical pharmacologists, and paediatric pharmacists. Furthermore, this year we launched a ground-breaking journal entitled The Patient: Patient-Centered Outcomes Research. The journal is published in collaboration with Johns Hopkins Bloomberg School of Public Health and over 10 000 copies of the first issue were distributed to interested readers. The high quality of a number of our titles was further recognized in the new ISI impact factors for 2007. The impact factor of CNS Drugs increased to 4.514 and PharmacoEconomics increased to 2.623. Clinical Pharmacokinetics, Sports Medicine and Clinical Drug Investigation also registered increases in their impact factors. Wolters Kluwer Health | Adis has been providing quality content to healthcare professionals for nearly 40 years and the following titles will celebrate major anniversaries in 2009: Sports Medicine (25), Clinical Drug Investigation (20), CNS Drugs and BioDrugs (15), and Pediatric Drugs (10). Next year we will also have a new online platform for our journals and we hope that the new functionality will help you navigate our content. Last, but not the least, we would like to say a big thank you to all the authors who have contributed articles to Sports Medicine in the last 12 months. Without their hard work and diligence we would not have been able to publish the journal. The quality of published articles reflects also the significant time and effort dedicated by the peer reviewers who ensure that we continue to publish content of the highest possible standard. In addition to the members of our Honorary Editorial Board, we would like to thank the following individuals who acted as referees for articles published in Sports Medicine in 2008:

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C.R. Abbiss, Joondalup, WA, Australia E.O. Acevedo, Richmond, VA, USA L. Andersen, Oslo, Norway M.W. Anderson, Charlottesville, VA, USA L. Ansley, Kingston-upon-Thames, Surrey, UK C.I. Ardern, Kingston, ON, Canada E. Arendt, Minneapolis, MN, USA N. Armstrong, Exeter, UK R.B. Armstrong, College Station, TX, USA A. Aubert, Leuven, Belgium M. Audran, Montpellier, France R. Bailey, London, UK R.J. Baker, Kalamazoo, MI, USA J.C. Baldi, Auckland, New Zealand N. Baume, Epalinges, Switzerland L. Bax, Kitasato, Japan M.F. Bergeron, Augusta, GA, USA T. Bernard, La Garde, France A. Bernard, Brussels, Belgium A. Biffi, Rome, Italy D. Bishop, Verona, Italy E.H. Blackstone, Cleveland, OH, USA N. Boisseau, Poitiers, France C. Brugnara, Boston, MA, USA D.J. Burgess, Sydney, NSW, Australia J. Bush, Houston, TX, USA C. Button, Otago, New Zealand J. Bytomski, Durham, NC, USA J.E.L. Carter, San Diego, CA, USA J.B. Carter, Burnaby, BC, Canada D. Casa, Storrs, CT, USA C. Castagna, Rome, Italy R-K Chang, Los Angeles, CA, USA M. Ciocca, Chapel Hill, NC, USA P.M. Clarkson, Amherst, MA, USA G.L. Close, Liverpool, UK A.R. Cooper, Bristol, UK G. Costa, Milan, Italy C. Cote, Kingston, ON, Canada A. Coutts, Sydney, NSW, Australia P.E. Cryer, St Louis, MO, USA G. Dalleau, Le Tampon, France S. de Castro, Rome, Italy G. del Rossi, Coral Gables, FL, USA J.J. Densmore, Charlottesville, VA, USA G.W. Dorshimer, Philadelphia, PA, USA A.L. Dunn, Golden, CO, USA T. Dusek, Zagreb, Croatia E. Eils, Muenster, Germany T.S. Ellenbecker, Scottsdale, AZ, USA N.A. Estes III, Boston, MA, USA M.J. Faber, Nijmegen, The Netherlands S.J. Fairclough, Liverpool, UK E. Fehrenbach, Tuebingen, Germany M. Fernstrom, Stockholm, Sweden C.M. Ferrara, Baltimore, MD, USA B.C. Focht, Columbus, OH, USA C. Foster, La Crosse, WI, USA M. Fredericson, Stanford, CA, USA L. Fried, Baltimore, MD, USA F. Furlanello, Milan, Italy N. Gaibazzi, Desenzano del Garda, Italy S. Garland, Tyne and Wear, UK N.J. Gibbs, Sydney, NSW, Australia J. Glatz, Maastricht, The Netherlands

ª 2008 Adis Data Information BV. All rights reserved.

C. Gomez-Cabrera, Valencia, Spain D. Gould, East Lansing, MI, USA M.T. Gross, Chapell Hill, NC, USA K.M. Guskiewicz, Chapel Hill, NC, USA G.G. Haff, Morgantown, WV, USA M. Haykowsky, Edmonton, AB, Canada B.C. Heiderscheit, Madison, WI, USA T.R. Henwood, Brisbane, QLD, Australia J. Hertel, Charlottesville, VA, USA J.A. Hess, Eugene, OR, USA F. Hettinga, Amsterdam, The Netherlands R.C. Hickner, Greenville, NC, USA J. Hoff, Trondheim, Norway J. Holloszy, St Louis, MO, USA J. Hoogsteen, Veldhoven, The Netherlands S.P. Hooker, Columbia, SC, USA W. Hopkins, Auckland, NZ N. James, Swansea, UK T.A. Jarvinen, La Jolla, CA, USA N.R. Jorgensen, Hvidovre, Denmark J.N. Kalavar, Upper Burrell, PA, USA B. Kayser, Geneva, Switzerland V. Khanduja, London, UK W.B. Kibler, Lexington, KY, USA B. Kiens, Kobenhavn, Denmark A. Koller, Innsbruck, Austria A.D. Korczyn, Tel Aviv, Israel A. Kraut, Winnipeg, MB, Canada M.W. Kreuter, St Louis, MO, USA P. Krustup, Copenhagen, Denmark S. Kuitunen, Jyvaskyla, Finland D.A. Lake, Savannah, GA, USA G. Lancaster, Bundoora, VIC, Australia K.B. Landorf, Melbourne, VIC, Australia A.B. Lanier, Kennesawga, GA, USA K. Larsen, Holstebro, Denmark D.E. Larson-Meyer, Baton Rouge, LA, USA P. Larsson, Umea, Sweden N. Latham, Boston, MA, USA M. Leunig, Berne, Switzerland M. Lindstrom, Malmo¨, Sweden M.S. Link, Boston, MA, USA M.J. Lipinski, Charlottesville, VA, USA M. Locke, Toronto, ON, Canada M.R. Lovell, Pittsburgh, PA, USA G. Lovell, Bruce, ACT, Australia C. Lundby, Copenhagen, Denmark R.F. Machado, Bethesda, MD, USA M. Maeder, St Gallen, Switzerland C. Maffeis, Verona, Italy M. Magnani, Urbino, Italy M. Mattsom, Baltimore, MD, USA L. Mayers, Pleasantville, NJ, USA A. Mayr, Brunico, Italy A. McArdle, Liverpool, UK S. McGill, Waterloo, ON, Canada T. McGuine, Madison, WI, USA M.P. McHugh, New York, NY, USA R. Meir, Lismore, NSW, Australia A. Mero, Jvyaskyla, Finland M.A. Merrick, Columbus, OH, USA W.P. Metheny, Providence, RI, USA

Sports Med 2008 2008; 38 (12)

Acknowledgement

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C. Milgrom, Jerusalem, Israel D. Minors, Manchester, UK T.A. Miszko, Decatur, GA, USA A-I Murgu, Bucharest, Romania J.B. Myers, Pittsburgh, PA, USA K. Nakken, Sandvika, Norway G. Naughton, Sydney, NSW, Australia J.F. Nichols, San Diego, CA, USA K. Niere, Bundoora, VIC, Australia T. Noakes, Cape Town, South Africa E.G. Noble, London, ON, Canada R. Nuss, Denver, CO, USA K. O’Connell, New York, NY, USA S.M. Ostojic, Belgrade, Serbia & Montenegro D.R. Patel, Kalamazoo, MI, USA B.M. Pluim, Amersfoorty, The Netherlands P. Portero, Paris, France J. Press, Chicago, IL, USA U. Proske, Melbourne, VIC, Australia Z. Radak, Budapest, Hungary C. Randolph, Waterbury, CT, USA N. Robinson, Lausanne, Switzerland S.E. Ross, Richmond, VA, USA T.W. Rowland, Springfield, MA, USA P.A. Ruell, Lidcombe, NSW, Australia J.M. Sacheck, Boston, MA, USA S.K. Saha, Stockholm, Sweden G.L. Salvagno, Verona, Italy A. Samb, Dakar, Senegal T.G. Sampson, San Francisco, CA, USA A. Saxena, Palo Alto, CA, USA J. Scharhag, Saarbrucken, Germany U. Sekir, Bursa, Turkey

O. Seynnes, Antipolis, France R.E. Shave, Middlesex, UK J.M. Sheppard, Ballarat, VIC, Australia S. Shoor, Santa Clara, CA, USA A.J. Siegel, Belmont, MA, USA D.K. Simonton, Davis, CA, USA M. Spencer, Crawley, WA, Australia D.J. Spitz, Bethesda, MD, USA A. St Clair Gibson, Newcastle upon Tyne, UK R. Steck, Brisbane, QLD, Australia D.J. Stensel, Loughborough, UK T.G. Sutlive, Houston, TX, USA G. Tenenbaum, Tallahassee, FL, USA A.M. Thomson, Antigonish, NS, Canada S. Tokmakidis, Komotini, Greece F.G.S. Toledo, Pittsburgh, PA, USA I. Torres-Aleman, Madrid, Spain N.T. Triplett, Boone, NC, USA C.K. Tsolakis, Athens, Greece C. Tudor-Locke, Mesa, AZ, USA L.P. Turcotte, Los Angeles, CA, USA Y. Vanlandewijck, Leuven, Belgium E. Verhagen, Amsterdam, The Netherlands A. Vinet, Orleans, France N.B. Vollaard, Colchester, UK D.E.R. Warburton, Vancouver, BC, Canada M. Whitlam, Bangor, Gwynedd, UK K.E. Wilk, Malvern, PA, USA L.R. Williams, Dunedin, NZ K. Woolf-May, Canterbury, Kent, UK

We look forward to your continued support in 2009 and to bringing you first-class content from around the globe. With best wishes from the staff of Sports Medicine and all at Wolters Kluwer Health | Adis.

ª 2008 Adis Data Information BV. All rights reserved.

Sports Med 2008 2008; 38 (12)

Sports Med 2008; 38 (12): 975-986 0112-1642/08/0012-0975/$48.00/0

CURRENT OPINION

ª 2008 Adis Data Information BV. All rights reserved.

Catastrophic Injury in Rugby Union Is the Level of Risk Acceptable? Colin W. Fuller Centre for Sports Medicine, University of Nottingham, Nottingham, UK

Abstract

Rugby union is a full contact sport with a relatively high overall risk of injury and a small specific risk of fatal and catastrophic spinal injury. Although catastrophic injuries in rugby union cause public concern and generate strong emotive reactions, the magnitude of society’s concern about this type of injury is often dominated by people’s perceptions rather than by actual levels of risk. This article assesses published values for the risk of catastrophic injuries in rugby union, evaluates these against the risk standards of the UK Health and Safety Executive (HSE) and compares the values with the risks associated with other common sport and non-sport activities. The assessment showed that the risks of sustaining a catastrophic injury in rugby union in England (0.8/100 000 per year), Ireland (0.9/100 000 per year) and Argentina (1.9/100 000 per year) were within the HSE’s ‘acceptable’ region of risk (0.1–2/100 000 per year), whilst the risks in New Zealand (4.2/100 000 per year), Australia (4.4/100 000 per year) and Fiji (13/100 000 per year) were within the ‘tolerable’ region of risk (2–100/100 000 per year). The risk of sustaining a catastrophic injury in rugby union was generally lower than or comparable with the levels reported for a wide range of other collision sports, such as ice hockey (4/100 000 per year), rugby league (2/100 000 per year) and American Football (2/100 000 per year). In addition, the risk of catastrophic injury in rugby union was comparable with that experienced by most people in work-based situations and lower than that experienced by motorcyclists, pedestrians and car occupants. Whilst ranking risks provides an effective way of assessing their acceptability, it is recognized that representing risks by a single risk value can be misleading, as account must also be taken of the public’s perception of the risks and the inherent differences in the types of risk being considered. However, an acceptable level of risk is often regarded as one that is no greater than the levels of risk that an individual encounters in everyday life. In this respect, the assessment indicated that the risk of sustaining a catastrophic injury in rugby union could be regarded as acceptable and that the laws of the game therefore adequately manage the risk.

Athletes normally participate in a particular sport on the basis of their conceptions or misconceptions about the level of risk associated with the sport and the nature of their own risk-

taking or risk-averse behaviours. Rugby union is a full contact sport[1] with a relatively high overall risk of injury[2] and, as with many individual and team sports,[3,4] a small risk of fatal and

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catastrophic spinal injury. Although fatalities do occur in sport, they are extremely rare and the majority are the result of cardiovascular disease.[5] Most spinal injuries associated with rugby union occur in the lumbar region,[6] but these are generally not life-threatening or incapacitating. Although the incidence of injuries in the cervical region is lower than that found for the lumbar region,[6] acute trauma sustained to the cervical spine is the most common cause of permanent disability in rugby union.[7] It is incumbent on all stakeholders, from international governing bodies to individual athletes, to control the risks of injury in sport through the process of risk management.[8] The normal way for this responsibility to be discharged is by completion of a risk assessment, which provides a qualitative or quantified estimate of risk. Quantified values, which are obtained using theoretical calculations and/or records of past failures, impart a degree of accuracy and precision to an assessment and assist in the development of risk control strategies. Measures of risk are by definition subject to uncertainty; therefore, however reliable the available statistical data may be, it is not possible to predict the exact outcome in any particular situation. However, although statistical uncertainty is associated with any assessment of risk, the uncertainty should be viewed against the alternative of using guesses, beliefs and prejudices. If this latter approach were to be adopted, it would be almost impossible to reach meaningful conclusions about the acceptability and control of risks in any situation. Injury risk can be defined as the expected loss within a stated period of time and this can be estimated using the product of the average consequence of all adverse events (injury severity) and the probability that these adverse events will occur within a specified period of time (incidence of injury).[8] Risk estimation, though, cannot define whether a level of risk is acceptable, either to the individual or to society, because acceptance is dependent on people’s perceptions of risk and the current norms within society.[9,10] The evaluation process is further confounded because individuals often view and react to the same risks in ª 2008 Adis Data Information BV. All rights reserved.

quite different ways; this happens because one person’s fears are not necessarily the same as another person’s. Risk perception is not an absolute measure as it is a personal view that is affected by a number of factors, such as the dread/non-dread and the known/unknown dimensions of risk.[9] Appreciating the role of these factors is important in order to develop an understanding of stakeholder views about risks such as catastrophic injury (CI) in sport. Fischoff et al.[11] studied how people viewed a wide range of risks using this two-factor framework and their results are summarized with selected examples in figure 1. Issues that fall into the region defined by unknown/dread dimensions relate to risks where the public has significant concerns and for which there will be calls for government intervention to control the risks. On the other hand, issues that fall into the region defined by known/non-dread dimensions relate to risks where the public has few concerns and for which there are more likely to be campaigns for non-intervention. Law[12] highlighted an important paradox associated with the evaluation of risk, whereby the public and media become less tolerant of rare adverse events and there is a tendency to amplify their importance and to over-regulate the risks due to an irrational perception about the acceptability of these residual levels of risk.[13]

Unknown risk Risks such as: · food preservatives · diagnostic x-rays · aspirin · microwave ovens

Non-dread risk Risks such as: · sport · chainsaws · motor vehicles · alcoholic drinks Issues in this region provoke calls for non-intervention

Issues in this region provoke calls for regulation

Risks such as: · DNA technology · space exploration · pesticides · nuclear power Risks such as: · crime · terrorism · aviation · construction

Dread risk

Known risk

Fig. 1. The known/unknown and dread/non-dread dimensions of risk perception.

Sports Med 2008; 38 (12)

Catastrophic Injury in Rugby Union

In occupational settings, acceptable levels of risk are often embedded within national health and safety legislation and guidance. In the UK for example, the Health and Safety Executive (HSE)[14,15] defined four regions of occupational risk in terms of the probability that a serious adverse event (normally taken to mean a fatality) would occur within 1 year; these regions of risk were negligible (<0.1 in 105 per year); acceptable (0.1–2 in 105); tolerable (2–100 in 105); unacceptable (>100 in 105). The de minimis principle of risk is adopted in many countries, including Australia, Canada, New Zealand and the US, and by organizations such as the WHO[12] in order to control societal risks associated with issues such as the environment, radiation, genetically modified organisms and food. This principle means that if a risk is sufficiently unlikely it can be ignored; in this context ‘unlikely’ is normally regarded as a probability of <1 in 106, which equates to the HSE region of negligible risk. Most people, however, find it easier to evaluate risks by comparing and ranking risks against activities with which they are familiar[16] rather than evaluating them against fixed standards of the type used by the HSE. Although governing bodies of sport do not have an employer-employee relationship with participants, their responsibility to manage risks at acceptable levels has been established in UK law (Watson vs British Boxing Board of Control [2001] QB1134; Vowles vs Evans and Welsh Rugby Union [2003] EWCA 318, [2003] 1 WLR 1607). CIs cause great public concern and they generate strong emotive reactions; the magnitude of society’s concern about this type of injury is, however, often dominated by people’s perceptions rather than by the actual levels of risk. The aims of this article are to estimate the level of risk associated with CI in rugby union and then to evaluate this level against the HSE risk standards and also to compare the risk with those associated with other common sport and non-sport activities. 1. Sources of Data on Catastrophic Injuries Data for CI in rugby union, other sports and other activities were obtained for the period ª 2008 Adis Data Information BV. All rights reserved.

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1950–2007 using literature searches of the EMBASE, PubMed, MEDLINE and SafetyLit databases. Specific searches were undertaken of the American Journal of Sports Medicine, British Journal of Sports Medicine, British Medical Journal, Clinical Journal of Sport Medicine, Clinics in Sports Medicine, Injury Prevention, Journal of Safety Research, Medical Journal of Australia, Medicine and Science in Sports and Exercise, Risk Analysis, Safety Science, Scandinavian Journal of Medicine and Science in Sport and Sports Medicine. In addition, the Cochrane Database of Systematic Reviews and the official websites of the Spinecare Foundation, International Rugby Board, Health and Safety Executive and the Australia, England, Ireland, New Zealand and South Africa rugby unions were searched. No universally accepted definition of a CI in sport was identified in the literature, although there was a high degree of consistency in many of the definitions used. For example:  cases of cervical spinal cord injuries resulting in admission to a hospital spinal unit with permanent neurological deficit such as tetraplegia;[17]  cases of permanent neurological deficit, such as paraplegia and quadriplegia;[18]  well documented objective permanent neurological deficit;[19]  injury that resulted in a brain or spinal cord injury or skull or spinal fracture;[20]  major trauma, such as admission to an intensive care unit, urgent surgery (within 24 hours of admission to hospital) involving intra-cranial, intra-thoracic, intra-abdominal injury or fixation of spinal or pelvic fracture;[4]  the occurrence of an acute, traumatic lesion of neural elements in the spinal canal (spinal cord and cauda equina) resulting in temporary or permanent sensory deficit, motor deficit, or bladder/ bowel dysfunction;[21]  a brain or spinal cord injury that results in permanent (>12 months) severe functional disability[22] (IRB consensus statement definition). In the context of this paper, a CI was taken to encompass fatalities and brain/spinal cord Sports Med 2008; 38 (12)

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injuries resulting in significant permanent neurological deficit and which were a direct consequence of playing rugby union: this definition is consistent with the international consensus statement on the recording of injuries in rugby union.[22] Deaths from other causes, such as cardiovascular disease and heat stress, were not included in the data analysis, as these were not considered to be a specific consequence of the sport of rugby union. The incidences of CIs in rugby union, other sports and other activities are generally low and, therefore, there were very few prospective studies reported in the literature. Most research related to CIs was undertaken and reported in the form of a case report or case series retrospective studies with ill-defined populations at risk; these studies seldom reported the incidence of CI. The main exceptions to this generalization were the data collected for CIs sustained in a range of sports in the US by the National Center for Catastrophic Sport Injury Research,[23] and the annual data collected on fatalities at work in the UK by the HSE[24] and in the US by the Bureau of Labor Statistics.[25] Acceptable levels of risk[14] are normally defined as the number of serious adverse events per 100 000 population at risk per year: for this reason, incidences of CIs are presented in this article in the same format. In studies where the incidence of injury was not specifically reported, a value was calculated using the number of injuries reported in the publication and the best estimate of the exposed population at risk available from other publications; in these cases, both sources of information are provided. In some publications, non-permanent spinal injuries were reported alongside injuries resulting in permanent disability; hence, where possible, values were corrected so that only CIs were included. However, it was not always possible to identify the exact number of less severe injuries so the results presented may in a few cases be higher than the true value. This approach was adopted as it was considered preferable to err on the side of inclusion rather than exclusion of cases in order that the risk of CI was not under-estimated. ª 2008 Adis Data Information BV. All rights reserved.

2. Causes of Catastrophic Injury In the UK,[26] the major causes of catastrophic spinal injuries were falls (42%), road traffic accidents (37%) and sport (12%); and in the US,[27] road traffic accidents (35%), falls (20%), gunshot wounds (16%) and sport (11%). The major sources of catastrophic sports injuries in the UK[26] were diving (3.5%), horse riding (2.6%) and rugby (2.4%); and in the US,[27] diving (6.7%), winter/snow sports (0.9%) and American Football (0.5%). These figures reflect the burden of CIs on society, but because they do not take into account the exposed population, the figures do not indicate the risk associated with each of the activities. The following sections provide estimates of the incidence of CIs associated with rugby union and a range of other sports and activities.

2.1 Rugby Union

Most publications recording CIs in England reported the number of cases in the form of case report/series studies; no publication reported the incidence of CIs. The incidences presented in table I were, therefore, derived from the number of injuries reported in various publications and the estimated average annual number of club and school rugby union players in England over the period 1992–2002 (490 000 players) reported by the Rugby Football Union Governance

Table I. Incidence of catastrophic injuries (CIs) [injuries/100 000 participants per year] in rugby union in England (1956–2002) Time period

Type of CI

Incidencea

Source of exposure data

Source of injury data

1956–82

Spinal

0.48

RFU[28]

Silver[29]

1976–93

Spinal

0.70

RFU[28]

Haylen[30]

1980–7

Spinal

1.5

RFU[28]

Silver and Gill[31]

1982–7

Spinal

0.73

RFU[28]

Silver[29]

1992–7

Spinal + fatal

0.82

RFU[28]

RFU[28]

0.78

[28]

RFU[28]

1997–2002 a

Spinal + fatal

RFU

Average incidence (all results) = 0.84.

RFU = Rugby Football Union.

Sports Med 2008; 38 (12)

Catastrophic Injury in Rugby Union

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Committee.[28] The validity of using this number of players was supported by the value of 400 000 used by Secin et al.,[32] although the source of this value was not reported. A current estimate of the total rugby-playing population in England is 627 000 (adult: 211 000; youth: 100 000; junior/mini: 317 000). It was not considered appropriate to include ‘mini’ players in the exposed population for this particular study, as these players were not subject to the same risk of injury from tackling and scrummaging as older players. Assuming that half of the ‘junior/mini’ group of players were ‘mini’ players (159 000), the total rugby-playing population exposed to the risk of CI would be 470 000 players, which is close to the value used (490 000). There have been no reports of CI sustained by ‘mini’ players, which supported the assumption about exposure to risk within this group of players. Exclusion of the ‘mini’ group of players from the exposed population also ensured that the risk of CI was not under-estimated by inflating the exposed population. The incidence values presented in table I remain, however, subject to three further potential sources of error: 1. the number of injuries reported in each study may not relate to the total population at risk; 2. the data in several publications were derived from the same database of injuries;

3. some data referred only to spinal injuries, whilst other data referred to spinal and fatal injuries. The incidences of CIs resulting from rugby union in other countries over the period 1976–2005 are presented in table II. These results are subject to the same potential sources of error as those presented for the England data. The rugby activities responsible for CIs reported over the period 1952–2005 are summarized in table III. 2.2 Other Sports

CIs also occur in many other sports and these risks are represented by results from Australia (table IV) and the US (table V). In addition, Boden et al.[42] reported the mean incidences (injuries/100 000 players per year) of injuries leading specifically to quadriplegia amongst US high-school (0.50) and college (0.82) American Football players over the period 1989–2002. Boden et al.[43] also reported an incidence of 0.6 fatal and catastrophic spinal injuries/100 000 cheerleaders per year for the period 1982–2002. Data presented by McCrory et al.[44] indicated that the incidences (injuries/ 100 000 jockeys per year) of fatal injury amongst professional jockeys were 14 for flat and 24 for

Table II. Incidence of catastrophic injuries (CIs) [injuries/100 000 participants per year] in rugby union in various countries (1970–2005) Country

Time period

Type of CI

Argentina

1977–97

Spinal + fatal

Australia

1.9

Source of exposure data

Source of injury data

Secin et al.[32]

Secin et al.[32]

1976–85

Spinal

4.1

Spinecare Foundation[19]

Haylen[30]

1984–96

Spinal

7.0

Rotem et al.[17]

Rotem et al.[17]

1986–96

Spinal

4.2

Spinecare Foundation[19]

Haylen[30]

1986–96

Spinal

3.5

Spinecare Foundation[19]

Spinecare Foundation[19]

1997–2002 Fiji

Incidencea

1997

Spinal Spinal + fatal

3.2 13.0

Carmody et al.

[33]

Maharaj and Cameron

Maharaj and Cameron[34]

Ireland

1995–2004

Spinal

0.89

Shelly et al.

Shelly et al.[18]

New Zealand

1976–85

Spinal

5.0

Quarrie et al.[7]

Haylen[30]

1986–2000

Spinal

7.4

Quarrie et al.[7]

Haylen[30]

1984–96

Spinal

2.3

Quarrie et al.[7]

Quarrie et al.[7]

1998–2005

Spinal

2.0

Quarrie et al.[35]

Quarrie et al.[35]

3.3

[36]

Wetzler et al.[36]

US a

1970–96

Spinal

[18]

Carmody et al.[33] [34]

Wetzler et al.

Average incidence: Australia = 4.4; New Zealand = 4.2; all results = 4.4; all countries = 4.6.

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Sports Med 2008; 38 (12)

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Table III. The cause of catastrophic injuries (CIs) in rugby union in various countries (1956–2004) Country

Time period

Phase of play in which CI occurred (%) tackle

scrum

ruck/maul

Reference other

Argentina

1977–97

28

61

11

0

32

Australia

1960–85

22

62

14

3

19

1986–96

16

39

42

3

19

1997–2002

41

32

27

0

33

Canada

1975–82

22

78

0

0

7

Ireland

1995–2004

63

13

25

0

18

New Zealand

1976–95

33

45

14

8

7

South Africa

1963–89

50

21

18

10

7

1985–89

50

40

3

8

37

1956–82

24

24

34

18

29

UK

1983–87

26

37

32

5

31

US

1970–84

31

58

12

0

7

Wales

1966–84

30

40

30

0

7

33.5

42.3

20.2

4.2

Average (all results)

jump racing in the UK over the period 1980–2001 and 18 for flat and 61 for jump racing in France over the period 1975–2001.

2.5 Other Activities

Incidences of fatal and catastrophic spinal injuries from a range of common activities in several countries are presented in table IX.

2.3 Work

CI occurs in most work activities; however, when work-based data are published, catastrophic spinal injuries are normally grouped with other serious injuries so it was not possible to identify the incidence of these injuries. The following results from the UK[24] and US[25] (table VI) therefore relate only to fatalities. The National Occupational Health and Safety Commission[45] of Australia prepared a comparison of work-related fatalities across several countries in the Established Market Economies group for the year 2001 (table VII). 2.4 Road Traffic

Although fatalities associated with road traffic accidents are very high throughout the world, the UK has one of the best road safety records in Europe.[46] The estimated incidences of fatalities for pedestrians, motor cyclists and car users in the UK during 2005 are presented in table VIII. ª 2008 Adis Data Information BV. All rights reserved.

3. Comparing Incidences of Catastrophic Injuries There are several problems associated with defining the incidences of CIs in sport and other activities: most of these problems relate to the usual difficulties encountered in epidemiological studies, such as variations in injury definition, Table IV. Incidence of catastrophic injuries (CIs) [injuries/100 000 participants per year] in a range of contact sports in Australia (1984–2002) Sport

Time period

Type of CI

Incidence

Reference

Rugby league

1984–96

Spinal

1.8

17

1986–96

Spinal

2.4

33

1997–2002

Spinal

1.5

33

Australian Rules

1986–96

Spinal

0.34

19

1997–2002

Spinal

0.52

33

Soccer

1986–96

Spinal

0.03

19

1997–2002

Spinal

0.19

33

Sports Med 2008; 38 (12)

Catastrophic Injury in Rugby Union

981

Table V. Incidence of catastrophic injuries (CIs) [injuries/100 000 participants per year] amongst male students in a range of sports in the US (1981–2002) Sport

Time period

Type of CI

Incidence of injury high-school level

Reference college level

Contact sports American Football

1982–97

Fatal + spinal

0.98

2.1

38

American Football

1989–2002

Head

0.67

0.21

39

Ice hockey

1982–97

Fatal + spinal

1.7

6.6

38

Lacrosse

1982–97

Fatal + spinal

0.23

1.7

38

Soccer

1982–97

Fatal + spinal

0.12

0

38

Wrestling

1981–99

Fatal + spinal

0.42

0.77

40

Baseball

1982–99

Fatal + spinal

0.28

1.1

41

Gymnastics

1982–97

Fatal + spinal

1.7

14.5

38

Swimming

1982–97

Fatal + spinal

0.16

0.43

38

Track and field

1982–97

Fatal + spinal

0.21

0.48

38

Non-contact sports

sample populations and units of exposure.[51] In addition, because CIs are rare events, most reported data were derived from retrospective study designs, which are not as reliable as data obtained from prospective studies.[52] The difficulty of obtaining adequate sample sizes to determine the incidence of CIs in rugby union can be illustrated by considering a published study of spinal injuries in professional rugby union.[6] In this study, players at Premiership clubs in England were followed prospectively over two seasons (around 400 players per season) and it was reported that no CIs were recorded. Despite the size of the study, it is only possible to conclude that the incidence of CIs amongst this group of players was <125 injuries/100 000 players per year. In order to be able to report that the incidence of CIs was <2 injuries/100 000 players per year, which is the upper limit of the ‘acceptable region’ of risk,[14] it would be necessary to follow a cohort of 400 players for 125 seasons or 50 000 players for one season. For these reasons, it is not possible to determine whether the differences in the incidences of CI observed between countries and within countries over time are significant or merely caused by differences in the quality of data collection and reporting. At the present time, New Zealand has the most comprehensive system for recording and accessing data on CIs in rugby ª 2008 Adis Data Information BV. All rights reserved.

union and other sports because of the no-fault national insurance scheme operated in this country. Many of the results presented in this assessment are summarized in figure 2 in order that the risk of CI in rugby union can be judged against the HSE[14] regions of negligible, acceptable, tolerable and unacceptable risks and also compared with the risks associated with other common sport and non-sport activities. The results indicated that for rugby union players in England, the risk of sustaining a CI came within the HSE ‘acceptable region’ of risk (0.1–2/100 000 per

Table VI. Incidence of fatal injuries (fatalities/100 000 workers per year) amongst employees in the UK and the US by work sector Work sector

Incidence of fatalities[24,25] UKa

USb

Agriculture, forestry

5.1

29.6

Extractive, utility supply

3.3

6.2

Manufacturing

1.4

2.7

Construction

3.6

10.8

Service sector

0.3

2.5

Average

0.6

3.9

a

Results based on data presented by the Health and Safety Executive for 2005/2006.

b

Results based on data presented by the Bureau of Labor Statistics for 2006.

Sports Med 2008; 38 (12)

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Table VII. Comparison of work-related fatalities (fatalities/100 000 workers per year) amongst employees across several countries (2001) Country

Incidence of fatalities[45]

UK

0.8

Sweden

1.4

Norway

1.6

Denmark

1.8

Switzerland

2.0

Finland

2.1

Australia

2.6

Germany

3.0

New Zealand

3.1

Belgium

3.3

US

4.0

Ireland

4.2

Austria

4.5

France

4.5

Greece

6.2

Italy

7.0

Canada

7.1

Spain

7.9

Portugal

8.7

year), whilst the average risk in all other countries fell within the ‘tolerable region’ of risk (2–100/100 000 per year). The risk of sustaining a CI in rugby union in England was generally lower than or comparable with that experienced in a wide range of other collision sports, such as ice hockey, rugby league, American Football and wrestling. In England, rugby union carried a much lower risk of CI than gymnastics, which is a popular activity amongst children. The risk of CI from rugby union in England is comparable with the risk of a fatality in work-related situations but less than that experienced by car occupants, pedestrians and motorcyclists. The risk of sustaining a CI in rugby union in England is an order of magnitude lower than the risk of death experienced by women during pregnancy.

4. Acceptability of the Risk of Catastrophic Injury in Rugby Union Having evaluated the risk of sustaining a CI in rugby union against the HSE risk standards and compared the risks with other common activities, what conclusions can be drawn? All athletes undertake sport on a voluntary basis with the knowledge that there is a risk of personal injury; however, individual opinions vary about what constitutes an acceptable level of risk. This situation arises because individuals view and react to the same risks in different ways. Some people try to reduce (risk-averse behaviour) whilst others look to increase (risk-taking behaviour) their exposure to risk. The ‘risk compensation’ model presented by Adams[53] attempted to explain these different viewpoints by postulating that everyone has a propensity to take risks within certain limits and that this level of risk depended on the individual’s personality, needs and motivational requirements. In a similar way, the ‘risk homeostasis’ model[54] postulated that individuals always worked towards a constant level of risk, irrespective of whether measures were put in place to reduce the levels of risk. In this latter case, an individual’s target level of risk is determined by where their personality/ behaviour sits within the risk-averse to risktaking continuum. A high level of risk does not make a risk unacceptable, per se, as people will accept risks that are taken on voluntarily at levels up to 1000-fold higher than risks taken on a non-voluntary basis;[55] for many athletes, the feeling of excitement created by high-risk sports is their raison d’eˆtre for participation. Individuals are generally not concerned about risks when they feel they understand the risks involved and they have control over their exposure to these risks; in these circumstances, they will often oppose measures to

Table VIII. Estimated incidence of road-related fatalities (fatalities/100 000 exposed population per year) for the UK (2005) No. of fatalities[46]

Estimated exposed population[47]

1762

27 000 000

Motorcyclists

467

250 000

Pedestrians

1008

60 000 000

Car users

ª 2008 Adis Data Information BV. All rights reserved.

Incidence of fatalities 2.9 190 3.7

Sports Med 2008; 38 (12)

Catastrophic Injury in Rugby Union

983

Table IX. Incidences of death in the UK and Australia from a range of causes (cases/100 000 population at risk per year) Cause

Type of CI

Country

Year

Incidence

Reference

Cancer (all)

Fatal

UK

1999

260

48

Violence

Fatal

UK

1985

40

14

Maternal death during pregnancy

Fatal

UK

1994–6

12

48

Lightning

Fatal

UK

1995–9

Falls (all )

Fatal

Australia

2003–4

14.0

0.005

48 49

men (>50 y)

Spinal

Finland

2004

17.4

50

women (>50 y)

Spinal

Finland

2004

6.4

50

Transport (all)

Fatal

Australia

2003–4

8.6

49

Poisoning

Fatal

Australia

2003–4

5.6

49

Drowning

Fatal

Australia

2003–4

1.3

49

Fire, smoke

Fatal

Australia

2003–4

0.7

49

Suicide

Fatal

Australia

2003–4

10.8

49

Homicide

Fatal

Australia

2003–4

1.1

49

CI = catastrophic injury.

limit their freedom of choice to take part in these activities.[13] However, when exposure is outside their control or unfavourable consequences occur, people usually at some point turn to the issue of compensation and attempt to identify the person(s) or organization(s) who they believe to be responsible for managing the risk. The law of negligence is the same in a sports environment as it is in any other context: to prove negligence, it is necessary to demonstrate that a duty of care existed and that there was a breach of that duty of care by, for example, the governing body. In sport, this duty depends on a range of factors related to the nature of the sport, such as the sport’s objectives, the normal demands made on participants, the hazards associated with the sport, the laws/rules applicable to the sport, the normal conventions and customs of the sport and the level of performance that one may reasonably expect from a participant.[56] Whilst, in most situations, there are bodies that regulate risks by establishing appropriate standards and procedures, society often does not trust these bodies, as they are perceived to have self-interests: this often leads to conflict between stakeholders and the governing bodies.[13] In addition, when a situation is viewed after an adverse outcome, an affected stakeholder will no longer be in a position to make an impartial and rational judgement ª 2008 Adis Data Information BV. All rights reserved.

about whether the level of risk was acceptable in the way that an independent person undertaking a conventional risk assessment is required to. Although it is recognized that ranking risks provides an effective way of assessing the acceptability of risks, it is also recognized that representing risks by a single risk value can be misleading, as account must also be taken of the public’s perception of the risks and of the inherent differences in the types of risk that are being considered.[57] Simple questions, such as whether a catastrophic spinal injury can or should be equated to a fatality, must be resolved before definitive risk comparisons can be made. A suggestion has been made that an acceptable level of risk should be regarded as one that is no greater than the levels an individual encounters in everyday life.[12] The comparative analysis presented in this assessment showed that the risk of sustaining a CI in rugby union was no greater than that encountered in many daily activities undertaken by the wider population. Hence, on the above premise, it would be reasonable to conclude that the risk of sustaining a CI in rugby union is acceptable. Risks associated with various sports were evaluated from the perspective of the dread/nondread and known/unknown dimensions of risk;[11] this evaluation placed sport generally in Sports Med 2008; 38 (12)

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HSE guidance on regions of risk

Rugby union

Other sports

Other activities

Traffic-related activities

1000 Region of UNACCEPTABLE risk

260 - X - Cancer

Cases/100 000 population at risk per year

100

61 - France Horse racing

10

Region of TOLERABLE risk

13 - Fiji

40 - X - Violence

14 - UK 8.2 - X - Gymnastics 4.1 - X - Ice hockey

1 Region of ACCEPTABLE risk

0.8 - England

0.1

0.01

190 - X - Motor cyclists (UK)

1.9 - X - Rugby league 1.0 - X - American Football 0.77 - X - Wrestling

14 - X - Falls 12 - X - Pregnancy 8.7 - Portugal Work-related activities (Europe)

3.7 - X - Pedestrians (UK) 2.9 - X - Car drivers (UK)

0.8 - UK 0.7 - X - Fire

0.43 - X - Australian Rules 0.11 - X - Soccer

Region of NEGLIGIBLE risk 0.005 - X - Lightning

0.001 Fig. 2. Comparison of the incidence of catastrophic injuries in various activities (deaths/injuries per 100 000 participants per year). HSE = Health and Safety Executive; X indicates the value.

the region of non-dread/known risk, where one would not expect to hear calls from the public for statutory intervention. This evaluation is supported by the responses made to a letter in the British Medical Journal[58] calling for the banning of all contested scrums in rugby union on the grounds that there was a risk of catastrophic spinal cord injury. Of the 25 responses to this letter (up to 14 December 2007), 24 of the respondents were against the call for a ban.[59] Among the reasons given by respondents for their opposition were voluntary participation by the players, the known risk undertaken by the players and a generally lower risk of sustaining a CI in rugby union than in many other common activities. The respondents were clearly not a randomly selected sample of the population, as all declared an involvement of some kind with rugby union; nevertheless, these respondents represented the views of a valid stakeholder group. On this basis, the current limited evidence again ª 2008 Adis Data Information BV. All rights reserved.

suggests that the risk associated with CI in rugby union is at an acceptable level. 5. Conclusions Although the conclusion from this assessment, based on several criteria, is that the risk of CI in rugby union is acceptable, it is not intended as a justification for the incidence of CI, but to place the risk in a wider context. Every effort should still be made by all stakeholders to reduce the level of risk to a value that is as low as reasonably practicable; a decision on whether this has been achieved is outside the scope of this assessment. Measures to reduce the risk of CI in rugby union can be addressed from two perspectives. In terms of injury incidence, great advances have been made in many countries in the development and provision of training programmes to reduce the incidence of CI in rugby union, for example: International Rugby Board[60] – Rugbyready; England[61] – Tackling Sports Med 2008; 38 (12)

Catastrophic Injury in Rugby Union

Safety; New Zealand[62] – Rugby Smart; South Africa[63] – Avoiding Neck Injuries. In terms of injury consequences, Sekhon and Fehling[64] estimated that one-third of all new cases of paraplegia and quadriplegia in the US died before they reached hospital so decisions made at the scene of a spinal cord injury and within the first 24 hours following injury were extremely important in determining the long-term consequences of this type of injury. The concept of specialist Spinal Injury Centres has been adopted in some countries in the belief that immediate referral to a Spinal Injury Centre resulted in a better patient outcome than referral at a later date. Jones and Bagnall,[65] however, stated that it was not possible to reach a conclusion on this issue as there was no valid evidence available. These authors concluded that ‘‘all of the studies identified were retrospective observational studies and of poor quality.’’ There remains, therefore, an urgent need to establish a consensus definition and a consistent system for recording and assessing CIs in rugby union. Acknowledgements The preparation of this assessment was partly funded by the Rugby Football Union (England) and the International Rugby Board (Ireland); the content of the assessment including the opinions and conclusions expressed are, however, those of the author alone and they do not necessarily reflect the views or policies of either the Rugby Football Union or the International Rugby Board. The author has no conflicts of interest directly relevant to the contents of this article.

References 1. International Rugby Board. Laws of the game [online]. Available from URL: http://www.irb.com/EN/Laws+and+ Regulations/Laws/laws.htm [Accessed 2007 Dec 12] 2. Brooks JHM, Fuller CW, Kemp SPT, et al. Epidemiology of injuries in English professional rugby union: part 1 – match injuries. Br J Sports Med 2005; 39: 757-66 3. Silver JR. Spinal injuries in sports in the UK. Br J Sports Med 1993; 27: 115-20 4. Gabbe BJ, Finch CF, Cameron PA, et al. Incidence of serious injury and death during sport and recreation activities in Victoria, Australia. Br J Sports Med 2005; 39: 573-7 5. Turk EE. Natural and traumatic sports-related fatalities: a 10 year retrospective study. Br J Sports Med 2008; 42: 604-8 6. Fuller CW, Brooks JHM, Kemp SPT. Spinal injuries in professional rugby union: a prospective cohort study. Clin J Sport Med 2007; 17: 10-6

ª 2008 Adis Data Information BV. All rights reserved.

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7. Quarrie KL, Cantu RC, Chalmers DJ. Rugby union injuries to the cervical spine and spinal cord. Sports Med 2002; 32: 633-53 8. Fuller CW. Managing the risk of injury in sport. Clin J Sport Med 2007; 17: 182-7 9. Slovic P. Perceptions of risk: reflections on the psychometric paradigm. In: Krimsky S, Golding D, editors. Social theories of risk. London: Praeger, 1992: 117-52 10. Royal Society. Risk: analysis, perception and management. London: The Royal Society, 1992 11. Fischoff B, Slovic P, Lichtenstein S, et al. How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sci 1978; 9: 127-52 12. Law R. Analysis of relative risks and levels of risk in Canada [online]. Available from URL: http://www.enerex.ca/ articles/risk.htm [Accessed 2007 Dec 12] 13. Interdepartmental Liaison Group on Risk Assessment. Use of risk assessment within government departments. Sudbury: HSE Books, 1996 14. Health and Safety Executive. The tolerability of risks from nuclear power stations. Sudbury: HSE Books, 1988 15. Health and Safety Executive. Generic terms and concepts in the assessment and regulation of industrial risks. Sudbury: HSE Books, 1995 16. Fuller CW, Myerscough FE. Stakeholder perceptions of risk in motor sport. J Safety Res 2001; 32: 345-58 17. Rotem TR, Lawson JS, Wilson SF, et al. Severe cervical spinal cord injuries related to rugby union and league football in New South Wales, 1984-1996. Med J Aust 1998; 168: 379-81 18. Shelly MJ, Butler JS, Timlin M, et al. Spinal injuries in Irish rugby: a ten-year review. J Bone Joint Surg 2006; 88B: 771-5 19. Spinecare Foundation. Spinal cord injuries in Australian footballers. ANZ J Surg 2003; 73: 493-9 20. National Center for Catastrophic Sport Injury Research (NCCSIR). Glossary of injury terms (1999) [online]. Available from URL: http://www.unc.edu/depts/nccsi/ InjuryTerms.htm [Accessed 2007 Dec 12] 21. Thurman DJ, Sniezek JE, Johnson D, et al. Guidelines for surveillance of central nervous system injury. Atlanta (GA): US Department of Health and Human Services, Centers for Disease Control and Prevention, 1995 22. Fuller CW, Molloy MG, Bagate C, et al. Consensus statement on injury definitions and data collection procedures for studies of injuries in rugby union. Br J Sports Med 2007; 41: 328-31 23. National Center for Catastrophic Sport Injury (NCCSI). Annual survey of catastrophic football injuries 1977-2005 [online]. Available from URL: http://www.unc.edu/depts/ nccsi/CataFootballData.htm [Accessed 2007 Dec 12] 24. Health and Safety Executive. Health and safety statistics 2006/07. Sudbury: HSE Books, 2007 25. Bureau of Labor Statistics. Consensus of fatal occupational injuries, 2006 [online]. Available from URL: http://www.bls.gov/ iif/oshwc/cfoi/cfch0005.pdf [Accessed 2007 Dec 12] 26. Apparelyzed. Traumatic spinal cord injury: facts & figures [online]. Available from URL: http://www.apparelyzed. com/statistics.htm [Accessed 2007 Dec 12] 27. National Spinal Cord Injury Statistical Center. 2006 Annual statistical report for the model spinal cord injury care systems. Birmingham (AL); University of Alabama, 2006

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28. 29. 30. 31. 32.

33.

34.

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37. 38.

39.

40. 41.

42.

43. 44.

45.

46.

47.

[online]. Available from URL: http://http://images.main. uab.edu/spinalcord/pdffiles/NSCIC%20Annual%2006.pdf [Accessed 2007 Dec 12] Rugby Football Union. Governance Committee: players’ safety. Twickenham: Rugby Football Union, 2002 Silver JR. Injuries of the spine sustained during rugby. Br J Sports Med 1992; 26: 253-8 Haylen PT. Spinal injuries in rugby union, 1970-2003: lessons and responsibilities. Med J Aust 2004; 181: 48-50 Silver JR, Gill S. Injuries of the spine sustained during rugby. Sports Med 1988; 5: 328-34 Secin FP, Poggi EJT, Luzuriaga F, et al. Disabling injuries of the cervical spine in Argentine rugby over the last 20 years. Br J Sports Med 1999; 33: 33-6 Carmody DJ, Taylor TKF, Parker DA, et al. Spinal cord injuries in Australian footballers 1997-2002. Med J Aust 2005; 182: 561-4 Maharaj JC, Cameron ID. Increase in spinal injury among rugby union players in Fiji. Med J Aust 1998; 168: 418 Quarrie KL, Gianotti SM, Hopkins WG, et al. Effect of nationwide injury prevention programme on serious spinal injuries in New Zealand rugby union: ecological study [online]. BMJ 2007; 334: 1150-3 Wetzler MJ, Akpata T, Laughlin W, et al. Occurrence of cervical spine injuries during the rugby scrum. Am J Sports Med 1992; 26: 253-8 Scher AT. Catastrophic rugby injuries of the spinal cord: changing patterns of injury. Br J Sports Med 1991; 25: 57-60 Cantu RC, Mueller FO. Fatalities and catastrophic injuries in high school and college sports, 1982-1997. Phys Sportsmed 1999 Aug; 27 (8): 35-48 Boden BP, Tacchetti RL, Cantu C, et al. Catastrophic head injuries in high school and college football players. Am J Sports Med 2007; 35: 1075-81 Boden BP, Lin W, Young M, et al. Catastrophic injuries in wrestling. Am J Sports Med 2002; 30: 791-5 Boden BP, Tacchetti R, Mueller FO. Catastrophic injuries in high school and college baseball players. Am J Sports Med 2004; 32: 1189-96 Boden BP, Tacchetti RL, Cantu C, et al. Catastrophic cervical spine injuries in high school and college football players. Am J Sports Med 2006; 34: 1223-32 Boden BP, Tacchetti R, Mueller FO. Catastrophic cheerleading injuries. Am J Sports Med 2003; 31: 881-8 McCrory P, Turner M, LeMasson B, et al. An analysis of injuries resulting from professional horse racing in France during 1991-2001: a comparison with injuries resulting from professional horse racing in Great Britain during 1992-2001. Br J Sports Med 2006; 40: 614-8 National Occupational Health and Safety Commission. Fatal occupational injuries: how does Australia compare internationally? Canberra (ACT): National Occupational Health and Safety Commission, 2004 National Statistics. Transport statistics bulletin. Road casualties in Great Britain: main results 2005. London: National Statistics, 2006 Department for Transport. Tomorrow’s roads: safer for everyone. London: Department of Transport, 2004

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48. Health and Safety Executive. Reducing risks, protecting people. Sudbury: HSE Books, 2001 49. Henley G, Kreisfeld R, Harrison J. Injury deaths, Australia 2003-04. Canberra (ACT): National Occupational Health and Safety Commission, 2007 50. Kannus P, Palvanen M, Niemi S, et al. Alarming rise in the number and incidence of fall-induced cervical spine injuries among older adults. J Gerontol A Biol Sci Med Sci 2007; 62: 180-3 51. Brooks JHM, Fuller CW. The influence of research design on the results and conclusions obtained from epidemiological studies: illustrative examples. Sports Med 2006; 36 (6): 459-72 52. Junge A, Dvorak J. Influence of definition and data collection on the incidence of injuries in football. Am J Sports Med 2000; 28: S40-6 53. Adams J. Risk. London: UCL Press, 1995 54. Wilde GJS. The concept of target risk and its implications for accident prevention strategies. In: Feyer AM, Williamson A, editors. Occupational injury: risk, prevention and intervention. London: Taylor & Francis Ltd, 1998: 82-105 55. Trimpop R, Zimolong B. Risk acceptance. In: Stellman JM, editor. Encyclopaedia of occupational health and safety [online]. 4th ed. Available from URL: http://www.ilo.org/ encyclopedia/?hdoc&nd=857000002 [Accessed 2007 May 20] 56. James M. Liability for professional athletes’ injuries: a comparative analysis of where the risk lies. Web journal of current legal issues 2006; 1 [online]. Available from URL: http://webjcli. ncl.ac.uk/2006/issue1/james1.html [Accessed 2007 Dec 12] 57. Interdepartmental Liaison Group on Risk Assessment. Risk assessment and risk management. improving policy and practice within government departments. Sudbury: HSE Books, 1998 58. Bourke JB. Rugby union should ban contested scrums. BMJ 2006; 332: 1281 59. BMJ. Rapid responses to James B Bourke: rugby union should ban contested scrums [online]. Available from URL: http://www.bmj.com/cgi/eletters/332/7552/1281 [Accessed 2007 Dec 12] 60. International Rugby Board. IRB Rugbyready (DVD). Dublin: International Rugby Board, 2007 61. Rugby Football Union. Tackling safety: peak performance and injury prevention (DVD). Twickenham: Rugby Football Union, 2004 62. New Zealand Rugby Union [online]. Available from URL: RugbySmart. http://rugbysmart.clients.chrometoaster.com [Accessed 2008 Oct 30] 63. KwaZulu-Natal Rugby Union. Avoiding neck injuries (DVD). Durban: KwaZulu-Natal Rugby Union, 2007 64. Sekhon LH, Fehlings MG. Epidemiology, demographics and pathology of acute spinal cord injury. Spine 2001; 26 (24 Suppl.): S2-12 65. Jones L, Bagnall A. Spinal injuries centres (SICs) for acute traumatic spinal cord injury. Cochrane Database Syst Rev 2004 Oct 18; (4): CD004442

Correspondence: Dr Colin W. Fuller, Centre for Sports Medicine, University of Nottingham, Nottingham, NG7 2UH, UK. E-mail: [email protected]

Sports Med 2008; 38 (12)

Sports Med 2008; 38 (12): 987-994 0112-1642/08/0012-0987/$48.00/0

LEADING ARTICLE

ª 2008 Adis Data Information BV. All rights reserved.

Cross-Sectional Area and Muscular Strength A Brief Review Eric J. Jones,1 Phil A. Bishop,2 Amanda K. Woods2 and James M. Green2 1 Department of Kinesiology and Health Science, Stephen F. Austin State University, Nacogdoches, Texas, USA 2 Human Performance Laboratory, University of Alabama, Tuscaloosa, Alabama, USA

Abstract

A brief review is provided on the relationship of strength to muscle crosssectional area (CSA). It is commonly believed that maximal force and CSA are strongly related. Studies examining varying levels of training status display discordant data suggesting complex relationships between training status, CSA and peak force. It has been reported that trained participants had a significantly larger force to CSA ratio (F/CSA) than untrained males and females. Therefore, it is difficult to attribute all force changes due to training to CSA changes. In general, studies of CSA and strength suggest that sex differences may exist. For example, recreationally trained female weightlifters produced higher F/CSA than males at lower velocities of contraction. Definitive conclusions regarding sex differences, force production and CSA are difficult because of limited studies and equivocal results among these studies. Some studies have also examined the impact of aging on F/CSA. These studies seem to follow the same pattern as studies on sex differences and training status, with data suggesting that F/CSA varies unpredictably across ages and that differences may be attributed to factors other than age alone. In the papers reviewed, the relationship between force and CSA is neither consistent nor simple. Although some of the discrepancies between studies could be attributed to methodological variations, this does not seem likely to explain all differences. The F/CSA relationship seems complex, and future studies are required to elucidate the relationships among key factors in the expression of strength.

It has been reported that muscle cross-sectional area (CSA) is a major predictor of force production.[1-3] Results from early studies, which suggested a positive linear force-CSA (F/CSA) relationship (force increasing as CSA increases), used pooled subject data with males/females and trained/untrained subjects to report a strong F/CSA relationship in non-normally distributed

data.[1] Only when pooling data from disparate groups did a positive relationship become apparent.[4] It is also interesting to note that Ikai and Fukunaga[1] did not report correlational data; rather, they presented a figure depicting the relationship. Differences in results concerning F/CSA that are discussed in this article have, in part, been attributed to the techniques used to

Jones et al.

988

measure or estimate CSA (anatomical vs physiological CSA) in early studies.[2] Numerous studies have been performed with different populations (both sexes, various age groups, trained vs untrained) using a variety of measurement techniques to evaluate F/CSA. Some studies indicate that a linear relationship between CSA and force production exists in these various populations.[5-7,11] As larger quantities of muscle mass would appear to lead to greater expressions of muscular force, a limiting factor of maximal strength in humans may be the ability to acquire muscle mass and thus a greater CSA.[8] However, as will be discussed, this conclusion may be tentative. While positive relationships between muscle force production and CSA have been shown, other studies examining F/CSA have reported inconsistent findings. Also, training studies have revealed either increases in strength with little to no change in CSA or improvements in CSA with non-significant changes in strength.[9,10] Differences in neural adaptation, fascicle length and pennation angles have been presented as possible explanations for some of the varying results discussed in this brief review.[7,8] Neural adaptation, such as increased neural activation of a muscle following short-term training regimens, as well as the specificity of neural adaptation (sprinters vs endurance runners) may have impacted results of the studies reviewed.[7] Muscular strength is a combination of neural expression and specific morphological characteristics of the muscle as well as muscle mass or CSA.[7,8] Neural expression affects muscular force production through the frequency of action potentials created as the intensity of the initial stimulus becomes greater (rate coding), which in turn dictates the number of motor units recruited. From this, excitation-contraction coupling (release of calcium from sarcoplasmic recticulum and the following muscle contraction) is then made possible. Muscle fibre type composition influences strength expression as well as CSA. When discussing CSA of a muscle, it should also be mentioned that CSA comprises the various parts that make up the total muscle CSA, ª 2008 Adis Data Information BV. All rights reserved.

including non-contractile tissue such as blood vessels. The volume of sarcomeres (i.e. contractile proteins) is often discussed with regards to CSA. Common adaptations based on activity levels, such as changes in intravascular and interstitial volume, mitochondrial density and muscle glycogen density also contribute to the CSA of muscle. Therefore, the purpose of this review is to assess relationships between F/CSA among various populations. Specific emphasis was placed on age, gender and training status. Possible explanations for discordant findings in previous research will also be discussed briefly. To simplify this brief review, the abbreviation F/CSA was used to summarize various results regardless of strength and area measurement techniques used to establish these ratios (e.g. isometric, isokinetic, dynamic, etc.). A table of differences (table I) from various studies is presented to help elucidate the equivocal nature of the studies reviewed.

1. Training Status and Force/ Cross-Sectional Area (F/CSA) It has been reported that the maximum force production is directly dependent upon muscle CSA.[1,5] Logically then, populations and individuals with larger CSA should produce more force. In the past, it has been suggested that regardless of sex difference or training status, F/CSA should be similar.[1] In a more recent study by Castro et al.,[7] it was reported that trained males and females had significantly higher (p < 0.0001) F/CSA than untrained males and females (2.79 N/cm2 – 0.37, 2.65 N/cm2 – 0.27, 2.13 N/cm2 – 0.49 and 2.09 N/cm2 – 0.23, respectively). A similar study by Ryushi et al.[14] revealed F/CSA was higher (p = 0.01) in a group of elite strength-trained subjects (55 N/cm2) compared with subjects labelled physically active (40 N/cm2, standard deviation not available). Neuromuscular adaptations are presented as a possible explanation for the increased efficiency of F/CSA in elite trained subjects. We believe this is a plausible explanation. However, the elite training status of the subjects in Ryushi et al. suggested much longer training time periods Sports Med 2008; 38 (12)

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Table I. Force/cross sectional area (CSA) comparisions and z scores for training-, sex- and age-difference studiesa Author

Subjects

Measurement technique force

Measurement technique CSA

Z scores (difference)

Castro et al.[7]

26 trained 26 untrained

Isometric torque (isokinetic dynamometer)

Limb circumference/skinfolds

Maughan et al.[11]

12 trained 30 untrained

Isometric chair (knee extensor)

CT scan

Dons et al.[4]

18 trained (preto post- trained)

Knee extension (dynamic)

Ultrasound

2.74

Castro et al.[7]

26 males 26 females

Isometric torque (isokinetic dynamometer)

Limb circumference/skinfolds

0.26

Hubal et al.[12]

342 females 243 males

Elbow flexion (dynamic)

MRI

0.40

Ichinose et al.[13]

28 males 33 females

Elbow extension Isokinetic (cybex)

Ultrasound

0.99

Training status 1.79 -0.11

Gender

a

Because of the variety of measurements used, for comparisons, a z-score difference was calculated by using the mean and standard deviations reported to express the number of z units (standard deviations) difference in F/CSA between the two comparison groups (trained vs untrained, pre-vs post-training, males vs females, and young vs old).

(5–10 years) than those traditionally associated with neuromuscular adaptation,[7,15] so there may be additional factors involved as well. Maughan et al.[11] assessed F/CSA in leg extensors in elite male sprinters, elite endurance runners and an untrained control group. Results revealed that while sprinters were significantly stronger (p < 0.01) than endurance subjects, there were no significant differences in CSA between groups. Maughan et al.[11] also reported that control-group subjects who ‘‘were not particularly successful at any athletic event’’ exhibited greater individual F/CSA ratios than athletes (13.57 N/cm2 vs 11.45 N/cm2) in either group. Possible reasons for these findings were cited as muscle fibre composition differences (volume of fast twitch vs slow twitch fibres) and varying contractile characteristics (i.e. explosive, endurance) based on different training regimens or self-selection into these competitive events. In a similar study, Alway et al.[16] reported lower F/CSA in elite bodybuilders (p < 0.05) versus recreational weightlifters at velocities between 1.05 and 4.19 rad/sec. These differences declined and were not significant when speed of movement reached 5.24 rad/sec. Actual F/CSA ª 2008 Adis Data Information BV. All rights reserved.

data were presented only in figures, preventing comparisons with other studies in table I. While no definitive conclusions were given, fibre type distribution, fibre pennation angles and neural adaptation were discussed as possible explanatory factors. A 7-week training study by Dons et al.[4] revealed smaller changes in CSA in subjects training at 80% of 1-repetition maximum (1RM) versus subjects training at 50% of 1RM (2.03 cm2 and 3.53 cm2, respectively), while strength increased by 42.3% and 23.9%, respectively. F/CSA ratios from post-training data (used to report differences in table I) were 89.6 – 7.65 and 75.4 – 2.7 N/cm2 for trained and untrained (control) subjects respectively. While this study does not speak directly to F/CSA, if indeed increased force were a product of increased CSA alone, comparable increases in force with comparable increases in CSA would have been observed. Also, while post-training data were not analysed for differences between trained and untrained subjects, there appears to be a substantial difference between these groups. In Dons et al.,[4] pubescent changes may have affected results, as subjects were high-school physical education students and the control Sports Med 2008; 38 (12)

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group CSA increased by 2.34 cm2 without training. However, levels of intensity during training may be another possible explanation for discordant changes in force and CSA. Castro et al.[7] also state that self-selection of subjects for success in strength measurements could also lead to variations in F/CSA among various states of training. Based on these studies (see table I), it seems that differences other than muscle CSA may account for the amount of force produced. Equivocal results suggest that relationships among training status, CSA and peak force are complex, making it difficult to attribute greater peak force solely to larger CSA.[16] 1.1 Sex Differences and F/CSA

It has been suggested that the ratio of F/CSA across genders should be similar among similarly trained people.[1,5,12,17] Differences in F/CSA between males and females have been assessed to determine whether variations in force production and adaptations to training between sexes actually exist.[7,12,16,18,19] If the maximum amount of force generated were directly dependent on F/CSA, it would be expected that similar tissue qualities between sexes would result in predictable force measurements in relationship to CSA. In a study that attempted to minimize behavioral physical activity differences between genders while comparing strength and CSA, 24 male and 25 female collegiate swimmers were studied. Of the variation in strength between sexes, 97% was attributed to fat free weight (FFW) and fat free CSA (FFCSA).[17] However, this study was performed using multiple joint exercises, which may have allowed for force production from muscle groups not accounted for by CSA measurements.[13] However, such differences should have acted to reduce the variation accounted for by FFW and FFCSA. Similarly, comparisons of 26 trained and untrained males and females revealed no significant differences (p £ 0.05) in F/CSA within respective training groups between sexes.[7] A 12-week training study of 585 subjects assessed elbow flexion 1RM and CSA.[12] Men and ª 2008 Adis Data Information BV. All rights reserved.

women revealed mean pre-trained F/CSA of 0.54 kg/cm2 and 0.45 kg/cm2, respectively, with post-trained F/CSA of 0.62 kg/cm2 in both groups. While Hubal et al.[12] found no significant difference in F/CSA attributable to the sex of untrained subjects; the study highlighted the importance of considering the training status of subjects when assessing differences between genders. Post-training percentage changes from baseline in CSA and muscle strength were also presented, with males increasing by 20% in CSA and 40% in strength, versus an 18% increase in CSA and a 64% increase in strength for females. From these data, it appears that female responses to training were significantly different (p = 0.001) to male responses. The larger increases in strength concurrent with comparable increases in CSA may be caused by differences between genders in skill acquisition during training and familiarity with exercises used.[12] It may also reveal the varying levels of untrained states between sexes before testing. While the above studies have revealed no sex differences in F/CSA, several studies have reported otherwise. Data extrapolated from figures in a study comparing male and female bodybuilders with male and female recreational weightlifters suggest the importance of training status and speed of movement and subsequent effects on F/CSA between sexes.[16] No significant difference was found between elite male and female bodybuilders (n = 13) regarding F/CSA across all velocities of movement (1.05– 5.24 rad/seconds). However, differences were found between recreationally trained males and females. While data were not directly reported, the differences between recreationally trained male and female subjects at low velocities appeared similar in magnitude to the differences (p = 0.05) observed between elite and recreational subjects. F/CSA ratios (extrapolated from figures) were approximately 3.4 Nm/m2 and 4.25 Nm/m2 for recreational males and females, respectively, at 1.05 rad/seconds, 2.8 Nm/m2 and 3.6 Nm/m2 at 2.07 rad/seconds, respectively, and 2.5 Nm/m2 for both males and females at 4.19 rad/seconds. As velocities increased, the differences between genders diminished. From these Sports Med 2008; 38 (12)

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data, it appears that long-term training may compensate for sex differences. Speed of movement may be another variable that should be considered, at least when studying untrained or recreationally trained subjects. Comparisons of Japanese Olympic athletes (soccer, judo and gymnastics) across various velocities of movement revealed no significant sex differences for F/CSA (p < 0.05) at velocities of either 60/sec or 180/sec in different athletic events.[13] However, it is noteworthy that male judo contestants had the lowest F/CSA at velocities of 60/sec (14.5 N/cm2 vs 16.7–22.2 N/cm2) out of any group, male or female. While F/CSA between sports was not analysed, the relatively large differences between judo athletes (14.5– 16.7 N/cm2) and women gymnasts (18.1 N/cm2) compared with soccer players (21.0–22.2 N/cm2) would suggest that sport participation and styles of training should be considered and accounted for in future work. Lastly, the higher velocity data reported by Ichinose et al.[13] support earlier contentions (Alway et al.[16]) regarding speed of movement. That is, when velocities were increased to 180/sec, disparities in F/CSA become non-existent between sport groups. Ryushi et al.[14] compared F/CSA of male strength athletes with male and female physically active subjects and reported (approximated from figures) significant differences (p < 0.01) between physically active males (3.2 kg/cm2) and females (2.4 kg/cm2). Across varied levels of training, a difference (p < 0.001) between physically active females (2.4 kg/cm2) and strengthtrained males (3.9 kg/cm2) was also found.[14] In a similar study by Sale et al.,[19] strength was tested at velocities of 30, 120, 180, 240 and 300/sec. Untrained females were reported to have a greater F/CSA than males and male bodybuilders (p = 0.01) at all velocities except 30/sec.[19] The F/CSA of untrained females varied from 1.8 to 2.2 Nm/cm2, while that of untrained males and elite bodybuilders varied from 1.1 to 1.4 Nm/cm2 across velocities of 120–300/sec. This study emphasized speed of movement, as in Ichinose et al.[13] and Alway et al.[16] However, there was no significant difference between groups at the 30/sec velocity,[19] ª 2008 Adis Data Information BV. All rights reserved.

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which was contradictory to the results of Ichinose et al.[13] and Alway et al.[16] While direct comparisons could not be made with the current review, Delmonico et al.[20] assessed muscle volume and peak power, and reported that changes in muscular force were dependent on muscular hypertrophy in men, but not in women. Martel et al.[21] evaluated changes in individual muscle fibre types with regard to strength training programmes. Whereas strength increased by 29% and 34% for men and women, respectively, the percentage of fibre types that made up total muscle volume as a result of strength training programmes varied between the sexes. Although discordance among studies means it is difficult to make definitive statements about sex differences and the relationship between force production and CSA, the studies do suggest that differences in F/CSA between sexes may exist with regard to variables such as velocities of movement, levels of training and type of sports. 1.2 Age and F/CSA

Age-related loss of muscle mass (sarcopenia) and strength is another area of discussion when considering relationships between force and CSA. Studies addressing age and F/CSA were not included in table I, as only one study supplied sufficient data (i.e. means and standard deviations). Past work has established that aging is associated with decreases in muscular size and force.[22-24] Declines in force of 12–14% per decade have been reported after the age of 50 years.[25] While specific mechanisms explaining force declines have not been offered, it has been suggested that declines in CSA may help to explain declines in force. An early study measuring grip strength in men and women aged 65–90 years suggested that the decline in strength was not accompanied by a decline in lean body mass.[26] However, because lean body mass was used rather than CSA, it is difficult to interpret these results, as muscles that account for grip strength make up such a small portion of total lean body Sports Med 2008; 38 (12)

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mass.[23] Comparisons of F/CSA between 30-, 50and 70-year-old women revealed no differences among age groups.[24] However, the author did note that while not significant, the variation in F/CSA within the 70-year-old group was greater, and correlations between maximal force and F/CSA were smaller, than for younger subjects (r = 0.67 for 70-year-olds compared with 0.86 and 0.72 for the 50- and 30-year-olds, respectively). Possible causes of greater variations in F/CSA of older subjects have been cited as due to decreases in neural stimulation and changes in qualitative characteristics of the muscle tissue itself.[24] Young et al.[23] performed a study on 20- and 70-year-old females and reported that older women’s quadricep strength averaged 6.9 N/cm2 – 0.24, similar to the 7.1 N/cm2 – 0.20 for younger females. This indicates that intrinsic strength among different age groups was unchanged and that CSA accounted for most of the changes seen. While these studies[23,24] suggested that the major factor accounting for declines in force production was loss of muscle mass or CSA, a study performed on males aged 20 and 70 years revealed that younger subjects were 39% stronger (8.7 N/cm2) than older subjects (7.1 N/cm2), but only 25% larger in CSA of the quadriceps muscle. Thus, F/CSA in the younger subjects was greater than that of the older subjects.[6] Young et al.[23] also noted the strength of younger male subjects was greater than what would normally be expected based on CSA. An analysis by Bruce et al.[27] of five studies on maximal force and CSA revealed a major difference in the rate of decline between maximal force and CSA over time. From a figure in this study, it appears that maximal force declines were much more rapid than CSA decline. While needed information was missing from this figure, it warrants further consideration when discussing F/CSA and age changes, since measurement and evaluation errors are cited as possible sources of variance among studies. More recent research has addressed muscular force production and age-related changes.[20,21,28] However, these studies chiefly addressed changes ª 2008 Adis Data Information BV. All rights reserved.

in muscle volume, muscle damage and individual fibre type changes, making them inappropriate for comparison in the context of the current review and F/CSA. However, it is worth mentioning that Martel et al.[21] found significantly different fibre type adaptations to strength training as a function of age. From the studies reviewed, it appears that a large portion of the strength loss associated with aging may be accounted for by a loss of CSA. However, while we cannot speak definitively from the limited studies assessed in this brief review, it appears that some of the age-related losses of strength may be associated with other factors (i.e. neural stimulation degradation, changes in muscle characteristics with age, and measurement and evaluation errors).

2. Summary The aim of this brief review was to compare results of F/CSA studies among various populations (training status, gender and age). Although we cannot fully explain the disagreement of results among these various populations, the existing data raise questions on the relationship between muscle force and CSA. While it is commonly recognized that the larger muscle CSA has greater force generation capacity, the large variations in F/CSA suggest that other variables account for differences in strength rather than size alone. Table I depicts the discrepancies among some of the papers reviewed; however, the utility of table I is limited because many of the studies reviewed could not be included as a result of missing means and standard deviations or the presentation of data in figure form only, with insufficient resolution for extracting useful data. Although making comparisons across studies is a daunting task because of the variations in reported units, meaningful assessments are possible using z scores (see table I). Among trained athletes, genetic predisposition is more likely to lead to increases in F/CSA than among sedentary subjects.[7] The recurring theme of changes in velocity of movement and training status when discussing F/CSA between Sports Med 2008; 38 (12)

Cross-Sectional Area and Muscular Strength

genders should not be overlooked. From the papers reviewed, it appears that these variables play an important role in discerning differences among these populations. Muscle tissue composition and neural adaptation have also been presented as possible explanations for differences seen among groups.[14,24] 3. Conclusions While precise explanations for differences seen in F/CSA between training status, genders, and ages are unknown, it seems apparent that force production is more complex than can be explained by muscle CSA alone. Additional research employing precise measures of the CSA of specific muscle groups for a given force measurement should help to clarify some of the questions related to CSA. Modern tissue and force measurement equipment should advance this research line over the next few years. Acknowledgements The authors received no funding for the preparation of this review and have no conflicts of interest directly relevant to its contents.

References 1. Ikai M, Fukunaga T. Calculation of muscle strength per unit cross-sectional area of human muscle by means of ultrasonic measurement. Int Z Agnew Phys 1968; 26: 26-32 2. Fukunaga M, Miyatani M, Tachi M, et al. Muscle volume is a major determinant of joint torque in humans. Acta Phys Scand 2001; 172: 249-55 3. Moss BM, Refsnes PE, Abildgaard A, et al. Effects of maximal effort strength training with different loads on dynamic strength, cross-sectional area, load-power and load-velocity relationships. Eur J Appl Phys 1997; 75: 193-9 4. Dons B, Bollerup K, Bonde-Peterson F, et al. The effect of weight-lifting exercise related to muscle fiber composition and muscle cross-sectional area in humans. Eur J Appl Phys 1979; 40: 90-106 5. Schantz P, Randall-Fox E, Hutchison W, et al. Muscle fibre type distribution, muscle cross-sectional area and maximal voluntary strength in humans. Acta Phys Scand 1983; 117: 219-26 6. Young A, Stokes M, Crowe M. The size and strength of the quadriceps muscles of old and young men. Clin Phys 1985; 5 (2): 145-54

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7. Castro MJ, McCann DJ, Shaffrath JD, et al. Peak torque per unit cross-sectional area differs between strengthtrained and untrained young adults. Med Sci Sports Exer 1995; 27: 397-403 8. Brechue WF, Abe T. The role of FFM accumulation and skeletal muscle architecture in powerlifting performance. Eur J Appl Phys 2002; 86: 327-36 9. Frontera WR, Meredith CN, O’Reilly KP, et al. Strength conditioning in older men: skeletal muscle hypertrophy and improved function. J Appl Phys 1988; 64: 1038-44 10. Sale DG, Martin JE, Moroz DE. Hypertrophy without increased isometric strength after weight training. Eur J Appl Phys 1992; 64: 51-5 11. Maughan RJ, Watson JS, Weir J. Relationships between muscle strength and muscle cross-sectional area in male sprinters and endurance runners. Eur J Appl Phys 1983; 50: 309-18 12. Hubal MJ, Gordish-Dressman H, Thompson PD, et al. Variability in muscle size and strength gain after unilateral resistance training. Med Sci Sports Exer 2005; 37 (6): 964-72 13. Ichinose Y, Kanehisa H, Ito M, et al. Morphological and functional differences in the elbow extensor muscle between highly trained male and female athletes. Eur J Appl Phys 1998; 78: 109-14 14. Ryushi T, Hakkinen K, Kauhanen H, et al. Muscle fiber characteristics, muscle cross-sectional area and force production in strength athletes, physically active males and females. Scand J Sports Sci 1988; 10 (1): 7-15 15. Hawley J, Stepto NK. Adaptations to training in endurance cyclists. Sports Med 2001; 31 (7): 511-20 16. Alway SE, Stray-Gundersen J, Grumbt WH, et al. Muscle cross-sectional area and torque in resistance-trained subjects. Eur J Appl Phys 1990; 60: 86-90 17. Bishop P, Cureton K, Collins M. Sex difference in muscular strength in equally-trained men and women. Ergonomics 1987; 30 (4): 675-87 18. Chilibeck PD, Calder AW. A comparison of strength and muscle mass increases during resistance training in young women. Eur J Appl Phys 1998; 77: 170-5 19. Sale DG, MacDougall SE, Alway SE, et al. Voluntary strength and muscle characteristics in untrained men and women and male bodybuilders. J Appl Phys 1987; 62: 1786-93 20. Delmonico MJ, Kostek MC, Doldo NA, et al. Effects of moderate-velocity strength training on peak muscle power and movement velocity: do women respond differently than men? J Appl Phys 2005; 99: 1712-8 21. Martel GF, Roth SM, Ivey FM, et al. Age and sex affect human muscle fibre adaptations to heavy-resistance strength training. Exp Physiol 2006; 91 (2): 457-64 22. Doherty TJ. The influence of aging and sex on skeletal muscle mass and strength. Cur Opin Clin Nutr Met Care 2001; 4 (6): 503-8 23. Young A, Stokes M, Crowe M. Size and strength of the quadriceps muscles of old and young women. Eur J Clin Inv 1984; 14: 282-7 24. Hakkinen K, Hakkinen A. Muscle cross-sectional area, force production and relaxation characteristics in women at different ages. Eur J Appl Phys 1991; 62: 410-4

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25. Lynch NA, Metter EJ, Lindle RS, et al. Muscle quality. I. Age-associated diffrerences between arm and leg muscle groups. J Appl Phys 1999; 86 (1): 188-94 26. Maclennan WJ, Hall MRP, Timothy JI, et al. Is weakness in old age due to muscle wasting? Age Ageing 1980; 9: 188-92 27. Bruce SA, Phillips SK, Woledge RC. Interpreting the relation between force and cross-sectional area in human muscle. Med Sci Sports Exer 1997; 29 (5): 677-83

ª 2008 Adis Data Information BV. All rights reserved.

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28. Roth SM, Martel GM, Ivey FM, et al. High-volume, heavy resistance strength training and muscle damage in young and older women. J Appl Phys 2000; 88: 1112-8

Correspondence: Dr Eric J. Jones, P.O. Box 13015, Stephen F. Austin State University, Nacogdoches, TX 75962-3015, USA. E-mail: [email protected]

Sports Med 2008; 38 (12)

Sports Med 2008; 38 (12): 995-1008 0112-1642/08/0012-0995/$48.00/0

REVIEW ARTICLE

ª 2008 Adis Data Information BV. All rights reserved.

Optimizing Performance by Improving Core Stability and Core Strength Angela E. Hibbs,1,3 Kevin G. Thompson,1,4 Duncan French,1 Allan Wrigley 2 and Iain Spears3 1 2 3 4

English Institute of Sport, Gateshead, UK Canadian Sport Centre Pacific, Vancouver, British Columbia, Canada University of Teesside, Middlesbrough, UK School of Psychology and Sports Science, Northumbria University, Newcastle, UK

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995 1. Definition of Performance, Core Stability and Core Strength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 996 2. Functional Anatomy of the ‘Core’ as it Relates to Athletic Performance . . . . . . . . . . . . . . . . . . . . . . . 997 3. Types of Core Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 998 4. Evidence of Core Training Benefits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 4.1 Rehabilitation Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001 4.2 Athletic Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1002 5. Measuring the Core and its Relation to Performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1004 6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006

Abstract

Core stability and core strength have been subject to research since the early 1980s. Research has highlighted benefits of training these processes for people with back pain and for carrying out everyday activities. However, less research has been performed on the benefits of core training for elite athletes and how this training should be carried out to optimize sporting performance. Many elite athletes undertake core stability and core strength training as part of their training programme, despite contradictory findings and conclusions as to their efficacy. This is mainly due to the lack of a gold standard method for measuring core stability and strength when performing everyday tasks and sporting movements. A further confounding factor is that because of the differing demands on the core musculature during everyday activities (low load, slow movements) and sporting activities (high load, resisted, dynamic movements), research performed in the rehabilitation sector cannot be applied to the sporting environment and, subsequently, data regarding core training programmes and their effectiveness on sporting performance are lacking. There are many articles in the literature that promote core training programmes and exercises for performance enhancement without providing a strong scientific rationale of their effectiveness, especially in the sporting sector. In the rehabilitation sector, improvements in lower back injuries have been reported by improving core stability. Few studies have observed any performance enhancement in sporting activities despite observing

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improvements in core stability and core strength following a core training programme. A clearer understanding of the roles that specific muscles have during core stability and core strength exercises would enable more functional training programmes to be implemented, which may result in a more effective transfer of these skills to actual sporting activities.

1. Definition of Performance, Core Stability and Core Strength Core stability and core strength have been subject to research since the early 1980s.[1-7] What is referred to as the core varies between studies, with many studies including upper and lower sections of the body including the shoulders, trunk, hips and upper leg.[8-11] Furthermore, many studies also fail to distinguish between core stability and core strength, two concepts that are fundamentally very different. The confusion over the precise definition of core stability and core strength is largely because what is included in these definitions differs greatly depending on the context in which they are viewed. For example, in the rehabilitation sector, the focus is on rehabilitation following injuries causing lower back pain (LBP), arm and leg pain and enabling the general population to perform everyday (low load) tasks using exercises that emphasize the control of spinal loading. This requires less core stability and core strength than elite and highly trained athletes in the sporting sector who have to maintain stability during highly dynamic and, in many cases, highly loaded movements.[12] The anatomy involved during sporting tasks includes much more of the body, i.e. shoulders and knees, which contribute to the transfer of forces through the body to produce effective sporting techniques resulting in a different definition of core stability and core strength. Therefore, although the process of core stability and core strength can be defined, what is anatomically included in these definitions varies. Panjabi[13] suggested that core stability is the integration of the passive spinal column, active spinal muscles, and the neural control unit, which when combined maintains the intervertebral range of motion within a safe limit to enable ª 2008 Adis Data Information BV. All rights reserved.

activities to be carried out during daily living. Kibler et al.[14] summarized core stability in a sporting environment as ‘‘the ability to control the position and motion of the trunk over the pelvis to allow optimum production, transfer and control of force and motion to the terminal segment in integrated athletic activities.’’ Akuthota and Nadler[15] defined core strength as the muscular control required around the lumbar spine to maintain functional stability. This is different to the traditional concept of strength in the sporting sector, which has been suggested by Lehman[8] as the maximal force that can be generated at a specific velocity by a muscle or muscle group. Faries and Greenwood[16] provide clearer definitions as to the difference between core stability and core strength for the rehabilitation sector by suggesting that core stability refers to the ability to stabilize the spine as a result of muscle activity, with core strength referring to the ability of the musculature to then produce force through contractile forces and intra-abdominal pressure. Due to the different demands placed on the body during sporting activities, more complex core exercises are trained (usually highly dynamic movements with added resistance) compared with those used for training the general population (mostly static in nature). As a result, the research findings performed in patients with LBP and the general population cannot be extended to the athletic and elite sports performer. This inability to generalize findings along with inconsistent definitions makes the collection and application of meaningful data difficult and has arguably lead to the inconclusive and contradictory findings reported to date. It has been suggested, however, that it is important to have sufficient strength and stability for the body to function optimally in both everyday and sporting environments[17] and that by having sufficient Sports Med 2008; 38 (12)

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stability and strength, athletic performance could be enhanced.[4] To establish whether training core stability and/or core strength are important in everyday and sporting activity, research needs to establish what impact training in these areas can have on resulting performance. What is termed as performance, as with the definitions of core ability (core stability and core strength), differs between the rehabilitation and athletic sectors. In the rehabilitation sector, an improved performance for a patient with LBP would be the ability to perform everyday tasks pain free;[9,18] whereas in the sporting sector, an improved performance may be characterized by not necessarily being pain free, but by improving technique in order to run faster, throw further or jump higher,[4] although it could also include the reporting of fewer injuries, which enhances performance in training.[19,20] Research performed to date has highlighted benefits of training core stability and core strength for patients with LBP and for carrying out everyday activities. However, less research has been performed on the benefits of core training for elite athletes and how this training should be carried out to optimize sporting performance. Although many studies have reported contradictory findings and conclusions,[3,6,8,16,21-25] many elite athletes continue to undertake core stability and core strength training as part of their training programme. 2. Functional Anatomy of the ‘Core’ as it Relates to Athletic Performance A number of models have been published that try to describe the core musculature and the complex integration of the separate processes that work together to bring about core stability. Physiologically, what is included as ‘the core’ varies from study to study[26] depending on the context (rehabilitation or athletic) that it is viewed in. The core has been described as a box or a double-walled cylinder[27] with the abdominals as the front, paraspinals and gluteals as the back, the diaphragm as the roof and the pelvic floor and hip girdle musculature as the ª 2008 Adis Data Information BV. All rights reserved.

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bottom.[28] Meanwhile, other researchers focusing on sports performance define the core as including all of the anatomy between the sternum and the knees with a focus on the abdominal region, low back and hips.[7] Other researchers conclude that the core musculature should include the muscles in the shoulder and pelvis as they are critical for the transfer of energy from the larger torso to the smaller extremities, which may be more involved in sporting movements rather than everyday tasks.[26,27,29,30] Leetun et al.[12] supports this by reporting that hip muscle activation significantly influences the ability to generate force in the upper leg muscles and it has been identified that hip muscle activation is important when looking at core stability and trying to improve core strength.[31] Elphinston[11] and Wilson[32] consider the gluteus maximus to have an essential role in core stability and hip control. A weak gluteus maximus muscle has an influence on the alignment of the lower knee and ankle, resulting in greater medial and rotational movement, which leads to an increase in strain on the joints, predisposing to a greater injury risk. Panjabi[13] summarized the contributors to spinal stability into three groups: passive (e.g. vertebrae, ligaments and intervertebral discs), active (muscles and tendons around the joints) and neural (CNS and other contributing nerves). Bergmark[33] developed a model to summarize the role of the trunk muscles and their contribution to core stability. Bergmark’s model labels muscles as ‘local’ (those with attachments to the lumbar vertebrae and which therefore influence inter-segmental control) and ‘global’ (those with attachments to the hips and pelvis and which therefore influence spinal orientation and control the external forces on the spine). It is important that both systems are integrated to establish normal movement function, for example, if only the global mobilizer muscles are trained, a muscular imbalance occurs because they ‘take over’ the role of the stabilizer muscles, resulting in restricted and compensatory movement patterns that are less efficient.[27] Stabilizing muscles are responsible for posture holding and distributing and absorbing force in the body, whereas mobilizing muscles contribute to rapid movement, Sports Med 2008; 38 (12)

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force and power[33] because of their multi-joint positioning and large moment arms. All of these above processes are important to train, whether in the rehabilitation or sporting sector, as they all contribute to performing movements safely and correctly. Lee[34] suggested that stability is not about the ‘quantity of motion’ and the ‘quality of the end feel’, but about the control of systems that allow load to be transferred and movements to be smooth and effortless. This may be true for sporting movements where the individual is looking solely to optimize their technique and not necessarily worry about pain, but for patients with LBP and the general population, the range of movement and ‘quality of the end feel’ (i.e. no pain) are more important. Brown[22] suggests that core stability is achieved by the muscular system of the trunk providing the majority of the dynamic restraint along with passive stiffness from the vertebrae, fascia and ligaments of the spine. Akuthota and Nadler[15] provide a detailed summary of the anatomy of the lumbar spine and the contribution of these parts to core stability and they draw attention to the contributions of the thoracolumbar fascia, osseous and ligamentous structures, paraspinals, quadratus lumborum muscle, abdominal muscles,[35,36] hip girdle musculature, diaphragm and the pelvic floor muscles. Lehman[8] identified certain muscles that are essential to monitor when analysing core stability and core strength. These include the transverse abdominis (TrA), rectus abdominis (RA), external oblique (EO), internal oblique (IO), erector spinae, quadratus lumborum and latissimus dorsi. The contribution of these abdominal muscles to stability is related to their ability to produce flexion, lateral flexion and rotation movements and control external forces that cause extension, flexion and rotation to the spine.[24,33] Comerford and Mottram[36] emphasise the importance of the RA muscle and believe that this muscle has a high recruitment threshold and is important in bracing the spine for high-load activities such as pushing or lifting heavy loads. The EO and IO have a lower threshold of recruitment and mostly contribute to posture and stability. The contribution to and ª 2008 Adis Data Information BV. All rights reserved.

precise roles of these muscles in core stability and core strength is not clear and future research needs to be performed to establish these links.[15] For example, McGill[9] found that the psoas muscle (the largest muscle in the lower lumbar spine[37]) does not provide much stability, whereas Gibbons[37] reported that this muscle does have a stability role through axial compression and suggested that it was involved with lateral flexion, rotation and extension as well as hip flexion. Despite the apparent confusion and complexity outlined here, it would seem reasonable to suggest that when training the core, it is essential to understand the contribution to stability and strength that all of the musculature, neural and other structures have, and subsequently to train each section depending on the requirements for that individual (i.e. whether they are an athlete needing higher stability and strength or from the general population and require the ability to maintain stability at lower loads). 3. Types of Core Training Core training programmes include processes that target muscular strengthening and motor control of the core musculature.[5] Core strengthening exercises are very popular in rehabilitation programmes despite little scientific evidence existing as to their efficacy on improving subsequent performance,[1,6,30] although some research has suggested that a number of methods can enhance neuromuscular control. These include joint stability exercises,[38] contraction exercises (concentric, eccentric and isometric),[39] balance training,[6] perturbation (proprioceptive) training,[40-43] plyometric (jump) exercises (plyometric training emphasises loading of joints and muscles eccentrically before the unloading concentric activity)[3] and sport-specific skill training.[8] In the field of physiotherapy, proprioceptive training is believed to be important and, consequently, programmes use methods and exercises that challenge proprioception using equipment such as wobble boards, roller boards, discs and Swiss balls. Comerford[27] believes, however, that to train core stability and strength it is important to Sports Med 2008; 38 (12)

Performance, Core Stability and Core Strength

perform both low- and high-load threshold training.[17] Comerford[27] identified the following sub-areas of core training that all need to be included when training core stability and strength: 1. Motor control stability: low-threshold stability where the CNS modulates the efficient integration and low-threshold recruitment of local and global muscle systems. 2. Core strength training: high-threshold and overload training of the global stabilizer muscle system and leads to hypertrophy as an adaptation to overload training.[44] 3. Systematic strength training: traditional highthreshold or overload strength training of the global mobilizer muscle system. Comerford[27] argues that it is essential for local muscles to be targeted and for low-load threshold training to be performed to avoid any muscle recruitment imbalance, which may lead to movement dysfunction and injuries. It is proposed that initial core strengthening programmes should enable people to become aware of motor patterns and allow them to learn to recruit muscles in isolation (it is possible to use biofeedback devices or verbal cues). Programmes can then progress to functional positions and activities.[15] Akuthota and Nadler[15] stated that re-learning the motor control of inhibited muscles may be more important than strengthening in patients with LBP. In this case, it may be that improvements in performance are a result of improved neural co-ordination and recruitment rather than specific improvements in core strength or stability. Careful performance measures are required in studies to identify which of these is ultimately targeted following intervention programmes. The choice of exercise is important as the magnitude of the muscle activation and the recruitment pattern of the motor units determines whether core stability or core strength is developed. Vezina and Hubley-Kozey[45] suggest that an activation of >60% maximal voluntary contraction (MVC) is required to result in strength benefits,[46] with stability and muscle endurance benefits resulting from MVCs of <25%.[21,45] Vezina and Hubley-Kozey[45] used surface electromyography (sEMG) on three ª 2008 Adis Data Information BV. All rights reserved.

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abdominal and two trunk extensor muscle sites and performed three low-load core exercises; pelvic tilt, abdominal hollowing and level 1 of the trunk stability test[47] to compare muscle activation. They identified that the three exercises recruited the five muscles differently, with the external oblique muscle showing the highest activation levels during the pelvic tilt (25% MVC). They concluded that the activation during these exercises would not elicit any strength benefits, but these exercises could be used to form an assessment of an individual’s core stability to formulate a more demanding training programme. Similarly, Davidson and Hubley-Kozey[48] observed muscle electromyogram (EMG) activity of 3–7% MVC during a progressive leg extension exercise test, which suggests that this exercise is not sufficient to result in muscle strength improvements, but would be sufficient to establish and maintain trunk stability.[23] Comerford[27] suggests that core stability training should range from isolated activation of the deep abdominal muscles to lifting weights on uneven surfaces. This is due to the different functional roles of the muscles during exercises and therefore it is advised[33] that a range of exercises be performed to challenge the core musculature in all three planes and ranges of movement to develop total core stability. For example, flexion (targeting hip flexors, back extensors, abdominal and glutei muscles, e.g. curl-ups, leg raising and squats with rotation), extension (e.g. targets hip extensors and hamstrings) and rotational exercises[49] should be included. Stephenson and Swank[26] believe that a core strength development programme should include: flexibility of the abdominal and lower back, hip extensor and flexor muscles; exercises in an unstable environment; as well as isometric and dynamic exercises. Lehman[8] believes that because only a minimal level of muscle contraction is required to stabilize the spine (1–3% MVC), muscle endurance may be more important than muscle strength. Lehman[8] identified exercises such as the curl up, bird dog, side and front support and loaded squat to develop core muscle endurance as these challenge all of the anterior, lateral and posterior trunk muscles and all sufficiently stress Sports Med 2008; 38 (12)

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the muscle, but do not exceed the thresholds for compression and shear loading, which may predispose the body to injuries. This is supported by McGill,[9,50] who suggests that muscular endurance is more important to stability than muscle strength, and by Faries and Greenwood,[16] who suggest that endurance should be trained before strength while focusing on establishing the correct motor control systems prior to increasing the bodies stabilization strength. Faries and Greenwood[16] suggest that endurance training focuses on low load, longer (30–45 seconds), less demanding exercises, while strength exercises are based on high-load, lowrepetition exercises. Speed, direction and order of limb movement during exercise are seen as critical factors when training. For example, the speed at which an exercise is performed will affect the gravitational and mechanical resistance experienced on the body. This is due to fast movements recruiting the fast motor units in the muscles when performing a movement optimally. Slow motor units of the muscle are utilized during low-threshold recruitment in postural sway and movements involved with unloaded limbs. It is important for optimum motor control to train both the fast and slow motor units in a muscle to optimize core stability and core strength.[24] The direction and order of limb movements also has a profound effect on muscle activation. Cresswell[51] found that the abdominal muscles, the RA, EO and IO were only active during acceleration, when they generated the movement, and deceleration, when they opposed the movement. The magnitude of movement has also been investigated; for example, feed-forward response in these muscles was identified when movements of the elbow and shoulder were performed, but not when the wrist and thumb were moved.[24] Furthermore, when the arm is moved, the onset of TrA activation precedes the deltoid by 30 ms,[52] when the leg is also moved, activation of the TrA precedes the deltoid by more than 100 ms.[53] This highlights the effect on muscle activity that increasing the demand on the core to maintain stability has on certain core muscles. Research suggests that limb movement is ª 2008 Adis Data Information BV. All rights reserved.

Hibbs et al.

delayed in tasks where the postural demand is increased[54,55] due to the extra time needed to prepare the body for the resultant forces.[24] Research on the optimum speed and order of loading on the muscles is limited; therefore, it remains unclear what speed and direction of movement should be used, only that it should be functional and sport-specific for the individual’s needs.[8,12] Future research should try to establish these characteristics to enable the most effective training programme to be implemented and to maximize the potential for the skills and training benefits to be transferred into performance.[56-58] Due to the many factors mentioned above in the paragraph above, the ability to train the muscles to improve core stability and/or strength relies on the training being functional and specific to the everyday or sporting movement that is to be performed. Any improvements in training can then be translated into improvements in performance. Therefore, whether the targeted movements are to be low or high load will have a significant effect on the type of training programme implemented. The apparent contradiction between the traditional dynamic approach of the strength and conditioning coach compared with the more modest movements prescribed by physiotherapists has typically led to confusion as to which method is most effective. Prior to any training programme being initiated, the exercises included and intensity of the programme should be carefully evaluated depending on the individual involved and their goals (i.e. to be pain free to improve sporting performance). Therefore, future research should focus on establishing which exercises are sufficient for improving each part of core stability (i.e. neural, passive and active systems) and core strength (e.g. neural adaptations) to be able to target these performance goals more effectively.

4. Evidence of Core Training Benefits Spinal instability and injuries to muscles (e.g. the core) and joints (e.g. knee, hips) sustained during movements are associated with insufficient strength and endurance of the Sports Med 2008; 38 (12)

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trunk-stabilizing muscles and inappropriate recruitment of the trunk and abdominal muscles.[45] It is important that any trunk-stabilizing muscle weakness is identified and corrected as this significantly increases an individual’s muscle and joint injury risk.[44] Neural adaptations from core training include: more efficient neural recruitment patterns, faster nervous system activation, improved synchronization of motor units and a lowering of neural inhibitory reflexes.[59] Highthreshold strength training results in hypertrophy of the muscles (structural change) and neural adaptations (e.g. of the motor units in the muscles) of the muscles, which benefits performance by increasing the possible force generation, CNS facilitation, improved intrinsic muscle stiffness and improved tissue mobilization.[15] Research stating whether there are any benefits of specific core stability or core strength exercises in activating muscles is limited and conflicting because of the wide variety of data collection methods, exercise techniques and subjects used for analysis. There is not one single exercise that activates and challenges all of the core muscles; therefore, a combination of exercises is required to result in core stability and strength enhancements in an individual.[23] Future research needs to identify which of these exercises are most effective in resulting in benefits depending on the performance goal. 4.1 Rehabilitation Sector

Most research in the rehabilitation sector focuses on how core stability influences LBP,[28,60-63] with many conditioning programmes being based around training the abdominal muscles to improve their strength and subsequently the stability of the spine.[64] This is based on the knowledge that strong abdominal muscles provide support for the lumbar spine during day to day activities.[64] Jeng[65] reported that the occurrence of LBP may be decreased by strengthening the back, legs and abdomen to improve muscular stabilization. Pollock et al.[39] showed that resistance training with pelvic stabilization improved development of lumbar extension strength, which may lead to an improvement in core stability and ª 2008 Adis Data Information BV. All rights reserved.

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therefore reduce the risk of LBP. One of the main muscles associated with ‘the core’ is the TrA. This is the deepest abdominal muscle and provides specific support to the lumbar spine and has been shown to be impaired in those with LBP.[24,36,52,53,61,66-68] Hodges and Richardson[53,69] observed that TrA activity in healthy individuals precedes that of arm and leg movement by approximately 30 and 100 ms, respectively, suggesting that this muscle has a preparatory stabilizing effect and assists in stabilizing the trunk, thereby enabling force production at the extremities. The TrA muscle is also found to be active regardless of body movement direction, unlike other core muscles such as the RA, EO and IO.[24] Therefore, theoretically, training the abdominal muscles and improving their strength should have beneficial effects on resultant stability and performance. Rehabilitation programmes have used Swiss balls to improve core stability with some benefits being documented.[70,71] Behm et al.[38] suggest that using a Swiss ball provides an unstable surface, which challenges the core muscles to a greater extent and improves trunk stability and balance. Cosio-Lima et al.[6] tested two groups of subjects, one training on the floor and one using a Swiss ball and found that the Swiss-ball group had a significantly greater change in muscle EMG activity during flexion and extension and greater balance scores than the floor-exercise group. Behm et al.[38] suggested that the Swiss ball can be used to increase stability, balance and proprioceptive ability, but not muscle strength.[24] As a result, many researchers advocate using a Swiss ball only as a low-threshold rehabilitation tool to improve joint position sense, balance, posture and proprioception.[40,41,43] This has led to modern day rehabilitation programmes using a mixed conditioning approach, which includes a range of methods to improve core stability and core strength. Saal and Saal[72] investigated the effectiveness of an exercise training programme on patients with LBP, which consisted of a flexibility programme, joint mobilization of the hip and the thoracolumbar spinal segments, a stabilization and abdominal programme (low-load exercises[73]) and an aerobic gym programme. The Sports Med 2008; 38 (12)

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authors reported successful recoveries for 50 of the 52 subjects (96%). However, it is not possible to conclude how much of this improvement was directly due to the core stability work (other factors such as medication, injections and healing over time would all have had an additional effect). Whether a training programme results in an improved performance or not depends on the effectiveness of the core exercise performed. This may explain why some research has resulted in contradictory research on the efficacy of some rehabilitation programmes to strengthen core muscles.[24,74] The effectiveness of an exercise is determined by factors such as functionality/specificity of the movement, intensity/threshold, familiarization and frequency. Different core exercises that challenge the core musculature at different intensities of muscle activation are required to result in stability or strength enhancements,[8] but these must be specific to the performance goals to result in any enhancement. In summary, research in the rehabilitation sector has been conducted, which has begun to assess how core muscles respond to low-load core stability exercises and their effect on LBP, and suggests that by performing core training exercises, performance relating to injury risk and recovery can be improved (table I). How core muscles respond to higher threshold exercises and movements/demands, seen regularly in sporting environments, however, cannot be elucidated by such methodologies.

4.2 Athletic Sector

There is a lack of research looking at the effect of core stability on athletic performance.[22] Although some studies have implied that there is an advantageous effect on performance by improving core stability and strength, these conclusions are largely assumptions based on basic testing.[16,77,78] Roetert[79] reported that core stability and balance are critical for good performance in almost all sports and activities. This is due to the 3-dimensional nature of many sporting movements, which demands that athletes must have good strength in the hip and ª 2008 Adis Data Information BV. All rights reserved.

trunk muscles to provide effective core stability. Some sports require good balance, some force production, and others body symmetry, but all require good core stability in all three planes of motion.[79] A lack of core strength and stability is thought to result in an inefficient technique, which predisposes the athlete to injury.[80] For example, LBP is a common problem in any sport that requires significant rotatory or twisting motions, repetitive flexion and/or extension.[81-83] In swimming, the maintenance of posture, balance and alignment is thought to be critical in maximizing propulsion and minimizing drag, yet it is not common practice for core muscles to be trained, with most strength programmes favouring arm exercises.[7] Leetun et al.[12] found that 41 (28 women, 13 men) of 139 athletes (basketball and track) sustained 48 back or lower extremity injuries during the season (35% of the women, 22% of the men). They identified that the athletes who sustained an injury generally had poor core stability (i.e. weaker hip abduction and external rotation strength, which decreased their ability to maintain stability) and also concluded that there were greater demands on the female lumbo-pelvic musculature, which resulted in a greater injury risk to the lower back for females (this is supported by previous research).[19,20,84,85] Subsequently, core training could play an important role in injury prevention, especially in females. Physiologically, core strength and stability training is believed to lead to a greater maximal power and more efficient use of the muscles of the shoulders, arms and legs.[8] This theoretically results in a lower risk of injury and positive effects on athletic performance, in terms of speed, agility, power and aerobic endurance.[30] Training programmes attempting to correct weak links in an individual’s core ability include strategies that regain control of the site and direction of the deficiency at the appropriate threshold of training. Typically, programmes are designed to:    

increase joint range and muscle extensibility; improve joint stability; enhance muscle performance; optimize movement function.[86]

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ª 2008 Adis Data Information BV. All rights reserved.

Stability improved

Stability improved

Stability effects

Increased muscle activity but no strength increase

Strength increase and fewer injuries

Poor strength led to more injuries

Improved muscle endurance but no effect on performance

Improved stability, but no effect on performance

Improved stability, strength and resultant performance

Liemohn et al. [75]

Vezina and Hubley-Kozey[45]

Urquhart and Hodges [76]

Cosio-Lima et al.[6]

Nadler et al.[5]

Leetun et al.[12]

Tse et al.[30]

Stanton et al.[1]

Myer et al.[4]

Single-leg hop and hold, and distance test used. Distance jumped and held increased following training programme. Found stability and strength improvements and enhanced performance following programme

Sahrmann core stability test, stature, . VO2max test, running economy. Found significant effect on core stability, but no significant improvement on resultant performance measures

Vertical jump, shuttle run, 40-m sprint, overhead medicine-ball throw, 2000-m ergo test. Found improved endurance, but no effect on performance

Weakness in hip abduction/external rotation led to more injuries

Strength increased and fewer injuries observed for males. Observed gender differences in response to the training on injuries reported

EMG muscle activity. Strength on Cybex machine (back, abdominals, knee). Found Swiss-ball group had greater change in EMG activity, but no strength changes

EMG muscle activity; found posture and stance affected muscle activity of the abdominal muscles. Muscles had different contributions/activity to each movement

Repeated tests 6 wk later. Found improved TST level 1 results

Time out of balance. Concluded exercises should be repeated over 4 d

Performance measure used/findings

Video, speed/strength and jump tests

Surface EMG (RA, EO, ES), video

EMG

Video, dynamometer, force, EMG

Force plate, dynamometer

Surface EMG (RA and ES) vs intramuscular EMG (TrA)

41 female college athletes (basketball, soccer, volleyball)

18 young male athletes

45 college rowers

140 basketball and track athletes (80 women, 60 males)

>200 college sports players

6-wk programme; plyometric and movement, speed, core strengthening, balance and resistance training

6 week programme; Swiss-ball exercises

8-wk programme; trunk extension and side flexion

Hip abduction strength (sit and hold with hips at 60), abdominal muscle activity, back extensor endurance

Structured core-strengthening programme

5-wk Swiss-ball training programme; curl-ups and back extensions

Rapid, unilateral shoulder flexion in sitting and standing

11 healthy nonathletic subjects

30 untrained college women

TST level 1, pelvic tilt, abdominal hollowing

24 healthy men

Surface EMG (3 abdominal and 2 trunk muscles) Intramuscular EMG (TrA, EO, IO), surface EMG (RA)

Forward and side bridge, plank, bird dog

Training programme/exercises used

16 healthy college students (9 men, 7 women)

Subjects

Stability platform

Data collection method

EMG = electromyography; EO = external oblique muscle; ES = erector spinae muscle; IO = internal oblique muscle; RA = rectus abdominis muscle; TrA = transverse abdominis . muscle; TST = trunk stability test; VO2max = maximal oxygen uptake.

Result

Study

Table I. A selection of research on core training and resultant benefits on core stability, core strength, muscular endurance and performance

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Many sport-specific training programmes fail to include low-load motor control training, which has been identified as an essential part of core strength training and improving core stability.[27] By neglecting the local muscles, the force produced by the global muscles will be too great for the local muscles to control and leads to greater injury risk.[16] It is believed that high-load training changes the muscle structure, whereas low-load training improves the ability of the CNS to control muscle coordination and hence the efficiency of the movement.[27] Therefore, by performing a well structured and functional programme using both low- and high-load training, improvements should be attained in all the processes contributing to core stability and core strength, which, it is reasoned, will in turn, impact on sporting performance. Low- and high-load training involves different types of movements; for example, low-load training involves less demanding, posture-related exercises that focus on muscle recruitment, whereas high-load training can involve exercises such as overhead weighted squats and hanging leg raises, which places a greater stress on the core musculature and also promotes core strength development.[87] Many questions remain regarding what type of core training programme is most effective for improving core ability, but if future research can establish (i) clear definitions; (ii) reliable methods for summarizing the effectiveness of different core exercises; and (iii) the extent to which these muscles need to be active to bring about sufficient core stability and strength improvements, these training programmes would be more effective and we should expect to see fewer injuries and subsequently to observe improved sporting performances. 5. Measuring the Core and its Relation to Performance Tse et al.[30] evaluated the effect of a core endurance programme (2 days a week for 30–40 minutes for 8 weeks) on 45 rowers. They measured trunk endurance (flexion, extension and side flexion tests) and functional performance tests including vertical jump, broad jump, shuttle run, ª 2008 Adis Data Information BV. All rights reserved.

Hibbs et al.

40-m sprint, overhead medicine-ball throw and a 2000-m maximum rowing test. The results revealed significant improvements in the side flexion tests of the core group; however, no significant differences were observed in the performance tests between the two groups. The authors stated that this may have been due to the margins for improvement in the subjects being relatively small in this highly conditioned group of athletes. Using a homogenous group of athletes, however, does enable a high level of sensitivity in the parametric statistic should any improvements be observed following an intervention programme, so the lack of significant differences in the study of Tse et al.[30] may also be due to the exercises performed not being functional enough to significantly improve performance. The length of intervention (8 weeks) may also have not been sufficient to elicit a performance enhancement (see figure 1). Stanton et al.[1] investigated the effect of shortterm Swiss-ball training on stature, bodyweight, EMG activity of abdominal and back muscles, treadmill maximal oxygen uptake, running economy and running posture. Each subject had familiarization sessions on the core activities to minimize the learning effect and then attended two sessions per week for 6 weeks. The authors used the Sahrmann core stability test[47] and a stabilizer pressure biofeedback unit (an inflatable pad that the subject lies supine on) and surface EMG from the RA, EO and erector spinae muscles. Stanton et al.,[1] Scibek et al.[88] and Cusi et al.[89] all observed significant effects on Swiss-ball stability; however, no significant differences in EMG activity or performance parameters were observed. Stanton et al.[1] speculated that the training may have had an effect on other muscles that were not analysed (e.g. pectorals, latissimus dorsi). Swiss-ball training alone, therefore, may not elicit the same performance advantage as explosive or high-intensity strength training. The lack of effect on performance observed in many studies may be due to the core training programmes not being functional enough to translate into improvements in sporting performance as a result of the poor understanding of the role that specific muscles have during these exercises. Future research needs to establish the Sports Med 2008; 38 (12)

Performance, Core Stability and Core Strength

ASSESSMENT

1005

PHYSIOLOGICAL CHANGES

TRAINING

Core Stability: Low-threshold training Identify weakness in core stability or core strength

Increased motor unit recruitment and synchronization patterns

No added weight Static/slow movements

Increased CNS control

Core Strength: High-threshold training

Added weight/ resistance Dynamic movements

Exercise specificity/ functionality Exercise familiarization

Hypertrophy of muscles Enhanced neural activation of motor units

PERFORMANCE OUTCOMES Increased muscle endurance observed

Decreased injury risk

Increased stability observed

No evidence of direct improvement in performance Increased force generation Increased muscle stiffness

Increased core strength

Performance enhancements observed (speed, agility, power)

Increased risk of injury when training due to high-threshold exercises

Fig. 1. Core training and potential performance benefits: principles of low- and high-load training with subsequent effects on core stability and core strength and the possible impact on performance as a result of scientific research carried out.

roles of specific muscles to be able to implement the optimum training programme for individuals. The lack of effect may also be due to the low-load exercises not being sufficient to result in a large enough improvement in core ability to affect the subsequent performance, and it may be that more demanding (high-load) exercises are required. As stated in section 3, Davidson and HubleyKozey[48] suggest that loads need to be 60–100% of one repetition maximum to result in a strength enhancement of the truck musculature; however, this depends on the training status of the individual. Myer et al.[4] found improvements in performance (vertical jump, single-leg hop distance, speed and improved biomechanical range of motion) following a high-load training programme (including squats and bench-press exercises that focused on improving core strength), which suggests that the core training programme improved individuals’ core ability and subsequently improved their ability to perform the tests. Nadler et al.[5] investigated how core strengthening influences hip muscle imbalance and LBP in trained athletes (by reducing the likelihood of segmental buckling).[8] Subjects performed a core-strengthening programme (abdominal, paraspinal and hip extensor strengthening) that included isolated abdominal strengthening (sit ups and pelvic tilts; rectus abdominis and abdominal obliques, squats and lunges (emphasizing multiple ª 2008 Adis Data Information BV. All rights reserved.

joint activation of ankle, knee and hip), leg press (to strengthen quadriceps and hamstring musculature and gluteus maximus) and strength training with free weights (dead lifts, hang cleans, using shoulder, upper leg and hip musculature). The study reported an increase in hip extensor strength for 90% of subjects, with the incidence of LBP decreasing by 47% in male athletes, but increasing slightly for females. This maybe due to the use of some unsafe exercises, such as the Roman chair exercise, and also due to females being more susceptible to LBP.[84] The exercises also only included frontal and sagittal plane movements and this may have affected the results by not being sport-specific enough to translate into improvements in sporting performance. Nadler et al.[5] concluded that the lack of significant findings in the study maybe due to the small number of subjects who reported LBP during the season, which may in itself reflect positively on the core training programme implemented. In summary, it remains unclear as to which exercises best rehabilitate an individual back to normal health or are optimal for improving core strength or stability gains for improving sporting performance. Despite widespread acceptance that core stability and core strength impacts on sports performance, further research needs to be performed to establish whether this can be substantiated. Sports Med 2008; 38 (12)

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6. Conclusions The definitions of core stability and core strength are yet to be clearly established in the rehabilitation and sporting sectors, and as a result, this has led to many contradictory and confusing findings in the area. These definitions need to be established before a clear conclusion as to which exercises and what type of training programme will most effectively result in performance enhancements, such as recovering from or lowering the risk of injury and improving the ability to perform everyday activities or enhancing sporting performance. If future research can establish clear definitions for core stability and core strength and reliable methods for summarizing the effectiveness of different core exercises, fewer injuries and subsequently improved performances in the rehabilitation and athletic sectors should be expected. There are many articles in the literature that promote core training programmes and exercises for performance enhancement without providing a strong scientific rationale of their effectiveness, especially in the sporting sector. In the rehabilitation sector, it has been reported that improving core stability leads to improvements in lower back injury. Few studies have observed any performance enhancement in sporting activities despite observing improvements in core stability and core strength following a core training programme. It might be that improvements made in stability and strength only impact indirectly on sporting performance by allowing athletes to train injury free more often. A clearer understanding of the roles that specific muscles have during core stability and core strength exercises would enable more functional training programmes to be implemented, which may result in a more effective translation of core training into improvements in sporting performance.

Acknowledgements The authors would like to thank the English Institute of Sport and University of Teesside for their support. No

ª 2008 Adis Data Information BV. All rights reserved.

sources of funding were received in the preparation of this article and the authors have no conflicts of interest directly relevant to its contents.

References 1. Stanton R, Reaburn PR, Humphries B. The effect of shortterm Swiss ball training on core stability and running economy. J Strength Cond Res 2004; 18 (3): 522-8 2. McGill SM. Low back stability: from formal description to issues for performance and rehabilitation. Exerc Sport Sci Rev 2001; 29 (1): 26-31 3. Axler CT, McGill SM. Low back loads over a variety of abdominal exercises: searching for the safest abdominal challenge. Med Sci Sports Exerc 1997; 29 (6): 804-11 4. Myer GD, Ford KR, Palumbo JP, et al. Neuromuscular training improves performance and lower-extremity biomechanics in female athletes. J Strength Cond Res 2005; 19 (1): 51-60 5. Nadler SF, Malanga GA, Bartoli LA, et al. Hip muscle imbalance and low back pain in athletes: influence of core strengthening. Med Sci Sports Exerc 2002; 34 (1): 9-16 6. Cosio-Lima LM, Reynolds KL, Winter C, et al. Effects of physioball and conventional floor exercises on early phase adaptations in back and abdominal core stability and balance in women. J Strength Cond Res 2003; 17: 721-5 7. Fig G. Sport-specific conditioning: strength training for swimmers - training the core. Strength Cond J 2005; 27 (2): 40-2 8. Lehman GJ. Resistance training for performance and injury prevention in golf. JCCA J Can Chiropr Assoc 2006; 50 (1): 27-42 9. McGill S. Low back disorders: evidence-based prevention and rehabilitation. Champaign (IL): Human Kinetics, 2002 10. Santana J. Sport-specific conditioning: the serape effect – a kinesiological model for core training. Strength Cond J 2003; 25 (2): 73-4 11. Elphinston J. Getting to the bottom of things. Sportex Dynam 2004; 2: 12-6 12. Leetun DT, Ireland ML, Willson JD, et al. Core stability measures as risk factors for lower extremity injury in athletes. Med Sci Sports Exerc 2004; 36 (6): 926-34 13. Panjabi M. The stabilising system of the spine, part I: function, dysfunction, adaptation and enhancement. J Spinal Disord 1992; 5: 383-9 14. Kibler WB, Press J, Sciascia A. The role of core stability in athletic function. Sports Med 2006; 36 (3): 189-98 15. Akuthota V, Nadler SF. Core strengthening. Arch Phys Med Rehabil 2004; 85 (3 Suppl. 1): S86-92 16. Faries MD, Greenwood M. Core training: stabilising the confusion. Strength Cond J 2007; 29 (2): 10-25 17. Comerford MJ. Performance stability, module 1: stability for performance. Course 1: core stability concepts. Ludlow: Comerford & Performance Stability; 2007 18. Hides JA, Jull GA, Richardson CA. Long-term effects of specific stabilizing exercises for first-episode low back pain. Spine 2001; 26 (11): E243-8

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19. National Collegiate Athletic Association. NCAA injury surveillance system. Overland Park (KS): NCAA, 1998 20. National Collegiate Athletic Association. NCAA injury surveillance system. Overland Park (KS): NCAA, 1999 21. Arokoski JP, Kankaanpaa M, Valta T, et al. Back and hip extensor muscle function during therapeutic exercises. Arch Phys Med Rehabil 1999; 80 (7): 842-50 22. Brown T. Getting to the core of the matter. Strength Cond J 2006; 28 (2): 552-61 23. Cholewicki J, VanVliet JJT. Relative contribution of trunk muscles to the stability of the lumbar spine during isometric exertions. Clin Biomech (Bristol, Avon) 2002; 17 (2): 99-105 24. Hodges PW. Is there a role for transversus abdominis in lumbo-pelvic stability? Man Ther 1999; 4 (2): 74-86 25. Hubley-Kozey CL, Vezina MJ. Muscle activation during exercises to improve trunk stability in men with low back pain. Arch Phys Med Rehabil 2002; 83 (8): 1100-8 26. Stephenson J, Swank AM. Core training: designing a program for anyone. Strength Cond J 2004; 26 (6): 34-7 27. Comerford MJ. Clinical assessment of stability dysfunctionperformance [online]. Available from URL: http:// 216.239.59.104/search?q=cache:skMpsUpvPzIJ:www. kineticcontrol.com/documents/others/MicrosoftWordRatingsystem0706.pdf+clinical+assessment+of+stability+ dysfunction&hl=en&ct=clnk&cd=2&gl=uk [Accessed 2008 Oct 29] 28. Richardson C, Jull G, Hodges P, et al. Therapeutic exercise for spinal segmental stabilisation in low back pain: scientific basis and clinical approach. London: Churchill Livingstone, 1999 29. Gracovetsky S, Farfan HF, Lamy C. The mechanism of the lumbar spine. Spine 1981; 6 (3): 249-62 30. Tse MA, McManus AM, Masters RS. Development and validation of a core endurance intervention program: implications for performance in college-age rowers. J Strength Cond Res 2005; 19 (3): 547-52 31. Bobbert MF, van Zandwijk JP. Dynamics of force and muscle stimulation in human vertical jumping. Med Sci Sports Exerc 1999; 31 (2): 303-10 32. Wilson E. Rehab tips: core stability: assessment and functional strengthening of the hip abductors. Strength Cond J 2005; 27 (2): 21-3 33. Bergmark A. Stability of the lumbar spine: a study in mechanical engineering. Acta Orthop Scand Suppl 1989; 230: 1-54 34. Lee D. The pelvic girdle. 2nd ed. London: Churchill Livingstone, 1999 35. McGill SM. A revised anatomical model of the abdominal musculature for torso flexion efforts. J Biomech 1996; 29 (7): 973-7 36. Comerford S, Mottram S. Transverse training: a waste of time in the gym? FitPro Network (Apr-May) [online]. Available from URL: http://www.kineticcontrol.com/ publication.asp [Accessed 2008 Oct 29] 37. Gibbons SGT. A review of the anatomy, physiology and function of psoas major: a new model of stability. Proceedings of the 11th Annual Orthopedic Symposium; 1999 Nov 6-7; Halifax (NS)

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38. Behm DG, Anderson K, Curnew RS. Muscle force and activation under stable and unstable conditions. J Strength Cond Res 2002; 16 (3): 416-22 39. Pollock ML, Leggett SH, Graves JE, et al. Effect of resistance training on lumbar extension strength. Am J Sports Med 1989; 17 (5): 624-9 40. Lewis FM, Hawke JR. Orthopaedic treatments – 1: the spine. Physiotherapy 1983; 69 (3): 76-7 41. Scott JJ, Pruce SP, Wilson DJ. Orthopaedic treatments – 2: the upper and lower limbs. Physiotherapy 1983; 69 (3): 78-80 42. Carriere B. The Swiss ball: theory, basic exercises and clinical applications. Berlin: Springer, 1998 43. Carriere B. The Swiss ball. Physiotherapy 1999; 83 (10): 552-61 44. Cotton T. Low back pain: does its management differ between athletes and non-athletes? Zurich: Schweizerischer Sportmedizin Kongress, 2005 45. Vezina MJ, Hubley-Kozey CL. Muscle activation in therapeutic exercises to improve trunk stability. Arch Phys Med Rehabil 2000; 81 (10): 1370-9 46. Andersson EA, Ma Z, Thorstensson A. Relative EMG levels in training exercises for abdominal and hip flexor muscles. Scand J Rehabil Med 1998; 30 (3): 175-83 47. Sahrmann S. The Shirley Sahrmann exercise series 1. St Louis (MO): Videoscope, 1991 48. Davidson KL, Hubley-Kozey CL. Trunk muscle responses to demands of an exercise progression to improve dynamic spinal stability. Arch Phys Med Rehabil 2005; 86 (2): 216-23 49. Jackson CP, Brown MD. Analysis of current approaches and a practical guide to prescription of exercise. Clin Orthop Relat Res 1983; (179): 46-54 50. McGill SM. Low back exercises: evidence for improving exercise regimens. Phys Ther 1998; 78 (7): 754-65 51. Cresswell AG. Responses of intra-abdominal pressure and abdominal muscle activity during dynamic trunk loading in man. Eur J Appl Physiol Occup Physiol 1993; 66 (4): 315-20 52. Hodges PW, Richardson CA. Contraction of the abdominal muscles associated with movement of the lower limb. Phys Ther 1997; 77 (2): 132-42 53. Hodges PW, Richardson CA. Feedforward contraction of transversus abdominis is not influenced by the direction of arm movement. Exp Brain Res 1997; 114 (2): 362-70 54. Cordo PJ, Nashner LM. Properties of postural adjustments associated with rapid arm movements. J Neurophysiol 1982; 47 (2): 287-302 55. Zattara M, Bouisset S. Chronometric analysis of the posturo-kinetic programming of voluntary movement. J Mot Behav 1986; 18 (2): 215-23 56. Morrissey MC, Harman EA, Johnson MJ. Resistance training modes: specificity and effectiveness. Med Sci Sports Exerc 1995; 27 (5): 648-60 57. Behm DG, Sale DG. Velocity specificity of resistance training. Sports Med 1993; 15 (6): 374-88

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58. Willardson J. Regarding ‘The effectiveness of resistance exercises performed on unstable equipment’. Response. Strength Cond J 2005; 27 (4): 11-3 59. Staron RS, Karapondo DL, Kraemer WJ, et al. Skeletal muscle adaptations during early phase of heavy-resistance training in men and women. J Appl Physiol 1994; 76: 1247-55 60. Panjabi MM. Clinical spinal instability and low back pain. J Electromyogr Kinesiol 2003; 13 (4): 371-9 61. Kankaanpaa M, Taimela S, Laaksonen D, et al. Back and hip extensor fatigability in chronic low back pain patients and controls. Arch Phys Med Rehabil 1998; 79 (4): 412-7 62. Hodges PW, Richardson CA. Inefficient muscular stabilization of the lumbar spine associated with low back pain: a motor control evaluation of transversus abdominis. Spine 1996; 21 (22): 2640-50 63. Fritz J, Whitman JM, Flynn TW, et al. Clinical factors related to the failure of individuals with low back pain to improve with a spinal manipulation. Phys Ther 2004; 84 (Feb): 173-90 64. Robinson R. The new back school prescription: stabilisation training, part I: occupational medicine. State Art Rev 1992; 7: 17-31 65. Jeng S. Lumbar spine stabilisation exercise. Hong Kong J Sport Med Sports Sci 1999; 8: 59-64 66. Beckman SM, Buchanan TS. Ankle inversion injury and hypermobility: effect on hip and ankle muscle electromyography onset latency. Arch Phys Med Rehabil 1995; 76 (12): 1138-43 67. Devita P, Hunter PB, Skelly WA. Effects of a functional knee brace on the biomechanics of running. Med Sci Sports Exerc 1992; 24 (7): 797-806 68. Marshall P, Murphy B. The validity and reliability of surface EMG to assess the neuromuscular response of the abdominal muscles to rapid limb movement. J Electromyogr Kinesiol 2003; 13 (5): 477-89 69. Hodges PW, Richardson CA. Relationship between limb movement speed and associated contraction of the trunk muscles. Ergonomics 1997; 40 (11): 1220-30 70. Check P. Swissball exercises for swimming, soccer and basketball. Sports Coach 1999; 21: 12-3 71. Fuller T. A ball of fun: programs using ‘Swiss balls’ can help junior participation at your facility. Tennis Industry 2002; 30: 48-9 72. Saal JA, Saal JS. Nonoperative treatment of herniated lumbar intervertebral disc with radiculopathy: an outcome study. Spine 1989; 14 (4): 431-7 73. Comerford MJ, Mottram SL. Movement and stability dysfunction: contemporary developments. Man Ther 2001; 6 (1): 15-26 74. Koes BW, Bouter LM, Beckerman H, et al. Physiotherapy exercises and back pain: a blinded review. BMJ 1991; 302 (6792): 1572-6 75. Liemohn WP, Baumgartner TA, Gagnon LH. Measuring core stability. J Strength Cond Res 2005; 19 (3): 583-6

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76. Urqhart DM, Hodges PW. Differential activity of regions of transversus abdominis during trunk rotation. Eur Spin J 2005; 14 (4): 393-400 77. Cholewicki J, McGill SM. Mechanical stability of the in vivo lumbar spine: implications for injury and chronic low back pain. Clin Biomech (Bristol, Avon) 1996; 11 (1): 1-15 78. McGill SM. Electromyographic activity of the abdominal and low back musculature during the generation of isometric and dynamic axial trunk torque: implications for lumbar mechanics. J Orthop Res 1991; 9 (1): 91-103 79. Roetert PE. 3D balance and core stability. In: Foran B, editor. High-performance sports conditioning: modern training for ultimate athletic development. Champaign (IL): Human Kinetics, 2001 80. Jeffreys I. Developing a progressive core stability program. Strength Cond J 2002; 24 (5): 65-6 81. Johnson H. Stressful motion: golfers at risk for low back pain. Sports Med Update 1999; 14: 4-5 82. Kerrigan DC, Todd MK, Della Croce U. Gender differences in joint biomechanics during walking: normative study in young adults. Am J Phys Med Rehabil 1998; 77 (1): 2-7 83. Nadler SF, Malanga GA, DePrince M, et al. The relationship between lower extremity injury, low back pain, and hip muscle strength in male and female collegiate athletes. Clin J Sport Med 2000; 10 (2): 89-97 84. Nadler SF, Wu KD, Galski T, et al. Low back pain in college athletes: a prospective study correlating lower extremity overuse or acquired ligamentous laxity with low back pain. Spine 1998; 23 (7): 828-33 85. McGill S. Stability: from biomechanical concept to chiropractic practice. J Can Chiropr Assoc 1999; 43: 75-88 86. Ball T, Comerford MJ, Mottram SL. Performance stability: a new system for providing stability and control for movement and performance [online]. Available from URL: http://www.performance-stability.com/documents/ TheCoacharticle_000.pdf [Accessed 2008 Sep 19] 87. Hasegawa I. Using the overhead squat for core development. NSCA Perform Train J 2004; 3 (6): 19-21 88. Scibek J, Guskiewicz KM, Prentice WE, et al. The effects of core stabilisation training on functional performance in swimming [abstract]. NATA Annual Meeting - Free Communications; 1999 Jun 17-18; Kansas City (MO) 89. Cusi M, Juska-Butel CJ, Garlick D, et al. Lumbopelvic stability and injury profile in rugby union players. NZ J Sports Med 2001; 29: 14-8

Correspondence: Angela E. Hibbs, English Institute of Sport, Gateshead International Stadium, Neilson Road, Gateshead, NE10 0EF, UK. E-mail: [email protected]

Sports Med 2008; 38 (12)

Sports Med 2008; 38 (12): 1009-1024 0112-1642/08/0012-1009/$48.00/0

REVIEW ARTICLE

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Exercise, Vascular Wall and Cardiovascular Diseases An Update (Part 1) Fung Ping Leung,1,3 Lai Ming Yung,1,3 Ismail Laher,4 Xiaoqiang Yao,1,2,3 Zhen Yu Chen5 and Yu Huang1,2,3 1 2 3 4

Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China Institute of Vascular Medicine, Chinese University of Hong Kong, Hong Kong, China Department of Physiology, Chinese University of Hong Kong, Hong Kong, China Department of Pharmacology and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada 5 Department of Biochemistry, Chinese University of Hong Kong, Hong Kong, China

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Exercise and Endothelial Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Exercise and Vascular Smooth Muscle Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Exercise and Antioxidant Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Exercise and Heat Shock Protein Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Exercise and Inflammation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Exercise and Vascular Remodelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Angiogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Coronary Heart Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.3 Hypertension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.4 Heart Failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.5 Diabetes Mellitus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Arteriogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Coronary Heart Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Peripheral Arterial Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Exercise and Pre-Eclampsia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

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Cardiovascular disease (CVD) remains the leading cause of morbidity and premature mortality in both women and men in most industrialized countries, and has for some time also established a prominent role in developing nations. In fact, obesity, diabetes mellitus and hypertension are now commonplace even in children and youths. Regular exercise is rapidly gaining widespread advocacy as a preventative measure in schools, medical circles and in the popular media. There is overwhelming evidence garnered from a number of sources, including epidemiological, prospective cohort and intervention studies, suggesting that CVD is largely a disease associated with physical inactivity.

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A rapidly advancing body of human and animal data confirms an important beneficial role for exercise in the prevention and treatment of CVD. In Part 1 of this review we discuss the impact of exercise on CVD, and we highlight the effects of exercise on (i) endothelial function by regulation of endothelial genes mediating oxidative metabolism, inflammation, apoptosis, cellular growth and proliferation, increased superoxide dismutase (SOD)-1, down-regulation of p67phox, changes in intracellular calcium level, increased vascular endothelial nitric oxide synthase (eNOS), expression and eNOS Ser-1177 phosphorylation; (ii) vascular smooth muscle function by either an increased affinity of the Ca2+ extrusion mechanism or an augmented Ca2+ buffering system by the superficial sarcoplasmic reticulum to increase Ca2+ sequestration, increase in K+ channel activity and/or expression, and increase in L-type Ca2+ current density; (iii) antioxidant systems by elevation of Mn-SOD, Cu/Zn-SOD and catalase, increases in glutathione peroxidase activity and activation of vascular nicotinamide adenine dinucleotide phosphate [(NAD(P)H] oxidase and p22phox expression; (iv) heat shock protein (HSP) expression by stimulating HSP70 expression in myocardium, skeletal muscle and even in human leucocytes, probably through heat shock transcription factor 1 activity; (v) inflammation by reducing serum inflammatory cytokines such as high-sensitivity C-reactive protein (hCRP), interleukin (IL)-6, IL-18 and tumour necrosis factor-a and by regulating Toll-like receptor 4 pathway. Exercise also alters vascular remodelling, which involves two forms of vessel growth including angiogenesis and arteriogenesis. Angiogenesis refers to the formation of new capillary networks. Arteriogenesis refers to the growth of pre-existent collateral arterioles leading to formation of large conductance arteries that are well capable to compensate for the loss of function of occluded arteries. Another aim of this review is to focus on exercise-related cardiovascular protection against CVD and associated risk factors such as aging, coronary heart disease, hypertension, heart failure, diabetes mellitus and peripheral arterial diseases mediated by vascular remodelling. Lastly, this review examines the benefits of exercise in mitigating pre-eclampsia during pregnancy by mechanisms that include improved blood flow, reduced blood pressure, enhanced placental growth and vascularity, increased activity of antioxidant enzymes, reduced oxidative stress and restored vascular endothelial dysfunction.

It has been known for some time that regular aerobic exercise is an effective means for lowering cardiovascular morbidity and mortality.[1-3] Physical inactivity is believed to be an independent risk factor for the development of coronary heart disease, stroke and peripheral vascular disease.[4] A sedentary lifestyle has been identified as a risk factor for development of cardiovascular disease (CVD), and there is a strong correlation between physical inactivity and cardiovascular mortality.[5] ª 2008 Adis Data Information BV. All rights reserved.

Although vigorous physical exertion is a precipitating factor for myocardial infarction, this adverse outcome is usually incurred by persons who otherwise lead a sedentary existence.[6,7] Thus, daily physical aerobic activity is considered as an effective component of both primary and secondary prevention of cardiovascular events.[8,9] This article summarizes recent findings on some specific mechanisms by which exercise confers cardiovascular protection and discusses Sports Med 2008; 38 (12)

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the multifactorial nature of exercise-induced improvements in cardiovascular function at the vascular wall (see figure 1). Exercise increases both the number (angiogenesis) and the diameter (arteriogenesis) of arterial blood vessels in skeletal muscle and the myocardium. In short, vascular remodelling is a response to exercise training. Exercise favourably modifies several CVD/risk factors, including coronary artery disease, aging, diabetes mellitus, hypertension, heart failure and peripheral arterial diseases (see figure 2). We also discuss emerging evidence for the possible association between physical activity and a reduction of preeclampsia and its complications during pregnancy.

1. Exercise and Endothelial Function The endothelium represents a dynamic interface between the intima of the vasculature and the luminal flow of blood. The cells of the endothelium line the blood vessel so that they are aligned in the direction of laminar blood flow, thus enabling the endothelial cells (EC) to respond to physical forces induced by blood flow, i.e. shear stress.[10] Exercise training in stable coronary artery disease (CAD) improves agonist-mediated

Inflammation ↑ IL-6, IL-1ra, IL-10 & IL-18 ↑ TNF-α ↓ C-reactive protein

endothelium-dependent vasodilatory capacity. The change in acetylcholine-induced vasodilatation is closely related to shear stress-induced/ Akt-dependent phosphorylation of endothelial nitric oxide synthase (eNOS) at serine-1177.[11] Physiological levels of sustained laminar shear stress (LSS) act as a stimulus for the differentiation of cultured human umbilical vein endothelial cells (HUVEC) to induce a ‘protective’, anti-atherosclerotic phenotype where genes upregulated by LSS in vitro would be similar to those expressed in EC in vivo.[12] By using the GeneCalling method, at least 107 genes have been identified as candidates for regulation by LSS at 10 dyn/cm2, a level of shear stress experienced by many arteries.[12] Endothelial genes specifically regulated by shear stress include intracellular adhesion molecule-1 (ICAM-1), cyclooxygenase 2, eNOS, SMAD6, transforming growth factor (TGF)-b1, Cu/Znsuperoxide dismutase (SOD), thrombomodulin, aldehyde dehydrogenase 6, heme oxygenase-1, amongst others (see figure 1). Interestingly, the LSS-regulated genes belong to a limited number of functional clusters that include those for oxidative metabolism, inflammation, apoptosis, cellular growth/proliferation and cell differentiation.[12] This suggests that flow is an important

Plasma ↑ Nitric oxide ↓ Cholesterol ↑ Shear stress during exercise

EC Heat shock proteins ↑ HSP72 in cardiac muscle ↓ HSP60 and HSP70 in aortic wall

VSMC

Others ↑ K+ channel expression and activity ↑ L-type Ca2+ current density ↑ Expression of PKC isoforms ↓ Ca2+ sensitivity

Endothelium ↑ Akt ↑ eNOS and phosphorylated eNOS

Antioxidant system ↑ Cu/Zn-SOD and Mn-SOD ↑ Catalase ↑ SOD-1 ↑ Glutathione peroxidase ↓ NAD(P)H oxidase and p22phox

Fig. 1. Effects of exercise on endothelial function, vascular smooth muscle function, antioxidant system, heat shock protein (HSP) system, inflammation and HSP expression. EC = endothelial cell; eNOS = endothelial nitric oxide synthase; ET-1 = endothelin; IL = interleukin; IL1ra = IL-1 receptor antagonist; ICAM = intercellular adhesion molecule; NAD(P)H = nicotinamide adenine dinucleotide phosphate; PKC = protein kinase C; SOD = superoxide dismutase; TNF = tumour necrosis factor; VSMC = vascular smooth muscle cell; › indicates increase; fl indicates decrease. Arrows indicate that shear stress is induced during exercise.

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Angiogenesis

Arteriogenesis

Coronary heart disease ↑ Circulating endostatin ↓ Cardiovascular risk factors (asymmetric dimethylarginine and myeloperoxidase) • Improves mobilization of endothelial progenitor cells

Coronary heart disease ↑ Coronary artery lumen diameter ↑ Angina threshold ↓ Maximum myocardial oxygen supply • Improves left ventricular systolic function

Hypertension ↑ Myocardial capillarization ↓ Myocardial mass ↓ Blood pressure

Aging ↑ VEGF serum level ↑ Capillary density and capillary-to-fibre ratio ↓ Expression of VEGF receptors e.g. fms-like tyrosine kinase-1 (Flt-1) and fetal liver kinase-1 (Flk-1)

Heart failure ↑ VEGF at both mRNA and protein levels

Peripheral arterial diseases ↑ Endothelial progenitor cells ↑ eNOS-mediated vasodilatation ↑ Collateral flow ↑ Pain-free and total walking distance ↑ Utilization and extraction of oxygen from erythrocytes ↑ Pain threshold ↓ Inflammatory markers ↓ Progression of atherosclerosis ↓ Blood viscosity

Diabetes mellitus ↑ Anti-angiogenic proteins (thrombospondin-1 and retinoblastoma like-2) ↓ Pro-angiogenic proteins (VEGF-A, VEGF-B, neurophilin-1, VEGFR-1 and VEGFR-2) Exercise

Fig. 2. Benefits of exercise on vascular remodelling (angiogenesis and arteriogenesis) in different cardiovascular diseases/risk factors including coronary artery disease, heart failure, diabetes mellitus, hypertension, aging and peripheral heart diseases. eNOS = endothelial nitric oxide synthase; mRNA = messenger RNA; VEGF = vascular endothelial growth factor; VEGFR = VEGF receptor; › indicates increase; fl indicates decrease.

biomechanical regulator of endothelial gene expression in vivo and that these LSS-regulated genes likely play a role in the maintenance of endothelial homeostasis in vivo. Furthermore, Rush et al.[13] demonstrated that prolonged, regular aerobic exercise training increased SOD-1 (present in cytosol and nucleus) protein in aortic EC and also the activity of SOD-1 in the aortae of swine.[13] Protein levels of p67phox, a subunit of the pro-oxidant enzyme nicotinamide adenine dinucleotide phosphate [(NAD(P)H] oxidase, ª 2008 Adis Data Information BV. All rights reserved.

were also reduced in exercised compared with sedentary animals[13] (see figure 1). Changes in intracellular calcium level ([Ca2+]i) in EC are a critical integrating signal for endothelium-dependent vasorelaxation.[14] The acetylcholine-evoked rise in endothelial [Ca2+]i and the concomitant vasorelaxation are impaired in hypercholesterolemic rabbit femoral arteries; importantly, exercise was able to restore both the stimulated endothelial [Ca2+]i response as well as the endothelium-dependent vasodilation.[15] Sports Med 2008; 38 (12)

Exercise and Cardiovascular Health (Part 1)

Because eNOS is a Ca2+-dependent enzyme, increases in endothelial [Ca2+]i signalling may be a key step in the enhanced nitric oxide (NO)dependent vasodilation after chronic exercise. Gadolinium, an inhibitor of mechanosensitive cationic channels, reduced the Ca2+ response to acetylcholine in tissues from exercise groups while it had little effect in the control groups, suggesting that mechanosensitive cationic channels may be upregulated by flow during exercise.[16] Exercise increases protein expression of eNOS in the porcine coronary arterial microcirculation but not in conduit coronary arteries.[17,18] Chronic levels of exercise also increase NO production and eNOS gene expression in coronary arteries of dogs.[19] Similarly, protein levels of eNOS and eNOS phosphorylated at serine-1177 are elevated in arterioles from exercise-trained animals with the greatest effects occurring in collateral-dependent regions of hearts exposed to chronic coronary arterial occlusion[20] (see figure 1). Moreover, there are also reports that interval sprint training increases endothelium-dependent vasodilation and eNOS and/ or SOD-1 protein content selectively in arterioles supplying the white muscles of the rat gastrocnemius.[21,22] A subsequent study by McAllister et al.[23] showed that the increased expression of eNOS also extended to second-, fourth- and fifthorder arterioles supplying red muscles of the gastrocnemius in exercised rodents (see figure 1).

2. Exercise and Vascular Smooth Muscle Function There is a growing body of evidence indicating that physical activity changes the functional properties of coronary vascular smooth muscle in both humans and animals.[24-26] For example, Haskell et al.[27] reported that coronary arteries of distance runners have a greater vasodilator response to sodium nitroprusside (SNP) than those of untrained subjects. This increased response could be due to structural differences (i.e. larger coronary arteries) or increased sensitivity of the coronary artery smooth muscle cells ª 2008 Adis Data Information BV. All rights reserved.

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to SNP. There are also a number of studies indicating that physical activity alters the characteristics of coronary vascular smooth muscle in animals including dysfunction of sarcolemmal K+ and L-type Ca2+ channels.[24-26] Chronic exercise training reduces the incidence and severity of CAD, as supported by extensive studies in animal models.[28] Exercise training decreases the Ca2+ sensitivity and contractile responses to endothelin in coronary arteries of adult female miniature swine (treadmill running for 16–20 weeks) [see figure 1]. Interestingly, arteries of trained animals also have an enhanced Ca2+ influx. However, it is likely that either an increased affinity of the Ca2+ extrusion mechanism or an augmented Ca2+ buffering system by the superficial sarcoplasmic reticulum induced by exercise enhances Ca2+ unloading, thus protecting the myofilaments from tonic activation. Hence, coronary smooth muscle of trained animals appears to adopt a different strategy for maintaining a sustained response to endothelin that may involve increased sarcolemmal Ca2+ cycling. Attenuation of the contractile response to endothelin may have important clinical significance, since endothelin is implicated in the pathology of coronary artery vasospasm,[29,30] myocardial infarction and hypertension.[31] Other studies also show that exercise training reduces receptor-mediated (endothelin-1 and U46619) vasoconstriction of coronary resistance arteries after ischaemia and reperfusion (I/R).[32] The regulation of coronary tone by exercise training may also involve changes in vascular K+ channel activity and/or expression. Coronary arteries of exercise-trained animals are more responsive to K+ channel blockers such as tetraethylammonium, iberotoxin or 4-aminopyridine. Although exercise regulates basal K+ channel activity in intact coronary arteries, there is paradoxically no effect of training on K+ current characteristics or membrane potential responses in isolated cells, suggesting that a requisite factor for enhanced K+ channel activation, which is present in intact arteries (for example, stretch or distention), is absent in isolated cells.[33] The same study also reported that exercise training increased L-type Ca2+ current density in the Sports Med 2008; 38 (12)

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coronary arterial bed. The increased voltagegated Ca2+ channel density might provide a crucial mechanistic link between functional and cellular adaptations in the coronary circulation during exercise training[33] (see figure 1). Endurance exercise in diabetic dyslipidemic pigs prevents the compensatory increased coupling of Ca2+ release to KCa channel activation in that occurs diabetic dyslipidemic coronary arteries.[34] Moreover, exercise attenuates diabetic dyslipidemia-induced impairment in intracellular Ca2+ regulation in isolated coronary smooth muscle[35] (see figure 1). It is also likely that gender influences the adaptation of the inward Ca2+ current in coronary smooth muscle to exercise training.[36] Gender-related changes in vascular function that are influenced by exercise include increases in protein kinase C (PKC)-bI, PKC-d, and PKC-z (see figure 1). The observed sex differences in PKC protein profiles may be related to the differences in cardiovascular risk patterns in males versus females.[37] 3. Exercise and Antioxidant Systems Exercise is known to protect against atherosclerosis, but at the same time also induces oxidative stress. This occurs primarily as a result of the inefficiency of the mitochondrial respiratory chain and the increase in fluid shear stress on the endothelium. Exercise training results in an upregulation of antioxidant defence mechanisms in various tissues, presumably due to increased exposure to oxidative stress. It is thought that habitual physical activity improves the intrinsic antioxidant potential and at the same time prevents lipids peroxidation in healthy, elderly men.[38] Protective antioxidant defence targets such as SOD, glutathione peroxidase and catalase are complex and multifactorial. Physiological levels of shear stress increase expression of Cu/Zn-SOD in human aortic endothelium[39] (see figure 1), while endurance training mainly induces Mn-SOD with elevations of both enzyme protein levels and activity skeletal muscle fibres.[40] Pig coronary arteriole SOD-1 messenger RNA ª 2008 Adis Data Information BV. All rights reserved.

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(mRNA), protein and activity levels all increase during chronic exercise training[13] (see figure 1), while other studies report increases in Cu/ZnSOD mRNA and protein levels after 24 hours of exposure to steady laminar shear.[41] Acute bouts of exercise in young animals increase the activities of Mn-SOD, Cu/Zn-SOD and catalase, while in older animals, only Mn-SOD is increased (see figure 1). Exercise increases glutathione peroxidase activity in the liver, kidney and heart,[42] as well as in skeletal muscle.[43] Exercise and, hence, changes in fluid shear stress activate vascular NAD(P)H oxidase and p22phox expression.[44] It is likely that p22phox affects NAD(P)H oxidase in response to shear stress, which may in turn regulate the amount of vascular antioxidant enzyme gene expression levels[45] (see figure 1).

4. Exercise and Heat Shock Protein Expression Damage to existing proteins or impaired protein synthesis will likely disturb cellular homeostasis. To combat this type of disturbance, cells respond by synthesizing heat shock proteins (HSPs), which are a multi-gene family of proteins ranging in molecular weights from 10 to 150 kDa and showing high homology between different species. However, the possible effect of physical exercise on vascular HSP expression remains unclear. Exercise stimulates HSP70 expression in the myocardium,[46] skeletal muscle[47,48] and, surprisingly, even in human leucocytes,[49] probably through heat shock transcription factor 1 (HSF1) activity.[50] Voluntary exercise improves vascular distensability in spontaneously hypertensive rats (SHRs), while at the same time producing a striking pattern of coordinated down regulation of HSP60 and HSP70, possible indicators of decreased oxidative stress in the aortic vascular wall[51] (see figure 1). Furthermore, endurance training (14 weeks of swimming) increases myocardial tolerance to doxorubicin-induced oxidative damage in mice, an effect that may be related to training-induced increases in total and reduced glutathione and HSP60.[52] Long-term Sports Med 2008; 38 (12)

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endurance training (24 weeks of running) induces discrete increases in antioxidant enzyme activities in the rat myocardium with a marked enhancement in HSP72 expression levels. However, a shorter training programme (12 weeks) was not effective in increasing heart antioxidant defence.[53] It is thought that the inducible HSP70 is cardioprotective. Exercise-induced cardiac expression of HSP70 can be modulated by estrogen, an effect that may confer gender-specific protection against ischaemic injury. After treadmill running, male rats exhibited a >2-fold increase in cardiac HSP70 levels than gonadally intact female rats, which was similar to the increase in ovariectomized female rats[54] (see figure 1). Estrogen treatment reversed the increased HSP70 expression induced by exercise in ovariectomized rats, suggesting that exercise may be of greater importance in males by providing HSP70-related cardiovascular protective mechanisms, and thus offering a novel manner by which males can mitigate hormone-linked susceptibility to adverse cardiac events.[55] Mild exercise training enhances myocardial defences and resistance to I/R by upregulating HSP70 and HSP72 and downregulating rodent HSP60 mRNA and protein levels;[56] however, these important findings needs to be confirmed in humans.

5. Exercise and Inflammation Inflammation is critically important in the pathogenesis of CVD, as reviewed by Libby.[57] Atherosclerosis is an inflammatory disease that is mediated by monocyte-derived macrophages, which accumulate in arterial plaques and become activated to release cytokines that cause tissue damage. In healthy young adults, a 12-week, high-intensity aerobic training programme downregulates cytokine release from monocytes.[58] Some studies suggest that exercise promotes cardioprotection through anti-inflammatory effects, which may be dose dependent.[59-61] Exercise produces a short-term inflammatory response, while extended exercise training produces a long-term ‘anti-inflammatory’ effect.[62] Recent ª 2008 Adis Data Information BV. All rights reserved.

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analysis concludes that two-thirds of 40 observational studies report an inverse relationship between inflammatory factors and fitness after adjustment for obesity/overweight.[63,64] Several studies have attempted to identify the mechanisms by which regular exercise reduces inflammation. A review by Petersen and Pedersen[65] concluded that regular exercise protects against diseases associated with chronic low-grade systemic inflammation. The long-term effects of exercise may be ascribed to the antiinflammatory responses elicited by an acute bout of exercise, which is partly mediated by muscle-derived interleukin (IL)-6 (see figure 1). Physiological concentrations of IL-6 induces the appearance of anti-inflammatory cytokines such as IL-1 receptor antagonist (IL-1ra) and IL-10 in the circulation, while at the same time inhibiting the production of the pro-inflammatory cytokine tumour necrosis factor (TNF)-a. Exercise also confers protection against TNF-induced insulin resistance[66] (see figure 1). Interestingly, IL-6 is the first reported ‘myokine’, which is defined as a cytokine that is produced and released by contracting skeletal muscle fibres and exerts its effects in other organs of the body.[67] Myokines may be involved in mediating the health benefits of exercise and are thought to play an important role in protection against chronic diseases associated with low-grade inflammation such as diabetes mellitus and CVD.[68] As evidence accumulates favouring the role of inflammation in the different phases of the progression of atherosclerosis, markers of inflammation such as high-sensitivity C-reactive protein (hCRP) may be used to provide additional insights on the biological status of atherosclerotic lesions. Both cross-sectional and longitudinal training studies demonstrate that physical activity reduces hCRP concentrations in a dose-dependent manner.[69] Inflammatory markers such as hCRP, fibrinogen and white blood cell count are stable for several days following a single session of moderate-intensity aerobic exercise in healthy men.[70] There is also substantial evidence demonstrating an inverse association between physical activity and the concentrations of acute phase reactants such as Sports Med 2008; 38 (12)

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hCRP[71-74] (see figure 1). In fact, even leisuretime physical activity (e.g. walking, jogging or running, cycling/use of an exercise cycle, swimming, aerobic dancing, other dancing, calisthenics, gardening/yard work and lifting weights) reduces hCRP concentrations in a graded manner.[75] In support of this are the recent findings of Kohut et al.,[75] who report that long-term aerobic exercising of older adults (aged ‡64 years) reduces serum inflammatory cytokines such as hCRP, IL-6, IL-18 and TNF-a (see figure 1). Another possibility is that the Toll-like receptor 4 (TLR4) pathways mediates the antiinflammatory effects of a physically active lifestyle, since it has been observed that TLR4 expression (mRNA and cell surface) is lower in physically active older women than in sedentary older women.[76] Moreover, McFarlin et al.[77] also suggest that TLR4 may play a role in regulating the link between inflammatory cytokine production and a physically active lifestyle. Thus there appears to be a clear inverse correlation between the extent of physical activity and levels of inflammatory markers, so providing an explanation of how exercise may mitigate inflammation. Additional details on the intensity, duration and type of physical activity required to attenuate local inflammatory responses, for example, in the arterial wall, will yield additional insights into the cardiovascular health benefits of exercise.[60] 6. Exercise and Vascular Remodelling Exercise is a powerful angiogenic stimulus within active muscles and leads to a functionally important increase in capillarity. Moreover, exercise enhances vasodilator and hence to flow capacity by increasing the calibre of arterial supply vessels. These adaptations are achieved by vascular remodelling, which can be divided into two major processes: (i) expansion of the capillary network, a process termed angiogenesis, which is effective at improving exchange properties between blood and tissue; (ii) enlargement of existing vessels, a process termed arteriogenesis, ª 2008 Adis Data Information BV. All rights reserved.

which is an effective means of increasing blood flow capacity to downstream vascular elements. 6.1 Angiogenesis

Changes of vascular morphology induced by exercise training in healthy subjects are critically dependent on the initial vessel size. An increased number of vessels resulting from exercise training, i.e. angiogenesis, appears to occur on the level of capillaries and resistance arterioles (<40 mm in diameter), but not in large arteries. 6.1.1 Aging

Aging is associated with reduced capacity to generate energy supplies, resulting in a consequent suppression of the angiogenic potential in the heart. Exercise training-induced increases of capillary density may be an advantageous adaptation in the aged heart since the remodelled capillary network is better able to maintain the supply of oxygen and energy substrates within the myocardium.[78,79] Vascular endothelial growth factor (VEGF) is a potent endothelial cell-derived mitogen and represents a major intrinsic stimulus in the process of angiogenesis of animals and humans.[80,81] VEGF activates the angiogenic signalling cascade, thus promoting angiogeneis in association with activation of Akt and the eNOS-related pathway.[82-84] Moreover, NO also regulates VEGF gene expression.[85] Several cross-sectional and longitudinal clinical studies demonstrate an increased capillary density and/or increased capillary-to-fibre ratio as a result of endurance exercise training.[86] Exercise training causes some degree of reversal of the age-induced down regulation of the VEGF angiogenic signalling cascade in rat hearts.[87] In a study by Iemitsu et al.,[87] the capillary density in rat hearts was significantly lower in sedentary aged rats than in hearts from sedentary young rats and, importantly, exercise training caused a significant recovery of angiogenesis in hearts from sedentary aged rats. The expression of VEGF receptors including fms-like tyrosine kinase-1 (Flt-1) and fetal liver kinase-1 (Flk-1) in Sports Med 2008; 38 (12)

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the hearts were lower in sedentary aged rats and were significantly improved by exercise. Of interest is the finding that exercise increases VEGF serum levels and circulating endothelial progenitor cells in patients with peripheral arterial disease increase[88] (see figure 2). 6.1.2 Coronary Heart Disease

Physical inactivity is now regarded as one of the most prevalent coronary heart disease risk factors.[89,90] Endurance training decreases the risk for coronary heart disease, possibly related to the reduction in circulating levels of cardiovascular risk markers such as asymmetric dimethylarginine and myeloperoxidase.[91] It has been speculated that these effects may be due to an exercise-induced stimulation of angiogenesis by (i) a formation of new vessels from existing blood vessels induced by either a division of preexisting endothelial cells; or (ii) by bone-marrow derived endothelial progenitor cells and monocyte-or macrophage-derived angiogenic cells.[92] Recent reports indicate that physical activity improves the mobilization of endothelial progenitor cells in healthy subjects and in patients with cardiovascular risk and coronary artery disease.[93,94] Endostatin is a 20 kDa C-terminal degradation product of collagen XVIII, an extracellular matrix protein that is diffusely expressed in basement membranes.[95,96] It is generated by cleavage of the extracellular matrix by matrix metalloproteases and elastases.[97] A recent study by Gu et al.[98] reported that circulating endostatin is significantly increased by exercise in healthy volunteers – the extent of endostatin increase being related to the peak oxygen consumption. The potent anti-angiogenic effects of endostatin are mediated by a combination of effects on endothelial cells where endostatin inhibits cellular proliferation and migration and stimulates apoptosis. An important additional beneficial anti-angiogenic effect of endurance training (running or cycling) also occurs via a reduction in the plasma concentrations of endostatin in obese men (aged 50–60 years)[99] (see figure 2). ª 2008 Adis Data Information BV. All rights reserved.

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6.1.3 Hypertension

It is likely that blood pressure is lowered by angiogenic growth factor therapy and increases with angiogenesis inhibitor therapy, as demonstrated by studies in animals and humans that demonstrate a drop in blood pressure with angiogenic growth factor administration.[100] Administration of lisinopril and exercise training independently enhances myocardial capillarization through a reduction of myocardial mass and stimulation of angiogenesis, respectively. A combination of the two treatments increases myocardial capillarisation more than either intervention alone. This approach may aid in the restoration of a normal nutritional status in cardiac myocytes compromised by the hypertrophic state of hypertension[101] (see figure 2). 6.1.4 Heart Failure

During the last decade, several angiogenic factors have been characterized, but it is unclear if exercise training increases the expression of these factors in patients with moderate heart failure (New York Heart Association II–III). Expression of VEGF at the mRNA and/or protein levels was studied before and after 8 weeks of training in patients with chronic heart failure. A positive local effect of exercise in the muscular bed was confirmed by a 46% increase in citrate synthase activity and a 36% augmentation in peripheral exercise capacity. These changes were accompanied by a 2-fold increase in VEGF at both the mRNA and protein levels. Therefore, the increase in VEGF gene expression in response to exercise training represents an important mediator of exercise-induced angiogenesis that may regulate an important and early step in adaptation to increased muscle activity in patients with chronic heart failure [102] (see figure 2). 6.1.5 Diabetes Mellitus

The cardiovascular complications in diabetes are largely the result of alterations in microvascular structure and function. In an animal model of streptozotocin-induced diabetes, hyperglycaemia reduces the mRNA levels of proangiogenic proteins (VEGF-A, VEGF-B, neuropilin-1, VEGFR-1 and VEGFR-2) and increases those of Sports Med 2008; 38 (12)

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anti-angiogenic proteins (thrombospondin-1 and retinoblastoma-like 2); there is also a decreased capillarization. These changes in the balance of pro- and anti-angiogenic processes may be one of the major reasons for the markedly increased risk for peripheral cardiovascular complications in diabetes.[103] Endurance training alleviates some of these changes but does not fully reverse the diabetes-induced defects. These training-induced effects at the mRNA levels of angiogenesisrelated genes may be one of the mechanisms responsible for the beneficial effects of regular endurance exercise in diabetic patients, and suggest that reduced skeletal muscle capillarization in type 1 diabetes may be associated with a dysregulation of complex angiogenesis pathways[103] (see figure 2). 6.2 Arteriogenesis 6.2.1 Coronary Heart Disease

It is well known that exercise training can increase the diameter of large arterioles, small arteries and conductance arteries. Another important aspect of exercise induced changes in capillarity is the onset and persistence of exerciseinduced arteriogenesis. The induction of arteriogenesis appears to be an important vascular adaptation.[104] Arteriogenesis leads to the formation of large conductance arteries that are quite capable of compensating for the loss of function of occluded arteries. Animal studies and clinical observations provide evidence for a significant correlation between regular physical exercise and increased coronary artery lumen diameter.[27,105-109] In one study, an 8-week training programme increased the contractile response to low doses of dobutamine in patients with chronic coronary artery disease and having a left ventricular ejection fraction below 40%. This implies that short-term exercise training may improve quality of life by improving left ventricular systolic function during mild to moderate physical activity in patients with ischaemic cardiomyopathy.[110] Moreover, eight patients with coronary heart disease and exertional angina pectoris successfully completed an 11–15 week programme of endurance exercise conª 2008 Adis Data Information BV. All rights reserved.

ditioning. Angina threshold was determined by upright bicycle ergometer exercise and by atrial pacing. The product of heart rate and arterial systolic blood pressure at the exercise angina threshold was higher after conditioning, suggesting that conditioning increased the maximum myocardial oxygen supply during exercise[111] (see figure 2). 6.2.2 Peripheral Arterial Diseases

Patients with peripheral arterial diseases often present with complaints of intermittent claudication, which negatively impacts the quality of life as a result of reduced walking endurance. Peripheral arterial disease is associated with an increased morbidity and mortality. In addition to other treatment modalities, exercise training has also been shown to be an effective treatment option for patients with intermittent claudication. A recently published meta-analysis reported exercise as being more effective than medical treatment or smoking cessation in alleviating the symptoms of intermittent claudication. The data from trials were either graded level 1 (randomized and double- or assessor-blind), level 2 (open randomized), or level 3 (non-randomized). Painfree and total walking distances were the main outcomes considered and, where feasible, endof-treatment results were combined with appropriate meta-analytical procedures. In level 2 studies, physical training significantly increased both pain-free (139.0 m to 246.9 m) and total walking distance (179.1 m to 298.1 m).[112] There are many possible reasons that can account for this improved walking distance – for example, exercise improves the utilization and extraction of oxygen from erythrocytes, influences blood viscosity, raises the pain threshold, inhibits the progression of atherosclerosis and, quite possibly, improves collateral flow. However, the exact mechanism whereby exercise influences arterogenesis in the peripheral circulation is currently unknown.[113] Moreover, Shaffer et al.[114] reported that exercise increases endothelial progenitor cells in patients with peripheral vascular diseases as determined with a multi-parameter flow cytometry assay that rigorously assesses endothelial progenitor cells Sports Med 2008; 38 (12)

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as well as mature endothelial cells in control subjects and patients with peripheral artery disease undergoing graded exercise. Most recently, Andreozzi et al.[115] examined the effects of maximal exercise and of physical training on endothelial function in patients with intermittent claudication by using ultrasonography of the brachial artery in 22 male patients with intermittent claudication before and after maximal treadmill test. The measurements were repeated after 18 days of supervised physical training (3 times weekly for 6 weeks). This study clearly validates supervised physical training (3 times weekly for 6 weeks) as the most effective means to increasing walking ability (as well as improving endothelial function) in patients with intermittent claudication. It is also probable that exercise induced changes in haemodynamic stress reduces inflammatory markers and increases flow-mediated vasodilation through ischaemic preconditioning. The increased walking ability and the related improvement in endothelial function suggest that exercise improves the systemic outcomes of claudicant patients, even in patients with coronary heart disease[115] (see figure 2).

7. Exercise and Pre-Eclampsia Hypertensive disorders during pregnancy are the second leading cause, after embolism, of maternal mortality in the US, accounting for ~15% of such deaths.[116] Hypertension during pregnancy is related to complications including cerebral haemorrhage, abruption placentae, hepatic failure and acute renal failure.[117] Pre-eclampsia, one of the hypertensive disorders in pregnancy, contributes to roughly 3–4% of perinatal morbidity and mortality.[118] Pre-eclampsia leads to vascular constriction in the placenta, so limiting the supply of food and oxygen to the fetus, and leading to retardation of fetal growth and development.[118] Although the pathophysiology of pre-eclampsia remains uncertain, placental ischaemia/hypoxia is widely regarded as a key factor. Pre-eclampsia manifests as maternal hypertension, increased urinary protein levels and ª 2008 Adis Data Information BV. All rights reserved.

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other associated metabolic disorders such as lipid peroxidation or oxidative stress,[119] insulin resistance,[120] systemic chronic inflammation[121] and elevated plasma homocysteine.[122] During ischaemia/hypoxia, the placenta produces and releases vasoactive factors including angiotensin II, angiotensin II type 1 receptor-auto antibodies[123] and cytokines.[124] The plasma concentration of angiotensin II[125] and renin activity[126] are both elevated in pregnancy. Women with a history of pre-eclampsia exhibited impaired endothelial function, as measured by stress-induced forearm blood flow with the aid of venous occlusion plethysmography.[127] The widespread dysfunction of the endothelium leads to reduced formation of vasodilatory factors, including NO and prostacyclin, and causes an imbalance between relaxing factors and contracting factors. These disturbances in the regulation of vasoactive factors predispose the vasculature a vasoconstrictor, pro-oxidant and pro-inflammatory environment. Chronic treatment of pregnant rats with NO synthase inhibitors induces hypertension and renal vasoconstriction as well as increased fetal morbidity and proteinuria.[128,129] Despite the complexity in the pathophysiology of pre-eclampsia, some investigators have expressed concerns about the untoward effects of increased physical activity on maternal and fetal wellbeing during pregnancy; as such, the benefits of regular exercise in preventing pre-eclampsia remain controversial. A case-controlled study of Canadian women who regularly participated in recreational physical activity during the first 20 weeks of pregnancy reported a 43% reduction in the risk of pre-eclampsia.[130] The beneficial effects of exercise, including improved blood flow, reductions in blood pressure and enhanced placental growth and vascularity have led some to advocate regular exercise as a means of reducing pre-eclampsia.[118] Regular aerobic exercise also increases the activity of antioxidant enzymes, reduces oxidative stress and restores vascular endothelial dysfunction in pregnant women.[131] In addition to the benefits gained from regular exercise activities, the incidence of pre-eclampsia is also attenuated by increased leisure time physical activity (e.g. cycling).[132] It may well Sports Med 2008; 38 (12)

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be that the cardiovascular benefits of increased regular physical activity reported in healthy nonpregnant people may also extend to prevention of pre-eclampsia. 8. Conclusions There is now plentiful epidemiologic and experimental evidence indicating that physical exercise slows the progression of vascular disease and reduces cardiovascular morbidity and mortality. The mechanisms of this effect include beneficial changes in cholesterol level, antioxidant systems, blood pressure, inflammation, HSPs and ion channel activity. Flow enhances endotheliumdependent vasodilation by increasing the vascular expression of NO synthase and by enhancing the release of NO. Exercise-induced increases in blood flow appear to have direct effects on vascular function and structure (angiogenesis and arteriogenesis). Beneficial effects of exerciseinduced vascular remodelling and reactivity are also prominent in different CVD including coronary heart diseases, hypertension, heart failure, peripheral heart diseases and pre-eclampsia. There are some notable exceptions to the widespread advocacy of exercise; there can be serious risks in patients who have underlying CAD or predisposing genetic abnormalities.[133,134] Therefore, the cellular mechanisms of exercise should be established in a clinical setting. Importantly, it is not known exactly how much exercise is needed to restore endothelial function, how long these effects might persist after exercise and to what degree these effects contribute to the beneficial effects of exercise on cardiovascular health. An investigation of these questions is likely to lead to novel approaches to pharmacotherapy and exercise rehabilitation for patients with cardiovascular heart diseases. Acknowledgements We are thankful to Prof. Bruce McManus and Dr Jonathan Wanagat for their helpful comments and suggestions on this manuscript. This study was funded by CUHK Direct Grant (2041380), Research Grants Council of Hong Kong SAR, CUHK Li Ka Shing Institute of

ª 2008 Adis Data Information BV. All rights reserved.

Health Sciences, CUHK Focused Investment Scheme, and The Canadian Heart and Stroke Foundation (IL). LMY and FPL were supported by these grants. The authors have no conflicts of interest directly relevant to the contents of this review.

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68. Libby P. Inflammation and cardiovascular disease mechanisms. Am J Clin Nutr 2006; 83: 456S-60S 69. Plaisance EP, Grandjean PW. Physical activity and high-sensitivity C-reactive protein. Sports Med 2006; 36: 443-58 70. Plaisance EP, Taylor JK, Alhassan S, et al. Cardiovascular fitness and vascular inflammatory markers after acute aerobic exercise. Int J Sport Nutr Exerc Metab 2007; 17: 152-62 71. Geffken DF, Cushman M, Burke GL, et al. Association between physical activity and markers of inflammation in a healthy elderly population. Am J Epidemiol 2001; 153: 242-50 72. Mattusch F, Dufaux B, Heine O, et al. Reduction of the plasma concentration of C-reactive protein following nine months of endurance training. Int J Sports Med 2000; 21: 21-4 73. Rohde LE, Hennekens CH, Ridker PM. Survey of Creactive protein and cardiovascular risk factors in apparently healthy men. Am J Cardiol 1999; 4: 1018-22 74. Dufaux B, Order U, Geyer H, et al. C-reactive protein serum concentrations in well-trained athletes. Int J Sports Med 1984; 5: 102-6 75. Kohut ML, McCann DA, Russell DW, et al. Aerobic exercise, but not flexibility/resistance exercise, reduces serum IL-18, CRP, and IL-6 independent of beta-blockers, BMI, and psychosocial factors in older adults. Brain Behav Immun 2006; 20: 201-9 76. McFarlin BK, Flynn MG, Campbell WW, et al. TLR4 is lower in resistance-trained older women and related to inflammatory cytokines. Med Sci Sports Exerc 2004; 36: 1876-83 77. McFarlin BK, Flynn MG, Campbell WW, et al. Physical activity status, but not age, influences inflammatory biomarkers and toll-like receptor 4. J Gerontol A Biol Sci Med Sci 2006; 61: 388-93 78. Bloor CM, Leon AS. Interaction of age and exercise on the heart and its blood supply. Lab Invest 1970; 22: 160-5 79. Tomanek RJ. Effects of age and exercise on the extent of the myocardial capillary bed. Anat Rec 1970; 167: 55-62 80. Neufeld G, Cohen T, Gengrinovitch S, et al. Vascular endothelial growth factor (VEGF) and its receptors. FASEB J 1999; 13: 9-22 81. Yancopoulos GD, Davis S, Gale NW, et al. Vascular-specific growth factors and blood vessel formation. Nature 2000; 14; 407: 242-8 82. Dimmeler S, Hermann C, Zeiher AM. Apoptosis of endothelial cells. Contribution to the pathophysiology of atherosclerosis? Eur Cytokine Netw 1998; 9: 697-8 83. Fulton D, Gratton JP, McCabe TJ, et al. Regulation of endothelium-derived nitric oxide production by the protein kinase Akt. Nature 1999; 399: 597-601 84. Ziche M, Morbidelli L, Choudhuri R, et al. Nitric oxide synthase lies downstream from vascular endothelial growth factor-induced but not basic fibroblast growth factorinduced angiogenesis. J Clin Invest 1997; 99: 2625-34 85. Morbidelli L, Chang CH, Douglas JG, et al. Nitric oxide mediates mitogenic effect of VEGF on coronary venular endothelium. Am J Physiol 1996; 270: H411-5

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86. Saltin B, Gollnick PD. Skeletal muscle adaptability: significance for metabolism and performance. In: Peachey LD, editor. Handbook of physiology: skeletal muscle. Baltimore, (MD): American Physiological Society, 1983: 555-631. 87. Iemitsu M, Maeda S, Jesmin S, et al. Exercise training improves aging-induced downregulation of VEGF angiogenic signaling cascade in hearts. Am J Physiol Heart Circ Physiol 2006; 291: H1290-8 88. Sandri M, Adams V, Gielen S, et al. Effects of exercise and ischemia on mobilization and functional activation of blood-derived progenitor cells in patients with ischemic syndromes: results of 3 randomized studies. Circulation 2005; 111: 3391-9 89. Thompson PD, Lim V. Physical activity in the prevention of atherosclerotic coronary heart disease. Curr Treat Options Cardiovasc Med 2003; 5: 279-85 90. Blair SN, Horton E, Leon AS, et al. Physical activity, nutrition, and chronic disease. Med Sci Sports Exerc 1996; 28: 335-49 91. Richter B, Niessner A, Penka M, et al. Endurance training reduces circulating asymmetric dimethylarginine and myeloperoxidase levels in persons at risk of coronary events. Thromb Haemost 2005; 94: 1306-11 92. Rehman J, Li J, Parvathaneni L, et al. Exercise acutely increases circulating endothelial progenitor cells and monocyte-/macrophage-derived angiogenic cells. J Am Coll Cardiol 2004; 43: 2314-8 93. Steiner S, Niessner A, Ziegler S, et al. Endurance training increases the number of endothelial progenitor cells in patients with cardiovascular risk and coronary artery disease. Atherosclerosis 2005; 181: 305-10 94. Laufs U, Urhausen A, Werner N, et al. Running exercise of different duration and intensity: effect on endothelial progenitor cells in healthy subjects. Eur J Cardiovasc Prev Rehabil 2005; 12: 407-14 95. Wenzel D, Schmidt A, Reimann K, et al. Endostatin, the proteolytic fragment of collagen XVIII, induces vasorelaxation. Circ Res 2006; 98: 1203-11 96. Marneros AG, Olsen BR. Physiological role of collagen XVIII and endostatin. FASEB J 2005; 19: 716-28 97. Felbor U, Dreier L, Bryant RA, et al. Secreted cathepsinL generates endostatin from collagen XVIII. EMBO J 2000; 19: 1187-94 98. Gu JW, Gadonski G, Wang J, et al. Exercise increases endostatin in circulation of healthy volunteers. BMC Physiol 2004; 4: 2 99. Brixius K, Schoenberger S, Ladage D, et al. Long-term endurance exercise decreases antiangiogenic endostatin signalling in overweight men aged 50-60 years. Br J Sports Med 2008; 42: 126-9 100. Sane DC, Anton L, Brosnihan KB. Angiogenic growth factors and hypertension. Angiogenesis 2004; 7: 193-201 101. Ziada AM, Hassan MO, Tahlilkar KI, et al. Long-term exercise training and angiotensin-converting enzyme inhibition differentially enhance myocardial capillarization in the spontaneously hypertensive rat. J Hypertens 2005; 23: 1233-40

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102. Gustafsson T, Bodin K, Sylve´n C, et al. Increased expression of VEGF following exercise training in patients with heart failure. Eur J Clin Invest 2001; 31: 362-6 103. Kivela¨ R, Silvennoinen M, Touvra AM, et al. Effects of experimental type 1 diabetes and exercise training on angiogenic gene expression and capillarization in skeletal muscle. FASEB J 2006; 20: 1570-2 104. Brown MD. Exercise and coronary vascular remodelling in the healthy heart. Exp Physiol 2003; 88: 645-58 105. Stevenson JAF, Feleki V, Rechnitzer V, et al. Effect of exercise on coronary tree size in the rat. Circ Res 1964; 25: 265-70 106. Leon AS, Bloor CM. Effects of exercise and its cessation on the heart and its blood supply. J Appl Physiol 1968; 24: 485-90 107. Kramsch DM, Aspen AJ, Abramowitz BM, et al. Reduction of coronary atherosclerosis by moderate conditioning exercise in monkeys on an atherogenic diet. N Engl J Med 1981; 305: 1483-9 108. Morris JN, Crawford MD. Coronary heart disease and physical activity of work: evidence of a national necropsy survey. BMJ 1958; 5111: 1485-96 109. Wyatt HL, Mitchell J. Influences of physical conditioning and deconditioning on coronary vasculature of dogs. J Appl Physiol 1978; 45: 619-25 110. Belardinelli R, Georgiou D, Ginzton L, et al. Effects of moderate exercise training on thallium uptake and contractile response to low-dose dobutamine of dysfunctional myocardium in patients with ischemic cardiomyopathy. Circulation 1998; 97: 553-61 111. Sim DN, Neill WA. Investigation of the physiological basis for increased exercise threshold for angina pectoris after physical conditioning. J Clin Invest 1974; 54: 763-70 112. Girolami B, Bernardi E, Prins MH, et al. Treatment of intermittent claudication with physical training, smoking cessation, pentoxifylline, or nafronyl: a meta-analysis. Arch Intern Med 1999; 159: 337-45 113. Remijnse-Tamerius HC, Duprez D, De Buyzere M, et al. Why is training effective in the treatment of patients with intermittent claudication? Int Angiol 1999; 18: 103-12 114. Shaffer RG, Greene S, Arshi A, et al. Effect of acute exercise on endothelial progenitor cells in patients with peripheral arterial disease. Vasc Med 2006; 11: 219-26 115. Andreozzi GM, Leone A, Laudani R, et al. Acute impairment of the endothelial function by maximal treadmill exercise in patients with intermittent claudication, and its improvement after supervised physical training. Int Angiol 2007; 26: 12-7 116. American College of Obstetricians and Gynecologists. Hypertension in pregnancy. ACOG Tech Bull 1996; 219: 1-8 117. National High Blood Pressure Education Program working group report on high blood pressure in pregnancy. Bethesda (MD): National Institutes of Health, Jul 2000. NIH publication No. 00-3029 118. Sorensen TK, Williams MA, Lee IM, et al. Recreational physical activity during pregnancy and risk of preeclampsia. Hypertension 2003; 41: 1273-80 119. Kaaja R, Tikkanen MJ, Viinikka L, et al. Serum lipoproteins, insulin, and urinary prostanoid metabolites in

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normal and hypertensive pregnant women. Obstet Gynecol 1995; 85: 353-6 Walsh SW, Wang Y. Deficient glutathione peroxidase activity in preeclampsia is associated with increased placental production of thromboxane and lipid peroxides. Am J Obstet Gynecol 1993; 169: 1456-61 Williams MA, Farrand A, Mittendorf R, et al. Maternal second trimester serum tumor necrosis factor-alphasoluble receptor p55 (sTNFp55) and subsequent risk of preeclampsia. Am J Epidemiol 1999; 149: 323-9 Rajkovic A, Mahomed K, Malinow MR, et al. Plasma homocyst(e)ine concentrations in eclamptic and preeclamptic African women postpartum. Obstet Gynecol 1999; 94: 355-60 Xia Y, Ramin SM, Kellems RE. Potential roles of angiotensin receptor-activating autoantibody in the pathophysiology of preeclampsia. Hypertension 2007; 50: 269-75 Heenan AP, Wolfe LA, Davies GA, et al. Effects of human pregnancy on fluid regulation responses to short-term exercise. J Appl Physiol 2003; 95: 2321-7 Eneroth-Grimfors E, Bevega˚rd S, Nilsson BA, et al. Effect of exercise on catecholamines and plasma renin activity in pregnant women. Acta Obstet Gynecol Scand 1988; 67: 519-23 Borekci B, Aksoy H, Al RA, et al. Maternal serum interleukin-10, interleukin-2 and interleukin-6 in preeclampsia and eclampsia. Am J Reprod Immunol 2007; 58: 56-64 Kassab S, Miller MT, Hester R, et al. Systemic hemodynamics and regional blood flow during chronic nitric oxide

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synthesis inhibition in pregnant rats. Hypertension 1998; 31: 315-20 Khalil RA, Crews JK, Novak J, et al. Enhanced vascular reactivity during inhibition of nitric oxide synthesis in pregnant rats. Hypertension 1998; 31: 1065-9 Agatisa PK, Ness RB, Roberts JM, et al. Impairment of endothelial function in women with a history of preeclampsia: an indicator of cardiovascular risk. Am J Physiol Heart Circ Physiol 2004; 286: H1389-93 Marcoux S, Brisson J, Fabia J. The effect of leisure time physical activity on the risk of pre-eclampsia and gestational hypertension. J Epidemiol Community Health 1989; 43:147-52 Meher S, Duley L. Exercise or other physical activity for preventing pre-eclampsia and its complications. Cochrane Database Syst Rev 2006; (2): CD005942 Rudra CB, Williams MA, Lee IM, et al. Perceived exertion during prepregnancy physical activity and preeclampsia risk. Med Sci Sports Exerc 2005; 37: 1836-41 McManus BM, Waller BF, Graboys TB, et al. Current problems in cardiology: exercise and sudden death. Part I. Chicago, IL: Year Book Medical Publishers, 1981 Dec: 1-89 McManus BM, Waller BF, Graboys TB, et al. Current problems in cardiology: exercise and sudden death. Part II. Chicago (IL): Year Book Medical Publishers, 1982 Jan: 1-67

Correspondence: Prof. Yu Huang and Dr Fung Ping Leung Department of Physiology, Faculty of Medicine, Chinese University of Hong Kong, Shatin, Hong Kong, China. E-mail: [email protected] and christinaleung@cuhk. edu.hk

Sports Med 2008; 38 (12)

Sports Med 2008; 38 (12): 1025-1043 0112-1642/08/0012-1025/$48.00/0

REVIEW ARTICLE

ª 2008 Adis Data Information BV. All rights reserved.

A Review of Vision-Based Motion Analysis in Sport Sian Barris and Chris Button Human Performance Centre, School of Physical Education, University of Otago, Dunedin, New Zealand

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Manual Vision-Based Tracking Systems: Notational Analysis in Sport. . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Racket Sports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Association Football (Soccer). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Limitations and Reliability of Manual Notational Analysis Techniques . . . . . . . . . . . . . . . . . . . . . 2. Automated Vision-Based Tracking Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Surveillance Tracking Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Outdoor Sports Tracking Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Limitations of Outdoor Automatic Motion Tracking Systems . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Indoor Sports Tracking Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Limitations and Reliability of Indoor Automatic Motion Tracking Systems . . . . . . . . . . . . . 3. Commercially Available Vision-Based Analysis Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Semi-Automatic/Online Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Limitations of Commercial Tracking Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

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Efforts at player motion tracking have traditionally involved a range of data collection techniques from live observation to post-event video analysis where player movement patterns are manually recorded and categorized to determine performance effectiveness. Due to the considerable time required to manually collect and analyse such data, research has tended to focus only on small numbers of players within predefined playing areas. Whilst notational analysis is a convenient, practical and typically inexpensive technique, the validity and reliability of the process can vary depending on a number of factors, including how many observers are used, their experience, and the quality of their viewing perspective. Undoubtedly the application of automated tracking technology to team sports has been hampered because of inadequate video and computational facilities available at sports venues. However, the complex nature of movement inherent to many physical activities also represents a significant hurdle to overcome. Athletes tend to exhibit quick and agile movements, with many unpredictable changes in direction and also frequent collisions with other players. Each of these characteristics of player behaviour violate the assumptions of smooth movement on which computer tracking algorithms are typically based. Systems

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such as TRAKUS, SoccerMan, TRAKPERFORMANCE, Pfinder and Prozone all provide extrinsic feedback information to coaches and athletes. However, commercial tracking systems still require a fair amount of operator intervention to process the data after capture and are often limited by the restricted capture environments that can be used and the necessity for individuals to wear tracking devices. Whilst some online tracking systems alleviate the requirements of manual tracking, to our knowledge a completely automated system suitable for sports performance is not yet commercially available. Automatic motion tracking has been used successfully in other domains outside of elite sport performance, notably for surveillance in the military and security industry where automatic recognition of moving objects is achievable because identification of the objects is not necessary. The current challenge is to obtain appropriate video sequences that can robustly identify and label people over time, in a cluttered environment containing multiple interacting people. This problem is often compounded by the quality of video capture, the relative size and occlusion frequency of people, and also changes in illumination. Potential applications of an automated motion detection system are offered, such as: planning tactics and strategies; measuring team organisation; providing meaningful kinematic feedback; and objective measures of intervention effectiveness in team sports, which could benefit coaches, players, and sports scientists.

The capacity of portable data recording and analysis systems to process large amounts of information for motion tracking has improved markedly in the last 10 years. Indeed, equipment such as global positioning systems (GPS), highspeed video and accelerometers are increasingly used in the measurement and evaluation of human physical activity. Consequently, the quantitative analysis of team and player activity is now an important aspect of the coaching process in sport.[1] Obtaining accurate positional information about sports players is of interest to coaches and high-performance support teams because of the potential to relate performance to tactics, and to assist in the design of better training programmes. Furthermore, sport scientists can utilize such information to understand the coordination dynamics of player activity and the most influential constraints acting upon them. For example, such data could be processed to calculate player velocities and accelerations and establish performance profiles in successful compared with unsuccessful games. Therefore, it would be of value to several groups to access an objective player tracking system that can ª 2008 Adis Data Information BV. All rights reserved.

depict performers’ movement patterns with accuracy, reliability and efficiency. However, despite considerable advances in technology, issues in automatic player tracking still present many challenges. This review evaluates existing published research on human motion tracking in sports. Given the recent and rapid developments in this field of study, an overview of available research would make an important and timely contribution to the literature. The following Internet search databases were used between January and March 2007 to identify relevant articles: PubMed, SportDiscus, Ovid and Web of Science. The search reference terms ‘movement patterns’, ‘movement analysis’, ‘spatio-temporal trajectories’, ‘automatic tracking’ and ‘sport’ were used. The inclusion criteria specified articles published between 1970 and 2007, in peer-reviewed scientific journals (n = 52), as papers published in scientific conference transactions or proceedings (n = 27), technical reports (n = 1) and unpublished dissertations (n = 2). Additionally, three peer-reviewed scientific journals were included on recommendation by an anonymous Sports Med 2008; 38 (12)

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reviewer. Papers that tracked movement using accelerometry or magnetic field (e.g. Polhemus) technology were excluded. Further, a Google Internet search was conducted using the reference terms ‘commercial tracking systems’, ‘motion analysis systems’ and ‘performance analysis systems’ to obtain information about existing commercial systems used in the analysis of sporting activities. This article is structured into three sections in a manner that is loosely chronological. The first section describes manual notational analysis methods that have largely involved the subjective monitoring of player activity. The second section, reviews automated tracking systems in sport that have emerged over the last 10 years and the exciting new possibilities for multiple, simultaneous person tracking. Finally, the strengths and weaknesses of some commercially available motion tracking systems that have been used for research and performance analysis purposes are discussed. The aim is to establish what visionbased tracking systems exist and to what extent they are able to record multiple player movement in different sports environments (i.e. outdoor and indoor sports).

1. Manual Vision-Based Tracking Systems: Notational Analysis in Sport Notational analysis is often recommended to coaches as an inexpensive way of providing insight into the physiological and technical demands of sports activities, by recording and quantifying the player movement patterns that characterize skilled performance.[2] This involves the subjective quantification of individual player(s) movements by an investigator, and the frequency and timing of particular movements are then related to their relative success.[3] Notational analysis has been used to investigate the activity patterns and techniques in various sports, such as rugby,[2,4] squash,[3,5,6] badminton,[7] football,[8-12] netball,[13-15] basketball,[16-18] Australian Football League (AFL),[19] volleyball[20] and roller hockey.[21] This section provides an overview of methods and scope of notational ª 2008 Adis Data Information BV. All rights reserved.

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analysis. Racket sports and football were chosen as our examples because they represent the most commonly analysed sports using notational analysis, they show the greatest technological developments and they demonstrate significant differences in player numbers, patterns of play and size of capture environment. For further examples of notational analysis in sport, the reader is recommended to consult Hughes and Franks.[22] 1.1 Racket Sports

Historically, researchers have used different procedures to carry out notational analysis, from simple observations with written notes to more complex systems using video and computers.[7] Sanderson and Way[6] first developed a hand notation method for sequential stroke analysis in squash using illustrative symbols to analyse 17 different strokes and court plans to gather accurate positional information. This method was later modified by Sanderson[5] and applied to match play to define winning and losing patterns of play in squash. Hughes et al.[23] introduced computerized movement tracking to squash, entering player position data with a digitizing pad and using custom-designed software to calculate distances travelled and velocity and acceleration time series. A more sophisticated notational system was developed by Hughes and Clarke[24] to study the effect of court surface on elite tennis strategy. Players’ court positions, the time taken per shot and the type of event itself were recorded using a graphical user-interface, which was analysed post event by video recorder. This analysis provided both temporal and positional information as well as frequency distribution of shots and rally-ending conditions.[24] Hong and colleagues[3] also used a notational analysis method in studying game strategies of 12 of the world’s top ranked male squash players. Video cameras were placed at the back of the court, at ground level, to provide a clear view of the movement of the player and the ball. Several variables were recorded and subsequently analysed including the strokes played by each individual, the position from where the shot was Sports Med 2008; 38 (12)

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played and where the ball landed.[3] Strokes were identified according to 13 pre-determined types and each return shot was classified into one of four categories (effective, ineffective, winning and losing) to determine the playing effectiveness of the competitors. 1.2 Association Football (Soccer)

Several approaches have been adopted to analyse the movement activities of football players. Notational analysis has been employed to explore the success of different playing formations, and in particular goal scoring patterns, and attacking moves.[25-29] Erdmann[30,31] developed a system for player tracking by televising the whole football pitch and gathering information on every moving object. A video camera, fitted with a wide-angle lens (130), was placed in a stand above the pitch to allow a view of the full field of play. White tape marked 1-m intervals longitudinally across the field prior to the game for camera calibration and to control for the radial distortion that occurs with a wide-angle lens. The position of the white tape was then transferred from a TV monitor onto a transparent foil to form a semi-permanent reference grid. Another foil was used to manually record the positional information about each player of the field. The resulting displacement, velocity and acceleration were calculated by measuring the relative displacement of the two foils.[31] Ali and Farrally[32] were able to identify and distinguish typical patterns of play for three professional teams, two club sides and an international team. Data were collected by an experimenter using 20 shorthand symbols that were representative of common game features. During matches, the movement of the ball was manually recorded in real-time on diagrammatic sheets that were later converted onto a 2-dimensional (2-D) coordinate grid (figure 1). Similarly, Yamanaka and colleagues[25] and Garganta et al.,[28] performed investigations into patterns of play associated with teams from different countries. Yamanaka et al.,[25] used a computerized notational analysis method for eight games in the Asian Qualifying tournament ª 2008 Adis Data Information BV. All rights reserved.

Barris & Button

for the 1994 World Cup. It was observed that Japan’s playing strategies were more predictable than those of many other countries. Garganta et al.[28] analysed goal scoring patterns from video recordings of five successful European football teams from different countries (Barcelona, Porto, Bayern Munich, A.C. Milan, and Paris Saint Germain), using a hand notation system. Data were collected from 44 matches (including 104 goals), with set plays filtered out and key features recorded for each scoring movement. It was concluded that the teams exhibited similar player movement patterns before scoring goals, independent of the number of matches observed, match difficulty, or the teams’ style of play.[28] Although the direct style of play seemed to be the most successful, it was also noted that the efficiency of the direct style was dependent on the team’s ability to change and adapt to the rhythm of the game and vary their attacking methods to outplay the opposition. A video-based notation analysis system was also used to conduct a time-motion analysis of football players in the 1994 World Cup.[33] The input data from each of the matches included the time taken for each action with the ball, spatial locations of players as x and y coordinates, selected qualitative manoeuvre variables, stops in play, injury time, and time in possession of the ball for each player.[33] Brazil demonstrated the most successful attacking plays, the most scoring opportunities, and the most shots for scoring goals.[29] A breakdown of their attacking manoeuvres showed more frequent passing and a more free style of play, shorter runs with the ball, more shots after receiving the ball and more direct free kicks than their opponents.[33] 1.3 Limitations and Reliability of Manual Notational Analysis Techniques

A potential limitation of notational methods described so far is the reliability of the data entry procedure, or the researcher’s ability to reproduce the observed value when the measure is repeated.[2] Inter-observer consistency is considered crucial in establishing the reliability of Sports Med 2008; 38 (12)

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Path of ball Route of player with ball Shot on target a

b

5

2 1 4

3

4 7 6

5

1

3

2

Fig. 1. Attacking patterns for (a) Scottish National Team and (b) Scottish Premier Team (adapted from Ali and Farrally,[32] with permission).

motion analysis systems where the total time, frequency and mean duration of movements can exhibit large variations. Blomqvist et al.[7] investigated the reliability of a notational analysis system in badminton. Two matches were analysed post-event by three trained observers who coded the matches twice using SAGE Game Manager for Badminton software (Newcastle, England). The results highlighted the good reliability and validity of the analysis system for evaluating playing time, player position and the type, quantity and length of shots, but showed less reliability in relation to shot execution.[7] ª 2008 Adis Data Information BV. All rights reserved.

The reliability of notational analysis in squash was evaluated by comparing the results in a repeated analysis.[3] The categorization of the 13 squash shot types and the classification of winning and losing shots found no errors. An error of 1.85% was found when classifying shots as effective or ineffective, a value that was considered acceptable.[3] Similarly, Duthie et al.[2] analysed the reliability of video-based time-motion analysis with ten rugby union players who were individually tracked during the 2001–2 Super 12 competitions in New Zealand. The footage was then analysed Sports Med 2008; 38 (12)

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by a single researcher on two occasions, 1 month apart. The test-retest reliability was quantified as the typical error of measurement (TEM) and rated as good, moderate or poor. The total time spent in the movement categories (walking, jogging, striding, sprinting, static exertion and being stationary) had moderate to poor reliability (5.8–11.1% TEM). The frequency of individual movements had good to poor reliability (4.3–13.6% TEM), while the mean duration of individual movements had moderate reliability (7.1–9.3% TEM). It was concluded that the time motion analysis system was a moderately reliable evaluation tool for examining movement patterns in competitive rugby.[2] The laborious process and extensive time requirements involved with manually tracking multiple players in team games has meant that researchers have typically avoided trying to analyse all the available players, and instead have focused only on the movements of players involved in active play. In one exception, Franks and Goodman (cited in Hughes et al.[23]) attempted to analyse player movements in an entire team of football players. Unfortunately, the analysis was presented incomplete because of difficulties with identifying players from the distance required to film the entire pitch, and the time needed to complete the digitization process.[23] Investigations by Docherty et al.[4] and Kingman and Dyson[21] alternately monitored players from two teams in rugby and roller hockey, respectively. Player movements and activities were manually coded into standard categories and general movement patterns were analysed individually. However, while both of these investigations involved the filming of multiple players, no information was provided regarding interactions between players and team movement patterns. In summary, early efforts at tracking player motion by methods of notational analysis have typically involved a range of data collection techniques from live observation to post-event video analysis. Notational analysis has emerged as a field of study involving observer(s) subjectively categorizing player movement patterns and relating this information to performance effectiveª 2008 Adis Data Information BV. All rights reserved.

ness. Due to the considerable time required to manually collect and analyse such data, research has tended to focus only on small numbers of players within pre-defined playing areas or on the player in possession of the ball and those in close proximity. Whilst notational analysis is a convenient, practical and typically inexpensive procedure, the validity and reliability of the process can vary depending on a number of factors including how many observers are used, their experience, and the quality of their viewing perspective.[22]

2. Automated Vision-Based Tracking Systems Unlike the manual visual tracking systems described in section 1, automatic motion tracking does not require human operators to locate manually and continually record the position of the tracked object. Theoretically, the process required for automated tracking involves a number of steps. The initial stage of automatic tracking is the detection of moving targets in the captured images. These images must then be processed and segmented before shape models are fitted and the foreground shapes are extracted from the overall image. Further filtering and tracking methods are then used to predict the future locations and shapes of the tracked object.[34] Whilst some commercial online motion analysis systems (e.g. MAC, Santa Rosa, CA, USA) alleviate the requirements of manual tracking, to our knowledge a completely automated system for sports performance is not yet commercially available. Automatic motion tracking has been used successfully in other domains outside of elite sport performance, notably for surveillance in the military and security industry where automatic initialization of moving objects is achievable because identification of the objects is not necessary. Human motion analysis, particularly automated motion analysis, is a broad field of study.[35] For this reason we will briefly discuss automatic tracking from a surveillance perspective, for background and context, but the focus of this section is on systems Sports Med 2008; 38 (12)

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that are currently used for sports analysis or those that have potential to be used to measure athletic performance. For a more detailed overview of developments in computer image processing see Poppe[36] or Moeslund and colleagues.[37,38] 2.1 Surveillance Tracking Systems

Tracking human motion in an indoor environment is of interest in applications of surveillance particularly in security-sensitive areas such as, banks, borders, airports and department stores.[38-40] As well as the obvious security applications, smart surveillance has also been proposed to measure traffic flow, monitor pedestrian congestion and behaviour and compile consumer demographics in shopping malls.[39] The monitoring of these activities requires the viewing system to be able to follow the image of the tracked object(s) within a large volume of space over a long period of time.[38] Human motion analysis in sport presents considerable difficulties to potential surveillance systems due to large variations in human motion and appearance, camera viewpoints and environment settings.[36] There have been many attempts at human motion detection, with varying degrees of success. Human body models that were initially described in 2-D have now evolved into highly articulated 3-D models. Deterministic linear tracking has been replaced by samplingbased tracking frameworks that sample according to some cost function, and the role of machine learning is playing an increasingly important role in human motion analysis.[36] Recent contributions have addressed previously limiting assumptions, and some systems can now cope with natural outdoor scenes and operate on long sequences of video containing multiple interacting and occluding people.[36,39,41] It has been suggested that these developments are due to advancement in segmentation algorithms.[35,36,38] Other explanations suggest improved modelbased pose estimation or in the representation and interpretation of actions and behaviour.[36-38] There are a number of motion-sensing devices with surveillance applications that do not ª 2008 Adis Data Information BV. All rights reserved.

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rely upon video technology (e.g. infrared, magnetic systems, ultrasonic, IEEE 802.11 radio frequency-based systems and radio frequency identification [RFID]). However, the purpose of surveillance systems does not typically demand a high level of spatio-temporal tracking precision, as long as the movements of individuals can be tracked, their identity, for surveillance purposes, is not usually necessary.[42] It is necessary in sport, however, to track individuals and know when errors in player identification occur, which may pose serious analytical problems. Additionally, the characteristics and complexity of player activities in many sports, means that the frequency of player occlusion behind other players and officials pose serious problems for accurate uninterrupted tracking. Together, these factors currently prevent the direct application of surveillance systems to sporting environments. 2.2 Outdoor Sports Tracking Systems

This section focuses on tracking systems that were designed to capture human motion performed in an outdoor environment. Outdoor tracking systems differ to those developed for indoor sports, as they have larger capture areas, more interacting players and variable lighting conditions. Traditionally, this has meant that analysis systems have needed multiple cameras positioned around stadia and have focused on the game’s ‘active play’. One of the first investigations into video-based automatic tracking of athletes involved the development and application of what has been called the ‘closed-world’ to an outdoor sports environment.[43] A closed-world was defined as a spatio-temporal region in which all identifiable objects present, such as players and boundary lines, were coded manually and each display pixel was allocated to one of those objects. This information enabled context-specific features to be dynamically selected as the basis for tracking. Intille and Bobick[43] define a context-specific feature as one that has been chosen, based upon its context, to maximize the chance of successful tracking between frames. In their analysis, two consecutive frames from television footage were Sports Med 2008; 38 (12)

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subtracted to detect the players. The area around these players was then expanded to form a closedworld relative to the selected and relevant players. This closed-world information was then used to match the visual characteristics of each player in future frames. Intille and Bobick[43] demonstrated the use of contextual knowledge and its application to closed-world football player tracking and assert its potential to work more effectively than common template matching in tracking isolated players. However, the authors acknowledge that the method is somewhat limited by its inability to accurately track more than one player.[43] Scene event analysis using image processing is becoming increasingly common. For example, tracking of players/athletes makes strategy analysis and scene recovery possible and can be achieved using many different methods.[12,44-48] Several studies have used computer vision processing, which groups together image pixels to form blob-like entities based on proximity and visual appearance. The blob representation was developed as a method of extracting compact and structurally meaningful descriptions of multispectral imagery.[46] Spatial coordinates are added to the spectral component of the image forming feature vectors for each pixel. These coordinates are clustered so image properties (colour and spatial similarity) combine and form coherent connected regions or ‘blobs’. Tracking of the blobs’ location on a playing surface is then carried out using motion features instead of structural features.[12] A fully automatic and computationally efficient framework for analysis and summarization of football videos was proposed by Ekin et al.[49] using cinematic and object features. The framework included some novel low-level football video processing algorithms including dominant colour region detection, robust shot boundary detection, and shot classification. High-level algorithms were also incorporated for goal detection, referee detection and penalty box detection. The system was capable of three types of summary outputs: slow motion segments, goals scored, and slow motion segments classified by object features.[49] The ª 2008 Adis Data Information BV. All rights reserved.

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results showed that this system was successful at detecting real-time events (using cinematic features) and filtering slow-motion replays (using object-based features) to allow semantic labelling. This automatic video analysis and summarization method, however, is currently only capable of detecting events (i.e. goal scoring) and is not able to provide more detailed information regarding player(s) location throughout the match.[49] Qi et al.[50] presented a multiple-player tracking framework using low resolution video of various sports, at eight frames per second. Tests showed that the algorithm could successfully deal with 2 minutes of non-occluded game situations and occluded situations between members of opposite teams. Motion model and shape information with a data association method could track objects when no occlusions occurred. However, when occlusion between members of the same team occurred, the similar appearance of players made differentiation very difficult.[50] Iwase and Saito[51,52] proposed a method for parallel tracking of all football players during games using multiple fixed cameras. From 25 people (22 players and 3 referees) tested, 14 of these could be tracked through a scene of 500 frames without problems. Each player image was provided with an identification number in the original video image frame. On occasions where the scene became too crowded and there were occlusions, identification numbers were replaced by numbers belonging to other players, allowing problems to be easily identified and easily corrected by manual intervention. A recent investigation by Barros et al.[53] used an automatic tracking system (DVideo, Campinas, Brazil) to measure distances covered by football players. In each game, four digital cameras were fixed at the highest points of the stadiums, each covering approximately a quarter of the field, but in overlapping regions.[53] The trajectories of 112 different players were tracked in four games, although only the results of the players who participated in whole games were analysed. Situations where players were not tracked automatically only occurred when players were occluded and were corrected manually.[53] Sports Med 2008; 38 (12)

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Most investigations that have attempted to auto-track objects have used fixed camera placements and focused on a single moving object.[54,55] Alternative automatic tracking methods for analysis of football, using a moving camera were suggested by Utsumi et al.[54] and Araki et al.,[55] The investigation by Utsumi et al.[54] detected the object for tracking and each pixel was evaluated based on its colour rarity and its local edge property, which meant that player regions could then be detected and tracked. Players without occlusion were easily identified and the average sizes of the upper and lower halves of the ‘player region’ of the image were calculated separately to provide more accurate positional information. Players who were occluded by another player or object were separated using colour-based template matching. While the overall player detection rate was not sufficient to label specific players and precisely describe the game, the additional information on object shape and colour was shown to improve tracking ability. Utsumi et al.[54] suggested that further improvement was required to automatically detect occluded players and that a more robust method of detecting changes in player’s positions could accurately measure player movement patterns.[54] Similarly, Araki et al.[55] proposed a method for real-time tracking from a moving camera image sequence using robust statistics and active contour models. Detection of moving objects was achieved by cancelling the apparent motion of a static background and applying split and merge models that set initial contours and surround the moving object on the first frame. Although this method was shown to detect and track multiple moving objects in real-time, it could not accurately track dynamic objects that stop temporarily and frequently change direction as it is limited to continuously moving edges.[35,55] 2.2.1 Limitations of Outdoor Automatic Motion Tracking Systems

Automatic tracking systems developed for outdoor applications appear to be restricted by several common limitations. The first of these is the inability to accurately track more than one ª 2008 Adis Data Information BV. All rights reserved.

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player.[43,49-51,56] Problems with player tracking are often encountered during periods of player congestion, where players are frequently occluded by others, particularly players of the same team. Situations where multiple players cluster in restricted playing areas (e.g. penalty area during a corner in football) often encounter such problems and consequently are typically low in tracking accuracy.[45,53] Conversely, players who are largely isolated during the game, (e.g. football goalkeepers) are tracked with the most accuracy. It has been suggested that the problems associated with occlusion could be overcome by increasing the number of cameras, or changing the placement of the existing cameras.[53] In instances where players are lost or cannot be tracked the operator is usually required to correct errors in the player’s path manually.[48] Studies that use computer vision processing to create blob-like entities for player tracking also report occlusion as the most significant limitation.[12,44-48] This is primarily because of the camera placement, which is usually on the side of the pitch at ground level and causes inherent difficulties when multiple players surrounded the ball. Finally, there are limitations associated with using a moving camera. Utsumi et al.[54] and Araki et al.[55] presented automatic tracking methods for analysis of football using a moving camera, and reported that the overall player detection rate was not sufficient to label specific players. They also suggest that further improvement is required to automatically detect occluded players and that a more robust method of detecting changes in player’s positions is needed to accurately measure player movement patterns.[54,55] Furthermore, the use of a moving camera increases the complexity of player tracking as both the objects and the sensor are moving independently with respect to the reference coordinate frame, requiring continuous calibration.[57-60] It appears that the moving camera approach is limited to predominately planar activities like walking, running, skating or skiing where athletes can remain within the calibrated space and the relationship between cameras can remain constant.[55] Sports Med 2008; 38 (12)

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2.3 Indoor Sports Tracking Systems

This section focuses solely on tracking systems that were designed to capture human motion performed inside a building. Although automatic tracking and image processing ability is developing rapidly, its application to sport has undoubtedly been hampered because of inadequate video and computational facilities available at sports venues. The complex nature of movement inherent to many physical activities also represents a significant hurdle to overcome. In many sports, such as basketball, handball and netball, participants exhibit quick and agile movements, with many unpredictable changes in direction and also frequent collisions with other players. Each of these characteristics of player behaviour violate the assumptions of smooth movement on which computer tracking algorithms are typically based.[61] As previously discussed in section 1, notational approaches have partially addressed the motion acquisition problem through manual tracking of single players for each recorded field with observational sheets or computerized digitizing pads.[30,31,57,61] Segen and Pingali[62] developed a fixed camera system for transforming recorded video footage of people moving through an environment into spatio-temporal coordinates. Feature points were identified in each video frame and these points were matched across frames to develop feature ‘paths’. The trajectories consisted of spatial (vertical and horizontal) and temporal coordinates and data were collected continuously as the individual moved through the environment in real-time.[62] This technique was developed further by Pers and Kovacic[59,61] who devised a computer vision system capable of tracking multiple players with a known accuracy, in real time and with minimal human operator intervention.[59,61] The system presents spatio-temporal trajectories for five players in a handball match and allows for further context-specific analysis (see figure 2). The automated tracking method of Pers and Kovacic[59,61,63] required the players to remain in the field of view for the duration of the capture period. Theoretically this requirement can be ª 2008 Adis Data Information BV. All rights reserved.

Fig. 2. Spatio-temporal trajectories of five handball players (denoted by different colours) for the first few minutes of a match, obtained by motion detection algorithm (adapted from Pers and Kovacic,[59] with permission. Copyright ª IEEE 2000).

achieved though camera panning, a moving camera system or appropriate camera placement. Pers and Kovacic[59,61] decided to place two static cameras with 103 wide-angle lenses at a height of 10 m above a handball playing court. Footage of the players was captured at 50 fields per second with an image resolution of 384 · 288 pixels (see setup in figure 3). Motion detection, template and colour-based tracking algorithms were tested by Pers and colleagues[59,61,63,64] to determine the most effective method for following player movements. Each video frame in a sequence was subtracted from the reference frame (image of an empty court) leaving the difference image. The image was then thresholded and filtered, resulting in the production of ‘blobs’ (that correspond to players), which were then analysed. The frequent collisions between players in handball required constant human intervention as sudden changes in player position occur when the tracker confuses two players during collisions.[57,59,61,63] The colour identification algorithm was shown to be successful even when the colour of the player is changed with signal distortion during tape recording.[59,61] The algorithm searched for the pixel most similar to the recorded colour of the player, in a limited area surrounding their previous known location. However, a highly diverse background with coloured areas (court surface advertisements) could potentially cause Sports Med 2008; 38 (12)

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a 40 m Camera 1

20 m

Camera 2

G o a l

Camera 1 G o a l

Goal

Camera 2

Goal

b

c

Fig. 3. (a) Camera placement (10 m above court). (b) Image obtained. (c) The resulting images after correction (adapted from Pers et al.,[63] with permission. Copyright ª Elsevier 2001).

differentiation problems for the system if the background colour is similar to the players’ kit.[59,61,63] The colour identification method is not always successful because the number of pixels required to construct the player images are often too great, and cannot be provided by the captured footage.[59,61,63] ª 2008 Adis Data Information BV. All rights reserved.

Vuckovic et al.[65] used a motion detection tracking algorithm to compare the work rates of international and national squash players during matches. Tracking of the players was initiated manually, by clicking near each player’s centre of gravity, and then automated at 15–20 frames per second. Video images of player positions were Sports Med 2008; 38 (12)

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smoothed using the Gaussian smoothing kernel to reduce measuring errors in path length and players’ velocity.[65] Information relative to the player’s movement patterns was then presented graphically as spatio-temporal trajectories, which showed that typical x-shaped motion patterns (see figure 4) of varied magnitudes were executed by both groups of athletes during rallies. The similarity of the player trajectories suggest that common play tactics are employed by players of different abilities. However, significantly shorter diagonals in motion trajectory can be observed for the national player, indicating national players play with less accuracy, which directly influences the distance covered and work-rate of opposing players.[65] Kristan et al.[66] used a colour-based tracking algorithm to study the interaction of multiple targets during team sports. Intille and Bobick’s[43] closed-world concept was applied to model the scene context, because of the sportspecific task constraints. This concept can be applied to a sporting context on the assumption that the camera above the playing field is static and its optical axis is perpendicular to the floor.[66] Furthermore, the texture of the players was known and varied throughout the game due to non-uniform lighting conditions, the background and variations in the players pose.

Fig. 4. Trajectory of (a) the international player during the rallies and (b) the national player during the rallies (adapted from Vuckovic et al.,[65] with permission. Copyright ª IEEE 2005).

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Needham and Boyle[67] presented an alternative method to those proposed by Pers and colleagues[57,59,61,63] by using a condensationbased approach. They tracked the movements of football players from a single camera on an indoor court. Each player automatically tracked was independently fitted to a model and the sampling probability was calculated as a function of the fitness score of each player. To evaluate the automated tracking system, a sequence was manually tracked four times and each resulting trajectory was compared with the other trajectories. The automated system showed a mean difference of 2.5 m to the manually digitized trajectories. Needham and Boyle[67] were able to make improvements to the tracking system using Kalman filtering, preventing it from switching between players, and consequently reducing the mean error to 1.16 m (where an error of up to 0.5 m was considered acceptable for handtracked data). 2.3.1 Limitations and Reliability of Indoor Automatic Motion Tracking Systems

Pers and colleagues[63,68] validated the use of their automated indoor tracking system by reporting the errors and mistakes associated with player tracking. First, the most significant sources of error capable of influencing accuracy were identified as: movement of player extremities, video tape noise; quantization error; imperfect camera calibration; operator mistakes. Error related to the movement of player extremities was attributed to viewing a 3-D situation through a 2-D medium. Ideally, player positions would change only when the player walked or ran from one position to another. However, because 2-D movement is assumed, changes in position due to vertical movement or movement of the extremities are not accounted for and can result in error.[63,68] Quantization error relates to the effect that radial distortion has on the input images, which is problematic particularly at boundary regions (see figure 5). To verify the ability of the tracking system, Pers et al.[63,68] conducted four experiments to investigate the system’s accuracy. Separate experiments were designed to look at (i) the Sports Med 2008; 38 (12)

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b

Fig. 5. The effect of the radial distortion. (a) The acquired image. (b) Demonstration of wide-angle lens distortion at court boundaries (adapted from Pers et al.,[68] with permission).

influence of ‘jitter’ (short trajectory length on stationary players) in the output trajectories; (ii) the influence of filtering on the accuracy of player positioning; (iii) the influence of filtering on calculated player velocity; and (iv) a comparison between the multi-object tracking system and a manual single-object, 3-D tracking system (Ariel Performance Analysis System [APAS]).[63,66,68] Five players participated in each experiment, initially they all stood still in various positions on the court before performing 180 seconds of physical activities.[63] Trajectory smoothing was found to significantly reduce the errors in player velocity and distance covered, but had minimal influence on player position error.[63,66] The second experiment found that heavy smoothing hid rapid changes in player trajectories.[63] In the third experiment, velocity errors were calculated from the length of the player’s circular trajectory and the time taken to complete the path. Average error ranged from 0.07 to 0.35 m/sec depending on the size of the smoothing kernel used.[63] In the final experiment, the participants ran around three markers in one corner of the court in a 3.4-second (25-Hz) video sequence. The results were consistent with previous experiments with a 0.36-m error in position. However a 0.5 m/sec error was reported in velocity, highlighting the important difference in capture detail between the systems.[63] ª 2008 Adis Data Information BV. All rights reserved.

Undoubtedly both the multiple and singleobject tracking systems describe the actual motion of the player. However, while the APAS system provided accurate information of the player’s acceleration from their centre of mass and considered the movement occurring at the extremities, the multi-object system provided more global information, accurately showing acceleration and changes in direction on a larger scale. This information was considered potentially more relevant to game play and necessary to identify interactions in team game situations.[63,68] 3. Commercially Available Vision-Based Analysis Systems 3.1 Semi-Automatic/Online Systems

Several sports performance analysis systems now exist on the commercial market providing different features of player tracking capacity.[34] In this section, examples of these systems will be introduced and their common limitations will be summarized. Basic video analysis systems such as Dartfish (Fribourg, Switzerland), Digital Soccer (Italy), Game Breaker (Sportstec, Australia) and Utilius VS (CCC-Software, Germany) are designed to provide extrinsic feedback information to coaches and athletes to enhance performance and learning. Systems such as Sports Med 2008; 38 (12)

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Game Breaker and Utilius VS do not perform actual analysis, but edit video recordings to create movie presentations and time-coded video clips to allow a frame by frame analysis of individual movements and skills. They do not currently provide information about movement patterns or player interactions. Systems that require players to wear special tracking devices (e.g. TRAKUS TKS Inc., MA, USA) are often unsuitable for competition because of regulations and the risk to player safety. The TRAKUS software is an example of a customized real-time system based on microwave receivers that analyse signals emitted from transmitters worn by the players to various interactive media.[68] To date, the TRAKUS system has been applied in sports such as ice hockey, golf and motor sports. The system provides data on location, speed, acceleration endurance and intensity. It is also capable of reconstructing sequences of events or plays and creates views of the action from multiple angles.[69] SoccerMan (Berne, Switzerland) is a football game-reconstruction system that is designed to generate a descriptive animated 3-D scene from a given video sequence. This description includes information on the ground texture as well as a 2-D description of the players. This 3-D scene can then be examined from any virtual viewpoint with a 3-D viewer, assisting in the verification of referee decisions, television game analysis, training support, teamwork evaluation and the classification of football scenes.[67,70] Initially, the cameras are calibrated to extract background and player information from the video sequence and the system assumes that the camera remains in the same position throughout the match. LucentVision provides a product for analysing and visually presenting tennis games.[69] The features of the product include presence maps that highlight where players spend most of their time during a match, virtual replays, where the ball can be viewed from any position and a variety of numerical statistics (velocity, distance). This system is a real-time networked visual information system that archives sports action using visual processing. Computer vision ª 2008 Adis Data Information BV. All rights reserved.

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techniques are used to track each player’s movement including the distance travelled and speed.[69] TRAKPERFORMANCE (Sportstec, Australia) is a video-based system developed by Sportstec that provides player tracking information from games in real-time. The system relies upon operators to manually track player positions frame by frame before providing information such as distances travelled, speed breakdowns of individual players, work to rest ratios and positionspecific information. It does not require players to wear additional transmitting devices, which means it is also capable of tracking opposing teams or individuals. This programme is marketed to be accurate to within 5% of true values and the breakdown of speeds is accurate to within –10% (Sportstec, Australia). Online motion analysis systems developed by companies such as Motion Analysis Corporation (MAC, Santa Rosa, CA, USA) capture movement in digital format in real time. Such systems are often used in animation production and for industrial measurement and control. MAC have developed a powerful 28-camera Eagle-4 Digital System that has the ability to capture ten performers at 60 frames per second in a 40 · 40 · 15 feet (12 · 12 · 5 m) capture area. Performers are prepared in advance of tracking with infrared light reflecting markers fixed to various body segments. The Eagle-4 cameras capture the motion of the markers and Motion Analysis EVaRT software identifies the actors’ movements in realtime. Some examples of other companies that produce similar motion analysis systems include Vicon (Vicon Motion Systems, Oxford, UK), BTS Elite (Milan, Italy) and Qualysis Motion Capture Systems (Gothenburg, Sweden). Pfinder (Cambridge, MA, USA) is a realtime digital video system for tracking and interpreting human movement.[46] Pfinder uses a multi-class statistical model of colour and shape to segment the video image and obtain a 2-D representation of head and hands in a wide range of viewing conditions.[46] These 2-D representations are ‘blobs’ or clusters of pixels with similar colour and spatial image properties. The ‘blobs’ are then automatically tracked by software that Sports Med 2008; 38 (12)

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uses a background subtraction technique to determine positional changes.[12,44,46] Amisco (Sport Universal, Nice, France) provides a passive (marker-less) tracking system that measures all moving objects on the football pitch. Sampling at 25 Hz, real-time information is provided from the multiple capture systems installed around the stadium. However, operators are required to note all events, such as fouls, off sides and cautions that occur throughout the game.[69] Similarly, ProZone (West Yorkshire, England) is another video tracking system designed for the analysis of football that requires an elaborate multi-camera system that is custom-fitted at sports stadia. The footage from these cameras is used by the Prozone company to track player positions manually over quadrants that make up the total playing area. The positional information of each player is then used to provide valuable statistics to the coaching staff.[34,69] Di Salvo et al.[71] attempted to validate the Prozone measurement system while analysing displacement and velocity on a football field. Participants performed a series of runs while being tracked by the Prozone system in different zones of the Old Trafford and Reebok Stadiums. The Prozone data were compared with timing gate data collected for the same runs.[71] The results indicated that the average speed recorded by the Prozone system during the paced runs of 60 m and 50 m showed an excellent correlation (r = 0.999) with the average speed measured by timing gates. Furthermore, the maximal 15-m sprint showed a correlation (r = 0.970). Therefore, it was concluded that Prozone represented a valid motion analysis system for analysing movement patterns of football players on a football field.[71] 3.2 Limitations of Commercial Tracking Systems

Clearly the commercial tracking systems described in this section have provided enhanced motion tracking capability in recent times. Such systems typically operate though highspeed digital video or infra-red light-detecting cameras. However, several of the systems are ª 2008 Adis Data Information BV. All rights reserved.

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limited by some common factors. For example, the main limitation of the SoccerMan, TRAKPERFORMANCE and ProZone systems is that significant manual intervention is required to accurately track the players. Thus, similar to notational analysis, it can be assumed that the accuracy of the positional information generated by these two systems will be dependent upon the training and experience of the observers. The MAC system is relatively expensive, has a limited capture volume, and the infra-red light-detecting cameras cannot be used for outdoor sports. The Pfinder tracking system is limited by its reliance on dynamic scenes and the assumption that only one person occupies a space at any one time, preventing multipleobject tracking.[46] A number of commercial motion tracking systems are now available with reasonably high levels of accuracy and reliability. The tracking software that accompanies some of these systems has developed to the extent now that much of the information can be encoded in real-time (online) meaning that reduced processing times are required. However, commercial tracking systems still require significant operator intervention to process the data after capture. Whilst several systems are expensive, purchase costs have decreased substantially in the last 10 years or so. The main limitations of the systems described in this section are the restricted capture environments that can be used and the necessity for individuals to wear tracking devices. Such limitations can prevent their usage in a number of competitive sports situations.

4. Conclusions The increasing capacity of digital technology to collect, manage and organize video images has made it possible to enhance existing sportspecific analytical procedures. A variety of systems and methods have been employed to analyse the motion of athletes during sports where the movements vary in duration, field position and surface, speed, direction technique and tactics.[3-5,13,25,30,32,45,53,67,70,72-74] The traditional Sports Med 2008; 38 (12)

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methods of manual data entry, using either hand or computer, can be lengthy and monotonous. Codes that are assigned to different actions or player’s position if not designed to provide some meaning to the operator, often results in the process being inefficient and inaccurate.[22] Video cameras linked with computers and automated analysis software provide a more sophisticated approach to motion analysis, but such systems have also experienced a number of practical limitations. For example, either the field of view of the camera has been too small to include the entire playing area or the system requires athletes to wear joint markers and move within a pre-set, restrictive calibration volume.[23,73] Notational analysis linked to a computerized coding system enables quick and immediate analysis of the video images but fast feedback is restricted to simple kinematic and temporal data. Such techniques are also vulnerable to inaccuracies within the qualitative analysis of the data collected and interpreted.[2,23,75] However, despite these limitations and advances in the field of image processing and automatic tracking, notational analysis systems remain an important sports analysis tool. While semi-automatic or automatic computerized systems provide objective measurements about player movements, notational analysis is still required to compliment these new systems and provide event-specific information about player activities such as, jumps, throws, shots and fouls. Tracking objects that take part in sporting events is inherently difficult because of athletes’ interaction with other athletes and hence the unpredictability of movement.[45,62] The development of an automated system that collects uninterrupted footage of games and provides accurate positional information would be desirable and advantageous to sporting teams and coaches. Although the results of the studies reviewed have shown various levels of successful tracking, they also have significant limitations and most are restricted by their inability to track multiple, dynamic, frequently changing movements in a large environment. The application of automated systems into sports is further restricted by the amount of ª 2008 Adis Data Information BV. All rights reserved.

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equipment required to collect and process the video images. For example, multiple cameras are regularly used to film the movements of multiple players in football. Iwase and Saito[51] used 15 cameras to record the movements of 22 players and three referees. Barros and colleagues[53] used four cameras and needed five computers to segment and process the movements of football players over 10 hours. Muller and Anido[45] used five cameras and needed 66 computers to process a short sequence of a professional football match. In addition to these large hardware demands, the sampling frequency of these systems is generally low. Iwase and Saito[51] sampled at 15 frames per second, and other systems sampled at a lower frequency (i.e. 7.5 Hz[53] or 8 Hz[50]). Furthermore, the duration of the playing sequences tracked is typically quite brief.[12] Iwase and Saito[51] recorded playing sequences of 500 frames (33.3 seconds), Utsumi et al.[54] tracked 30 seconds, Needham and Boyle[67] tracked 835 frames, whereas Muller and Anido[45] analysed 4 minutes, 42 seconds and Qi et al.[50] tracked 2 minutes of real football game video. The indoor systems tested by Pers and colleagues[57,59,61,63–66,68] had similar processing problems. The system described by Pers et al.[64] tracked 120 seconds of player activity at 10 frames per second and captured the image using one video camera. Another investigation by Pers et al.[61] used two cameras, captured sequences of 30 and 50 seconds and sampled at 25 Hz, processing this information on one computer running at 500 MHz. A third investigation also used two cameras, and tracked 14 players at a speed of 4.5 frames per second, requiring approximately one operator intervention per player during the 30-second sequence.[63] These requirements, of multiple cameras and multiple computers, mean that these systems are not easily installed next to playing courts or fields. The time required to process entire games also means that investigations are currently limited to only short playing sequences. Finally, sampling at low frequencies may influence the tracked data by reducing the number of frames processed and potentially minimizing the influence or effect of some movements recorded. Sports Med 2008; 38 (12)

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5. Future Research The application of computer technology to the sporting domain presents an arduous research challenge because of the variable nature of human movement, the complexity of team ball sports and significant equipment requirements. The current challenge is to obtain appropriate video sequences that can robustly identify and label people over time, in a cluttered environment containing multiple interacting players in the absence of markers. Although systems that can achieve this goal are currently being developed for surveillance and security purposes, the problems associated with their application into the sporting environment is compounded by the quality of video capture, the relative size and occlusion frequency of people within that scene, and also changes in illumination.[35-37,61] Significant progress towards fully automated human motion tracking and reconstruction has occurred in recent years. These advancements have been largely in the initialization of model shape appearance and pose, tracking of multiple people in unstructured outdoor scenes, the reconstruction of human movement from multiple views, monocular motion reconstruction, pose estimation in natural scenes and the understanding of behaviour and actions.[35,37,38] Future developments may focus on body part detectors, body shape and movement of clothing, which would improve tracking reliability and pose estimation in clutter scenes.[38] Other areas of focus are on the development of reliable detection of people and behaviour from low quality imagery, and on the advancement of behaviour representation for dynamic scenes.[38,39] Further research is also required to reduce the computational costs while maintaining processing capacity and therefore increase processing speeds of automatic tracking.[35] An automated system capable of capturing, analysing and quantifying player information in real-time would greatly enhance research in game analysis. For example, in indoor sports such as handball or basketball, detailed information regarding player’s velocities, distances travelled, speed of attacking play, and frequencies related ª 2008 Adis Data Information BV. All rights reserved.

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to the direction of passes, and the number of passes can be obtained. Furthermore, the ability of the system to track the movement of the ball and its interaction with players means that information regarding successful and unsuccessful passes between team mates can be quantified. The expression of players’ activities as measurable physical quantities is important for developing position-specific training programmes for players, creating more informed team strategies and developing profiles on opposing teams. As well as being of benefit to coaches and athletes, sport and exercise science researchers would benefit from information derived from an automated player tracking system. For example, sport psychologists could examine team interactions to relate theories of group dynamics to actual team performances. Sports biomechanists might use the information to create simulations of team behaviour and to model hypothetical game situations. Motor control researchers could examine the stability and variability of intraperson coordination as a function of different decisions made by key players. Clearly, a wealth of fascinating information related to multiple player interaction will soon become available that has the potential to enhance understanding of sports performance to a new level. Acknowledgements The authors would like to thank Professor Roger Bartlett, Kathryn Phillips and two anonymous reviewers for their help and advice whilst preparing the manuscript. No funding, no potential conflicts of interest.

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41. Hu W, Tan T, Wang L, et al. A survey on visual surveillance of object motion and behaviours. IEICE Trans Inf Syst Man Cybernetics 2004; 34 (3): 334-52 42. Ni L, Liu Y, Lau Y, et al. Indoor location sensing using active RFID. Wireless Networks 2004; 10: 701-10 43. Intille S, Bobick A. Visual tracking using closed worlds. MIT Media Laboratory Perceptual Computing Section, 1994. Technical report no. 294. Los Alamito (CA) 44. Bobick A, Davis J. The recognition of human movement using temporal templates. IEEE Transact Pattern Analysis Machine Intelligence 2001; 23 (3): 257-67 45. Muller B, Anido R. Distributed real-time soccer tracking. ACM 2nd International Workshop on Video Surveillance and Sensor networks conference proceedings. New York: ACM Press, 2004 Oct 15; 97-103 46. Wren C, Azarbayejani A, Darrell T, et al. Real-time tracking of the human body. IEEE Transact Pattern Analysis Machine Intelligence 1997; 19 (7): 780-5 47. Figueroa P, Leite N, Barros R. Background recovering in outdoor image sequences: an example of soccer players segmentation. Image Vision Comput 2006; 24: 363-74 48. Taki T, Hasegawa J, Fukumura T. Development of motion analysis system for quantitative evaluation of teamwork in soccer games. IEEE; International Conference Image Processing 1996; 3: 815-8 49. Ekin A, Tekalp M, Mehrotra R. Automatic soccer video analysis and summarization. IEEE Transact Image Process 2003; 12 (7): 796-807 50. Qi F, Luo Y, Hu D. Visual tracking of players through occlusions in low resolution. IASTED International Conference; 2004 Aug 23-25; Honolulu (HI), 375-80 51. Iwase S, Saito H. Parallel tracking of all soccer players by integrating detected positions in multiple view images 17th International Conference on Pattern Recognition; 2004 Aug 23-26; Cambridge, UK, 2004: 751-54 52. Iwase S, Saito H. Tracking soccer players based on homography among multiple views. Proceedings of SPIE; 2003 Visual Communications & Image Processing (VGP) VCIP 2003: 283-92 53. Barros R, Misuta M, Menezes R, et al. Analysis of the distances covered by first division Brazilian soccer players obtained with an automatic tracking method. J Sports Sci 2007; 6: 233-42 54. Utsumi O, Miura K, Ichiro I, et al. An object detection method for describing soccer games from video; 2002 Multimedia Expo 2002. ICME. Proceedings of the IEEE International Conference 2002; 1: 45-8 55. Araki S, Matsuoka T, Yokoya N, et al. Realtime tracking of multiple moving object contours in a moving camera image sequence. IEICE Trans Inf Syst 2000; E83-D (7): 1583-91 56. Ohashi J, Miyagi O, Nagahama H, et al. Application of an analysis system evaluating intermittent activity during a soccer match. In: Spinks RaM, editor. Proceedings of Science and Football IV; 2002 Aug 26-29; Lausanne, Switzerland: Taylor & Francis, 2002: 329-33 57. Pers J, Kovacic S. Tracing people in sport: making use of partially controlled environment. Ljubljana: Faculty of Electrical Engineering, University of Ljubljana, 2001

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58. Cai Q, Aggarwal K. Tracking human motion using multiple cameras. IEEE Proceedings ICPR 1996; Vienna, 1996: 68-72 59. Pers J, Kovacic S. Computer vision system for tracking players in sports games. First International Workshop on Image and Signal Processing and Analysis; 2000 Jun 14-15; Pula: IWISPA, 2000: 81-6 60. Lafontaine D, Lamontagne M. 3-D Kinematics using moving cameras. J Appl Biomech 2003; 19: 372-7 61. Pers J, Kovacic S. A system for tracking players in sports games by computer vision. Electrotechnical Rev 2000; 67 (5): 281-8 62. Segen J, Pingali S. A camera based system for tracking people in real time. IEEE Proceedings of 13th International Conference on Pattern Recognition ICPR; 1996: 63-7 63. Pers J, Bon M, Kovacic S, et al. Observation and analysis of large-scale human motion. Hum Move Sci 2002; 21: 295-311 64. Pers J, Vuckovic G, Kovacic S, et al. A low cost real time tracker of live sport events. Ljubljana: Faculty of Electrical Engineering, University of Ljubljana, 2001 65. Vuckovic G, Dezman B, Pers J, et al. Motion analysis of the international and national rank squash players. 4th International Symposium on Image and Signal Processing and Analysis (ISPA); 2005 Sep 15-17; IEEE, 2005, 334-8 66. Kristan M, Pers J, Perse M, et al. Multiple interacting targets tracking with application to team sports. 4th International Symposium on image signal processing and analysis; 2005 Sep; ISPA, 2005; 322-7 67. Needham C, Boyle R. Tracking multiple sports players through occlusion, congestion and scale. Proceedings British Machine Vision Conference; 2001 Sep 10-13; Manchester, 2001; 1: 93-102 68. Pers J, Bon M, Kovacic S. Errors and mistakes in automated player tracking. Sixth Computer Vision Winter Workshop; 2001; Bled: 2001 Feb 7-9; IEEE, 2001: 25-36 69. Setterwall D. Computerised video analysis of football: technical and commercial possibilities for football coaching [dissertation]. Stockholm: CID, NADA, 2003 70. Bebie T, Bieri H. Reconstructing soccer games from video sequences. International Conference on Image Processing; 1998 Oct 4-7; Chicago (IL): IEEE, 1998: 898-902 71. Di Salvo V, Collins A, McNeill B, et al. Validation of Prozone: a new video-based performance analysis system. Int J Perform Analysis Sport 2006 Jun; 6 (1): 108-19 72. Cruz J, Tavares F, editors. Notational analysis of the offensive patterns in cadet basketball teams. Cardiff: UWIC, 2001 73. Hughes M, Bartlett R. Performance analysis. J Sports Sci 2002; 20: 735-7 74. Grehaigne J, Godbout P, Bouthier D. The foundations of tactics and strategy in team sports. J Teach Phys Educ 1999; 18: 159-74 75. Liebermann D, Katz L, Hughes M, et al. Advances in the application of information technology to sport performance. J Sports Sci 2002; 20 (10): 755-70

Correspondence: Sian Barris, School of Physical Education, University of Otago, Dunedin, New Zealand. E-mail: [email protected]

Sports Med 2008; 38 (12)

Sports Med 2008; 38 (12): 1045-1063 0112-1642/08/0012-1045/$48.00/0

REVIEW ARTICLE

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Understanding Change of Direction Ability in Sport A Review of Resistance Training Studies Matt Brughelli,1 John Cronin,1,2 Greg Levin1 and Anis Chaouachi3 1 School of Exercise, Biomedical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia 2 Institute of Sport and Recreation Research New Zealand, AUT University, Auckland, New Zealand 3 Research Unit ‘‘Evaluation, Sport, Sante´’’, National Center of Medicine and Science in Sports (CNMSS), Tunis, Tunisia

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Search Strategy for Correlational and Training Sections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Selection Method of the Studies Gathered during the Literature Search . . . . . . . . . . . . . . . . . . 1.2 Evaluation of Methodological Quality of Training Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Effect Sizes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Classification and Characteristics of Different Change of Direction (COD) Tests . . . . . . . . . . . . . . . . 2.1 Reliability of the Different Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Correlational Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Delimitations and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 COD Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Straight Sprinting Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Strength and Power. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Maximal Leg Strength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Leg Power (Expressed in Watts) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Leg Power (Jump Height) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Leg Power (Jump Horizontal Distance) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 Summary of Correlational Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Training Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Sprint Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Leg Strength and Power Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 COD-Specific Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions and Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

1045 1046 1047 1047 1047 1048 1051 1051 1054 1054 1054 1055 1055 1055 1056 1056 1057 1057 1057 1058 1059 1060

The ability to change direction while sprinting is considered essential for successful participation in most team and individual sports. It has traditionally been thought that strength and power development would enhance change of direction (COD) performance. The most common approach to quantifying these relationships, and to discovering determinants (physiological and mechanical) of COD performance, is with correlation analysis. There have not been any strength or power variables that significantly correlated with COD performance on a consistent basis and the magnitude of the correlations were, for the most part, small to moderate. The training studies

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in the literature that have utilized traditional strength and power training programmes, which involved exercises being performed bilaterally in the vertical direction (e.g. Olympic-style lifts, squats, deadlifts, plyometrics, vertical jumping), have mostly failed to elicit improvements in COD performance. Conversely, the training protocols reporting improvements in COD performance have utilized exercises that more closely mimic the demands of a COD, which include horizontal jump training (unilateral and bilateral), lateral jump training (unilateral and bilateral), loaded vertical jump training, sport-specific COD training and general COD training.

Agility has been defined as a rapid whole-body movement with change of velocity or direction in response to a stimulus.[1] Implicit in this definition is that agility comprises both a perceptual decision-making process and the outcome of this process, a change of direction (COD) or velocity. Of interest in this article is the COD component of agility. COD can be described as a movement where no immediate reaction to a stimulus is required, thus the direction change is pre-planned. Several authors have argued that COD ability is a prerequisite for successful participation in modern-day sports.[2-5] These arguments have been supported in the literature by talent identification/selection studies, which have reported that COD ability was the most important performance variable for (i) predicting player selection (soccer);[6] (ii) American National Football League draft status (wide receivers, running backs, defensive backs and quarterbacks);[7] (iii) predicting on-field performance (American football);[8] and (iv) for distinguishing between elite and sub-elite soccer players.[5] Given the proposed importance of COD ability in sporting performance, it would seem beneficial for strength and conditioning practitioners to identify those training techniques that may best optimize COD performance. Sheppard and Young[1] have described a number of factors that are considered important in determining COD ability, which can be observed in figure 1 and include technical, speed and leg muscle qualities. Whilst such a model appears intuitively appealing, the function of deterministic models is to actually identify those factors that will predict performance and, if trained, will make a functional ª 2008 Adis Data Information BV. All rights reserved.

difference to the variable or component of interest. With this in mind, the purposes of this review are to identify the determinants of COD performance and to investigate the longitudinal training studies in this area. First, a number of COD tests have been classified into categories to enable a better understanding of whether speed and leg muscle qualities better predict certain types of COD tests. Secondly, the research that has quantified the relationship between COD and straight sprinting speed and leg muscle qualities are investigated. Thirdly, the longitudinal studies in this area that have assessed COD performance after a training intervention are critically reviewed. Finally, future research directions that could enhance our understanding of COD performance are suggested. 1. Search Strategy for Correlational and Training Sections Four researchers independently searched the electronic databases of AUSPORT, Expanded Academic ASAP, ProQuest 5000, PubMed, Change of direction

Technique

Leg muscle qualities

Straight sprinting speed

Strength

Power

Reactive strength

Fig. 1. Modified deterministic model of change of direction (reproduced from Sheppard and Young,[1] with permission from Taylor & Francis Ltd, http://www.tandf.co.uk/journals).

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SPORTDiscus, Web of Science and Google Scholar for the years 1985–2007. The following keywords were used in different combinations: ‘agility’, ‘leg strength’, ‘leg power’, ‘running’, ‘sprint’, ‘COD’, ‘speed’ and ‘multi-directional’. 1.1 Selection Method of the Studies Gathered during the Literature Search

The reviewers carried out the selection of studies in two consecutive screening phases. The first phase consisted of selecting articles based on the title and abstract. The second phase involved applying the selection criteria to the articles. Studies were chosen if they fulfilled the following two selection criteria: (i) the study detailed the COD test and outcome measures of leg strength, power and/or running speed; and (ii) the study must have been written in the English language and published as an article in a peer-reviewed journal or conference proceeding. 1.2 Evaluation of Methodological Quality of Training Studies

Evaluating the methodological quality of experimental research is often quantified with one of the following scales: (i) the Delphi scale; (ii) the PEDro scale; or (iii) the Cochrane scale. However, these scales are typically designed to investigate the specific methodological quality of healthcare research and interventions. Training studies that involve strength and conditioning interventions usually score very low on these methodological scales. For example, Markovic[9] conducted a meta-analysis on the effects of longitudinal plyometric training on jumping performance. The PEDro scale was used to quantify the methodological quality of each individual study. Of the 51 training studies analysed, the average score was a 4.6/10 with an overall range of 3–5/10. Furthermore, none of the 51 studies were able to accomplish items 3, 5, 6, 7 and 9. These items were as follows: allocation was sealed (item 3), blinding of the subjects (item 5), blinding the therapists who administered the treatment (item 6), blinding the testers (item 7), and intention-to-treat analysis (item 9). ª 2008 Adis Data Information BV. All rights reserved.

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As can be observed from this analysis, many of the criteria are not relevant to strength and conditioning methodologies, such as blinding participants. We feel that if exercise training studies are evaluated with the above-mentioned methodological scales, the quality of the studies (individually and as a whole) will be classified as poor. Thus, we have created an evaluation of methodological quality for exercise training that is derived from a combination of items from the previous three scales. It is thought that this scale would be appropriate if two goals were accomplished, which were not accomplished with the PEDro scale in Markovic:[9] (i) the best methodological studies scored highly; and (ii) the scale was sensitive with a wide range of scores from poor quality to high quality. This scale includes a 10-item scale (range 0–20) designed for rating the methodological quality of exercise training studies. The score for each criteria was as follows: 0 = clearly no; 1 = maybe; and 2 = clearly yes. The items included: 1. inclusion criteria were clearly stated; 2. subjects were randomly allocated to groups; 3. intervention was clearly defined; 4. groups were tested for similarity at baseline; 5. use of a control group; 6. outcome variables were clearly defined; 7. assessments were practically useful; 8. duration of intervention practically useful; 9. between-group statistical analysis appropriate; 10. point measures of variability. As can be seen in table I and table II, the items that had the greatest range in scores (i.e. the most sensitive) included 2, 4, 7 and 10. From this information, it is recommended that future exercise training studies improve their methodological quality by including a control group, randomizing their subjects, including groups that have similar pre-values, and reporting the variability of their tests. 1.3 Effect Sizes

In the training section, the results of each strength and speed measurement have been presented in terms of p-values (<0.5) for statistical Sports Med 2008; 38 (12)

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Table I. Resistance training studies that have failed to improve change of direction (COD) performance Study

Subjects

Frequency; duration of training

Type of training

Agility test

Results (effect size)

Quality score

Fry et al.[10]

10 female NCAA division 1 volleyball players

4 ·/wk; 12 wk

Performed traditional resistance training and plyometric training

t-test

+3.6% (0.76)

13

Cronin et al.[11]

40 recreationally active males and females

2 ·/wk; 10 wk

JSB and JS

Modified t-test

JSB group -0.7% JS group -1.4%

19

Tricoli et al.[12]

32 male physical education students

3 ·/wk; 8 wk

OL and VJ and control group

Box test

OL group -2.8% (0.66) VJ group -3.6% (0.82)

17

Hoffman et al.[13]

20 male NCAA division III football players

4 ·/wk; 15 wk

OL, PL and control group. Additional sprint and COD training for the final 5 wk for both groups (ten sessions)

t-test

OL group -1.6% (0.34) PL group -2.0% (0.5) Control group – no change

14

Kraemer et al.[14]

30 female college tennis players

3 ·/wk; 9 mo

NPT, PT and control group

PT group +5.0% (0.38) NPT group +2.8% (0.14) Control group – no

17

Hoffman et al.[15]

47 male NCAA division III football players

4 ·/wk; 15 wk

LSJ or USJ. Additional sprint and COD training for the final 5 wk for both groups (ten sessions), and a control group

t-test

LSJ group -2.8% (0.6) USJ group -1.8% (0.1) Control group – no change

16

Gabbett[16]

69 sub-elite male rugby league players

2 ·/wk; 9 wk

SSS group or TC group for rugby league

L-run

SSS group -0.5% (0.75) TC group -0.7% (1.0)

16

Harris et al.[17]

41 strength-trained males (could squat 1.4 · body mass)

4 ·/wk; 9 wk

ST performed strength exercises at high intensities relative to 1RM. PO performed strength exercises at low intensities relative to 1RM., and combined group performed both of above

10-yd (9-m) shuttle run

ST group +1.0% (0.8) PO group +1.7% (1.3) Combined group -2.3% (1.4)

15

1RM = one repetition maximum; JS = jump-squat training on a modified leg-press machine without bands; JSB = jump-squat training on a modified leg-press machine with bands; LSJ = Olympic lifting and strength training with loaded; NCAA = National Collegiate Athletic Association; NPT = non-periodized resistance training group; OL = Olympic-lifting training; PL = power-lifting group; PO = power group; PT = periodized resistance training group; SSS = sport-specific skill-based training; ST = strength group; TC = traditional conditioning; USJ = unloaded squat jump training; VJ = vertical jump training.

significance, percentage change and effect sizes. Percentage changes in performance measures are commonly reported in the literature. However, calculation of percentage change does not take into consideration the variance of strength, power, COD and/or speed improvements.[30] By including the effect size (pre-test minus post-test divided by the standard deviation of the pre-test), the variance of each measurement is included, thus making it a standardized and more accurate description of the treatment effect.[30] The effect size allows us to compare the magnitude of the treatments on the outcome variables of interest ª 2008 Adis Data Information BV. All rights reserved.

between studies. We describe the effects as ‘trivial’, ‘small’, ‘moderate’ and ‘large’ based on the description of effects for untrained, recreationally trained and highly trained athletes.[30] Such classification means that effect sizes are not described in a uniform manner throughout the different populations. 2. Classification and Characteristics of Different Change of Direction (COD) Tests Many different tests have been used to assess COD performance and more are continually Sports Med 2008; 38 (12)

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93 male physical education students

19 NCAA division 1 soccer players

36 elite female soccer players

18 adolescent male soccer players

28 recreational male and females

48 college-aged male and females

Markovic et al.[24]

Cressey et al.[25]

Polman et al.[26]

Christou et al.[27]

Miller et al.[28]

Deane et al.[29] 3 ·/wk; 8 wk

2 ·/wk; 6 wk

2 ·/wk; 16 wk

2 ·/wk; 12 wk

27 sessions; 10 wk

3 ·/wk; 10 wk

2 ·/wk; 4 wk

2 ·/wk; 8 wk

3 ·/wk; 8 wk

2 ·/wk; 14 wk

6 · 5-m shuttle run

L-run

t-test

Agility test

t-test

20-yd (18-m) shuffle

RT group -1.4% (0.6) SCE group -4.2% (1.6) SC group -3.8% (1.2)

Stable group -4.4% (1.0) Unstable group -2.9% (1.6)

ST group -4.3% (1.1) PT group – no change Control group – no change

-3.2% (0.3)

JS30 -1.7% (1.2) JS80 -2.4% (1.3) Control group – no change

STCOD group -2.7% (0.8) ST group – no change Control group – no change

-3.6% (2.1)

Juniors -17.7% (no SD) Seniors -16.2% (no SD)

-5.2% (3.6)

Results (SD)

PL group -2.9% (0.3) Control group – no change Illinois agility test

Male HF group -10% (1.8) Female HF group -8.3% (1.1) Male control – no change Female control – no change

PL group -5.5% (0.7) Control group – no change t-test

10 · 5-m shuttle CSST group -4.0% (1.1) runs AGT group -5.4% (1.7) Control group – no change

HF training group performed hip flexor strength 23.2-m shuttle training with elastic tubing. Also a control group run

PL performed horizontal, vertical, and lateral hops, jumps and bounds. Also a control group

One group performed CSST; one group performed the above with AGT and control group

Three groups performing sport-specific training: L-run RT, SCE, SC

t-test Sport-specific training with COD. One lowerbody exercise/session was performed on either stable or unstable surface

Two groups performing either ST or PT, and a control group

Running, jumping, movement and reaction-time Sideways training shuffle

JS30 or JS80 and a control group

30-m with five Two groups performing ST with COD or ST without COD included in the sprint, and a control CODs group

SSC training including vertical jumping and horizontal jumping both performed bilaterally and unilaterally

Sport-specific training including COD training and skill-based games

Sport-specific training based on the court (passing, setting, blocking and spiking, and small-sided games)

Type of training

16

14

19

18

14

20

10

16

15

12

15

14

Quality score

1RM = one repetition maximum; AGT = CSST and additional general strength training; CSST = COD training plus soccer-specific training; HF = hip flexor; JS30 = jump squat training at 30% of 1RM; JS80 = jump squat training at 80% of 1RM; NCAA = National Collegiate Athletic Association; PL = plyometric group; PT = plyometric training; RT = regular training; SC = speed, agility and quickness training plus COD training with no equipment; SCE = speed, agility and quickness training plus COD training with equipment; SSC = stretchshortening cycle; ST = sprint training.

139 young adolescents

Dean et al.[32]

27 recreational athletic males 2 ·/wk; 6 wk

Young et al.[21]

26 resistance trained male athletes

8 healthy active males

Malisoux et al.[20]

McBride et al.[22]

77 sub-elite rugby players; juniors group (n = 36) and seniors group (n = 41)

Gabbett[19]

3 ·/wk; 8 wk

26 junior volleyball players

Gabbett et al.[18]

Frequency and duration of training

Subjects

Study

Table II. Resistance training studies which have shown improvements in change of direction (COD) performance

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Table III. Characteristics of the different agility tests commonly used Time to complete test 0–5 sec

t-test,[11] 10-yd (9-m) shuttle,[17] 20-yd (18-m) shuffle,[24] 5-0-5[31]

5–9 sec

t-test,[13,15,25] 48-ft (14.6-m) sideways shuffle,[32] 4 · 5.8-m shuttle,[29] L-run,[18,26,19] tennis-specific shuttle,[14] zigzag test;[4] up and back[31]

>10 sec

10 · 5 m shuttle,[27] t-test,[3,10,22,28] 6 · 5-m shuttle,[20] Illinois,[31,28] Box test,[12] 30 m with 5 CODs,[21] slalom run,[33] hurdle test[33]

No. of CODs 2–3

48-ft (14.6-m) sideways shuffle,[32] 4 · 5.8-m shuttle,[29] L-run,[18,26,16] 10-yd (9-m) shuttle,[17] tennis-specific shuttle,[14] 20-yd (18-m) shuffle,[24] zigzag test,[4] 5-0-5,[31] up and back[31]

4–6

t-test,[3,10,11,13,15,22,28,25] 6 · 5-m shuttle,[20] 30 m with 5 CODs[21]

>7

10 · 5 m shuttle,[27] Illinois,[31,28] box test,[12] slalom run,[33] hurdle test[33]

Primary application of force throughout the entire test Horizontal

10 · 5 m shuttle,[27] t-test,[3,25] 4 · 5.8-m shuttle,[29] L-run,[18,26,16] 10-yd (9-m) shuttle;[17] tennis-specific shuttle;[14] 6 · 5-m shuttle,[20] 20-yd (18-m) shuffle,[24] Illinois,[31,28] box test,[12] 30-m with 5 CODs,[34] zigzag test,[4] slalom run,[33] hurdle test,[33] 5-0-5,[31] up and back[31]

Lateral

48-ft (14.6-m) sideways shuffle[32]

Both

t-test[10,11,13,15,22,28]

COD = change of direction.

being developed in order for researchers to assess the specific demands of the sport for which they are used. Table III details those tests that have been used by researchers in this area as used in this review. As can be observed, there is a great deal of variety in the type and number of assessments that have been used to assess COD ability. We have attempted to classify each test into three areas (energetic requirements, type of force application and number of changes of direction) that may allow a better understanding of the relationships between these tests and variables of interest in this review. The duration and intensity of the COD test will determine the relative contribution of the energy systems in providing the main source of fuel for performance. In his review article, Gastin[35] explains that the anaerobic energy ª 2008 Adis Data Information BV. All rights reserved.

system depends on phosphocreatine for the first 5 seconds of exercise and then utilizes the glycolytic energy pathway followed largely by energy produced from the aerobic system. Thus, tests of different durations may be subject to influences of energetics rather than just assessing COD ability. The complexity of each test can be categorized either by the number of changes of direction required or by the type of movements and forces that are primarily used throughout the test. Certain tests (shuttles or L-runs) can have as few as two or three directional changes, whereas others (Illinois test) can incorporate as many as 12 changes of direction. Each COD requires a braking force followed by a propulsive force, which in turn may increase the importance of eccentric-concentric force capability of muscle and endurance as the number of turns increase. The application of force during the actual COD is more difficult to determine because it would rely heavily on individual technique. However, it is accepted that lateral forces would be involved in certain COD movements such as those in a t-test when the COD is preceded by shuffling movements. In terms of the interrelationships amongst these tests, some researchers have found that there was a significant correlation between the Illinois test and the up and back test (r = 0.63) and between the up and back test and a 5-0-5 test (r = 0.51), but no significant relationship between the Illinois test and the 5-0-5 test (0.25).[31] The researchers suggested that the results of most COD tests were independent from one another and they believed that this was a result of the duration and complexity of each COD test. We would also assert that in some circumstances this independence is due to differences in direction of force application and/or energetic requirements as discussed previously. However, the actual threshold at which the number of changes of directions and/or different forces and/or energetic requirements ensure that a test is a true measure of COD is far from clear. For example, Young et al.[34] found that a single 20 COD in an otherwise straight 20-m sprint produced almost identical results to the sprint with no COD. Sports Med 2008; 38 (12)

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Already it would seem a difficult if not impossible task to discern those factors that are important predictors of COD ability, given the huge variety of tests, the many components of COD ability and the ambiguity as to what constitutes a true ‘COD’ test. The reader needs to appreciate these complexities when reading the ensuing discussion and conclusions.

2.1 Reliability of the Different Tests

To date, not many authors have accurately reported the reliability coefficients of the COD test that they have used. The reliability and variation of the results are important especially for training studies when it is essential to know if the exercises performed result in a real and worthwhile change to the measured variable. Only nine studies have reported the reliability of their measurements (see table IV). This belies one of the limitations of research in this area as indicated by the methodological scores observed later in the review and discussed previously. Nonetheless, regardless of the duration of the

test, the number of CODs, or the direction in which most of the forces were applied all the tests that have been used to measure COD ability, show similar reliability (intra-class correlation 0.8–0.96; coefficient of variation 1–5%).

3. Correlational Research If we observe the model described in figure 1, we note that Sheppard and Young.[1] proposed that straight running speed and leg muscle qualities were important determinants of COD ability. One approach we can use to quantify the importance of straight running speed and leg muscle qualities to COD ability is to use correlational analysis. In this section, we have used correlational research that has been published in peer-reviewed journals or conference proceedings only (see table V), to insure some measure of methodological qualities was adhered to and have some confidence in our conclusions. Also, only isoinertial (constant gravitational load) strength and power measures were included in the analysis, as this is the resistance type

Table IV. Measurements of reliability for specific change of direction (COD) tests Study

COD test

Reliability

Time to complete (sec)

Christou et al.[27]

10 · 5 m shuttle

ICC = 0.94 CV = 1.01%

20

Cronin et al.[11]

Modified t-test

ICC = 0.88 CV = 2.1%

Gabbett et al.[18]

L-run

ICC = 0.90 TEM = 2.8%

Gabbett[3]

t-test

ICC = 0.85 CV = 2.9%

Gabbett[19]

L-run

Markovic et al.[24]

Application of force throughout the entire test

No. of CODs

Horizontal

9

4

Horizontal and lateral

4

6

Horizontal

3

11

Horizontal

4

ICC = 0.90 TEM = 2.8%

6

Horizontal

3

20-yd (18-m) shuffle

ICC > 0.9 CV < 4.1%

5

Horizontal

2

McBride et al.[22]

t-test

ICC = 0.94% TEM = 2.09

11

Horizontal and lateral

4

Tricoli et al.[12]

Box test

ICC = 0.80

16

Horizontal

11

Alricsson et al.[33]

Slalom run

ICC = 0.96 CV = 2.3%

>10

Horizontal

10

Hurdle test

ICC = 0.90 CV = 4.9%

>10

Horizontal

7

CV = coefficient of variation; ICC = intra-class correlation; TEM = typical error of measurement.

ª 2008 Adis Data Information BV. All rights reserved.

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ª 2008 Adis Data Information BV. All rights reserved.

62 National Collegiate division III American football players (19.7 – 1.4 y)

106 male professional soccer players (division 1 and 2)

76 male physical education students

Hoffman et al.[37]

Little and Williams[4]

Marcovic[38]

53 College football players

12 hockey and 6 Australian football players of representative level

Draper and Lancaster[31]

Mayhew et al.[39]

Subjects (age)

21 male junior national and state representative soccer players (16.1 – 1.23 y)

Study

Buttifant et al.[36]

Component and correlations (r)

20-m sprint time (0.055) 20-m sprint time (0.495)a

5-0-5 agility test (2.36 sec); 1 COD Up and back agility test (6.26 sec); 1 COD

Single non-dominant VJ (-0.36) Single non-dominant VJ (-0.37)

Non-dominant leg

Isoinertial squat (-0.21) Isometric squat (0.08) One-leg rising (-0.35) SJ power (W/kg -0.33) Hopping power (W/kg -0.26) Standing LJ (-0.12)

Slalom run (6.9 sec)

Continued next page

1RM bench press (0.35) 10-yd (9-m) dash (0.50) 40-yd (37-m) dash (0.46)

Isoinertial squat (0.31) Isometric squat (0.03) One-leg rising (-0.44) SJ power (W/kg -0.35) Hopping power (W/kg -0.30) Standing LJ (-0.27)

20-m shuttle run (5 sec)

SEMO agility run (10.92 sec); 5 CODs

Isoinertial squat (-0.17) Isometric squat (-0.25) One-leg rising (-0.3) SJ power (W/kg -0.15) Hopping power (W/kg -0.22) Standing LJ (-0.19)

Lateral stepping (7.24 sec)

10-m sprint time (0.346)a Flying 20-m sprint time (0.458)a

Bilateral VJ (r = -0.39)

Dominant leg

Zigzag test (5.34 sec); 3 CODs

Bilateral VJ (-0.34)

Dominant leg Non-dominant leg

Three cone drills (~8 sec); 3 CODs

20-m sprint time (0.472)a

20-m sprint time (0.33)

Illinois agility test (17.28 sec); 9 CODs

20 m agility test (6.13 sec); 4 CODs

Agility (time)

Table V. Correlations between change of direction (COD) tests and measures of straight sprinting speed and leg muscle qualities

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ª 2008 Adis Data Information BV. All rights reserved.

This study normalized these measures by BW.

7 · 8 m sprint with single directional changes at a variety of angles (1.65–2.85 sec)

Three 90 directional changes (5.14 sec)

20-m COD (3 CODs)

Component and correlations (r)

Bilateral DJ (-0.35 to -0.65) Unilateral DJ (-0.23 to 0.71)

Unloaded CMJ (r =-0.10) DJ (0.36) CMJ -50% BW (0.01) 20-m sprint time (0.27)

VJ height (r = -0.34)a

1RM squat (0.408)a 1RM squat/mass (-0.633)a VJ height (-0.713)a VJ peak power (-0.210) HJ (-0.788)a Sprint acceleration (-0.630)a Sprint velocity (-0.693)a

1RM squat (-0.169) 1RM squat/mass (-0.333) VJ height (-0.261) VJ peak power (-0.033) HJ (-0.613)a Sprint acceleration (-0.491)a Sprint velocity (-0.579)a

VJ height (-0.49)a 40-yd (37-m) sprint (0.55)a

VJ height (-0.55)a 40-yd (37-m) sprint (0.73)a

SLVJ height (-0.38)a,b SLHJ distance (-0.65)a,b

1RM = one repetition maximum; BW = bodyweight; CMJ = counter-movement jump; DJ = drop jump; HJ = horizontal jump; LJ = long jump; SEMO = Southeast Missouri; SJ = squat jump; SLHJ = single-leg horizontal jump; SLVJ = single-leg vertical jump; VJ = vertical jump.

Significant at p < 0.05 and less.

a

15 male soccer, basketball, Australian football and tennis players (18–28 y)

Young et al.[34]

b

18 senior football league players (18–22 y)

Young et al.[44]

Spider run (18.85 sec); 10 CODs

t-test (female = 11.48 sec)

Females (n = 36)

83 ranked male tennis players (11.62 – 0.62 y)

t-test (male = 9.89 sec); 4 CODs

First year college athletes (19.4 y) Males (n = 19)

t-test (male average = 10.54 sec)

Females (n = 152)

Roetert et al.[43]

Peterson et al.[42]

t-test (female average = 12.33 sec) 4 CODs

College students (22.3 y)

Pauole et al.[41]

Agility (time) Diamond shape: males (~100 sec), females (~135 sec)

Males (n = 152)

Subjects (age)

29 men and 31 women (24.5 y)

Study

Negrete and Brophy[40]

Table V. Contd

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encountered in most training environments. Inherent in calculating correlation coefficients is the assumption that a number of statistical criteria are met, namely normality, linearity, homoscedasticity and adequate sample size related to the number of variables being analysed. Many of the studies do not report or violate these assumptions, so the reader needs to be cognizant of this limitation and the interpretation of the results herewith.

3.1 Delimitations and Limitations

Delimitations refer to the populations to which generalizations can be safely made. A total of 795 athletes were used in the research cited in table V of which 576 (~73%) were males. In terms of age, most of the researchers used athletes in their twenties, two studies using for the most part younger athletes.[36,43] The sports most represented in this sample include soccer, Australian Rules and American football players, although 38% of the sample was made up of college students. Training status varied from professional athletes to college students. The results of the following analysis are most relevant to this demographic. Limitations refer to the restrictive weakness of a study. Some of the limitations of the research used in this analysis relate to the statistical procedures discussed previously. For example, one assumption is that there is an adequate number of subjects per variable of interest. The studies of Young and colleagues[34,44] used 18 and 15 subjects to study the relationship between 8 and 14 variables, respectively. These studies clearly violate this assumption. Furthermore, when performing correlations using both male and female subjects, it is accepted practice not to pool the data, since the heterogeneity of the population will artificially inflate the correlation coefficient; this is evidenced in the study of Peterson et al.,[42] who present the pooled data as well as the data separated by gender. Negrete and Brophy;[40] however, present pooled correlations only, so their correlation statistics need to be viewed with caution. ª 2008 Adis Data Information BV. All rights reserved.

In terms of the methodology, great variation was noted in the surfaces on which the tests took place (grass, artificial grass, indoor synthetic pitch, hardwood floors), timing equipment (stop watches to electronic timing gates), familiarization (Buttifant et al.[36] and Pauole et al.[41] were the only studies to mention any familiarization), test order (jumps to agility/speed tests and vice versa), data analysis (best performance data vs the average of a number of trials) and starting stances (a variety of standing stances to threepoint starting stance). Comparing results between tests is further compounded by authors making slight modifications to existing agility tests, for example, the t-test. The reader needs to be cautious about any deductions made from the analysis given the delimitations and limitations described. 3.2 COD Tests

As can be observed from table V, a great variety of COD tests were used in the research reported. These tests necessitated different energetic requirements (~1.65 to ~135 seconds), CODs (2–10) and primary force production as described previously in section 2. Given this variety, it would seem difficult to reach any form of consensus as to the correlates or predictors of COD performance. Nonetheless, some discussion of best predictors of COD performance ensues. To describe the magnitude of the correlations, we use the work of Cohen,[23] who has written extensively in this area and has described the magnitude of correlations as: >0.5 is large, 0.5–0.3 is moderate, 0.3–0.1 is small, and anything <0.1 is insubstantial or trivial. 3.3 Straight Sprinting Speed

The relationship between straight running speed and COD tests is of interest on a number of counts. First, is running speed a determinant of COD, as the Young et al.[34] model suggests? Secondly, if so, then it should be sufficient to test the different qualities with the same test as there should be a great deal of shared variance between the tests. Thirdly, training to improve speed Sports Med 2008; 38 (12)

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should improve COD and vice versa; this contention is discussed later in this article in section 4.1. In terms of the correlations reported in table V, most correlations between COD and straight running speed would be described as moderate (r = 0.3–0.5) the lowest correlate reported for the 20-m sprint and 5-0-5 agility test (r = 0.055) and the highest significant correlates reported in females between the t-test and sprint acceleration and velocity (r = -0.630 to -0.693). Sheppard and Young[1] stated that generally, the more changes in direction, the less the transfer from straight running speed to COD. This does not seem the case given the data above, the 5-0-5 test involving one COD, the t-test four CODs and the majority of the correlations of moderate magnitude regardless of the number of directions. In terms of the shared variance (i.e. coefficient of determination or R2) between variables, it would seem that straight sprinting speed and COD seem to be, for the most part, separate motor qualities, the R2 < 50% for all the research reported in table V. 3.4 Strength and Power

It has been proposed by Young et al.[34] that leg muscle qualities such as strength, power and reactive strength are important determinants of COD ability as indicated by their deterministic model. We believe that this classification overcomplicates the analysis, as most assessments that are included as measures of reactive strength are also representative measures of power. That is, measures of reactive strength such as the countermovement jump or drop jump are also measures of leg power. Perhaps a better classification would be to classify the power variables in terms of the direction of force application (i.e. vertical, horizontal and lateral) and whether the movement involves unilateral or bilateral force production. 3.5 Maximal Leg Strength

In terms of maximal isoinertial strength, only two studies have quantified the relationship between maximal isoinertial strength (with ª 2008 Adis Data Information BV. All rights reserved.

1055

multi-joint and closed-chain exercises) and COD. Markovic[38] found mostly small correlations (r = -0.17 to -0.31) between their isoinertial squat (one repetition maximum [1RM]) in a Smith machine and their three COD tests. Peterson et al.[42] investigated the relationship between the 1RM (barbell squat) and COD (t-test) and found that the male correlation (r = -0.169) was weaker than the significant female correlation (r = 0.408). Given the direction of the association in females, it seems that the stronger people were slower, which may be explained by larger subjects lifting greater loads; however, as a result of their greater mass, their COD ability was compromised. Expressing maximal strength relative to bodyweight would account for this problem as evidenced in the results of Petersen et al.,[42] who found the male (r = -0.33) and female (-0.633) correlations to be stronger when expressed in such a fashion. Even given the expression of strength relative to mass, the shared variance between maximal strength and COD ability is <41% for the strongest correlates in both studies. This could be attributed to many factors such as the depth of squat assessment (i.e. range of motion 80–90 used in the studies cited) not simulating COD range of motion. Interestingly, the relationship (r = 0.03 to -0.25) between maximal isometric strength and COD was mostly trivial,[38] supporting the contention that static and dynamic movement have very little in common, i.e. shared variance. 3.6 Leg Power (Expressed in Watts)

Strictly speaking, in terms of power production, only two studies[38,42] investigated the relationship between power (W) and COD ability, so studies that have included jump height and distance measures will be included in this discussion. This is not technically correct as there is an assumption that jump height is a measure of leg power or at the very least closely related. We need only look at the work of Peterson et al.[42] to observe that this is not necessarily the case, the relationship between the height and peak power output of a vertical jump for females (r = 0.420) and males (r = 0.734) indicates minimal shared Sports Med 2008; 38 (12)

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variance (~18–54%) between the measures, and as such we draw the reader’s attention to this limitation. With this in mind, we first discuss those studies that have quantified power output and then discuss the relationship between other jump measures (height and distance) and COD ability. Peterson et al.[42] used an indirect measure of power, as they calculated the power output for their bilateral vertical jump using the formula of Sayers et al.[45] They found trivial to small (r = -0.03 to -0.21) correlations between power output and the t-test for males and females, respectively. Markovic,[38] using a force platform to derive power output, found that the relative power output (W/kg) for a concentric-only squat jump was poorly to moderately related (r = -0.15 to -0.35) to the three tests of COD ability. Markovic[38] also tested the relationship between stretch-shorten cycle (SSC) leg power, as measured by a single-leg hop in place (ten jumps) and their tests of COD ability. The results were very similar to the squat jump, with the strength of most correlations classified as small (r < 0.30). 3.7 Leg Power (Jump Height)

The most common type of jump used to predict COD was the vertical jump and its derivatives, the outcome measure of interest and comparison being the height jumped. There are many different classification systems of vertical jumps from jumps without use of arms to use of arms, drop jumps from various heights, different contact times (e.g. slow SSC vs fast SCC, unilateral vs bilateral). In terms of the kinetic determinants of such jumps, the vertical ground reaction forces (VGRF) and the time over which the forces are applied would seem fundamental to jump performance. For the sake of simplicity and clarity, therefore, we will broadly classify all the different jumps that apply VGRF in a unilateral or bilateral manner, based on the premise that unilateral jump performance may be better related to COD ability, given all COD tests described in this article require unilateral propulsion. Regarding the tests of bilateral leg power, most studies that used males reported small to moderate correlations (-0.261 to -0.49) between the vertical ª 2008 Adis Data Information BV. All rights reserved.

Brughelli et al.

jump test and the respective COD test. Two interesting observations were that the female correlations were typically stronger than the males (r = -0.55 to -0.713). Furthermore, the bilateral drop jump correlations (r = -0.31 to -0.65) reported by Young et al.[34] were typically stronger than other vertical jumps. However, we must be careful about making any conclusions given that this is the only study that has reported this relationship and it has methodological limitations in terms of the sample size and the amount of variables investigated. The unilateral findings were, for the most part, similar to the bilateral in that most correlations were small to moderate (r = -0.36 to -0.38) the exceptions being the unilateral drop jumps reported by Young et al.[34] In summary, the majority of correlations between vertical leg power (jump height) and COD for males are moderate (mean r » 0.4). That is, there is approximately 16% common variance between the tests of COD and leg power that use predominantly VGRF. For female college students, the relationship is higher (mean r = 0.63), which corresponds to a shared variance of 40%. 3.8 Leg Power (Jump Horizontal Distance)

Intuitively, it would seem more appropriate to use jump tests that not only involve the application of VGRF, but also horizontal ground reaction forces (HGRF) to predict COD performance, given that most human motion is a combination of these two types of forces. However, only three studies have investigated the relationship between horizontal jump performance and COD ability. Markovic,[38] using a bilateral long jump with arm swing, reported small correlations (r = -0.12 to -0.27) to their three tests of COD ability. Peterson et al.,[42] using a standing broad jump, found that horizontal jump distance was significantly correlated to the t-test for both males (r = -0.613) and females (r = -0.713). Once more, the female correlation coefficient was higher than the male. Finally, Negrete and Brophy[40] reported a correlation of r = -0.65 between a single-leg hop for distance and a diamond-shaped agility test. This correlation is most Sports Med 2008; 38 (12)

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probably artificially elevated due to the pooling of the male and female subjects into a single sample. Nonetheless, this horizontal jump measure was greater than their vertical jump measure (r = -0.38). Furthermore, the Peterson et al.[42] horizontal jump correlations were greater than the vertical jump correlations. Given the results, it may be tentatively claimed that jumps that involve the combination of both HGRF and VGRF may better predict COD ability. However, there is still much unexplained variance between COD and horizontal jump measures and still a great deal more research needs to be conducted before definitive conclusions can be made. 3.9 Summary of Correlational Research

Given the delimitations, limitations and the great variety in COD tests and sprint test distances, it is difficult to disentangle any relationship between COD and leg strength and power qualities with any real certainty from the studies presented in this section. Also, it is important to note that the strength and significance of a relationship provide no insight into whether the relationship between two variables is causal. Correlational analysis, therefore, is of limited value in identifying the causal relationship between certain variables and COD ability. It is concluded that the preoccupation of correlational studies to find the best strength and/or power predictors of functional performance is fundamentally flawed due to other factors such as body mass, physique, flexibility, technique and leg strength qualities having diverse effects on the statistical models. Sport practitioners and researchers are interested in determining the effect of various training programmes on the variable of interest, in this case COD. To do this, the changes in leg muscle qualities and straight running speed need to be mapped over longitudinal training interventions. Such an approach provides the focus for the remainder of this review. By adopting such an approach, it is hoped that those variables that strongly influence COD ability will be elucidated and, as a result, give the reader insight and focus ª 2008 Adis Data Information BV. All rights reserved.

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as to what variables should be assessed, developed and monitored. 4. Training Studies Several studies have reported improvements in functional performance (i.e. sprinting and jumping) after training for peak power, maximum strength and/or reactive strength.[12,46-49] It is thought that strength and power development would have a carry-over effect on COD performance. The following sections of this article will review the studies and training protocols that have enhanced COD performance, along with those that have failed to enhance COD performance.[10-13,15] We feel that this approach will give insight into the specific and effective COD training protocols. In addition, this approach will give information on why other COD training protocols have not been effective. 4.1 Sprint Training

Only two studies have investigated the effects of straight-ahead sprint training on COD and reported conflicting arguments. In the study of Young et al.,[21] one group of recreationally trained athletes performed straight-ahead sprint training (n = 13), and another group performed COD training (n = 13) for a 6-week period. The objectives of the study were to determine if sprint training transferred to COD performance, and if COD training transferred to sprint performance. The subjects in the training groups performed between 10 and 12 total sessions, which included 5–8 total sprints (20–40 m). The sprint-trained group significantly improved sprint times, but did not significantly improve COD times. Conversely, COD training improved COD times, but did not significantly improve sprint times. The conclusion was that sprint training and COD training were specific and do not readily transfer to each other. Markovic et al.[24] also performed straightahead sprint training over a period of 10 weeks (3 sessions per week) with greater volume in each training session (9–12 total sprints). There were a total of 30 physical education students Sports Med 2008; 38 (12)

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performing the sprint training, and each was assessed before and after training with the 20-yard (18-m) shuttle COD test. The sprint training significantly decreased 20-yard shuttle times by 4.3% with an effect size of 1.1 after the 10-week period. It should be noted that the samples used in both papers were not well trained athletes. Given the paucity of research, the conflicting results, the differences in COD tests used and the samples used in the research, it would seem difficult to arrive at a definitive conclusion on how straight sprint training affects COD ability in both recreational and well trained athletes. 4.2 Leg Strength and Power Training

There are several longitudinal studies that have attempted to improve COD performance with resistance training.[10-13,15] Since COD ability is thought to be influenced by strength and power, many authors have implemented the following training protocols: heavy lifts (e.g. squats, deadlifts, goodmornings, lunges), Olympic-style lifts, and plyometrics. Each of these protocols has been effective at enhancing strength, power and sprint performance in athletic and non-athletic populations. However, each of these studies has failed to improve COD performance (see table I). Hoffman et al.[13,15] performed traditional strength and power training over a 15-week period with division III American football players in the US. Neither of the studies elicited an improvement in COD performance as measured by the t-test. The athletes in Hoffman et al.[13] performed either Olympic-style lifts with strength exercises or strength exercises only. Both groups also performed COD and sprint training in the final 5 weeks (two sessions per week) of the 15week period. The athletes in Hoffman et al.[15] performed a traditional strength and conditioning programme (e.g. Olympic-style lifts, squats, step-ups, deadlifts) for 15 weeks. One group performed jump squats in the final 5 weeks using both concentric and eccentric phases of contraction, and another group performed the jump squat exercise using the concentric phase only in the final 5 weeks. Both groups also performed agility and sprint training (two sessions per week) ª 2008 Adis Data Information BV. All rights reserved.

Brughelli et al.

in the final 5-week cycle. It could be speculated that a 5-week speed and agility training period (ten total sessions) is not a sufficient training load/stimulus to significantly improve COD times in division III American football players. Tricoli et al.[12] investigated the effects of Olympic-style weightlifting and vertical jump training on COD performance in recreationally active subjects. The subjects either performed the weightlifting exercises (high pulls, power clean, clean and jerk, half squat) or vertical jumps (single- and double-leg hops, drop jumps, half squat) for an 8-week period. No significant improvements were reported for COD performance. Similarly, Fry et al.[10] investigated the effects of a traditional strength-training programme (Olympic-style lifts and strength exercises), with plyometric training on COD performance in division I volleyball players. The athletes performed the training for 12 weeks in the off-season and did not significantly improve COD performance. Harris et al.[17] also investigated the effects of a similar strength and conditioning programme for 9 weeks on strengthtrained males, and reported no significant changes in COD performance. Furthermore, Kraemer et al.[14] investigated the effects of a traditional periodized strength and conditioning programme on college female tennis players over a 9-month period. It was thought that a periodized strength and conditioning programme over a long period of time would elicit an improvement in most performance variables, including COD. However, COD times significantly increased after the training period (2.8–5.0%; effect size = 0.14–0.38). Lastly, Cronin et al.[11] investigated the effects of squat-jump training with a modified Smith machine, with and without elastic bands, on COD performance in recreationally active subjects. Neither intervention improved COD performance. It could be speculated from the above studies that strength and power development in the vertical direction does not transfer to COD performance, which is typified by unilateral VGRF and HGRF production. However, one study has shown an improvement in COD performance with resistance training in the vertical Sports Med 2008; 38 (12)

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direction.[22] The subjects in the study of McBride et al.[22] performed jump squats with either 30% or 80% of their 1RM, which was taken from the squat exercise, for 8 weeks. This is the only COD training study that has incorporated jumping with an external load. Both groups significantly decreased their times in the t-test (1.7–2.4%; effect size = 1.2–1.3) after the training period, with greater improvements in the heavier group (80% 1RM). Although the jump-squat exercise is performed in the vertical direction, it is different to the exercises presented above. As the subjects squat down, high velocities and eccentric forces are developed while the muscles elongate, similar to a COD. In order to take advantage of these high forces, a strong eccentric strength base would be needed, which could be developed through jump-squat training. Thus, it could be suggested that since strength and power training in the vertical direction does not appear to enhance COD performance, greater eccentric loading/training stimulus may be the reason for the enhanced COD performance. In a study by Malisoux et al.,[20] recreationally active men performed 8 weeks of jump training and reported significant improvements in the shuttle run (6 · 5 m). The subjects performed both vertical and horizontal jumps (unilateral and bilateral). COD times significantly decreased (-3.6%; effect size = 2.1) after the training period. It should be noted that Malisoux et al.[20] scored a 12/20 on the methodological quality scale (see table I), due to receiving scores of zero on the following items: 2 (not randomizing groups), 4 (groups not similar at baseline) and 5 (no control group). Miller et al.[28] also reported improvements in COD performance after a 6-week training intervention that included vertical, horizontal and lateral jumping (unilateral and bilateral). COD times were significantly reduced for the t-test and the Illinois agility test (-5.5%; effect size = 0.7). It could be speculated that unilateral and bilateral horizontal jump training contributed more to the improvements in COD performance than vertical jump training, since other studies have reported no improvements in COD performance with vertical jump training alone. Alternatively, at the very least, a ª 2008 Adis Data Information BV. All rights reserved.

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combination of both vertical and horizontal or lateral jumping-type movements are needed to elicit improvements in COD ability. The results of these two studies are encouraging for athletic populations, and suggest that the inclusion of horizontal and lateral jumping should be researched (i.e. movement mechanics and training effects) more thoroughly in the future. Unfortunately, there have been no studies that have investigated the effects of horizontal and/or lateral jump training versus vertical jump training on COD performance. 4.3 COD-Specific Training

The majority of training studies that have reported significant improvements in COD performance have performed either sport-specific COD training or traditional COD training (see table II). Gabbett et al.[18] investigated the effects of a volleyball-specific training programme on t-test times in junior female volleyball players. The training programme included technical and instructional coaching, coupled with skill-based games to develop passing, setting, serving, spiking, and blocking accuracy and technique. The training protocol lasted for 8 weeks and significantly decreased t-test times (-5.2%; effect size = 3.6). Polman et al.[26] found similar results in elite female soccer players with specific COD training. The athletes performed a 12-week training period (two sessions per week), which included soccer-specific and traditional speed, COD and power exercises. One group performed the exercises with additional equipment (resistance cords, k-boards, hurdles), and another group performed the exercises without the equipment. The COD test that was utilized involved sprinting and several CODs of 90 or 180 over 18 m. Both groups significantly improved COD performance (-3.8% to -4.2%; effect size = 1.2–1.6). It appears that sport-specific or traditional COD training alone can improve COD performance in female athletes over a period of 8–12 weeks. Gabbett[19] investigated the effects of specific field training (including COD exercises) in combination with a traditional strength and Sports Med 2008; 38 (12)

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conditioning programme on COD performance in junior and senior level rugby league players. The field training was performed twice a week for 14 weeks. General COD exercises were performed in the off-season, and specific COD exercises were performed in the pre-season. After the 14-week training period, COD performance significantly decreased COD times by -17.7% for the junior players and -16.2% for the senior players, respectively. In another rugby league study by Gabbett,[16] both traditional COD training and skill-based COD training were performed over a 9-week in-season period. The skill-based COD training involved exercises that were meant to improve rugby skills (e.g. passing, catching, tackling) and enhance COD performance. It was reported that both training groups maintained COD times over the competitive season. Dean et al.[32] and Christou et al.[27] both investigated the effects of COD training on young athletes (12–16 years old) and reported significant improvements. The athletes in the study of Dean et al.[32] performed COD training for 4 weeks and reported a significant improvement in the 20-yard shuttle times (-3.2%; effect size = 0.3). It should be noted that Dean et al.[32] received a low score of 10/20 on the methodological quality scale (see table I), due to receiving a zero on the following items: 2, 4, 5 and 10. Christou et al.[27] also reported a significant improvement in COD times after 16 weeks of both COD training and strength and conditioning training in young soccer players (-4.0 to 5.4%; effect size = 1.1–1.7). The subjects in Christou et al.[27] performed traditional and soccer-specific COD exercises over the 16 weeks, and reported significant decreased in the shuttle run (10 · 5 m). There are two COD training studies that have incorporated non-traditional training protocols. Cressey et al.[25] examined the effects of a strength and conditioning programme that included stable (stable group) and unstable surfaces (unstable group) during a variety of lower body exercises (lunges, squats, deadlifts). All subjects (male soccer players) also performed soccer-specific COD training, and both groups significantly decreased COD times (-2.9% to 4.4%; effect ª 2008 Adis Data Information BV. All rights reserved.

Brughelli et al.

size = 1.0–1.6). Deane et al.[29] investigated the effects of hip flexor strength training with elastic tubing on COD performance in untrained subjects. After the 8-week intervention, COD times were significantly decreased (-8.3% to 10%; 1.1–1.8).

5. Conclusions and Future Research Directions Given the limitations cited throughout this article, it is difficult to discern with any great certainty those factors that influence COD ability. The model proposed by Young et al.[34] is somewhat simplistic, and it is more likely that the model needs to view the determinants of COD ability as interrelated qualities, this combination of qualities explaining COD ability to better effect. Furthermore, there needs to be a great deal more research into those force/power qualities (e.g. horizontal and lateral) and technique factors that influence event- or sport-specific COD ability. In terms of the training studies, traditional strength and power training methods (i.e. Olympic-style weightlifting, traditional strength training, plyometrics, and vertical jump training) have been shown to enhance functional performance (i.e. running and jumping) in athletic and nonathletic populations.[12,46-48] These training methods have been utilized in several training studies and are commonly used by strength and conditioning coaches.[50] However, these traditional training methods have failed to improve COD performance. We feel that this failure is due to the commonality in the design of these exercises, which include bilateral movements in the vertical direction. Conversely, COD movements occur unilaterally in the vertical-horizontal and/or lateral direction, and require anteriorposterior (breaking and propulsive) and mediolateral force production. Unfortunately, there have been no studies that have investigated the correlations between unilateral horizontal jumping and COD performance. It could be speculated that since CODs require vertical-horizontal force production, horizontal jumping would be highly correlated with COD performance and Sports Med 2008; 38 (12)

Change of Direction

could enhance COD performance with training. Furthermore, for those tests requiring lateral force production, the effect of lateral-type jumps needs to be investigated. Improvements in COD performance seem better accomplished with the following types of exercise and training: general COD training, sport-specific COD training, jump squat training, unilateral and bilateral horizontal jump training. Not surprisingly, training that involves sprinting with direction changes (i.e. COD tests themselves) has been shown to enhance COD performance. The only other types of exercises that have enhanced COD performance have been horizontal jumping (in combination with vertical and lateral jumping) and loaded vertical jumping. Unfortunately, there have been no studies that have investigated the effects of primarily horizontal jump training (unilateral and bilateral) on COD performance. Since vertical jumping has failed to enhance COD performance, it is thought that the horizontal jump training enhanced COD performance. Therefore, the inclusion of such jumps in the athlete’s assessment and training programme would seem fundamental to improved COD ability. It is thought that since vertical jump and squat training have failed to enhance COD performance, the eccentric strength gained from the weighted vertical jumps enhanced COD performance. It could be speculated that the eccentric strength gained form weighted jumping could have been the stimulus for the enhanced COD performance. Unfortunately, there have been no studies that have investigated the effects of eccentric training on COD performance. Furthermore, improvements in COD performance have been reported over a range of subjects (young, recreational, sub-elite athletes, elite athletes) with these training methods. Thus, the findings of this article can be applied to a wide variety of athletes and subjects. It is suggested that specific methods and exercises should be developed in order to enhance COD performance. These exercises should include horizontal and unilateral movements, closed-chain exercises, multi-joint movements and movements that can be safely and effectively ª 2008 Adis Data Information BV. All rights reserved.

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overloaded, cost-effective and easy to implement. Research is needed on the relationships between these types of exercises and COD performance, and their training effects on COD performance. Furthermore, it appears that training involving COD movements themselves has been effective at improving COD performance. Acknowledgements No sources of funding were received in the preparation of this review and the authors have no conflicts of interest directly relevant to its contents.

References 1. Sheppard JM, Young WB. Agility literature review: classifications, training and testing. J Sports Sci 2006; 24 (9): 919-32 2. Gil SM, Gil J, Ruiz F, et al. Physiological and anthropometric characteristics of young soccer players according to their playing position: relevance for the selection process. J Strength Cond Res 2007; 21 (2): 438-45 3. Gabbett TJ. A comparison of physiological and anthropometric characteristics among playing positions in sub-elite rugby league players. J Sports Sci 2006; 24 (12): 1273-80 4. Little T, Williams AG. Specificity of acceleration, maximum speed, and agility in professional soccer players. J Strength Cond Res 2005; 19 (1): 76-8 5. Reilly T, Williams AM, Nevill A, et al. A multidisciplinary approach to talent identification in soccer. J Sports Sci 2000; 18 (9): 695-702 6. Gil S, Ruiz F, Irazusta A, et al. Selection of young soccer players in terms of anthropometric and physiological factors. J Sports Med Phys Fitness 2007; 47 (1): 25-32 7. Mcgee K, Burkett L. The National Football League combine: a reliable predictor of draft status? J Strength Cond Res 2003; 17 (1): 6-11 8. Davis DS, Barnette BJ, Kiger JT, et al. Physical characteristics that predict functional performance in division I college football players. J Strength Cond Res 2004; 18 (1): 115-20 9. Markovic G. Does plyometric training improve vertical jump height? A meta-analytical review. Br J Sports Med 2007; 41 (6): 349-55 10. Fry A, Kraemer WJ, Weseman C, et al. The effects of an off-season strength and conditioning program on starters and non-starters in women’s intercollegiate volleyball. J Strength Cond Res 1991; 5 (4): 174-81 11. Cronin J, McNair PJ, Marshall RN, et al. The effects of bungy weight training on muscle function and functional performance. J Sports Sci 2003; 21 (1): 59-71 12. Tricoli VA, Lamas L, Carnevale R, et al. Short-term effects on lower-body functional power development: weightlifting vs vertical jump training programs. J Strength Cond Res 2005; 19 (2): 433-7

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13. Hoffman JR, Cooper J, Wendell M, et al. Comparison of Olympic vs traditional power lifting training programs in football players. J Strength Cond Res 2004; 18 (1): 129-35 14. Kraemer W, Hakkinen K, Triplett-Mcbride N, et al. Physiological changes with periodized resistance training in women tennis players. Med Sci Sport Exerc 2003; 35 (1): 157-68 15. Hoffman JR, Ratamess NA, Cooper JJ, et al. Comparison of loaded and unloaded jump squat training on strength/power performance in college football players. J Strength Cond Res 2005; 19 (4): 810-5 16. Gabbett TJ. Skill-based conditioning games as an alternative to traditional conditioning for rugby league players. J Strength Cond Res 2006; 20 (2): 309-15 17. Harris G, Stone M, O’bryant H, et al. Short-term performance effects of high power, high force, or combined weight-training methods. J Strength Cond Res 2000; 14 (1): 14-20 18. Gabbett T, Georgieff B, Anderson S, et al. Changes in skill and physical fitness following training in talentidentified volleyball players. J Strength Cond Res 2006; 20 (1): 29-35 19. Gabbett TJ. Performance changes following a field conditioning program in junior and senior rugby league players. J Strength Cond Res 2006; 20 (1): 215-21 20. Malisoux L, Francaux M, Nielens H, et al. Stretch-shortening cycle exercises: an effective training paradigm to enhance power output of human single muscle fibers. J Appl Physiol 2006; 100 (3): 771-9 21. Young WB, McDowell MH, Scarlett BJ, et al. Specificity of sprint and agility training methods. J Strength Cond Res 2001; 15 (3): 315-9 22. McBride JM, Triplett-McBride T, Davie A, et al. The effect of heavy- vs light-load jump squats on the development of strength, power, and speed. J Strength Cond Res 2002; 16 (1): 75-82 23. Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale (NJ): Lawrence Erlbaum, 1988 24. Markovic G, Jukic I, Milanovic D, et al. Effects of sprint and plyometric training on muscle function and athletic performance. J Strength Cond Res 2007; 21 (2): 543-9 25. Cressey EM, West CA, Tiberio DP, et al. The effects of ten weeks of lower-body unstable surface training on markers of athletic performance. J Strength Cond Res 2007; 21 (2): 561-7 26. Polman R, Walsh D, Bloomfield J, et al. Effective conditioning of female soccer players. J Sports Sci 2004; 22 (2): 191-203 27. Christou M, Smilios I, Sotiropoulos K, et al. Effects of resistance training on the physical capacities of adolescent soccer players. J Strength Cond Res 2006; 20 (4): 783-91 28. Miller M, Herniman J, Ricard M, et al. The effects of a 6-week plyometric training program on agility. J Sports Sci Med 2006; 5: 459-65 29. Deane RS, Chow JWC, Tillman MD, et al. Effects of hip flexor training on sprint, shuttle run, and vertical jump performance. J Strength Cond Res 2005; 19 (3): 615-21

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30. Rhea MR. Determining the magnitude of treatment effects in strength training research through the use of the effect size. J Strength Cond Res 2004; 18 (4): 918-20 31. Draper JA, Lancaster MG. The 505 test: a test for agility in the horizontal plane. Aust J Sci Med Sport 1985; 17 (1): 15-8 32. Dean W, Nishihara M, Romer J, et al. Efficacy of 4-week supervised training program in improving components of athletic performance. J Strength Cond Res 1998; 12 (4): 238-42 33. Alricsson M, Harms-Ringdahl K, Werner S, et al. Reliability of sports related functional tests with emphasis on speed and agility in young athletes. Scand J Med Sci Sports 2001; 11 (4): 229-32 34. Young WB, James R, Montgomery I, et al. Is muscle power related to running speed with changes of direction? J Sports Med Phys Fitness 2002; 42 (3): 282-8 35. Gastin P. Energy system interaction and relative contribution during maximal exercise. Sports Med 2001; 31 (10): 725-41 36. Buttifant D, Graham K, Cross K. Agility and speed in soccer players are two different performance parameters. In: Spinks W, Reilly T, Murphy AJ, editors. Science and football IV. London: Routledge, 2002: 329-32 37. Hoffman J, Ratamess N, Klatt M, et al. Do bilateral power deficits influence direction-specific movement patterns? Res Sports Med 2007; 15 (2): 125-32 38. Markovic G. Poor relationship between strength and power qualities and agility performance. J Sports Med Phys Fitness 2007; 47 (2146-JSM) 39. Mayhew JL, Piper FC, Schwegler TM, et al. Contributions of speed, agility and body composition to anaerobic power measurement in college football players. J Appl Sport Sci Res 1989; 3 (4): 101-6 40. Negrete R, Brophy J. The relationship between isokinetic open and closed kinetic chain lower extremity strength and functional performance. J Sports Rehab 2000; 9: 46-61 41. Pauole K, Madole K, Garhammer J, et al. Reliability and validity of the t-test as a measure of agility, leg power, and leg speed in college-aged men and women. J Strength Cond Res 2000; 14 (4): 443-50 42. Peterson M, Alvar B, Rhea M, et al. The contribution of maximal force production to explosive movement among young collegiate athletes. J Strength Cond Res 2006; 20 (4): 867-73 43. Roetert EP, Garrett GE, Brown SW, et al. Performance profiles of nationally ranked junior tennis players. J Appl Sport Sci Res 1992; 6 (4): 225-31 44. Young W, Hawken M, McDonald L, et al. Relationship between speed, agility and strength qualities in Australian Rules football. Strength Cond Coach 1996; 4 (4): 3-6 45. Sayers SP, Harackiewicz DV, Harman EA, et al. Cross-validation of three jump power equations. Med Sci Sport Exerc 1999; 31 (4): 572-7 46. Blazevich AJ, Jenkins DG. Effect of the movement speed of resistance training on sprint and strength performance in concurrently training elite junior sprinters. J Sport Sci 2002; 20: 981-90

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47. Coutts AJ, Murphy A, Dascombe BJ, et al. Effect of direct supervision of a strength coach on measures of muscular strength and power in young rugby league players. J Strength Cond Res 2004; 18 (2): 316-23 48. Kotzamanidis C, Chatzopoulos D, Michailidis C, et al. The effect of a combined high-intensity strength and speed training program on the running and jumping ability of soccer players. J Strength Cond Res 2005; 19 (2): 369-75 49. Murphy AJ, Wilson GJ. The ability of tests of muscular function to reflect training-induced changes in performance. J Sport Sci 1997; 15: 191-200

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50. Simenz C, Dugan C, Ebben W, et al. Strength and conditioning practices of national basketball association strength and conditioning coaches. J Strength Cond Res 2005; 19 (3): 495-504

Correspondence: Matt Brughelli, School of Exercise, Biomedical and Health Sciences, Edith Cowan University, 100 Joondalup Drive, Joondalup, Western Australia 6027, Australia. E-mail: [email protected]

Sports Med 2008; 38 (12)

Sports Med 2008 2008; 38 (12): 1065-1079 0112-1642/08/0012-1065/$48.00/0

REVIEW ARTICLE

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Keeping Pace with ACE Are ACE Inhibitors and Angiotensin II Type 1 Receptor Antagonists Potential Doping Agents? Pei Wang, Matthew N. Fedoruk and Jim L. Rupert School of Human Kinetics, University of British Columbia, Vancouver, British Columbia, Canada

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1065 1. Variants in the ACE gene and Athletic Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1066 2. The ACE Insertion/Deletion Polymorphism and Variation in Plasma ACE Activity . . . . . . . . . . . . . . . . 1068 3. The Renin-Angiotensin-Aldosterone System Pathway and Mechanism(s) by which it May Influence Performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1070 4. ACE Inhibitors and Angiotensin Receptor Blockers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1071 5. Effects of ACE Inhibitors and Angiotensin II Type 1 Receptor Antagonists on Physical Performance . . . 1072 5.1 Effects on Maximum Oxygen Uptake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1072 5.2 Effects on Muscle Composition and Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1073 6. Dosage Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1073 7. Pharmacogenomic Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1074 8. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1074

Abstract

In the decade since the angiotensin-converting enzyme (ACE) gene was first proposed to be a ‘human gene for physical performance’, there have been numerous studies examining the effects of ACE genotype on physical performance phenotypes such as aerobic capacity, muscle function, trainability, and athletic status. While the results are variable and sometimes inconsistent, and corroborating phenotypic data limited, carriers of the ACE ‘insertion’ allele (the presence of an alu repeat element in intron. 16 of the gene) have been reported to have higher maximum oxygen uptake (VO2max), greater response to training, and increased muscle efficiency when compared with individuals carrying the ‘deletion’ allele (absence of the alu repeat). Furthermore, the insertion allele has been reported to be over-represented in elite athletes from a variety of populations representing a number of endurance sports. The mechanism by which the ACE insertion genotype could potentiate physical performance is unknown. The presence of the ACE insertion allele has been associated with lower ACE activity (ACEplasma) in number of studies, suggesting that individuals with an innate tendency to have lower ACE levels respond better to training and are at an advantage in endurance sporting events. This could be due to lower levels of angiotensin II (the vasoconstrictor converted to active form by ACE), higher levels of bradykinin (a vasodilator degraded by ACE) or some combination of the two phenotypes. Observations that individuals carrying the ACE insertion allele (and presumably lower ACEplasma) have an enhanced response to training or are

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over-represented amongst elite athletes raises the intriguing question: would individuals with artificially lowered ACEplasma have similar training or performance potential? As there are a number of drugs (i.e. ACE inhibitors and angiotensin II type 1 receptor antagonists [angiotensin receptor blockers – ARBs]) that have the ability to either reduce ACEplasma activity or block the action of angiotensin II, the question is relevant to the study of ergogenic agents and to the efforts to rid sports of ‘doping’. This article discusses the possibility that ACE inhibitors and ARBs, by virtue of their effects on ACE or angiotensin II function, respectively, have performance-enhancing capabilities; it also reviews the data on the effects of these medications on . VO2max, muscle composition and endurance capacity in patient and nonpatient populations. We conclude that, while the direct evidence supporting the hypothesis that ACE-related medications are potential doping agents is not compelling, there are insufficient data on young, athletic populations to exclude the possibility, and there is ample, albeit indirect, support from genetic studies to suggest that they should be. Unfortunately, given the history of drug experimentation in athletes and the rapid appropriation of therapeutic agents into the doping arsenal, this indirect evidence, coupled with the availability of ACE-inhibiting and ACE-receptor blocking medications may be sufficiently tempting to unscrupulous competitors looking for a shortcut to the finish line.

The ACE gene is the most commonly investigated gene in the study of the genetics of human physical performance. Numerous investigators have reported an association between variants in the ACE gene that appear to lower the circulating levels of the enzyme and endurance performance in elite athletes.[1-8] These data suggest that, as individuals with naturally lower ACE levels appear to have greater performance potential, either due to some intrinsic physiological advantage or improved response to training, people in whom ACE levels or activity are artificially lowered may experience an improvement in endurance performance. This article addresses this possibility, first by reviewing the data on the contribution of the ACE genotype to performance and some of the possible mechanisms by which lower levels of circulating ACE could enhance endurance. Secondly, this article examines the limited pharmacological and clinical data on the effects of ACE inhibition and angiotensin II . blockade on maximum oxygen uptake (VO2max) and other performance parameters in patient and non-patient populations. The purpose of this article is not to provide a comprehensive ª 2008 Adis Data Information BV. All rights reserved.

review of the genetics of ACE and human performance, but rather to evaluate the evidence that drugs that affect the renin-angiotensin pathway have potential performance-enhancing capabilities. 1. Variants in the ACE gene and Athletic Status The renin-angiotensin-aldosterone system (RAAS) is important in the regulation of blood pressure and fluid homeostasis. Renin converts angiotensinogen to angiotensin 1 (Ang I), which is in turn converted by ACE to angiotensin II (Ang II), a vasoactive peptide that mediates vascular resistance by binding to endothelial receptors causing vasoconstriction, and regulates salt and water balance via the aldosterone pathway. ACE exists as both membrane-bound (especially in pulmonary endothelium) and circulating (ACEplasma) isoforms and is a major contributor to cardiovascular homeostasis.[9] Although intra-individual variation in circulating ACE levels is slight, there is substantial inter-individual variation,[10] much of which is Sports Med 2008 2008; 38 (12)

RAAS Medications and Doping

genetically determined.[11] Ang II is also synthesized locally in a variety of tissue including the heart, kidneys, brain and muscle.[12,13] The circulating and tissue systems appear to be linked by dependence on plasma renin levels; however, synthesis of the final product (Ang II) is not coupled,[14] suggesting some degree of independence between the systems at the ACE level. Ten years ago, Montgomery and colleagues[15] published a paper describing a genetic association study that demonstrated an over-representation of a variant in the gene encoding ACE (ACE)1 in successful high-altitude mountaineers. The paper, entitled Human Gene for Physical Performance inspired researchers to investigate the so-called ACE ‘insertion/deletion’ polymorphism (often abbreviated to ‘in/del’), previously studied primarily in patient populations, in high-altitude populations ranging from indigenous Andean and Tibetan highlanders[16,17] to pilots,[18] and prompted numerous investigations of the effect of the ACE genotype on physical performance in athletes ranging from ‘weekend warriors’ to elite amateurs and professionals. These latter studies were diverse and involved athletes from many sport disciplines and a variety of populations (table I). Both Myerson et al.[25] and Nazarov et al.[22] reported a significant over-representation of the ACE ‘insertion’ (ACE-in) allele in national-calibre middle- and long-distance runners and an under-representation in short-course runners among British and Russian competitors, respectively. Alvarez et al.[20] genotyped 60 professional Spanish endurance athletes and found the ACE-in allele was significantly more common than in the control population. They also reported a correlation between the ACE genotype and ACEplasma levels; however, the measurements were made in the control population, not in the athletes. The ACE-in allele has also been shown to be significantly over-represented in Australian national-class rowers,[19] Italian Olympic endurance athletes[4] and elite Spanish runners,[6] while the ACE-del allele was more

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common in Spanish cyclists,[6] elite Israeli marathon runners[24] (this result is at odds with other studies in which the ACE-in allele is associated with endurance ability and suggests population differences in the ACE in/del phenotype) and Russian ‘short-distance’ athletes.[22] In contrast to the varied but ‘positive’ previous studies, Rankinen et al.[5] reported no significant association between the ACE genotype and athletic status in the large GENATHLETE database (although ACE-in was more common in the athletes at p = 0.09). Unlike the preceding studies, Rankinen et al.[5] did not focus on elite athletes and there is speculation that the effects of the ACE genotype may be subtle and manifest only at very high levels of performance (e.g. see Nazarov et al.[22]). This hypothesis was not supported by Scott et al.,[23] who reported no overrepresentation of either allele in elite Kenyan runners; however, the relationship between the ACE in/del polymorphism and serum ACE in Africans is ambiguous. For example, Forrester et al.[26] reported a significant correlation between ACE in/del genotype and serum ACE in Black Jamaicans, while a recent study of Black South Africans found no association.[27] The studies described above and in table I are just a representation of the numerous studies that have investigated relationships between the ACE genotype and performance status, and several comprehensive reviews have already been written on the subject.[7,8,28-30] Although the results are far from consistent or conclusive, the general trend supports the hypothesis that the ‘insertion’ allele is associated with success in endurance events, while the ‘deletion’ allele is associated with success in power events (although the recent paper by Amir et al.[24] found the opposite in elite Israeli runners [see table I]). The mechanism by which lower circulating ACE would improve performance[31] is unknown; however, there are a number of studies demonstrating that the ACE-in allele is associated with a greater response to training in both the skeletal muscle and cardiovascular systems (table II).

1 By convention, abbreviations for human genes are presented in italicized capitals (i.e. ACE is the gene, ACE is the protein).

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Table I. Studies demonstrating an association between ACE insertion (I)/deletion (D) genotype and training responses Study

Discipline

Level of athletes

Population

Sample

Results: association of allele/genotype (p-values)

Gayagay et al.[19]

Rowers

National

Caucasian

64 athletes 114 controls

Excess of I allele (<0.02) and II genotype (0.03) in elite rowers

Alvarez et al.[20]

Cyclists Runners

International

Caucasian

60 athletes 400 control

Excess of II/ID genotypes (<0.001) and I allele (<0.001) in athletes

Collins et al.[21]

Triathlons

Mixed

Caucasiana

100 fastest finishers 100 slowest finishers 166 control

Excess of I allele in ‘fast finisher’ group (0.036) and a linear trend for increasing I allele frequency from the controls through the slow finishers to the fast finishers (0.033)

Scanavini et al.[4]

Multiple disciplineb

Olympic-class

Caucasian

‘Aerobic’ 71 ‘Anaerobic’ 55

Low II genotype frequency in anaerobic compared with aerobic athletes (0.03), no difference in ACE I/D allele frequency

Lucia et al.[6]

Cyclists Runners

Pro-peloton Olympic-class

Caucasian

77 athletes 119 control

Excess of DD genotype in cyclists (<0.05) Excess of II genotype in runners (<0.05) Excess of D allele in cyclists and controls compared with runners (<0.001) Excess of I allele in runners (<0.001)

Taylor et al.[1]

Multiple disciplinec

National

Caucasian

120 athletes 685 control

No association (>0.05)

Nazarov et al.[22]

Multiple disciplined

Regional and national

Caucasian

217 athletes 449 control

Excess of D in short-distance athletes (0.001) Excess of I in middle- to shortdistance athletes (0.03)

Scott et al.[23]

Longdistance runners

National and international

African

291 athletes 85 control

No association reported (0.39)

Multiple discipline

National and international

Caucasian

192 athletes 189 control

No association (>0.05)

Runners

National

Caucasian

121 athletes (79 marathon, 42 sprint) 247 control

Excess of D allele and DD genotype in marathoners compared with sprinters (0.002, 0.004) and controls (0.01, 0.004)

Amir et al.[24]

a

South African born, no association seen in other populations.

b

Aerobic: cycling, running, skiing. Anaerobic: kayaking.

c

Hockey swimming, track and field, rowing, gymnastics.

d

Swimming, skiing, triathlon, track and field.

2. The ACE Insertion/Deletion Polymorphism and Variation in Plasma ACE Activity The ACE-in allele refers to the presence of a 287 base alu repeat segment in intron 16 of the ACE gene on chromosome 17 (the ‘deletion’ [del] allele refers to the absence of the repeat and is ª 2008 Adis Data Information BV. All rights reserved.

unlikely to be due to an actual deletion event). Alu repeats are short segments of DNA interspersed throughout the genome, members of the SINE (short interspersed nuclear element) family of repeat elements (reviewed by Jasinska and Krzyzosiak[40]). Quite common in primates, there may be as many as 1.1 million alu units scattered throughout the human genome. Depending on Sports Med 2008 2008; 38 (12)

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Table II. Studies demonstrating an association between ACE insertion (I)/deletion (D) genotype and training responses Study

Measure

Training regimen

Population

Sample

Williams et al.[32]

D efficiency (muscle) (D work/D energy)

11 wk endurance (military)

Caucasian

58 M

Woods et al.[33]

. VO2max . HR/VO2 . VO2/work

As above

Results: association of allele/genotype (p-values) No association with baseline values; significant association (<0.025) between ACE II genotype and training effect No association with base-line values . or with training effects on VO2max or . HR/VO2max; significant (0.02) association between training effect . on VO2/work at maximum output

Thompson et al.[34]

Adherence to aerobic exercise programme

6 mo aerobic

Caucasian

53 M 57 F

II, ID genotypes associated with higher adherence to aerobic training than DD (<0.05, <0.01)

Folland et al.[35]

Quadriceps strength (isometric and dynamic leg extension)

9 wk Strength

Caucasian

33 M

D allele is associated with greater isometric strength gains (<0.05)

Thomis et al.[36]

Elbow flexor mass and strength

10 wk, resistance

Caucasian

57 M

No association between ACE I/D genotype and baseline strength or muscle mass (>0.05) or change in these variables in response to training (>0.05)

Pescatello et al.[37]

Peak elbow flexor muscle strength

12 wk unilateral resistance

Mixed, 79.5% Caucasian

265 M 366 F

No association between ACE I/D genotype and baseline strength and muscle size. Increase in maximum voluntary contraction response to training greater in II/ID (<0.05), but no association for CSA or 1RM. Increase in CSA and 1RM in contralateral arm associated with D/D and I/D genotypes (<0.05)

Montgomery et al.[15]

Elbow flexor strength/local muscular endurance

10 wk general military

Caucasian

123 M

No association with baseline values; significant semi-dominant associations between improvement in flex duration and genotype (I/I >D/D [0.001])

. VO2peak, sit-ups, push-ups, endurance (3.3-km run)

8 wk US army Basic

Caucasian, African American

62 M 85 F

Caucasian

140 M

Sonna et al.[38]

Montgomery et al.[39]

No association between genotype and baseline values or training responses (>0.05)

Greater left ventricular enlargement in response to training associated with I/D and D/D compared with I/I genotype (0.001) . . 1RM = one repetition maximum; CSA = cross-sectional area; F = female; HR = heart rate; M = male; VO2 = oxygen uptake; VO2max = maximum . oxygen uptake; VO2peak = peak oxygen uptake. Left ventricular enlargement (mass)

10 wk strength and endurance

location in the gene, the presence of an alu repeat can alter expression levels, disrupt messenger RNA (mRNA) splicing and processing, interfere with translation of the mature transcript into protein, or have no measurable effect whatsoever. The alu repeat in ACE, which is not present in ª 2008 Adis Data Information BV. All rights reserved.

chimpanzees, integrated into the ancestral human gene sometime in the recent evolutionary past and is now present at some frequency in almost every population that has been studied (see ALFRED: the ALelle FREquency Database for allele frequency data[41]). The consequences Sports Med 2008 2008; 38 (12)

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(if any) of carrying the intron 16 alu on ACE expression are not fully understood; however, most of the inter-individual variation in ACEplasma is genetically determined[11] and numerous studies have shown a semi-dominant effect of the in/del alleles on ACEplasma and tissue ACE, with lower levels of the enzyme associated with the I allele ACE-in.[42-51] These changes likely occur at the transcriptional level, as lower ACE mRNA levels in peripheral blood cells have been associated with the ACE-in allele;[52] however, whether this is due to the presence of the alu is unknown. While intronic polymorphisms can have phenotypic effects,[53] there is a possibility that the ACE in/del alleles are not causal, but are instead in linkage disequilibrium (i.e. not randomly segregating during intergenerational transmission) with other causal variants elsewhere in, or near, ACE. According to the HapMap database (http://www.hapmap.org), there are at least 40 single nucleotide polymorphisms (SNPs) in the 20.5 kb region that encompasses the gene. Two of these, one in the promoter region (ACE-4 [dbSNP rs4291])2 and one in exon 17 (ACE-8 [dbSNP rs4343]), have alleles shown by Zhu et al.[54] to associate with circulating ACE levels and, as alleles at both of these loci are in linkage disequilibrium with the alleles at the ACE in/del locus in a number of populations,[54-56] the ACE in/del alleles may simply be proxies for the variants at these loci. In 2002, Cox et al.[57] identified four other polymorphic loci in ACE (A23495G, A31958G, 31839insC, and A6138C) with alleles that associate with ACEplasma and noted that patterns of linkage disequilibrium between alleles at the loci varied between populations. Studies that measure levels of the ACE protein are far less common than the genetic studies. ACEplasma was significantly associated with the ACE genotype (del/del>in/del>in/in) and correlated with baseline muscle strength, but not training response;[50] however, neither lower circulating ACE levels or ACE in/del genotype were determinants of endurance performance (oxygen

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. uptake [VO2] and mechanical efficiency).[51] This work was carried out in untrained subjects, whereas much of the ACE in/del literature suggests that effects manifest during training, or in trained individuals.[15,22,58] Whether the physiological effects of the ACE genotype are through the circulatory or tissue Ang II synthesis is unknown. Plasma Ang II levels are not affected by ACE genotype, suggesting that some homeostatic mechanism is ameliorating the effects of increased ACEplasma, which supports a phenotypic role for the tissue Ang II synthesis.[13] The evidence that alleles associated with lower ACEplasma are also associated with enhanced response to physical training and the frequent (although not universal) observations that the same alleles are over-represented in endurance athletes beg the following questions: (i) would inhibiting ACE function increase physical performance; and (ii) as ACE inhibitors and Ang IIreceptor blockers are frequently prescribed for hypertension, should the anti-doping authorities be concerned about these common prescription medications being co-opted into the doper’s pharmacopoeia? 3. The Renin-Angiotensin-Aldosterone System Pathway and Mechanism(s) by which it May Influence Performance The RAAS is a principal regulator of blood pressure. Ang II is a potent vasoconstrictor that binds to G-protein-coupled angiotensin receptors, of which the ubiquitous AT1-receptor is the most common. In vascular smooth muscle, activation of this receptor triggers an intracellular cascade that results in the Ca2+-mediated phosphorylation of myosin and induction of smooth muscle contraction.[59] Ang II:AT1-receptor binding also stimulates tissue growth, cell proliferation, cardiovascular remodelling and neovascularization, Na+ reabsorption and stress responses; conversely, Ang II activation of the

2 rs numbers are polymorphism identifiers used in the dbSNP database, see http://www.ncbi.nlm.nih.gov/ projects/SNP/.

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less common (and less understood) AT2-receptor seems to induce vasodilatation, and impede tissue growth and development.[60] Ang II also promotes the release of aldosterone from the adrenal cortex and ADH release from the pituitary, both of which are involved in Na+ and H2O retention. In addition to catalysing conversion of Ang I to Ang II, ACE breaks down and inactivates the hypotensive agent bradykinin, an endotheliumdependant vasodilator (via nitric oxide [NO]) that also increases vascular permeability. The exact mechanism by which the ACE-in allele, and presumably lower ACE levels, influences physical capacity is unknown. Endurance performance, especially at elite levels, is determined by a combination of cardiovascular, pulmonary, neural and musculoskeletal factors. In athletes, these physiological and anatomical factors are optimized by a combination of genetic predisposition and environment response (especially to training). . VO2max, which reflects the efficiency of oxygen uptake, delivery and utilization, is a frequently used measure of aerobic performance. An early study reported a correlation between the presence . of the ACE-in allele and VO2max in postmenopausal women,[61] but subsequent studies in young women,[62] and in a large mixed cohort of men and women[63] did not corroborate this finding and a study in Chinese males found the opposite: an . association between the ACE-del allele and VO2max.[64]. Woods et al.[33] also found no association with VO2max (pre- and post-training), but noted that aerobic .training did have more effect on submaximal VO2 in ACE in/in homozygotes than in ACE del/del homozygotes at the study’s maximum workload (80 W). Williams et al.[32] reported that improvements in muscle efficiency (measured as energy used/ power produced) were greater in ACE in/in individuals. Although there were no genotypic associations at baseline levels differences in the aforementioned study, Zhang et al.[65] found a higher percentage of slow-twitch fibre in untrained ACE in/in individuals, suggesting an innate, muscle-based predilection for aerobic sports. ª 2008 Adis Data Information BV. All rights reserved.

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An alternative pathway, in which the ACE variants manifest through the bradykinin pathway was proposed by Williams et al.,[66] who reported that alleles in the kinin b2-receptor gene that were associated with higher receptor activity were also associated with metabolic efficiency and endurance performance. Bradykinin activity maintains NO production, thereby inhibiting cytochrome-c oxidase and limiting mitochondrial oxygen consumption. As ACE degrades bradykinin, lower ACEplasma (the initial phenotype of the ACE-in allele) may contribute to greater b2-receptor activity and thus improve endurance performance by improved contractile efficiency.

4. ACE Inhibitors and Angiotensin Receptor Blockers Not surprisingly, given the central role of the RAAS in modulating blood volume and vascular resistance, drugs that affect the RAAS pathway are commonly used in treating cardiovascular pathologies such as hypertension, congestive heart failure (CHF) and managing myocardial infarcts,[67,68] as well as diabetic nephropathy, which responds to both changes in systemic blood pressure and effects of the medication on renal remodelling and tissue fibrosis.[69] The two most common of these pharmacological agents are ACE inhibitors, which block the synthesis of Ang II and the breakdown of bradykinin, and angiotensin II type 1 receptor antagonists (angiotensin receptor blockers [ARBs]), which block the binding of the hormone to the AT1-receptor. Both drugs alleviate hypertension by suppressing vasoconstriction, but with different ancillary effects.[70] By inhibiting ACE, ACE inhibitors maintain bradykinin activity and thereby facilitate vasodilatation, but prevent interactions between Ang II and any of its receptors; conversely, ARBs (which block only the AT1-receptor) do not maintain bradykinin activity, but do allow the continued Ang II stimulation of the AT2-receptor, which may be refractory to AT1-receptor activity and therefore beneficial in treating hypertension. Also, Ang II may be generated by enzymes other than ACE Sports Med 2008 2008; 38 (12)

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(e.g. chymase), which may explain why circulating Ang II levels recover after prolonged ACEinhibitor treatment, although the role and significance of other Ang I activators is contentious (see Danser[13] for discussion). Responses to ARB drugs are not limited to the Ang II pathway; for example, losartan has anti-inflammatory properties[71] and acts on several ion channels in cardiomyocytes[72] and may exert an anti-arrhythmic therapeutic effect through mechanisms other than AT2-receptor blockade. The two families of drugs (ACE inhibitors and ARBs) are often used in combination and, as there are a variety of commercially available drugs within each family that vary both chemically and pharmacologically,[73] a wide variety of treatment options exist. If the ACE-in endurance phenotype is primarily due to the ACE-bradykinin interaction,[66] then ACE inhibitors, which would suppress bradykinin degradation, would be more likely to benefit performance than ARBs. 5. Effects of ACE Inhibitors and Angiotensin II Type 1 Receptor Antagonists on Physical Performance A number of studies have examined the effects of ACE inhibitors and ARBs on fitness and physical performance; however, few have been done in non-patient populations and none have been done in athletes. Given the wide range of medications classified as ACE inhibitors and ARBs and the variation in subject characteristics, the studies are difficult to compare. Furthermore, many are small studies that lack the statistical power to detect subtle changes in performance that could be relevant to athletes. Nevertheless, the data they contain are significant to the discussion of the potential of ACE inhibitors and ARBs to enhance athletic performance. 5.1 Effects on Maximum Oxygen Uptake

In a study of 16 patients with CHF, Vescovo et al.[74] reported. significant increases in peak oxygen uptake (VO2peak) and ventilation threshold after 6 months on the ARB losartan (+31%, p = 0.011; +32%, p = 0.049, respectively) or the ª 2008 Adis Data Information BV. All rights reserved.

Wang et al.

ACE inhibitor enalapril treatment (+42%, p = 0.014; +28%, p = 0.039, respectively). In an. other cohort of CHF patients, VO2max and exercise duration both increased by ~11% (nonsignificant) following 12 weeks of the ACE inhibitor cilazapril,[75] whilst neither captopril (another ACE inhibitor) nor enalapril had much effect on maximum endurance duration (+3%) or . VO2max (no change) in patients with uncomplicated essential hypertension.[76,77] The aforementioned studies involved either non-athletes or individuals whose athletic capacity was undefined. Endurance capacity in hypertensive ‘sportsmen’ was measured following administration of .enalapril and, although there was an increase in VO2max (+3%, non-significant); there was no improvement in performance.[78] Although the.subjects were described as active in sports, their VO2max values (average ~3.1 L/min) were substantially lower than the values usually associated with high levels of athletic performance. Significant increases in . endurance performance (as measured by VO2max) have resulted from combination treatments, in which patients are given ACE inhibitors and ARBs. In one study of CHF .patients, both enalapril and losartan increased VO2max by approximately 15% (p < 0.01); however, when administered in combination, the improvement was almost double this (+26%, p < 0.01).[79] The significant synergistic effect (p < 0.05, compared with either single treatment) was ascribed to enalapril improving pulmonary function and losartan improving aerobic work efficiency. A similar combination of ACE inhibitor (ramipril, enalapril or fosinopril) and losartan also resulted in an increase (albeit non. significant) in VO2max (+4.7%) in a study of patients with left ventricular systolic dysfunction.[80] There have been few studies of ACE inhibition and performance in healthy, normotensive subjects and, as the effects on patients may be the reversal of cardiovascular dysfunction or CHFinduced peripheral muscle atrophy (see review by Munzel et al.[81]) rather than improvement per se, the patient models may not be predictive . of effects in the general population. The VO2max in . eight healthy ‘non-athletic subjects’ (average VO2max of 2.7 L/min) given captopril for 3 days Sports Med 2008 2008; 38 (12)

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showed no change despite marked reduction (-50%) in circulating ACE.[82] A similar lack . of effect on VO2max was seen in normotensive physical education students with .a somewhat higher average level of fitness (VO2max = 49.7 mL/min/kg).[83] Predel and colleagues[84] looked at the effects of the ACE inhibitor trandolapril on healthy, trained men and found no effect on . VO2max. Although the subjects were . active in a number of sports, their moderate VO2max values (average 48.5 mL O2/min/kg) do not suggest that they were high performance, endurance athletes, unlike the subjects in participating in the study of Carre et al.,.[85] who were runners or cyclists with an average VO2max of 56.8 mL/min/kg. This study compared a variety of performance measures before and after 1 month of captopril treatment and . reported slight, non-significant differences in VO2max (+0.5%), maximum power (+2%), maximum heart rate (+1%) or blood pressure at peak effort. In a recent study of older individuals (>65 years of age) with some mobility problems, but healthy cardiovascular systems and who ‘‘maintained health-related quality of life’’, Sumukadas et al.[86] reported an increase in exercise capacity after perindopril treatment that was significant and the equivalent of a 6-month training programme.

5.2 Effects on Muscle Composition and Function

Enalapril treatment in patients with chronic heart . failure resulted in non-significant increases in VO2max and exercise capacity (wattage) and significant increases in skeletal muscle fibre area.[87] The increase was most pronounced in the type 1 (slow-twitch) fibres associated with aerobic and endurance performance. Continued use of ACE inhibitors limited the decline of muscle strength and helped maintain walking performance in older hypertensive women.[88] Similarly, in a large prospective study on an older mixed active population, use of ACE inhibitors was associated with greater lower extremity muscle . mass.[89] The significant increase in VO2peak and ventilation threshold reported by Vescovo ª 2008 Adis Data Information BV. All rights reserved.

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et al.[74] was accompanied by a concomitant and significant increase in slow myosin isoforms (MHC1) in the gastrocnemius that correlated . with the increase in VO2peak resulting from either losartan or enalapril. The authors of that study postulated that this biochemical change was responsible for the improvements in exercise capacity. The potential effects of ACE inhibitors on skeletal muscle structure and function was recently reviewed by Onder et al.,[88] who propose a number of mechanisms by which muscle performance could be enhanced by ACE inhibition including changes in fibre type, reduced inflammation, improved neuromuscular transmission, increased muscle vascularization and improved metabolic efficiency. The therapeutic effects of ACE inhibitors on CHF may be due to improvements in muscle function reducing the work load on the heart rather than simply to vascular dilation.[31] The data on the effects of ACE inhibitors and ARBs on performance in animal models (which, for the sake of brevity, we have not included in this article) are difficult to extrapolate to humans. Like the human studies, variations in drugs and dosage, frequent use of disease models, and the lack of data about trained, healthy subjects make drawing conclusions about the performanceenhancing capacity of the drugs difficult. For example, in the rat, Minami et al.[90] found captopril to reduce exercise capacity in normotensive animals, but later (in 2007) reported that perindopril increases the untrained exercise capacity in spontaneously hypertensive rats (but not the training response, although a positive effect on training-induced muscle capillarization and percentage of type I fibres was noted).[91] Bahi et al.[92] found no effect of perindopril on exercise capacity in healthy but sedentary rats; whereas, the same drug impaired exercise capacity in diabetic animals.[93] 6. Dosage Effects As can be seen in the studies described in section 5, there is a substantial range of performance responses to ACE inhibitors in patient populations. One possible contributor to this variance is Sports Med 2008 2008; 38 (12)

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dosage. Pascual Figal et al.[80] noted . that the studies that showed an increase in VO2max following losartan treatment[79,94] were using lower doses than their own study, in which no difference was detected, and postulate that the performance-enhancing capacity of the drug may manifest only at low doses. Similarly, the ACE inhibitor lisinopril significantly increased the . VO2max (+7%) and exercise duration (+29%) of patients with CHF;[95] however, the ergogenic effect was observed only at a low dose (5 mg). The . VO2max of patients given 20 mg (the recommended dose) did not differ from that in the control group, and exercise duration, although higher than in the controls, was lower than that of patients receiving 5 mg. The same authors suggest that excess vasodilatation leading to hypotension might account for the failure of other studies to detect significant improvements in aerobic capacity following treatment with ACE inhibitors.[96] The mechanism(s) by which ACE inhibitors improve cardiac function (and presumably contribute to any improvements in performance) in CHF patients are not fully understood and Montgomery and Brull[31] speculate that the therapeutic effects of the medications may be related to improved muscle metabolic efficiency in addition to circulatory changes. Whether a similar low-dose performance enhancement would occur in healthy subjects is unknown. In the studies of non-patient populations cited above, the captopril and the trandolapril dosages used (25–150 and 2 mg/day, respectively) are within in the therapeutic range of the drugs. Whether sub-therapeutic doses of these medications would be performance enhancing is an intriguing and untested possibility.

7. Pharmacogenomic Issues Genetic background may be a confounding factor in the evaluation of the effects of ACE inhibitors on physical performance; however, the data are inconsistent and difficult to compare. For example, the vaso-relaxant effects of enalaprilat (the activated form of enalapril) was greater in ACE in/in homozygotes than in ACE ª 2008 Adis Data Information BV. All rights reserved.

del/del homozygotes,[97] but no effect of ACE genotype was observed for captopril.[98] This discrepancy could reflect differences in action between the drugs or could be due to other genetic differences between the study populations (the former study was conducted in Scotland, whereas the latter was done in Japan). Population differences in response to ACE inhibitors have been shown by Taylor and Ellis,[99] who reported lower response in Black heart-failure patients than in White patients. Further complicating the issue, carriers of the C variant at the A/C1166 polymorphism in the angiotensin receptor type 1 gene showed less haemodynamic response to the ACE inhibitor perindopril than did A/A homozygotes,[100] raising the possibility that variations in genes other than ACE may influence response to ACE inhibitors. In a recent review of the abundant literature pertaining to the relationships between ACE genotype and the ACE inhibitor action, Danser et al.[10] concludes that individuals with the higher ACEplasma genotype (del/del) would not be under-treated by typical prescribed dosages of inhibitors and that early results that suggested an effect of genotype on the blood pressure response to the medications were not supported by later, larger studies. This would argue that no genotypic effects on a putative performance response would be observed at therapeutic doses, although nothing can be concluded about non-therapeutic doses.

8. Conclusions Athletic capability is determined by the interaction of a number of internal and external parameters. Muscle efficiency (the . ratio of work output to metabolic cost) and VO2max (the point beyond which no further increase in oxygen uptake is possible) are two of the internal factors that set the upper level for performance in endurance events, and strategies, both licit and illicit, to increase these rate-limiting physiological parameters are commonplace in training and competition. Lower ACEplasma is associated with improved muscle efficiency and, at least in studies of patient populations, ACE inhibition Sports Med 2008 2008; 38 (12)

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frequently increases muscle mass and capacity. Whether there would be a similar, potentially ergogenic effect in healthy, athletic individuals has yet to. be investigated; however, if the increases in VO2max reported in some of the studies on patients with CHF following ACE inhibition or Ang II receptor blockade (e.g. 15–26%,[79] 31–42%,[74] 7%[95]) could be induced in athletes, the drugs would have a huge impact on performance. In comparison, 6 months of .endurance training in elite cyclists increased VO2max by 13%,[101] 5 weeks of high-intensity .training in moderately fit runners increased VO2max by 5.2%,[102] and the administration of recombinant human erythropoietin (rhEPO), the scourge of professional cycling and other endurance. focused sports, increased VO2max by 7–12%.[103,104] At this time, there is no evidence that increases of such magnitude would occur in elite athletes (the limited number of studies on healthy, fit individuals suggest that they would not); however, this does not rule out that smaller, competitively relevant improvements could occur. Regrettably, the absence of direct evidence that ACE medications would be effective performance-enhancing drugs does not mean that unscrupulous athletes, or their entourages, will not experiment with them. ACE inhibitors and ARBs are common and relatively inexpensive medications, the possibility that non-therapeutic doses would be ergogenic has not been disproved, and, as many races are won by non-statistically significant margins, the absence of significant results in the studies cited above may not be a deterrent to statistically savvy cheaters. Athletes should be made aware of the risks of such experimentation. According to Health Canada, ACE inhibitors (e.g. captopril, DIN 01999559) may cause dizziness, fainting, sensitivity to the sun and lower resistance to infection, and possible adverse effects include coughing, fast or irregular heartbeat, muscle cramps and joint pain. The adverse effects of ARBs (e.g. lorsatan, DIN 02182874) include dizziness, and overdoses can result in arrhythmias. Also, both ACE inhibitors and ARBs are particularly dangerous in pregnant women as they can harm the fetus if taken in the last two trimesters. The drugs, which can be excreted with ª 2008 Adis Data Information BV. All rights reserved.

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milk, have been implicated in fetal or newborn death and ACE inhibitors are a known cause of birth defects during early development.[105] This review grew out of teaching about the contribution of genetic background to physical performance and athleticism. Inevitably, after discussing the ACE in/del polymorphism, students ask if any drugs exist that lower ACE levels, and if they do, are they performance enhancing. To these students, and others who have moved through the literature to the same hypothesis, we would say that the data suggest that ACE inhibitors and ARBs are unlikely to enhance endurance performance in athletes, but surety awaits studies on the effects of low doses of these medications in healthy, athletic individuals. Whether surreptitious tests of this have been undertaken is unknown; however, given the rapid adoption of pharmaceuticals into the doping toolbox, the sporting world should be aware, and wary, of the possibility. Acknowledgements PW is the recipient of a UBC University Graduate Fellowship. MNF is supported through research funds to JLR from the World Anti-Doping Agency (WADA). The authors have no conflicts of interest directly relevant to the contents of this article.

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Correspondence: Dr Jim L. Rupert, School of Human Kinetics, University of British Columbia, 6081 University Boulevard, Vancouver, BC, V6 T 1Z1, Canada. E-mail: [email protected]

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