sports medicine Volume 41 Issue 10 October 2011

Sports Med 2011; 41 (10): 793-800 0112-1642/11/0010-0793/$49.95/0

CURRENT OPINION

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Principles for the Use of Ball Projection Machines in Elite and Developmental Sport Programmes Ross A. Pinder, Ian Renshaw, Keith Davids and Hugo Kerherve´ Institute of Health and Biomedical Innovation, and School of Human Movement Studies, Queensland University of Technology, Brisbane, QLD, Australia

Abstract

Use of ball projection machines in the acquisition of interceptive skill has recently been questioned. The use of projection machines in developmental and elite fast ball sports programmes is not a trivial issue, since they play a crucial role in reducing injury incidence in players and coaches. A compelling challenge for sports science is to provide theoretical principles to guide ‘how’ and ‘when’ projection machines might be used for acquisition of ball skills and preparation for competition in developmental and elite sport performance programmes. In this article, we propose how principles from an ecological dynamics theoretical framework could be adopted by sports scientists, pedagogues and coaches to underpin the design of interventions, practice and training tasks, including the use of hybrid video-projection technologies. The assessment of representative learning design during practice may provide ways to optimize developmental programmes in fast ball sports and provide information on the principled use of ball projection machines.

Ball projection machines typically play an integral role in practice and training environments in many sports, from cricket and baseball to volleyball and tennis. Recently, it has emerged that not all skilled performers agree that the use of projection machines in practice provides a functional task to practice interceptive actions, leading to some contention among expert coaches. For example, some high performance and developmental programmes have taken steps to extensively reduce the use of these machines in the sport of cricket.[1] Greg Chappell, a former prominent Australian cricket batsman and head coach of the National Centre of Excellence, now talent development manager, describes his stance:

‘‘... what my intuition told me for years was that the bowling machine was a totally different exercise from batting against the bowler. From my own personal experience of batting against the bowling machine, it wasn’t a great experience because once I’ve done it a few times I decided that it wasn’t going to help me with batting. I was better off not to bat at all than to go and bat on a bowling machine because the activity is so different. [In an actual cricket match] you know the bowler’s preparation to bowl; you know everything – all of the cues and clues that you’re getting from the bowler is[sic] really important to get into the rhythm of the bowler and to get the timing of your

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movements. You take the bowler out of the equation, you stick a machine there that spits balls out at you and you’ve lost all those cues and clues. What I’ve subsequently found is that research is telling us what my intuition and my experience was telling me. The other thing is that the research into expertise tells you that experts are better at picking up the cues and clues than the average player. So why take it away from everyone and stop them from developing the things that will help them get better?’’[1] Conversely, the late Bob Woolmer, an equally respected and renowned former international batsman and national coach of South Africa and Pakistan, considered the ball machine one of the ‘‘most essential tools in modern cricket.’’[2] One of the primary uses of projection machines in practice seems predicated on movement repetition,[2] considered traditionally as an essential feature of ‘perfecting’ a putative ‘ideal’ technique in the process of skill acquisition.[3,4] This idea is exemplified in the most up-to-date coaching literature on cricket batting, which discusses the use of ball machines to help ‘groove the skill’.[2] Additionally, projection machines allow individuals to achieve a high volume of practice by facing more balls in a short period of time in order to practice specific actions hundreds of times. This is not only a critical issue for sports coaches, but a very important theoretical and methodological one for sport science researchers. 1. The Problem of Practice Volume Projection machines provide relatively consistent and accurate practice conditions, which developing athletes (e.g. pitchers, bowlers) may not be capable of producing for their peers. This is important, since skill acquisition in interceptive actions has been associated with large volumes of task-specific practice.[5] However, an overreliance on projection machines may have been inadvertently induced by some perspectives of expertise that have over-emphasized practice volume (e.g. quantity of balls hit) over the quality of practice task design. Practice volume is central to many prevalent perspectives on expertise, such as the 10 000 hour rule,[6] the power-law of practice,[7] and deliberate practice.[8] To exemplify, ª 2011 Adis Data Information BV. All rights reserved.

the most comprehensive and relevant coaching literature in cricket batting proposes that batsmen require ‘‘10 000 repetitions of an action or skill to penetrate the subconscious’’ and that this conditioning ‘‘enables the batsmen to react instinctively in match conditions.’’[2] In complex skilled actions, it is important to consistently achieve a particular performance outcome; however, it has been demonstrated that skilled movement patterns are rarely repeated in an identical way on two or more occasions as performance outcomes are achieved.[9] The need for ‘repetition without repetition’ in practice is a critical feature of successful motor learning,[10,11] with performers using movement variability and stability paradoxically to achieve performance outcome consistency and movement pattern adaptability.[11] This is important to note, since the ways in which projection machines are currently used appears to be focused on stability and blocked practice of isolated movement aspects (e.g. blocked practice of a single type of shot). But how does one practice multi-articular actions for an extended number of trials and for prolonged periods of time, without placing too much stress on the bodies of coaches, pitchers or bowlers? The practical benefits of projection machines have been highlighted by research into overuse injuries in sports relying heavily on multiarticular projecting actions (e.g. baseball pitching, cricket bowling). A clear advantage gained from using ball projection machines is that they alleviate the workload required from bowlers or pitchers during batting practice. This is most important since, in cricket, bowling injuries are heavily attributed to overuse through high bowling workloads, particularly at developmental stages.[12,13] Critically, once a player sustains an injury, the likelihood of re-occurrence is increased.[13,14] Similar findings exist in baseball, with overuse injuries of the shoulder being a primary concern for developing performers.[15,16] A recent prospective study demonstrated that youth athletes pitching more than 100 innings per calendar year were significantly more likely to sustain injury. To counter the problem of injuries, many coaches in cricket rely heavily on providing simulated actions such as ‘throw downs’ (over-arm throws from a reduced distance) to replicate ball flight Sports Med 2011; 41 (10)

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information and maintain the temporal demand reminiscent of bowling. However, there is some anecdotal evidence from the coaching literature of similar levels of overuse injuries in coaches using ‘throw downs’ to simulate bowling deliveries in cricket.[2] In light of these data it is apparent that use of projection machines in practice programmes remains important in reducing the risks of injury incidence. 2. Implications for Skill Acquisition As well as being perceived as a benefit in reducing injury incidence, ball projection machines are also primarily considered as useful tools for the acquisition of skilled hitting actions, allowing a performer to focus on one isolated movement aspect (e.g. a specific shot or stroke), practice individually, and complete large volumes of practice in a short period of time. Despite these reasons, some studies assessing the use of projection machines in sports performance have questioned their role in athlete preparation, skill acquisition and assessment. There is clear evidence that use of projection machines in tennis and cricket creates significant differences in timing and control of performers’ actions, as well as a reduction in the quality of interception compared with facing a ‘live’ opponent delivering a ball with the same characteristics.[17-20] In developing junior performers, especially, these differences are manifested in significant delays in movement initiation times that increase the temporal demand on the unfolding action. For example, it has been reported that developing junior cricket batters initiated the backswing of the bat and front foot movement significantly later when performing front foot shots (e.g. moving the front foot towards ball bounce) against a projection machine set to the same speed (»28 m/sec) and with similar trajectory characteristics as a ‘live’ performer.[17] Critically, these delays in movement initiation resulted in a reduction in quality of contact of the interceptive action, a reduction in bat swing speeds and significantly shorter step lengths;[19] the need to place the foot as close to the pitch of the ball in these type of shots (e.g. minimizing the impact of ª 2011 Adis Data Information BV. All rights reserved.

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late ball-flight deviation) is a well established cricket coaching principle. Changing the practice task constraints and using projection machines in junior cricket batting leads performers to reorganize their actions in attempts to achieve the required spatial-temporal orientation.[19,21] Here we argue that differences exist between performance contexts and training task constraints because the latter are used to simulate the former. Differences may exist between specific training tasks and competitive match contexts. This is a rich area for future research to address using the principles we outline below (see section 3). Critically relevant information sources from the competitive performance environment are not available under practice task constraints involving projection machines. Research suggests that in their current mode of use, prolonged exposure to projection machine practice tasks may lead athletes to attune to information sources that are not present during competitive performance, leading to a predictive rather than prospective control strategy emerging in learners.[18,22] Renshaw and colleagues[18] demonstrated that, contrary to data reported for junior performers, experienced cricket batters initiated the backswing of the bat earlier against a projection machine than when batting against an experienced medium-paced bowler at the same bowling speed (»27 m/sec) [also see Gibson and Adams[23]]. It has also been demonstrated recently that highly distinctive visual search patterns are used by experienced cricket batters when practising with projection machines, since they ‘park’ their gaze at a point on the anticipated trajectory of the ball before release.[22] Although it is intuitive to predict differences between batting performance contexts, no research has yet compared visual strategies of batters under ball projection machine and ‘live’ bowler task constraints. The use of a principled framework for these comparisons would support analyses to observe whether differences between the two tasks might emerge. However, a key point to note is that the use of projection machines reduces the opportunities for developing batters to attune to subtle and relevant sources of pre-ball release information from a bowler/pitcher’s movements for differentiating ball trajectory, speed or Sports Med 2011; 41 (10)

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ball type variations (e.g. different spin rotations) – a critical feature of expertise in interceptive skill.[24-26] This criticism can also be directed at the use of ‘throw downs’ to simulate bowling deliveries in cricket. For these reasons, Pinder et al.[17] have cautioned against an ‘‘over-reliance of ball projection machines in developmental programmes.’’ But it is important to note that this message should not be interpreted as ‘ball machines should not be used at all during practice’. Rather, practitioners and sport scientists need to develop a principled theoretical rationale for their use as a skill enhancement tool in sport, which is elucidated in section three. 3. Principles for Future Work A compelling challenge for sports science is to understand ‘how’ and ‘when’ projection machines might be used for acquisition of ball skills and preparation for competition. Ecological dynamics is a theoretical framework that could underpin a reasoned analysis for the use of ball machines in developmental and elite sport programmes. Ecological dynamics is predicated on ideas of ecological psychology and dynamical systems theory, with a level of analysis embedded in the performerenvironment relationship.[27,28] This theoretical framework proposes that movement behaviours emerge from dynamic interactions between neurobiological movement systems and their performance environments.[9,29] The interaction between performer, environmental and task constraints results in the emergence of patterns of movement behaviour that become stabilized through learning and practice. A model based on the tenets of ecological dynamics has already been outlined for sport scientists, coaches, experimental psychologists and pedagogues, to underpin the design of training interventions practice tasks in sport.[30] The model was predicated on concepts from ecological dynamics and a nonlinear pedagogy (see Renshaw et al.[11] for recent reviews of skill acquisition in sport). Assessment of ‘representative learning design’ in specific practice tasks allows sport scientists to understand the functionality and limitations of particular training environments. Understanding ª 2011 Adis Data Information BV. All rights reserved.

representative learning design may provide opportunities to optimize learning programmes in sport and provide information on the use of performance enhancement tools, such as projection technology, during practice. To assess representative learning design of specific tasks, practitioners should consider the functionality of the practice task constraints in allowing performers to pick up and use information sources representative of the performance context (e.g. by comparing visual search strategies between projection and ‘live’ bowler/pitcher situations). Since information regulates actions, an important principle is that the key perception and action processes that are coupled in a competitive performance environment should be maintained in the design of practice task constraints. In simulations, the degree of association between practice and performance contexts should be analysed by considering the fidelity of the performer’s actions (for a detailed overview see Pinder et al.[30]), such as by measuring and comparing movement organization between the different contexts (see figure 1). Principles of representative learning design are summarized in figure 1. The use of projection machines should be considered to understand how they might alter learners’ emergent spatiotemporal responses, movement coordination and visual search behaviours, compared with facing a real bowler during competitive performance. Future research is needed to explore ways to increase the functionality of current practice tasks involving ball projection machines. For example, it was recently reported that a specific ‘near lifesize’ video simulation task, which maintained a coupling between perception and action processes, allowed the action fidelity of cricket batters’ preparatory and initial movement responses to be maintained compared with facing a ‘live’ bowler.[19] Recent technological advances that combine both video and ball projection machines (e.g. ‘ProBatter’ [ProBatter Sports, LLC, Milford, CT, USA]), may have a significant future in elite sport and development programmes. However, caution is needed with these new technologies, and the assessment of their representative learning design may help identify the benefits and limitations of these hybrid training Sports Med 2011; 41 (10)

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Principles for the assessment and use of projection machines

Representative learning design[30] Theoretical framework A principled theoretical model based on concepts from ecological dynamics and nonlinear pedagogy

Supporting concepts

Action fidelity

Functionality of research, practice or learning design

Experimental/learning designs viewed as simulators of a simulated (performance) system

Functional coupling between critical perception and action processes

Assessment of dynamic patterns of behaviour under changing environmental and task constraints Explanations Fidelity exists when performers’ responses remain the same in two or more contexts

Variables

Intraindividual

Interindividual

Kinetic/kinematic or coordination profiling (e.g. 2D/3D movement analysis) or bat/racquet swing characteristics

Spatiotemporal responses (e.g. movement initiations, shot selections)

Outcomes

Informational sources

Degree of success should be controlled for and assessed between contexts

Achievement to be based on comparable information sources to those in a performance environment

Performance analysis (e.g. quality of contacts, performance outcomes)

Visual search and information pickup (e.g. eye movement between machine and ‘live’ contexts)

Fig. 1. A principled theoretical framework for the future design of experimental and practice tasks involving ball projection machines. 2D = two dimensional; 3D = three dimensional.

tasks. As discussed, Croft et al.[22] found that against projection machines, experienced batters fixated their gaze at a point on the anticipated trajectory of the ball from the machine. As ‘ProBatter’ systems release the ball from a specific position (a screen with one hole), it needs to be verified whether the pickup and use of information in that simulation task actually replicates the competitive performance context. Because of the high current cost of high fidelity ball projection systems, the standard projection machine is likely to remain prominent in development programmes for some time. For this reason, researchers should focus on carefully assessing and designing the informational properties of a competitive performance environment that might be replicated at different development and skill levels. This level of analysis is needed to provide insights into the nature of the transfer of interceptive actions ª 2011 Adis Data Information BV. All rights reserved.

performed against projection machines and real bowlers, for instance when comparing visual search strategies under both task constraints. 4. A Future Role for Ball Projection Machines? The relevance of projection machines as part of training programmes for team game performance is not under question. The key issue is how best to use them during practice. Current research does not advocate removal of ball projection machines from cricket training programmes, since investigation of their use is still in its infancy. Research has not yet examined their role in high and low ball delivery speeds or looked at their effect on timing and coordination in back foot shots (where a cricket batter moves backwards from their initial position to intercept a Sports Med 2011; 41 (10)

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ball that bounces closer to the bowler and reaches the batter around or above waist height). An important challenge is to examine their role in developing interceptive actions in early and more advanced learners in ball sports. In the very early stages of learning when the focus should be on the construction of a basic coordination pattern from all the possible degrees of freedom, it is expected that the stable and consistent practice conditions would greatly benefit the rate of learning[31] (also see Davids et al.[9] for a review). To exemplify this in cricket batting, ball projection machines could be used to deliver the ball to a restricted spatial location so that learners stabilize a functional stroke, such as a forward defensive or straight drive. Developing athletes, in particular, need to be provided with opportunities to establish functional and stable relationships between perception of information from the performance environment and their movements.[9,32] However, later in learning it is clear that stability and variability of practice task constraints may allow for the development of more adaptable performers.[33] At later stages of learning, ball projection machines could be used to locate the learner in a meta-stable region of a perceptual-motor workspace. In this region, learners remain in a state of relative coordination with the practice environment, being unable to function completely independently, nor dependently, on environmental information to regulate their actions. In the metastable region, functional movement solutions can emerge during task performance, for instance when learners need to decide whether to move forward or backward in playing cricket batting strokes. By accurately projecting the ball onto specific locations of the cricket pitch, more advanced learners can be forced to enter a metastable region of batting performance to enrich performance during practice (see Hristovski et al.[34] for an example in boxing). These theoretical ideas imply that traditional blocked practice methods utilizing ball projection technology may prevent more advanced learners from harnessing motor system degeneracy to functionally adapt stable patterns of movement organization, and may actually be dysfunctional when transferring to more dynamic performance contexts.[35] ª 2011 Adis Data Information BV. All rights reserved.

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As a principle, therefore, it seems that, at all stages of learning, the role of projection machines may be to ‘supplement’ rather than ‘replace’ the role of the ‘live’ bowler in the acquisition of batting actions. Ensuring a balance between the use of projection machines and bowlers may also ensure that batters are able to constantly attune and recalibrate (recognize and adjust) to differences in ball flight characteristics under the distinct practice contexts and establish important informationmovement couplings. However, further research is needed to assess the effects on skill acquisition of different volumes (amount of time) of supplementary practice in fast ball sports (e.g. visual training through video simulation designs or ball projection machines). Additionally, variations in ball speed and trajectories (e.g. constant changes in bounce location) may allow increased opportunities for batters to exploit and master the perceptual degrees of freedom that support adaptive movement behaviours needed during performance (see Savelsbergh et al.[36]). Intuitively, it would be predicted that when there is a reduced temporal constraint on batters’ actions, such as when playing against slow bowling, ball-flight information might become more salient (i.e. less reliance on pre-release information) and could provide opportunities for increased action fidelity when using projection machines. Ball projection machines, as outlined in section 2, are able to replicate the same ball speeds and angle of release as a ‘live’ performer, providing some level of ‘representativeness’ of practice task information. Therefore, the consideration and assessment of the representative design of ball projection machines nested within particular performance contexts (e.g. middle wicket practice with typical game demands) may increase action fidelity at more elite levels. With respect to this idea, it is important to note that most projection systems available to practice batting in cricket do not currently support the use of balls with the same properties as those typically used during competitive performance. This is a major issue, since expert performers have been shown to use seam characteristics of balls to support perceptual decision making in fast ball sports.[37] The use of tasks that more closely replicate the flight Sports Med 2011; 41 (10)

Principles for Learning Design in Sport

and bounce characteristics of a ball used in competitive performance should become a focus for future work.

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2. 3.

5. Conclusions 4.

Using projection machines in sport should not be considered dysfunctional. Importantly, they help alleviate workload stresses on bowlers or pitchers during ball delivery that can lead to overuse injuries in developing and elite performers. Nevertheless, it is disingenuous to call ball projection machines ‘bowling machines’, because they can only generally simulate post-ball release information sources during batting practice. They do not allow learners to pick up specific sources of pre-ball release information from bowlers’ actions, a vital part of the information-movement coupling link in batting performance. Current practices of using such projection technology in athlete development programmes can be enhanced by using theoretically guided principles to underpin their implementation as a skill acquisition and performance preparation tool in ball sports. Principles of ecological dynamics suggest that (i) their use is likely to differ according to the needs of different skill groups; (ii) they are most functional when used in high fidelity simulations of performance environments (e.g. nested in performance settings such as in middle wicket practice in cricket, or in combination with video simulations); and (iii) they should supplement practice with ball projection with real individuals so that all learners can attune to the affordances provided by movements of opponents during ball delivery.

5.

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12.

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16.

Acknowledgements 17.

This article was written while the first author, R.A. Pinder, was supported by a Queensland University of Technology International Postgraduate Scholarship Award. The authors have no conflicts of interest to declare that are directly relevant to the content of this article. No funding was used by the remaining authors I. Renshaw, K. Davids and H. Kerherve´, to assist in the preparation of this article.

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Correspondence: Ross A. Pinder, School of Human Movement Studies, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Brisbane, QLD 4059, Australia. E-mail: [email protected]

Sports Med 2011; 41 (10)

Sports Med 2011; 41 (10): 801-814 0112-1642/11/0010-0801/$49.95/0

REVIEW ARTICLE

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Effects of Acute Alkalosis and Acidosis on Performance A Meta-Analysis Amelia J. Carr,1,2 Will G. Hopkins3 and Christopher J. Gore1,4 1 2 3 4

Physiology, Australian Institute of Sport, Canberra, ACT, Australia School of Sport Science, Exercise and Health, University of Western Australia, Perth, WA, Australia Sport Performance Research Institute of NZ, AUT University, Auckland, New Zealand Exercise Physiology Laboratory, Flinders University of South Australia, Bedford Park, SA, Australia

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Study Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Data Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Physiological Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Publication Bias and Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Meta-Analytic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Physiological Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Outcome Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Physiological Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

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Ingestion of agents that modify blood buffering action may affect highintensity performance. Here we present a meta-analysis of the effects of acute ingestion of three such agents – sodium bicarbonate, sodium citrate and ammonium chloride – on performance and related physiological variables (blood bicarbonate, pH and lactate). A literature search yielded 59 useable studies with 188 observations of performance effects. To perform the mixedmodel meta-analysis, all performance effects were converted into a percentage change in mean power and were weighted using standard errors derived from exact p-values, confidence limits (CLs) or estimated errors of measurement. The fixed effects in the meta-analytic model included the number of performance-test bouts (linear), test duration (log linear), blinding (yes/no), competitive status (athlete/nonathlete) and sex (male/female). Dose expressed as buffering mmoL/kg/body mass (BM) was included as a strictly proportional

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802

linear effect interacted with all effects except blinding. Probabilistic inferences were derived with reference to thresholds for small and moderate effects on performance of 0.5% and 1.5%, respectively. Publication bias was reduced by excluding study estimates with a standard error >2.7%. The remaining 38 studies and 137 estimates for sodium bicarbonate produced a possibly moderate performance enhancement of 1.7% (90% CL – 2.0%) with a typical dose of 3.5 mmoL/kg/BM (~0.3 g/kg/BM) in a single 1-minute sprint, following blinded consumption by male athletes. In the 16 studies and 45 estimates for sodium citrate, a typical dose of 1.5 mmoL/kg/BM (~0.5 g/kg/BM) had an unclear effect on performance of 0.0% (–1.3%), while the five studies and six estimates for ammonium chloride produced a possibly moderate impairment of 1.6% (–1.9%) with a typical dose of 5.5 mmoL/kg/BM (~0.3 g/kg/BM). Study and subject characteristics had the following modifying small effects on the enhancement of performance with sodium bicarbonate: an increase of 0.5% (–0.6%) with a 1 mmoL/kg/BM increase in dose; an increase of 0.6% (–0.4%) with five extra sprint bouts; a reduction of 0.6% (–0.9%) for each 10-fold increase in test duration (e.g. 1–10 minutes); reductions of 1.1% (–1.1%) with nonathletes and 0.7% (–1.4%) with females. Unexplained variation in effects between research settings was typically –1.2%. The only noteworthy effects involving physiological variables were a small correlation between performance and pre-exercise increase in blood bicarbonate with sodium bicarbonate ingestion, and a very large correlation between the increase in blood bicarbonate and time between sodium citrate ingestion and exercise. The approximate equal and opposite effects of sodium bicarbonate and ammonium chloride are consistent with direct performance effects of pH, but sodium citrate appears to have some additional metabolic inhibitory effect. Important future research includes studies of sodium citrate ingestion several hours before exercise and quantification of gastrointestinal symptoms with sodium bicarbonate and citrate. Although individual responses may vary, we recommend ingestion of 0.3–0.5 g/kg/BM sodium bicarbonate to improve mean power by 1.7% (–2.0%) in high-intensity races of short duration.

Induced blood acidosis and alkalosis via ingestion of supplements has been researched thoroughly, particularly with respect to the two alkalotic agents, sodium bicarbonate and sodium citrate, and one acidotic agent, ammonium chloride. It is widely held that increased intramuscular acidity can limit the capacity to perform highintensity exercise,[1] and that buffering supplements can have an ergogenic effect, while acidic supplements can be ergolytic. Despite this view, the magnitude of benefit or detriment to performance associated with changes in blood pH has been summarized inadequately to date. The investigation of the extent to which blood buffering agents modify performance began in ª 2011 Adis Data Information BV. All rights reserved.

the early 1930s.[2,3] Since then many studies have reported performance enhancements with sodium bicarbonate. More than 15 years ago, a metaanalysis of the performance benefits associated with sodium bicarbonate ingestion yielded an average standardized effect size of 0.44,[4] which is regarded as small.[5] However, effect sizes are not intuitive to understand in terms of the magnitude by which an athlete might improve their race performance. Moreover, the first meta-analysis did not take account of differences between studies in terms of the dose of sodium bicarbonate, calibre of subjects and test protocols, each of which could modify the effect of the sodium bicarbonate on performance.[6-8] Sports Med 2011; 41 (10)

Meta-Analysis: Alkalosis, Acidosis and Performance

Changes to both the data extraction process and meta-analytic approach would address limitations of the prior analysis. Matson and Tran[4] treated all outcomes as standardized effects, that is, as fractions or multiples of the pooled standard deviation of the two conditions, regardless of the units of measure such as time, distance and speed. The data extraction process, and its subsequent interpretation, could be improved by converting all performance data to a common metric of the percentage change in mean power. Contemporary mixed-model meta-analysis would have two main advantages over the fixed-effect approach of Matson and Tran.[4] First, incorporating study and subject characteristics as multiple predictors within a mixed-model meta-analysis would allow the effect on performance of a specific characteristic to be isolated and quantified. A second advantage is that random differences in the magnitude of the performance effect between studies could be estimated. The inclusion of ergolytic effects with ammonium chloride supplementation in a meta-analytic review could promote further understanding of performance effects when blood pH is altered. The same reasoning supports consideration within a new meta-analysis of sodium citrate as another alkalotic supplement that has the potential to enhance performance, possibly inducing fewer gastrointestinal symptoms than sodium bicarbonate.[6] The primary aim of this review was to use a mixed-model meta-analysis to estimate the performance effects of sodium bicarbonate, sodium citrate and ammonium chloride supplementation, as well as estimating the modifying effects of study and subject characteristics. A secondary aim was to quantify the supplement-induced changes in blood bicarbonate concentration ([HCO3-]), lactate concentration ([La-]) and pH. Meta-analysing these physiological variables may enhance an understanding of the corresponding performance effects. 1. Methods 1.1 Study Selection

A literature search of PubMed and Google Scholar using the keywords ‘sodium bicarbonª 2011 Adis Data Information BV. All rights reserved.

803

ate’, ‘exercise’, ‘performance’, ‘alkalosis’ and ‘pH’, and reference lists of review and original research articles published in English up to and including December 2009, was conducted by one of the authors for the performance effects of sodium bicarbonate, sodium citrate and ammonium chloride supplementation. The search yielded 91 potentially eligible studies and 330 observations of performance effects. For each potentially eligible study, two authors recorded study and subject characteristics, and performance test results in a spreadsheet. Many studies yielded multiple observations arising from multiple performance bouts, supplement doses, treatments, exercise protocols or outcome measures. Observations of performance effects were excluded for the following reasons: a performance test with a prior exercise bout (i.e. a pre-load) [23 estimates]; any supplementation before the day of the performance test (2 estimates); failure to randomize or balance the sequence of supplement and comparison (control or placebo) treatments (39 estimates); exercise protocols that focused on only small muscle groups (4 estimates); combining sodium bicarbonate, sodium citrate or ammonium chloride with other supplements or treatments (15 estimates); insufficient methodology detail or performance results (47 estimates); and the failure to give sufficient details of supplement dose (11 estimates). The final data set consisted of 188 estimates from 59 studies, all of which were crossovers. 1.2 Data Extraction 1.2.1 Performance Measures

Performance effects from all studies were converted to a percentage change in mean power in a time trial, so that meta-analysed performance effects could be applied directly to athletic performance. For performance effects reported in units of speed, distance or time, mean power was calculated from the speed-power relationship (equation 1):
P ¼ k Sx ¼ k D=t x

(Eq: 1Þ

where P is power, S is speed, D is distance, t is time, and k and x are constants.[9] The constant x was 1.0 for running and cycle ergometry, 2.0 for Sports Med 2011; 41 (10)

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swimming and 3.0 for rowing ergometry.[9] For sub-maximal time-to-exhaustion tests, the exercise duration was converted to a percentage change in power using the model of Le´ger and Mercier[10] (equation 2): P ¼ a  b  lnðTÞ

(Eq: 2Þ

where P is power and T is the test duration, and the constants a and b were estimated. from the mean maximal oxygen consumption (VO2max) of the subjects in the study using simple linear regression (equation 3): . . a ¼ 111:4 þ 0:11 VO2max and b ¼ 12:1  0:042 VO2max (Eq: 3Þ

. where VO2max is expressed in mL/min/kg the constants in these equations were derived from a meta-analysis of effects of carbohydrate on performance (Vandenbogaerde TJ and Hopkins WG, personal communication). The critical-power model was used for time-to-exhaustion tests where the . intensity was above VO2max; power was calculated using (equation 4): P ¼ CP þ AWC=T

(Eq: 4Þ

where P is power, CP is critical power, AWC is the anaerobic work capacity, and T is the test [9] CP and AWC were estimated from duration. . VO2max using simple linear equations described by Hopkins et al.[9] After converting performance estimates to power using these four equations, the value for the experimental condition was divided by the control value, with the result expressed as a percentage to give the change in mean power. For each converted performance effect, standard errors were calculated to indicate the level of imprecision. Where exact p-values were given, standard errors were directly calculated via the use of the t-distribution; for those effects without an exact p-value, standard errors were calculated via typical errors of measurement, on the assumption that we could estimate the typical error reasonably accurately for these studies. The assumption was that studies with similar test protocols and subject characteristics would have similar typical errors. We then calculated standard error from the relationship between ª 2011 Adis Data Information BV. All rights reserved.

standard error and typical error: standard error = (typical error O2)/O(sample size). 1.2.2 Physiological Measures

For [HCO3-], pH and [La-] we used a similar process as with performance measures to determine standard error and typical error values. Note, however, that p-values were almost invariably for comparisons between pre- and postsupplementation within a condition, rather than changes between treatments at a given timepoint, as was the case with performance results. For the studies that allowed direct standard error calculation from exact p-values, we fitted a linear regression line to the scatter for the relationship between effect magnitude and typical error. There was a trend for greater error values with increased effect magnitude. We excluded observations that deviated from this trend by reporting improbably small typical errors, as this suggested either that the errors or effects were incorrect. We then analysed the remaining results by treating [HCO3-], pH and [La-] values from all studies as those for subjects in an experimental study with repeated measures. Given that error values were derived from the repeated measures for each variable, standard errors were not used as determinants when weighting observations in this part of the analysis. 1.2.3 Publication Bias and Outliers

To reduce effects of publication bias, we examined the standard error against the t-value[11] for each predicted effect, and inspected the plot for signs of asymmetrical scatter (figure 1). This plot is effectively an improved version of the funnel plot, because the scatter of the effects is adjusted for the uncertainty in the estimates and for the contribution of study covariates. Asymmetrical scatter may indicate a bias toward publication of large performance effects, evidenced by a dearth of negative t-values at a given standard error value. A vertical line was drawn at the standard error value that divided the scatter into a symmetrical plot on the left and an asymmetrical plot on the right. The meta-analysis was performed only for those estimates within the symmetrical plot. The descriptive statistics for the 39 useable Sports Med 2011; 41 (10)

Meta-Analysis: Alkalosis, Acidosis and Performance

5

t-Value

3

1

−1 −3 −5 0

1

2 3 4 Standard error (%)

5

6

Fig. 1. Scatter plot to investigate publication bias for performance measures. The dashed vertical line at a standard error of ~2.7% divides the plot into a region with symmetric scatter to the left and a region to the right where a dearth of t-values within the dashed rectangle is apparent.

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tional linear effect interacted with all effects except subject blinding. Reference conditions for each supplement were determined from the typical values of each predictor as derived from eligible studies (e.g. 3.5 mmoL/kg/BM sodium bicarbonate dose, 1-minute test duration). Supplement performance effects were calculated as the predicted performance effect under the reference conditions. The modifying effects on the overall performance outcome of changing values of specific predictors were also calculated: either differences between levels of a nominal covariate (e.g. male-female) or approximately two standard deviations of a numeric covariate (i.e. a typically low value to a typically high value). Unexplained true variation within and between studies was estimated from random effects and expressed as standard deviations. 1.3.2 Physiological Measures

studies and corresponding 83 observations included in the meta-analysis are shown in table I. There were 38 studies and 137 observations for sodium bicarbonate, 16 studies and 45 observations for sodium citrate and 5 studies and 6 observations for ammonium chloride. 1.3 Meta-Analytic Model 1.3.1 Performance Measures

A mixed-model meta-analysis was used to quantify overall mean performance effects for each supplement, and modification of effects with different subject and study characteristics. The Statistical Analysis System (Version 9.2, SAS Institute, Cary, NC, USA) was used to perform the metaanalyses. Percentage effects on mean power output were converted to factors (=1 + effect/100), log transformed for the analysis, and then backtransformed to percentages. Effects were weighted with the inverse of the standard error squared for that performance effect. Fixed effects (predictors) for the meta-analytic model were subject blinding (yes or no), test duration (minutes), performance bouts (number), subject competitive status (athlete or nonathlete) and subject sex (male or female). Supplement dose (mmoL/kg/body mass [BM]) was modelled as a strictly proporª 2011 Adis Data Information BV. All rights reserved.

The meta-analytic model was different from that used for performance. Here, the values for each study were analysed similar to the repeated measurements from a subject in a crossover study of the time-course of the four treatments (placebo, bicarbonate, citrate, ammonium chloride). In the meta-analytic model for the physiological variables, the fixed-effect terms consisted of three interactions of variables specifying six timepoints (TimeGroup, with values pre-supplementation, pre-exercise, and four post-exercise times), the four supplement conditions (Condition, with values bicarbonate, citrate, ammonium chloride, control), the dose of supplement (in mmoL/kg/BM), and dummy variables with values of 0 and 1 specifying whether the given observation was before or after exercise (Exercise01) and before or after supplementation (Supplement01). The terms represented a different contribution for exercise at each exercise timepoint (TimeGroup*Exercise01), a different contribution for each of the four supplement conditions at each supplement timepoint (Condition*TimeGroup*Supplement01), and a linear contribution of dose of each supplement that was different for each supplement but the same at every timepoint for the given supplement (Condition*Supplement01*Dose). The random effects were study identity, the interaction Sports Med 2011; 41 (10)

Study (y)

Dose (mmoL/kg/BM)

Duration (min)

No. of bouts

Double-blind designa

Proportion of male subjects

Athletes

3.6

6.0

1

Yes

1.0

Yes

3.6

0.4

1

Yes

1.0

Yes

Power effect (%)

806

ª 2011 Adis Data Information BV. All rights reserved.

Table I. Study and subject characteristics for performance effects included in the meta-analysis. Studies have been sorted from the largest to the smallest effects on power Power SE (%)

Sodium bicarbonate McNaughton and Cedaro[12] (1991) Artioli et al.

[13]

Bishop et al.

Zajac et al.

Gao et al.

(2007)

[14]

[15]

[16]

(2004)

3.6

(2009)

3.6

(1988)

Balberman and Roby

2.9

[17]

(1983)

Lindh et al.[18] (2008) Goldfinch et al.

[19]

2.0

3.8

2.4

2

5.8

3

5.5

1

Yes

0.0

No

0.8

2

3.5

3

4.4

4

5.4

5

5.6 Yes

0.4

1

5.3

1.5

0.5

2

0.0

1.5

0.5

3

2.4

1.5

0.5

4

3.6

1.8

-1.2

1.1

Yes

1.0

1.0

Yes

1.8

0.9

1

0.9

2

Yes

-1.2

0.9

3

-1.2

0.9

4

2.5

1.0

5

3.8

3.6

1.0

1

Yes

1.0

No

3.4

2.5

3.6

1.9

1

Yes

1.0

Yes

3.2

1.3

4.8

1.0

1

Yes

1.0

No

3.0

0.6

George and MacLaren[20] (1998)

2.4

26

1

Yes

1.0

No

2.7

1.2

Van Montfoort et al.[21] (2004)

3.6

1.3

1

Yes

1.0

Yes

2.7

1.1

3.6

2.1

1

Yes

1.0

Yes

1.2

1.4

Pruscino et al.

[22]

(1988)

0.1

8.2

(2008)

2 3.6

2.1

1

Yes

1.0

Yes

No McCartney et al.[24] (1983)

3.6

0.1 0.5

1

Yes

1.8

0.7

2.4 1.0

No

2.2

2.3

0.9 Continued next page

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Sports Med 2011; 41 (10)

Wilkes et al.[23] (1983)

2.6

Study (y)

Dose (mmoL/kg/BM)

Duration (min)

Lavender and Bird[25] (1989)

3.6

0.2

No. of bouts 1

Double-blind designa

Proportion of male subjects

Athletes

Yes

0.4

No

Power effect (%)

Power SE (%)

0.8

0.4

2

0.9

0.4

3

0.9

0.4

4

0.8

0.4

5

1.2

0.4

6

1.2

0.4

7

1.4

0.5

8

1.1

0.4

9

1.5

0.5

10

2.0

0.6

Kozac-Collins et al.[26] (1994)

3.6

8.4

1

Yes

0.0

Yes

1.8

1.1

Pierce et al.[27] (1992)

2.4

0.9

1

Yes

1.0

Yes

1.7

1.5

Bird et al.[28] (1995)

3.6

4.3

1

1.0

Yes

-2.9

No Yes No

1.1

0.3

1.6

Klein[29] (1987)

3.6

2.0

1

Yes

1.0

No

1.1

0.5

Siegler et al.[30] (2008)

3.6

2.1

1

Yes

1.0

No

0.9

1.6

Katz et al.[31] (1984)

2.4

1.6

1

Yes

1.0

No

0.5

1.5

Linderman et al.[32] (1992)

2.4

6.9

1

Yes

1.0

No

0.5

1.1

Stephens et al.[33] (2002)

3.6

59

1

Yes

1.0

Yes

0.2

2.1

Kowalchuk et al.[34] (1984)

3.6

10

1

Yes

1.0

No

0.0

1.7

Brien and McKenzie[35] (1989)

3.6

6.0

1

Yes

1.0

Yes

-0.1

2.0

Marx et al.[36] (2002)

3.6

0.1

1

Yes

1.0

No

-0.2

1.3

-0.2

0.1

Tiryaki and Atterbom[37] (1995)

1.5

-0.6

1.5

-0.6

3.6

2.0

1.7

1.0

Meta-Analysis: Alkalosis, Acidosis and Performance

ª 2011 Adis Data Information BV. All rights reserved.

Table I. Contd

1

Yes

0.0

No

-0.9

0.4

1

Yes

1.0

No

Sodium citrate Cox and Jenkins[38] (1994)

1.5

1.7

1.6

3

3.9

1.8

4

-1.9

1.9

5

-1.9

2.0

Continued next page

807

Sports Med 2011; 41 (10)

-0.3

2

808

ª 2011 Adis Data Information BV. All rights reserved.

Table I. Contd Study (y) Schabort et al.[39] (2000)

Dose (mmoL/kg/BM)

Duration (min)

0.7

60

No. of bouts 1

Double-blind designa

Proportion of male subjects

Athletes

Yes

1.0

Yes

Power effect (%) 3.9

1.4

-1.6

2.0

-4.7

Power SE (%) 2.0

Potteiger et al.[40] (1996)

1.7

59

1

Yes

1.0

Yes

3.0

1.3

Linossier et al.[41] (1997)

1.7

4.3

1

Yes

0.6

No

2.7

1.5

(2003)

1.7

20

1

Yes

1.0

No

2.7

0.9

(2001)

1.8

10

1

Yes

0.8

Yes

1.8

0.8

Feriche Fernandez-Castanys et al.[44] (2002)

1.4

2.8

1

Yes

1.0

No

1.0

1.0

Van Montfoort et al.[21] (2004)

1.8

1.3

1

Yes

1.0

Yes

0.5

1.1

1.0

2.0

1

Yes

0.0

No

0.4

0.4

1.7

0.6

1

Yes

1.0

Yes

0.3

0.6

Ball and Maughan[46] (1997)

1.0

3.4

1

Yes

1.0

No

-0.1

1.2

Ball and Maughan[46] (1997)

1.0

3.4

1

Yes

1.0

No

-0.1

1.2

Oopik et al.[47] (2008)

1.4

5.3

1

Yes

0.0

Yes

-1.2

0.6

1.7

18

1

Yes

1.0

Yes

-1.6

0.9

Kowalchuk et al.[34] (1984)

5.6

10

1

Yes

0.0

No

-8.0

1.7

Brien and McKenzie[35] (1989)

5.6

6.0

1

Yes

1.0

Yes

-4.9

0.6

McCartney et al.[24] (1983)

5.6

0.1

1

Yes

1.0

No

-1.5

2.3

Oopik et al.

[42]

Shave et al.

[43]

Tiryaki and Atterbom Ibanez et al.

Oopik et al.

[45]

[48]

[37]

(1995)

(1995)

(2004)

Ammonium chloride

-4.9

0.5

Robergs et al.

[49]

(2005)

Balberman and Roby[17] (1983) a

3.8

26

1

Yes

1.0

No

-3.4

1.2

5.6

2.3

1

Yes

1.0

Yes

-1.4

0.6

3.6

1.0

1

Yes

1.0

No

-0.1

2.5

The three observations taken from sodium bicarbonate studies are coded as ‘nonblinded’ because in the control condition of those studies participants were aware that they were not ingesting any supplement. When the subjects from these same three studies were given supplements, appropriate blinding was used.

BM = body mass; SE = standard error.

Carr et al.

Sports Med 2011; 41 (10)

George and MacLaren[20] (1988)

Meta-Analysis: Alkalosis, Acidosis and Performance

of study identity with Condition, Exercise01 and Supplement01 (to allow for these three clusters of repeated measurement within studies) and the residual. The observations from a given study were weighted by a value corresponding to the number of subjects in the study divided by the total number of subjects in all studies. To investigate the extent to which changes in blood [HCO3-] accounted for changes in performance, we plotted the relationship between pre-exercise [HCO3-] and performance. We also examined the scatter for the effect of the supplementation period on blood [HCO3-]. For these two analyses, we fitted simple regression lines for each variable and interpreted the magnitude of each effect using a published scale for interpreting correlation coefficients: 0.0 trivial; 0.1 small; 0.3 moderate; 0.5 large.[11] 1.3.3 Outcome Statistics

Performance effects were reported as the effect (%) –90% confidence limits (CLs). We made probabilistic magnitude-based inferences about the true values of outcomes, based on the likelihood that the outcome was substantially positive or substantially negative.[11] Thresholds for small, moderate and large effects on performance were set at 0.3, 0.9 and 1.6 of the variation in the performance of elite athletes from one competition to another, which is ~0.8% and ~3.5% for running and cycling, respectively (Vandenbogaerde TJ and Hopkins WG, personal communication); therefore, the corresponding smallest effects are ~0.25% (0.8 · 0.3) and ~1.0% (3.5 · 0.3).[11] Given that running, cycling and other exercise types featured in the exercise protocols in our eligible studies, we chose 0.5% for the smallest effect for all included exercise protocols. The corresponding thresholds for moderate and large effects were 1.5% and 2.7%. For performance under reference conditions, an effect was deemed clear if there was a possible benefit (>25%) and harm was sufficiently unlikely that the odds ratio of benefit/ harm was >66. A modifying effect was deemed unclear if its 90% confidence interval overlapped thresholds for the smallest worthwhile positive and negative effects. Correlations and their CLs for the relationships between effects on perforª 2011 Adis Data Information BV. All rights reserved.

809

mance and blood measures were based on the assumption of equal contribution of each study and are therefore only approximate. 2. Results 2.1 Performance Measures

Figure 1 shows the plot used to assess the presence of outliers and publication bias. No study had a sufficiently large t-value to justify its exclusion as an outlier, but an asymmetrical scatter consistent with publication bias is apparent for predicted effects with standard errors greater than ~2.7%. Performance effects with errors greater than this value were therefore excluded from the analysis. The meta-analysed performance effects of sodium bicarbonate, sodium citrate and ammonium chloride are shown in table II. For sodium bicarbonate ingested under reference conditions (3.5 mmoL/kg/BM dose) a moderate performance enhancement was likely. Small extra increases in this performance effect were possible with an increase in dose to 4.5 mmoL/kg/BM and when performing six repeated 1-minute sprints instead of a single 1-minute sprint. With sodium citrate supplementation, the reference dose (1.5 mmoL/kg/BM) produced an unclear effect on performance. The modifying effects of changes in dose and test duration, and performing additional sprint bouts were also unclear. With ammonium chloride ingested under reference conditions (a dose of 5.5 mmoL/kg/BM), a moderate performance impairment was likely; there were further moderate detriments with a 10-fold increase in test duration to 10 minutes and for nonathletes compared with athletes. 2.2 Physiological Measures

Figure 2 shows the meta-analysed time-course of blood [HCO3-], pH and [La-] for three supplement conditions: sodium bicarbonate, sodium citrate, ammonium chloride, and placebo. The only clear mean (–90% CL) differences in [HCO3-] in comparison to the placebo condition were an overall 3.9 (–0.9) mmol/L increase with sodium bicarbonate, and an overall 4.2 (–1.7) mmol/L Sports Med 2011; 41 (10)

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Table II. Effects on mean power (with –90% confidence limits [CLs] and inferences) for sodium bicarbonate, sodium citrate and ammonium chloride in the reference condition,a with modifying effects of study and subject characteristics Agents

Effect (%)

–90% CL

Inference

2.0

Moderate ›

Sodium bicarbonate Reference condition

1.7

Modifying effects › 1 mmoL/kg/BM

0.5

0.6

Small ›

Five extra bouts

0.6

0.4

Small ›

10 · duration

-0.6

0.9

Small fl

Nonathletes

-1.1

1.1

Small fl

Females

-0.7

1.4

Unclear

0.2

0.7

Unclear

0.0

1.3

Unclear

0.0

0.8

Unclear

Five extra bouts

-2.8

3.5

Unclear

10 · duration

-0.1

1.1

Unclear

Nonathletes

1.8

1.5

Moderate ›

Females

0.1

2.0

Unclear

-1.6

1.9

Moderate fl

Nonblinded Sodium citrate Reference condition

3. Discussion

Modifying effects › 1 mmoL/kg

Ammonium chloride Reference condition Modifying effects › 1 mmoL/kg

-0.3

0.3

Trivial fl

10 · duration

-2.5

2.0

Moderate fl

Nonathletes

-2.6

2.8

Moderate fl

Females

-1.2

4.2

Unclear

a

citrate did not fit this trend, with increased preexercise alkalosis associated with a small, unclear effect on performance (r = 0.10; 90% CI -0.56, 0.68). There was a very high correlation (figure 4) between increased pre-exercise [HCO3-] and time from sodium citrate ingestion (r = 0.76; 90% CI 0.25, 0.94). For sodium bicarbonate, there was a trivial effect of supplement absorption time on [HCO3-] (r = 0.07; 90% CI -0.37, 0.48), and there was a moderate decrement in [HCO3-] as time from ammonium chloride ingestion increased (r = 0.42; 90% CI -0.61, 0.92); however, both these effects were unclear.

Reference condition: a single 1-minute sprint with blinded male athletes consuming 3.5 mmoL/kg/BM sodium bicarbonate, or 1.5 mmoL/kg/BM sodium citrate, or 5.5 mmoL/kg/BM ammonium chloride. Effect magnitudes are interpreted with reference to thresholds for small (0.5%), moderate (1.5%) and large (2.7%) effects. Overall between-study random differences: –1.2% (90% CL –0.5%).

BM = body mass; fl indicates decrease; › indicates increase.

decrease with ammonium chloride. There was also a clear mean increase in pH of 0.069 (–0.018) with sodium bicarbonate compared with placebo. There was a moderate correlation (figure 3) between performance and pre-exercise blood [HCO3-] after sodium bicarbonate ingestion (r = 0.33; 90% CI -0.10, 0.65) and moderate performance impairments with increased preexercise blood acidosis with ammonium chloride (r = 0.33; 90% CI -0.78, 0.85). Data for sodium ª 2011 Adis Data Information BV. All rights reserved.

This is the first meta-analytic review of research on the acute performance effects of agents that modify the body’s blood pH (sodium bicarbonate, sodium citrate and ammonium chloride). The most effective supplement of these three is sodium bicarbonate, which enhances performance by a clear 1.7% (–2.0%) under the typical testing conditions of blinded male athletes ingesting a 0.3 g/kg/BM dose prior to a 1-minute sprint. The effectiveness of sodium bicarbonate is enhanced with an increased dose and when performing repeated sprints, and there is a reduction in benefit with nonathletes and when increasing the test duration to 10 minutes or longer. The modifying effect with females and the placebo effect are unclear and at most small in magnitude. Sodium citrate under typical testing conditions has an unclear performance effect that could at most be a small beneficial or a small harmful effect. Ammonium chloride under typical test conditions has a moderately harmful effect. The similar magnitude in performance impairment with ammonium chloride ingestion, which was associated with a dose-dependent decrease in [HCO3-], to that of the performance enhancement and pre-exercise increase in [HCO3-] with sodium bicarbonate, provides support for the concept that extracellular buffering is an important modifier of performance in athletes. We found a ~2% performance improvement with sodium bicarbonate supplementation under typical test conditions. We interpret this to be a Sports Med 2011; 41 (10)

Meta-Analysis: Alkalosis, Acidosis and Performance

Sodium bicarbonate Sodium citrate Ammonium chloride Placebo SD

a 40

[HCO3−] (mmol/L)

811

30

20

10

0 b 7.5 7.4

pH

7.3 7.2 7.1 7.0 6.9 c 20

[La−] (mmol/L)

15

10

5

0 −95

−1

2 5 12 Time from exercise (min)

33

Fig. 2. Time-course of (mean – 90% CL) blood bicarbonate concentration ([HCO3-]), pH and lactate concentration ([La-]) in relation to exercise commencement. Negative values are for time before exercise; positive values indicate post-exercise timepoints. Standard deviation (SD) bars represent the mean between-subjects SD where provided. CL = confidence limit.

moderate performance enhancement, using a scale to evaluate the magnitude of improvement in the performance of elite athletes who are reª 2011 Adis Data Information BV. All rights reserved.

quired to win medals in competitive events.[11] The previous meta-analysis[4] found a standardized improvement in performance of 0.44 (0.44 of the between-subject standard deviation in performance). This discrepancy may arise from our conversion of all data to a common metric prior to analysis to more accurately represent performance effects, or the use of a scale[5] by Matson and Tran[4] that is less appropriate for the interpretation of improvements in competitive athletic performance. Despite a similar pre-exercise perturbation to both [HCO3-] and pH with sodium citrate as with sodium bicarbonate, we found that the performance effect of sodium citrate ingestion is unclear. Supplement mechanisms were not the focus of this review, but our physiological measures may assist in providing information that can contribute to explaining the disparity between these two alkalotic agents. Sodium bicarbonate ingestion was associated with a trend toward improved performance with increased [HCO3-], but sodium citrate was not. There are limitations to the aforementioned observed trends, in that the correlations depicted in figure 3 are based on few data and have wide confidence intervals. However, our observations suggest that the mechanism(s) that potentially account for the ergogenic effect of sodium bicarbonate, such as improved extracellular buffering and subsequent increased intramuscular pH or potassium concentration ([K+])[50] during high-intensity exercise[1] and subsequent fatigue offset,[1,50] may be counteracted in the case of sodium citrate ingestion by some inhibitory effect, such as an increased intracellular citrate[51] inhibiting phosphofructokinase[52] and thus adenosine triphosphate production.[53] The unclear effect of sodium citrate on performance notwithstanding, we have demonstrated that several hours after sodium citrate ingestion, [HCO3-] is higher than with an equimolar dose of sodium bicarbonate. The greater buffering potential with sodium citrate could be explained by citrate ions having three negative charges that consume H+ and thereby raise [HCO3-],[28] whereas bicarbonate ions have only one.[21] In the majority of studies we meta-analysed sodium citrate was taken 90–120 minutes prior to exercise,[46,54,55] but our Sports Med 2011; 41 (10)

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Change in power (%)

10

5

0

−5

−10 −10

−5

0

5

10

Change in [HCO3−] (mmol/L) Fig. 3. Individual study estimates of percentage effects on performance plotted against pre-exercise change in blood bicarbonate concentration ([HCO3-]) after supplement ingestion. Regression lines are shown for the three supplements.

results suggest that the greatest [HCO3-] change would be more than 180 minutes after ingestion. Therefore, peak [HCO3-] with sodium citrate seems to occur later than the 60–90 minutes seen with sodium bicarbonate.[56,57] The underlying explanation for the disparity between the two alkalotic agents is unclear and requires further investigation. Future research should also focus on the timecourse of induced alkalosis with sodium citrate and the timing of pre-exercise supplement ingestion required to consistently enhance performance. The unexplained variation in the performance effects between research settings was typically –1.2% for the three supplements. This percentage represents the variation in performance that will occur if, for the reference conditions, a study was replicated in an independent laboratory. For instance, if a new team of investigators used the reference conditions for sodium bicarbonate, with an adequate sample size, they might easily attain a performance benefit as low as 0.5% (1.7% - 1.2%) or as high as 2.9% (1.7% + 1.2%). The magnitude of unexplained variation between studies also casts doubt on the results of groups reporting mean performance benefits of 10–15% (when expressed as a percentage change in mean power) with bicarbonate supplementation.[58-60] In any case, these studies had standard errors above ª 2011 Adis Data Information BV. All rights reserved.

the threshold we chose to reduce publication bias and were eliminated from the analysis. The incidence of side effects after supplement ingestion was not quantified in this review. Side effects were quantified in only a small number of studies, which is a limitation in this area of research and, therefore, a limitation of this metaanalytic review. Gastrointestinal disturbance is often associated with sodium bicarbonate[15,21,26,58] and in some cases sodium citrate ingestion.[43,48] Failure to quantify the type and severity of side effects experienced by participants is a limitation of previous investigations, and future studies should be designed to redress this shortcoming. Some recent studies have incorporated the objective measurement of gastrointestinal symptoms,[61,62] and other future investigations should include similar measurements. Since we found that a higher dose of sodium bicarbonate, increasing from 3.5 to 4.5 mmoL/kg/BM, would result in additional improvements in performance, it follows that the associated changes in gastrointestinal symptoms are equally important to assess from a perspective of utility. Regardless of mean results modelled by our meta-analysis, an athlete’s personal experience of gastrointestinal side effects after supplement ingestion could modify the benefits of bicarbonate supplementation. There could also be other influences, such as differences in endogenous Sodium bicarbonate Sodium citrate Ammonium chloride

6 Change in [HCO3−] per unit dose (mmol/L [mmol/kg/BM])

Sodium bicarbonate Sodium citrate Ammonium chloride

4 2 0 −2 −4 0

30

60

90 120 Time (min)

150

180

Fig. 4. Change in pre-exercise blood bicarbonate concentration ([HCO3-]) with time from ingestion. Regression lines are shown for each supplement.

Sports Med 2011; 41 (10)

Meta-Analysis: Alkalosis, Acidosis and Performance

buffer levels (arising from training or diet) responsible for individual responses to bicarbonate supplementation and subsequent high-intensity exercise performance. This meta-analysis, like all others, produces estimates for only mean effects on performance. Estimation of individual responses will be possible only when authors provide adequate information. 4. Conclusions We recommend ingestion of sodium bicarbonate at a dose of 0.3 g/kg/BM for performance enhancements of ~2% in short-duration (~1-minute), high-intensity sprints, for male and female athletes. The performance effect will be greater with an increase in dose and when performing repeated sprints, and gains will be diminished with nonathletes and with performances lasting ~10 minutes or more. We do not recommend sodium citrate supplementation as it is currently implemented, but more research is needed to investigate the possibility that there could be a performance benefit if exercise commences ~3 hours after ingestion. Future investigations should also include detailed documentation of gastrointestinal side effects, which could give more information about individual responses to the supplement. Acknowledgements No funding has been received for the preparation of this manuscript. The authors declare that there are no conflicts of interest that are directly relevant to the content of this review.

References 1. Gledhill N. Bicarbonate ingestion and anaerobic performance. Sports Med 1984; 1: 177-80 2. Dennig H, Talbott JH, Edwards HT, et al. Effects of acidosis and alkalosis upon capacity for work. J Clin Invest 1931; 9: 601-13 3. Dill DB, Edwards HT, Talbott JH. Alkalosis and the capacity for work. J Biol Chem 1931; 97: 58-9 4. Matson LG, Tran ZV. Effects of sodium bicarbonate ingestion on anaerobic performance: a meta-analytic review. Int J Sport Nutr 1993; 3 (1): 2-28 5. Cohen J. Statistical power analysis for the behavioural sciences. Hillsdale (NJ): Lawrence Erlbaum Associates, 1988 6. Burke LM, Pyne DB. Bicarbonate loading to enhance training and competitive performance. Int J Sports Physiol Perform 2007; 2: 93-7

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7. Requena B, Zabala M, Padial P, et al. Sodium bicarbonate and sodium citrate: ergogenic aids? J Strength Cond Res 2005; 19 (1): 213-24 8. McNaughton L, Siegler J, Midgley A. Ergogenic effects of sodium bicarbonate. Curr Sports Med Rep 2008; 7 (4): 230-6 9. Hopkins WG, Schabort EJ, Hawley JA. Reliability of power in physical performance tests. Sports Med 2001; 31 (3): 211-34 10. Le´ger L, Mercier D. Gross energy cost of horizontal treadmill and track running. Sports Med 1984; 1: 270-7 11. Hopkins WG, Marshall SW, Batterham AM, et al. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41 (1): 3-12 12. McNaughton L, Cedaro R. The effect of sodium bicarbonate on rowing ergometer performance in elite rowers. Aust J Sci Med Sport 1991; 23 (3): 66-9 13. Artioli GG, Gualano B, Coelho DF, et al. Does sodiumbicarbonate ingestion improve simulated judo performance? Int J Sport Nutr Exerc Metab 2007; 17: 206-20 14. Bishop D, Edge J, Davis C, et al. Induced metabolic acidosis affects muscle metabolism and repeated-sprint ability. Med Sci Sports Exerc 2004; 36 (5): 807-13 15. Zajac A, Cholewa J, Poprzecki S, et al. Effects of sodium bicarbonate ingestion on swim performance in youth athletes. J Sports Sci Med 2009; 8: 45-50 16. Gao J, Costill DL, Horswill CA, et al. Sodium bicarbonate ingestion improves performance in interval swimming. Eur J Appl Physiol 1988; 58: 171-4 17. Balberman SE, Roby FB. The effects of induced alkalosis and acidosis on the work capacity of the quadriceps and hamstrings muscle groups [abstract]. Int J Sports Med 1983; 4: 143 18. Lindh AM, Peyrebrune MC, Ingham SA, et al. Sodium bicarbonate improves swimming performance. Int J Sports Med 2008; 29: 519-23 19. Goldfinch J, McNaugton L, Davies P. Induced metabolic acidosis and its effects on 400-m racing time. Eur J Appl Physiol 1988; 57: 45-8 20. George KP, MacLaren DPM. The effect of induced alkalosis and acidosis on endurance running at an intensity corresponding to 4 mM blood lactate. Ergonomics 1988; 31 (11): 1639-45 21. Van Montfoort MCE, Van Dieren L, Hopkins WG, et al. Effects of ingestion of bicarbonate, citrate, lactate and chloride on sprint running. Med Sci Sports Exerc 2004; 36 (7): 1239-43 22. Pruscino CL, Ross MLR, Gregory JR, et al. Effects of sodium bicarbonate, caffeine and their combination on repeated 200-m freestyle performance. Int J Sport Nutr Exerc Metab 2008; 18: 116-30 23. Wilkes D, Gledhill N, Smyth R. Effect of acute induced metabolic akalosis on 800-m racing time. Med Sci Sports Exerc 1983; 15 (4): 277-80 24. McCartney N, Heigenhauser GJF, Jones NL. Effects of pH on maximal output and fatigue during short-term dynamic exercise. J Appl Physiol 1983; 55: 225-9 25. Lavender G, Bird SR. Effect of sodium bicarbonate ingestion upon repeated sprints. Br J Sports Med 1989; 23 (1): 41-5 26. Kozac-Collins K, Burke ER, Schoene R. Sodium bicarbonate ingestion does not improve performance in women cyclists. Med Sci Sports Exerc 1994; 26 (12): 1510-5

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27. Pierce EF, Eastman NW, Hammer WH, et al. Effect of induced alkalosis on swimming time trials. J Sports Sci 1992; 10: 255-9 28. Bird SR, Wiles J, Robbins J. The effect of sodium bicarbonate ingestion on 1500-m racing time. J Sports Sci 1995; 13: 399-403 29. Klein L. The effect of bicarbonate ingestion on upper body power in trained athletes [abstract]. Med Sci Sports Exerc 1987; 19: 567 30. Siegler JC, Keatley S, Midgley AW, et al. Pre-exercise alkalosis and acid-base recovery. Int J Sports Med 2008; 29: 545-51 31. Katz A, Costill DL, King DS, et al. Maximal exercise tolerance after induced alkalosis. Int J Sports Med 1984; 5: 107-10 32. Linderman JK, Kirk L, Musselman J, et al. The effects of sodium bicarbonate and pyridoxine-alpha-ketoglutarate on short-term maximal exercise capacity. J Sports Sci 1992; 10: 243-53 33. Stephens TJ, McKenna MJ, Canny BJ, et al. Effect of sodium bicarbonate on muscle metabolism during intense endurance cycling. Med Sci Sports Exerc 2002; 43 (4): 614-21 34. Kowalchuk JM, Heigenhauser GJF, Jones NL. Effect of pH on metabolic and cardiorespiratory responses during progressive exercise. J Appl Physiol 1984; 57 (5): 1558-63 35. Brien DM, McKenzie DC. The effect of induced alkalosis and acidosis on plasma lactate and work output in elite oarsmen. Eur J Appl Physiol 1989; 58: 797-802 36. Marx JO, Gordon SE, Vos NH, et al. Effect of alkalosis on plasma epinephrine responses to high intensity cycle exercise in humans. Eur J Appl Physiol 2002; 87: 72-7 37. Tiryaki GR, Atterbom HA. The effects of sodium bicarbonate and sodium citrate on 600 m running time of trained females. J Sports Med Phys Fit 1995; 35: 194-8 38. Cox G, Jenkins DG. The physiological and ventilatory responses to repeated 60 s sprints following sodium citrate ingestion. J Sports Sci 1994; 12: 469-75 39. Schabort E, Wilson G, Noakes TD. Dose-related elevations in venous pH with citrate ingestion do not alter 40-km cycling time-trial performance. Eur J Appl Physiol 2000; 83: 320-7 40. Potteiger JA, Nickel GL, Webster MJ, et al. Sodium citrate enhances 30 km cycling performance. Int J Sports Med 1996; 17: 7-11 41. Linossier MT, Dormois D, Bre´ge`re P, et al. Effect of sodium citrate on performance and metabolism of human skeletal muscle during supramaximal cycling exercise. Eur J Appl Physiol 1997; 76: 48-54 42. Oopik V, Saaremets I, Medijainen L, et al. Effects of sodium citrate ingestion before exercise on endurance performance in well trained college runners. Br J Sports Med 2003; 37: 485-9 43. Shave R, Whyte G, Siemann A, et al. The effects of sodium citrate ingestion on 3,000-meter time-trial performance. J Strength Cond Res 2001; 15 (2): 230-4 44. Feriche Ferna´ndez-Castanys B, Delgado-Ferna´ndez M, Alvarez Garcı´ a J. The effect of sodium citrate intake on anaerobic performance in normoxia and after sudden ascent to moderate altitude. J Sports Med Phys Fitness 2002; 42 (2): 179-85 45. Ibanez J, Pullinen T, Gorostiaga E, et al. Blood lactate and ammonia in short-term anaerobic work following induced alkalosis. J Sports Med Phys Fit 1995; 35: 187-93

ª 2011 Adis Data Information BV. All rights reserved.

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46. Ball D, Maughan RJ. The effect of sodium citrate ingestion on metabolic response to intense exercise following diet manipulation in man. Exp Physiol 1997; 82: 1041-56 47. Oopik VT, Timpmann S, Kadak K, et al. The effects of sodium citrate ingestion on metabolism and 1500-m racing time in trained female runners. J Sports Sci Med 2008; 7: 125-31 48. Oopik V, Saaremets I, Timpmann S, et al. Effects of acute ingestion of sodium citrate on metabolism and 5-km running performance: a field study. Can J Appl Physiol 2004; 29 (6): 691-703 49. Robergs R, Hutchinson K, Hendee S, et al. Influence of preexercise acidosis and alkalosis on the kinetics of acid-base recovery following intense exercise. Int J Sport Nutr Exerc Metab 2005; 14: 59-74 50. Sostaric S, Skinner SL, Brown MJ, et al. Alkalosis increases muscle K+ release, but lowers plasma [K+] and delays fatigue during dynamic forearm exercise. J Physiol 2005; 570 (1): 185-205 51. Kowalchuk JM, Maltais SA, Yamaji K, et al. The effect of citrate loading on exercise performance, acid-base balance and metabolism. Eur J Appl Physiol 1989; 58: 858-64 52. Horswill CA. Effects of bicarbonate, citrate and phosphate loading on performance. Int J Sport Nutr 1995; 5: 111-9 53. Newsholme EA, Leech AR. Biochemistry for the Medical Sciences. Chichester, London: John Wiley & Sons Ltd, 1983 54. Potteiger JA, Webster MJ, Nickel GL, et al. The effects of buffer ingestion on metabolic factors related to distance running performance. Eur J Appl Physiol 1996; 72: 365-71 55. McNaughton L. Sodium citrate and anaerobic performance: implications of dosage. Eur J Appl Physiol 1990; 61: 392-7 56. Price MJ, Singh M. Time course of blood bicarbonate and pH three hours after sodium bicarbonate ingestion. Int J Sports Physiol Perform 2008; 3: 240-2 57. Renfree A. The time course for changes in plasma [H+] after sodium bicarbonate ingestion. Int J Sports Physiol Perform 2007; 2: 323-6 58. McNaughton L, Cedaro R. Sodium citrate ingestion and its effects on maximal anaerobic exercise of different durations. Eur J Appl Physiol 1992; 64: 36-41 59. McNaughton L, Strange N, Backx K. The effects of chronic sodium bicarbonate ingestion on multiple bouts of anaerobic work and power output. J Hum Move Stud 2000; 38: 307-22 60. McNaughton L, Dalton B, Palmer G. Sodium bicarbonate can be used as an ergogenic aid in high-intensity, competitive cycle ergometry of 1 h duration. Eur J Appl Physiol 1999; 80: 64-9 61. Tan F, Polglaze T, Cox G, et al. Effects of induced alkalosis on simulated match performance in elite female water polo players. Int J Sport Nutr Exerc Metab 2010; 20 (3): 198-205 62. Cameron SL, McLay-Cooke RT, Brown RC, et al. Increased blood pH but not performance with sodium bicarbonate supplementation in elite rugby union players. Int J Sport Nutr Exerc Metab 2010; 20 (4): 307-21

Correspondence: Dr Amelia J. Carr, Physiology, Australian Institute of Sport, Bruce, ACT, 2617, Australia. E-mail: [email protected]

Sports Med 2011; 41 (10)

Sports Med 2011; 41 (10): 815-843 0112-1642/11/0010-0815/$49.95/0

REVIEW ARTICLE

ª 2011 Adis Data Information BV. All rights reserved.

Compression Garments and Exercise Garment Considerations, Physiology and Performance Braid A. MacRae,1,2 James D. Cotter 2 and Raechel M. Laing1 1 Clothing and Textile Sciences, Department of Applied Sciences, University of Otago, Dunedin, New Zealand 2 School of Physical Education, University of Otago, Dunedin, New Zealand

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Garment Considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Garment Properties and Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Garment Sizing and Applied Pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Effects During Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Exercise Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Jumping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Sprinting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Prolonged Running or Cycling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4 Varied-Activity Exercise and Simulated Team Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.5 Fatigue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Physiological and Physical Effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Cardiorespiratory Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Thermoregulatory Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Proprioception and Muscle Oscillation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Perceptual Responses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Effects on Cardiovascular Function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Systemic Cardiovascular and Haemodynamic Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Effects on Local Blood Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Effects During Recovery from Exercise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Subsequent-Performance Measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Physiological and Physical Effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Myocellular Proteins and Metabolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Swelling, Range of Motion and Proprioception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Perceptual Responses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

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Compression garments (CGs) provide a means of applying mechanical pressure at the body surface, thereby compressing and perhaps stabilizing/ supporting underlying tissue. The body segments compressed and applied pressures ostensibly reflect the purpose of the garment, which is to mitigate exercise-induced discomfort or aid aspects of current or subsequent exercise performance. Potential benefits may be mediated via physical, physiological or psychological effects, although underlying mechanisms are typically not well elucidated. Despite widespread acceptance of CGs by competitive and

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recreational athletes, convincing scientific evidence supporting ergogenic effects remains somewhat elusive. The literature is fragmented due to great heterogeneity among studies, with variability including the type, duration and intensity of exercise, the measures used as indicators of exercise or recovery performance/physiological function, training status of participants, when the garments were worn and for what duration, the type of garment/ body area covered and the applied pressures. Little is known about the adequacy of current sizing systems, pressure variability within and among individuals, maintenance of applied pressures during one wear session or over the life of the garment and, perhaps most importantly, whether any of these actually influence potential compression-associated benefits. During exercise, relatively few ergogenic effects have been demonstrated when wearing CGs. While CGs appear to aid aspects of jump performance in some situations, only limited data are available to indicate positive effects on performance for other forms of exercise. There is some indication for physical and physiological effects, including attenuation of muscle oscillation, improved joint awareness, perfusion augmentation and altered oxygen usage at sub-maximal intensities, but such findings are relatively isolated. Sub-maximal (at matched work loads) and maximal heart rate appears unaffected by CGs. Positive influences on perceptual responses during exercise are limited. During recovery, CGs have had mixed effects on recovery kinetics or subsequent performance. Various power and torque measurements have, on occasions, benefitted from the use of CGs in recovery, but subsequent sprint and agility performance appears no better. Results are inconsistent for postexercise swelling of limb segments and for clearance of myocellular proteins and metabolites, while effects on plasma concentrations are difficult to interpret. However, there is some evidence for local blood flow augmentation with compression. Ratings of post-exercise muscle soreness are commonly more favourable when CGs are worn, although this is not always so. In general, the effects of CGs on indicators of recovery performance remain inconclusive. More work is needed to form a consensus or mechanisticallyinsightful interpretation of any demonstrated effects of CGs during exercise, recovery or – perhaps most importantly – fitness development. Limited practical recommendations for athletes can be drawn from the literature at present, although this review may help focus future research towards a position where such recommendations can be made.

Compression has been used in therapeutic medicine for many years, and applications include prophylaxis against lymphoedema or other oedema,[1,2] pulmonary embolism,[3] stasis and deep vein thrombosis,[4] and in management of wound, scar and venous leg ulcers.[5] Compression has since been used in sporting applications, with compression garments (CGs) manufactured and marketed as a means of improving numerous aspects of exercise performance, although the basis of such claims often lacks satisfactory supporting ª 2011 Adis Data Information BV. All rights reserved.

evidence or the evidence is taken out of context. The aim of this article was to review the published research on effects of wearing CGs during exercise and/or exercise recovery. Our preference was to provide extensive, accessible information rather than strongly integrative findings because this topic has been particularly susceptible to generalization and extrapolation of study findings where it may be imprudent to do so. Literature was located over a 2-year period (up to July 2010) using a combination of database Sports Med 2011; 41 (10)

Compression Garments and Exercise

searches (PubMed, MEDLINE, Google Scholar; with key words including ‘compression’, ‘compressive’, ‘garment’, ‘stocking’, ‘exercise’, ‘sport’, ‘performance’, ‘recovery’, ‘muscle soreness’ and ‘haemodynamic’) and extensive follow-up through reference sections of identified papers. To establish inclusion criteria, a CG (in the context of sport and exercise) was defined as a garment that is: (i) worn to apply pressure to particular areas of the body with the intention of mitigating exerciseinduced discomfort, or aiding aspects of current or subsequent exercise performance; and (ii) of a construction that permits prolonged wear (if required). 1. Garment Considerations 1.1 Garment Properties and Characterization

Many styles of CGs exist, including stockings (knee length, thigh length), sleeves, upper-body garments (covering the torso and the upper limbs in full or part) and lower-body garments (from the waist, covering the lower limbs in full or part). The pressures applied to the body depend primarily on mechanical properties of the garment, which are derived from both the properties of the fabric and garment fit (i.e. garment vs body dimensions). Mechanical properties, in particular those contributing to extension and recovery behaviour, are also important for maintaining compression within and across wear sessions, and hence determine the effective life of the garment. Attention directed towards understanding the mechanical and physical properties of sporting CGs in the published literature is rare.[6] When examining the ergogenic effects of wearing CGs on aspects of exercise and recovery, considerations regarding the garments themselves are infrequently noted. Physical properties of the constituent fabrics have several pertinent effects including influences on transfer of heat and moisture (liquid and vapour). While measurement of physical or mechanical fabric properties (e.g. thermal resistance,[7] extension and recovery behaviour/elasticity) may not always be possible because of instrument requirements, basic characterization of the garment and fabric provides information that permits comparison among studies ª 2011 Adis Data Information BV. All rights reserved.

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and thus should be included. Such information includes fabric structure (CGs are usually made from knit fabrics), thickness,[8] mass per unit area[9] and fibre content,[10,11] in addition to explicitly stating the area of the body covered by the garment (e.g. ankle to waist). 1.2 Garment Sizing and Applied Pressures

Sizing is generally more critical for ‘performance’ garments than for fashion/dress,[12] and the likely relationship between CG dimensions and compression-applied pressures emphasize this importance. However, ready-to-wear garments, such as commercially-available CGs, must usually fit individuals whose actual body dimensions are unknown to the manufacturer/supplier and, thus, generalized sizing systems are used. Sporting CGs are commonly fitted according to generic anthropometric features such as body mass, height and chest circumference. Yet, people in the same dimensional range for a particular garment size (e.g. medium lower-body CG) would be likely to vary in body morphology (e.g. calf circumference, tibia length). Indeed, the proportional variation in any population is not well addressed with many sizing systems.[13] The applied pressures are too often unreported in studies on CGs in sport, although pressure measurement has become more common (e.g.[14-16]) and includes pressure measurement during movement.[17] When pressures are stated but not measured by the investigators, the source needs to be explicit (e.g. manufacturer-reported pressures). Information about the applied pressures is essential for subsequent efforts to compare findings across studies, and hence measurement of these pressures is strongly encouraged. It is recommended that standardized anatomical locations be used and reported to define measurement positions for the lower body,[18,19] with preference given to sites B1[19] (area at which the Achilles tendon changes into the calf muscles), C (calf at the maximum girth) and F (mid thigh). Other recommendations are that resting pressures when standing are reported as convention, supplemented by pressures in other postures or during movement if relevant and practicable. Sports Med 2011; 41 (10)

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Whether the existing sizing systems used for sporting CGs adequately account for the variation in size and shape of the end user in terms of the application of ‘intended’ pressure profiles, seems unknown. Evidence from clinical studies on graduated compression stockings (GCSs) indicates that measured pressures frequently differ from those intended[20,21] and, therefore, achieving specific compression profiles is likely to be difficult, particularly with generic sizing of products. Graduated compression implies that the applied pressures are highest distally, and decrease proximally, with use originating from medical applications that relate primarily to circulatory roles such as the reduction of venous pooling and augmentation of venous blood flow velocity.[22,23] The claim of graduated compression is common among sporting CGs covering the limbs, although the implications are not well understood. Issues with sizing notwithstanding, the extent to which body size and shape differences actually influence the applied pressures and, more importantly, whether variability in these pressures actually affects the potential benefits of CGs in exercise applications remains unclear. In the medical literature, attempts have been made to identify an ‘optimal’ pressure gradient for the lower limbs (e.g.[22-25]), although common measures used (e.g. venous flow velocities and vein cross-sectional areas of stationary limbs) and body posture (typically lying supine) are not directly applicable to dynamic exercise. Lessons from the medical literature pertinent to research on CGs and exercise include that movement,[26] body or limb posture,[21,27] the site of measurement and underlying tissue[28,29] all influence in vivo pressures. Effective pressure gradients for CGs used in sport do not seem to have been studied systematically, which is not surprising given the modest demonstrated effects of CGs during or following exercise. Compression applied during exercise encompasses many variables (e.g. physiological or physical effects) that may or may not be interrelated with respect to a particular applied pressure. If indeed ‘optimal’ compression profiles exist for any particular variable related to exercise or recovery performance (e.g. tissue inflammation or metabolite clearance), those same ª 2011 Adis Data Information BV. All rights reserved.

pressures may be less optimal, have little effect or may even be detrimental to another variable (e.g. adaptations to strength or metabolic pathways). Hence, the issue of compression for exercise is complex and multifaceted, and the applied pressures of sporting CGs seem likely to be somewhat arbitrary rather than based on specific empirical evidence. It is difficult to delineate the contribution of differences in CG pressure, pressure distribution and body coverage across studies to explain differences in outcome effects because of the extent of heterogeneity among other aspects of those studies. Additionally, reliability of reported pressures is uncertain due partly to the different methods of measurement and sites used (if measured). Because CGs apply pressure to the body, a difficulty in experimental design and, hence, in interpretation of experimental results, is that participants are typically not blinded to experimental condition. No-garment conditions do not control for body-coverage and placebo effects, and even placebo garments are likely to be noticeably less tight. Nevertheless, garments that look similar should be used where practicable to minimize placebo effects (e.g.[14]). 2. Effects During Exercise Relatively few ergogenic effects have been demonstrated when wearing CGs during exercise, as summarized in table I. Note that effects during exercise that are assessed/re-assessed during or following a defined recovery period are included in section 4 (recovery). Physical and physiological effects that have been observed include attenuation of muscle oscillation and improved joint awareness,[42] perfusion augmentation,[32] altered oxygen usage at sub-maximal intensities[33] and, not surprisingly, higher skin temperatures for areas covered by CGs.[35,36,39] However, while CGs appear to help aspects of jump performance in some situations,[34,41,42] only limited data (e.g.[40,44]) are available that indicate positive effects on performance for other forms of exercise. As previously indicated, information on the applied pressures is important for comparison of results among studies; however, such information is often lacking. Sports Med 2011; 41 (10)

Study Ali et al.[30]

No. of participants; sex; fitness level

CGs type

applied pressure (mmHg)

14; M; active

GCS (knee length)

18–22 anklea

Exercise protocol

Performance indicator

Effects of CG

Multistage shuttle running

Distance run

2

HR (throughout; mean, max)

2

RPE (end exercise)

2

Run time (paced)

2

HR (throughout)

2

RPE (end exercise)

2

. VO2 (throughout)

2b

HR (throughout)

2b

10-km road run

Ali et al.[14]

9; M, 1; F; run trained

Berry and McMurray[31]

6; M; active

GCS (knee length); 3 types: high pressure, low pressure, control

GCS (covered calf)

High pressure: 26 ankle, 15 calf; low pressure: 11 ankle, 8 calf; control: 4 ankle, 4 calf

~18 ankle, ~8 calfa

Bochmann et al.[32]

9; M; healthy

Compression sleeve (wrist to elbow)

~16

Bringard et al.[33]

6; M; run trained

Lower-body CG (coverage NR)

NR

40-min running at . ~80% VO2max (fixed velocity)

. Running VO2max test (incremental, 2-min stages)

10; M, 10; F; sprint or jump trained

Compression shorts (above knee to waist)d

NR

2b

Time to exhaustion . VO2max

2

Forearm perfusion (throughout)

. Running VO2max test (incremental, 3-min stages)

. VO2max . VE (at each stage)

60-m sprint

2

›

2 2

Oxygen cost (mL O2 ? kg-1 ? m-1): at 12 km/h

fl

at 10, 14, 16 km/h

2

HR (at each stage)

2

RPE (end exercise) . VO2 slow componentc . VE (min 2 and 15)

2

HR (min 2 and 15)

2

RPE (end exercise)

2

fl 2

Sprint time

2

Hip ROM

fle

Knee ROM

2 Continued next page

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Doan et al.

2b

RPE (throughout)

70-min light rhythmic hand-grip exercise

15-min running at . ~80% VO2max

[34]

[La ]P (throughout)

-

Compression Garments and Exercise

ª 2011 Adis Data Information BV. All rights reserved.

Table I. The effect of wearing compression garments (CGs; vs without CGs) during exercise

Study

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Table I. Contd No. of participants; sex; fitness level

CGs type

Exercise protocol

Performance indicator

Effects of CG

CMJ

Height of maximum CMJ

›e

Longitudinal muscle oscillation

fl

Anterior-posterior muscle oscillation

fl

Skin temperature (thigh)

›

applied pressure (mmHg)

5-min cycling Duffield and Portus[35]

Duffield et al.[36]

Duffield et al.[37]

Higgins et al.[38]

10; M; cricket players

14; M; trained rugby players

Upper- and lower-body CGs (wrist and neck to waist; ankle to waist)

Lower-body CG (coverage NR)

NR

2 2

Sprint time (for 10 and 20 m; mean, total, % decline)

2

Distance covered (total)

2

HR (throughout)

2

Skin temperature (weighted mean, 3 sites)

›

Mass loss

2

RPE (throughout)

2

20-m repeat sprint time

2

Peak power (dynamometer cart)

2

HR (throughout)

2

Tympanic temperature (throughout)

2

Skin temperature (thigh; throughout)

›

Mass loss

2

10-min repeat 20-m sprint and plyometric bounding

Sprint time (mean, total, % decline)

2

10 bound distance (mean, total, % decline)

2 ( fl )f

4 · 15-min gamespecific circuit for netball

20 m sprint time (each quarter)

2

CMJ flight time (each quarter)

2

30-min repeat 20-m sprints and shuttle running

NR

11; M; moderatelytrained rugby players

Lower-body CG (coverage NR)

NR

9; F; trained netball players

NR, presumably lower-body CG (coverage NR)

NR

80-min simulated team game

HR (throughout)

2

[La-]P (end quarter)

2

Distance covered (total)

2 ( › )g Continued next page

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Sports Med 2011; 41 (10)

Distance performance measures Accuracy performance measures

Ball throwing

Study Houghton et al.[39]

Kemmler et al.[40]

Kraemer et al.[41]

Kraemer et al.[42]

No. of participants; sex; fitness level

CGs type

10; M; moderatelytrained hockey players

Upper- and lower-body CGs (mid-arm and neck to waist; above knee to waist)

21; M; moderatelytrained runners

18; M, 18; F; volleyball players

10; M, 10; F; athletic 10 M, 10 F; nonathletic

Compression stockings (presumably ankle to knee)

Lower-body CG (knee to waist)

Lower-body CG (aboveknee to waist)

Exercise protocol

Performance indicator

Effects of CG

4 · 15-min intermittent shuttle running tests

15-m sprint time (each quarter)

2

HR (each quarter)

2

Core temperature (each quarter)

2

Skin temperature (weighted mean, 4 sites; each quarter)

›

applied pressure (mmHg) NR

24 ankle, 18–20 calfa

NR

NR

Running to exhaustion (incremental, 5-min stages)

10 max CMJ

10 max CMJ

[La-]P (end quarter)

2

Mass loss

2

RPE (each quarter)

2

Duration

›

Total work . . VO2peak, VEpeak, EQ, RER

2

HR (max)

2

[La-]P (max)

2

Interpolated running velocity at AnT . . HR, VO2, VE, RER at AnT

2

Interpolated running velocity at AT . . HR, VO2, VE, EQ, RER at AT

2

Compression Garments and Exercise

ª 2011 Adis Data Information BV. All rights reserved.

Table I. Contd

›

›

›

2 M ›, F2

Mean power (10 jumps)

›

Mean force (10 jumps)

›

Max power (highest vertical jump)

2

Athletic and nonathletic M – AUC power in CMJ

›

Athletic and nonathletic F – AUC power in CMJ

› 2h

10 max CMJ, preceded by: 30-min run (70% max HR)

› Continued next page

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Max power (highest vertical jump) Max force (highest vertical jump)

Study

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ª 2011 Adis Data Information BV. All rights reserved.

Table I. Contd No. of participants; sex; fitness level

CGs type

Exercise protocol 4 sets of leg presses at 10RM

6; M, 6; F; active 5; M, 5; F; active

Maton et al.[15]

Scanlan et al.[44]

10; M, 10; F; active

15; NR; healthy

12; M; trained cyclists

Lower-body CG (aboveknee to waist)

Effects of CG

NR

GCS (presumably foot/ankle to mid-thigh)

~17–24 ankle, ~14 calf, ~7 thigh

Lower-body CG (anklewaist)

~20 ankle, ~17 calf, ~15 thigh

Athletic and nonathletic M – AUC power in CMJ Athletic and nonathletic F – AUC power in CMJ

›

Athletic and nonathletic M – AUC power in CMJ

›

Athletic and nonathletic F – AUC power in CMJ

›

Joint position replication (hip)

30, 90 (error scores)

2

45, 60 (error scores)

fl

3 · 6 max CMJ

Vertical muscle oscillation

fl

Horizontal muscle oscillation

2

70% 1RM squats to fatigue

Number of repetitions

2

3 · 50 isokinetic knee extension and flexion

Knee extensions (total work, peak torque)

2

Knee flexions (total work, peak torque)

2

Sustained static ankle dorsal flexion (50% of max) to fatigue

Time sustained

2

Slope of EMG – mean power frequency/time

2

Slope of EMG – total power/time . . VO2max, PO at VO2max

2

Relative and absolute PO at AnT . HR and VO2 at AnT

2 ( › )i

[La-]P (max)

2

Peak muscle oxygen utilization

2

Mean PO, peak PO, work output . VO2, economy (throughout)

2

Muscle oxygenation, muscle oxygenation economy (throughout)

2 ( › )j

[La-]P (throughout)

2

HR (throughout)

2

10 · 10 max CMJ

Kraemer et al.[43]

Performance indicator

applied pressure (mmHg)

. Cycling VO2max test (incremental, 3-min stages)

1-h cycling time trial

2 2

Continued next page

MacRae et al.

Sports Med 2011; 41 (10)

2

Study Sperlich et al.[45]

No. of participants; sex; fitness level

CGs type

15; M; run and triathlon trained

Compression socks, lowerbody CG, full-body CG (exact coverage NR)

Exercise protocol

Performance indicator

Effects of CG

. VO2 (last 30 sec)

2k

Oxygen saturation and partial pressure (end exercise)

2k

[La-]P and pH (end exercise)

2k

RPE, muscle soreness (end exercise)

2k

Time . VO2peak

2k

applied pressure (mmHg) Reportedly ~20 mmHg, but not measured

15 min running at . ~70% VO2max (fixed velocity)

Run to volitional exhaustion (fixed velocity)

a

Reported by the manufacturer or presumably reported by the manufacturer (measurement method not described).

b

Among CG conditions (was no without CG condition).

2k

Oxygen saturation and partial pressure (end exercise)

2k

Blood [La-] and pH (end exercise)

2k

RPE, muscle soreness (end exercise)

2k

Compression Garments and Exercise

ª 2011 Adis Data Information BV. All rights reserved.

Table I. Contd

. . . c The VO2 slow component was taken as the difference between VO2 at min 2 and end exercise (min 15); the actual VO2 values were not reported. d

Atypical CG; made from neoprene and rubber, ~5 mm thick.

e

Significant when M and F combined, but not significant in either M or F when separate.

f

Moderate effect size (0.53) for smaller decline in bound distance over the ten sets in CG condition.

g Large effect size (0.86) for greater distance travelled at a fast pace (3.5 m/sec) in CG condition. h

Two different CGs were used; in each case (athletic and nonathletic), control condition was different to one CG condition but not the other.

i

Likely, practically significant (Z2 = 0.6) for greater in the CG condition.

j

Possibly, practically significant (Z2 = 0.6) for greater in the CG condition.

. . AnT = anaerobic threshold; AT = aerobic threshold; AUC = area under the curve; CMJ = countermovement jump; EMG = electromyogram; EQ = ventilatory equivalent (VE/VO2); PO = power F = female; GCS = graduated compression stockings; HR = heart rate; [La ] = lactate concentration; [La ]P = plasma [La ]; M = male; max =. maximum; NR = not reported; . . output; RER = respiratory exchange ratio; RPE = ratings of perceived exertion; RM = repetition maximum; VE = minute ventilation; VEpeak = peak VE; . . ROM . = range of motion; . . VO2 = oxygen uptake; VO2max = maximal VO2; VO2peak = peak VO2; › indicates significantly higher than the no CG (control) condition; fl indicates significantly lower than the no CG (control) condition; 2 indicates not significantly different from the no CG (control) condition; % indicates percentage; ~ indicates approximately.

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k Among all conditions (three compression, one control).

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2.1 Exercise Performance 2.1.1 Jumping

The effect of CGs on jumping performance is somewhat mixed. None of the identified studies reported the garment-applied pressures, which makes comparison of results more difficult. Performance of the maximum jump in one set of three to ten countermovement jumps (CMJs) was generally similar when CGs were worn or not worn.[34,41,42] Wearing compression shorts did not significantly affect maximum power of the highest vertical jump (in a set of ten) for athletic and nonathletic males and females,[42] and male and female volleyball players,[41] although the maximum force generated within the highest jump (measured in the latter study only) was larger for the males when wearing correctly fitted or undersized CGs than when wearing regular gym shorts. Height of the maximum CMJ was ~5% greater with compression shorts for sprint- or jump-trained athletes; however, the difference was not significant for the males and females separately.[34] The influence of compression may become more apparent as fatigue develops. Mean power output over ten repeated maximal vertical jumps was typically higher when wearing CGs than without,[41,42] including when following other exercise.[42] Kraemer et al.[42] found mean jump performance to be better with CGs (than without) following various exercise types intended to elicit endurance, strength and power fatigue (CGs were not worn during the fatiguing exercise). The three fatigue protocols, respectively, were a 30-minute run at 70% maximal heart rate (HR), four sets of ten-repetition maximum leg presses, and ten sets of ten maximal-effort CMJs. In other situations, however, use of CGs did not improve performance. Wearing CGs had little effect on CMJ flight times performed during or following other exercise in a netball-specific circuit (at the 9th minute of each quarter; section 2.1.4).[38] Similarly, CGs had no significant effect on the performance of two-legged bounds during 10 minutes of exercise, consisting of a 20-m sprint followed by ten bounds each minute. Performance indicators were ten-bound distance and decline within the set.[37] A moderate effect size ª 2011 Adis Data Information BV. All rights reserved.

(0.53) was present for the distance decline in CGs (10%) compared with the non-CG condition (14%). Possible explanations for any improvements in jumping performance include enhanced proprioception of the hip, reduced muscle oscillation upon landing and positive psychological feeling of ability when wearing CGs, all of which have some empirical support.[42] 2.1.2 Sprinting

Sprint performance appears to be unaffected by wearing CGs. For a 60-m sprint performed in isolation from other activity, the time taken was similar when wearing compression shorts or normal loose-fitting shorts.[34] Similarly, CGs had no observable effect on repeat sprints or sprints performed in association with other exercise.[35-39] During simulated sport (section 2.1.4), intermittent-sprint performance over 10–20 m was not significantly influenced by various CGs for rugby,[36] netball,[38] field hockey[39] and cricket[35] players, with decline in sprint performance also similar with or without CGs.[35] In a shorter duration exercise protocol, moderately-trained rugby players completed ten sets of a 20-m sprint followed by ten plyometric bounds (section 2.1.1) every minute.[37] Wearing lower-body CGs had no demonstrable effect on mean sprint time, total sprint time or decline in 20-m sprint time. 2.1.3 Prolonged Running or Cycling

Most identified studies indicate that CGs have little or no benefit for measures of exercise performance during prolonged exercise. During multistage 20-m shuttle running at an increasing speed, the total distance covered was similar irrespective of whether or not knee-length GCSs were worn.[30] Similarly, differences in distance covered were not statistically supported when wearing fullbody CGs during 30 minutes of repeated 20-m sprints separated by sub-maximal exercise[35] or lower-body CGs during a netball-specific circuit (distance covered at a higher speed rate appeared higher for the CG condition; section 2.1.4).[38] When running at a fixed speed, time to exhaustion was no different from the control when run/triathlon-trained males wore each of three types of CGs (compression socks, ankle-waist Sports Med 2011; 41 (10)

Compression Garments and Exercise

lower-body CGs or full-body CGs).[45] Berry and McMurray[31] similarly found that time to exhaustion was not influenced with knee-length GCSs versus no GCSs for active males during a . running maximal oxygen uptake (VO2max) test, and. Scanlan et al.[44] found cycling power output at VO2max to be equivalent when wearing anklewaist, lower-body CGs versus normal underwear for well trained male cyclists. Power and work during a 1-hour time trial was also unchanged by CGs in this cohort.[44] In contrast, Kemmler et al.[40] found the duration of a running test to exhaustion to be longer and, hence, total work higher, when knee-length GCSs were worn compared with the control (no GCSs). Excluding the Sperlich et al. study,[45] in. which the participants were highly trained (VO2max »64 mL ? kg-1 ? min-1), the aerobic fitness of participants in the other three studies was compa-1 -1 [31,40,44] The CGs rable (52–55 mL ? kg . ? min ). or peak oxygen uptake had little effect on VO 2max . (VO2peak; section 2.2) in all of the studies.[31,40,44] In all cases CGs covered at least the ankle to knee; however, the applied pressures in the Kemmler et al. study[40] were perhaps slightly higher and were reported (but not measured) as 24 mmHg at the ankle and 18–20 mmHg at the calf. These values compare with 20 and 17 mmHg (measured),[44] and 18 and 8 mmHg (not measured)[31] for the ankle and calf, respectively. The applied pressure was reported as ‘targeting 20 mmHg’ in the Sperlich et al. study.[45] Work directly investigating the influence of different applied pressures on performance or physiological effects during exercise is rare. In one identified study, the level of compression offered by GCSs was not found to affect any of several physiological variables during prolonged running (section 2.2).[14] While further investigation is required, such studies may be limited by the general lack of demonstrable compression effects per se. There is some evidence for meaningful compression effects on sub-maximal exercise intensity thresholds, but such findings may be in contrast with a general lack of effect on time to exhaustion[31,45] or 1 hour of work.[44] When CGs were worn (vs control) during incremental exercise tests, interpolated running velocity at both anaerobic ª 2011 Adis Data Information BV. All rights reserved.

825

and aerobic thresholds was significantly greater (by 1.5% and 2%, respectively),[40] and absolute and relative cycling power output at the anaerobic threshold was reportedly greater (by 6% and 4%, respectively), which may have indicated some practical significance (likely practically significant, Z2 = 0.6) but was not supported by any statistical significance (p = 0.19–0.24).[44] 2.1.4 Varied-Activity Exercise and Simulated Team Games

During protocols of varied-activity exercise, including simulated team games, CGs have had little influence on a number of performance measures, including sprint times,[35,36,38,39] CMJs,[38] ball throwing[35] and scrum performance.[36] When assessed before and after 30 minutes of repeated sprints, no differences in throwing distance or accuracy performances were evident among four conditions (three sets of full-body CGs and a control garment) for ten male club-level cricket players.[35] Distance measures were maximum distance, summated distance, distance decline across five throws, while accuracy measures were of individual throws, total accuracy score and time to complete the test. Findings were similar for simulated scrum activity among trained rugby players.[36] Wearing CGs had no demonstrated effect on peak power output during brief, maximal scrummaging efforts, determined immediately before and after half time and before full time of an 80-minute simulated team game (fourquarters of repeat circuits involving high-intensity sprints, walking, jogging and agility).[36] Intermittent, varied-intensity 20-m shuttle running was used to investigate CG effects on 12 male field hockey players during four 15-minute bouts.[39] Exercise intensities were walking, maximal sprinting and running at 55% and 95% of . VO2max. In the compression condition, knee-length CG shorts and a short-sleeved CG top were worn under the ‘normal’ hockey attire used in the control condition. Fifteen metre sprint time was used to indicate performance throughout the exercise, and no differences were identified with compression. During a simulated game-specific circuit for netball arranged into 15-minute quarters, distance covered and ‘speed rates’ for distance Sports Med 2011; 41 (10)

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covered (fast [3.5 m ? s-1] or slow [1.5 m ? s-1]) were not different among CGs, placebo (‘elastanebased long pants’), and control (normal netball garments) conditions,[38] although a large effect size (0.86) was evident for the observation of 20% and 34% greater distance travelled at a fast pace across four-quarters, compared with the control and placebo conditions, respectively. The authors speculated that CGs could facilitate cardiovascular function during low-to-moderate activity, decrease energy expenditure and assist in conserving high-energy phosphates for subsequent short anaerobic bursts,[38] but that is not supported by any improvement in either 20-m sprint times or CMJ flight times. The authors also attributed the lack of effect to their participants’ high anaerobic fitness or competitive nature,[38] which may be so, but this would apply to many users of such garments. If benefits were evident in people of modest fitness, then fitness development would be as relevant as CG usage. 2.1.5 Fatigue

Among the studies identified, none reported significant detrimental effects of wearing CGs on any performance measure during exercise. For example, neither compression shorts[43] nor compression stockings[15] had significant ergogenic or ergolytic effects during sustained and repeated muscle contractions to fatigue, when compared with wearing regular gym shorts. Performance measures were peak torque and total work during three sets of 50 maximal isokinetic knee extension/ flexion movements at 180/s, and maximal number of squat repetitions at 70% of one-repetition maximum load among 20 active men and women.[43] Likewise, during maintained ankle dorsal flexion at 50% of maximum force, tolerance time was equivalent for GCSs versus control.[15] 2.2 Physiological and Physical Effects 2.2.1 Cardiorespiratory Measures

CGs have overwhelmingly had little influence on sub-maximal or maximal HR[14,30,33,35,36,38-40,44] or sub-maximal and maximal plasma lactate[14,38,40,44,45] across various exercise protocols with participants of various training statuses. Despite the limited data on cardiovascular effects ª 2011 Adis Data Information BV. All rights reserved.

during exercise (besides HR), altered blood flow is a commonly – perhaps ostensibly – stated physiological effect of CGs and, thus, proposed as a mediator of potential benefits. Data from non-exercise studies are often taken out of context, but may still apply to recovery situations. A more detailed examination of cardiovascular effects is warranted and, because of pertinence to both exercise and recovery, is discussed separately (section. 3) to avoid overlap. . Measured VO2max/VO2peak were similar among garment conditions during various incrementalintensity exercise protocols in both moderately[33,45] particitrained[31,40,44] . and well-trained . pants (mean VO2max/VO2peak of ~52–55 and ~60–64 mL ? kg-1 ? min-1, respectively). While it appears CGs do not benefit peak delivery and utilization of oxygen, there is some evidence that CGs may improve economy of oxygen use during sub-maximal exercise when expressed per unit of not found difdistance,[33] although others have . ferences for oxygen uptake (VO2) per se during exercise at fixed loads.[14,45] The oxygen cost of running (mL? O2 ? kg-1 ? m-1) was evaluated using six trained runners . (VO2max »60 mL ? kg-1 ? min-1) at running velocities of 10, 12, 14, and 16 km ? h-1.[33] At 12 km ? h-1 the oxygen cost was ~9% lower for compression tights and classic elastic tights than conventional shorts; the same trend was present at 10 and 14 km ? h-1 but not 16 km ? h-1. Wearing tights was suggested to improve economy by increasing proprioception, muscle coordination and the propulsive force.[33] It is interesting that results were similar for compression and elastic tights, with the former presumably applying greater pressure (not reported). In a second protocol, the . upward. drift in VO2 was assessed with participants (VO2max »52 mL.? kg-1 ? min-1) running at a constant pace of 80% VO2max (~13.8 km ? h-1) for 15 minutes.[33] The upward .drift was taken as the slow component . of rising VO2, expressed as the difference in VO2 between the second minute and end of exercise.[33] The slow component was smaller when wearing compression tights than elastic tights (by 26%) or conventional shorts (by 36%), but not different between wearing . elastic tights and shorts. Importantly, actual VO2 were Sports Med 2011; 41 (10)

Compression Garments and Exercise

. not reported, so influences of resting VO2 and fast components are unknown. In some support of the findings of Bringard et al.,[33] ~10% and ~14% improvements in muscle oxygenation (percentage) and muscle oxygenation economy (W/percentage), respectively, were reported beneath compression tights (vs loose underwear) throughout a 1-hour cycling time trial, indicating some possible practical significance (Z2 = 0.6) but no supporting statistical sig[44] nificance . (p = 0.40 and p = 0.25, respectively). Mean VO2, power output (W), economy (W ? mL O-1 2 ), and work output (kJ) were similar for CGs . and control across the time trial.[44] Indeed, VO2 was similar for fixed running intensities and durations, irrespective of the pressures applied by knee-length GCSs (from low pressure of 4–4 mmHg to high pressure of 26–15 mmHg; ankle to calf),[14] or irrespective of whether CG coverage was absent, below the knee, ankle. to waist, lower body or full body.[45] While the VO2 at aerobic[40] and anaerobic[40,44] thresholds were similar between compression and control conditions, performance measures (interpolated running velocity,[40] power output (not statistically significant)[44]) at the respective thresholds were slightly greater (section 2.1.3). More research is required to validate potential improvements in aspects of sub-maximal exercise economy. 2.2.2 Thermoregulatory Responses

Wearing CGs resulted in higher skin temperatures for CG-covered areas,[34-36,39] unsurprisingly, although fabric and garment effects on heat exchange were not sufficient to cause differences in core body temperature[36,39] or mass loss[35,36,39] in cool to moderate environmental conditions. During prolonged exercise, thigh temperature was consistently ~2C warmer when lower-body CGs were worn (vs conventional shorts) throughout an 80-minute simulated team game in ambient conditions of 16–18C and 30% relative humidity.[36] Similarly, compared with control conditions (no CGs), weighted-mean skin temperature was 1.5–3.0C higher when wearing full-body CGs during 30-minutes of repeat-sprint exercise (ambient temperature ~15C),[35] and 0.5–1C higher when wearing short-sleeved/legged upperª 2011 Adis Data Information BV. All rights reserved.

827

and lower-body CGs during 60 minutes of variedintensity intermittent shuttle running (ambient conditions ~17C, ~60% relative humidity).[39] While it appears CGs do not adversely affect core body temperature or moisture loss in cool to moderate conditions, effects for warmer environments and during high intensity endurance exercise are of interest. 2.2.3 Proprioception and Muscle Oscillation

There is some indication that CGs can improve awareness of joint position.[42,46] With and without compression shorts (applied pressures not reported), 12 active males and females replicated hip positions while supine.[42] When wearing CGs, error scores were lower at 45 and 60, while no differences were identified at 30 and 90. The authors[42] suggested surface compression could act on receptors in the skin to enhance proprioception,[47,48] especially when the limb is distanced from an endpoint of the range of motion. Wearing CGs has also resulted in better performance during visuomotor tracking immediately and one day following eccentric exercise (section 4.2.2).[46] Use of CGs may reduce muscle oscillation, at least vertically. Both longitudinal and anteriorposterior muscle oscillation of the thigh were decreased during vertical jump landing when compression shorts were worn; however, the garments used were atypical, being 4.76 mm thick and made from 75% closed-cell neoprene and 25% butyl rubber.[34] Another vertical jump landing study[42] used more conventional compression shorts and showed reduced muscle oscillation of the thigh in the vertical but not horizontal direction. Whether similar effects are present during continuous exercise such as running is unknown, and may warrant further investigation in view of the economy-related findings discussed previously (section 2.2.1). 2.3 Perceptual Responses

Placebo effects are difficult to control for and, when differences in exercise performance are found, to delineate from physiological effects. Notwithstanding placebo effects, psychophysical Sports Med 2011; 41 (10)

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benefits, if present, are benefits nonetheless. Following fatigue-inducing exercise, ten CMJs were performed by athletic and nonathletic participants when wearing each of two compression shorts or regular gym shorts (section 2.1.1).[42] For participant perceptions of how the garment may have affected their jumping ability, both CGs were rated as having ‘moderate’ to ‘big’ difference (3–5 on a scale from 0–10), while the gym shorts were rated as having no effect (rating of 0). This perceived benefit was thought to contribute to the typically better jump performance when CGs were worn during CMJs. Similar positive perceptual effects do not appear to be present during prolonged exercise. Wearing CGs have had little influence on the ratings of perceived exertion during various running or repeatsprinting protocols.[14,30,33,35,39,45] Surprisingly, few data are available on the comfort of CGs per se and wearer acceptability. In two studies,[14,30] Likert scales were used to assess comfort, tightness and pain associated with GCSs, on scales ranging from 1 (very uncomfortable, slack/loose, no pain) to 10 (very comfortable, very tight, very painful), before and after exercise. For knee-length GCSs, intended to apply 18–22 mmHg at the ankle, mean scores were 6–8 for comfort, 6–8 for tightness and 1–3 for pain.[30] In the second study,[14] three sets of knee-length GCSs were compared: ‘control’ (4 mmHg at ankle and calf), ‘low pressure’ (11 and 8 mmHg, ankle and calf) and ‘high pressure’ (26 and 15 mmHg, ankle and calf). The trend for comfort was such that as the applied pressures increased, the comfort decreased (from ~9.5 for the control to ~7.5 for the high-pressure garment). Not surprisingly, the trend for tightness ratings reflected the applied pressures, with mean scores for the control, low- and high-pressure garments being ~3, ~5, and ~8, respectively. Pain ratings were slightly higher for high-pressure garments, although still minor at 2–2.5. Elsewhere, ratings of clothing comfort, ‘clothing sweating’ and ‘whole thermal’ sensations were similar among compression tights (pressures not reported), classic elastic tights and conventional shorts during running at an increasing intensity to voluntary exhaustion (average duration not reported; ambient temperature of ª 2011 Adis Data Information BV. All rights reserved.

. ~31C), and at 80% VO2max for 15 minutes (ambient temperature of ~24C).[33] 3. Effects on Cardiovascular Function A section on the effects of CGs on cardiovascular function is included because of relevance to both exercise and recovery. Wearing CGs is widely reported by manufacturers and the popular press to improve cardiovascular function; however, most data do not relate to orthostatically stressful postures or dynamic exercise (e.g. running, cycling). Thus, cardiovascular influences of CGs in exercise applications remain largely unclear. Cardiovascular strain has typically been assessed only using HR, and findings indicate little effect of CGs during various exercise types.[14,30,33,35,36,38,39,44,49] No studies have been identified, which show that wearing CGs increases local or systemic blood flow (including venous return) during dynamic exercise at intensities reflecting training or competition. Aspects of cardiovascular function have been the focus of considerable attention in the medical literature on CGs, but such studies are often undertaken on patients while resting supine.[22,23,50] Common foci include effects of CGs on haemodynamic function (particularly venous blood flow velocity), venous cross-sectional area and physical characteristics of the applied pressures. Emphasis is placed on reducing venous stasis, achieved through related decreases in venous diameter and increases in venous blood flow velocity. Findings from studies on recumbent and stationary participants ought not to be extrapolated to imply circulatory benefits during dynamic exercise, but could have relevance for post-exercise recovery. Indeed, sleeping in CGs for recovery purposes is, anecdotally, common among athletes, although whether the practice has demonstrable desirable effects is not a simple issue (section 4). 3.1 Systemic Cardiovascular and Haemodynamic Effects

The suggested mechanism by which compression alters variables such as venous blood velocity and venous pooling is by reducing total crosssectional area of the veins.[51,52] That is, CGs Sports Med 2011; 41 (10)

Compression Garments and Exercise

compress superficial limb tissues, which in turn compress underlying veins, reduce their diameter, increase velocity and reduce pooling. Demonstrated effects of compression, often in people at rest, include decreasing venous diameter and volume,[53-56] and increasing venous flow velocity,[22,23] although, such changes are not always observed, particularly when the person is upright.[56,57] While venous pressures are low when supine, they increase when upright due to gravity and, hence, achieving venous compression and flow velocity augmentation becomes more difficult. In people without vascular disorders, various compression stockings with applied pressures ranging from 20–50 mmHg (higher than typical sporting CGs) were found not to reduce the diameter of the long saphenous, popliteal and common femoral veins,[56,57] and not to increase peak venous velocity[57] when in upright positions. However, in other studies, compression tights (23–24 mmHg at the calf) reduced venous pooling in the gastrocnemius medialis when resting in both supine and standing positions,[58] and compression hosiery (reportedly 7.6–15.4 mmHg at the ankle, 5.2–9.0 mmHg at the thigh; measured in vitro) attenuated the increase in diameter of popliteal and posterior tibial veins over an 8-hour standing fatigue protocol.[59] Note that changes to variables such as vein diameter and venous blood velocity do not necessarily indicate changes to flow volume per unit time and, hence, venous return. Data directly demonstrating influences on venous return, cardiac output or stroke volume appear to be sparse,[60] and none were identified for people engaged in exercise. When supine, venous outflow at the calf was shown to be higher by ~20–50% when wearing knee- or thigh-length compression stockings (pressures not reported) than without stockings.[50] Outflow was determined using venous occlusion plethysmography, as the rate of decrease in calf circumference between 0.5 and 2 seconds after releasing the occlusion. Whether this post-occlusion increase in venous outflow would be applicable under normal conditions (i.e. without the altered flow pressure associated with occlusion), and especially when upright or ambulant, is not clear. Interestingly, the authors also found an increase ª 2011 Adis Data Information BV. All rights reserved.

829

in the maximum percentage change in calf volume during the occlusion when the stockings were worn, interpreted as an increase in venous capacitance. Another explanation could be a compression-induced increase in local arterial flow. With ankle-to-knee compression bandaging of ~30 mmHg, below-knee pulsatile blood flow was acutely higher by ~45% relative to the contralateral leg with noncompressive bandaging in resting patients in the supine position.[61] Increased pulsatile blood flow (evaluated using nuclear magnetic resonance flowmetry) was attributed to an overall increase in below-knee blood flow. Possible mechanisms for flow augmentation have been discussed[62] and include a myogenic vasodilatory response (section 3.2). However, these increases were not sustained after 7 hours of normal activity. The sub-bandage pressures had reduced by ~40–50% to 16–19 mmHg by the second measurement period (i.e. after 7 hours), so this may represent a causal relationship between the flow and applied pressures. Effects of compression stockings on venous haemodynamic parameters such as ambulatory venous pressure,[63] muscle pump function,[64] venous reflux[65] and ejection fraction[55] have been investigated during movement, but typically in patients with chronic venous insufficiency. However, some studies have included ‘healthy’ people with normal vascular function. Direct measurements of venous pressure in a dorsal foot vein of ten healthy participants (aged 29–48 years) revealed that with repetitive tiptoe exercises (ten repetitions at 1 ? s-1), the ‘ambulatory’ venous pressure was ~10 mmHg higher when wearing compression stockings with sub-stocking pressures in the range of 30–50 mmHg.[57] The ‘ambulatory’ venous pressure was defined as the lowest pressure reached after the ten tiptoes. During this same exercise protocol, venous refill time and the maximum venous pressure with the tiptoe exercise were not significantly different from when stockings were not worn. Further investigation on the effects of CGs during movement and on muscle pump function in healthy limbs is required. The application of pressure to limb segments also has potential implications for reducing plasma loss to the interstitial spaces. Over the course of Sports Med 2011; 41 (10)

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daily activity, or simulated daily activity, oedema and swelling in the legs of healthy people has been shown to be reduced with compression stockings (14–18 mmHg at the ankle)[66] and compression hosiery (7.6–15.4 mmHg at the ankle).[59] These effects presumably result from a reduced hydrostatic pressure gradient across the vessel walls. Effects of CGs on exercise-induced swelling are discussed in section 4.2.2. 3.2 Effects on Local Blood Flow

The effect of a compression sleeve (13–23 mmHg) on arterial inflow to the forearm was investigated using venous occlusion plethysmography.[32] Compression increased arterial perfusion by more than 200% compared with the control (contralateral forearm without compression), and the increases were sustained for the duration of applied compression (10 minutes to 3 hours). The applied pressure of ~20 mmHg resulted in the largest flow increase. This flow augmentation was also maintained during ~70 minutes of light rhythmic handgrip exercise with both hands (5–10% of maximum voluntary contraction, 1 : 2 second contractionrelaxation duty cycle). Increases in arterial inflow were attributed to the myogenic response, primarily of arterioles. However, reductions in muscle blood flow have also been observed with external compression during dorsiflexion exercise.[67] Muscle relaxation pressure (intramuscular pressure between contractions) of tibialis anterior was increased from 14 – 3 mmHg to 40 – 9 mmHg with inflation of a 200 mm wide circumferential cuff. During exercise, muscle blood flow decreased from 35 – 10 mL ? 100 g-1 ? min-1 without compression to 11 – 5 mL ? 100 g-1 ? min-1 with compression. Unfortunately, applied pressures were not reported. These,[32,67] and other[62] data, indicate that a pressure range may exist for blood flow augmentation, whereby pressures too low result in no change, and pressures too high have adverse effects. The proposed involvement of the myogenic response associated with compression-induced increases in tissue perfusion is as follows: garmentapplied pressure and resultant compression of underlying tissues reduces transmural pressure in ª 2011 Adis Data Information BV. All rights reserved.

local arterioles, causing vessels to reflexively relax (i.e. dilate), thereby increasing flow.[32,62,68,69] A key role of the myogenic response is thought to be autoregulation of blood flow and capillary hydrostatic pressure.[70] Under normal conditions, that is, without tissue compression, a reduction in transmural pressure indicates a reduction in the perfusion pressure of blood in that vessel. Under these conditions, the vessel reflexively dilates to increase flow back to ‘normal’. Because the transmural pressure difference is relative, increases in tissue pressure with external compression is likely to reduce transmural pressure similarly to decreases in perfusion pressure. In this way, externally applied compression may lead to arteriolar dilation and therefore, potentially, also higher perfusion in underlying tissues.[32,62] Further work investigating local blood flow responses and associated effects with the use of CGs is required, particularly with respect to exercise involving large muscle groups (when cardiac output may be limiting) and recovery situations. Indeed, when resting (not following exercise) in both supine and standing positions, muscle oxygenation of the gastrocnemius medialis was higher when wearing compression tights (23–24 mmHg at the calf) than without, as indicated by a higher tissue oxygenation index and lower deoxyhaemoglobin (measured using near infrared spectroscopy).[58] Changes to muscle oxygenation were perhaps facilitated by changes in local perfusion, although the implications of these and other potential perfusion-related effects during exercise recovery are not yet clear. 4. Effects During Recovery from Exercise Studies of CG effects on exercise recovery show equivocal findings, as summarized in table II. Whereas some potential mechanisms underlying effects of compression during recovery are identified (e.g.[80]), the lack of consistent findings with sporting CGs could reflect the many aspects of heterogeneity among studies, including the type, intensity and duration of the preceding exercise, the training status of the participants (hence resultant soft tissue damage), and the duration and extent of compression in recovery. Sports Med 2011; 41 (10)

Study

Ali et al.[30]

Ali et al.[14]

Berry and McMurray[31]

No. of participants; sex; fitness level

CGs type

14; M; active

GCS (knee-length)

9; M, 1; F; run trained

6; M; active

GCS (knee-length); 3 types: high pressure, low pressure, control

applied pressure (mmHg)

when worn

18–22 anklea

DE

High pressure: 26 ankle, 15 calf Low pressure: 11 ankle, 8 calf Control: 4 ankle, 4 calf

GCS (covered calf) ~18 ankle, ~8 calfa

DE, FE (for 48 h)

DE, FE (for 1 h)

Exercise protocol

Recovery indicator (time following exercise)

Effects of CG

Multistage shuttle running

Muscle soreness (1, 24 h)

2

10-km road run

Muscle soreness (24 h)

fl

40-min running at . ~80% VO2max

Muscle soreness (0, 24, 48 h)

2b

[CK]P (0, 24, 48 h)

2b

[Mb]P (0, 24, 48)

2b

CMJ height and peak power (0, 24, 48 h)

2b

. VO2 (for 60 min)

2

. Running VO2max test; 5-min warm down then 55-min seated rest

[La-]P: 2

(5, 30, 45, 60 min) (15 min)

DE, FE (for 30 min); or DE only

Carling et al.[71]

7; M, 16 F; healthy (between-groups design)

Compression sleeve (deltoid insertion to wrist)

~17

FE (72 h)

3-min cycling at . 110% VO2; 30-min recovery supine

70 maximal eccentric contractions of elbow flexors

Compression Garments and Exercise

ª 2011 Adis Data Information BV. All rights reserved.

Table II. The effect of wearing compression garments (CGs; vs without CGs) on indicators of recovery from exercise

fl

. VO2 (for 60 min)

2

[La-]P: GCS worn DE, FE (time NR)

fl

GCS worn DE only (time NR)

2

Muscle soreness (10 min; 24, 48, 72 h)

2

Arm circumference (10 min; 24, 48, 72 h)

2

Arm volume (10 min; 24, 48, 72 h)

2

Elbow extensor ROM (10 min; 24, 48, 72 h) 2 Isokinetic peak torque (10 min; 24, 48, 72 h) 2

Chatard et al.[49]

12; NR; trained elderly cyclists

GCS (ankle to thigh)

33 ankle, 18 calf, 13 thigha

2 · 5-min maximal cycling exercises (M1, M2), 80 min apart

Between-exercise recovery (20-min intervals): [La-]P

fl

haematocrit

fl

plasma volume

2

HR, [La-]P, RPE in M2

2

Mean power output in M2 (80 min after M1) › Continued next page

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Sports Med 2011; 41 (10)

FE (between exercise sessions; for 80 min)

Study

Davies et al.[72]

Duffield and Portus[35]

Duffield et al.[36]

832

ª 2011 Adis Data Information BV. All rights reserved.

Table II. Contd No. of participants; sex; fitness level

CGs type

7; F, 4 M; netball and basketball players

Lower-body CG (ankle to waist)

10; M; cricket players

14; M; trained rugby players

applied pressure (mmHg)

Exercise protocol

Recovery indicator (time following exercise)

Effects of CG

5 · 20 plyometric drop jumps

Muscle soreness (24, 48 h)

2c

[CK]P (24, 48 h)

2d

when worn

~15 graduated from FE (for 48 h) lower to upper legsa

Upper- and lower- NR body CG (wrist and neck to waist; ankle to waist)

DE, FE (for 24 h)

Lower-body CG (coverage NR)

DE, FE (for 2 · 80-min 15 h) both days simulated team game, 24 h apart

NR

30-min repeat sprints; ball throwing

[LDH]P (24, 48 h)

2

Mid-thigh girth (24, 48 h)

2

CMJ height (48 h)

2

5-, 10-, 20-m sprint (48 h)

2e

5–0–5 agility (48 h)

2

Muscle soreness, arms (24 h)

fl

Muscle soreness, legs (24 h)

fl

[CK]P (24 h)

fl

Muscle soreness (pre- and 0, 48-h post-day fl 2 exercise) [CK]P (pre- and 48-h post-day 2 exercise)

2

[La-]P (pre, during and 10, 15 min following 2 exercise both days)

Duffield et al.[37]

11; M; moderatelytrained rugby players

Lower-body CG (coverage NR)

NR

DE, FE (for 24 h)

20-m sprint and 10 plyometric bounds every min for 10 min

Day 2 repeat sprint performance (time)

2

Day 2 peak power (dynamometer cart)

2

Muscle soreness: 2 ( fl )f

(2 h) (24 h)

fl 2

[CK]P and C-RPP (2, 24 h)

2

ASTP (2, 24 h)

2 ( fl )g

Evoked knee extensor peak twitch force (0, 2, 24 h)

2

Voluntary peak knee extension force (0, 2, 24 h)

2

Voluntary peak knee flexion force (0, 2, 24 h)

2

Continued next page

MacRae et al.

Sports Med 2011; 41 (10)

[La-]P and pHP (0, 2 h)

Study

French et al.[73]

No. of participants; sex; fitness level

CGs type

26; M; active

Lower-body CG (ankle to waist)

Exercise protocol applied pressure (mmHg)

when worn

12 calf, 10 thigha

FE (for 12 h)

6 · 10 parallel squats (100% body mass) plus 5-sec 1RM eccentric squat per set

Recovery indicator (time following exercise)

Effects of CG

Muscle soreness (1, 24, 48 h)

2

[CK]P (1, 24, 48 h)

2

[Mb]P (1, 24, 48 h)

2

Mid-thigh girth (48 h)

fl

Mid-calf girth (48 h)

2

CMJ height (48 h)

2

10-, 30-m sprint (48 h)

2

Multiplanar speed (48 h)

2

5RM parallel back squat (48 h)

2 fl

Gill et al.[74]

23; M; elite rugby players

Lower-body CG (coverage NR)

NR

FE (for 12 h)

Competition rugby

[CK]transdermalh (36, 84 h)

Jakeman et al.[75]

17; F; active (between-groups design)

Lower-body CG (ankle to waist)

Not measured, but reported as ~17 calf, ~15 thigh

FE (12 h)

10 · 10 plyometric drop jumps

Muscle soreness: (1, 24, 48, 72 h)

fl

(96 h)

2

Compression Garments and Exercise

ª 2011 Adis Data Information BV. All rights reserved.

Table II. Contd

2

[CK]P (1, 24, 48, 72, 96 h) CMJ height (1, 24, 72, 96 h)

2

(48 h)

›

Squat jump height: (1 h)

2

(24, 48, 72, 96 h)

›

Knee extensor isokinetic muscle strength:

Kraemer et al.[76]

~10 (pressure 20; F; non-strength Compression sleeve (axillary line modelling) trained (betweento mid-forearm) groups design)

FE (5 days)

2

(24, 48, 72, 96 h)

›

Soreness (global assessment): (1, 2, 3 days)

2

(4, 5 days)

fl

[CK]P: (1 day) (2, 3, 4, 5 days) [cortisol]P (1, 2, 3, 4, 5 days)

2 fl 2 Continued next page

833

Sports Med 2011; 41 (10)

2 · 50 passive arm curls, maximal eccentric contraction every fourth passive repetition

(1 h)

Study

834

ª 2011 Adis Data Information BV. All rights reserved.

Table II. Contd No. of participants; sex; fitness level

CGs type

Exercise protocol applied pressure (mmHg)

when worn

Recovery indicator (time following exercise)

Effects of CG

[LDH]P (1, 2, 3, 4, 5 days)

2

Change in biceps brachii circumference (1, 2, 3, 4, 5 days)

fl

Change in relaxed elbow angle (1, 2, 3, 4, 5 days)

fl

Elbow flexion peak power: (1, 2 days)

2

(3, 4, 5 days)

›

Elbow flexion peak torque:

Kraemer et al.[77]

15; M; non-strength Compression ~10 (pressure trained (betweensleeve (axillary line modelling) groups design) to mid-forearm)

FE (for 72 h)

2 · 50 passive arm curls, maximal eccentric contraction every fourth passive repetition

(1, 2 days)

2

(3, 4, 5 days)

›

Soreness (global assessment): (24, 48 h)

›

(72 h)

fl

[CK]P: (24, 48 h)

2

(72 h)

fl

Change in biceps brachii circumference: (24, 72 h)

2

(48 h)

fl

Change in relaxed elbow angle: (24 h)

2

(48, 72 h)

fl

Elbow flexion peak torque:

Kraemer et al.

11; M, 9 F; resistance trained

Whole-body CG (ankle/wrist to neck)

NR

FE (for 24 h)

8-exercise, wholebody heavyresistance workout

2

(48, 72 h)

›

Elbow flexion peak power (24, 48, 72 h)

›

General muscle soreness (24 h)

fl

Vitality ratings (24 h)

›

Fatigue ratings (24 h)

fl

Quality of sleep ratings (24 h)

2 Continued next page

MacRae et al.

Sports Med 2011; 41 (10)

[78]

(24 h)

Study

No. of participants; sex; fitness level

CGs type

Exercise protocol applied pressure (mmHg)

when worn

Recovery indicator (time following exercise)

Effects of CG

[CK]P (24 h)

fl

[LDH]P (24 h)

fl

Thigh swelling (24 h)

fl

Limb circumferencei (24 h)

2

Distal patellar tendon thickness (24 h)

2

CMJj (24 h)

2

Squat jump (24 h)

2

Reaction time (24 h)

2

Bench throw (24 h)

›

Maton et al.[15]

15; NR; healthy

GCS (presumably foot/ankle to mid-thigh)

~17–24 ankle, ~14 calf, ~7 thigh

DE, FE (for 30 min)

Recovery of max ankle dorsal flexion Sustained static ankle dorsal flexion force (30 s-1; first 10 min of recovery) (50% of max) to fatigue

Pearce et al.[46]

8; M; healthy (between-groups design)

Upper-body CG (wrist and neck to waist)

NR

FE (during laboratory visits only)

35 max voluntary eccentric contractions of elbow flexors

Visuomotor elbow flexion/extension tracking:

8; M; active

GCS (covered calf of one leg only)

NR

FE (5 h/day at 2, 24, 48, 72 h)

30-min backwards downhill walking

Muscle soreness:

Perrey et al.[79]

Compression Garments and Exercise

ª 2011 Adis Data Information BV. All rights reserved.

Table II. Contd

2

(0, 1 days)

›k

(2, 3 days)

2l

(5, 7, 14 days)

2

(2, 24, 48 h)

2

(72 h)

fl

Peak evoked twitch of plantar flexors: 2

(2, 48, 72 h) (24 h)

Trenell et al.[16]

11; M; active

Lower-body CG (ankle to waist on one leg only)

~17 calf, ~10 thigh

FE (for 48 h)

30-min downhill walking

›

Plantar flexor peak voluntary torque (2, 24, 48, 72 h)

2

Muscle soreness (1, 48 h)

2

[PDE]m: › 2

[PME]m, PCr/Pim, pHm, [Mg2+]m (1, 48 h) a

Reported/presumably reported by the manufacturer (measurement method not described).

b

Among CG conditions.

2

Continued next page

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(1 h) (48 h)

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ª 2011 Adis Data Information BV. All rights reserved.

1RM = one repetition maximum; AST = aspartate transaminase; CK = creatine kinase; CMJ = countermovement jump; C-RP = c-reactive protein; DE = during main exercise protocol; F = female; FE = following exercise; GCS = graduated compression stockings; HR = heart rate; La- = lactate; LDH = lactate dehydrogenase; M = male; max = maximum; Mb = myoglobin; Mb2+ = Mb oxidation; M1/M2 = maximal cycling exercise test 1/2; NR = not reported; P = plasma; PCr.= phosphocreatine; . . PDE = phosphodiester; Pi = inorganic phosphate; PME = phosphomonoester; ROM = range of motion; RPE = rating of perceived exertion; VO2 = oxygen uptake; VO2max = maximal VO2; › indicates significantly higher than the no CG (control) condition; fl indicates significantly lower than the no CG (control) condition; 2 indicates not significantly different from the no CG (control) condition; ~ indicates approximately; [] indicates concentration.

c Perceived muscle soreness was higher (i.e. more severe) at 48 h than baseline in the control condition but not in the CG condition; however, no significant difference was identified between conditions at baseline or 48 h. d For F subjects only, [CK]P was higher at 24 h than baseline in the control condition but not in the CG condition; however, no significant difference was identified between conditions at baseline or 24 h. e Five-metre sprint time was longer at 48 h than baseline in the CG condition but not in the control condition; however, no significant difference was identified between conditions at baseline or 48 h. f Moderate effect size (0.62) for less muscle soreness in CG condition. g Moderate effect size (0.58) for lower ASTP in CG condition at 24 h. h Expressed as percentage recovery. i Upper arm, forearm, upper leg, lower leg and ankle. j Peak power, mean power and maximum performance decrement. k Tracking error was lower than without CG (i.e. performance was better). l Conditions were significantly different, but performance appeared neither better nor worse. m Measured using or calculated from results of 31P-Magnetic Resonance Spectroscopy.

Table II. Contd

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Compression has been implicated in roles that include mitigating the physiological or mechanical strain associated with exercise itself, facilitating removal of muscle metabolites, reducing exercise-induced swelling and muscle soreness, promoting cellular repair, and improving the subsequent range of motion. For example, it has been suggested that following soft tissue injury, compression can mitigate pain sensation and promote the physiological actions of healing by reducing the magnitude of inflammation-associated swelling and assisting with clearance of myocellular proteins and inflammatory mediators from the injured area.[80] Although several studies report positive effects on perceptual responses such as ratings of muscle soreness,[30,35-37,75-79] data demonstrating effects of CGs on physiological and subsequent-exercise performance variables are still relatively few and do not form a consensus or mechanistically-insightful interpretation on these effects (table II). Further data are required; particularly those on trained participants, with exercise type and wear durations typical of practice, adequate blinding to limit placebo effects, and consideration of longer term (adaptive) effects. The purpose of using CGs during exercise or recovery is, ultimately, to improve exercise performance acutely (i.e. within that or the next session) or chronically (i.e. to aid adaptive effects). The role of exercise training itself is to promote exposure to several forms of strain for mediating long-term adaptations. Therefore, the real value in subsequent exercise bouts during training is the extent of physiological and functional adaptation which occurs, and these factors are not addressed by (and may typically be counter to) studies of acute CG effects. Based on findings that blunting the magnitude or duration of recovery processes also blunts such adaptations,[81] acute effects of compression may actually attenuate useful adaptive processes. Studies of chronic CG effects are therefore essential for their common applications in a sporting context. 4.1 Subsequent-Performance Measures

A primary desired effect of CG use during recovery is to improve aspects of subsequent exercise Sports Med 2011; 41 (10)

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performance. For squat and CMJ performance, as well as sprint and agility performance, wearing CGs during recovery periods typically resulted in no demonstrable difference from control conditions.[14,36,72,73,78] Various power and torque measurements have benefitted from CG use following exercise,[75-79] although this is not always the case.[37,71,79] This discrepancy is possibly due to exercise type and familiarity and, thus, the potential for adverse effects on subsequent exercise, since the studies that used unaccustomed exercise (e.g.[76,77]) appeared more likely to observe an effect than those that used accustomed exercise.[37] Also, the timing of the subsequent exercise on occasions influenced whether effects were observed or not (e.g. benefits were identified at 24 hours but not 2, 48 and 72 hours for peak evoked twitch of plantar flexors[79]). To be of greatest practical value, conditions should reflect actual use. Following exercise involving eccentric contractions of the elbow flexors, use of a compression sleeve resulted in higher peak power and peak torque of the elbow flexors in men[77] and women,[76] although in a separate group of men and women[71] peak torque of the elbow flexors was no different (vs without compression). Similarly, mixed results are evident among studies involving the lower limb. In the 24 hours following sprint and plyometric bounding exercise, no significant differences between compression and control conditions were found for peak force of knee flexion and extension, as well as evoked twitch force of knee extensors.[37] However, torque of the knee extensors was higher 24–96 hours following plyometric drop jumps when lowerbody CGs were worn for 12 hours following exercise.[75] After backwards downhill walking, peak torque of the plantar flexors was unchanged with CGs (vs no compression) for voluntary contractions, as was evoked twitch force at 2, 48 and 72 hours following exercise (but higher in CGs at 24 hours).[79] Peak power output of rugby players performing simulated scrummaging during two simulated games (24 hours apart) was also unaffected by wearing lower-body CGs (pressures not reported) during exercise on both days, and for 15 hours during recovery from the first session.[36] ª 2011 Adis Data Information BV. All rights reserved.

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Heights of CMJs and squat jumps were better at certain post-exercise intervals for females who wore a lower-body CG for 12 hours following plyometric drop jumps compared with females who did not.[75] However, CMJ height,[14,72,73] peak power[14,78] and mean power,[78] and squat jump performance[78] were all unchanged (CGs vs control) following exercise that included 40-minutes running,[14] plyometric drop jumps,[72] parallel squats[73] and a workout of eight resistance exercises.[78] Sprint or repeat-sprint performance (5, 10 and 20 m,[72] 20 m,[36] 10 and 30 m[73]) and agility (5–0–5 agility test,[72] multiplanar speed[73]) were no better when CGs were worn (vs without compression) 24–48 hours after various forms of exercise. Wearing GCSs (~33–18 mmHg, ankle-thigh) between two 5-minute bouts of maximal cycling exercise performed 80 minutes apart significantly (p < 0.01) mitigated the reduction in mean power of the second bout (vs first bout) from ~3% to ~1%.[49] The authors suggested that lower plasma lactate concentrations ([La-]P) during recovery with GCSs may have contributed to the better subsequent performance, although psychological influences cannot be overlooked, and lower lactate concentrations in plasma might also reflect higher concentrations in muscle (which might ultimately be advantageous anyway). 4.2 Physiological and Physical Effects 4.2.1 Myocellular Proteins and Metabolites

Blood concentrations of various muscle proteins and metabolites have been used to investigate CG effects on post-exercise recovery. Berry and McMurray[31] hypothesized that compression-induced augmentation of venous blood flow could aid the removal of muscle metabolites, with the premise that studies of GCSs in clinical applications indicated altered venous haemodynamics (e.g.[22]) and decreased ambulatory venous pressure.[82]. In a first experiment[31] comprising a running VO2max test before seated rest, a trend for lower [La-]P was evident throughout the 60-minute recovery period when GCSs were worn during exercise and recovery (vs no stocking control), although only the values at 15 minutes differed Sports Med 2011; 41 (10)

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significantly. In a second experiment,[31] GCSs were worn either during exercise only (cycling at . and recovery or 110% VO2max), during exercise . not at all. While recovery VO2 and plasma volume were each similar among all conditions, recovery [La–]P was lower when the GCSs were worn throughout exercise and recovery than when removed after exercise or not used at all. With no evidence of altered lactate oxidation, the authors concluded it was most likely that lactate was being retained in muscle rather than being cleared more quickly. In addition to the difficulties in interpreting lower values as augmented or impaired clearance from muscle, lactate concentration per se is not necessarily a valid indicator of recovery quality.[83] In another study,[49] wearing GCSs throughout an 80-minute recovery period resulted in lower [La-]P (than without compression) following 5-minutes of maximal cycling in trained elderly cyclists (mean 63 – SD3 years of age), although there was also a trend for [La-]P to be lower at the start of the recovery period (when GCSs were donned). The GCSs used in the latter study[49] were ankle-to-thigh length, and at 33–13 mmHg applied higher pressures than the stockings used by Berry and McMurray[31] (18–8 mmHg), which were ankleto-knee length. When lower-body CGs were worn during exercise and recovery, no significant differences in [La-]P were evident compared with the control (no compression) condition immediately or 2 hours following short sprints and plyometric bounding[37] or 10 and 15 minutes following an 80-minute simulated team game.[36] Applied pressures were not reported in these two studies.[36,37] Various muscle metabolites were used to investigate CG effects on intracellular metabolic function following 30 minutes of downhill walking.[16] A lower-body CG that covered one leg only was donned immediately following exercise and worn for 48 hours. Using phosphorous magnetic resonance spectroscopy, intracellular phosphodiester concentration in the quadriceps was found to be higher in legs with compression than legs without at 1 hour after exercise, but not 48 hours after exercise. With elevated phosphodiester reported to represent increased membrane ª 2011 Adis Data Information BV. All rights reserved.

MacRae et al.

turnover,[84] the authors suggested that compression accelerated the inflammatory and repair timeframe.[16] No effects of the garments or the exercise itself were evident on phosphomonoester, phosphocreatine/inorganic phosphate ratio or intracellular free magnesium. Use of myocellular proteins as markers of muscle damage and inflammation have yielded mixed results. Lower plasma concentrations of lactate dehydrogenase ([LDH]P)[78] and creatine kinase ([CK]P)[35,76-78] as well as transdermal [CK][74] have been reported with CG use in some situations. On the other hand, no significant difference between compression and control conditions have been identified for post-exercise [LDH]P,[72,76] [CK]P,[36,37,72,73,75] plasma myoglobin,[14,73] Creactive protein[37] and aspartate transaminase.[37] Differences in the exercise used to induce muscle damage, and therefore the extent of myocellular release of specific proteins, may contribute to the inconsistency of findings. On the two occasions where no difference was found between compression and control conditions for [LDH]P,[72,76] the post-exercise values were not significantly different to baseline (pre-exercise) values and hence an apparent lack of CG affect was not surprising. However, on most occasions, the exercise used was sufficient to induce significant increases in indicators of muscle damage in at least one postexercise measurement (typically multiple measurements are taken, for example at 24-hour intervals). It is also worth noting that while muscle enzymes and other proteins are commonly used as indirect indicators of muscle damage, there can be large inter-individual variability in the response, and concentrations in the blood merely reflect the net balance of release and clearance.[85,86] Hence (as with lactate), interpreting lower concentrations in the plasma as being improved clearance or impaired release from the muscle, may not be defensible, and may not have a simple relationship with long-term adaptive responses. 4.2.2 Swelling, Range of Motion and Proprioception

Following eccentric contractions of the elbow flexors, wearing a compression sleeve (compared with no sleeve) made little difference to arm circumference, arm volume or elbow extensor range Sports Med 2011; 41 (10)

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of motion for a group of non-strength-trained men and women,[71] but did limit the rise in midbiceps brachii circumference in other groups of men[77] and women,[76] as well as limit the loss of range of motion (less change in relaxed elbow angle).[76,77] In the first study,[71] arm circumference was taken at the distal-, mid- and proximalbiceps brachii: the CG was worn for 3 days and covered the arm from the deltoid insertion to the wrist, applying ~17 mmHg of pressure. In the latter two studies,[76,77] the CGs applied ~10 mmHg and covered the arm from the axillary line to the mid-forearm, with wear durations being 3 days[77] or 5 days.[76] While these studies appear to provide some evidence of a relationship between the reduction of swelling and preservation of range of motion, elsewhere,[73] postexercise range of motion data from movements at the ankle, hip and knee did not provide a consistent trend in relation to CG effects on thigh and calf circumference measurements following a parallel squat exercise protocol. After 24 hours of wearing full-body CGs (applied pressures not reported) following an eight-exercise resistance training protocol, swelling of the vastus lateralis was reduced but circumference measurements at five upper- and lower-limb sites were not different when compared with the control condition (normal clothing).[78] Circumference measurements (mid-thigh) were also not different between CG and control conditions 24–48 hours following plyometric drop jumps.[72] Garments (lower-body CGs, reported as providing a ‘graduation of 15 mmHg’) were worn for 2 days following exercise. Effects of CGs on circumference or volume measurements during recovery do not necessarily relate to the effects on ratings of muscle soreness.[71-73,76-78] Wearing CGs reportedly improved visuomotor tracking immediately after and 1 day following eccentric exercise, although CGs were not used at baseline (performance – tracking error – was expressed as the percentage of baseline).[46] Thirty five maximal voluntary eccentric contractions of the elbow flexors were performed through 130 of extension at 90 ? s-1. Participants then either wore or did not wear a long-sleeved upperbody CG (applied pressures not reported) when ª 2011 Adis Data Information BV. All rights reserved.

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visuomotor tracking performance was assessed on several occasions from immediately afterwards to 14 days following exercise. Performance was also significantly different at days 2 and 3, although appeared neither better nor worse (tracking error [percentage of pre-exercise value] was ~120% for control, and ~80% for CGs). No significant differences were present at days 5, 7 and 14. 4.3 Perceptual Responses

Exercise-induced muscle soreness accompanies muscle damage, and ratings of perceived muscle soreness, particularly delayed onset muscle soreness (DOMS), are widely used to evaluate CG effects during recovery. The cause of this soreness may be a combination of factors including swelling, increases in noxious chemicals and byproducts of inflammation.[85] DOMS characteristically peaks 24–72 hours following exercise.[87] Wearing CGs has had mixed effects on the ratings of immediate muscle soreness and DOMS. Wearing CGs during and following exercise, or only following exercise, has sometimes shown little influence on perceived muscle soreness (0–72 hours following exercise),[14,16,71-73] and has sometimes shown attenuated soreness compared with no garment controls (1–120 hours following exercise).[30,35-37,75-79] Of the five studies that found no significant differences between the CGs and control, only one[14] had little or no increase in post-exercise muscle soreness compared with baseline, and four[14,16,72,73] used moderately- to highly-trained participants. Thus, exercise familiarity may influence results, although trained participants have reportedly benefitted from CG use in other situations.[30,35-37,78] One study[30] was identified in which CGs were worn during exercise only. No difference in perceived soreness was apparent at 1 and 24 hours following multistage intermittent shuttle running with or without GCSs, although the exercise protocol did not induce soreness above baseline. Ratings of vitality were higher and resting fatigue lower when a full-body CG was worn for 24 hours after whole-body, heavy-resistance exercise in resistance-trained men and women, although other mood states and quality of sleep Sports Med 2011; 41 (10)

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were not different from the control condition (normal clothing).[78] Following arm-curl exercise in untrained men[77] and women,[76] arm function during daily activities, soreness with range of motion and soreness with palpation were rated more favourably (at 72–120 hours following exercise) when wearing a compression sleeve than without. However, at 24 hours following exercise, no differences were evident between conditions for the women, and the men rated the compression condition less favourably than the control for soreness with range of motion and soreness with palpation. A between-groups study design was used for these two studies.[76,77] Overall, CG effects on various perceptual responses tend to be better or no worse than without compression. Difficulties obvious with perceptual measures, however, include that participants are often likely to have pre-conceived expectations regarding the purpose of CGs, and are not blinded for experimental condition (control condition is typically no CG). 5. Conclusions Anecdotal evidence indicates that CGs are widely used throughout sport and exercise in an attempt to improve aspects of exercise performance or recovery quality. Current empirical evidence indicates that wearing these garments has limited physiological or performance effects, although reports of detrimental effects are rare. There is some evidence that CGs may attenuate muscle oscillation, improve joint awareness, alter submaximal oxygen usage during exercise, alter local blood flow and protein or metabolite clearance, mitigate swelling and reduce perceived muscle soreness during post-exercise recovery, but the findings are often isolated (and need corroboration) or inconclusive (mixed results across studies). The fragmented nature of the literature, with respect to experimental variables, further complicates reaching a consensus. The temptation to take findings from one cohort or exercise type (e.g. untrained, jumping) and apply to others (e.g. trained, prolonged running) is cautioned. That garment type, applied pressures and wear durations differ complicates the matter further; hence, ª 2011 Adis Data Information BV. All rights reserved.

more research is required before practical recommendations can be made. To ensure practical conclusions can be drawn from future research, researchers are encouraged to investigate (i) what variables are most consistently influenced by compression and whether training status has any influence on the effects; (ii) if different applied pressures produce different results, and what role body coverage (i.e. garment type) has; and (iii) underlying mechanisms for demonstrable performance, physiological or psychological effects. Adequate blinding is important for minimizing placebo effects. Essential information that needs to be reported to aid comparisons across studies includes the applied pressures (at what sites) and area of the body covered. If the pressures were not measured by the authors, then the source of the information should be stated explicitly. Any suggestion of ‘optimal’ pressures is currently unjustified. While the benefits of CGs include that they are relatively low cost, easy to use and are noninvasive, whether they ultimately lead to meaningful effects on athletic performance remains to be seen. Acknowledgements The preparation of this review was supported by the University of Otago, Dunedin, New Zealand, through an award of a Division of Sciences Scholarship and a Postgraduate Publishing Bursary. The authors have no conflicts of interest that are directly relevant to the content of this review.

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7. International Organization for Standardization. ISO 11092: Textiles – physiological effects – measurement of thermal and water-vapour resistance under steady-state conditions (sweating guarded-hotplate test). Geneva: International Organization for Standardization, 1993 8. International Organization for Standardization. ISO 5084: Textiles – determination of thickness of textiles and textile products. Geneva: International Organization for Standardization, 1996 9. British Standards Institution. BS 12127: Textiles – fabrics – determination of mass per unit area using small samples. London: British Standards Institution, 1998 10. Denton MJ, Daniels PN. Textile terms and definitions. 11th ed. Manchester: The Textile Institute, 2002 11. International Organization for Standardization. ISO 2076: Textiles – man-made fibres-generic names. Geneva: International Organization for Standardization, 1999 12. Laing RM, Sleivert GG. Clothing, textiles and human performance. Textile Prog 2002; 32 (2): 1-132 13. Ashdown SP. An investigation of the structure of sizing systems. Int J Cloth Sci Technol 1998; 10 (5): 324-41 14. Ali A, Creasy RH, Edge JA. Physiological effects of wearing graduated compression stockings during running. Eur J Appl Physiol 2010; 109 (6): 1017-25 15. Maton B, Thiney G, Dang S, et al. Human fatigue and elastic compressive stockings. Eur J Appl Physiol 2006; 97 (4): 432-42 16. Trenell MI, Rooney KB, Sue CM, et al. Compression garments and recovery from eccentric exercise: A 31P-MRS study. J Sports Sci Med 2006; 5: 106-14 17. McLaren J, Helmer RJN, Horne SL, et al. Preliminary development of a wearable device for dynamic pressure measurement in garments. Procedia Eng 2010; 2 (2): 3041-6 18. Comite´ Europe´en de Normalisation. ENV 12718: Medical compression hosiery. Brussels: Comite´ Europe´en de Normalisation, 2001 19. Partsch H, Clark M, Bassez S, et al. Measurement of lower leg compression in vivo: recommendations for the performance of measurements of interface pressure and stiffness. Dermatol Surg 2006; 32 (2): 224-3 3 20. Best A, Williams S, Crozier A, et al. Graded compression stockings in elective orthopaedic surgery. J Bone Joint Surg Br 2000; 82-B (1): 116-8 21. Wildin CJ, Hui ACW, Esler CNA, et al. In vivo profiles of thigh-length graduated compression stockings. Br J Surg 1998; 85 (9): 1228-31 22. Lawrence D, Kakkar VV. Graduated, static, external compression of the lower limb: a physiological assessment. Br J Surg 1980; 67 (2): 119-21 23. Sigel B, Edelstein AL, Savitch L, et al. Type of compression for reducing venous stasis: a study of lower extremities during inactive recumbency. Arch Surg 1975; 110 (2): 171-5 24. Liu R, Lao TT, Kwok YL, et al. Effects of graduated compression stockings with different pressure profiles on lower-limb venous structures and haemodynamics. Adv Ther 2008; 25 (5): 465-78 25. Sparrow RA, Hardy JG, Fentem PH. Effect of ‘antiembolism’ compression hosiery on leg blood volume. Br J Surg 1995; 82 (1): 53-9

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43. Kraemer WJ, Bush JA, Triplett-McBride NT, et al. Compression garments: influence on muscle fatigue. J Strength Cond Res 1998; 12 (4): 211-5 44. Scanlan AT, Dascombe BJ, Reaburn PRJ, et al. The effects of wearing lower-body compression garments during endurance cycling. Int J Sports Physiol Perform 2008; 3: 424-38 45. Sperlich B, Haegele M, Achtzehn S, et al. Different types of compression clothing do not increase sub-maximal and maximal endurance performance in well-trained athletes. J Sport Sci 2010; 28 (6): 609-14 46. Pearce AJ, Kidgell DJ, Grikepelis LA, et al. Wearing a sports compression garment on the performance of visuomotor tracking following eccentric exercise: a pilot study. J Sci Med Sport 2009; 12: 500-2 47. Barrack RL, Skinner HB, Buckley SL. Proprioception in the anterior cruciate deficient knee. Am J Sports Med 1989; 17: 1-6 48. Perlau R, Frank C, Fick G. The effect of elastic bandages on human knee proprioception in the uninjured population. Am J Sports Med 1996; 23 (2): 251-5 49. Chatard JC, Atlaoui D, Farjanel J, et al. Elastic stockings, performance and leg pain recovery in 63-year-old sportsmen. Eur J Appl Physiol 2004; 93 (3): 347-52 50. Benko¨ T, Cooke EA, McNally MA, et al. Graduated compression stockings: knee length or thigh length. Clin Orthop Relat Res 2001; 383: 197-203 51. Stanton JR, Freis ED, Wilkins RW. The acceleration of linear flow in the deep veins of the lower extremity of man by local compression. J Clin Invest 1949; 28 (3): 553-8 52. Morris RJ, Woodcock JP. Evidence-based compression. Prevention of stasis and deep vein thrombosis. Ann Surg 2004; 239 (2): 162-71 53. Partsch B, Partsch H. Calf compression required to achieve venous closure from supine to standing positions. J Vasc Surg 2005; 42 (4): 734-8 54. Coleridge Smith PD, Hasty JH, Scurr JH. Deep vein thrombosis: effect of graduated compression stockings on distension of the deep veins of the calf. Br J Surg 1991; 78 (6): 724-6 55. Ibegbuna V, Delis K, Nicolaides AN. Effect of lightweight compression stockings on venous haemodynamics. Int Angiol 1997; 16 (3): 185-8 56. Lord RSA, Hamilton D. Graduated compression stockings (20-30 mmHg) do not compress leg veins in the standing position. ANZ J Surg 2004; 74 (7): 581-5 57. Mayberry JC, Moneta GL, De Frang RD, et al. The influence of elastic compression stockings on deep venous hemodynamics. J Vasc Surg 1991; 13 (1): 91-100 58. Bringard A, Denis R, Belluye N, et al. Effects of compression tights on calf muscle oxygenation and venous pooling during quiet resting in supine and standing positions. J Sports Med Phys Fitness 2006; 46 (4): 548-54 59. Kraemer WJ, Volek JS, Bush JA, et al. Influence of compression hosiery on physiological responses to standing fatigue in women. Med Sci Sports Exerc 2000; 32 (11): 1849-58 60. Watanuki S, Murata H. Effects of wearing compression stockings on cardiovascular responses. Ann Physiol Anthropol 1994; 13 (3): 121-7

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61. Mayrovitz HN. Compression-induced pulsatile blood flow changes in human legs. Clin Physiol 1998; 18 (2): 117-24 62. Mayrovitz HN, Larsen PB. Effects of compression bandaging on leg pulsatile blood flow. Clin Physiol 1997; 17 (1): 105-17 63. Somerville JJF, Brow GO, Bryne PJ, et al. The effect of elastic stockings on superficial venous pressures in patients with venous insufficiency. Br J Surg 1974; 61 (12): 979-81 64. Struckman J. Compression stockings and their effect on the venous pump: a comparative study. Phlebology 1986; 1: 37-45 65. Labropoulos N, Leon M, Volteas N, et al. Acute and long term effect of elastic stockings in patients with varicose veins. Int Angiol 1994; 13 (2): 119-23 66. Jonker MJ, de Boer EM, Ade`r HJ, et al. The oedemaprotective effect of Lycra support stockings. Dermatology 2001; 203 (4): 294-8 67. Styf J. The influence of external compression on muscle blood flow during exercise. Am J Sports Med 1990; 18 (1): 92-5 68. Mayrovitz HN, Sims N. Effects of ankle-to-knee external pressures on skin blood perfusion under and distal to compression. Adv Skin Wound Care 2003; 16 (4): 198-202 69. Nielsen HV. External pressure-blood flow relations during limb compression in man. Acta Physiol Scand 1983; 119 (3): 253-60 70. Davis MJ, Hill MA. Signaling mechanisms underlying the vascular myogenic response. Physiol Rev 1999; 79 (2): 387-423 71. Carling J, Francis K, Lorish C. The effects of continuous external compression on delayed-onset muscle soreness (DOMS). Int J Rehabil Health 1995; 1 (4): 223-35 72. Davies V, Thompson KG, Cooper S-M. The effects of compression garments on recovery. J Strength Cond Res 2009; 23 (6): 1786-94 73. French DN, Thompson KG, Garland SW, et al. The effects of contrast bathing and compression therapy on muscular performance. Med Sci Sports Exerc 2008; 40 (7): 1297-306 74. Gill ND, Beaven CM, Cook C. Effectiveness of post-match recovery strategies in rugby players. Br J Sports Med 2006; 40 (3): 260-3 75. Jakeman JR, Byrne C, Eston RG. Lower limb compression garment improves recovery from exercise-induced muscle damage in young, active females. Eur J Appl Physiol 2010; 109 (6): 1137-44 76. Kraemer WJ, Bush JA, Wickham RB, et al. Influence of compression therapy on symptoms following soft tissue injury from maximal eccentric exercise. J Orthop Sports Phys Ther 2001; 31 (6): 282-90 77. Kraemer WJ, Bush JA, Wickham RB, et al. Continuous compression as an effective therapeutic intervention in treating eccentric-exercise-induced muscle soreness. J Sport Rehabil 2001; 10 (1): 11-23 78. Kraemer WJ, Flanagan SD, Comstock BA, et al. Effects of a whole body compression garment on markers of recovery after a heavy resistance workout in men and women. J Strength Cond Res 2010; 24 (3): 804-14 79. Perrey S, Bringard A, Racinais S, et al. Graduated compression stockings and delayed onset muscle soreness (P105). The Engineering of Sport 7. In: Estivalet M, Brisson P, editors. Paris: Springer, 2008: 546-54

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80. Kraemer WJ, French DN, Spiering BA. Compression in the treatment of acute muscle injuries in sport. Int Sport Med J 2004; 5 (3): 200-8 81. Yamane M, Teruya H, Nakano M, et al. Post-exercise leg and forearm flexor muscle cooling in humans attenuates endurance and resistance training effects on muscle performance and on circulatory adaptation. Eur J Appl Physiol 2006; 96: 572-80 82. Norris GS, Turley G, Barnes RW. Noninvasive quantification of ambulatory venous hemodynamics during elastic compressive therapy. Angiology 1984; 54: 560-7 83. Barnett A. Using recovery modalities between training sessions in elite athletes: does it help? Sports Med 2006; 36 (9): 781-96 84. Sprott H, Rzanny R, Reichenback JR, et al. 31P magnetic resonance spectroscopy in fibromyalgic muscle. Rheumatology 2000; 39: 1121-5

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85. Clarkson PM, Hubal MJ. Exercise-induced muscle damage in humans. Am J Phys Med Rehabil 2002; 81 (11 Suppl.): S52-69 86. Nosaka K, Clarkson PM. Variability in serum creatine kinase response after eccentric exercise of the elbow flexors. Int J Sports Med 1996; 17 (2): 120-7 87. Cheung K, Hume PA, Maxwell L. Delayed onset muscle soreness: treatment strategies and performance factors. Sports Med 2003; 33 (2): 145-64

Correspondence: Mr Braid MacRae, Clothing and Textile Sciences, Department of Applied Sciences, University of Otago, PO Box 56, Dunedin, New Zealand. E-mail: [email protected]

Sports Med 2011; 41 (10)

Sports Med 2011; 41 (10): 845-859 0112-1642/11/0010-0845/$49.95/0

REVIEW ARTICLE

ª 2011 Adis Data Information BV. All rights reserved.

Genetic Influences in Sport and Physical Performance Zudin Puthucheary,1 James R.A. Skipworth,1 Jai Rawal,1 Mike Loosemore,2 Ken Van Someren2 and Hugh E. Montgomery1 1 UCL Institute for Human Health and Performance, London, UK 2 English Institute of Sport, Bisham Abbey National Sports Centre, Marlow, Buckinghamshire, UK

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Skeletal Muscle Form and Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Cytokines and Growth Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Endocrine Influences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Vitamin D and Skeletal Muscle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Muscle Fibre Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Muscle Collagen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Bone Size Shape and Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Cardiac Size and Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Lung Development and Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Genes and Sports Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Genetic Influences on Injury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

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The common inheritance of approximately 20 000 genes defines each of us as human. However, substantial variation exists between individual human genomes, including ‘replication’ of gene sequences (copy number variation, tandem repeats), or changes in individual base pairs (mutations if <1% frequency and single nucleotide polymorphisms if >1% frequency). A vast array of human phenotypes (e.g. muscle strength, skeletal structure, tendon elasticity, and heart and lung size) influences sports performance, each itself the result of a complex interaction between a myriad of anatomical, biochemical and physiological systems. This article discusses the role for genetic influences in influencing sporting performance and injury, offering specific exemplars where these are known. Many of these preferable genotypes are uncommon, and their combination even rarer. In theory, the chances of an individual having a perfect sporting genotype are much lower than 1 in 20 million – as the number of associated polymorphisms increase, the odds decrease correspondingly. Many recently discovered polymorphisms that may affect sports performance have been described in animal or other human based models, and have been included in this review if they may apply to athletic populations.

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Muscle performance is heavily influenced by basal muscle mass and its dynamic response to training. Genetic factors account for approximately 50–80% of inter-individual variation in lean body mass, with impacts detected on both ‘training-naive’ muscle mass and its growth response. Several cytokines such as interleukin-6 and -15, cilliary neurotrophic factor and insulinlike growth factor (IGF) have myoanabolic effects. Genotype-associated differences in endocrine function, necessary for normal skeletal muscle growth and function, may also be of significance, with complex interactions existing between thyroxine, growth hormone and the downstream regulators of the anabolic pathways (such as IGF-1 and IGF-2). Almost 200 polymorphisms are known to exist in the vitamin D receptor (VDR) gene. VDR genotype is associated with differences in strength in premenopausal women. VDR expression decreases with age and VDR genotype is associated with fatfree mass and strength in elderly men and women. Muscle fibre type determination is complex. Whilst initial composition is likely to be strongly influenced by genetic factors, training has significant effects on fibre shifts. Polymorphisms of the peroxisome proliferator-activated receptor a (PPARa) gene and R577x polymorphism of the ACTN3 gene are both associated with specific fibre compositions. Alterations in cardiac size have been associated with both increased performance and excess cardiovascular mortality. PPARa is a ligand-activated transcription factor that regulates genes involved in fatty acid uptake and oxidation, lipid metabolism and inflammation. Psychology plays an important role in training, competition, tolerance of pain and motivation. However, the role of genetic variation in determining psychological state and responses remains poorly understood; only recently have specific genes been implicated in motivational behaviour and maintenance of exercise. Thyroid hormone receptors exist within the brain and influence both neurogenesis and behaviour. With the current state of knowledge, the field of genetic influences on sports performance remains in its infancy, despite over a decade of research.

1. Introduction The common inheritance of approximately 20 000 genes defines each of us as human. However, substantial variation exists between individual human genomes, including ‘replication’ of gene sequences (copy number variation, tandem repeats), or changes in individual base pairs (mutations if <1% frequency and single nucleotide polymorphisms [SNPs] if >1% frequency). Such variation is common; indeed, approximately 10 million SNPs alone are thought to exist.[1] All variation in human traits (or phenotypes) results from the interaction between an individual’s unique genotype and environmental stimuli. Heritability (H2) is defined as the proportion of phenotypic variation in a population attributable to genetic variation ª 2011 Adis Data Information BV. All rights reserved.

(rather than variation in environment) among individuals: H2 ¼

Variation ðgenotypeÞ Variation ðphenotypeÞ

This holds true not just for disease, but for health and for sporting phenotypes. A vast array of human phenotypes (e.g. muscle strength, skeletal structure, tendon elasticity, and heart and lung size) influence sports performance, each itself the result of a complex interaction between a myriad of anatomical, biochemical and physiological systems. Thus, muscle strength is influenced by fibre types, angle of pennation, innervation, fibre size and blood flow, to name but a few. These phenotypes themselves will be influenced by a variety of other processes (inSports Med 2011; 41 (10)

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cluding appetite, dietary volume and characteristics, muscle protein synthesis) and cellular types governing these processes (gut epithelium, muscle proteolysis and synthesis pathways, hepatic transport). In turn, each of these phenotypes will be influenced by a large number of individual genes; the broader the phenotype, the larger the number of relevant genes. Our final form and function will be the result of these numerous genetic factors interacting with the diverse environmental stimuli to which we are exposed. In terms of sporting abilities, then, diverse genetic influences (some overlapping and some unique) affect our ‘untrained form’; some genes our willingness to engage in exercise and others our body’s response to such exercise. This article discusses the role for genetic influences in influencing sporting and physical performance and injury, offering specific exemplars where these are known. An exhaustive review of genetic influences on sports and physical performance is not possible in a single article. Some variants, such as those in the genes encoding the angiotensin converting enzyme (ACE) or bradykinin, are worthy of their own separate reviews, given the strength of evidence suggestive of their importance.[2-7] Many recently discovered polymorphisms that may affect sports and physical performance have been described in animal or other human based models, and have been included in this review if they may apply to athletic populations. 2. Methods This article was not intended to be a formal structured and systematic review, but rather as a discussion of the field and of the key relevant papers. As such, we used PubMed, MEDLINE and Google Scholar to identify articles of relevance published in the last 20 years from January 1990 to January 2011. The primary search terms were ‘skeletal muscle’, ‘endocrine’, ‘vitamin D’, ‘bone’, ‘cardiac’, ‘lung’, ‘psychology’ and ‘injury’, with genotype/polymorphism. Search results were then narrowed using terms relevant to performance phenotypes, including ‘performance’ ‘power’, ‘strength’, ‘athlete’ and ‘elite’. Studies were excluded if there was no English language translation available. ª 2011 Adis Data Information BV. All rights reserved.

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3. Skeletal Muscle Form and Function Muscle performance is heavily influenced by basal muscle mass and its dynamic response to stressors (e.g. training). Genetic factors account for approximately 50–80% of inter-individual variation in lean body mass,[8] with impacts detected on both ‘training-naive’ muscle mass and its growth response. Similar genetic influences are seen on muscle function: heritability of grip and pull and push strength ranges from 44% to 83%.[9] This influence appears greater in males, especially for static strength and power compared with muscular endurance,[10] and also varies with age.[9,11-13] In addition to mass, the efficiency of muscle activity and contraction is likely to be influenced by genetic factors.[14] 3.1 Cytokines and Growth Factors

Interleukin (IL)-15 is a myoanabolic cytokine whose actions are (in part) mediated through its a-receptor (IL15RA). In young men and women undergoing 10 weeks of resistance exercise, SNP in exon 7 of the IL15RA gene accounted for 7.1% of the variation in muscle anabolism.[15] A polymorphism in exon 4 was also independently associated with muscle hypertrophy and accounted for an additional 3.5% of the variation in hypertrophy. Variation in the IL15RA gene may thus be responsible for a significant proportion of the variability in the skeletal muscle hypertrophic response to exercise.[15] Meanwhile, IL-6 is an inflammatory cytokine associated with skeletal muscle wasting in animal models and with lower muscle mass and strength in healthy older individuals.[16-18] In keeping, a G174C promoter polymorphism of the IL-6 gene seems associated with a variation in fat-free mass in men.[19] Ciliary neurotrophic factor (CNTF; another member of the IL-6 family) seems trophic to skeletal muscle,[20] protecting rat soleus muscle from wasting after sciatic denervation,[21] and increasing the cross-sectional area of innervated soleus muscle fibres.[22] A C174T polymorphism in the CNTF gene has been associated with differences in human fat-free mass,[23] and the A allele (of the G1357A polymorphism) with higher peak Sports Med 2011; 41 (10)

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torque of both knee extensor and flexor muscle groups.[24] The powerful mitogen insulin-like growth factor (IGF)-2 could potentially influence ageassociated loss in human muscle mass (sarcopaenia) and strength. In keeping with this hypothesis, adult males homozygous for the A (rather than G) IGF-2 ApaI polymorphism have lower fat-free mass and also lower isokinetic grip strength than those of the GG genotype.[25] This difference was maintained at age 65 years and across the adult age span (p < 0.05). The IGF-2 genotype has also been associated with grip strength in middle-aged men.[26]

terol levels and increased insulin sensitivity.[33] In a cohort of 350 subjects observed from age 13 years for 13 years, noncarriers and carriers (27 individuals, 8%) of the ER22/23EK variant were compared.[34] In the males at 36 years of age, ER22/23EK carriers were taller, with greater lean body mass, greater thigh circumference and greater limb strength. The female ER22/23EK carriers had smaller waist and hip circumferences. The investigators concluded that the ER22/23EK polymorphism is associated ‘‘with a sex-specific, beneficial body composition at young-adult age, as well as greater muscle strength in males’’.[34] One specific endocrine system deserves special comment and that is the vitamin D system.

3.2 Endocrine Influences

Genotype-associated differences in endocrine function, necessary for normal skeletal muscle growth and function, may also be of significance, with complex interactions existing between thyroxine, growth hormone and the downstream regulators of the anabolic pathways (such as IGF-1 and IGF-2). Specifically, thyroid hormones are essential for normal muscle growth and development,[27] directly alter metabolic efficiency of muscle[28] and are essential for normal production of growth hormone both in vitro[29,30] and in vivo.[31] Deiodinases convert thyroxine to tri-iodothyronine (the more active form of thyroid hormones). Two polymorphisms of the type I deiodinase (D1) seem associated with serum iodothyrodine levels,[32] the D1 haplotype 2 allele (aT-bA) showing lower concentrations, and the haplotype 3 allele higher activity (aC-bG, respectively). Amongst 350 elderly men, carriers of the D1a-T variant had higher lean body mass (p = 0.03), as well as higher isometric grip strength (p = 0.047) and maximum leg extensor strength (p = 0.07), suggesting that this polymorphism is associated with increased muscle mass through associated decreased D1 activity and increased IGF-1 levels concurrently shown in the study.[32] Glucocorticoid hormones have a powerful impact upon body composition. Polymorphism in the glucocorticoid receptor gene at codons 22 and 23 (ER22/23K) is associated with relative glucocorticoid resistance as well as low cholesª 2011 Adis Data Information BV. All rights reserved.

3.3 Vitamin D and Skeletal Muscle

The vitamin D compounds (D1–D5) are fatsoluble pro-hormones, of which the predominant forms are D2 (ergocalciferol, made from ergosterol) and D3 (cholecalciferol, made from 7-dehydrocholesterol). Hepatic vitamin D hydroxylase converts D3 to 25-dihydroxyvitamin D3 (25[OH]D3), the main circulating vitamin D metabolite. This is converted to 1,25-dihydroxyvitamin D3 by further enzymatic action within the kidney, and subsequently transported through the blood stream by vitamin D binding protein. The hormonally active forms of vitamin D mediate their effects through agonist action at the vitamin D receptor (VDR), a transcription regulator principally located in the nuclei of target cells.[35,36] The traditional roles of vitamin D include regulation of serum calcium and phosphorous levels through promotion of their intestinal absorption and renal calcium reabsorption, and promotion of bone formation and mineralization. However, vitamin D has pleiotropic actions; the VDR has been identified in a wide range of tissues,[37,38] its activation modulating the expression of over 200 genes, affecting (amongst others) cellular proliferation and differentiation and modulation of the immune response.[39] An influence on muscle function is also suggested.[40,41] Data from VDR-null mice confirm that the nuclear ligand-receptor VDR complex leads to messenger RNA (mRNA) transcription and protein synthesis Sports Med 2011; 41 (10)

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capable of influencing proliferation and differentiation of cells into mature muscle fibres,[42,43] through a mechanism involving the mitogenactivated protein kinase pathway.[44,45] In addition, nongenomic signal transduction occurs more rapidly through binding to a membrane-bound VDR, leading to enhanced calcium influx.[37,46] Thus, VDR-null, 3-week-old mice (that still have normal mineral ion and vitamin D metabolite levels) have smaller muscle fibres and persistently elevated expression of markers of early muscle differentiation such as myogenin, Myf5 and neonatal myosin heavy chain.[42] In vitro studies have shown that 1,25-dihydroxyvitamin D3 can have rapid effects on muscle through phosphorylation and activation of secondary messengers.[47] In support of such influence, profound vitamin D deficiency is associated with (predominantly proximal) muscle weakness.[48] In such cases, predominantly type II fibre atrophy is identified, accompanied by fibre necrosis and fatty infiltration,[49-51] possibly occurring secondary to reduced calcium uptake by the sarcoplasmic reticulum and phosphate depletion impairing glycolysis.[52] Oral vitamin D supplementation in the elderly reduces the incidence of falls in both residential[53] and community settings,[54] as well as increasing lower limb and handgrip strength.[53] Furthermore, the effects of training are enhanced by vitamin D supplementation.[55] The VDR gene is located on chromosome 12 (12q12–q14) and contains two promoter regions and 14 exons (8 protein coding and 6 untranslated), all of which are alternatively spliced.[56,57] Almost 200 polymorphisms exist in the VDR gene, the most studied mainly being anonymous restriction fragment length polymorphisms (RFLPs). Restriction sites are specific nucleotide sequences recognized by ‘restriction enzymes’ that cleave them. The lengths of the intervening pieces of DNA that lie between cleavage sites vary; these are RFLPs. In addition, the presence (or absence) of enzyme-specific restriction sites may also be used to define variation between individuals. Such RFLPs include the ApaI,[58] EcoRV,[59] BsmI,[59,60] TaqI[60] and Tru9I[61] discovered at the 30 end of the VDR gene. The only known functional polymorphism is the FokI polyª 2011 Adis Data Information BV. All rights reserved.

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morphism, in which the presence of the FokI f allele in the 50 promoter region of the VDR gene results in the production of a less effective transcriptional activator.[62,63] VDR genotype is associated with differences in strength in premenopausal women and in elderly men.[64,65] VDR expression decreases with age,[66] and VDR genotype is associated with fat-free mass and strength in elderly men[67] and women.[68] In elderly postmenopausal women, the presence of the BsmI SNP in the VDR gene is associated with quadriceps and grip strength.[65,68] Furthermore, in these elderly cohorts, both low vitamin D levels and high parathyroid hormone levels were associated with a decline in lower limb muscle bulk and handgrip strength,[69] as well as an increased tendency to fall.[70] Few studies have looked specifically at the relationship between vitamin D receptors and sport, and none at global performance. Tajima et al. examined the interaction of the FokI polymorphism upon resistance training to discover that homozygotes without FokI had an increased period of suppression of bone resorption, as well as a greater increase in bone formation, following 1 month of weight training.[71] A further crosssectional study examining 44 athletes and 44 matched, nonathletic controls found that the athletes had a significantly higher bone mineral content, resulting from both increased volume and density, at both the lumbar spine and femoral neck.[72] When the FokI subsets were compared, the increased spinal volume was found only in those homozygotes without the FokI endonuclease, therefore, suggesting that individuals lacking FokI are capable of adapting to impact loading by producing stronger bone structures . Rabon-Stith et al. evaluated the effect of VDR polymorphisms upon bone mineral density in 206 healthy men and women (aged 50–81 years) in response to 5–6 months of either aerobic or strength training to find that FokI was related significantly to strength training-related (but not aerobic training) changes in femoral neck bone mineral density.[73] Diogenes et al. similarly evaluated the impact of VDR polymorphisms on longitudinal changes in bone mass in 46 adolescent, Brazilian soccer players (aged 11.8–14.2 years) to show that Sports Med 2011; 41 (10)

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those with at least one non-FokI allele had higher total body bone mineral content and density and that this difference was maintained after 6 months, suggesting that any effect of the FokI polymorphism upon bone mineralization may occur from as early as the initial stages of puberty.[74] These findings were further confirmed by Chatzipapas et al. in a study of 64 military personnel, which demonstrated that patients with stress fractures were much more likely to have the FokI polymorphism (2.7-fold increase in risk of stress fractures with the f allele).[75] The B allele of the BsmI polymorphism was also noted to be an independent risk factor for the development of stress fractures (2.0-fold increase in risk of stress fractures with the B allele). 3.4 Muscle Fibre Type

Human skeletal muscle is composed of varying proportions of three different myofibres, each with its own functional and metabolic profiles: type I (slow twitch) and type IIA and IIx (the currently accepted term for IIB) [fast twitch]. In a large study of Caucasian men and women, 25% of subjects had <35% or >65% type I fibres.[76] Whilst the initial composition is likely to be strongly influenced by genetic factors, the product is likely resultant from gene-environment interaction (i.e. training).[77] The latter may be more likely; fibre type shift has been described from IIx to IIA in resistance training and from I to II in disease states.[78-81] Whilst fibre type shifts away from IIx are seen in endurance training, the replacement fibre type is variable.[80] In a study of 26 pairs of male and female dizygotic twins and 35 pairs of male and female monozygotic twins, genetic differences seemed to account for 45–50% of variation in the proportion of type I fibres.[82] The peroxisome proliferatoractivated receptor a (PPARa) is a transcriptional regulator that controls genes responsible for skeletal and heart muscle fatty-acid oxidation. In one study of 40 men, significant correlation was seen between a PPARa intron 7 G/C polymorphism and composition by muscle fibre type.[83] XX homozygotes of R577x polymorphism of the ACTN3 gene are deficient in a-actin-3, a structural ª 2011 Adis Data Information BV. All rights reserved.

protein found only in type II fibres. In a single study of 44 volunteers (22 homozygotes for XX, 22 homozygotes for RR) greater numbers of type II fibres were seen in the RR homozygotes.[84] In a separate study the presence of the X allele and XX genotype was seen to be significantly lower in power athletes than in controls.[85] Whilst various associations have been seen with the R577X polymorphism with fibre type and mass, its relationship to performance remains unclear.[86-90] Variations in the vascular endothelial growth factor receptor (VEGFR)-2 have also been associated with muscle fibre type composition.[91] 3.5 Muscle Collagen

Type I collagen is a triple-stranded fibrillar protein, and is the major collagen of tendon and bone, and is also found in both the epimysium and perimysium of skeletal muscle.[92] It comprises two a1 polypeptide chains (encoded by the collagen type I a1, COL1A1, gene) and one a2 chain (encoded by the COL1A2 gene). Whilst fast twitch (fibre type 2) muscle has more type III collagen, slow twitch (fibre type 1) muscle fibres contain more type I collagen. Both types serve as a supportive structure in muscle tissue where they attach myocytes and muscle bundles to each other.[92] The collagen fibre network of skeletal muscle has been shown to be a major contributor to the integrity and tensile strength of muscle tendon and bone.[93,94] A polymorphic binding site of the Sp1 transcription factor in the gene encoding the a1 chain of type I collagen exists, and the s (rather than S) allele of this polymorphism has been associated with lower grip and biceps strength on the dominant side, with the difference between the two homozygous genotype groups amounting to 21% and 30%, respectively.[94] 4. Bone Size Shape and Density The demand of competition and rigorous training schedules takes its toll on competitors, stress fractures being a major problem among both professional and amateur athletes. In younger adults, bone mineral density (BMD) has not been Sports Med 2011; 41 (10)

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shown to relate closely to fracture risk, unlike in the elderly.[95,96] Whilst the role of genetic variation on BMD has been explored, the role of other determinants on bone strength is less clear. Other properties of bone, such as elasticity and anatomical development, are clearly of importance and contribute greatly to bone mechanical properties. Nonetheless, BMD continues to be used as a surrogate marker for bone strength. In several studies, heritability estimates for BMD at the lumbar spine and femoral neck range from 57% to 92%.[97,98] Among female members of the same family, significant correlations have also been observed in the rates of fragility fractures.[99,100] Several polymorphic variants have been associated with static BMD, and its response to environmental stimuli involving calcium and phosphate metabolism, parathyroid hormones, estrogen receptor-a, and aromatase enzymes.[101-106] Other molecules affecting bone metabolism include a2-HS glycoprotein and IL-6.[107,108] The VDR genotype (BsmI) has also been associated with variation in BMD in children,[109] but not in premenopausal women.[110] Such subgroup-specific associations may account for the finding of only nonsignificant trends when the BsmI genotype was correlated with BMD in a 16-study metaanalysis.[111] Further studies and meta-analyses, however, have suggested that VDR genotypes associated with reduced receptor function, may be associated with enhanced risk of osteoporosis.[112-115] Mechanostatic theories define muscle and bone as one functional unit under the influence of individual stimuli, one of which might thus be vitamin D.[116] As a result, caution must be applied in the interpretation of gene association data (such as those relating to the VDR gene): a polymorphism might influence bone structure directly, or indirectly through associated alterations in the loading applied by skeletal muscle. 5. Cardiac Size and Function Alterations in cardiac size has been associated with both increased performance and excess cardiovascular mortality.[117-121] Investigation into the genetic factors that influence left ventricular (LV) growth responses have thus been performed ª 2011 Adis Data Information BV. All rights reserved.

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in both health and disease. One important polymorphism offers insight. PPARa is a ligand-activated transcription factor.[122] In addition to influences on muscle fibre type (see section 3.4), it regulates genes involved in fatty-acid uptake and oxidation, lipid metabolism and inflammation.[123] Substrate utilization appears important in the pathogenesis of ventricular hypertrophy. The hypertrophied heart exhibits an increase in the utilization of glucose with a corresponding decrease in fattyacid oxidation attributable to the downregulation of the fatty-acid oxidation enzyme mRNA levels.[124] Both in vitro and in vivo studies demonstrate that PPARa is down-regulated in cardiac hypertrophy.[125] This ‘metabolic switch’ may in fact be a cause rather than just a consequence of hypertrophy, with inhibition of fatty acid oxidation in animal models causing cardiac hypertrophy.[126,127] The influence of a G/C polymorphism of intron 7 of the PPARa gene has been investigated in 144 young male British Army recruits undergoing a 10-week period of uniform physical training.[128] Here, LV mass increased by 6.7 – 1.5 g in G allele homozygotes, but significantly more so in those heterozygous for the C allele (11.8 – 1.9 g) and in CC homozygotes (19.4 – 4.2 g). Meanwhile, in 578 men and 564 women participating in the (population-based) third MONICA (Multinational Monitoring of Trends and Determinants in Cardiovascular Disease) Augsburg survey,[128,129] C allele homozygotes had a significantly higher LV mass; an effect amplified in hypertensive subjects. 6. Lung Development and Function In animals, lung function is a result of complex genetic influences and interactions.[130] In humans, there appears to be a strong genetic influence on forced vital capacity (FVC), a measure of lung volume.[131] Examining 1045 individuals from 309 families in Saskatchewan, Canada and adjusting for height, weight and age, significant sibling-sibling and parent-offspring correlations were seen.[132] This is in keeping with studies performed in other countries and races.[133] Data from the Framingham, MA, USA, (populationSports Med 2011; 41 (10)

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based) study showed that the loci with the most influence on the forced expiratory volume in 1 second (FEV1) localized to chromosome 6 and for FVC to chromosome 21 (logarithm of odds scores of 2.4 and 2.6, respectively).[134] One study from Western Australia estimates heritability of FEV1 and FVC to be 38.9% and 40.6%, respectively, consistent with significant genetic determination.[135] Decreased flow generation can limit the master athlete or asthmatic.[136] Arterial desaturation does occur in healthy endurance athletes, and has been reported in cyclists, rowers and cross country skiers, implying limitation of performance by lung function.[137-140] Swimmers have been noted to have greater lung function than controls, although little has been done to address the potential confounding role of training as a stimulus to lung growth.[141] 7. Genes and Sports Psychology Psychology plays an important role in training, competition, tolerance of pain and motivation. However, the role of genetic variation in determining psychological state and responses remains poorly understood. Only recently have specific genes been implicated in motivational behaviour and maintenance of exercise.[142] While thyroid hormone influences skeletal muscle performance,[28] its receptors exist within the brain and influence both neurogenesis and behaviour. In particular, mice lacking the thyroid hormone receptor-a show decreased expression of genes such as that for the glucocorticoid receptor, growth-associated protein-43 and neurogranin (all known to modulate learning and memory) as well as decreased activity.[143] Brain-derived neurotrophic factor (BDNF) has a diverse influence on neuronal and vascular growth, and development and regeneration in the brain (centred on the hippocampus), spinal cord and skeletal muscle. Polymorphisms of the BDNF gene are associated with differences in mood, and in perception of exercise.[144] Athletes are often exposed to high levels of emotional stress, and polymorphism of the 50 -flanking regulatory regions of serotonin transporter gene (5HTT) ª 2011 Adis Data Information BV. All rights reserved.

may be associated with differences in emotional control.[145] Neuropeptide Y2 receptor (NPYR2) knockout mice demonstrate improved stress coping abilities.[146] Spatial awareness is central to many sports. Mice lacking the receptor for the glutamate analogue L-2-amino-4-phosphonobutyric acid show impaired spatial accuracy.[147] Altered habituation has been shown in rodents with adenosine A1 receptor knockouts.[148] Mice with low levels of IGF-1 have reduced adult hippocampal neurogenesis and spatial awareness, which recover with IGF-1 infusions.[149] However, the influence of homologous polymorphic variation in humans largely remains to be demonstrated. Pain remains a barrier to be overcome by athletes. Animal models suggest that nociception (the feeling of pain) is strongly influenced by genetic elements.[150,151] A complete review of pain genetics is beyond the scope of this article, but a comprehensive review is available.[151] However, by way of example, the first stage in the induction of pain is the depolarization of sensory neurons. Three genes encoding for sodium channels are expressed selectively in sensory neurons, and knockout studies have shown that SCN9A is involved in perception of peripheral pain, SCN10A cold pain, and SCN11A in setting pain thresholds.[152-154] Reports exist of humans with channelopathies leading to ‘complete indifference to pain’, i.e., the ability to sense but not to be affected by pain. Interestingly, the index case here performed street theatre by walking across burning coals and using knives for entertainment.[155] Whilst those with extreme forms of pain tolerance are rare, there is potential for the existence of less stark phenotypes. Polymorphic variation in the genes of diverse systems have the capacity to influence human physical performance through associated differences in the regulation of (amongst others) the structure and function of skeletal and cardiac muscle, bone and lung. Indeed, it is now clear that genetic variation does account for differences in human physical performance. Several candidate genes may well affect overall sporting prowess, though a full discussion of specific loci associated with human global performance measures is beSports Med 2011; 41 (10)

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yond the remit of this review. Several functional polymorphisms have been demonstrated to affect sporting phenotypes, by acting in a variety of fashions. The ACE genotype is by far one of the best known of these.[2,156-161] Others of note include the functional allele (577r) of ACTN3 (coding for human a actin 3), which has been associated with elite ‘sprinter’ athletic status.[162,163] PPARa (discussed in sections 3.4 and 5) has been shown to act on a variety of tissues and so may contribute to the overall sporting phenotype.[83] The reality is that a combination of rare alleles is needed for the making of a ‘super’ athlete.[164,165] Thus, genotype can influence sporting intermediate phenotypes, as well as more global measures of sporting performance. But genotype may also influence propensity to sporting injury. 8. Genetic Influences on Injury Musculoskeletal injury and subsequent recovery seem likely to result from the interaction of environmental stimuli (training or competitionrelated mechanical load patterns, or surgery/ unloading) and genotype. Thus, high-velocity throwing is a frequent cause of supraspinatus muscle injury,[166] with a relative risk of 2.85–4.65 amongst siblings of those injured than amongst controls.[167,168] In the triceps surae (Achilles) tendon, tenocytes and tenoblasts lie parallel to the fibres and are the main cellular constituents.[169] However, the extracellular tendon matrix is key to the structural integrity of the tendon, and comprises proteoglycans, glycosaminoglycans, cellular adhesion molecules and collagen in its various forms. Dry tendon mass is 30% of the total, of which type 1 collagen accounts for 65–80%, and elastin 2%. Other forms of collagen are also present. Type V collagen is thought to play a role in determining collagen fibre size and assembly,[170] while type II and III are localized principally at the fibrocartilagenous tendon insertion (ideally situated to bear compressive loads). Tenascin C is a small structural protein found in tendons, myotendinous junctions, perichondrium and periosteum.[171] The change in collagen type thus mirrors the ª 2011 Adis Data Information BV. All rights reserved.

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functional requirements of the tendon along its length.[172,173] The tendon itself is subject to large transmitted forces, to which the matrix must respond. Failure to do so leads to injury, which is both debilitating and common, with a reported annual incidence of 7–9% in top-level runners[174] or two injuries per 1000 km of endurance running.[175] Achilles tendinopathy seems the most common injury, but tendon rupture is also common.[173] The matrix response to loading or injury is achieved through modulation of expression of matrix metalloproteases (MMP) and tissue inhibitors of matrix metalloproteases (TIMP), which may thus influence propensity to tendon injury and repair. Expression of TIMPs and MMPs (as measured with real-time polymerase chain reaction analysis) is thus altered in ruptured (compared with adjacent healthy) areas.[176-178] Expression of the COLIA1 gene (which encodes the aI chain of type 1 collagen) is also increased in ruptured areas. MMP3 and MMP10, and TIMP3 expression seems downregulated in Achilles tendinopathy, whilst MMP2 and MMP23 are upregulated.[176] The role of such changes in the causation of injury (rather than in the response to it) remains to be proven. However, a vascular aetiology is often proposed for Achilles tendinopathy and rupture, given that injury generally occurs at watershed vascular zones, where angiogenesis is also found in the event of injury.[179] In human studies, the vascular endothelial growth factor can be identified using immunostaining in the tenocytes of injured (but not normal) Achilles tendons, whilst the VEGFR could be identified in the microvessels.[180] Using these elements to suggest potential candidate genes, what progress has been made? Investigating 85 Achilles tendinopathy cases, 41 cases of Achilles tendon rupture and 125 controls, the frequency of a G>T substitution within intron 1 of the COLIA1 gene did not differ between cases and the controls.[181] However, the BstUl DpnII restriction fragment length polymorphism of the COL5A1 (a1 type V collagen) gene has been associated with symptomatic Achilles tendinopathy and tendon rupture,[170] while variation in the gene encoding tenascin C (found Sports Med 2011; 41 (10)

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on chromosome 9q32-q34) has been associated with Achilles tendon injury. Using 114 cases with Achilles tendon pathology (tendinopathy tendinosis or rupture) and 127 asymptomatic tendons, the tenascin C allele containing 12 or 14 repeats of guanine-thymine dinucleotide has a 6-fold higher risk of Achilles tendon injury compared with those with alleles containing 13 or 17 repeats.[171] Such studies are hampered by variation in subject race, age and sex, as well as in past and current loading history. They are also prone to ascertainment bias, and incomplete phenotyping (that can ‘lump’ diverse disease states together by presenting complaint). Nonetheless, genetic study of the injured athlete may yet offer great insight into the propensity to injury (allowing subjectspecific tailoring of training regimen), and its mechanism (leading to the development of new preventative and therapeutic strategies). 9. Conclusions Human physical performance is the result of interaction between genetic inheritance and environmental stimuli. Over 200 autosomal gene variants and quantitative trait loci have been associated with human physical performance.[182] Many of these preferable genotypes are uncommon, and their combination even rarer. In theory, the chances of an individual having a perfect sporting genotype are much lower than 1 in 20 million and as the number of associated polymorphisms increase, the odds decrease correspondingly.[165] With the current state of knowledge, the field of genetic influences on sports performance remains in its infancy, despite over a decade of research. Sport genetic studies have been hampered by their small cohort sizes, and some may argue that few candidate genes have sufficient evidence to implicate them in affecting sporting performance. Larger studies are desperately needed, and engagement of science with the major national and international sports regulating authorities is paramount. Acknowledgements The authors have no conflicting interests to declare that are directly relevant to the content of this review. No funding

ª 2011 Adis Data Information BV. All rights reserved.

was received for this review. All contributors have met criteria for authorship.

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117. Young LE, Rogers K, Wood JL. Left ventricular size and systolic function in thoroughbred racehorses and their relationships to race performance. J Appl Physiol 2005; 99 (4): 1278-85 118. Koren MJ, Devereux RB, Casale PN, et al. Relation of left ventricular mass and geometry to morbidity and mortality in uncomplicated essential hypertension. Ann Intern Med 1991; 114 (5): 345-52 119. Levy D, Garrison RJ, Savage DD, et al. Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study. N Engl J Med 1990; 322 (22): 1561-6 120. Vakili BA, Okin PM, Devereux RB. Prognostic implications of left ventricular hypertrophy. Am Heart J 2001; 141 (3): 334-41 121. Buhl R, Ersbøll AK, Eriksen L, et al. Changes over time in echocardiographic measurements in young standardbred racehorses undergoing training and racing and association with racing performance. J Am Vet Med Assoc 2005; 226 (11): 1881-7 122. Issemann I, Green S. Activation of a member of the steroid hormone receptor superfamily by peroxisome proliferators. Nature 1990; 347 (6294): 645-50 123. Fruchart JC, Duriez P, Staels B. Peroxisome proliferatoractivated receptor-alpha activators regulate genes governing lipoprotein metabolism, vascular inflammation and atherosclerosis. Curr Opin Lipidol 1999; 10 (3): 245-57 124. Sack MN, Rader TA, Park S, et al. Fatty acid oxidation enzyme gene expression is downregulated in the failing heart. Circulation 1996; 94 (11): 2837-42 125. Barger PM, Brandt JM, Leone TC, et al. Deactivation of peroxisome proliferator-activated receptor-alpha during cardiac hypertrophic growth. J Clin Invest 2000; 105 (12): 1723-30 126. Binas B, Danneberg H, McWhir J, et al. Requirement for the heart-type fatty acid binding protein in cardiac fatty acid utilization. FASEB J 1999; 13 (8): 805-12 127. Chiu HC, Kovacs A, Ford DA, et al. A novel mouse model of lipotoxic cardiomyopathy. J Clin Invest 2001; 107 (7): 813-22 128. Jamshidi Y, Montgomery HE, Hense HW, et al. Peroxisome proliferator: activated receptor alpha gene regulates left ventricular growth in response to exercise and hypertension. Circulation 2002; 105 (8): 950-5 129. Schunkert H, Hengstenberg C, Holmer SR, et al. Lack of association between a polymorphism of the aldosterone synthase gene and left ventricular structure. Circulation 1999; 99 (17): 2255-60 130. Reinhard C, Meyer B, Fuchs H, et al. Genomewide linkage analysis identifies novel genetic loci for lung function in mice. Am J Respir Crit Care Med 2005; 171 (8): 880-8 131. Lewiiter FI, Tager IB, McGue M, et al. Genetic and environmental determinants of level of pulmonary function. Am J Epidemiol 1984; 120 (4): 518-30 132. Chen Y, Rennie DC, Lockinger LA, et al. Major genetic effect on forced vital capacity: the Humboldt Family Study. Genet Epidemiol 1997; 14 (1): 63-76 133. Coultas DB, Hanis CL, Howard CA, et al. Heritability of ventilatory function in smoking and nonsmoking New

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Mexico Hispanics. Am Rev Respir Dis 1991; 144 (4): 770-5 Joost O, Wilk JB, Adrienne Cupples L, et al. Genetic loci influencing lung function: a genomewide scan in the Framingham Study. Am J Respir Crit Care Med 2002; 165 (6): 795-9 Palmer LJ, Knuiman MW, Divitini ML, et al. Familial aggregation and heritability of adult lung function: results from the Busselton Health Study. Eur Respir J 2001; 17 (4): 696-702 Dempsey JA, Johnson BD, Saupe KW. Adaptations and limitations in the pulmonary system during exercise. Chest 1990; 97 (3 Suppl.): 81S-7S Dempsey JA, Wagner PD. Exercise-induced arterial hypoxemia. J Appl Physiol 1999; 87 (6): 1997-2006 Gavin TP, Stager JM. The effect of exercise modality on exercise-induced hypoxemia. Respir Physiol 1999; 115 (3): 317-23 Nielsen HB. Arterial desaturation during exercise in man: implication for O-2 uptake and work capacity. Scand J Med Sci Sports 2003; 13 (6): 339-58 Holmberg HC, Rosdahl H, Svedenhag J. Lung function, arterial saturation and oxygen uptake in elite cross country skiers: influence of exercise mode. Scand J Med Sci Sports 2007; 17 (4): 437-44 Doherty M, Dimitriou L. Comparison of lung volume in Greek swimmers, land based athletes, and sedentary controls using allometric scaling. Br J Sports Med 1997; 31 (4): 337-41 Lippi G, Longo UG, Maffulli N. Genetics and sports. Br Med Bull 2010; 93: 27-47 Wilcoxon JS, Nadolski GJ, Samarut J, et al. Behavioral inhibition and impaired spatial learning and memory in hypothyroid mice lacking thyroid hormone receptor alpha. Behav Brain Res 2007; 177 (1): 109-16 Bryan A, Hutchison KE, Seals DR, et al. A transdisciplinary model integrating genetic, physiological, and psychological correlates of voluntary exercise. Health Psychol 2007; 26 (1): 30-9 Maliuchenko NV, Sysoeva OV, Vediakov AM, et al. Effect of 5HTT genetic polymorphism on aggression in athletes [in Russian]. Zh Vyssh Nerv Deiat Im I P Pavlova 2007; 57 (3): 276-81 Tschenett A, Singewald N, Carli M, et al. Reduced anxiety and improved stress coping ability in mice lacking NPY-Y2 receptors. Eur J Neurosci 2003; 18 (1): 143-8 Gerlai R, Roder JC, Hampson DR. Altered spatial learning and memory in mice lacking the mGluR4 subtype of metabotropic glutamate receptor. Behav Neurosci 1998; 112 (3): 525-32 Gimenez-Llort L, Masino SA, Diao L, et al. Mice lacking the adenosine A1 receptor have normal spatial learning and plasticity in the CA1 region of the hippocampus, but they habituate more slowly. Synapse 2005; 57 (1): 8-16 Trejo JL, Llorens-Martin MV, Torres-Aleman I. The effects of exercise on spatial learning and anxiety-like behavior are mediated by an IGF-I-dependent mechanism related to hippocampal neurogenesis. Mol Cell Neurosci 2008; 37 (2): 402-11

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150. Mogil JS, Lichtensteiger CA, Wilson SG. The effect of genotype on sensitivity to inflammatory nociception: characterization of resistant (A/J) and sensitive (C57BL/6J) inbred mouse strains. Pain 1998; 76 (1-2): 115-25 151. Foulkes T, Wood JN. Pain genes. PLoS Genet 2008; 4 (7): e1000086 152. Priest BT, Murphy BA, Lindia JA, et al. Contribution of the tetrodotoxin-resistant voltage-gated sodium channel NaV1.9 to sensory transmission and nociceptive behavior. Proc Natl Acad Sci U S A 2005; 102 (26): 9382-7 153. Nassar MA, Stirling LC, Forlani G, et al. Nociceptorspecific gene deletion reveals a major role for Nav1.7 (PN1) in acute and inflammatory pain. Proc Natl Acad Sci U S A 2004; 101 (34): 12706-11 154. Zimmermann K, Leffler A, Babes A, et al. Sensory neuron sodium channel Nav1.8 is essential for pain at low temperatures. Nature 2007; 447 (7146): 855-8 155. Cox JJ, Reimann F, Nicholas AK, et al. An SCN9A channelopathy causes congenital inability to experience pain. Nature 2006; 444 (7121): 894-8 156. Collins M, Xenophontos SL, Cariolou MA, et al. The ACE gene and endurance performance during the South African Ironman Triathlons. Med Sci Sports Exerc 2004; 36 (8): 1314-20 157. Costa A, Silva A, Garrido N, et al. Association between ACE D allele and elite short distance swimming. Eur J Appl Physiol 2009; 106 (6): 785-90 158. Gayagay G, Yu B, Hambly B, et al. Elite endurance athletes and the ACE I allele: the role of genes in athletic performance. Hum Genet 1998; 103 (1): 48-50 159. Jones A, Woods DR. Skeletal muscle RAS and exercise performance. Int J Biochem Cell Biol 2003; 35 (6): 855-66 160. Williams AG, Rayson MP, Jubb M, et al. The ACE gene and muscle performance [letter]. Nature 2000; 403 (6770): 614 161. Woods DR, Humphries SE, Montgomery HE. The ACE I/D polymorphism and human physical performance. Trends Endocrinol Metab 2000; 11 (10): 416-20 162. Moran CN, Yang N, Bailey MES, et al. Association analysis of the ACTN3 R577X polymorphism and complex quantitative body composition and performance phenotypes in adolescent Greeks. Eur J Hum Genet 2006; 15 (1): 88-93 163. Yang N, MacArthur DG, Gulbin JP, et al. ACTN3 genotype is associated with human elite athletic performance. Am J Hum Genet 2003; 73 (3): 627-31 164. Ahmetov II, Williams AG, Popov DV, et al. The combined impact of metabolic gene polymorphisms on elite endurance athlete status and related phenotypes. Hum Genet 2009; 126 (6): 751-61 165. Williams AG, Folland JP. Similarity of polygenic profiles limits the potential for elite human physical performance. J Physiol 2008; 586 (1): 113-21 166. Kannus P, Natri A. Etiology and pathophysiology of tendon ruptures in sports. Scand J Med Sci Sports 1997; 7 (2): 107-12 167. Gwilym SE, Watkins B, Cooper CD, et al. Genetic influences in the progression of tears of the rotator cuff. J Bone Joint Surg Br 2009; 91 (7): 915-7

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168. Harvie P, Ostlere SJ, Teh J, et al. Genetic influences in the aetiology of tears of the rotator cuff: sibling risk of a fullthickness tear. J Bone Joint Surg Br 2004; 86 (5): 696-700 169. Sharma P, Maffulli N. Tendon injury and tendinopathy: healing and repair. J Bone Joint Surg Am 2005; 87 (1): 187-202 170. Mokone GG, Schwellnus MP, Noakes TD, et al. The COL5A1 gene and Achilles tendon pathology. Scand J Med Sci Sports 2006; 16 (1): 19-26 171. Mokone GG, Gajjar M, September AV, et al. The guaninethymine dinucleotide repeat polymorphism within the tenascin-C gene is associated with achilles tendon injuries. Am J Sports Med 2005; 33 (7): 1016-21 172. Waggett AD, Ralphs JR, Kwan AP, et al. Characterization of collagens and proteoglycans at the insertion of the human Achilles tendon. Matrix Biol 1998; 16 (8): 457-70 173. Vogel KG. What happens when tendons bend and twist? Proteoglycans. J Musculoskelet Neuronal Interact, 2004; 4 (2): 202-3 174. Lysholm J, Wiklander J. Injuries in runners. Am J Sports Med 1987; 15 (2): 168-71 175. Knobloch K, Yoon U, Vogt PM. Acute and overuse injuries correlated to hours of training in master running athletes. Foot Ankle Int 2008; 29 (7): 671-6 176. Jones GC, Corps AN, Pennington CJ, et al. Expression profiling of metalloproteinases and tissue inhibitors of metalloproteinases in normal and degenerate human achilles tendon. Arthritis Rheum 2006; 54 (3): 832-42 177. Karousou E, Ronga M, Vigetti D, et al. Collagens, proteoglycans, MMP-2, MMP-9 and TIMPs in human achilles tendon rupture. Clin Orthop Relat Res 2008; 466 (7): 1577-82 178. Corps AN, Jones GC, Harrall RL, et al. The regulation of aggrecanase ADAMTS-4 expression in human Achilles tendon and tendon-derived cells. Matrix Biol 2008; 27 (5): 393-401 179. Pufe T, Petersen WJ, Mentlein R, et al. The role of vasculature and angiogenesis for the pathogenesis of degenerative tendons disease. Scand J Med Sci Sports 2005; 15 (4): 211-22 180. Petersen W, Pufe T, Zantop T, et al. Expression of VEGFR-1 and VEGFR-2 in degenerative Achilles tendons. Clin Orthop Relat Res 2004; (420): 286-91 181. Posthumus M, September AV, Schwellnus MP, et al. Investigation of the Sp1-binding site polymorphism within the COL1A1 gene in participants with Achilles tendon injuries and controls. J Sci Med Sport 2009; 12 (1): 184-9 182. Bray MS, Hagberg JM, Perusse L, et al. The human gene map for performance and health-related fitness phenotypes: the 2006-2007 update. Med Sci Sports Exerc 2009; 41 (1): 35-73

Correspondence: Dr Zudin Puthucheary, UCL Institute for Human Health and Performance, 2nd Floor, Charterhouse Building, UCL Archway Campus, Highgate Hill, Archway, London N19 5LW, UK. E-mail: [email protected]

Sports Med 2011; 41 (10)

Sports Med 2011; 41 (10): 861-882 0112-1642/11/0010-0861/$49.95/0

REVIEW ARTICLE

ª 2011 Adis Data Information BV. All rights reserved.

Physiological and Nutritional Aspects of Post-Exercise Recovery Specific Recommendations for Female Athletes Christophe Hausswirth and Yann Le Meur National Institute of Sport, for Expertise and Performance (INSEP), Research Department, Paris, France

Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Recovery and Maintenance of Energy Stores: A Gender Difference?. . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Metabolic Responses During Prolonged Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Carbohydrate Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Lipid Utilization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Protein Utilization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4 Beyond Gender Differences: the Effect of Menstrual Cycle Phase Upon Exercise Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Metabolic Responses After Prolonged Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Strategies for Nutritional Recovery and Repletion of Energy Substrates After Prolonged Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Carbohydrate Intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Lipid Intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Protein Intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Metabolic Responses during and following Brief Intense Exercise . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Chronic Fatigue and Management of Daily Energy Balance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Recovery and Musculoskeletal Regeneration Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Recovery From Exercise-Induced Muscle Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Severity of Muscle Damage and Post-Exercise Inflammatory Response. . . . . . . . . . . . . . . 3.2 Recovery From Exercise-Induced Bone Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Bone Turnover and Stress Fracture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Recovery and Return to Homeostasis: Is there a Gender Difference? . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Recovery and Metabolic Disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Active Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Recovery by Immersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Recovery and Thermoregulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Evaporation and Hydration Strategies for Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Thermoregulation and Post-Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract

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Gender-based differences in the physiological response to exercise have been studied extensively for the last four decades, and yet the study of postexercise, gender-specific recovery has only been developing in more recent years. This review of the literature aims to present the current state of knowledge in this field, focusing on some of the most pertinent aspects of

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physiological recovery in female athletes and how metabolic, thermoregulatory, or inflammation and repair processes may differ from those observed in male athletes. Scientific investigations on the effect of gender on substrate utilization during exercise have yielded conflicting results. Factors contributing to the lack of agreement between studies include differences in subject dietary or training status, exercise intensity or duration, as well as the variations in ovarian hormone concentrations between different menstrual cycle phases in female subjects, as all are known to affect substrate metabolism during submaximal exercise. If greater fatty acid mobilization occurs in females during prolonged exercise compared with males, the inverse is observed during the recovery phase. This could explain why, despite mobilizing lipids to a greater extent than males during exercise, females lose less fat mass than their male counterparts over the course of a physical training programme. Where nutritional strategies are concerned, no difference appears between males and females in their capacity to replenish glycogen stores; optimal timing for carbohydrate intake does not differ between genders, and athletes must consume carbohydrates as soon as possible after exercise in order to maximize glycogen store repletion. While lipid intake should be limited in the immediate post-exercise period in order to favour carbohydrate and protein intake, in the scope of the athlete’s general diet, lipid intake should be maintained at an adequate level (30%). This is particularly important for females specializing in long-duration events. With protein balance, it has been shown that a negative nitrogen balance is more often observed in female athletes than in male athletes. It is therefore especially important to ensure that this remains the case during periods of caloric restriction, especially when working with female athletes showing a tendency to limit their caloric intake on a daily basis. In the post-exercise period, females display lower thermolytic capacities than males. Therefore, the use of cooling recovery methods following exercise, such as cold water immersion or the use of a cooling vest, appear particularly beneficial for female athletes. In addition, a greater decrease in arterial blood pressure is observed after exercise in females than in males. Given that the return to homeostasis after a brief intense exercise appears linked to maintaining good venous return, it is conceivable that female athletes would find a greater advantage to active recovery modes than males. This article reviews some of the major gender differences in the metabolic, inflammatory and thermoregulatory response to exercise and its subsequent recovery. Particular attention is given to the identification of which recovery strategies may be the most pertinent to the design of training programmes for athletic females, in order to optimize the physiological adaptations sought for improving performance and maintaining health.

1. Introduction The large majority of exercise physiology research has been performed exclusively on male populations. Until the 1980s, it was widely preª 2011 Adis Data Information BV. All rights reserved.

sumed that the physiological responses to exercise did not truly differ between males and females. From this assumption, the design of training programmes and the recommendations for recovery strategies have been generalized to females, Sports Med 2011; 41 (10)

Post-Exercise Recovery and Gender Differences

without any prior determination of whether such a direct transfer was viable. Since then, numerous studies focusing on gender[1,2] have uncovered some specificity in females’ physiological response to exercise, and determined that gender is an important variable to control for in order to design robust research protocols. The females’ response to various types of exercise and physical training is now better understood. It appears that the aerobic power and muscular strength of females are naturally lower than males, due to differences in body size and composition, hormonal status, socio-cultural influences and dietary habits.[3] Yet, despite these factors, well trained athletic females can deliver performances that are by far superior to those of poorly trained males. Within this context, a growing number of studies have turned their focus toward the effects of gender on recovery in sport, thereby contributing to a better comprehension of the similarities – and disparities – in the post-exercise recovery processes occurring in males and females. Nevertheless, the factors contributing to the lack of a global consensus can be attributed to differences observed in training and nutrition status, and to females’ hormonal variations over the course of the menstrual cycle and the influence of the latter upon energy metabolism during exercise.[4] This review aims to emphasize the subtle, yet potentially important characteristics observed in athletic females’ post-exercise physiology, to identify the recovery strategies best suited to meet the demands of their sporting activity. This work will discuss which recovery practices should be prioritized in well trained athletic females, while also critiquing the effectiveness of the principal modalities of recovery. Recovery in this review is defined as the return to homeostasis of the various physiological systems presented,[5] following the metabolic, thermoregulatory, inflammatory challenges and muscle damage incurred by exercise training sessions. Optimal recovery therefore enables the athlete to perform the next training session feeling rested, not fatigued, healthy and injury-free. This article reviews some of the major gender differences in the metabolic, inflammatory and thermoregulatory responses to exercise and its ª 2011 Adis Data Information BV. All rights reserved.

863

subsequent recovery, focusing on those that may be most pertinent to the design of training programmes for athletic females, specifically, in order to optimize the physiological adaptations sought for improving performance and maintaining health. All studies presented in this review were chosen for their relevance and quality of design and findings. The databases and other sources searched were the US National Library of Medicine (PubMed), Adis, Elsevier and Human Kinetics. Using the search terms ‘gender’, ‘sex differences’, ‘exercise’ and ‘recovery’, papers were then selected for the quality of design and results and relevance to our themes. While the most recent and pertinent studies were selected, early, ‘pioneer’ studies from the 1970s and 1980s were also included when research on gender differences during exercise bloomed. 2. Recovery and Maintenance of Energy Stores: A Gender Difference? Performance in long-duration activities that rely upon aerobic metabolism is related to the availability of endogenous energy substrates. In light of this, the fatigue induced by exercise can be linked to the athlete’s inability to continue supplying adenosine triphosphate (ATP) to the working muscles, due to the exhaustion of endogenous energy stores.[6] This type of fatigue can also occur in athletes training multiple times per day, even in those not necessarily specialized in endurance activities, and can cause large quantities of energy to be expended.[7] The depletion of energy stores may set in progressively, when daily caloric intake does not compensate for the total energy expenditure linked to both basal metabolism and the practice of a sport. Recovery strategies must therefore take into account the specificities of females’ metabolic response to exercise to ensure the maintenance of energy stores and to support the training workloads. 2.1 Metabolic Responses During Prolonged Exercise

Even though females were excluded from participating in the Olympic marathon until Sports Med 2011; 41 (10)

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1984, several studies demonstrated that they could actually perform better than males in ultraendurance events.[8,9] For instance, Speechly et al.[8] showed that females, despite slower performances in a marathon than a given group of males, performed better than the latter when the race distance exceeded 90 km. Bam et al.[9] showed by way of linear regression analysis that females may potentially hold an advantage over males when the race distance of a running race reaches or exceeds 66 km. For Tarnopolsky,[1] this phenomenon would be linked to genderbased metabolic differences during prolonged exercise and, more specifically, to a females greater capacity for lipid oxidation, allowing them to maintain normoglycaemia and preserve muscle glucose during these very long events. Tarnopolsky explains that the first studies to compare the metabolic responses of males and females during prolonged exercise date from the 1970s and 1980s.[10,11] All reported a gender effect, except that of Costill et al.[12] These results, however, were considered with caution, as these investigations had neither accounted for the occurrence of menstrual cycles, nor evaluated precisely, the training status of the subjects involved. Also, exercise intensity was determined relative to maximal oxygen consumption . (VO2max) without adjusting for each individual’s body mass. Since the 1990s, many studies have added to these preliminary results by factoring in these important methodological details in their protocols.[13,14] 2.1.1 Carbohydrate Utilization

Any discussion of gender differences in exercise metabolism must address the different hormonal environment that males and females are exposed to. Numerous studies have documented that estrogen is, at least in part, responsible for the decreased reliance upon hepatic glycogen stores, increased availability and oxidation of fatty acids, and decreased amino acid breakdown during exercise.[15-18] In human studies, 17b-estradiol, the most abundant form of estrogen, has been found to decrease the hepatic glucose rate of appearance and disappearance and total oxidation, resulting ª 2011 Adis Data Information BV. All rights reserved.

in a relative sparing of hepatic glycogen stores during exercise.[17,18] The finding that exogenous estradiol administration reduces the magnitude of the adrenergic response (assessed by circulating epinephrine levels) to exercise in amenorrhoeic females[15] conveys that estrogen may influence exercise metabolism via its action on the sympathetic system. Ettinger et al.[19] have postulated that estrogen-induced lipolysis could be reducing epinephrine secretion via negative feedback by free fatty acids. In accordance with the greater reliance on carbohydrate oxidation during exercise, males display a larger catecholamine response to a given moderate-to-high intensity of exercise than females who are provided with a similar training status.[14,20] Tarnopolski et al.[14] showed that after 3 days under a controlled diet in equally trained males and females running 15.5 km on a. treadmill at a speed corresponding to 65% of VO2max, males displayed 25% greater rates of muscle glycogen utilization and 30% greater amounts of protein catabolism (determined by urea nitrogen excretion) than females. Roepstorff et al.[21] found that even though the rate of glucose appearance (glucose Ra; representing the rate of hepatic glucose production) was lower in females during a . 90-minute cycling exercise at 58% of VO2max, glucose balance within the working muscle (consumption and release) did not differ between the genders. 2.1.2 Lipid Utilization

Estrogen has been repeatedly found to promote free fatty acid availability and lipid oxidation during exercise, possibly via an increased sensitivity to the lypolytic action of catecholamines.[19,22] Also, even though intramuscular triglyceride content is dependent upon dietary fat consumption[23] these intramuscular stores are generally much larger in females than in males,[24] and are utilized to a greater extent in females during prolonged exercise at. a moderate intensity (90 minutes, 58% of VO2max).[21,25] During this exercise trial, Roepstorff et al.[21] demonstrated, by measuring the arterio-venous difference in nonesterifed fatty acids, that females consume 47% more nonesterifed fatty acids within the working Sports Med 2011; 41 (10)

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865

Table I. Summary of studies where whole-body substrate metabolism was reported in trained males and trained females with exercise duration >60 min: only data from studies using trained subjects are reported in the present table. Significant gender difference was calculated at p = 0.02 using a two-tailed independent t-test (adapted from Tarnopolsky,[27] with permission) Study (y)

Exercise

Costill et al.[28] (1976) Blatchford et al.[29] (1985) [14]

Tarnopolsky et al.

(1990)

Philips et al.[30] (1993) Tarnopolsky et al.[14] (1990) Tarnopolsky et al.[13] (1997) Roepstorff et al.[21] (2002) Melanson et al.[31] (2002) Riddell et al.[32] (2003) Zehnder et al.[33] (2006)

. 60 min run at 70% VO2max . 90 min walk at 35% VO2max . 15.5 km run at ~65% VO2max . 90 min cycle at 35% VO2max . 60 min cycle at 75% VO2max . 90 min cycle at 65% VO2max . 90 min cycle at 58% VO2max . 400 kcal at 40 + 70% VO2max . 90 min cycle at 60% VO2max . 180 min cycle at 50% VO2max

No. of subjects

. RER = respiratory exchange ratio; VO2max = maximal oxygen consumption.

muscles than their male counterparts. This result is verified by Mittendorfer et al.[26] during a prolonged exercise protocol (90 minutes) of . moderate intensity (50% VO2max). Aside from the greater utilization of plasma nonesterifed fatty acids, Mittendorfer et al.[26] observed greater lipolytic activity in female than in male subjects that were matched by training level and fat-mass percentage. Taken together, these findings convey that females possess larger intramuscular triglyceride stores and rely more heavily upon this substrate during exercise, as is confirmed by Tarnopolsky[27] in a meta-analysis of the literature. The genderbased differences in whole-body substrate oxidation during exercise are reflected in the lower respiratory exchange ratio of females compared with males for a given exercise intensity and duration of exercise, as displayed in table I. 2.1.3 Protein Utilization

Tarnopolsky et al.[14] showed that a rise in urinary nitrogen concentration (i.e. an indicator of protein utilization) occurs in males within the 24 hours following endurance exercise compared with a control day, while no significant difference is observed in females. This reveals that proportionally greater amounts of amino acids are oxidized by males during exercise than females. These results were then confirmed in a study by ª 2011 Adis Data Information BV. All rights reserved.

RER

females

males

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Phillips et al.[30] who used nitrogen balance and L-[1-13C]-leucine tracing to demonstrate that males oxidize more protein and leucine than females. These findings were completed by McKenzie et al.[34] who showed that females utilize leucine as an energy substrate to a lesser extent than males . during a 90-minute pedalling exercise at 65% VO2max, both before and after a 31-day endurance training programme. Interestingly, these authors also reported that even though before the training programme, leucine oxidation doubled during exercise compared with the resting state, no differences were observed in this variable at the end of the training period. Given that the enzyme, that limits the intramuscular oxidation of branched-chain amino acids, branched chain-2-oxodehydrogenase (BCOAD), does not differ between the genders,[34] the difference in branched chain amino acid utilization could be of hepatic origin, and likely be linked to the sparing of glycogen occurring in this same organ.[35] 2.1.4 Beyond Gender Differences: the Effect of Menstrual Cycle Phase Upon Exercise Metabolism

A recent review by Oosthuyse and Bosch,[36] gathers the current state of knowledge on the influence of ovarian hormones on carbohydrate, protein and fat metabolism throughout the menstrual cycle phase. Several studies have Sports Med 2011; 41 (10)

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attempted to describe how the changing levels of estrogen and progesterone throughout the menstrual cycle could alter the metabolic response to exercise. Many report conflicting results due to important confounding factors that outweigh the effect of ovarian hormone variations, including nutritional status, fitness level, the intensity of exercise and the total energy demand of exercise. Studies that carefully controlled for these variables reported some significant alterations in carbohydrate, free fatty acids and protein metabolism in the early follicular phase (characterized by low circulating estrogen and progesterone concentrations) compared with the mid-luteal phase (high estrogen and progesterone concentrations). Substrate oxidation during exercise is influenced by the modulation of sympatho-adrenergic receptors. Via its action upon b-adrenergic receptors,[22] estrogen reduces the rate of carbohydrate oxidation, while increasing free fatty acid availability and oxidation capacity during exercise.[15,37] Some studies have identified such variations in carbohydrate and fat oxidation between the follicular and the luteal phases, provided that the . intensity of exercise was high enough (60% of VO2max or more) and that the subjects were in an overnight-fasted state.[38,39] Estrogen was also found to augment the capacity for muscle glycogen storage, as has been observed in the luteal phase compared with the early follicular phase.[40] Increased protein catabolism . during exercise (60 minutes of running at 70% of VO2max) has been observed during the luteal phase compared with the early follicular phase,[41] as shown by greater excretion of urinary urea nitrogen. This is thought to be caused by higher circulating progesterone levels (or a lower estrogen to progesterone ratio), promoting protein catabolism,[42] while estrogen reduces protein oxidation.[37] It has also been reported, however, that this increase in amino acid catabolism was diminished when carbohydrate supplementation was provided during exercise,[43] demonstrating that the hormonal fluctuations over the menstrual cycle may only be secondary to other factors such as substrate availability. Despite these metabolic alterations during exercise across the different menstrual phases, ª 2011 Adis Data Information BV. All rights reserved.

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controversy still exists, as other well controlled studies have found no differences between menstrual cycle phases.[44,45] Casazza et al.[44] have proposed that the hierarchy of factors affecting substrate oxidation during a given intensity of exercise in females was: energy flux > oral contraceptive use > recent carbohydrate nutrition, menstrual cycle effects. Little is known on how extensively these ovarian hormone fluctuations may actually impact the athletes’ post-exercise needs for optimal recovery of energy stores; are these hormone-driven fluctuations important enough to recommend that the diet composition be altered to optimize the replenishment of glycogen stores, intramuscular triglyceride stores or protein turnover? Again, the influence of ovarian hormones on exercise metabolism appears secondary to factors such as nutritional status/energy availability, exercise intensity, and overall energy demand of exercise.[44] 2.2 Metabolic Responses After Prolonged Exercise

Henderson et al.[46] showed that if a greater fatty acid mobilization occurs in females during prolonged exercise compared with males, the inverse is observed during the recovery phase. This could explain why, even though females mobilize lipids to a greater extent than males during endurance exercise,[46] they lose less fat mass than their male counterparts during a physical training programme, as several studies have demonstrated.[47] Some experimental data have indeed revealed that some metabolic disturbances caused by an exercise session could still be observed several hours after its completion.[20,46,48,49] Henderson et al.[46] showed that the rate of lypolysis was still elevated 21 hours . after a 90-minute pedalling exercise at 45% of VO2max, or 60 minutes at 65% . of VO2max in males, whereas significant differences in lipolysis are no longer observed in females by that point (figure 1). To further elucidate the effect of gender on the evolution of post-exercise metabolism, Henderson et al.[50] compared the evolution of glycaemia in sedentary males and females over the 3 hours succeeding a 90-minute pedalling Sports Med 2011; 41 (10)

Post-Exercise Recovery and Gender Differences

Day 1 Other FA Plasma FA

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Fig. 1. Lipid oxidation (LOX). Fatty acid (FA) oxidation rate in (a) males and (b) females. FA oxidation by a combination of tracerderived measurement and indirect calorimetry on day 1 and solely by indirect calorimetry on day 2. Values are mean – standard error of the mean. Males: n = 10 for days 1 and 2; females: n = 8 for day 1 and n = 6 for . day 2; control (CON) . trial; 45, 45% peak oxygen consumption (VO2peak) trial; 65, 65% VO2peak trial. Recovery, day 1; average from 30 min post-exercise. Recovery, day 2; the next day following exercise bouts (reproduced from Henderson et al.,[46] with permission). . FFM = fat-free mass; * indicates total FA oxidation in 45% VO2peak trial that was significantly different from corresponding timepoints in the CON trial, p < 0.05. FA oxidation was significantly elevated above CON during exercise at either intensity in both genders (p < 0.05). Plasma FA oxidation was elevated above CON during exercise and recovery for both exercise intensities, while the other (non-plasmatic) FA oxidation was significantly elevated above (p < 0.05), but not during . postexercise recovery; # indicates total FA oxidation in 65% VO2peak trial that was significantly different from corresponding timepoints in the CON trial, p < 0.05; // indicates only one separating day1 and day 2.

. bout at 45% of VO2max . and after pedalling for 60 minutes at 65% of VO2max using labelled glucose. Because euglycaemia is influenced by the ª 2011 Adis Data Information BV. All rights reserved.

time of day, a control situation during which the sedentary subjects continued to go about their normal routine was included. Results revealed an increase in the rates of blood glucose appearance and disappearance as well as a greater metabolic clearance during the two exercise situations compared with the control setting in both genders. However, differences between males and females appeared when comparing the metabolic responses post-exercise with the control situation. Three hours after ceasing exercise, male subjects were found to have higher rates of blood glucose appearance and disappearance, higher metabolic clearance and a lower glycaemia, while no differences were found in females for any of these parameters between the post-exercise and control situations. These results suggest that compared with males, females have a greater ability to maintain glycaemia during the recovery period following prolonged exercise, which could explain why lipolysis occurs to a lesser degree in females during this phase. They also confirm their previous results,[46] which revealed a weaker reliance on post-exercise lipolysis and a more precise glucoregulation in females compared with males. In all, given that females mobilize lipids to a greater extent during exercise, that their lipid stores are greater and that they show a better propensity to spare glycogen, females have a greater ability than males to maintain constant energy substrate stores during exercise, as well as during the recovery period.[50] These metabolic specificities imply the necessity for genderspecific nutritional recovery strategies.[1,35] 2.3 Strategies for Nutritional Recovery and Repletion of Energy Substrates After Prolonged Exercise

Most of the research performed in sports nutrition and energy metabolism during exercise has been performed on male subjects. Nevertheless, numerous recent studies have determined some of the specificities for restoring energy parameters in female subjects. 2.3.1 Carbohydrate Intake

Kuipers et al.[51] studied glycogen resynthesis following a bike ergometer exercise to exhaustion Sports Med 2011; 41 (10)

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in endurance-trained athletes (i.e. seven males and nine females). During the 2.5 hours following the end of exercise, subjects consumed a 25% maltodextrin-fructose solution (carbohydrates: 471 – 5 g and 407 – 57 g for males and females, respectively). Glycogen repletion occurred in similar proportions in males and females. Different studies demonstrated an improved glycogen repletion when carbohydrates (or carbohydrates + proteins) are consumed immediately after exercise instead of a few hours later.[52,53] Tarnopolsky et al.[13] compared the rate of glycogen resynthesis in males and females . following a 90-minute exercise bout at 65% of VO2max and the ingestion of three solutions: one a placebo, one containing 1 g/kg of carbohydrate, and one containing 0.7 g/kg of carbohydrate/0.1 g/kg of protein/0.02 g/kg of lipids, immediately after and 1 hour following exercise. Glycogen resynthesis occurred faster for both genders with both test solutions than with the placebo, with no differences between males and females (figure 2). Finally, a study by Roy et al.[54] showed that, in ten young females, the post-exercise intake of 1.2 g/kg of carbohydrate, 0.1 g/kg of protein and 0.02 g/kg of lipids during a period of training (four training sessions per week) resulted in an Females Males

60

*

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* 50

40

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20

10

. increased time to exhaustion at 75% of VO2max and tended to diminish protein oxidation over the week following the protocol. These data therefore show that, when carbohydrate intake is proportional to body mass, no significant differences appear between males and females in their capacity to replenish their glycogen stores. In light of these findings, when the time allotted between two training sessions is l<8 hours, both female and male athletes must consume carbohydrates as soon as possible after exercise in order to maximize the replenishment of glycogen stores. There is an undeniable interest in breaking up the total intake of carbohydrates into smaller portions in the early phase of recovery, especially when it is not possible or practical for the athlete to eat a full meal shortly after a training session.[53] Carbohydrate-rich meals are recommended during recovery. Adding 0.2–0.5 g of protein per day and per kg to carbohydrates in a 3 : 1 (carbohydrate : protein) ratio is recommended. This holds particular importance when the athlete’s training sessions occur twice daily and/or are very prolonged.[52,55] Let us recall that no difference is observed in terms of glycogen repletion when carbohydrates are consumed in the form of solids and liquids.[56] Carbohydrates with a moderate-to-high glycaemic index supply energy quickly for glycogen resynthesis during recovery and must therefore be prioritized in energy-replenishing snacks following exercise.[57] In all, the daily intake of female athletes must reach 5 g/kg of carbohydrates of bodyweight per day.[58] If the training volume is high and training sessions occur at least once daily, this can increase to 6–8 g/kg of bodyweight per day. Given the tendency of female athletes to limit their daily caloric intake, it appears necessary to ensure that these needs be met daily in order to maintain adequate energy balance.[59] 2.3.2 Lipid Intake

0 CHO pro-fat

CHO

Placebo

Fig. 2. Rate of glycogen resynthesis for each trial over the first 4 h post-exercise (reproduced from Tarnopolsky et al.,[13] with permission). CHO = carbohydrate; dm = dry matter; * indicates p < 0.05 compared with placebo.

ª 2011 Adis Data Information BV. All rights reserved.

Despite the importance of sufficient daily intake in essential fatty acids, many female athletes considerably limit their consumption of lipids (down to lipids only providing 10–15% of daily caloric intake), believing that they may compromise performance and increase body fat mass.[60] Sports Med 2011; 41 (10)

Post-Exercise Recovery and Gender Differences

However, in addition to compromising their health, these very low-fat diets reduce intramuscular triglyceride stores that are crucial for supplying free fatty acids to the muscles during recovery, especially for athletes in long-duration disciplines or those often training multiple times per day. Larson-Meyer et al.[60] showed that the lipid intake for female athletes specializing in long events must reach 30% of daily caloric intake to ensure a rapid resynthesis of intramuscular triglyceride stores. If post-exercise lipid intake is insufficient, the depletion of intramuscular triglyceride stores is still observed 2 days following exercise.[60,61] As some studies have demonstrated the very low lipid intake in this population,[62] it has also been hypothesized that a very low lipid intake (i.e. 10–15% of caloric intake) coupled with a high endurance training volume could be detrimental to performance by compromising intramuscular triglyceride stores.[63] Also, even though this study by Larson-Meyer et al.[61] did not report any decrease in performance during a 2-hour running trial following a short-term low-lipid diet (3 days, 10% of caloric intake), this diet did yield a modification of the athletes’ lipid profiles, as had already been proven in another study by Leddy et al.[64] They showed that a low-lipid diet increases triglyceride concentration and decreases the concentration of high-density lipoprotein cholesterol (HDL-C), leading to a lowered ratio of total cholesterol; HDL-C, an adverse consequence in terms of cardiovascular health. Even though no study to date has yet demonstrated this, this observation suggests, according to Larson-Meyer et al.,[61] that endurance athletes must make sure that they consume a sufficient amount of lipids daily, particularly if they or someone in their family have any problems linked with their lipid profiles. Future studies are needed to observe the effect of a chronic hypolipidic diet on prolonged exercise performance. In conclusion, it appears that while carbohydrate and protein intake should be favoured immediately post-exercise, in the scope of the athlete’s general diet, lipids intake should be maintained at a sufficient level. If fat consumption supplies less than 15% of daily caloric intake, there exists the risk of a deficiency in essential ª 2011 Adis Data Information BV. All rights reserved.

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fatty acids and vitamin E.[59] Female athletes should therefore be advised to consume vegetable oils, nuts and ‘fatty’ fish, such as tuna and salmon, on a regular basis.[59] 2.3.3 Protein Intake

It is well known that chronic aerobic exercise diminishes amino acid utilization. However, the metabolic oxidation capacity of amino acid augments as a result of an improved activity of the limiting enzyme, glutamate dehydrogenase (BCOAD).[16] For this reason, male and female athletes must increase their daily protein intake. Concerning proteins, it is recommended that female athletes maintain a daily energy intake superior to those recommended for the standard population, in order to ensure that the mechanisms of recovery, on a structural standpoint, are able to occur (1.2–1.4 g/kg of bodyweight per day, compared with 0.8 g/kg of bodyweight.[58] Phillipps et al.[30] demonstrated that a daily protein intake of 0.8 g/kg/day leads to a negative nitrogen balance in female athletes. Many studies performed on female athletes present evidence for the fact that this intake is often at the lower end of these recommendations, if not below them, particularly in amenorrhoeic athletes.[65,66] For female athletes specializing in strength sports, these recommendations reach 1.4–1.8 g/kg of bodyweight, given the extent of muscular damage caused by this type of activity. Foods containing proteins with a high biological availability (a high retention and utilization rate by the body) should be emphasized. Meats, fish, eggs and dairy products offer complete sources of protein (providing all essential amino acids), and it is particularly important for vegetarian or vegan athletes to pay attention to their protein sources to ensure that all essential amino acid requirements are met, usually by combining different sources such as nuts, whole grains and legumes.[67] Also, proteinrich animal foods are an important source of dietary iron, the latter helping to limit the risk of anaemia, a problem of particular concern for female athletes.[68] Volek et al.,[69] also consider that female athletes should be aware that animal protein sources provide vitamin B12 and D, thiamin, riboflavin, calcium, phosphorus, iron and zinc.[70] Sports Med 2011; 41 (10)

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2.4 Metabolic Responses during and following Brief Intense Exercise

During brief, high-intensity exercise, the work performed by skeletal muscle relies heavily upon the breakdown of phosphocreatine stores and the recruitment of glycolysis to ensure the resynthesis of energy. Under these conditions, the resulting decrease in intramuscular pH and the accumulation of inorganic phosphate play an important role in the development of muscular fatigue.[71] For this reason, it has been suggested that a rapid clearance of lactate and H+ ions after intense exercise is essential for recovery in order to restore work capacity quickly, particularly in the context of sports activities involving repetitive short and intense exercise bouts.[72] Several studies performed on the effect of gender upon the metabolic response during and post-exercise contribute interesting clues regarding the methods of recovery that must be prioritized in athletic females after such brief, high-intensity exercise. The pre-exercise phosphagen (ATP, adenosine diphosphate, phosphocreatine) concentrations do not differ between genders.[73] Nonetheless, Ruby and Robergs[74] reported a greater glycolytic activity in males than females during exercise. In this regard, Jacobs et al.[75] had already shown that males reach higher concentrations of intramuscular lactate than females following anaerobic exercise tests. These results were then confirmed by the in vivo study of Russ et al.,[76] which showed that males rely more heavily upon glycolysis than females, even though their reliance upon the flux of phosphocreatine and aerobic pathways for force production did not show parallel gender effects. This difference could be explained by a greater cross-sectional area of type II muscle fibres[77] and/or a greater glycolytic enzyme activity.[77] Recently, Wu¨st et al.[78] reinforced this hypothesis by demonstrating that the lower resistance of males to fatigue would be more strongly linked to gender differences in muscle fibre type distribution than to differences in motivation, muscular size, oxidative capacity or local blood flow. In this context, Esbjo¨rnsson-Liljedahl et al.[73] showed that over 3 all-out 30-second sprints sepª 2011 Adis Data Information BV. All rights reserved.

arated by 20 minutes of passive recovery, females were able to maintain a steadier level of performance than males. Despite similar fitness levels (physically active, noncompetitive) females showed a lower accumulation of metabolic products than males, particularly within the type II fibres.[73] During the recovery phase, females have a greater capacity for recycling their ATP supply than males, granting them a better aptitude to take on the next intense, brief exercise bout. 2.5 Chronic Fatigue and Management of Daily Energy Balance

In female athletes, the maintenance of energy balance, is a topic in need of particular attention, because of the frequently observed concerns with physical appearance and the social pressures pushing them to maintain a low percentage of fat mass.[58,69,70,79] A positive energy balance is indeed necessary to ensure optimal metabolic recovery and to promote the muscular regeneration processes in order to withstand the training loads.[7] However, the literature reports the daily caloric intake as often insufficient in high-level female athletes, particularly those involved in sports in which a thin silhouette confers an advantage.[59,80] Figure 3[80] shows that the energy balance of high-level gymnasts remains almost always negative throughout the day. Maintaining the equilibrium between daily energy intake and expenditure by way of well adapted nutritional recovery methods concerns all athletes in any sport who must train daily or multiple times per day. A negative energy balance is associated with a chronic state of fatigue, decreased alertness, sleep disturbances and a rise in protein catabolism, which may compromise the mechanisms for muscle re-synthesis.[7] It therefore appears necessary to inform female athletes of the importance of maintaining an adequate daily energy intake, especially since two-thirds of them express the desire to lose weight and to limit their daily caloric intake.[81] However, these dietary behaviours are not always associated with weight loss (due to a decrease in resting metabolism[80]), but rather to disturbances in the menstrual cycle.[82] Sports Med 2011; 41 (10)

Post-Exercise Recovery and Gender Differences

871

600 400

Energy balance (Kcal)

200 0 −200 −400 −600 −800 −1000 Artistic gymnasts

Rhytmic gymnasts

Middle-distance runners

Long-distance runners

Fig. 3. Comparison of within-day energy balance in four groups of elite athletes. Each group has 24 bars, beginning with wake-up and ending 24 h later. The bars represent 4 h for each h in the day. Energy surpluses and deficits are represented, respectively, by variations above and below the zero (0) energy-balance line (reproduced from Deutz et al.,[80] with permission).

The majority of female athletes require a minimum of 2300–2500 kcal/day (9263–10 460 kJ) in order to maintain their body mass (45– 50 kcal/kg/day).[58,59] Those involved in endurance sports, such as marathon running or triathlon, have energy needs susceptible to reach as much as 4000 kcal/day (16 736 kJ).[59] The literature reveals that a relatively high carbohydrate intake is usually recommended for athletes during the recovery period, independently of gender, due to the role of carbohydrates in maintaining glycogen stores.[83] However, increasing the daily caloric intake of female athletes solely via increased carbohydrate consumption does not appear as an optimal solution.[35] A female athlete consuming about 2000 kcal/day is not able to significantly increase her glycogen stores solely by increasing the percentage of carbohydrates that compose her diet. In fact, she would have to boost her caloric intake by 30% over the course of 4 days in order to reach a carbohydrate intake superior to 8 g/kg.[25] From a practical standpoint, this is neither habitual nor acceptable among athletes watching their weight permanently. Moreover, such a large shift of caloric sources toward carbohydrates ª 2011 Adis Data Information BV. All rights reserved.

is likely to lead to deficiencies in some essential proteins and lipids, and compromises nitrogen balance necessary for maintaining normal sexual steroid hormone levels.[84] In this context, several authors[60,69] recommend a lipid intake of 30% of daily energy needs for female athletes. 3. Recovery and Musculoskeletal Regeneration Processes 3.1 Recovery From Exercise-Induced Muscle Damage

Certain exercises that induce important local mechanical constraints can be accompanied, in the hours and days following their execution, by muscle aches. This delayed-onset muscle soreness is symptomatic of a number of physiological disturbances at the level of the muscle cell and interstitial fluid: inflammatory processes, oedema, muscle fibre lysis, etc. The recovery methods following any exercise able to generate this type of response in female athletes must take into account the extent of the muscle damage caused and the inflammatory response observed. Sports Med 2011; 41 (10)

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3.1.1 Severity of Muscle Damage and Post-Exercise Inflammatory Response The Case of Long-Duration Exercise

Several studies revealed a smaller elevation in plasma creatine kinase (CK; an enzyme indicating the severity of muscular damage) in females than in males, following aerobic exercise.[85,86] Shumate et al.[85] examined the evolution of plasma CK after graded exercise on a bike ergometer. The mean increase in CK levels was 664 U/L in males and 153 U/L in females. Similarly, Janssen et al.[87] showed that male marathon runners displayed a higher plasma CK elevation than females. Apple et al.,[86] also demonstrated that the rise in plasma CK levels in female runners was 5.5 times inferior to their male counterparts after a marathon. By contrast, Nieman et al.[88] did not observe any gender differences in the post-exercise inflammatory response following a marathon. These results should, however, be considered with caution, as this type of field study limits the ability to quantify the actual workload that such an event imposes upon each participant; some of these discrepancies could be because of the lower workload performed by females. The Case of Strength Exercise

Muscle function: Studies comparing the evolution of strength following a strength exercise bout in males and females report conflicting results, some showing no gender-based difference, and others a better recovery in females.[2] Borsa and Sauers[89] examined the effect of gender on recovery following a traumatizing strength exercise consisting of 50 eccentric and concentric maximal contractions of the elbow flexors. After the exercise bout, both genders revealed important muscle aches, a reduced amplitude of joint movement and a significant decrease in strength. After adjusting for the pre-exercise value, the degree of strength loss was similar for both genders (-15.3% vs -18.2% for males and females, respectively). In the same manner, Rinard et al.[90] did not report any gender differences in strength loss and recovery kinetics after 70 maximal eccentric contractions. Hakkinen[91] had earlier observed that after a strenuous leg ª 2011 Adis Data Information BV. All rights reserved.

extensor exercise (20 maximal contractions), even though the percentage of strength lost over the repetitions did not differ between genders, the recovery of maximal force 1-hour post-exercise was greater in females. Later, Linnamo et al.[92] demonstrated the same trend toward a greater central fatigue in males after an explosive strength loading exercise. Moreover, Sayers and Clarkson[93] did not observe any gender differences in relative strength loss after 50 maximal voluntary eccentric contractions, but they did find a greater incidence of extreme strength losses (>70%) in females compared with males. These authors also added that the females showing the largest losses actually recovered faster than the males who had shown the same degree of strength loss post-exercise. This finding suggests that females are able to recover to a functional level more rapidly than males. This hypothesis has also been strengthened by Sewright et al.,[94] who revealed a greater strength loss in females than in males immediately following 50 maximal eccentric contractions. This significant difference, however, disappeared 6-hours post-exercise (figure 4). Taken together, the heterogeneity of these results does not allow us to state that females differ significantly from males in terms of the strength aspects of recovery.[2] However, these findings show that females are subjected to muscle damage from the practice of strength exercises, and therefore this must be taken into account in the planning of recovery methods following this type of exercise. Muscle damage and inflammatory response: The protective effect of estrogen upon skeletal muscle inflammation and repair has been well documented in animal models.[95,96] Studies have reported that 17b-estradiol exerted a protective effect upon the extent of total muscle damage,[95,97] in part by reducing leucocyte infiltration into damaged muscle cells thus preventing additional or excess release of oxidizing agents. The few studies performed on human subjects report differing results. MacIntyre et al.[98] examined muscle aches, strength loss and the intramuscular accumulation of neutrophils (playing a role in the immune system) in premenopausal females and males after Sports Med 2011; 41 (10)

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300 maximal voluntary knee extensions. After this exercise, the evolution of muscle pain strength loss differed between genders, females generally showing a greater degree of damage after 20–24 hours compared with males. They showed a greater neutrophil accumulation than males at +2 hours (but not at +4 hours), despite males having accomplished a larger workload. These results suggest that females have a stronger neutrophil response than males in the early post-exercise period. Stupka et al.[99] have compared the evolution in granulocyte (i.e. phagocyte) count in the plasma, of CK and histological markers of muscle damage 24, 48 and 144 hours after an eccentric exercise at 120% of the one-repetition maximum. The results of this study reveal that if disruptions of the Z lines were visible at the level of the sarcomeres within the vastus lateralis muscle involved in that exercise, no significant gender difference was observed in the extent of post-exercise damage. But this study also revealed a higher plasma granulocyte count in males 48 hours after the exercise, suggesting that the inflammatory response was attenuated in

females compared with males by that point in time. While these findings could appear to conflict with those of MacIntyre et al.,[98] the absence of any difference 4 hours post-exercise in the latter’s study could imply a different evolution of post-exercise inflammatory cell infiltration in females and males. It is important to note, however, that in the Stupka et al. study the females[99] were all oral contraceptive users and tested during the equivalent of the late follicular phase, whereas MacIntyre et al.’s protocol[98] did not appear to account for the menstrual cycle phase. Estrogen levels in females taking oral contraceptives are known to be lower than non-oral contraceptive users, and this could have accentuated the discrepancy between the studies’ findings. Furthermore, Tiidus et al.[100] demonstrated that in rats, the protective effect of estrogen on neutrophil infiltration did not occur with synthetic estrogen supplementation. In light of these results, Clarkson and Hubal[2] stated that females demonstrate an earlier post-exercise inflammatory response, but that this response remains weaker than in males over the long term.

Males Females

120

Relative strength (%)

100

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60 * 40

20

0 Pre-exercise

Postexercise

0.5 days

3 days

4 days

7 days

10 days

Time Fig. 4. Relative strength loss and recovery over time (at baseline, immediately after exercise, and 0.5, 3, 4, 7, 10 days after exercise) expressed as mean – standard error of the mean. Females exhibit significantly greater strength loss immediately after exercise, but there is no significant effect of gender at any other timepoint (reproduced from Sewright et al.,[94] with permission). * indicates p < 0.05.

ª 2011 Adis Data Information BV. All rights reserved.

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For Peake et al.,[101] a potential explanatory hypothesis would involve the infiltration of leucocytes into muscle cells post-exercise, which could differ in males and females because of the differences they show in membrane permeability following a muscle-damaging exercise. In this perspective, recovery strategies making use of cold exposure to reduce the post-exercise inflammatory response appear particularly beneficial to the athletic woman. It has been proven that vasoconstriction and the reduction of metabolic activity had the effect of decreasing tissue swelling, inflammation, the immediate sensation of pain and the degree of severity of an injury.[102] Experimental evidence shows that the local application of cold, started promptly after a muscle injury and maintained for a prolonged time period, limits the process of cell destruction by leucocytes and improves nutrient perfusion through the tissues.[103] In the acute treatment of musculoskeletal trauma, cold application is an adequate method by which to improve cellular survival against local hypoxia generated by the inflammatory process and the formation of oedema.[104] Future studies will need to determine whether recovery by cold-water immersion or whole-body cryostimulation would be susceptible to yield greater benefits to female athletes, given the specificity of their inflammatory response, compared with that of males. 3.2 Recovery From Exercise-Induced Bone Damage 3.2.1 Bone Turnover and Stress Fracture

The majority of physical activities exert strong pressures upon the skeletal system, thereby increasing bone cell turnover. The mechanisms for osteogenesis and bone recovery being largely dependent upon calcium intake and overall caloric intake[58,105] as well as hormonal status.[106,107] Howat et al.[66] report that the diets of female athletes are frequently poor in calcium, especially when dairy product consumption is low or nonexistent. This contributes to the compromising of their bone health and increasing their risk for stress fractures.[59,105] It therefore seems essential ª 2011 Adis Data Information BV. All rights reserved.

during recovery, to promote calcium consumption to aid the bone remodelling processes.[105] In spite of this, female athletes often fail to maintain a sufficient calcium intake, especially when dairy products are not included in their diet. Several studies have thus shown that daily calcium intake can vary between 500 and 1623 mg/day in female athletes, with most of them not even reaching 1000 mg/day.[108] By contrast, the recommended daily intake of calcium is 1300 mg/day for females aged between 9 and 18 years and 1000 mg/day for females aged between 19 and 50 years. The results of a recent study by Josse et al.[109] illustrate the beneficial impact of milk consumption on bone turnover in young females over the course of a 12-week strength training programme. Five times per week, subjects consumed either twice 500 mL of fat-free milk (1200 mg/day of calcium and 360 IU/day of vitamin D), or an isoenergetic carbohydrate drink immediately and 1 hour after each strength training session (figure 5a and b). The milk group showed a larger rise in serum 25-hydroxyvitamin D (vitamin D acting to promote calcium absorption and bone turnover) levels than the control group, while parathyroid hormone (responsible for bone resorption by enhancing the release of calcium into the bloodstream) levels decreased in the milk group only. The greater lean mass gains observed in the milk group also suggest larger gains in bone mass and, finally, fat mass decreased in the milk group only. These results emphasize the important role of calcium as part of nutritional strategies for optimal bone health and recovery. Inadequate caloric intake also undermines bone health by suppressing hypothalamic-pituitary gonadal axis activity, resulting in low estrogen production and ensuing menstrual cycle disturbances.[110,111] Estrogen plays a multi-factorial role in maintaining bone health in premenopausal females, by both slowing bone resorption[107] and stimulating its formation.[106] The elevated occurrence of osteopaenia and increased rate of bone fractures is well documented in premenopausal athletic females with hypothalamic amenorrhoea and in anorexic females.[112,113] Athletes who restrict caloric intake despite important training loads, compromise the hormonal Sports Med 2011; 41 (10)

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875

b a

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CON

−500 −1000 −1500 −2000

2000

*

1500 1000 500

*

Change in fat mass (g)

0

Change in lean mass (g)

2500

−2500

0 Milk

CON

Fig. 5. Evolution of fat mass and lean mass before and after 12 weeks of strength training in groups having consumed (a) milk following training sessions (n = 10) or (b) an isoenergetic carbohydrate drink (control [CON], n = 10). Values are mean – SE (reproduced from Josse et al.,[109] with permission). * indicates significantly different from CON in the pre- to post-training difference (p < 0.05).

processes involved in bone remodelling, placing themselves at a risk for bone loss comparable to postmenopausal females. In this regard, maintaining adequate energy availability holds particular importance through puberty and until late adolescence, when females reach their peak bone mass.[114] Vitamin D status is another important factor in preserving bone health. Athletes living in Nordic countries and training mostly indoors often show a poor vitamin D status; a level ‡80 nM is necessary to maintain good bone health.[115] Studies show that the daily supply in vitamin D averages 5 mg per day (200 IU/day) in females aged between 19 and 50 years. It is therefore advisable to compensate for this deficit with a well adapted diet, or by way of luminotherapy. Finally, adequate protein intake should be included in the nutritional recovery strategies aiming to preserve bone health, in order to limit, over the short term, the risks of stress fractures, and over the long term, the onset of osteoporosis. 4. Recovery and Return to Homeostasis: Is there a Gender Difference? 4.1 Recovery and Metabolic Disturbances 4.1.1 Active Recovery

An analysis of the literature on the benefits of active recovery reveals the latter as especially advantageous when performed between two exercise sessions that are closely scheduled in time (<1 hour) and which rely heavily upon anaerobic ª 2011 Adis Data Information BV. All rights reserved.

processes.[116] It is therefore particularly useful when two maximal performances must be executed within a time frame that is too short to allow a return to homeostasis by way of passive recovery. Many studies have shown that an active recovery yields a more rapid return to resting blood lactate concentrations, compared with a passive recovery.[117] Similarly, Yoshida et al.[118] discerned that active recovery limits the accumulation of inorganic phosphate after an intense, 2-minute exercise bout compared with a passive mode of recovery. Other results showed that maintaining a sub-maximal intensity of exercise enables a quicker return of intramuscular pH to its resting value after a high-intensity effort.[118,119] It appears appropriate to recommend that females maintain a moderate intensity of exercise between two high-intensity exercise bouts that are separated by a relatively short time span for recovery (i.e. <1 hour). Carter et al.[120] showed a greater decrease in arterial blood pressure after exercise in females than that in males, such that it may decrease below its pre-exercise value during passive recovery. Given that the return to homeostasis after an effort relying heavily upon glycolytic processes appears to be linked to maintaining good venous return,[121] it is conceivable that female athletes would find a greater advantage to active recovery modes than their male counterparts at the circulatory level.[120] Furthermore, a recent study by Jakeman et al.[122] has proven the effectiveness Sports Med 2011; 41 (10)

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of wearing compression socks during the recovery period in females. These authors demonstrated that muscle aches were significantly diminished by their use, and that the loss in knee extensor strength was reduced. In addition, jumping performance (squatting jump with counter movement) was significantly superior following this mode of recovery. Such findings strongly support the concept that improving venous return during recovery in athletic females is an essential parameter contributing to muscle performance. 4.1.2 Recovery by Immersion

To our knowledge, only one study has examined the effect of gender on recovery by immersion after aerobic exercise.[123] The results of this study show that a 30-minute immersion in alternating hot (36C) and cold (12C) baths generates a greater drop in blood lactate concentrations than a passive recovery mode. However, we will mention that even though this method appears to accelerate post-exercise lactate clearance significantly, these differences remain small. New studies are necessary to determine whether this type of recovery can yield positive effects upon the level of performance of female athletes, when used between repeated anaerobic exercise sessions. Nonetheless, it appears that basic indications can be provided to female athletes regarding this recovery mode, and this not only for traumatizing exercises. 4.2 Recovery and Thermoregulation

Reaching a high core temperature as a result of an imbalance between the mechanisms of thermogenesis and thermolysis during prolonged exercise has been well identified in the literature as a physiological factor that limits performance.[124] The rise in core temperature during exercise is a normal consequence to any form of exercise over long durations and is controlled by the interaction of vascular, muscular and metabolic responses.[125] In response to these, the organism adapts by augmenting both peripheral blood flow and sweat loss. We will show that post-cooling methods during recovery can offer specific benefits to the female athlete. ª 2011 Adis Data Information BV. All rights reserved.

4.2.1 Evaporation and Hydration Strategies for Recovery Sweat Loss during Exercise

In their review of the literature on the effects of gender upon thermoregulation during exercise, Kacibua-Uscilko and Grucza[126] report that females differ from males in their exogenous heat storage and thermolytic capacities, as well as their endogenous heat production during exercise. These differences are mainly attributed to a lower body mass to surface area ratio, greater subcutaneous fat stores and a lower exercise capacity in females. A higher fat-mass percentage augments thermal stress by increasing the metabolic cost of performing a given exercise task.[127,128] In addition, the larger subcutaneous fat layer in females increases the distance between the active, heat-producing tissues (i.e. skeletal muscle) and the skin, reducing the rate of nonevaporative heat loss, and decreasing the body surface area to bodyweight ratio.[129] Nevertheless, when these differences are accounted for in research studies, differences between the genders are still observed. When subjected to an identical thermal rating, females were found to sweat less than males while maintaining the same body temperature as a result of greater efficiency of the sweat evaporation process. Grucza[130] also showed that females sweat less than males, without demonstrating a greater concomitant rise in rectal and body temperature. This author has provided evidence for the fact that this difference can be explained by a greater amount of evaporated sweat (58.7% vs 52.0% of total sweat lost, respectively) and a lower amount of dripping sweat in females, compared with males (21.2% vs 34.1% of total sweat loss in females and males, respectively). Even though these experimental data suggest that the extent of water loss associated to exercise is proportionally lesser in females, they should, nonetheless, compensate for these losses with well adapted rehydration strategies. Post-Exercise Rehydration Strategies

Recommendations for post-exercise rehydration are similar in males and females and aim to compensate for the water and electrolyte losses Sports Med 2011; 41 (10)

Post-Exercise Recovery and Gender Differences

activated by a given effort (figure 6). This difference appears linked to a higher threshold for females for post-exercise cutaneous vasodilation,[135] and to a greater drop in post-exercise arterial pressure, compared with males.[135] In addition, the hormonal variations of the menstrual cycle modify the thermoregulatory response to exercise in females, such that the core temperature during rest and exercise may differ according to the phases of the menstrual cycle. In this way, Kacibua-Uscilko and Grucza[126] explain that the internal temperature fluctuates by more than 0.6C, depending on the menstrual cycle phase, both at rest[136] as well as during

a

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occurring during exercise. However, few studies have compared the hydration strategies adopted by both genders. On this topic, Broad et al.[131] have quantified the sweat losses and fluid intake of female soccer players. These authors reported that females drank, on average, 0.4 L/h during training and competition periods, which is lower than that reported for males. This difference may be mainly linked to morphological differences between genders, but also to lower metabolic heat production rates in females. However, electrolyte losses comparing the sweat of female soccer players less than 21 years of age and seniors on a British team over the course of one practice and one game appeared identical to those measured in their male counterparts.[132] In light of these results, it appears that the same recommendations for rehydration during recovery given to males may be well adapted to females. Burke[79] did not bring forth any postexercise hydration strategy specific to females. We therefore retain, that in the post-exercise period, it is necessary to replace, as rapidly as possible, the fluids lost during exercise. In subjects having lost 3% of body mass due to dehydration, Shirreffs et al.[132] showed that ingesting, small fractions of a 23 mmol/L sodium solution (150–200% of the water volume deficit) was the most effective method to compensate for the fluid losses caused by exercise. To speed the rehydration process in the context of multiple practices or competitions performed in one day, it appears advantageous to consume cool water (i.e. 12–15C), slightly flavoured and containing 2% of carbohydrates and 1.15 g/L of sodium.[133] When the amount of time separating the performances is greater, the fluid recovery of female athletes can occur via a combination of water and solid food intake. In this context, urine clarity may serve as the most practical method for athletes to evaluate their hydration status.

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4.2.2 Thermoregulation and Post-Cooling Thermoregulatory Response Post-Exercise

Kenny and Jay[134] showed that, compared with males, females reveal a diminished ability to lower their temperature at the level of the oesophagus as well as that of the muscles previously ª 2011 Adis Data Information BV. All rights reserved.

Fig. 6. (a) Changes from pre-exercise rest in esophageal temperature. (b) Vastus medialis muscle temperature measured 10 mm from the deep femoral artery and femur. (c) Triceps brachii muscle temperature measured 10 mm from the superior ulnar collateral artery and humerus. Data separated according to males and females. Values are mean – standard error of the mean (reproduced from from Kenny and Jay,[134] with permission). * indicates significant difference between genders at an alpha level of p < 0.05.

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exercise,[137] and even under warm conditions for exercise.[136] The rise in progesterone levels during the luteal phase (i.e. the post-ovulation phase) triggers a rise in body and skin temperature that delays the activation of perspiration mechanisms in both ambient and warm environmental conditions. However, if Marsh and Jenkins[138] report that no tangible proof exists to claim that the female athlete is confronted with a greater risk of heat stroke during exercise than that of the male athlete, then the findings gathered in this review still suggest that strategies of post-cooling are likely to be at least as beneficial for females as they may be for males. Strategies for Post-Cooling

Given the lower thermolytic capacities of females in the post-exercise period, the cooling-off methods performed after an exercise session (post-exercise cooling), in particular those involving cold water immersion or wearing a cooling jacket, appear as a particularly interesting recovery method for females. Indeed, cold water immersion and the use of a cooling jacket both seem to reduce body temperature and heart rate efficiently.[139] Moreover, cold water immersion may cause an adjustment of blood flow distribution that could benefit the quality of performance delivered during the next exercise session.[139] The benefits of post-exercise cooling are most apparent following exercise performed in the heat.[140,141] A high internal thermal load can lead to an immediate drop in the level of performance and can slow the recovery to an optimal state of functioning.[142] In this way, the degree of thermal stress induced by exercise, or the inability to tolerate an imposed workload may result in a prolonged drop in performance. Previous studies highlighted the reduction in voluntary muscle activation following an exercise protocol to exhaustion due to whole-body hyperthermia, implying a selective reduction, by the CNS, of muscle activity following exercise.[143] According to these results, a decrease in maximal force could be the consequence of decreased CNS command upon the active muscles, acting as a protection mechanism aiming to limit metabolic heat production, and to thereby limit further rise in body temperature.[143] This also indicates that maintaining a high ª 2011 Adis Data Information BV. All rights reserved.

internal thermal load can impair performance during a subsequent exercise bout. While contradiction exists in the literature, post-exercise cooling is certainly beneficial as far as reducing the internal thermal load after exercise.[141] Even though future studies will need to confirm this hypothesis, it is conceivable that the reduction of thermal stress tied to exercise via post-cooling methods will demonstrate benefits of greater magnitude in females, given their lesser capacity to lower their internal temperature after exercise. 5. Conclusions Performing elevated training workloads is susceptible to yielding deleterious effects if the organism is unable to recuperate completely between training sessions. In this context, the recovery phase must be considered as an inherent component of the training process and must, therefore, be granted the same degree of attention in its programming and management as the exercise sessions themselves. Given the effects of gender upon the physiological responses to exercise and during the post-exercise recovery period, it appears essential to customize recovery methods according to gender, so as to optimize the processes of physiological recovery and supercompensation while minimizing the risks of injury in the female athlete. Both athletes and those surrounding them must now acknowledge the necessity of investing time and financial means into the optimization of recovery. Further research must be performed in order to comprehend clearly the potential benefits that new methods of recovery may bring to the female athlete. Acknowledgements No sources of funding were used to assist in the preparation of this review. The authors have no conflicts of interest that are directly relevant to the content of this review. The authors would like to thank Karine Schaal (IRMES) for her English editing assistance and the relevant advice she brought to this review.

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Correspondence: Dr. Christophe Hausswirth, National Institute of Sport, for Expertise and Performance (INSEP), Research Department-11 Avenue du Tremblay, 75012 Paris, France. E-mail: [email protected]

Sports Med 2011; 41 (10)