Sports Med 2010; 40 (4): 271-283 0112-1642/10/0004-0271/$49.95/0
CURRENT OPINION
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Expert Performance in Sport and the Dynamics of Talent Development Elissa Phillips,1,2,3 Keith Davids,2 Ian Renshaw2 and Marc Portus3 1 Biomechanics and Performance Analysis, Australian Institute of Sport, Canberra, Australian Capital Territory, Australia 2 School of Human Movement Studies, Queensland University of Technology, Brisbane, Queensland, Australia 3 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane, Queensland, Australia
Abstract
Research on expertise, talent identification and development has tended to be mono-disciplinary, typically adopting genocentric or environmentalist positions, with an overriding focus on operational issues. In this paper, the validity of dualist positions on sport expertise is evaluated. It is argued that, to advance understanding of expertise and talent development, a shift towards a multidisciplinary and integrative science focus is necessary, along with the development of a comprehensive multidisciplinary theoretical rationale. Here we elucidate dynamical systems theory as a multidisciplinary theoretical rationale for capturing how multiple interacting constraints can shape the development of expert performers. This approach suggests that talent development programmes should eschew the notion of common optimal performance models, emphasize the individual nature of pathways to expertise, and identify the range of interacting constraints that impinge on performance potential of individual athletes, rather than evaluating current performance on physical tests referenced to group norms.
Research on expertise and talent identification and development has been dominated by opposing genocentric or environmentalist positions.[1] Recently, Dunwoody[2] has challenged psychologists to look beyond the individual, suggesting researchers have neglected the role of the environment and focused on organism structure and process in isolation. Although there is a need to move away from the cognitive psychology asymmetric-organismic focus,[2,3] the tendency to overemphasize the role of environmental constraints on expertise acquisition also needs to be eschewed. For example, Ericsson and colleagues[4,5] emphasized an environmental perspective, ad-
vocating that expertise is acquired as performers specialize at an early age and engage in deliberate practice.[4] Other investigators have associated the hereditary nature of physiological, anthropometric and psychological characteristics with elite performance in sports.[6] For example, early anthropometric research on Olympic athletes advocated a close relationship between physical characteristics and specific Olympic events.[7] This line of evidence has been somewhat over-interpreted, leading to the questionable practice of anthropometric profiling of adolescents to identify potential for early specialization in a sport. Consequently, anthropometric profiling of physical
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dispositions has tended to skew the rationale for talent identification models in sport, despite a lack of supportive evidence and the unstable nature of anthropometric and physical parameters during adolescence.[8,9] Recently, there have been proposals to integrate polar perspectives on sports performance and expertise into a multidisciplinary approach, to enhance understanding of the athleteenvironment relationship as exemplifying a complex, dynamical system. In this article we identify limitations of existing research on expert performance and talent development, and provide evidence-based arguments for adopting a multidisciplinary research perspective for the comprehensive study of sport expertise. To support our argument we review and evaluate extant literature on expertise in psychology and sport science. The search reference terms used included ‘talent development’, ‘talent identification’ and ‘sport expertise’ in PubMed, SportDiscus and Ovid databases. A major reason for adopting polarized, monodisciplinary positions on the acquisition of sport expertise has been the absence of a powerful multidisciplinary theory to act as an integrative conceptual framework. Although some models have advocated a multidisciplinary approach to talent identification and development, such as Simonton’s[10] model of talent as a multiplicative, dynamic process and the Differentiated Model of Giftedness and Talent,[11] these approaches tend to be operational and propositional in nature. These models have not provided a detailed, explanatory theoretical rationale underpinning a dynamic and multidimensional basis for expertise and how it may support the process of identifying and developing talent. One suitable theoretical approach to the study of performance, expertise and talent development in sport conceptualizes the athlete as a complex, dynamical system. In such systems there is great potential for interactions between system components and the environment, often leading to rich and unique patterns of behaviour. To date, studies of complex system behaviour in sport include match play and behaviour during competitions,[12,13] decision-making,[14,15] motor learning,[16,17] coordination,[18,19] human gait and ª 2010 Adis Data Information BV. All rights reserved.
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injury,[20,21] and medicine in sports.[22] In this article, relevant theoretical concepts and insights from dynamical systems theory, complex systems theory, ecological psychology and evolutionary sciences are identified and their implications for the acquisition of sports expertise and talent development are discussed. 1. Traditional Expertise Approaches As noted, much research has focused on the role of environmental constraints in expertise acquisition, including participation in play and practice activities and the role of family and environmental contexts.[23,24] For example, the expert performance approach of Ericsson and Smith[25] explored the contribution to expertise of specific practice environments. Ericsson and colleagues’[5] deliberate practice approach has highlighted the importance of structured activities involving goal-directed skill learning, which requires effort and concentration. It was estimated that experts spend typically ~10 years or 10 000 hours in deliberate practice to attain exceptional performance.[26] The unidimensional nature of the deliberate practice approach led Starkes[27] to label it a ‘very environmentalist’ theory of expertise acquisition, while others (e.g. Arau´jo[28]) have proposed it has an organismic bias. Additionally, researchers studying deliberate practice in sport have encountered some incongruities with the tenets of the main theory. For example, in contrast to previous findings, many athletes tend to find appropriate practice and training enjoyable and motivating across all development stages.[29] Moreover, early specialization has not been found to be essential for acquisition of expert sport skills in adulthood.[30-32] Time spent in sport-specific training does discriminate between experts and non-experts in some sports, although the relationship between practice and performance is non-linear.[23] Further, retrospective analysis of historical data on time spent in practice was unable to clarify differences between experts and novices based on microstructure of practice activities.[33] For example, Weissensteiner et al.[34] examined the practice histories of under 15, under 20 and adult cohorts Sports Med 2010; 40 (4)
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of skilled and less skilled cricket batsmen and found that hours spent in practice explained only a small proportion of variance in the development of anticipation skills. A particular methodological concern in this approach is the proliferation of ‘weaker’ data from a plethora of cross-sectional studies, relative to the lack of longitudinal research. More evidence on putative benefits of deliberate practice is needed, following cohorts of young athletes through developmental pathways. To summarize, although numerous hours of training are necessary for success in sport at the elite level, research suggests that attainment of an expert level is not always accomplished by engaging in deliberate practice alone. In another polarized approach to elite sports performance, some research has investigated the genetic makeup of individual athletes. Molecular testing has attempted to identify single gene variants deemed ‘responsible’ for performance in specific sports (e.g. a gene responsible for physical power or endurance capacity). This ‘single gene as magic bullet’ philosophy has led to claims that elite performers are born to succeed. For example, there have been attempts to identify sprinters and endurance runners on the basis of differing alleles (i.e. forms) of a single gene known as a-actinin-3.[35,36] Besides obvious ethical issues, reports of gene-profiling and gene-transfer technology raise more general theoretical and practical questions about the nature of genetic and environmental constraints on skill acquisition and performance.[37] Research on human behaviour has yet to reveal that the function of a single gene variant can be inferred from identifying performance phenotypic variance (e.g. power athletes and endurance athletes), supporting the notion of non-specificity of genetic constraints.[38] 1.1 Methods for Assessing Expert Performance in Traditional Research
Empirical expertise research has typically been conducted with two main methods involving: (i) quantitative analyses, which include implementation of multiple test batteries on developing athletes; and (ii) qualitative examination of developmental histories of past or present elite ª 2010 Adis Data Information BV. All rights reserved.
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athletes, with methods such as interviews, questionnaires or self-reported, retrospective recall of practice histories. 1.2 Outcomes of Research with Quantitative Methodologies
Attempts to distinguish athletic expertise based on skills testing have been unsuccessful or have been confounded by the overriding importance of maturational factors on observed performance.[9] The over-use of ‘closed-skill drill tasks’ to elucidate athletic potential has been a major weakness in extant research because they lack task representativeness, resulting in a failure to identify skilled players from lesser skilled individuals. For example, Gabbett et al.[39] attempted to assess skills of passing (digging), setting, serving and spiking as part of a test battery to discriminate between junior volleyball players of different abilities. The only test that revealed differences between selected and non-selected players was passing. This may have been because skills in volleyball (digging, setting, spiking and blocking) are not performed in stable environments but are dynamic and characterized by marked temporal and spatial variability dependent on preceding play (e.g. quality of contact of the ball by team-mates and opponents). In other work, Hoare and Warr[40] assessed talented athletes (non-football players) aged 15–19 years to identify and develop female football players. Initial screening required coaches to grade players by assessing skill performance such as ball juggling, dribbling (around cones) and passing. Players were grouped by performance test scores for playing small-sided games before progressing to full-size games for coach observation and selection. They used an innovative quasi-applied research model to identify and develop talent in women’s soccer, and highlighted the need to assess athletes over a longer time period than typical in sport talent assessment, suggesting that 2–3 months is necessary for coaches to make a valid assessment of athlete potential. They also proposed that components of performance require different weightings, since the importance of speed and acceleration was underestimated in their tests. Finally, the need to develop an objective test of game sense was suggested, as Sports Med 2010; 40 (4)
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some athletes performed well on physical tests, but weakly in terms of general tactical understanding.[40] Other quantitative approaches in empirical talent identification research have attempted to examine sport-specific, relevant performance characteristics, through anthropometric, physical, technical, tactical and psychological testing.[40-44] In the main, these tests have been limited in utility for identifying talented performers in sport. For example, test batteries focusing on discrete performance measures have provided limited information of athletic potential and adaptability in different performance environments.[8,41] Questions on the data of these studies, and the efficacy of traditional talent identification practices, arise due to constraints on individuals like growth, maturation, development and training.[42] To exemplify, Elferink-Gemser et al.[43] employed a test battery to examine 30 elite and 35 sub-elite youth hockey players over three competitive seasons. Their research established differences in technical and tactical variables between skill levels at 14 years as well as variations in endurance capacity in the subsequent 2 years. However, the task demands of the technical test did not provide a valid representation of the competitive performance setting (cf. the importance of representative experimental design in studies of human behaviour).[44] The use of closed skill proficiency tests, without opposition and in a static environment, and physical performance measures considered in isolation need to be carefully evaluated, because they have a low correlation with the specific demands on an individual during a dynamic competitive situation. Several studies have highlighted the importance of using more sportsspecific assessments of tactical and technical competence rather than generic physical measures such as strength or endurance tests.[40,45] Skill-based differences in perceptual-cognitive skills such as anticipation and decision making have been successfully identified in a number of studies (see Williams and Ward[46]). Although a number of generalized visual training programmes are predicated on the idea that a superior visual hardware system provides perceptual advantages to skilled performers, research evidence does not support this view. Rather, it has been ª 2010 Adis Data Information BV. All rights reserved.
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argued that expertise advantages emerge as a result of sport-specific practice and experience.[46-48] Despite the large volume of studies examining the role of perceptual-cognitive expertise in sport, there has been some confusion over whether research has actually demonstrated the true extent of expertise. Van der Kamp et al.[49] criticised the lack of precision in perceptual-cognitive research, highlighted the magnitude of errors reported in many occlusion-based studies of anticipation skill and suggested a weakness in a popular experimental methodology. In a popular approach, liquid crystal spectacles permit information sources available to participants to be manipulated by limiting vision during key moments of performance.[50] It was argued that high levels of error observed in these studies may have been due to the failure of the methodology to capture the complementary efforts of the ventral and dorsal cortical visual systems in regulating perception and action. This is important since, as Williams and Ericsson[47] indicated, the first stage in determining expertise is that ‘‘performance be observed in situ in an attempt to capture the essence of expertise in the domain of interest and to design representative tasks that allow component skills to be faithfully reproduced in the laboratory’’ (p. 286). Van der Kamp et al.[49] also argued that a weakness of previous work was requiring participants to undertake non-representative actions such as pressing buttons, marking crosses on images of court surfaces and verbally reporting findings. Another common error is the provision of video footage that does not replicate the view of performers in action, thereby limiting understanding of the true extent of the expert advantage. Some researchers, such as Mu¨ller and Abernethy,[50] have developed and incorporated what they termed an ‘‘ecologically valid’’ batting test to measure a batsman’s perceptual-action coupling and found skilled players utilized prospective ball flight information to a greater extent than less skilled players. 1.3 Outcomes of Research Using Qualitative Methods
Qualitative studies on the development histories of elite athletes have been somewhat limited in Sports Med 2010; 40 (4)
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number, examining physical, psychological, environmental and social factors that shape performance.[51,52] They have established the importance of environmental constraints, including support from family or friends and the opportunity to participate in residency development programmes. A range of individual constraints – including mental preparation, focus and commitment, and clear goal setting – have been found to contribute to the development of expertise.[8] Durand-Bush and Salmela[52] examined qualitatively the development and maintenance of expert performance in Olympic and world championship competitions. Their data revealed that athletes did not all follow the same pathway to become world and Olympic champions, highlighting how individuals can take different routes to expert status, use various resources and strategies, and be innovative and creative as they develop and maintain their expertise in sport. However, some common factors were observed, including: (i) all athletes underwent stages described as the sampling, specializing, investment and maintenance years;[53] (ii) high levels of self confidence and motivation; and (iii) high levels of creativity and innovation during the maintenance years, a continuous drive to learn and improve, and a strong work ethic. 2. Dynamics Systems Theory and Constraints on the Acquisition of Expertise in Sport In contrast to more traditional approaches to studying expertise, a potentially valuable conceptual framework for modelling the acquisition of expertise exists in understanding performers as complex, dynamical systems. Complexity sciences have been used to study and explain the rich patterns formed in complex systems such as animal collectives, weather systems, the human brain and movements in team sports, where patterns emerge from seemly random component trajectories.[54-56] From this description an emerging expert can be viewed as a complex system, composed of many degrees of freedom on many system levels. The potential for interaction between system components provides the platform ª 2010 Adis Data Information BV. All rights reserved.
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for rich patterns of behaviour to emerge as individuals interact with dynamically changing environments. This new perspective reveals that adaptation in performance occurs as the result of system trade-offs between specificity and diversity of behaviours.[57] Dynamical systems theory emphasizes the influence of interacting constraints on performance and provides a framework showing how expertise can be achieved in diverse ways as individual performers attempt to satisfy the unique constraints on them.[22] Some current models of talent development are harmonious with these theoretical ideas, although their tenets are not necessarily predicated conceptually on these insights. For example, Simonton[10] proposed that talent emerges from multidisciplinary, multiplicative and dynamic processes and is likely to operate as an intricate system beyond the scope of the polar naturenurture debate. He pioneered mathematical equations to operationalize the potential components that contribute to talent development. These components were weighted by relevance and included reference to genetic dispositions (e.g. height or endurance capacity), environmental (e.g. social and familial support) and developmental constraints. Subsequently, the model was described as emergenic and epigenetic, comprising components that interact and change with time.[10] The emergenic aspect proposed that potential talent consists of multiple components, including all physical, physiological, cognitive and dispositional traits that facilitate the manifestation of superior expertise in a specific domain. Beyond individual differences, Simonton’s[10] model captured the dynamics of epigenetics. Epigenetics were seen in the diverse components that make up talent, which slowly appear and differentiate over time in an individual and ultimately depend on underlying neurological, muscular, cultural, skeletal, social, psychological, physiological and environmental variables.[58] However, they emerge gradually during the course of long-term interactions between the internally developing organism and appropriate environmental constraints.[10] This system is complicated, as it includes the evolutionary interaction of components, and any examination Sports Med 2010; 40 (4)
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of talent development that utilizes this model needs to be holistic, impartial and sophisticated.[10] Although this mathematical model is a useful starting point for sport scientists, since it attempts to operationalize key concepts such as multidisciplinary talent development practices and multiple developmental trajectories towards potential expertise,[8] it lacks theoretical power because it is not conceptualized within a theoretical framework. This weakness could be mediated by including a dynamical systems theory as a viable rationale for talent development as an emergenic and epigenetic process. Furthermore, on a practical level, adopting Simonton’s[10] model may be extremely difficult, as its efficacy is predicated on identifying all components that contribute to expertise in any one specific sporting domain. Identification of every component is essential because the model is proposed to be multiplicative, and any score of zero for any specific factor signifies that expertise cannot be achieved. Vaeyens et al.[9] provided an insightful model of talent identification, capturing the dynamic nature of talent and its development and focusing on potential for development and inclusion rather than early identification. Such a model can be strengthened with a dynamical system theoretical approach also, and in the next section we elucidate key ideas in this framework. 2.1 The Role of Neurobiological Degeneracy in Expertise Acquisition
Athletes considered as complex, neurobiological systems are composed of many interacting parts and levels, which self-organize under constraints.[59] These systems have been conceptualized recently as pleiotropic and degenerate with the ability to adapt to different task and environmental demands.[60] Pleitropy provides neurobiological systems with a variety of alternative performance solutions.[61] Neurobiological degeneracy refers to the ability of structurally different components to be coordinated together to achieve the same behavioural goal.[57] At the level of gene networks, degeneracy promotes evolutionary fitness by ensuring that genetic diversity ª 2010 Adis Data Information BV. All rights reserved.
supports functional adaptation to variable environments.[62] The degenerate relationship between system components and system output in developing experts implies that there are many different pathways to achieving expert performance. Genetic diversity is responsible for a portion of training or performance response differences between individuals, and when there is a favourable interaction with important environmental constraints, performance benefits may be observed. Given differences in genetic contributions, performance variations are more likely to assert themselves under intensive practice regimens. The characteristics of pleiotropy and degeneracy in athletes highlight the need for a systemsoriented, multidimensional framework.[24] Expertise attainment in a particular sport depends on many additional constraints outside the cognitive domain, including but not limited to genetics, social and physical environment, opportunity, encouragement and the effect of these variables on physical and psychological traits.[63] Monodisciplinary approaches to the acquisition of expertise fail to capture the complementary nature of the relationship between individual, task and environmental constraints.[8,24,64] For these reasons, Davids and Baker[24] highlighted the need for an interactionist explanatory framework to examine performance attainment in sport. As we note in the following sections of this paper, this theoretical approach provides a viable platform for explaining the dynamic relationship between an individual’s genetic disposition and the environment and the acquisition of expertise in sport through variable pathways and processes. 2.2 Constraints on Acquiring Expertise
In sport, the expression of expertise is limited or shaped by interacting constraints at many system levels. The concept of constraints (boundaries that constrain the interactions of system components) in human movement science was readdressed by Newell,[65] who classified them into organismic, task and environmental constraints. Constraints in the expertise acquisition context can be conceived of as the numerous variables that form each individual expert’s Sports Med 2010; 40 (4)
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developmental trajectory, and it is important to identify the range of constraints on the acquisition of expertise. Given that an individual is born with distinguishing physical characteristics (with a degree of genetic influence), expertise research is concerned with how environmental constraints interact to affect the development of skill and the expression of genotypes. Performance emerges from the intrinsic dynamics of experts, the preferred behavioural tendencies that arise from the interaction of environmental, task and organismic constraints (including development, experience, genes and learning of each performer).[66] Kelso[66] proposed that intrinsic dynamics reflect the organisational tendencies of an individual. Thelen[67] referred to intrinsic dynamics as ‘‘the preferred states of the system given its current architecture and previous history of activity’’ (p. 76). The intrinsic dynamics of each individual are unique and shaped by many constraints, including experience, learning, development, morphology and genes, which interact to shape performance and the acquisition of expertise in sport.[68] In developing experts, these contributions can lead to significant variations in performance solutions. The manner in which each developing expert attempts to satisfy constraints during performance and learning is determined by the matching of his/her intrinsic dynamics with the specific task dynamics.[69] By knowing an individual’s intrinsic dynamics, one can specify what in the performance repertoire actually changes due to environmental, learned or intentional influences.[70] Due to variations in intrinsic dynamics, expert performers are able to generate different types of functional performance solutions. This idea fits with data in cricket fast bowling reported by Pyne et al.,[71] who examined the relationship between junior and senior high performance athletes’ anthropometric and strength characteristics and bowling speed. They observed differences in the variables and strength of correlation of predictors of peak ball speed between age groups, and suggested growth and biological maturation largely accounted for greater peak ball speed in seniors. This line of thinking could be followed in ª 2010 Adis Data Information BV. All rights reserved.
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a comprehensive performance analysis including technical/coordination variants under a variety of conditions, such as different delivery types, to gain an understanding of the relationship between the intrinsic dynamics and performance solutions of individuals in developmental groups. Therefore, understanding the nature of each individual’s intrinsic dynamics is central to understanding how expert performance develops in sport. Due to different relationships between an individual’s intrinsic dynamics and a set of task dynamics, each athlete may harness system variability in a different way, implying that expertise may develop in quite unique ways. As individuals progress towards a state of expertise and explore different performance solutions, their intrinsic dynamics will alter and diversify. If the behavioural requirements of a task provide a close match with the pre-existing intrinsic dynamics of an individual learner, the rate of expertise acquisition is likely to be enhanced. The matching of intrinsic and task dynamics may explain praecocial behaviour in sport when some athletes can perform incredibly well from an early age and forms the basis of talent transfer in sport. In contrast, acquiring task dynamics that are dissimilar to those of a previously learned task (e.g. tennis and squash movement pattern dynamics) may lead to a longer process of learning because the specific learner’s intrinsic dynamics may need to be significantly re-shaped.[72] These ideas in dynamical systems theory have important implications for understanding the development and maintenance of expert performance. How the intrinsic dynamics of developing experts are continually shaped by genetic and environmental constraints needs to be understood. The effects of environmental constraints on phenotypic gene expression suggest that athletes with what may be perceived as less favourable genetic dispositions may still achieve expert levels of performance given an appropriate skill acquisition environment.[73] Alternatively, genetically gifted athletes may fail to achieve expert status without an environment that allows rich interactions for acquiring and practising skills (for a discussion of rich learning environments see ideas of Hammond and Bateman[74]). Rich Sports Med 2010; 40 (4)
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learning environments provide many opportunities to engage in continuous interactions with skilled peers, mentors, skilled coaches and supportive parents as athletes acquire movement skills and knowledge in specific sports. In Australia, Abernethy[75] discussed the disproportionate success of athletes from rural locations as the ‘Wagga-effect’. Wagga Wagga is a rural town with a population of 46 735 people,[76] where children typically play many different sports, often engaging in senior or adult competition prior to specialization. It has produced an inordinately high number of international and professional athletes, more than any other urban conurbation in Australia. Coˆte´ et al.[77] suggested that smaller cities (defined as having populations 82% after 60 seconds. The increase in oxidative phosphorylation was accompanied by a significant increase (107%) in heat production, without any significant change in PO. The most likely explanation for this finding is that substrate phosphorylation from PCr hydrolysis and glycolysis results in lesser heat liberation per ATP produced than oxidative phosphorylation. These results are confirmed by the study of Krustrup et al.,[65] who found a decrease in mechanical efficiency from 56 – 5% to 32 – 3% during low intensity knee-extensor exercise and a decrease from 47 – 4% to 36 – 3% during a 90-second high intensity bout. The studies of Gonza´lez-Alonso et al.[64] and Krustrup et al.[65] supported the hypothesis that the mechanical efficiency is higher for anaerobic ATP re-synthesis than for aerobic ATP-resynthesis. This important finding of a higher anaerobic mechanical efficiency than aerobic mechanical efficiency has to be taken into account when the MAOD values expressed in mL O2 eq (oxygen equivalent) per kg body mass (mL O2 eq kg-1) are converted to concentrations of the different anaerobic components in mmol kg-1. Thus, as long as we speak about the anaerobic capacity in oxygen equivalents or in mechanical work performed, the difference in anaerobic and aerobic mechanical efficiency is of no hindrance.
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2. The Construction of a Linear Relationship Between Exercise Intensity and Oxygen Uptake 2.1 Exercise Modality
In the original study of Medbø et al.,[3] the chosen exercise modality was treadmill running. Besides treadmill running, Medbø and Tabata[50,51] also used bicycle exercise to determine anaerobic capacity with the MAOD method. Since these early studies of Medbø et al.[3,66] and Medbø and Tabata[50,51] the MAOD has also been determined for supramaximal rowing,[19,53] arm cranking,[42] swimming[24,25,42] and one-legged, dynamic, knee-extensor exercise.[67] We will only discuss the concerns that come along with the estimation of anaerobic capacity during running and bicycling exercise, as those exercise modalities are most often used. 2.1.1 Running
Several studies investigated the effect of different treadmill inclinations on the MAOD attained during running, from which it can be concluded that uphill running results in significantly higher anaerobic capacities than horizontal running.[20,68,69] Olesen[20] found that the MAOD increased from 39.5 – 10.7 and 56.9 – 8.0 mL O2 eq kg-1 at a 1% grade to 69.4 – 8.3 and 99.8 – 7.1 mL O2 eq kg-1 at a 20% grade in anaerobically untrained (+76%) and anaerobically trained (+75%) runners, respectively. The results of the study of Craig and Morgan[69] supported the findings of Oleson[20] but they did not find a correlation between the treadmill belt inclination and the MAOD attained during treadmill running (r = -0.15), which suggests that certain subjects show a higher running economy during horizontal running while other subjects show a higher running economy during uphill running. The finding that the percentage inclination and the MAOD are unrelated[69] is in contrast with the finding of Olesen.[20] The oxygen deficit reached a maximum value at a 15% gradient and a further increase to a 20% gradient did not result in an additional increase in oxygen deficit, which suggests the attainment of the anaerobic capacity for running.[20]
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The MAOD Method
Medbø et al.[3] concluded that the MAOD is heavily dependent on the active muscle mass, which suggests that during uphill running a larger muscle mass is activated than during horizontal running. Sloniger et al.[70] used exercise-induced contrast shifts in magnetic resonance images to determine the pattern and intensity of muscle activation during treadmill running. A significant 9% increase in the active muscle mass was found during uphill running as compared with horizontal running, which was achieved by an altered pattern of muscle activation, i.e. the percentage of muscle volume increased significantly (p £ 0.004) for the Musculus vastus group [i.e. the M. vastus lateralis, M. vastus medialis and the M. vastus intermedius] (23%) and the Musculus soleus (14%), and decreased significantly (p £ 0.004) for the Musculus rectus femoris (29%), the Musculus gracilis (18%) and the Musculus semitendinosus (17%) from horizontal to uphill running.[70] However, they only found a moderately strong correlation between the increase in MAOD and the percentage increase in muscle activation.[68] It can be concluded that the percentage active muscle mass only explains a small part of the increase in MAOD from horizontal to uphill running and that other factors must be responsible for the difference.[20,57,68,70] Thus, for the determination of anaerobic capacity during treadmill running it is important to make a carefully considered decision about the treadmill inclination, because the MAOD at a 15% gradient possibly reflects the anaerobic capacity of a different set of muscles to those used during horizontal running.[21] Besides a higher MAOD found during uphill running compared with level running, there is another concern related . to the inclination of the treadmill, namely the VO2 slow component (SC), which is the late (i.e. secondary) increase in . VO2.[71] Pringle et al.[72] studied the oxygen kinetics during horizontal and uphill (10% grade) . running and found that the amplitude of the VO2 SC was significantly higher (40%) during uphill running compared with horizontal running. Based on this finding, it can be concluded that uphill running is less suitable for the determination of anaerobic capacity than horizontal running. ª 2010 Adis Data Information BV. All rights reserved.
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2.1.2 Cycling
Just as treadmill inclination was a confounding factor for the determination of anaerobic capacity during running, pedalling rate could influence the estimation of anaerobic capacity during cycling. Several studies used a fixed ped. alling frequency to establish the linear PO-VO2 relationship and eventually used this relationship to determine the MAOD during all-out exercise, throughout which pedalling frequency was free to vary,[5,35,38,39,44,45,52] without having studied the effect of pedalling frequency on MAOD. The effect of pedalling frequency on the MAOD was investigated by Woolford et al.,[6] . who established three different PO-VO2 relationships, based on submaximal exercise tests conducted on different cycle ergometers, which required pedalling frequencies in the range of 90–100 revolutions per minute (rpm), 120–130 rpm and 90–130 rpm. A higher pedalling frequency resulted in a higher oxygen demand of cycling and a higher resultant MAOD. The MAOD derived during cycling at pedalling frequencies between 90 and 100 rpm was approximately 30% lower than during cycling at frequencies between 120 and 130 rpm or at frequencies between 90 and 130 rpm.[6] Zoladz et al.[73] investigated the effect of ped. alling frequency on VO2 kinetics and. found a gradual, non-significant increase in VO2 when pedalling frequency increased from 60 to 100 rpm and a significant increase in oxygen uptake when pedalling frequency was further augmented to 120 rpm. Besides an effect of pedalling frequency . on the PO-VO2 relationship and on anaerobic capacity, there . is also an effect of pedalling frequency on VO2 SC.[74] Pringle et al.[74] found . a significant increase in the amplitude of the VO2 SC when pedaling frequency increased from 35 rpm . to 115 rpm, even when the amplitude of the VO2 SC . was expressed relatively. to the end exercise VO2. The increase in the VO2 SC was probably due to a change in motor unit recruitment patterns. . Thus, for the use of a linear PO-VO2 relationship it is important to choose a fixed pedalling frequency in the range of 60–100 rpm and to maintain this pedalling frequency during the Sports Med 2010; 40 (4)
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exhausting exercise bout used to determine the MAOD. 2.1.3 Running versus Cycling
Billat et al.[75,76] published two studies on the . SC, from effect of exercise modality on the VO 2 . which it became clear that VO2max did not differ between running .and cycling exercise for triathletes but that the VO2 SC was significantly higher during cycling compared with running (20.9 – 2 vs 268.8 – 24 mL min-1). Based on these results it. can be concluded that for the use of a linear POVO2 relationship horizontal running exercise . is preferred above cycling exercise, because the VO2 SC is less. during running compared with cycling and the VO2 SC is less during horizontal running compared with uphill running.
2.2 The Duration of Submaximal Exercise Bouts
Medbø et al.[3] suggested that submaximal exercise bouts of 10-minutes .duration are required to construct . the linear PO-VO2 relationship from which the VO2 demand of supramaximal exercise bouts can be predicted. The required duration of the submaximal exercise bouts, as proposed by Medbø et al.,[3] has been questioned by others.[57,58] Bangsbo[57,58] stated that the duration of the submaximal exercise bouts influences the . measured VO2 values. Whipp and Wasserman[77] and Barstow and Mole´[78] found that at .low cycling intensities (W) (50, 75 and 100 W) VO2 reached a steady-state value within 3 minutes. . VO2 continued to increase after 3 minutes and did not reach a steady-state value within 6 minutes for higher exercise intensities (125, 150, and 175 W). . The question that rises is: does the SC of the VO2 kinetics substantially affect the computation of the MAOD? . Bangsbo[57] constructed two . linear PO-VO2 relationships, one based on VO2 measurements after 4–6 minutes of submaximal running and one . based on VO2 measurements after 8–10 minutes of . submaximal running. The difference in the VO . 2 demand, estimated from the two linear POVO2 relationships, resulted in a difference in MAOD of 10.5 mL O2 eq kg-1 (21%), with the longer submaximal bouts yielding a higher pre-
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. dicted VO2 demand and thus computed MAOD. Thus, the duration of the submaximal exercise . bouts can substantially influence the PO-VO2 relationship and the calculated MAOD.[57,58] Based on these results, Bangsbo[57,58] concluded that it is hard to validate the use of a linear relationship between exercise intensity (i.e. running . speed) and VO2 for the determination of anaerobic capacity. Buck and Mc Naughton[46] studied the effect of submaximal exercise bout durations of 2–4 minutes, 4–6 minutes, 6–8 minutes, and the proposed . 8–10 minutes[3] on the estimated VO2 demand, and thus the calculated MAOD. Increasing durations of the submaximal cycling bouts. resulted in increasing values of the estimated VO2 demand and, accordingly MAOD. The linear . PO-VO2 relationship based on submaximal cycling bout durations of 2–4 minutes resulted in a 25.8 – 8.7% lower MAOD than the linear relation. ship based on the VO2 during minutes 8–10. The results of Buck and Mc Naughton[46] supported the findings of Bangsbo[57] that the duration of the submaximal . exercise bouts indeed influences the measured VO2 and thus the calculated MAOD. However, Buck and Mc Naughton[46] drew a different conclusion from their results. They concluded that 10-minute bout durations are ne. cessary to establish a linear PO-VO2 relationship, because otherwise the calculated MAOD is too low. [32] also compared . the Maxwell . and Nimmo average VO2 during minutes 4–6 with the VO2 measured during minutes 8–10 of submaximal exercise bouts. Significant (p < 0.001). differences were found between the average VO2 of 4–6 minutes versus 8–10 minutes (53.8 – 3.7 vs 55.8 – 3.9 mL kg-1 min-1.), which resulted in a different treadmill speed-VO2 relationship, and thus different MAOD values. Although Maxwell and Nimmo[32] . found significant differences in the average VO2 between 4–6 minutes and 8–10 minutes, no significant differences were found between 8–9 minutes and 9–10 minutes or between 7–10 minutes and 8–10 minutes. Based on these results they recommended the use of 8–10 minutes as an appropriate interval to define . the linear treadmill speed-VO2 relationship.
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The MAOD Method
In summary, the conclusions drawn by Buck and Mc Naughton[46] and by Maxwell and Nimmo[32] are in conflict with the conclusion drawn by Bangsbo.[57] In the study of Buck and Mc Naughton[46] and in the study of Maxwell and Nimmo[32] they did not . consider the possibility that the steady-state VO2 measured during minutes 8–10 of the highest submaximal exercise bouts could be the asymptotic value . that was. attained after a late increase in VO2 (the VO2 [78,79] SC). Green and Dawson[79] found a large SC . in VO2 between minutes 3–6 for high submaximal POs and a smaller amplitude between minutes [78] even found that the 6–10. Barstow and . Mole´ amplitude of the VO2 SC increased with increas. ing PO. This SC causes the steady-state VO2, . if attained, to be larger .than the VO2 demand predicted from the PO-VO2 relationship based on the lower submaximal exercise intensities.[78] Thus, we can conclude that the relationship be. tween treadmill speed and/or PO and VO2 may be non-linear for exercise intensities above lactate threshold (LT) and that the degree of non-linearity increases with increasing exercise intensity. 2.3 The Effect of the Number of Submaximal Exercise Bouts on the Linear Power Output-Oxygen Uptake Relationship
Medbø et al.[3] used an iterative procedure to determine the minimum number of submaximal exercise bouts necessary to construct a linear . PO-VO2 relationship. The iterative procedure . consisted of three steps: (i) a linear PO-VO2 relationship based on the first two submaximal exercise bouts was established; (ii) the MAOD of the supramaximal constant intensity exercise bout was determined; and (iii) the next submaximal exercise intensity and the corresponding . VO2 was added to the linear regression line. These three steps were repeated until the MAOD started to converge. At least ten submaximal exercise bouts were necessary for the MAOD to converge to a stable value.[3] Despite this early proposed number of submaximal exercise bouts, different numbers of submaximal exercise. bouts have been used in the literature. PO-VO2 relationships have been based on ª 2010 Adis Data Information BV. All rights reserved.
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two,[16,32] three,[13,15,35,36,41,56,80] four,[5,19,23,26,28,29,66,67] five,[21,31,40,42,44,71] six,[7-9,11,12,24,25,30,37,38] or eight[69] submaximal exercise bouts. Buck and Mc Naughton[47] systematically studied the effect of different numbers of sub. maximal exercise bouts on the linear PO-VO2 relationship, established for bicycle exercise, .and thus the calculated MAOD. Steady-state VO2 values were determined during 10 · 10-minute submaximal .exercise bouts at intensities between 30 and 90%. VO2max and were used to construct a linear PO-VO2 relationship.[47] Data points were . sequentially removed from the linear PO-VO2 relationship based on 10 · 10-minute submaximal exercise bouts, starting with the lowest PO, the highest PO or the most central PO. This systematic removal of data points resulted in 17 . different linear PO-VO2 relationships, which were compared with the linear relationship based on 10 · 10-minute submaximal exercise bouts, as proposed by Medbø et al.[3] Sequentially removing the lowest or highest submaximal exercise bout from the linear relationship resulted in an increasingly larger difference in MAOD when compared with the MAOD . calculated from the 10 · 10-minute linear PO-VO2 relationship. Removing the most central PO from the linear relationship predictably resulted in smaller differences in MAOD compared with the other methods for removing data points.[47] It was concluded that the effect of changing the number of . submaximal exercise bouts on the linear POVO2 relationship and thus MAOD is dependent on the intensity of the bouts used to establish the linear relationship. Removing the highest submaximal exercise intensities had the most pronounced effect on MAOD and removing the most central exercise intensities had the least pronounced effect. That the effect of removing the lowest or the highest submaximal exercise bouts was not the same. implies that the relationship between PO and VO2 is not truly linear. The outcome of a reduced number of submaximal exercise bouts, with and without the use of a fixed value of the y-intercept, on MAOD was studied by Russell .et al.[14] The accuracy of the estimations of the VO2 demand and the MAOD was evaluated with 95% confidence intervals Sports Med 2010; 40 (4)
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(CIs), because they questioned the use of the Pearson correlation coefficient, as large differences in MAOD were found despite correlation coefficients ‡0.99 in the study of Buck and McNaughton.[47] Submaximal exercise bouts at intensities of 50, 60, 70, 80 and 90% of the average PO during a 2000 m rowing ergometer test, with a duration of 5 to 7 minutes, and a forced y-inter-1 min-1 were used to estabcept of 5.1 mL kg . lish a linear PO. VO2 relationship (5+Y). A second linear PO-VO2 relationship was established using the same submaximal exercise bouts but -1 without a forced y-intercept of 5.1 mL kg . -1 min (5-Y). The third and last linear PO-VO2 relationship required only two submaximal bouts, . at intensities between 75–82% and 85–92% of VO2peak, and a fixed value for the y-intercept (MED). The third regression line is named ‘procedure 3’ in the study of Medbø et al.,[3] and is suggested as a less time consuming alternative procedure from which the resultant MAOD differed by only 2 mL O2 eq kg-1 from the MAOD estimated with the 10-point regression line. However, the use of ‘procedure 3’ and thus the use of a fixed y-intercept of 5.1 mL kg-1 min-1, as suggested in the study of Medbø et al.,[3] has only been confirmed for treadmill exercise at an inclination of 10.5%.[3] The three different PO. VO2 relationships in the study of Russell et al.[14] did not result in statistically . significant different values for the estimated VO2 demand and, accordingly, the MAOD. However, the size of the 95% CI was significantly smaller when using the regression line based on the 5+Y method (12.88 – 2.93) than when using the 5-Y method (27.63 – 14.47). The length of the 95% CI was of similar magnitude for the methods 5+Y and MED (16.89 – 10.07). Thus, Russell et al.[14] concluded that the three different regression lines . resulted in similar VO2 demand and MAOD. values, but that the precision of estimating the VO2 demand and thus the MAOD improved when a y-intercept of 5.1 mL kg-1 min-1 was added to the regression line (5+Y method). Bickham et al.[81] evaluated the effect of different methodological improvements on the anaerobic capacity attained during treadmill . running at a grade of 1%. The first PO-VO2 re-
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lationship was based on 10 · 4-minute submaximal bouts and a forced y-intercept, the second . PO-VO2 relationship was based on 4 · 4-minute submaximal bouts and a forced y-intercept, and the third relationship was based on 2 submaximal bouts and a forced y-intercept, as suggested by Medbø et al.[3] Five of the ten submaximal exercise intensities used in the first linear regression line were below LT and five of them were above LT. When only four submaximal bouts were used to establish a linear regression line, the chosen submaximal .intensities averaged 69%, 76%, 86% and 93% VO2max. Average submaximal intensities . of 83% and 93% were used for the linear PO-VO2 relationship that was based on two submaximal bouts and a y-intercept. The three different regression lines did not result in significant differences in MAOD and in the accompanying 95% CIs, which means that reducing the number of submaximal bouts is acceptable when a fixed value . of the y-intercept is used and submaximal VO2 data is carefully collected. [82] doubted the validity of Green and Dawson . relationship for the prediction using the POVO 2 . of the VO2 demand of maximal and supramaximal cycling exercise. Therefore, they compared . the linear PO-VO2 relationship based on exercise intensities below LT with the linear relationship based on exercise intensities above LT. The use of submaximal exercise intensities above LT resulted in a 14% and 6% greater slope of the regression line for cyclists and untrained men, respectively, compared with the regression line based on exercise intensities below LT. This difference was due to the disproportionally higher . VO2 attained during exercise above LT. Based on these results, .Green and Dawson[82] concluded that the PO-VO2 relationship is non-linear for exercise intensities above LT, and that the degree of non-linearity increases with increasing fitness of the subjects. The results of Russell et al.[14] and Bickham et al.[81] are not consistent with the findings of Buck and McNaughton[47] and Green and Dawson,[82] that changing the number of submaximal exercise . bouts influences the estimated VO2 demand and thus the MAOD. This may be due to the use of only 4-minute duration exercise bouts for the Sports Med 2010; 40 (4)
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submaximal bouts in the study of Bickham et al.,[81] which is much shorter than the 10-minute duration of the submaximal exercise bouts in the study of Buck and McNaughton,[47] as well as in the original studies of Medbø.[3,50,51] However, Green and Dawson[82] also used 4-minute sub. maximal bouts to establish the linear PO-VO2 relationship, but the difference between the study of Bickham et al.[81] and the study of Green and and Dawson[82] comDawson[82] is that Green . pared the linear PO-VO2 relationship based on exercise intensities below LT with the linear relationship based on exercise intensities above LT and that Bickham et al.[81] only used exercise intensities above LT for the regression lines that were based on two or four submaximal bouts. The other difference is that Russell et al.[14] and Bickham et al.[81] used a fixed value for the y-intercept, which influences the characteristics of the regression line. The y-intercept was forced to be 5.1 mL kg-1 min-1[3] in the study of Russell et al.[14] and the mean y-intercept was 5.5 – 0.4 mL kg-1 min-1 in the study of Bickham et al.,[81] where the mean y-intercept was 6.1 – 2.3 mL kg-1 min-1 in the study of Buck and McNaughton.[47] In the study of Green and Dawson,[82] three different y-intercept values were found for data below, at or above LT, which were 8.8, 7.2 and 2.6 mL kg-1 min-1 in well trained cyclists, and 9.5, 8.7 and 8.0 mL kg-1 min-1 in untrained men. These data suggest that there is large variation in the y-intercept . when this value is not determined from resting VO2 data or set at a fixed value of 5.1 mL kg-1 min-1 as has been reported by Medbø et al.[3] The changes in y-intercept for the data below, at or above LT were also accompanied by changes in the slope of the regression line, and even small changes. resulted in large differences in the estimated VO2 demand due to extrapolation of . the regression line. The robustness of the PO-VO2 relationship increased with the use of a fixed. y-intercept, the power of predicting the VO2 demand improved by approximately 45% in the study of Russell et al.[14] when a fixed value of the y-intercept was forced in the linear regression line. Although, it has to be mentioned that even with the use of a fixed y-intercept there was still a
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95% CI of 12.88 – 2.93 mL kg-1. min-1 with a mean value for the estimated VO2 demand of 71.28 – 9.49 mL kg-1 min-1.[14] Based on the results of the described studies, we can conclude that there is an effect of changing the number . of submaximal exercise bouts on the linear PO-VO2 relationship and thus MAOD. This effect is .most likely due to the non-linearity of the PO-VO2 relationship for exercise intensities above LT. The effect of reducing the number of submaximal exercise bouts is reduced when a fixed value of the y-intercept has been used to establish the linear regression line. Thus, . the robustness of the VO2 demand prediction increases with the use of a fixed y-intercept, since values at the extremes of the regression line can . profoundly affect the extrapolated VO2 demand.
3. The Influence of Supramaximal Exercise Protocol on the Computed Maximal Accumulated Oxygen Deficit (MAOD) Values In the original protocol of Medbø et al.,[3] a supramaximal constant intensity exercise bout was used to determine the MAOD. In more recent publications, different exercise protocols have been used to determine the MAOD. In this section we will discuss the effect of different supramaximal exercise protocols on the computed MAOD values. Craig et al.[52] compared the MAOD .of a supramaximal constant intensity (115% VO2max) exercise test with the anaerobic capacity attained during 70, 120 and 300 seconds of supramaximal all-out cycling. Twelve of their eighteen subjects were high performance track endurance cyclists and the other six were sprint cyclists. Track endurance cyclists attained the highest MAOD values during the 300-second all-out protocol, and the sprint cyclists reached their highest MAOD values during the 70-second all-out protocol. This suggests that it is important to determine the MAOD during an exercise protocol that is specific for the athlete’s event. In a study of Gastin et al.,[37] untrained and endurance trained subjects performed supramaximal constant intensity and supramaximal Sports Med 2010; 40 (4)
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all-out exercise protocols for the determination of anaerobic capacity. They found no significant differences between the mean MAOD values of the different exercise protocols. However, closer analyses of the individual data showed that individual subjects may perform better on a specific protocol. Part two of their study showed that untrained subjects achieved a higher MAOD during the supramaximal all-out protocol and that endurance-trained subjects attained a higher MAOD during the supramaximal constant intensity protocol.[37] The effect of a continuous ramp exercise protocol on the computed value of the anaerobic capacity was investigated by Pouilly and Busso.[22] No significant difference was found between the MAOD attained during the supramaximal constant intensity protocol and during two ramp exercise protocols. However, the variability in MAOD between the two ramp exercise protocols was very high (54.5%).[22] Tabata et al.[11] even showed that a high intensity intermittent exercise protocol could be used to determine anaerobic capacity. The MAOD of intermittent exercise was calculated by . subtracting the difference between the VO2 de. mand at rest . and the VO2 during the rest periods from the VO2 deficit of each bout of exercise. The attainment of anaerobic capacity depends on the duration of the exercise intervals and the duration of the resting periods.[11] In summary, we can say that different supramaximal exercise protocols (i.e., constant, all-out, ramp and intermittent) can be used to determine the MAOD with broadly comparable results. Nevertheless, it is probably important to choose an exercise protocol that is specific for the athlete’s event. 4. The Validity of the MAOD Method In order to be able to use the MAOD method for determining anaerobic capacity we have to be sure that the method is valid. Is the MAOD method correct in measuring what it is designed to measure, that is, anaerobic capacity? Bangsbo et al.[67] evaluated the validity of the MAOD method by comparing the MAOD atª 2010 Adis Data Information BV. All rights reserved.
tained by untrained males during a supramaximal constant intensity one-legged knee-extension protocol, during which only the quadriceps muscles were active, with the amount of anaerobic energy release estimated from metabolic measurements. The changes in ATP, creatine phosphate, inosine monophosphate and lactate concentration ([La-]) were determined from muscle biopsy samples taken from .the active muscles. Further, lactate efflux and VO2 of the active muscles were estimated from measurements of blood flow and the femoral arterialvenous difference for lactate and oxygen. In order to avoid blood flow from the lower leg from influencing the measurements, an occlusion cuff was placed just below the knee. The anaerobic ATP production of the active muscle mass was calculated from these metabolic measurements and was. related to the leg and pulmonary (wholebody) VO2 deficit of the exhaustive work bout. Bangsbo et al.[67] reported that the MAOD was closely related to the amount of anaerobically attributable energy in a single muscle group during one-legged exercise. Based on these results it was concluded that the MAOD is a quantitative expression of anaerobic capacity when exercising with a single muscle group.[67] Green et al.[83] assessed the validity of the MAOD method in well trained male cyclists by inspecting the relationship between MAOD, the amount of anaerobic ATP produced, and measures of the anaerobic potential of muscles. Muscle biopsy samples were taken from the M. vastus lateralis and analysed for ATP, PCr, creatine, [La-] and ADP. Equation 1 was used to determine the amount of anaerobic ATP produced from differences in pre- and post-exercise concentrations of muscle metabolites:[83] anATPm ¼ 1:5D½La þ D½PCr þ ð2D½ATP D½ADPÞ (Eq: 1Þ No relationship was found between the MAOD of well trained male cyclists and the amount of anaerobically attributable ATP produced, in vitro b (muscle buffer value) or enzyme activities. However, there were significant relations between the amount of anaerobic ATP produced and in vitro b or enzyme activities, from which Sports Med 2010; 40 (4)
The MAOD Method
Green et al.[83] concluded that their measures of the muscle anaerobic ATP content are valid.[83] The results of the study of Green et al.[83] were not in agreement with the results of Medbø and Tabata[51] who found a close relationship between rates of anaerobic ATP turnover in the active muscles and the anaerobic energy production in the whole body, as measured by the MAOD method. Muscle metabolite concentrations (ATP, PCr and [La-]) were determined from muscle biopsy samples taken from the M. vastus lateralis after termination of the exhausting cycling bout, as in the study of Green et al.[83] Equation 2 was used to calculate the muscle anaerobic ATP production (anATPm) in the study of Medbø and Tabata:[51] anATPm ¼ 1:5D½La þ D½PCr þ ½ATP (Eq: 2Þ However, there were some differences between both studies that could have resulted in different outcomes. Green et al.[83] used an extended version of the equation used by Medbø and Tabata,[51] (equation 1). Neither equation accounts for the amount of lactate released to the blood, which accounts for somewhere between 5 and 38% of the total anaerobic ATP production according to Bangsbo.[58] Thus, the estimated amount of anaerobic ATP produced is underestimated by both Green et al.[83] and Medbø and Tabata.[51] Further, it is unknown if the measured muscle metabolite concentrations are representative of the concentration in the whole muscle or of the whole muscle group[84] and the main limitation of the validation attempts of Medbø and Tabata[51] and Green et al.[83] is that the active muscle mass is unknown during whole body exercise. The amount of energy release estimated from muscle biopsy data and the MAOD were significantly correlated when the working muscle mass was estimated to be 25% of the body mass.[51] Medbø et al.[3] also used this percentage in the discussion of the metabolic components of the MAOD during running exercise. However, the estimation of the active muscle mass from total body mass is not based on direct measures and does not take into account differences in fat percentage between men and women and between different ª 2010 Adis Data Information BV. All rights reserved.
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subjects. Additionally, the percentage of active muscle mass can also vary between subjects. The mean active muscle mass determined from magnetic resonance images in the study of Sloniger et al.[68] was 5.6 kg for horizontal running and 6.1 kg for uphill running with a mean body mass of 59.7 – 8.2 kg, which corresponds to 9.4 and 10.2% of the body mass of physically active female students. A lower percentage of active muscle mass, 10.2% instead of 25%, results in a much higher concentration of anaerobic ATP produced per unit of active muscle mass, 141 mmol ATP/kg instead of 63.5 mmol ATP/kg, in the same subjects.[68] These uncertainties about the active muscle mass result in significantly different MAOD values. Besides that, there is also some doubt about the ratio between high energy phosphate produced per oxygen molecule consumed (P/O), used for the conversion of oxygen deficit in mL O2 eq kg-1 to moles. P/O ratios between 2.5 and 3.0 mmol ATP (mmol O2)-1 have been used in the literature,[64,65] but the results of the study of Krustrup et al.[65] suggest that the additional recruitment of type II fibres during intense exercise may result in a lower P/O ratio, which would disprove the use of a fixed value for the P/O ratio. Further research is necessary to investigate the relationship between muscle fibre type and the P/O ratio. Another difference between studies was that Green et al.[83] determined the MAOD during a supramaximal constant intensity exercise bout of 173 – 24 seconds in duration, and Medbø and Tabata[51] established the MAOD during exhausting exercise of ~30 seconds, 1 minute and 2–3 minutes. Based on the difference in number of exhausting exercise bouts, Green et al.[83] reported that the large range of values in the study of Medbø and Tabata[51] caused a spuriously high correlation coefficient. Bangsbo[58] determined, from the data of Medbø and Tabata,[51] the relationship between the MAOD and muscle anaerobic ATP production for the three different exercise durations (~30 seconds, 1 minute and 2–3 minutes), which resulted in non-significant correlation coefficients. In addition, a 2-fold range in MAOD was found for the same muscle anaerobic ATP production.
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From the studies described, we must conclude that the most precise assessment of the anaerobic energy yield has been performed by Bangsbo et al.[67] during intense dynamic knee-extension exercise. From the results of this study it could be concluded that in a small muscle mass the MAOD method is a valid method to determine anaerobic capacity. 5. The Reliability of the MAOD Method Anaerobic capacity is an important determinant of high-intensity exercise. Thus, it would be helpful for coaches, athletes, and sports scientists if anaerobic capacity could be measured accurately. The MAOD method not only has to be valid but it also has to be reliable. Coaches need to be sure that test-retest differences are real differences in anaerobic capacity and are not due to random error. Doherty et al.[56] evaluated the reproducibility of the MAOD and the exercise time to exhaustion, their results are summarized in table I. Based on intraclass correlation coefficients and sample coefficients of variation they concluded that the MAOD method is a reliable measure. However, they also determined the 95% limits of agreement for the MAOD, which measures the absolute reliability. Doherty et al.[56] found that 95% of the agreement ratios had to be between 0.80 and 1.28, which means that a subject with a MAOD of 70.0 mL O2 eq kg-1 the first time, could have a MAOD between 70.0 · 0.80 = 56.0 mL O2 eq kg-1 and 70.0 · 1.28 = 89.6 mL O2 eq kg-1 during the second supramaximal exercise bout. So, based on the 95% limits of agreement they concluded that the MAOD method was not a reliable measure. In the study of Bickham et al.,[81] the effect of different methodological improvements on the . precision of the estimated VO2 demand and on the MAOD was investigated, . using 95% CIs (table I). The 95% CI of the VO2 demand, estimated from the regression line based on ten submaximal bouts and a fixed y-intercept (11-point) was 7.8 – 2.7 mL O2 kg-1 and was not significantly different between the 11-point, 5-point (four submaximal bouts and a y-intercept) and
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3-point (two submaximal bouts and a y-intercept) regression line. When additional error data from the metabolic system was included, the width of . the 95% CI of the estimated VO2 demand became even larger, namely 13.9 – 2.8 mL O2 kg-1 for the 11-point regression line, which shows that half of the possible error is related to the regression line. It also became clear that a relatively small 95% CI . (7.8 mL O2 kg-1) for the estimated VO2 demand could result in a large 95% CI for the MAOD (22.4 mL O2 eq kg-1), the increase in the 95% CI will be larger when the length of the supramaximal exercise bout is longer. The study of Bickham et al.[81] showed that the 95% CI of the MAOD is relatively large, which suggests that estimating anaerobic capacity with the MAOD method is not very accurate. Although the precision of a measurement influences the reliability, the 95% CI is not a complete measure of relia[81] did not bility. Unfortunately, Bickham et al. . determine the reproducibility of the VO2 demand and MAOD. Jacobs et al.[85] determined the MAOD during two trials in order to evaluate the reliability of the MAOD method. The test-retest correlation coefficient was 0.97, from which they concluded that the MAOD method is a reliable method. This conclusion is supported by the study of Weber and Schneider,[8] who investigated the reliability of the MAOD method at POs calculated . to require 110% and 120% VO2peak with the use of intraclass correlation coefficients. The intraclass correlation coefficients for the supramaximal exercise tests were 0.95 and 0.97, respectively. This study showed that the MAOD method resulted in highly repeatable values.[8] However, the conclusions of Jacobs et al.[85] and Weber and Schneider[8] did not support the findings of Doherty et al.[56] In a letter to the editor, Doherty and Smith[55] questioned the outcomes of the study of Weber and Schneider.[8] Both Weber and Schneider[8] and Jacobs et al.[85] used a traditional measure of reliability, namely intraclass correlation coefficients, which has some limitations.[55] One of the limitations is that the correlation coefficient is dependent on the range of values that are attained by the subjects. With a very heterogeneous
Sports Med 2010; 40 (4)
Reference (y)
Subjects (n)
Ergometer
Number of submaximal tests
Length of submaximal tests (min)
Protocol
MAOD (mL O2 eq kg-1)
CIa O2 demand (mL O2 eq kg-1)
Muscle biopsies
Bangsbo et al.[53] (1993)
Distance runners (14) Soccer players (150) Oarsmen (5) Cyclists (3)
Treadmill (5% gradient) Rowing ergometer Cycle ergometer
6–10
6
Constant PO protocol (120–140% of the PO at . VO2max)
Soccer players (running) 49.5 – 3.0 Runners 51.9 – 3.8 Oarsmen running) 47.3 – 6.3 Oarsmen (rowing) 64.1 – 4.4 Cyclists 56.5 – 8.4
Soccer players (running) [62.2, 68.8] Runners [75.6, 80.6] Oarsmen (running) [66.3, 71.7] Oarsmen (rowing) [63.1, 69.7] Cyclists [62.9, 84.9] (mL O2 eq min-1 kg-1)
Runners, Musculus gastrocnemius Cyclists, Musculus vastus lateralis (fibre type distribution, capillaries, buffer capacity and enzyme activities)
11-point regression 43.3 – 5.3 5-point regression 44.2 – 7.2 3-point regression 42.6 – 8.2
11-point regression 7.8 – 2.7 [95% CI 1.96 SEp] 5-point regression 7.7 – 3.5 3-point regression 6.7 – 2.9 With inclusion of error data: 13.9 – 2.8, 13.8 – 3.6, and 12.8 – 2.7 (mL O2 eq min-1 kg-1)
Bickham et al.[81] (2002)
Trained male distance runners (7)
Treadmill (1% gradient)
10 and a fixed y-intercept, 4 and a fixed y-intercept, and 2 and a fixed y-intercept
4
Constant intensity protocol . (110% VO2max)
The MAOD Method
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Table I. Quality criteria of studies challenging the maximal accumulated oxygen deficit (MAOD) method
Craig and Morgan[69] (1998)
Male distance runners (9)
Treadmill (1% and 10.5% gradient)
8
6
(1%, linear) 45.0 – 6.9 (10.5%, linear) 63.2 – 10.6 (1%, curvilinear) 59.3 – 10.1 (10.5%, curvilinear) 93.6 – 19.7
(1%, linear) [65.4, 80.2] (10.5%, linear) [76.8, 94.8] (1%, curvilinear) [69.8, 87.8] (10.5%, curvilinear) [83.8, 120.6] (mL O2 eq min-1 kg-1)
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Constant intensity protocol (2 trials: 1% and 10.5% gradient)
Reference (y)
Subjects (n)
Ergometer
Number of submaximal tests
Length of submaximal tests (min)
Protocol
MAOD (mL O2 eq kg-1)
CIa O2 demand (mL O2 eq kg-1)
Doherty et al.[56] (2000)
Physically active male students (15)
Treadmill (10.5% gradient)
3 and a fixed y-intercept
6
Constant intensity protocol (3 trials: 125% . VO2max)
Trial 1, 69.0 – 13.1 Trial 2, 71.4 – 12.5 Trial 3, 70.4 – 15.0
Agreement ratios MAOD 0.8, 1.28 (log-transformed)
Green and Dawson[82] (1995)
Male cyclists (10), untrained men (9)
Cycle ergometer
6 cyclists 5 untrained men
4
Green and Dawson[79] (1996)
Male cyclists (7)
Cycle ergometer
5–8
4–15; CON4, CON10, DISCON4, DISCON6, DISCON10, DISCON15
Green et al.[83] (1996)
Well trained male cyclists (10) [2 pursuitists, 1 sprinter, 7 endurance cyclists]
Cycle ergometer
¨ zyener O et al.[71] (2003)
Male subjects (10)
Cycle ergometer
5
4
10
Muscle biopsies
0.04, CON4 0.06, DISCON4 CI between 0.06 and 0.07 mL O2 eq min-1 W-1 for DISCON6, DISCON10, and DISCON15
Constant PO trial (432 – 41 W)
55.2 – 10.3
Three constant supra-lactate threshold work rates (moderate, heavy, very heavy and severe)
Moderate, 0.81 – 0.2 L Heavy, -1.70 – 1.6 L Very heavy, -1.43 – 2.3 L Severe, 1.69 – 0.4 L
[164.9, 286.9] (mL kg-1)
M. vastus lateralis 202.7 – 46.9 mmol ATP kg-1 dw r = -0.38 (p >0.05)
The reported confidence intervals (CIs) of the studies of Bangsbo et al.[53] (1993), Craig and Morgan[69] (1998) and Green et al.[83] (1996) are values calculated from the published standard deviations of the energy demands (mean – 1.96 SD). Bickham et al.[81] (2002), Doherty et al.[56] (2000) and Green and Dawson[79] (1996) did report the length of the 95% CIs.
ATP = adenosine triphosphate; CI = confidence.interval; CON = continuous; DISCON = discontinuous; dw = dry weight; O2 eq = oxygen equivalent; PO = power output; r = correlation coefficient; SEp = standard error of prediction; VO2max = maximum oxygen uptake.
Noordhof et al.
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a
8–12
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Table I. Contd
The MAOD Method
group of subjects (i.e. males and females, individuals of varying fitness levels) it is easy to find a strong correlation coefficient.[86] Atkinson and Nevill[86] suggested the use of the standard error of measurement when heteroscedasticity is absent and the use of the coefficient of variation (CV) and the limits of agreement method when heteroscedasticity is present. These measures all evaluate the absolute reliability and are not influenced by the range of measurements. In summary, most studies[8,85] used traditional measures of reliability, which are not sensitive enough for assessing the reliability.[86] Adequate measures of reliability are the CV, the limits of agreement method and the standard error of measurement.[86] Doherty et al.[56] used the limits of agreement method (i.e. 95% CI), which resulted in the conclusion that the MAOD method is not a reliable measure of anaerobic capacity. However, the study of Doherty et al.[56] is one of the first studies which assessed the reliability of the MAOD method with an adequate measure of reliability and further research is therefore necessary to study the reliability of the MAOD method. 6. Conclusions The purpose of this review was to evaluate the procedures and outcomes of studies that used the MAOD method to determine anaerobic capacity, as proposed by Medbø et al.[3] From the reviewed literature it can be concluded that the most widely used approach to determine anaerobic capacity (the MAOD method), although clearly reasonable from a conceptual standpoint, is probably not (as currently used) a fully defensible method to determine anaerobic capacity. Further research is necessary to determine how to approach the problem of measuring anaerobic capacity. Clearly the most profitable areas of immediate concern are standardization of the number, duration and intensity of submaximal . exercise bouts necessary to define the . PO-VO2 relationship, whether forcing the PO-VO2 relationship into a linear analysis mode is the correct approach to allowing computation of an extrapolated value . for VO2 demand, and the relative PO (e.g. duraª 2010 Adis Data Information BV. All rights reserved.
299
tion) and character (square wave, all-out, free range) of the supramaximal exercise bout. We suggest the use of the following methodology for the determination of anaerobic capacity with the MAOD method. Relatively short submaximal exercise bouts are .preferred for the construction of the linear PO-VO2 relationship, . as VO2 shows a secondary increase after 3 minutes of exercise at the highest submaximal exercise intensities, which will lead to non-linearity . of the PO. VO2 relationship. To construct a robust PO-VO2 relationship, about ten submaximal exercise tests at intensities evenly distributed be. tween 30 and 90% VO2max are necessary, as is the use of . a fixed y-intercept, determined from resting VO2 data. Furthermore, it is advisable to use horizontal running or cycling exercise at a fixed and relatively low pedalling frequency as this will . also minimize the VO2 SC. Linear regression . analysis can be used to determine the PO-VO2 relationship, as it has been shown that a curvilinear fit did not improve the relationship.[69] A carefully considered decision has to be made about the supramaximal exercise protocol used to determine the MAOD, it is probably best to use a protocol and exercise modality specific for the athlete’s chosen event. The last recommendation is to evaluate the reliability of the MAOD method with the use of measures of absolute reliability, when heteroscedasticity is present the CV or the limits of agreement method (ratios) should be used and when heteroscedasticity is absent the standard error of measurement or the limits of agreement method (absolute values) should be reported.[86] Thus far, the MAOD method is the most widely used and probably the best noninvasive method to determine anaerobic capacity. 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
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Correspondence: Dionne A. Noordhof, MSc, Van der Boechorststraat 7, 1081 BT Amsterdam, the Netherlands. E-mail:
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Sports Med 2010; 40 (4): 303-326 0112-1642/10/0004-0303/$49.95/0
REVIEW ARTICLE
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A Biomechanical Evaluation of Resistance Fundamental Concepts for Training and Sports Performance David M. Frost,1,2 John Cronin1,3 and Robert U. Newton1 1 School of Exercise, Biomedical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia 2 Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada 3 School of Sport and Recreation, Auckland University of Technology, Auckland, New Zealand
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Constant Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Concentric Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Kinematics and Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Stretch-Shortening Cycle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Kinematics and Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Ballistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Kinematics and Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Accommodating Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Hydraulic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Kinematics and Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Isokinetic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Kinematics and Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Variable Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Cam- or Lever-Based System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Kinematics and Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Band and Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Kinematics and Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Pneumatic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Kinematics and Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
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Newton’s second law of motion describes the acceleration of an object as being directly proportional to the magnitude of the net force, in the same direction as the net force and inversely proportional to its mass (a = F/m). With respect to linear motion, mass is also a numerical representation of an
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object’s inertia, or its resistance to change in its state of motion and directly proportional to the magnitude of an object’s momentum at any given velocity. To change an object’s momentum, thereby increasing or decreasing its velocity, a proportional impulse must be generated. All motion is governed by these relationships, independent of the exercise being performed or the movement type being used; however, the degree to which this governance affects the associated kinematics, kinetics and muscle activity is dependent on the resistance type. Researchers have suggested that to facilitate the greatest improvements to athletic performance, the resistance-training programme employed by an athlete must be adapted to meet the specific demands of their sport. Therefore, it is conceivable that one mechanical stimulus, or resistance type, may not be appropriate for all applications. Although an excellent means of increasing maximal strength and the rate of force development, freeweight or mass-based training may not be the most conducive means to elicit velocity-specific adaptations. Attempts have been made to combat the inherent flaws of free weights, via accommodating and variable resistancetraining devices; however, such approaches are not without problems that are specific to their mechanics. More recently, pneumatic-resistance devices (variable) have been introduced as a mechanical stimulus whereby the body mass of the athlete represents the only inertia that must be overcome to initiate movement, thus potentially affording the opportunity to develop velocity-specific power. However, there is no empirical evidence to support such a contention. Future research should place further emphasis on understanding the mechanical advantages/disadvantages inherent to the resistance types being used during training, so as to elicit the greatest improvements in athletic performance.
Dynamic strength training can be classified into the following three different categories: (i) constant, isoinertial or free-weight resistance; (ii) accommodating resistance; and (iii) variable resistance, based on the nature in which the resistance is imposed/applied on the contracting musculature,[1] thereby resulting in significant differences in the associated kinetics, kinematics and muscle activity.[2-5] However, to date, the majority of research into resistance training is conducted with the aim of examining specific performance parameters, while failing to address or even understand the biomechanical determinants underlying the resistance or muscular effort being used. An increased understanding of the biomechanical properties that govern each resistance type, and/or how they can be manipulated, will provide the researcher, clinician and practitioner with a much greater appreciation of the benefits and limitations associated with each resistance-training mode. With this in mind, we will ª 2010 Adis Data Information BV. All rights reserved.
briefly introduce some mechanical formulae that are important for understanding the mechanics of loading muscle. Newton’s second law of motion (law of acceleration) dictates that the acceleration of an object, as produced by a force, is directly proportional to the magnitude of the net force, in the same direction as the net force and inversely proportional to its mass. For example, an object with a smaller mass requires less force to elicit the same acceleration (see equation 1): Fnet ¼ m a
(Eq: 1Þ
where Fnet is the sum of all external forces acting on the object (N), m is the mass of the object (kg) and a is the acceleration of the object (m/sec2). For example, see equation 2: X Fnet ¼ Fext ¼ Fapp þ Fload (Eq: 2Þ where Fext is equal to Fnet (N), Fapp is the force being applied by the athlete (N) and Fload is Sports Med 2010; 40 (4)
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resistive force of the load (N). The acceleration of an object can also be defined as a first-order derivative of the velocity time curve, or the change in velocity over a specific period of time (see equation 3): a ¼ ðv2 v1 Þ=ðt2 t1 Þ (Eq: 3Þ where v2 is the velocity (m/sec) of the object at time, t2 (seconds) and v1 is the velocity (m/sec) of the object at time, t1 (seconds). Therefore, equation 1 can be expressed as follows (see equation 4): Fnet ¼ m ðv2 v1 Þ=ðt2 t1 Þ (Eq: 4Þ By rearranging and expanding equation 4 we get equation 5: Fnet ðt2 t1 Þ ¼ ðm v2 Þ ðm v1 Þ (Eq: 5Þ Newton’s first law of motion (law of inertia) describes how an object at rest will remain at rest and an object in motion will remain in motion with the same magnitude and direction of velocity unless acted on by an unbalanced force. An object’s resistance to a change in its state of motion, defined as its inertia, is directly proportional to its mass (mass of an object is a numerical value of its inertia). Once an object acquires a velocity, it can also be characterized as having momentum, which is the product of its mass and velocity (see equation 6): p¼mv (Eq: 6Þ where p is the momentum of the object (kg m/sec). By comparing equation 6 with equation 5, it is evident that the change in the momentum of an object is directly proportional to the magnitude and duration of the net force on the object (impulse); however, for resistance-training applications in which the net force does not remain constant (as a result of changing mechanical and physiological advantage) during the muscular action, equation 5 must be integrated with respect to time to provide a representation of the change in momentum that occurs over the entire muscular effort (see equation 7): Z t2 Z t2 Fnet dt ¼ m dv (Eq: 7Þ
t1
t1
R t2 where t1 Fnet dt is the integral of the forcetime curve (n sec) between time t2 and time t1,
ª 2010 Adis Data Information BV. All rights reserved.
R t2 and t1 dv is the integral of the velocity (m/sec) between time t2 and time t1. Newton’s third law (law of action/reaction) affirms that for every action, there is an equal and opposite reaction, implying that each time an athlete applies a force against an object, an equivalent force in the opposite direction is exerted on the athlete by the object (see equation 8): (Eq: 8Þ Fapp ¼ Freact where Fapp is the sum of the net force and the resistive force of the load and Freact is the force exerted by the object on the athlete. The product of this applied force and the object’s resultant movement velocity (v) is defined as the power output (P) of the system, as measured in watts (W) for a specific instant in time (see equation 9). (Eq: 9Þ P ¼ Fapp v In the subsequent sections of this review, these kinetic and kinematic formulae will be discussed with respect to each resistance type. Understanding the biomechanical differences between these resistance types will allow researchers and strength coaches alike to utilize the resistancetraining mode that is most conducive to their specific exercise or training goal. Although not the focus of this review, it is important to note that the calculation and reporting of some variables (e.g. power) will depend on the inclusion or exclusion of bodyweight. An athlete’s performance is often dictated by their ability to generate sufficient force to move a load at a given velocity, and for the purpose of understanding various resistance types, it must be realized that bodyweight is a load (bodyweight = m · g) and must be included in all kinematic and kinetic calculations. Because there remains to be consistency in the literature in regards to the reporting of such variables for whole-body movements (e.g. squats), the discussion in this review will be focused on the bench-press movement (limited bodyweight involved), unless otherwise stated. 1. Constant Resistance Dynamic training with constant resistance, or isoinertial training,[6,7] is characterized by exercises in which the entire resistive force is Sports Med 2010; 40 (4)
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dependent on the mass of the object being lifted (force = mass · [gravity + acceleration]), and is currently the most widely used method to enhance strength and power capacity for sport. The use of constant resistance, or free weight, allows the strength coach/practitioner to prescribe sport-specific, multiple-joint efforts that involve both concentric and eccentric muscular actions, at various velocities. However, to increase the movement velocity, and thus the acceleration, a proportional increase in the net force is required when using the same load (equation 1). As a result, an athlete’s ability to move quickly might be limited by his/her rate of force development, potentially resulting in the development of force-specific and not velocity-specific power. Furthermore, when using constant resistance, athletes may not be able to elicit maximal activation of the primary musculature throughout the entire range of motion[8] as a result of mechanical advantages or disadvantages at specific joint angles. This can result in significant reductions in movement velocity and power output during the early and/or later stages of the concentric phase.[9] Researchers have investigated the kinematics and kinetics of various non-ballistic (concentric-only, stretch-shortening cycle [SSC]) and ballistic constant resistance efforts with the intent of identifying possible mechanical benefits that may lead to improved transference to sport performance. A concentric-only, nonballistic effort differs from SSC and ballistic movements in that the concentric phase begins without any pre-load from the eccentric phase and it ends without projecting the load into free space, respectively. Given that each of these efforts has different mechanical characteristics, each should be investigated separately to determine their role as a stimulus for strength and power adaptation. 1.1 Concentric Only
The initiation of the concentric (ascending) phase during constant resistance movements requires that the applied force be greater than the weight of the load, thus resulting in a net force ª 2010 Adis Data Information BV. All rights reserved.
and a proportional acceleration in the positive direction (equation 2). 1.1.1 Kinematics and Kinetics Velocity
Mean concentric velocity is a function of the displacement and duration of the concentric action. Therefore, the mean movement velocity should increase proportionally with a decrease in movement duration, assuming that the displacement (dictated in the bench press by limb length) is unchanged. Cronin et al.[2] have shown that an increase in the mean velocity is associated with a decrease in both the duration of the concentric phase and the magnitude of the load lifted, although such changes are not proportional (table I). Using the mean velocities and the durations of the concentric phase reported by Cronin et al.[2] for loads of 30–80% of the one repetition maximum (1RM), the calculated displacement was found to vary by as much as 0.169 metres. Because of the explosive nature of the efforts performed, such a difference in the displacement is likely due to greater protraction of the scapula with lighter loads. Similar to the mean concentric velocity, greater peak concentric velocities are produced with lighter loads. However, Cronin et al.[2] reported that a 100% increase in mean velocity necessitated a 37.5% reduction in the load, whereas a 50% reduction in load was required to attain an equivalent increase in the peak velocity (tables I and II). Conversely, the time to peak velocity, a key variable of interest among researchers,[9,12,13] was greater for heavier loads, even when expressed as a percentage of the total duration of the concentric phase (table I). Its position marks the point at which acceleration is equal to zero (equation 3), and the applied force is equivalent to the load (equation 2) and, thus, the separation of the acceleration and deceleration phases of the movement. These results highlight one of the major disadvantages of using constant resistance for high-velocity training: lighter loads are required to increase the movement velocity (1.49 vs 0.68 m/sec for 30% and 80% 1RM loads, respectively) but this is associated with a decrease in the duration of Sports Med 2010; 40 (4)
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Table I. A comparison of power and velocity across loads[2,10] Load
Power
% 1RM
MP
PP
% PP/MP
TPP
% TTP/DC
MV
Velocity PV
TPV
% TPV/DC
80
232.2
515.1
221.8
0.784
63.6
NA
NA
NA
NA
80
207.8
440.3
211.9
0.717
61.2
NA
NA
NA
NA
80
222.0
478.2
215.4
NA
NA
0.33
0.68
0.928
82.9
70
266.8
542.4
203.3
NA
NA
0.36
0.86
0.774
80.3
60
280.8
549.3
196.0
NA
NA
0.55
1.01
0.620
75.8
60
314.6
572.7
182.0
0.595
71.3
NA
NA
NA
NA
60
237.6
501.8
211.2
0.564
67.3
NA
NA
NA
NA
50
271.1
552.1
203.7
NA
NA
0.64
1.16
0.528
69.4
40
290.8
613.6
211.0
0.452
65.7
NA
NA
NA
NA
40
201.1
445.1
221.3
0.434
63.0
NA
NA
NA
NA
40
250.0
532.0
212.8
NA
NA
0.73
1.35
0.459
67.2
30
211.1
467.4
221.4
NA
NA
0.82
1.49
0.401
63.8
1RM = one repetition maximum; DC = duration of concentric phase (sec); MP = mean power (W); MV = mean velocity (m/sec); NA = not reported/could not calculate; PP = peak power (W); PV = peak velocity; TPP = time to PP (sec); TPV = time to PV (sec).
the acceleration phase during the concentric effort; 63.8% and 82.9% of the duration of the concentric phase for 30% and 80% 1RM loads, respectively.[2] Acceleration and Force
Distinct start- and endpoints of the concentric phase, at which the movement velocity is zero, result in a mean acceleration of zero for all nonballistic movements (equation 3). Furthermore, since force is directly proportional to the acceleration of the load, the mean net force will be equal to zero and the mean applied force will be equivalent to the weight of the load (equation 1), regardless of mass. This is illustrated in table II; mean forces are equal to the load lifted despite differences in strength or percentage of the 1RM being used. The magnitude of the peak force produced is directly proportional to the peak acceleration and the mass (inertia) of the object (equation 1); thus, to achieve greater concentric accelerations, peak force must be equal to a higher percentage of the object weight (table II). However, the peak force required with a specific load to elicit an acceleration or movement velocity similar to athletic performance may not be achievable. Therefore, a lighter load must be lifted to produce the desired acceleration (table II), possibly reduª 2010 Adis Data Information BV. All rights reserved.
cing the training specificity; with constant resistance, concentric-only efforts, extremely light loads may not provide an appropriate training stimulus even if the acceleration is high at the onset of the movement; peak force (acceleration) has been shown to occur at the onset of the concentric action,[9,11] which in turn leads to more variation in the force curve, greater changes in momentum[2,12,14] and, thus, may be one of the reasons for an extended deceleration phase. The displacement-limited nature of these efforts requires that the movement velocity be zero at the end of the concentric phase; thus, larger accelerations will need to be matched by larger decelerations or an extended deceleration phase. Therefore, concentric-only, non-ballistic movements may be an appropriate stimulus to improve an athlete’s rate of force development; however, caution must be exercised since this will occur in concert with a reduction in velocity towards the end range of movement.[13] Power
Power output, as defined in equation 9, is a function of the applied force and movement velocity of an object. Researchers and practitioners alike have regarded improvements in power output as being important in improving athletic performance.[15-18] Since mean acceleration Sports Med 2010; 40 (4)
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Table II. A comparison of force and acceleration across loads[2,3,9,11] Load % 1RM
W
M
Force MF
PF
% PF/W
% PPF/TCD
Acceleration MA PA
100
1668
170.0
NA
NA
NA
NA
NA
2.08
2.27
100
2349
239.5
NA
2621
111.6
2.46
NA
1.91
1.14
100
1814
184.9
NA
2004
110.5
2.60
NA
2.21
1.00
100
995
101.4
NA
1349
135.6
2.49
NA
3.54
1.76
90
1370
139.7
NA
2003
146.2
NA
NA
NA
1.30
81
1381
140.8
NA
NA
112.1
4.38
NA
NA
NA
80
677
69.0
678
826
122.0
NA
0.00
1.93
1.12
75
1138
116.0
NA
2097
184.3
NA
NA
NA
0.86
70
593
60.4
589
766
129.3
NA
0.06
2.43
0.96
60
508
51.8
509
661
130.0
NA
0.03
2.96
0.82
50
423
43.2
429
584
138.0
NA
0.14
3.78
0.76
40
339
34.5
340
506
149.4
NA
-0.03
4.63
0.68
30
254
25.9
258
414
163.0
NA
0.15
5.87
0.63
DC
1RM = one repetition maximum; DC = duration of concentric phase (sec); M = mass of load (kg); MA = mean acceleration (m/sec2); MF = mean force (N); NA = not reported/could not calculate; PA = peak acceleration (m/sec2); PF = peak force (N); PPF = position of peak force (m); TCD = total concentric displacement (m); W = weight of free weight load (N).
ª 2010 Adis Data Information BV. All rights reserved.
These values were subsequently used to determine the respective contributions of force and velocity for each power output, expressed as a percentage of the maximum (table III). Maximum mean power was produced with approximately one-half (45.4%) of the maximum force and one-quarter (25.0%) of the maximum velocity for a non-ballistic, concentric-only bench press (table III). Such an analysis might provide greater insight into the components that contribute to power output, which may in turn be used to monitor and programme for power development in a more systematic and sportspecific fashion (e.g. monitoring the effects of (a)
(b) b'
b' Force
a'
Force
and mean net force for non-ballistic movements are both zero, the mean concentric power becomes a product of the magnitude of the resistance (force) and the mean movement velocity over the concentric phase. However, identifying the degree to which each variable (force and velocity) contributes may prove to be of greater theoretical and practical significance. Most researchers continue to report an optimal load that disregards separate force and velocity contributions,[2,10,12,15-30] which could limit the transference to athletic performance. Although it has been stated that maximum power is developed at one-third of the maximum speed and one-quarter of the maximum force,[24,31] figure 1 depicts the potential effect of increasing either the maximal strength or peak velocity on the power output of muscle; both require an alteration in the force capability, but are the product of different training stimuli. Cronin et al.[2] reported minimum and maximum mean power outputs for the concentric phase of the bench press of 211 W (30% 1RM) and 281 W (60% 1RM), respectively (table II). Extrapolating their force and velocity data from tables I and II (figure 2) using a second-order polynomial, maximum force and velocity were estimated at 1120 N and 2.20 m/sec, respectively.
a'
b a
a Velocity
b
Velocity
Fig. 1. The effect of increasing: (a) maximal strength; and (b) contraction velocity of muscle on power output (dashed line). a = before; b = after.
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1400
Mean Peak Peak trendline Mean trendline
1200 Force (N)
1000 800 600 400 200 0 −0.25 −200
0.25
0.75
1.25
1.75
2.25
Velocity (m/sec)
Fig. 2. Extrapolated force and velocity curves (reproduced from Cronin et al.,[2] with permission from Lippincott Williams & Wilkins).
different training methods on either the force or velocity capabilities of muscle, with the aim of determining whether an athlete is force or velocity deficient). In this example, maximum mean power during constant resistance exercise appeared to have a greater dependence on the applied force than the movement velocity (table III). In such cases, it may be advantageous to attempt to increase the movement velocity contribution to power output and, thus, the transference to sport performance. However, as previously outlined, loading with constant resistance necessitates a proportional increase in force to attain this velocity, which in turn limits the possibility of achieving such training goals. Peak power must be identified via an analysis of the force-velocity curve for each of the loads, which makes a similar extrapolation of the data impossible. Table I shows similar peak power outputs for all loads between 30% and 80% 1RM, despite reports stating that a load of 40–60% 1RM is optimal for producing maximum power.[2,10]
Furthermore, as reported by Cronin et al.,[10] time to peak power is reduced with a subsequent decrease in the load, although there is little difference when it is expressed as a percentage of the total concentric duration (table I). However, as can be seen in table I, the point of the concentric phase at which peak velocity occurs is reduced with a decrease in load. Therefore, the contributions from force and velocity are changing despite similar power outputs. If velocity-specific power is of greater importance to improving sport specificity, then future research should separate the components of power output to better understand the adaptations to training with various loads. Concentric Phases
Lander et al.[3] has proposed that concentric action of a constant resistance, non-ballistic movement actually involves two acceleration and deceleration phases when using loads between 75% and 100% of the 1RM. The first and true acceleration phase is defined as the initial portion of the ascending phase that describes the period of maximum acceleration, whereas the late stages described by the period of maximum deceleration is defined as the second and true deceleration phase. The middle portion of the ascent is composed of the first deceleration phase, or the sticking region, and is defined as the portion of the effort where the applied force falls below the weight of the load. The second acceleration phase, or the maximum strength region, is defined as the period where the applied force becomes greater than the load for the second time (figure 3).
Table III. Force and velocity contributions to concentric-only and stretch-shortening cycle (SSC) mean power [reproduced from Cronin et al.,[2] with permission from Lippincott Williams & Wilkins] Load
Concentric only
% 1RM
MP (W)
% MF/Fmax
% MV/Vmax
MP (W)
SSC % MF/Fmax
% MV/Vmax
80
222.0
60.5
15.0
261.3
60.6
17.7
70
266.8
52.6
16.4
316.3
53.5
24.1
60
281.8
45.4
25.0
315.4
45.8
28.2
50
271.1
38.3
29.1
312.8
38.1
34.1
40
250.0
30.3
33.2
283.1
30.4
37.7
30
211.1
23.0
37.3
237.0
23.4
41.4
1RM = one repetition maximum; Fmax = maximum force (N); MF = mean force (N); MP = mean power (W); MV = mean velocity (m/sec); Vmax = maximum velocity (m/sec).
ª 2010 Adis Data Information BV. All rights reserved.
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1500
AP
SR
MSR
DP
1450 100% 1RM
1400 Force (N)
Load weight 1350 1300 1250
81% 1RM
1200 AP
1150
DP
1100 0
0.5
1.0 Time (sec)
1.5
2.0
Fig. 3. Force-time curve for 81% and 100% one repetition maximum (1RM) loads (reproduced from Elliott et al.,[9] from Lippincott, Williams & Wilkins). AP =acceleration phase; DP = deceleration phase; MSR = maximum strength region; SR = sticking region.
Using athletes with similar bench-press experience, Elliott et al.[9] found that a 100% 1RM load produced the same four phases as reported by Lander et al.;[3] however, an 81% load was described as having one acceleration and deceleration phase (figure 3). The researchers suggested that the differences between the two studies could have resulted from the sequencing of the lifts: the heavier load was lifted first in the study by Lander et al.,[3] resulting in muscular fatigue and, thus, a reduced ability to accelerate the load through the sticking region. Although Elliott et al.[9] reported that only 48.3% of the concentric phase was spent accelerating with a load of 81% of the 1RM, combining the duration of the acceleration phase and maximum strength region and the sticking region and deceleration phase for the 100% 1RM condition resulted in similar acceleration and deceleration durations (47.9% and 52.1%, respectively). Expressed as the point of the concentric phase at which peak velocity occurred, Cronin et al.[2] reported acceleration phases lasting between 63.8% and 82.9% of the duration of the concentric effort for loads of 30–80% 1RM, respectively (table II). However, force and velocity profiles were not described, making it difficult to determine whether the curves were similar to the 81% or 100% 1RM load as shown in figure 3. Changing the load lifted or the way a repetition is performed may alter the point at which peak velocity occurs and, thus, ª 2010 Adis Data Information BV. All rights reserved.
the duration of the acceleration phase; however, in the absence of the force and velocity data for the entire concentric movement, conclusions may be misrepresentative. Regardless of the attempts made to increase the length of the acceleration phase during a nonballistic movement, mean force will always equal the weight of the load. This implies that if a higher peak force is achieved early in the concentric phase, a subsequent decrease in force will be seen during the later stages of the movement or, conversely, if the time to peak velocity is delayed and the subsequent acceleration phase is longer, there will be a greater decline in force and a drastic deceleration at the conclusion of the concentric phase. This highlights another disadvantage of using non-ballistic, constant resistance movements; the peak forces required to elicit high-movement velocities will cause greater variation in the force-displacement curves, via accentuated accelerations and decelerations, which will lead to changes in the muscle activity and might increase the risk of injury.[13] 1.1.2 Muscle Activity
Elliott et al.[9] has reported large sustained increases of the agonist musculature during the acceleration phase of a concentric effort. However, once the load begins to decelerate, muscle activity subsides and subsequent reductions in agonist activity are seen. Using 60% and 80% Sports Med 2010; 40 (4)
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1RM loads, McCaw and Friday[5] found that peak triceps brachii activity occurred at 70% and 60% of the total concentric duration, peak anterior deltoid activity was produced at approximately 60% and 75%, although this dropped dramatically after an additional 10% duration, and peak pectoralis major activity occurred at 17% and 10%, although this was halved after completing only 50% of the movement. An inability to maintain a greater percentage of the maximum activity throughout the entire range of motion may inhibit an athlete’s ability to maximize any number of kinetic and kinematic variables (e.g. power, force and velocity); however, such a contention requires further investigation. Researchers have reported antagonist activity;[5,9,32,33] however, additional research is required to investigate the electromyographic profiles, particularly during high-velocity movements. A stronger antagonist will allow for a shorter deceleration phase, thus enabling the agonists to develop higher movement velocities,[34] although, as stated above, this may increase the risk of injury.[13] 1.2 Stretch-Shortening Cycle
Muscle actions in which the concentric action is immediately preceded by an eccentric action (pre-stretch) are referred to as SSC movements.[35] Use of the SSC alters the profiles of several kinetic, kinematic and electromyographic variables because of the inertial properties of mass and the physiological characteristics of the muscle. Researchers have shown that a prestretch can increase mean velocity and power output by as much as 16% and 6%, respectively,[2,21,36-38] although the mechanisms responsible for the augmentation to the concentric action remain unclear. Potential mechanisms include the utilization of elastic strain energy stored in the series elastic component, reflexively induced neural input and a higher active muscle state prior to the onset of the concentric muscle action; however, a detailed explanation of each is beyond the scope of this review and the authors refer those interested to several papers on this topic.[10,12,35,36,38-43] ª 2010 Adis Data Information BV. All rights reserved.
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1.2.1 Kinematics and Kinetics Velocity
Similar to concentric-only non-ballistic movements, the mean velocity of non-ballistic SSC movements is equal to the change in displacement divided by the duration of the displacement. However, it has been consistently shown that the use of a pre-stretch can augment the velocity curve by decreasing the duration of the concentric phase.[2,36-38] Cronin et al.[2] reported that mean velocity could be increased by as much as 47% with a 70% 1RM load and by an average of 20% across loads of 30–80% 1RM (table IV). Similarly, using ballistic efforts, Newton et al.[12] presented an average increase of 30%, with the largest increase being produced with the greatest load tested (90% 1RM). Conversely, the use of an SSC has been unable to elicit any change in the peak velocity or the time to peak velocity, expressed as a percentage of the total concentric duration, at any load tested.[2,12] As can be observed in equation 4, the change in movement velocity over a specific duration is proportional to the net force being applied and inversely proportional to the mass of the load being lifted. Therefore, an increase in mean velocity requires that a greater force be generated over the same time or that the same force be applied over a longer time. However, changing the peak velocity necessitates the production of greater accelerations over the same period of time, or the same acceleration over a longer period of time. If it is not possible to extend the duration, as is the case during movements with a fixed range of motion (e.g. limb length), larger accelerations and, thus, larger forces are needed. This may be difficult to accomplish with constant resistance. Acceleration and Force
For non-ballistic SSC movements, mean acceleration and, thus, mean force are zero since there is no change in the velocity between the start and end of the muscular effort. Conversely, peak acceleration and peak force have been shown to increase by as much as 69.5% and Sports Med 2010; 40 (4)
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Table IV. A comparison of the velocity and power between a concentric-only and stretch-shortening cycle (SSC) bench press [reproduced from Cronin et al.,[2] with permission from Lippincott Williams & Wilkins] Load
Concentric only
% 1RM
MV
PV
% TPV/DC
MP
PP
MV
SSC PV
% TPV/DC
MP
PP
80
0.33
0.68
82.9
222.0
478.2
0.39
0.65
75.4
261.3
468.9
70
0.36
0.86
80.3
266.8
542.4
0.53
0.83
76.2
316.3
542.3
60
0.55
1.01
75.8
280.8
549.3
0.62
0.99
73.8
315.4
550.6
50
0.64
1.16
69.4
271.1
552.1
0.75
1.21
68.7
312.8
557.9
40
0.73
1.35
67.2
250.0
532.0
0.83
1.37
66.2
283.1
536.3
30
0.82
1.49
63.8
211.1
467.4
0.91
1.52
56.7
237.0
463.2
1RM = one repetition maximum; DC = duration of concentric phase (sec); MP = mean power (W); MV = mean velocity (m/sec); PP = peak power (W); PV = peak velocity (m/sec); TPV = time to PV (sec).
19.0% at loads of 70% and 60% 1RM, respectively (table V). An SSC movement requires sufficient force to reduce the eccentric velocity of the load to zero prior to beginning the concentric action (figure 4). As can be observed in figure 4, the change in momentum is directly proportional to the change in velocity and the mass of the load. This change in momentum is also proportional to the force that is causing such a change, and the duration over which the change takes place (see equation 5). Compared with a non-ballistic, concentriconly movement that requires no reduction in eccentric velocity, the sum of the external forces (equation 2) may be greater, thus changing the external demands placed on the athlete. Consequently, higher accelerations may be produced during the initial portion of the concentric action, although this is not always the case. Without a subsequent increase in the peak velocity or duration of the acceleration phase, it is difficult to speculate as to whether the SSC action alone is advantageous to athletic perfor-
mance when the movement duration exceeds ~200 ms.[10,12] Perhaps, the benefits of such a movement stem from an ability to utilize the increased force quickly, via muscle tendon interactions in terms of elastic and contractile tension, thus allowing for a greater peak velocity to be achieved. In any case, further research is required to investigate the effects of an increased peak force and, thus, variation in the force-displacement curve, on the kinetic, kinematic and electromyographic profiles during the later stages of the movement. Power
Non-ballistic, SSC movements are characterized by mean forces that are identical to the magnitude of the resistance being lifted (equation 1) and mean velocities that are significantly greater than their concentric-only equivalents.[2] Since power is the product of force and velocity, mean power should be increased to the same degree as the mean velocity. Cronin et al.[2] reported an average increase in mean power of 14.8%
Table V. A comparison of the acceleration and force between a concentric-only and stretch-shortening cycle (SSC) bench press[2,44] Load
Concentric only
% 1RM
MA
SSC PA
MF
PF
MA
PA
MF
PF
80
0.00
1.93
677.8
826.3
0.02
3.19
679.0
906.5
70
-0.06
2.43
588.8
766.4
0.11
4.12
599.1
876.6
60
0.03
2.96
509.4
660.9
0.11
4.97
513.4
786.4
50
0.14
3.78
429.2
583.8
0.09
6.33
427.2
692.7
40
0.02
4.63
339.5
505.9
0.05
7.11
340.5
596.1
30
0.15
5.87
257.9
414.1
0.31
8.54
262.1
491.8
1RM = one repetition maximum; MA = mean acceleration (m/sec2); MF = mean force (N); PA = peak acceleration (m/sec2); PF = peak force (N).
ª 2010 Adis Data Information BV. All rights reserved.
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t1 = t s v1 = v m/sec
313
CO Fs = ma
SSC Fs = ma
t1 = t s v1 = v m/sec m
a
a
vtrans = −vecc t2 = ttrans
m
m
t0 = 0 s v0 = 0 m/sec a0 = 0 m/sec2
t0 = 0 s v0 = 0 m/sec a0 = a m/sec2 Fg = mg Ftotal = mg + ma
Fg = mg Change in momentum = m(v0 − vtrans) = Fsttrans a0 = Fs/m m/sec2 Ftotal = mg + ma0
Fig. 4. Force contributions during concentric-only (CO) and stretch-shortening cycle (SSC) movements. The total force (Ftotal) at time zero (t0), which represents the transition between the eccentric and concentric phase, is the sum of the system weight (Fg = mg) and the force due to acceleration (Fs = ma). The total force during SSC movements will be greater than the CO equivalent if the acceleration (a0) at this timepoint is greater than zero. The velocity at a specific instant in time (ttrans), prior to the completion of the eccentric phase, is represented by vtrans.
(velocity was 20%) across loads of 30–80% 1RM, compared with a concentric-only movement. The increase in mean power production for a SSC movement is a function of the greater relative contribution from velocity (table III). However, upon further examination of the power profiles for SSC actions, significant increases are limited to the initial 200 ms of a movement,[10,12,42,43] and any benefit of the pre-stretch has been reported lost if the concentric duration exceeds 0.37 seconds.[10,41,42] Cronin et al.[2,10] have provided further support for these claims by finding no difference in the peak power or time to peak power, expressed as a percentage of the concentric duration, between SSC and concentric-only conditions. However, in the absence of any acute or long-term training studies investigating the force and velocity contributions to power output, any conclusions pertaining to the effect of an SSC action on power development, or sport-specific applications may be misleading and/ or misrepresentative. 1.2.2 Muscle Activity
Although the electromyographic (EMG) profiles of the agonist musculature have been shown to parallel the force profiles for SSC actions,[13] there remains to be sufficient evidence to make ª 2010 Adis Data Information BV. All rights reserved.
definitive claims regarding this relationship. If the mechanism responsible for the augmentation to the concentric action stems from an ability to utilize stored elastic energy, then there should not be any change in the muscle activity at the beginning of the concentric phase. However, if the SSC enhancement is the result of a higher active muscle state, then increases in agonist activity could be expected during the initial duration of the concentric phase. Newton et al.[12] found that pectoralis major and triceps brachii activity were significantly higher during the first 50 ms of the concentric phase for an SSC movement, although no differences were seen when expressed as mean or peak values of the entire repetition. Consequently, additional research is required to verify the underlying mechanism(s), identify the role of the agonist and antagonist musculature throughout an SSC action and improve the transference of these training principles to sportspecific applications. 1.3 Ballistic
In the strength-training literature, ballistic denotes accelerative, of high velocity and with projection into free space.[45] Ballistic efforts have been introduced as a potentially superior form of Sports Med 2010; 40 (4)
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constant resistance training because the athlete is no longer limited by having to decelerate the load at the end of the concentric phase,[12] consequently resulting in higher mean velocity, force and muscle activity. This type of training may also provide a stimulus more representative of athletic movements in which objects are thrown or the body is propelled in a certain direction as quickly as possible.[46] 1.3.1 Kinematics and Kinetics Velocity
The mean velocity produced with a ballistic effort is equal to the change in displacement between the onset of the concentric action and the point at which the load is projected, divided by the duration of this phase. Furthermore, it should exceed the mean velocity of a non-ballistic movement performed with the same load. The point at which the load loses contact with the athlete, he/she is no longer able to apply any muscular force and, therefore, any subsequent change in velocity is a result of the force of gravity and should not be included in any kinetic or kinematic calculations. Comparing ballistic and non-ballistic techniques at a load of 45% 1RM, Newton et al.[13] found that projecting the load at the end of the concentric phase increased the mean velocity by 27.3%. Conversely, Cronin et al.[2] reported similar mean velocities between ballistic and non-ballistic efforts for loads ranging from 30% to 80% 1RM; however, the velocity data were calculated by differentiating the entire displacement curve, including the duration of the movement during which the load had been released (the barbell was no longer in contact with the hands but it was still travelling in an upwards direction), making it a non-ballistic analysis. Ballistic movements have been regarded as a superior form of power training because they allow for a greater portion of the concentric phase to be spent accelerating; 96% compared with 60% of total displacement with a 45% 1RM load,[13] which resulted in a 36.5% increase in peak velocity.[13] However, additional research is required to fully understand the transference of such a stimulus to sport performance. ª 2010 Adis Data Information BV. All rights reserved.
Acceleration and Force
The kinetics and kinematics of ballistic efforts are not constrained by a predetermined displacement or a final concentric velocity of zero. Consequently, mean acceleration and mean force will both be greater than their non-ballistic equivalent, contrary to some reports.[2] Comparing a ballistic and non-ballistic bench press, Newton et al.[13] found that projecting the load at the end of the concentric phase could increase the mean force by 35%. This finding, in combination with the increases to mean and peak velocity, led the researchers to conclude that ballistic training provided superior loading conditions for the neuromuscular system and a more sport-specific training stimulus. However, projecting the load failed to increase the peak acceleration, peak force or the time to peak force, even when utilizing an intermediate load of 45% 1RM. This implies that the maximum rate of force development or the ability to produce maximal force in minimal time,[24] and the force achieved early in the concentric phase (e.g. 30 ms) or the ability to increase force as rapidly as possible once the effort has begun,[24] was not changed. Participants in the study simply extended the duration of the acceleration phase, thereby increasing the peak velocity, without changing the ‘explosiveness’ of the movement. Extending the duration of the acceleration phase simply requires that an athlete provide a force greater than the weight of the load over a longer period of time. Increasing the explosiveness of a movement demands that a greater percentage of the absolute maximum force is produced during the initial portion of the concentric effort. With regards to athletic ability, it is important that the practitioner and researcher understand the demands of the sport and the training status of the athlete in question so that they are able to provide the stimulus most conducive to improving performance. Power
The ability to increase the mean velocity and mean force via ballistic efforts will result in greater mean power (equation 9), provided that the kinetic and kinematic analysis is completed from the point at which the load is projected and Sports Med 2010; 40 (4)
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does not include data from thereafter. Using a 45% 1RM load, Newton et al.[13] found that mean power could be increased by 70% in comparison to a non-ballistic equivalent, whereas Cronin et al.[2] failed to report any significant differences for loads of 30–80% 1RM. However, the data from Cronin et al.[2] were analysed from the onset of the concentric phase to the point at which the load achieved a velocity of zero, making it a non-ballistic analysis and thereby masking any differences that may have been produced. Compared with a non-ballistic movement, similar increases in peak power output have been reported,[2] whether performed with or without an SSC action (13.3% and 9.2% for SSC and concentric-only, respectively). Newton et al.[13] reported an increase of 67%, although it was not made clear whether the enhancement was due to a greater contribution from force or velocity. Ballistic training may provide a means of increasing the specificity of training; however, there may be disadvantages associated with a protocol that simply emphasizes the projection of the load (e.g. reduced rate of force development). An in-depth analysis of the force and velocity profiles of ballistic movement is required to fully understand their potential, or limitations, and identify possible means to increase the degree of specificity. 1.3.2 Muscle Activity
Ballistic movements have been described as having a particular neural pattern of motor unit firing known as the tri-phasic or ‘ABC’ pattern.[47,48] This pattern is characterized by a large burst of activity by the agonist musculature followed by a brief period of concomitant inactivity of the agonist and activity of the antagonist musculature, concluding finally with a second burst of agonist activity.[47-49] Behm[48] contended that while both the initial agonist and antagonist muscle activity are pre-programmed and immutable, the agonist has the responsibility of driving the movement while the antagonist protects the integrity of the joint and provides greater accuracy. Behm[48] also suggested that the pre-movement silent period of the agonist may help to synchronize the motor units for the subsequent second burst of activity by the agonist ª 2010 Adis Data Information BV. All rights reserved.
315
musculature, which is largely controlled by sensory feedback. Because the net force produced during a movement is a trade off between the force of the agonist muscles and the counteracting force of the antagonist muscles, the interaction between these bursts of myoelectrical activity warrants further research. Although more prevalent in trained individuals,[48] the interfering effect of the co-contraction between agonist and antagonist muscles in ballistic movements is reduced with strength training. Therefore, a more efficient control of the ABC pattern may benefit the power athlete. Newton et al.[13] compared the electromyographic profiles between a non-ballistic and ballistic SSC movement, but did not address the notion of an ABC pattern. The researchers reported increases in the peak and mean EMG for each muscle investigated; pectoralis major (70–84% 1RM and 69–70% 1RM), deltoid (70–93% 1RM and 56–68% 1RM), triceps brachii (59–85% 1RM and 57–68%) and biceps brachii (65–83% 1RM and 61–67% 1RM). The most significant differences were seen during the later portion of the concentric phase, which is reflected by the changes in the force-displacement curve. Although increasing an individual’s muscle activity during the concentric phase of movement may elicit performance improvements, the complexity of intermuscular and intramuscular coordination make a direct correlation highly unlikely. 2. Accommodating Resistance Accommodating resistance training allows for the development of maximal tension throughout the complete range of motion rather than at a particular point.[50] The use of accommodating resistance allows athletes to develop maximal force at various velocities, while being unaffected by the inertial properties of the load. That is, if velocity remains constant for the entire muscular effort, acceleration will be zero, meaning that the force produced is not dependent on the mass of the load. This reportedly results in increased force and motor unit recruitment at training Sports Med 2010; 40 (4)
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velocities similar to those achieved with a constant resistance equivalent.[4] However, many researchers question the validity and efficiency of these devices because they are biomechanically different from natural movements, and the degrees of freedom, or permissible movement directions, are reduced from six to one.[50] Currently, there are two types of devices used to accomplish these goals: hydraulics and isokinetics. The kinetics, kinematics and muscle activity associated with both these devices will form the basis of the discussion in this next section. 2.1 Hydraulic
Hydraulic devices do not offer a true type of accommodating resistance, in the sense that they do not provide a resistance equal to the force that is being applied.[51] Instead, hydraulic devices provide a resistance that is proportional to the applied force and accommodate for the movement velocity[50] by allowing the user to adjust the opening diameter of a valve that controls the speed at which hydraulic fluid flows through the system;[52,53] the greater the velocity, the greater the resistance.[50] 2.1.1 Kinematics and Kinetics
There are a limited number of researchers who have used hydraulic equipment in their studies[51,53-59] and even fewer who have reported the associated kinetics and kinematics.[53,55] Most studies using hydraulics have tested with isokinetic devices[54,57-59] and simply used these devices as a means of training. In one of the only studies reporting kinetics, Hortobagyi et al.[53] compared slow (0.037 m/sec) and fast (0.126 m/sec) hydraulic bench-press movements with a 1RM free-weight effort. For the slow and fast conditions, respectively, the mean force was found to be 8.3% greater and 62.6% lower, relative to the free-weight condition. However, the researchers failed to report either peak forces or accelerations, which would have allowed for better comparisons with free weights and an improved insight into the sportspecific training effects of hydraulic resistance. ª 2010 Adis Data Information BV. All rights reserved.
Burke et al.[55] found that forces equivalent to 171% and 231% of the mean force could be produced with slow (2.5-second) and fast (0.8-second) concentric efforts, respectively. Comparing these results with similar free-weight efforts is difficult because the total displacement was not reported and the subjects in the study by Burke et al.[55] performed the concentric action from a seated position. Cronin et al.[2] found that for a similar concentric duration (0.76 seconds), the peak force produced was only equivalent to 130% of the mean value, suggesting that there are much greater variations in the magnitude of the force-displacement curves for hydraulic resistance. Telle and Gorman[56] have advocated the combination of free weights and hydraulics as a superior strength-training mode, rationalizing that the inherent disadvantages associated with either type of resistance will compensate for the other. Hydraulic devices provide a resistance that is negligible at the onset of the concentric phase, gradually increases over the duration of the effort and is reduced to zero for the eccentric phase. Despite these claims, researchers and strength coaches alike do not regard hydraulic resistance as a superior stimulus to free weights as it lacks the training specificity thought to be necessary to elicit improvements in athletic performance. 2.1.2 Muscle Activity
At the time of writing this review, the authors were unable to locate any research that had investigated the acute or long-term effect of hydraulic-resistance exercise on muscle activity. Since hydraulics are a form of accommodating resistance, it could be hypothesized that maximum agonist activity is produced; however, this is assuming maximum voluntary efforts, which may not always be the case. 2.2 Isokinetic
The concept of isokinetic exercise was introduced in 1967 by Hislop and Perrine[4] as an alternative to isotonic (constant resistance) and isometric exercise. It was proposed that by keeping a constant velocity, and thus zero acceleration, Sports Med 2010; 40 (4)
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they could provide a more suitable mechanical means of obtaining a maximal concentric effort throughout a range of motion compared with isotonic training, while still allowing for work to be done (work = force · displacement), in contrast to isometric training. Although some degree of generality may exist between single-joint isokinetic efforts and athletic movement, it is thought that being limited to machine use, the lack of mechanical feedback and the absence of an eccentric component on some of the earlier systems do not provide a training stimulus conducive to high degrees of sport specificity; therefore, some researchers have began to challenge the effectiveness of isokinetic dynamometry.[8,50,60,61] However, isokinetics do offer a unique method of altering the kinetics and kinematics associated with resistance training and have assisted in improving our understanding of velocity specificity;[62-64] thus, they are still regarded as an effective means of high-velocity training. 2.2.1 Kinematics and Kinetics
The majority of studies that have used isokinetic devices as a means of training or testing have attempted to identify a velocity-specific response or an increase in torque over a specific training period.[41,62-68] Few researchers have actually attempted a comparison of the kinetics between isokinetics and other resistance-training modes.[3]
317
centric phase,[3,65,69,70] and can limit the constant velocity duration to 16% of the exercise if the movement velocity is high enough.[65] Although an increase in velocity is associated with a subsequent reduction in force, the magnitude of such a reduction is much less than for constant resistance efforts. A 100% increase in the mean angular velocity of a knee extension only elicited a 10.5% reduction in force, or torque[62] compared with a 37.5% reduction during constant resistance exercise (37.5%).[2] In order to reduce the torque by 37%, researchers have had to increase the angular velocity by 400% (38%),[65] 500% (42%)[62] and 1000% (37%).[66] Furthermore, the group of subjects who required a 1000% increase in velocity were able to attenuate their reduction in force to just 30% after becoming familiarized with the isokinetic device.[66] Acceleration and Force
The load acting in an isokinetic exercise is the result of the mechanical process of energy absorption.[4] Compared with constant resistance exercise in which part of the energy is dissipated with accelerations, isokinetic acceleration is, theoretically, always zero, allowing for complete energy absorption and a resisting force that is proportional to the magnitude of the input.[4] As such, the mean force for an isokinetic movement should be greater than its free-weight equivalent, at a comparable movement velocity. Comparing the force-time profiles of equivalent isokinetic
Velocity
ª 2010 Adis Data Information BV. All rights reserved.
60 Angular velocity (°)
Isokinetic devices are characterized by their ability to control the movement velocity and keep the acceleration at zero. However, all movement must start from a position of rest, or zero velocity, and terminate in a similar fashion, implying that exercises completed within a specific range of motion will involve some form of an acceleration and deceleration phase.[69,70] Chen et al.[69] has stated that the duration of an isokinetic movement spent at constant velocity and the accuracy of measurements are limited by acceleration, oscillation and deceleration phases of the exercising limb (figure 5). As the movement velocity is increased, the three non-constant velocity phases represent a greater relative portion of the con-
AP
OP
CV
DP
50 40 30 20 10 0 0
0.5
1.0
1.5 2.0 Time (sec)
2.5
3.0
Fig. 5. An example isokinetic angular velocity-time curve for a movement performed at 30/sec (reproduced from Chen et al.,[69] with permission from the Orthopaedic and Sports Physical Therapy Sections of the American Physical Therapy Association). AP = acceleration phase; CV = constant velocity; DP = deceleration phase; OP = oscillation phase.
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2000
Isokinetic
Force (N)
1600
Load
1200
Free weight
800 SR 400
AP
MSR OP
DP
0 0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Time (sec) Fig. 6. Slow isokinetic (0.45–0.84 rad/sec) and heavy free-weight (90% one repetition maximum) bench press force-time profiles (reproduced from Lander et al.,[3] with permission from Lippincott Williams & Wilkins). AP = acceleration phase; DP = deceleration phase; MSR = maximum strength region; OP = oscillation phase; SR = sticking region.
and free-weight resistance exercises, Lander et al.[3] reported 5% and 6% greater mean forces during the isokinetic condition when testing at velocities equivalent to a 90% (0.45–0.84 rad/sec) and 75% (0.79–1.61 rad/sec) 1RM lift, respectively. Furthermore, isokinetic mean forces during the sticking and maximum-strength regions (oscillation phase) were equivalent to 130.2% and 116.2% of the 90% 1RM free-weight movement and 133.3% and 126.6% of the 75% 1RM movement (figure 6). Although there were no significant differences between the peak forces seen during the acceleration phase, peak forces were consistently lower for the isokinetic condition. Peak force is directly proportional to the peak acceleration achieved in the initial stage of the concentric phase. Because isokinetics seek to limit the duration of the acceleration phase, the maximum force that an athlete is able to produce may be compromised using this form of dynamometry; however, there is no evidence to substantiate this contention. Power
Referring to equation 9, the power output of a muscle is the product of its velocity of shortening and force production.[71] Kanehisa and Miyashita[68] and Caiozzo et al.[62] both reported maximum mean powers at an angular velocity of 4.19 rad/sec. However, it is difficult to assess this claim in terms of sport-specific power ª 2010 Adis Data Information BV. All rights reserved.
development because there is an important difference between the power produced during coordinated multiple-joint efforts and the power generated during a single joint movement,[71] as is traditionally the case with isokinetics. Furthermore, controlling the movement velocity will encourage the development of force-specific power development, resulting in an upward shift of the force-velocity curve (figure 1a) and potentially less transference to sport. 2.2.2 Muscle Activity
Isokinetics have been designed as a way to maximize the force and, thus, muscle activity at a specific movement velocity.[4] This type of dynamometry has received a great deal of advocacy as a means of testing pre- and post-measures of strength by allowing an individual to maximally contract throughout the range of motion.[64,68,72-74] However, upon further examination of the EMG-torque relationship during a single repetition of isokinetic knee flexion, Onishi et al.[75] found that all agonists do not achieve maximum levels of activity during maximal efforts, and that there are varying degrees of synergistic involvement depending on the knee-joint angle. Furthermore, dissimilar movement patterns, associated with isokinetic dynamometry, have relegated the importance of whole-body coordination and control and, thus, may not provide the most conducive means to improve athletic performance. 3. Variable Resistance From as early as 1900, attempts have been made to develop innovative training devices that could combat the changing mechanical advantage and inertial properties associated with free weight or constant resistance.[76] At constant velocity, the resistance offered to the moving body segment remains constant; however, the resistance applied to the muscle varies with a change in joint angle.[4,77] Conversely, the acceleration of a load (equation 1), or a change in its velocity, necessitates an increase in peak force,[2,12] variation of the applied force[12,14] and an increase in the duration of the deceleration Sports Med 2010; 40 (4)
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phase.[3,9] As much as 67% of the concentric effort may be spent decelerating with a load of 19.4% 1RM.[14] Variable resistance devices alter the resistive force during a movement to match changes in the joint leverage,[50] and provide compensatory accelerations[76,78] with the intent of reducing the effect of these limitations. 3.1 Cam- or Lever-Based System
Arthur Jones and Harold Zinkin, founders of Nautilus and Universal, respectively, began their careers with a similar hypothesis: a muscle works at maximum capacity during a very small portion of a dynamic repetition. Therefore, to facilitate maximum muscular involvement, the resistance must be varied. Both inventors developed a cam- or lever-based system that would vary the resistance to coincide with the changing leverage of the body during movement.[8,79] Unfortunately, individual differences in limb length, point of muscle attachment, velocity of movement and maximum-force development affect the torque-angle curve of the machines, rendering them ineffective for their intents and purposes.[8,67,80] 3.1.1 Kinematics and Kinetics
The inherent flaws associated with cam-based variable resistance have limited the amount of available research on these devices. Those who have used them in their studies have done so with the intent of examining their effectiveness[67,80] or as a means of increasing strength,[57,58] and not for the purpose of a kinetic or kinematic comparison. Although found to be equally effective as hydraulics and surgical tubing to increase strength,[57,58] researchers have shown that there is little similarity between machine-resistive torque and human torque capabilities.[80] The torque-angle curve for the Universal bicep curl has been shown to be more representative of the maximum torque capabilities of the muscle than its free-weight counterpart;[79] however, these data were presented in a review on the device and not in a research study. Harman[80] showed that for the same exercise, machineª 2010 Adis Data Information BV. All rights reserved.
319
resistive torque varied up to 12% below maximum during the exercise movement, while human torque capabilities were as much as 59% below maximum at the extremes in the range of motion. Given the limitations of this type of equipment and the dissimilar resistance profiles to open- and closed-chain athletic movements, it should be deemed ineffective for the development of athletic performance and be reserved for recreational use. 3.1.2 Muscle Activity
To the authors’ knowledge, there has been no research to investigate the EMG profiles of a cam-based variable resistance effort. As stated in the previous section,[9] the inertial properties of a constant resistance load cause muscle activity to fluctuate throughout the repetition; therefore, it was speculated that cam-based devices would provide a resistance that resulted in near maximum muscular involvement throughout the range of motion. However, such a contention is theoretically based, and now receives little to no support from researchers and practitioners alike. 3.2 Band and Chain
Bands and chains are used as a means of overcoming the mechanical disadvantages associated with specific joint angles[76,78,81-83] and increasing the degree of sport specificity.[84] Reviews by Simmons[76,78] have resulted in an increased advocacy among powerlifters and strength coaches alike, despite claims that they are not an appropriate mode for resistance training as they work in opposition to the way human muscles contract and produce force.[84] Often reviewed simultaneously,[78,81] different inertial properties between the two training modalities necessitate separate discussions of the associated kinetics and kinematics. 3.2.1 Kinematics and Kinetics
The addition of bands and chains to a constant resistance movement will alter the kinematic and kinetic profiles of the respective lift; however, the manner in which changes and, thus, the training effect occur is governed by the inertial properties of the additional resistance. Sports Med 2010; 40 (4)
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The acceleration of any object is proportional to the force causing such an acceleration and inversely proportional to its mass or inertia (equation 1). Therefore, using bands, which have a minimal mass, will allow for greater accelerations to be produced compared with a constant resistance movement with an equivalent load. However, the load being lifted and, thus, the force required to elicit movement will increase proportionally with the displacement or stretch of the band (see equation 10): Fband ¼ k l
(Eq: 10Þ
where k is the stretch coefficient of the band and l is the distance that the band isP being stretched (m). Using equation 2, Fnet ¼ Fext ¼ Fapp þ Fload ; Fload becomes equation 11: Fload ¼ Fband þ Fmass
(Eq: 11Þ
where Fmass is the gravitational force of the load (N). Therefore, it becomes increasingly difficult to maintain high-movement velocities and accelerations towards the end of the concentric phase. However, the benefit of such a modality stems from these characteristics. A load that increases proportionally with the concentric displacement necessitates sufficient acceleration in the early stages of the effort to overcome the elastic recoil of the bands and still complete the movement. Because the band resistance is primarily a function of its elastic properties and not the force of gravity, there is also opportunity for much greater eccentric accelerations to be achieved. Greater eccentric accelerations will result in proportional increases in eccentric forces and a greater involvement of the SSC when applicable. Behm[85] has suggested that the benefits of band resistance are most apparent when combined with free weights so that the inherent shortcomings of either resistance type compensate for one another. Used alone, the bands do not provide sufficient overloading to the initial portion of the movement, thereby potentially reducing any opportunity to increase the rate of force development. However, used in combination with free weights, the bands may benefit the end range of motion by controlling the associated momentum and providing needed additional resisª 2010 Adis Data Information BV. All rights reserved.
tance, which allows for the lift to be completed with greater effort later in the movement. Contrary to bands, chains are an additional system mass. The magnitude of this mass is proportional to the percentage of the concentric phase completed, assuming that the chains are evenly distributed on the floor prior to the initiation of the concentric effort. Equation 11 becomes the sum of Fmass and Fchain where Fchain at position z is (equation 12): Fchain ¼ mz g (Eq: 12Þ where mz is the mass of the chain (kg) at position z (m) and g is gravity (m/sec2). However, mz can be stated as (see equation 13): mz ¼ ðmchain zÞ=ðd cÞ (Eq: 13Þ where mchain is the total mass of the chain (kg), d is the length of the concentric displacement (m) and c is the portion of the concentric displacement completed prior to the addition of any links in the chain (m). An increase in system mass also means that greater peak forces are required to achieve specific accelerations and movement velocities (equation 1 and 5). These forces, applied in ‘x’ amount of time, result in a change in momentum of the system, which is counteracted to some degree by the inertia of additional mass as subsequent chain links leave the ground. Similar to bands, chains compensate for mechanical advantages at the end range of motion of a squat or bench press[76] by increasing the resistance and limiting the effects of momentum. As a result, the rate of force development, the time to peak force and time to peak power may be improved; however, this contention has yet to be researched. 3.2.2 Muscle Activity
Despite their emergent popularity, a limited number of studies have examined the muscle activity associated with bands or chains.[81,86] Cronin et al.[86] compared the concentric and eccentric EMG of the vastus lateralis for two supine squats: band and no band conditions, with an equivalent mean load. The only significant differences observed between the two conditions were in the last 40% of eccentric displacement Sports Med 2010; 40 (4)
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where the band condition was greater. Similarly, Ebben and Jensen[81] examined quadriceps and hamstring EMG over the eccentric and concentric phase of a back squat over three conditions: squat with barbell and weight; squat with barbell, weights and chains to replace 10% of the load; and squat with barbell, weights and elastics to replace 10% of the load. No significant differences in the mean EMG were found between any of the conditions; however, presenting data as a mean may have masked any variations that did exist through the range of motion. 3.3 Pneumatic
The term ‘pneumatic’ denotes relating to or using air. Analogous to the resistive forces provided by band tension, pneumatic devices offer loads that are not dependent on an object’s mass. Conversely, they are a function of the air pressure being produced and the area through which this pressure is exerted (see equation 14): (Eq: 14Þ P ¼ Fpneumatic =A where P is the air pressure (Pa), Fpneumatic is the resultant force (N) and A is the area through which the air is compressed (m2). As a result, pneumatic loads will, theoretically, exhibit less variation in the muscular force required to complete an exercise stroke, thereby reducing the risk of strain and injury to the operator.[87] The first pneumatic exercising device was developed in 1978,[88] with the aim of duplicating the superior operative characteristics of ‘weight type’ exercising machines while avoiding the undesirable characteristics.[88] Dennis Keiser, founder of Keiser’s pneumatic technology, introduced a device that would not permit the operator to employ inertia to his/her advantage once the movement was initiated, but to exert a force during the return stroke, allowing for greater movement velocity to be achieved with an equivalent load.[88] Limiting the mass of the load will augment the kinetic, kinematic and electromyographic profiles of a movement, potentially resulting in dissimilar training adaptations; however, this remains to be investigated. The numerous researchers that have implemented pneumatic ª 2010 Adis Data Information BV. All rights reserved.
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technology in their respective protocols have done so as a means of training for the elderly[89-95] and not to assess the biomechanical characteristics of the resistance type. Therefore, the purpose of this section is to characterize pneumatic resistance in terms of Newton’s laws of motion and provide the theoretical framework for future experimental research. 3.3.1 Kinematics and Kinetics Velocity
As stated previously (see section 1), velocity is a function of the duration over which a specific displacement takes place. If the movement is nonballistic in nature, then both the initial and final velocities of the eccentric and concentric phase will be zero, meaning that the only way to change the mean velocity, given a fixed range of motion, is via changes in the duration of the movement. Therefore, for mean velocity to be greater during a pneumatic effort, the duration of that effort needs to be reduced. Peak velocity is a measure of the maximum instantaneous velocity at any point in time during the movement. Because it also represents a zero crossing on the acceleration curve (point that separates acceleration and deceleration phases), larger accelerations over a greater duration of the dynamic effort will elicit higher peak velocities. If pneumatic resistance does provide a means of increasing such acceleration, then there should be a proportional increase in the peak velocity. Acceleration and Force
The proposed advantages of pneumatic technology stem from Newton’s second law, which outlines the relationship between force, acceleration and mass. Referring to equation 1, the acceleration of an object is directly proportional to the sum of the net forces acting on the object and inversely proportional to its mass. Pneumatic devices utilize air pressure as a means of resistance, thereby reducing the mass component of the load to near zero. The magnitude of this mass component will vary in accordance with the body segments involved in producing such movement, but remain significantly lower than the freeweight equivalent, with greater differences being Sports Med 2010; 40 (4)
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Fa = 1081 N (applied force)
Fa = 1081 N (applied force)
FW
Pneumatic a = 1 m/sec2
a = 20 m/sec2
Fnet = 100 N
m = 100 kg
Fpneumatic = 932 N
Fr = 981 N (resistive force)
m = 5 kg
Fr = 981 N (resistive force)
Fig. 7. A theoretical kinematic and kinetic comparison of freeweight (FW) and pneumatic resistance using equivalent loads.
seen at higher loads. Consequently, an athlete will be able to achieve greater accelerations with a pneumatic load of the same weight if an identical force is applied (figure 7). Newton’s first law implicitly states that an object will maintain its state of motion unless acted on by an unbalanced force. Resistance to change in this present state, defined as inertia, will depend on the mass of the object, with larger masses characterized by greater resistance. Furthermore, in equation 5, we note that specific changes in the movement velocity necessitate proportional increases in either the magnitude of the applied force or the duration of the applied force for a given mass. Considering sport-specific velocity and power development, the only viable option is to increase the magnitude of the applied force, which consequently limits the load that can be lifted (table I). Because an equivalent pneumatic load uses less mass, less force will be required to produce the same desired velocities, or conversely, the same force will produce greater velocities via greater acceleration (table VI), possibly improving the transference of highvelocity resistance training to sport.
Power
A negligible mass component and near zero momentum makes projecting a pneumatic load near impossible, which results in a mean force that is always equal to the weight of load. Therefore, any increase to mean power must be from a proportional increase in the mean velocity. There are two ways to increase mean velocity: increase the duration of the phase spent accelerating; or increase the magnitude of the acceleration. Pneumatic resistance provides a training stimulus conducive to increased accelerations by reducing the inertia, momentum and peak force requirements of a movement, which may possibly lead to greater mean power. Furthermore, less mass implies less variation in the force curve, and the potential for peak power to be produced with a greater contribution from the movement velocity than the force (a rightward shift of the force-velocity curve; figure 1b). Peak power is an instantaneous measure of the optimal force-velocity combination at a specific point during the movement; however, to increase sport specificity, the velocity component should reflect the speeds at which movements are performed in athletic events. Pneumatic resistance may enable athletes to increase the peak power by producing greater movement velocities without necessitating proportional increases in the force; however, this contention needs validation from experimental research. 3.3.2 Muscle Activity
Newton et al.[12] has shown that during a bench press, the muscle activity of the agonists follow a similar pattern to the force-displacement curve; dramatic reductions in EMG activity are observed during the deceleration portion of the
Table VI. Force-velocity comparison of free-weight (FW) [reproduced from Lander et al.,[3] with permission from Lippincott Williams & Wilkins] and pneumatic resistance using the impulse-momentum theory Resistance
Fload
M
Vo
PV
MF
% MF/Fload
TPV
FW
1370
139.7
0.00
0.54
1704
1.24
0.225
Pneumatic
1370
10.0
0.00
0.54
1394
1.02
0.225
Pneumatic
1370
10.0
0.00
7.52
1704
1.24
0.225
Equation 5: Fnet ðt2 t1 Þ ¼ ðm v2 Þ ðm v1 Þ Fload = resistive force (N); M = mass of load (kg); MF = mean force between Vo and TPV (N); PV = peak velocity (m/sec); TPV = time to PV (sec); Vo = velocity at start of concentric phase (m/sec).
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Sports Med 2010; 40 (4)
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movement. Because pneumatic resistance will theoretically limit the variation in force, this resistance mode may also allow an athlete to maintain a more consistent muscular effort throughout a specified range of motion. An increase in agonist activity may result in a longer acceleration phase, thus allowing for greater movement velocities to be produced during the later stages of the concentric phase. However, as was stated for the isokinetic dynamometry, greater muscle activity does not necessarily equate to superior performance. Thus, it should be noted that increasing muscle activity alone is not sufficient evidence to validate a device as superior for performance development. 4. Conclusions Mechanical disadvantages inherent to constant resistance movements may limit the degree to which velocity-specific power output can be produced and, therefore, transference to athletic performance may be compromised. Dramatic variations in the kinetics, kinematics and muscle activity have been reported for both concentriconly and SSC movements because of the inertial properties of the load. Ballistics can reduce the magnitude of these variations via increases in mean force and mean and peak velocity; however, they fail to increase the rate of force development. Consequently, alternative resistance-training strategies continue to be investigated. Isokinetics, hydraulics and variable-resistance devices using cams and levers have offered possible solutions to the constant resistance problems by controlling the movement velocity and/or system load, but have done so by introducing limitations of their own. Movements are restricted to machines, thereby reducing the permissible movement directions from six to one, and have negated the importance of an acceleration component to training, likely decreasing the transference of gym-based strength and power gains to sporting activity. The addition of bands to constant resistance efforts may afford a promising solution, via compensatory accelerations, a variable resistance and a reduced mass. Compared with a constant resistance effort with an equivalent load, bands ª 2010 Adis Data Information BV. All rights reserved.
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allow for greater accelerations and, thus, velocities to be produced at the onset of the concentric effort, and prevent a reduction in force, velocity, power and muscle activity during the later stages of the movement. As a result, transference to sport may be improved, although this contention is yet to be shown through research; most evidence reports no acute differences compared with similar free-weight movements. Possible characteristics, such as a reduced load at the onset of the concentric phase or large variations in the load-displacement curve, may result in adverse training effects with extended use. Pneumatic resistance offers similar mechanical advantages to both constant resistance and band training: a reduced mass may allow for an increased acceleration and movement velocity at a given load; force, velocity, power and muscle activity should be maintained throughout the concentric phase; and the same load will theoretically be maintained for the duration of the movement. As a result, pneumatics may offer a resistance-training stimulus more conducive to sport-specific velocity and power development; however, this hypothesis remains to be investigated. Alternatively, perhaps it is naive to assume that one resistance type offers a mechanical stimulus conducive to improving all aspects of athletic performance. Sport is multifaceted and necessitates that a methodical strategy be implemented in order to achieve the best results. Understanding the biomechanical advantages and disadvantages associated with each resistance type may simply provide researchers and practitioners with a greater appreciation for the complex nature of performance enhancement, thereby prompting the evolution of hybrid or mixed-method training strategies. 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
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Correspondence: David Frost, Department of Kinesiology, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada. E-mail:
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Intravenous versus Oral Rehydration in Athletes Simon Piet van Rosendal,1 Mark Andrew Osborne,2 Robert Gordon Fassett,3 Bill Lancashire4 and Jeff Scott Coombes1 1 2 3 4
School of Human Movement Studies, University of Queensland, Brisbane, Queensland, Australia Queensland Academy of Sport, Brisbane, Queensland, Australia Renal Medicine, Royal Brisbane and Women’s Hospital, Brisbane, Queensland, Australia Port Macquarie Base Hospital, Port Macquarie, New South Wales, Australia
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Overview of Intravenous Fluid and Exercise Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Physiological Responses to Intravenous Fluid Infusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Cardiovascular Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Plasma Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Heart Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Thermoregulatory Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Core Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Skin/Forearm Blood Flow and Skin Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Sweat Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Endocrine Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Fluid-Regulating Hormones: Antidiuretic Hormone and Aldosterone. . . . . . . . . . . . . . . . . . 2.3.2 Stress Hormones: Adrenocorticotropic Hormone and Cortisol . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Catecholamines: Noradrenaline and Adrenaline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Subjective Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Thirst Sensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Thermal Sensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Rating of Perceived Exertion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Exercise Performance Following Intravenous versus Oral Rehydration . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Effects of Intravenous Fluids on Recovery from Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Areas for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Volume of Fluid Replenished . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Effects of Using Partial Intravenous and Partial Oral Rehydration . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Effect of Intravenous Fluid on Acid-Base Physiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Effect of Intravenous Fluid on Respiratory Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Adverse Effects from Intravenous Rehydration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
327 329 333 334 334 335 335 338 338 339 339 339 339 340 341 341 341 342 342 342 343 343 343 343 344 344 344
Fluid is typically administered via intravenous (IV) infusion to athletes who develop clinical symptoms of heat illness, based on the perception that dehydration is a primary factor contributing to the condition. However, other athletes also voluntarily rehydrate with IV fluid as opposed to, or in
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conjunction with, oral rehydration. The voluntary use of IV fluids to accelerate rehydration in dehydrated, though otherwise healthy athletes, has recently been banned by the World Anti-Doping Agency. However, the technique remains appealing to many athletes. Given that it now violates the Anti-Doping Code, it is important to determine whether potential benefits of using this technique outweigh the risks involved. Several studies have shown that rehydration is more rapid with IV fluid. However, the benefits are generally transient and only small differences to markers of hydration status are seen when comparing IV and oral rehydration. Furthermore, several studies have shown improvements in cardiovascular function and thermoregulation with IV fluid, while others have indicated that oral fluid is superior. Subsequent exercise performance has not been improved to a greater extent with one technique over the other. The paucity of definitive findings is probably related to the small number of studies investigating these variables and the vast differences in the designs of studies that have been conducted. The major limitation of IV rehydration is that it bypasses oropharyngeal stimulation, which has an influence on factors such as thirst sensation, antidiuretic hormone (arginine vasopressin) release, cutaneous vasodilation and mean arterial pressure. Further research is necessary to determine the relative benefits of oral and IV rehydration for athletes.
It is well accepted that moderate to severe dehydration detrimentally affects endurance exercise performance,[1,2] and also probably attenuates strength, power and high-intensity endurance (30 seconds to 2 minutes effort).[3] Traditionally, a 2% bodyweight reduction has been recognized as a physiological ‘threshold’ sufficient to consistently reduce endurance performance.[4] However, the magnitude of this threshold has recently been questioned,[1] based on multiple studies suggesting that athletes who performed the best in marathon and iron-man triathlon races were frequently also the most ‘dehydrated’.[5,6] Although the measurement of ‘dehydration’ in these studies is ambiguous, they raise the contentious issues at the centre of this debate. What level of dehydration constitutes a physiological perturbation sufficient to reduce performance? And how much fluid is required to stay on the euhydration side of this threshold, thereby maintaining performance? These questions are important because they influence the rehydration behaviours of athletes. Many athletes have recorded sweat rates of 2–3 L/h or greater,[7] and therefore may require ª 2010 Adis Data Information BV. All rights reserved.
very large volumes of fluid for adequate rehydration to stay above the ‘dehydration threshold’. The most logical method for replacing fluid deficits is via oral fluid ingestion. However, there are athletes for whom oral rehydration is difficult, leading to the use of intravenous (IV) fluids in athletes in two contexts. The first is as a medical intervention for collapsed athletes who show clinical symptoms and signs of heat illness and are generally unable to consume fluids orally. Under these circumstances it is typically assumed that dehydration is a primary cause of the condition and that rapidly administering fluids is imperative to reverse the symptoms and signs.[8] As discussed by Casa et al.,[9] the World AntiDoping Agency (WADA) permits IV infusions in these circumstances as a legitimate medical treatment.[10] Several papers have been published in recent years discussing issues associated with IV use in collapsed athletes, such as the importance of measuring serum sodium before providing large volumes of IV fluid to eliminate the risk of iatrogenic hyponatraemia.[8,11-14] This is a large topic worthy of a review in itself and is not the focus of the present article. Sports Med 2010; 40 (4)
Intravenous versus Oral Rehydration
The second and more contentious scenario of IV fluid infusion is as an ergogenic aid to rapidly restore the body’s fluid balance in ‘healthy’ athletes. Two groups of athletes are typically represented in this situation: athletes from predominantly individual sports such as triathlon and cycling who voluntarily rehydrate via this technique; and athletes from team sports for whom IV rehydration is included as a component of recovery practices administered by the sports club. The voluntary use of IV rehydration is often based on the perception that IV fluids are more effective at replenishing lost body water than oral fluid, leading to a shortened recovery time.[15] Indeed, IV fluid more rapidly enhances circulating blood volume because it bypasses the absorption delays associated with oral rehydration.[16-18] However, for many of these athletes IV fluid is also considered to be a logistically beneficial rehydration technique when large volumes are required with little time available for rehydration. For example, in a sport such as American football, athletes weighing upwards of 135 kg undertake pre-season training sessions of 2–3 hours, twice a day, with approximately 4 hours recovery between sessions. With average sweat rates of 2–3 L/h, these athletes may be required to replenish more than 6 L after each training session, or 10–12 L in a 10-hour stretch. It is therefore desirable for these groups of athletes and their athletic trainers or physicians to consider IV rehydration. However, while IV fluid is allowed as a medical intervention,[9] WADA has recently added IV infusions to their list of prohibited ergogenic methods.[10] Therefore, an athlete can only legally receive IV fluids for rehydration when it is medically warranted.[9] The present review focuses on the use of IV fluids as an ergogenic rehydration technique in healthy athletes. The primary aim is to establish whether IV rehydration may be physiologically advantageous compared with oral rehydration for athletes who are dehydrated and otherwise healthy (e.g. not experiencing heat-associated illnesses). Further articles comparing the use of IV fluids with differing compositions (hypotonic and isotonic) are also discussed. When discussing ª 2010 Adis Data Information BV. All rights reserved.
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specific studies within this review, unless otherwise indicated, oral fluid refers to 0.45% sodium chloride (NaCl) solution ingested orally, and hypotonic and isotonic IV indicate 0.45% and 0.9% NaCl solutions given intravenously. 1. Overview of Intravenous Fluid and Exercise Research Throughout this review, the terms ‘study’ and ‘article’ refer to a specific publication in a journal, while the terms ‘trial’, ‘experiment’ and ‘investigation’ refer to the physical process of testing subjects. The MEDLINE database was searched using PubMed for articles published before 12 January 2009 that investigated the use of IV fluid for rehydration in athletes. The search found 3 articles investigating the admission of IV fluids during exercise and 11 articles reporting the use of IV fluids when rehydrating following exercise-induced dehydration (table I). A number of issues need to be raised before discussing the findings. Firstly, data in the 11 published rehydration articles originate from only 5 trials. One of these was from a trial administering IV fluid following a marathon race and therefore contained little scientific intervention.[22] The remaining ten articles are from four laboratory based experiments.[15-18,23-28] To assist the reader understanding the protocols used in these trials, figures 1–4 have been constructed to illustrate the differences between the conditions (e.g. what rehydrating solution was administered) and the timepoints at which outcomes were assessed. As can be seen from these figures, the five articles by Castellani et al.,[15,24] Kenefick et al.[26,28] and Riebe et al.[23] report different aspects of the same major experiment (figure 1), as do those by Casa et al.[17,25] and Maresh et al. (figure 2).[16] Thus, more research in the area is still needed. Secondly, many of the discrepant findings reported between studies may be related, at least in part, to differences in study designs and timepoints at which primary outcome measures were assessed. As can be seen in figures 1–4, the typical design of IV versus oral rehydration studies has been to: (i) dehydrate subjects via exercise in the heat; (ii) rehydrate subjects with IV or oral fluid; Sports Med 2010; 40 (4)
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Table I. Summary of protocols of studies investigating intravenous (IV) rehydration in relation to exercise Study
Subjects
Treatments
Dehydration protocol
Dehydration Rehydration protocol % BW
Rehydration Exercise % BW environment
Exercise protocol
Isotonic IV No fluid
No pre-exercise dehydration
NA
1.28 L of 0.9% NaCl infused as rapidly as possible (69 mL/min) IV = 24C
NR
24C, 26% RH
Cycling at 73% . VO2max until exhaustion 50 min cycling at . 60% VO2max; IV infused between min 20–50
IV during exercise 9 males, healthy fit adults
Nose et al.[20]
6 males, healthy adults Isotonic IV, warm environment No fluid, warm environment Isotonic IV, cool environment No fluid, cool environment
NA 30 min passive equilibrium period preexercise Cycled at 60% . VO2max for 50 min then further 40 min passive rest
IV infusion at 29 mL/kg BW during min 20–50 of exercise
NR
22C, 5 m/sec) significantly more frequently than forwards and backs.[17,18] Nomadic players totalled 98.5 – 26.1 surges >5 m/sec compared with 87.1 – 22.9 and 81.0 – 20.7 for forwards and backs, respectively.[18] Dawson et al.[12] had previously reported that high intensity efforts numbered between 158 (full forward/backs) and 208 (midfielders) per game, with approximately 80–85% of sprint efforts lasting 2.2 m/sec) per game was significantly greater in nomadic players (24 min 40 sec) than both forwards (21 min 10 sec) and backs (21 min 28 sec).[18] Considering players average 100 minutes of on-field playing time,[18] steady-state running represents a relatively minor component of the game. The longest continuous periods of time spent moving at velocities 2.22 m/sec and ‘recovery’ was the total time below, Wisbey and Montgomery[15] found the average work-recovery ratio in the 2005 season to be 1 : 2.6, 1 : 2.3 and 1 : 2 for forwards, defenders and nomadic players, respectively (more recent data are not available). These ratios may not reflect ª 2010 Adis Data Information BV. All rights reserved.
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current game demands, given the recent changes in game structure. Further analysis of GPS data is likely to reveal the frequency and lapsed time between work periods and short, moderate and long recovery periods. Such information would aid in the design of game-specific conditioning drills. 3.2 Heart Rate Responses
Despite the ease with which heart rate data can now be collected during exercise, only two studies have reported the heart rate response during Australian Football games. In the 1970s, Pyke and Smith[8] collected heart rate data on two positions, a ‘small’ back and a midfielder, over one quarter of a game. Mean heart rates were 160 bpm and 178 bpm for the back and midfielder, respectively. It was found that during intense periods of play, the heart rate of the back reached 170–180 bpm (»94% maximum heart rate), dropping below 140 bpm during lengthy recovery periods. In contrast, the heart rate of the midfielder did not fall below 150 bpm and remained between 170 bpm and 185 bpm for most of the quarter. The authors concluded that the running demands of ‘nomadic’ players such as midfielders and ruckmen mean they have little opportunity to recover, taxing their cardiovascular system to a greater extent.[8] In the only other study to report heart rates, two midfielders were monitored during an entire game by Hahn and colleagues in 1979.[9] The heart rates of the players ranged from 140 to 180 bpm with averages of 164 bpm and 159 bpm reported. Updated analysis of the heart rate responses of players during Australian Football would be of value. Such information may be used in the development and evaluation of game-specific training drills where GPS technology is unavailable. Heart rate monitoring has been used to quantify exercise intensity[28] in continuous aerobic exercise, and training load[29,30] in intermittent exercise bouts such as those performed in Australian Football. Currently, GPS is used in preference to heart rate monitoring as it is able to provide a more accurate description of the movement patterns during play. Heart rate monitoring Sports Med 2010; 40 (4)
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in intermittent sports is perhaps more appropriately used to monitor weekly or monthly training loads. 4. Physiological and Anthropometric Characteristics 4.1 Mass, Stature and Body Composition
The average height of current elite Australian Football players is ~1.87 m,[31,32] with sub-elite and elite juniors slightly shorter (table V).[33,36] Young et al.[32] found that height was not significantly different between midfielders, forwards and backs. However, other studies have found substantial positional differences in height, with small forwards and small midfielders shorter than the mean height, whilst key position forwards/ backs and ruckmen are all taller.[35] A recent study investigating the relationship between anthropometric/fitness measures and playing performance found height to be the only significantly different variable between players who achieved more than six hit outs (hitting the ball clear of a ruck contest such as a bounce down) per game compared with those who achieved less.[36] This confirms the importance of having the tallest players (ruckmen) competing in ruck contests, at least at the elite junior level. On average, body mass has been found to range between 85 and 90 kg at the elite level in the AFL with some key position players weighing more than 100 kg; draftees and elite juniors are slightly lighter and have body masses ranging from 74 to 82 kg.[32-35] This alone suggests greater mass (presumably muscle mass) is necessary to play Australian Football at the elite level and confirms the importance of strength and size. Elite juniors identified as taking significantly more marks over an eight-game period were found to be significantly heavier.[36] However, at an elite level, body mass has not been found to differentiate between ‘successful’ and ‘unsuccessful’ players in the AFL,[32] nor between young players at the national draft camp.[34] Body mass appears to be more advantageous for competitors at the sub-elite level. It has been stated that as an elite junior, the biggest physical ª 2010 Adis Data Information BV. All rights reserved.
change needed to play in the AFL is an increase in body mass with proportionate increases in strength.[33] Interestingly, long term increases in body mass are evident especially at the juniorelite level. The average body mass of a state under-18-years squad in 1999 was approximately 75 kg, while in 2005 a larger cohort of players at the same level weighed approximately 80 kg. Skinfold data reveal that AFL players have low levels of body fat and that these have not changed with time. However, no consistent relationship has been found between estimates of body fat and playing position,[35] nor is level of body fat able to differentiate between ‘successful’ or ‘unsuccessful’ players.[32,34] Average ‘sum of seven’ and ‘sum of eight’ skinfold data from several studies are summarized in table V. 4.2 Maximal Oxygen Uptake
Several studies have reported on maximal aerobic power values of Australian Football players at the elite and junior elite levels of competition.[31-36] Estimated from the 20 m shuttle run test, these studies found that the maximal oxygen uptake . (VO2max) values of players ranged from 55 to 65 mL/kg/min with little variation existing between junior elite and elite level players. This is consistent with elite players in other running-based sports such as soccer (57.8 – 6.5 mL/kg/min)[38] and field hockey (61.8 – 1.8),[39] but greater than professional rugby league and rugby union players (~50 mL/kg/min).[26,40] In the few studies that have considered playing position, no positional differences were identified;[32] however, a high . VO2max has been found to be a defining quality of successful draftees to AFL clubs.[34] Physiological profiles of sub-elite or state league Australian Football players are not available for comparison. Further examination of current players from a variety of playing levels assessed in a laboratory setting may be of interest to those developing junior athletes or for talent identification purposes. 4.3 Muscular Strength and Power
Bumping, tackling and ‘wrestling’ opposition players when contesting a mark or loose ball on Sports Med 2010; 40 (4)
Study Buttifant[31]
Keogh[33]
Pyne et al.[34]
Pyne et al.[35]
Young et al.[32]
Young and Pryor[36]
Year of data collection
1993–7
1999
1999–2001
1999–2004
2004
2005
No. of players
18
29
283
495
38
177–200
Level of competition
AFL (elite)
State U/18
AFL draft camp
AFL draft camp
AFL (elite)
State U/18
Age (y)
22.7 [1993]–26.8 [1997]
15.9
17–18
17–18
22.6 – 2.9
16–18
Positions
All
All
All
All
Nomadic
Forwards
Backs
All
Anthropometry height (cm)
186
180.2 – 7.2
186 – 6.62
187 – 6.6
188 – 9
186 – 10
187 – 5
183.9 – 6.9
body mass (kg)
85.7 [1993]–90.5 [1997]
74.6 – 8.3
80.5 – 7.82
81 – 7.6
86.8 – 8.9
87 – 7.75
87.7 – 7.5
79.8 – 8.3
skinfolds (mm)
55.4 [1993]–63.6 [1997]b
56 – 3.4b
55.3 – 12.6b
47 – 7.8c
59.7 – 16c
53.3 – 12c
57.8 – 3.4
57.8 – 3.4
61.6 – 3.5
57.8 – 5.1
61.1 – 3.5
747 – 123
656 – 128
743 – 142
1.9 – 0.06
1.93 – 0.1
1.88 – 0.07
Game Demands of Australian Football
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Table V. Physiological characteristics of Australian Football playersa
Endurance capacity
. 20 m shuttle run (VO2max; mL/kg/min)
53.6 [1993]–59.2 [1997]
57.7
yo-yo IR2d test (m)
57.3 – 3.5
Speed and acceleration 1.09 – 0.06
5 m (s) 1.83 [1993]–1.66 [1997]
1.81 – 0.07
20 m (s)
3.06 [1993]–2.92 [1997]
3.04 – 0.09
40 m (s)
5.37 [1993]–5.40 [1997]
10 m (s)
1.12 – 0.05 3.04 – 0.09
3.13 – 0.09 3.48 – 0.11
Flying 30 m split (s)
3.6 – 0.15
3.49 – 0.06
Strength 97.9 – 11.9
93 – 10.4
98.8 – 8.4
3RM leg press (kg)
399 – 48
348 – 64
410 – 44
3RM chin ups (kg)
109 – 10
103 – 12
106 – 5
3RM bench press (kg)
63.8 – 10.7
Leg power concentric squat jump (W/kg)
68.3 – 6.2
70.5 – 7.4
68.1 – 7.1
countermovement squat jump (W/kg)
70.2 – 8.8
65.1 – 9.4
69.1 – 7.8
62.3 – 3.7
58.4 – 5.1
63.4 – 2.4
Jumping ability countermovement jump (cm)
55.2 – 7.9
60.1 – 5.76 322 – 11.1
absolute running vertical jump (cm)
322 – 10.9
Flexibility 10.7 – 6.4
Performance and anthropometric values are expressed as mean – SD.
b
Sum of seven skinfolds.
8.8 – 7.5
c Sum of eight skinfolds. d
There are two yo-yo protocols: level 1 and level 2. Level 2 is reported here and has higher initial running speeds.[37]
. AFL = Australian Football League; IR2 = intermittent recovery level 2; RM = repetition maximum; U/18 = under 18 y age group; VO2max = maximal oxygen uptake.
357
Sports Med 2010; 40 (4)
sit and reach test (cm) a
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the ground requires high levels of upper and lower body strength and power. Although not statistically significant, the data of Young et al.[32] suggest elite forwards and backs possess slightly greater upper body strength than elite midfielders (table V). Mean 3 repetition maximum (3RM) bench press values for players at the highest level (~95–100 kg) are far superior to junior elite players (~63 kg), reinforcing the significant gains in size and strength junior athletes must attain to be competitive in the AFL. Young et al.,[32] measured leg strength by 3RM leg press, again finding higher values for forwards and backs (~400–410 kg) than for midfielders (~350 kg). Measures of leg power and jumping ability assessed using the vertical jump are higher for Australian Football players (~62 cm) than for rugby league players (~55 cm)[26] but comparable to rugby union backs (~60 cm).[40] Pyne et al.[34] identified that junior players drafted to AFL clubs who went on to debut in the AFL had better jumping and agility qualities than those who did not. These fitness components reflect the skill demands required of the various positional groups. 4.4 Speed and Repeated Sprint Ability
The ability to outrun defenders and ‘find space’ or chase down opponents is integral to Australian Football; good acceleration and sprint ability are required by players in all positions, particularly with the speed of the game having increased in recent years. Similar to aerobic capacity, speed over 10–40 m does not appear to vary greatly between positions. Mean times over these distances for junior elite and elite players are summarized in table V. Recent estimates of maximal running speeds determined from ‘flying’ split times following 20 m of acceleration have been found to be 8.6 and 8.7 m/sec for two elite AFL teams.[41] In a practical sense, these players are capable of accelerating to speeds >80% of their maximal velocities in just a few seconds; a desirable quality that influences team selection at the elite level.[32] Young et al.,[32] also found the ability to recover from intermittent high intensity exercise to be a defining quality of ‘better’ players in elite ª 2010 Adis Data Information BV. All rights reserved.
Australian Football. Again, given that the game is becoming faster, repeated sprint ability is becoming increasingly important for players. Surprisingly, there has been only one published article describing the repeated sprint abilities of Australian Football players. Using a 6 · 30 m sprint protocol (on a 20-second cycle), Pyne et al.[42] reported the mean total time of all six sprints to be 25.83 – 0.6 seconds for 60 junior elite players attending the AFL national draft camp. A performance decrement of 4–6% was determined. Future research should assess repeated sprint ability at both elite and sub-elite levels of Australian Football competition. Knowledge of the degree to which repeated sprint ability varies relative to playing position would also be of value. In addition to the acknowledged importance of aerobic fitness, strength, power and speed for players who play Australian Football at a competitive level, there is evidence to suggest that reduced flexibility may predispose athletes to soft tissue injury.[43] The importance of maintaining flexibility should therefore not be overlooked.
5. Practical Applications for Conditioning Given that recent GPS research reports describe a high intensity intermittent movement profile for all playing positions, development of repeated sprint ability should be a primary focus for gamespecific player preparation. However, the specific nature of repeated high intensity exercise training may need to differ across playing positions. For example, nomadic players complete a greater number of high intensity efforts during a game, sprint for longer and have shorter recovery periods between bouts compared with players in other positions. Although steady-state running represents less than a quarter of total game demands, aerobic or endurance capacity will remain an extremely important component of fitness for all AFL players; recovery from high intensity exercise (e.g. removal of lactic acid and the resynthesis of creatine phosphate) is recognized as being strongly related to peripheral oxidative capacity.[44] This may be developed through various exercise modes. Sports Med 2010; 40 (4)
Game Demands of Australian Football
The relatively short duration of sprint efforts (2–3 seconds) suggests players seldom reach their maximal speed in games. Thus, development of acceleration may be of greater benefit than a focus on improving maximal speed during game-specific training. Nonetheless, players with greater speed have a distinct advantage when longer efforts are required. The importance of developing maximal speed should therefore not be underestimated. Recovery periods could be manipulated to alternate between developing ‘pure’ speed/acceleration (e.g. long recovery with a focus on technique) and repeated sprint ability (e.g. short recovery periods between bouts mimicking game demands). Future match analysis research should aim to couple preceding movement velocities with the frequency and type of accelerations/decelerations performed. This may help assess the value of standing starts, ‘flying starts’ and ‘varied pace’ acceleration drills. 6. Conclusions As is the case in most team sports, the available literature relating to Australian Football clearly reveals significant differences in game demands between different positions. The preparation of players for competition needs to reflect these positional differences and also take into general consideration the acknowledged increase in the speed of the game. It is reasonable to conclude that repeated sprint ability is more important for elite AFL players now than it was 20 or 30 years ago. Nonetheless, endurance (aerobic) fitness will remain a central determinant of the ability of the player to compete effectively in the AFL, not only given the large accumulated distances covered by all players during a game but also because of the need for players to recover rapidly between bouts of repeated high intensity exercise. GPS technology is providing coaches with objective and detailed data relating to specific movement demands of players. However, the accuracy and reliability of distance and velocityderived GPS data in highly specific game-related activities need to be established. Once this is achieved, GPS data that describe changes in veª 2010 Adis Data Information BV. All rights reserved.
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locity, repeated sprint intensities and subsequent recovery periods can be used to develop positionspecific training programmes for players and to monitor workloads both in competition and in training. Assessment of the relationships between the workload and various overuse injuries can follow. Furthermore, the potential of GPS data to differentiate movement patterns between elite and non-elite competition may aid in the development of junior players. 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.
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Correspondence: Mr Adrian J. Gray, School of Human Movement Studies, The University of Queensland, St Lucia Campus, Brisbane, QLD, 4072, Australia. E-mail:
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