MECHANICAL POWER OUTPUT AND

NEUROMUSCULAR ACTIVITY DURING

AND FOLLOWING RECOVERY FROM

REPEATED-SPRINT EXERCISE IN MAN

by

Alberto Mendez Villanueva

This thesis is submitted to the School of Human Movement and Exercise Science at

The University of Western Australia in fulfilment for the degree of Masters of

Science (by Research)

March, 2004

II

ACKNOWLEDGEMENTS

This has been a fantastic year. However, it has not been an easy one. I would like to

thank the following people for their help throughout the year. Without them, this thesis

would have not been possible.

Supervisors

Dr David Bishop

David, I thank you for everything you did for me more than I can say. Your talent,

dedication, wisdom and support have made my time in Australia the best professional

experience in my life. It has been a real privilege to work with you through this year.

Dr Peter Hamer

I greatly appreciate your effort, professionalism and patience because being a

biomechanist, many times you had to deal with all my physiological “stuff”. I am also

very thankful for all the time you devoted to teach me about EMG signal capturing and

processing.

Subjects

Thank you very much. Without your maximal effort this study could not be possible.

Technicians

Your help was invaluable. Special thanks to Tony for taking care of the bike, Peter for

the time developing the EMG capture sensors and Colin because you were always there

ready to help me.

Human Movement Staff

I would like to thank all the clerks, Brenda and specially Dr Brian Blanskby for trusting

me.

“Lab Assistants”

I would like to thank Steve for teaching me so many things within the physiology lab, I

really appreciate it.

III

To Nicol for your professional help during your stay with us.

To Hans, I extend my gratitude for your patience and time, not only during the testing

sessions, but also for your extra help and support during all my time in Australia. I wish

you all the best with your future.

Postgraduate students

Thank you Rob D, Rob S, Kuan, Lian and Sami to make my time at the school more

enjoyable. Good luck for the future!

I also wish to express my sincere appreciation to the following people:

To Pawel, thank you very much for your hospitality, friendship, understanding and help

during all my time in Australia. I always will be in debt with you.

To Thelma and relatives, because with you I felt like being a part of your family.

To my friends in Spain (Eduardo, Octavio, Marcos, Jaime, Tomy, Pablo, Bis, Pedro

Pablo and Guillermo) because your emails gave me a lot of happiness. Un abrazo

amigos!!

Finally, this thesis is dedicated:

To my parents, for providing me everything that you never had.

To Diana, because you complete me.

IV

ABSTRACT

The purpose of the present study was to examine the time-course of mechanical power

output and neuromuscular activity during fatiguing repeated-sprint exercise and

recovery in man. Prior to the main study, we also investigated the reproducibility of

power output during a single 6-s cycling sprint. For this study, eleven healthy

moderately trained males performed a 6-s standing sprint on the front-access cycle

ergometer on four separate occasions. The results of the study showed that reliable

power outputs can be obtained after one familiarization session in subjects unfamiliar

with maximal cycling sprint exercise. However, the inclusion of an extra familiarization

session ensured more stable power outputs. Therefore, two trials should allow adequate

familiarization with the maximal 6-s cycling test. For the main study, eight young

moderately trained adult men performed an exercise protocol that consisted of ten, 6-s

sprints on a wind-braked cycle ergometer interspersed with 30 s of recovery. After 6

min of passive recovery, five, 6-s sprints were repeated, again interspersed by 30 s of

recovery. Peak power output (PPO) and mean power output (MPO) were measured

during each sprint and EMG data (i.e., RMS) from the vastus lateralis muscle were also

recorded. A one-way ANOVA with repeated measures (i.e., sprint number) was used to

allocate the significant differences in each dependent variable over time. Analysis

revealed a decline in power output during the fatiguing exercise that was accompanied

by a decrease in EMG amplitude of the vastus lateralis muscle. Six minutes after the

fatiguing exercise, power output during sprint 11 significantly recovered with respect to

values recorded in sprint 10, but remained significantly lower than that recorded in the

initial sprint. Thus, 6 min was insufficient to fully recover from the fatiguing repeated-

sprint protocol utilised in this study. The main findings in the present study were that: 1)

the partial recovery of power output in sprint 11 was not accompanied by the recovery

V

of EMG amplitude; 2) similar mean power outputs were recorded during sprint 4 and 11

despite a significantly lower EMG activity recorded during the latter sprint; and 3)

despite comparable mean power outputs during sprint 4 and 11, the decrease in power

output over the next five sprints was greater for sprints 11 to 15 than for sprints 4 to 8.

VI

TABLE OF CONTENTS

Page ACKNOWLEDGEMENTS II ABSTRACT IV TABLE OF CONTENTS VI LIST OF TABLES VIII LIST OF FIGURES VIII CHAPTER ONE Introduction 1.1. Introduction 2 1.2. Statement of the problem 4

1.3. Hypothesis 4

1.4. Significance / Justification 4

1.5. Limitations 5

1.6. Delimitations 5 CHAPTER TWO Literature review 2.1. Introduction 7 2.2. Performance and muscular fatigue during intermittent exercise 9 2.3. Possible sites of fatigue during repeated-sprint exercise 10

2.3.1. Peripheral fatigue 13 2.3.1.1. Muscle excitability 13 2.3.1.2. Excitation-contraction coupling 15

2.3.1.2. Metabolic energy supply 18 2.3.1.2.1. PCr availability/Pi accumulation 18 2.3.1.2.2. Muscle glycogen 21 2.3.1.2.3. Acidosis 22

2.3.2. Central fatigue 25

VII

2.4. Neuromuscular activity monitored by surface electromyography (EMG) 30

2.4.1. EMG changes in the time domain 31 2.5. Conclusions 33 CHAPTER THREE Reproducibility of a 6-s maximal cycling sprint test 3.1. Introduction 36 3.2. Methods 38 3.2.1. Subjects 38 3.2.2. Ergometer 38 3.2.3. Procedures 39 3.2.4. Statistical analyses 40 3.3. Results 40 3.4. Discussion 43 CHAPTER FOUR Mechanical power output, the power-fatigability relationship and EMG activity during and following recovery from repeated-sprint exercise 4.1. Introduction 48 4.2. Methods 51 4.2.1. Subjects 51 4.2.2. Protocol 51 4.2.3. Ergometer 52 4.2.4. Muscle EMG 53 4.2.5. Statistics 54 4.3. Results 54 4.4. Discussion 59 CHAPTER FIVE SUMMARY 5.1. Summary 70 5.2. Conclusions 72 5.3. Recommendations for further study 73

VIII

REFERENCES 75 APPENDIX A Statement of informed consent 99 APPENDIX B Raw data / Statistical analysis (Chapter 3) 105 APPENDIX C Raw data / Statistical analysis (Chapter 4) 117

LIST OF TABLES

Page 42

Table 1.1. Reliability of performance between trials for peak and mean power expressed as a coefficient

of variation (CV) and confidence limits (95% CL).

LIST OF FIGURES

Page 11

Figure 2.1. Schematic illustration of the successive stages involve in generation of muscle force or power

output. Modified from McComas (1996), pp 231.

Page 41

Figure 3.1. Peak power recorded during the four 6-s maximal cycling sprint trials. *Significantly higher

(P < 0.05) compared with trial 1. Values are mean ± SD for the four trials (N = 11).

Page 42

Figure 3.2. Mean power recorded during the four 6-s maximal cycling sprint trials. *Significantly higher

(P < 0.05) compared with trial 1. Values are mean ± SD for the four trials (N = 11).

IX

Page 55

Figure 4.1. Average peak power output (A) and average mean power output (B) for the entire group over

the 15 sprints. Data are presented as mean ± SEM (n = 8). * Significantly lower (P < 0.01) than values

recorded at the first sprint. ** Significantly lower (P < 0.05) than values recorded at the tenth sprint.

Page 56

Figure 4.2. Average mean power output changes during five successive sprints when the initial sprints

were matched for power. The rate of decrease in power output for sprints 11 to 15 was greater than for

sprint 4 to 8 (P < 0.05). Data are presented as mean ± SEM (n = 8).

Page 57

Figure 4.3. Mean time course of power output (W) second by second (expressed in absolute values)

during sprints 1, 10, 11 and 15. Performance in all seconds during sprint 1 was significantly higher (P <

0.05) than in sprints 10, 11 and 15. Data are presented as mean ± SEM (n = 8).

Page 58

Figure 4.4. Normalised EMG amplitude (root mean square) changes of the vastus lateralis muscle for the

entire group over the 15 sprints. Data are presented as mean ± SEM (n = 8). * Significantly lower (P <

0.01) than values recorded at the first sprint. ** Significantly lower (P < 0.05) than values recorded at the

tenth sprint.

CHAPTER 1

Introduction

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1.1. Introduction To introduce this thesis the role of sport within the social and economic context of the

global community need to be considered. In 2002, the FIFA Soccer World Cup Final,

between Brazil vs Germany, was watched by 1.1 billion individuals in 213 countries,

virtually every country in the world (FIFA, 2004). Similarly, the Rugby World Cup

2003, celebrated in Australia, was broadcasted in 209 countries with an estimated

cumulative audience of 4 billion (RWC, 2003). These figures are impressive. These

sporting events can have far-reaching social and economic impacts. Soccer and rugby

are among a group of sports with movement patterns that involve periods of short

intense work interspersed with recovery of variable length. Further examples of such

sports include tennis, basketball, field hockey and handball. These sports are usually

classified as multiple-sprint sports (Hamilton et al., 1991). One important fitness

component in these sports is what has been termed repeated-sprint ability (RSA)

(Fitzsimons et al., 1993; Bishop et al., 2001). That is, the ability of the athletes to

reproduce their maximal performance, for example as determined by peak running

speed during a sprint, after previous exercise.

Performance in multiple-sprint sports is a complicated issue because of the complex

interaction between technical, tactical, psychological and physiological factors

(Bangsbo, 1994). However, the ability to sustain high-intensity intermittent loads

throughout a match is believed to be crucial for the final outcome of the match (Reilly,

1996). Owing to its importance, many studies have investigated the physiological

responses (Gaitanos et al., 1993; Bishop et al., 2003, Bishop et al., 2004b) or the effects

of ergogenic aids (Mujika et al., 2000; Paton et al., 2001; Bishop et al., 2004a) on this

mode of exercise. One common outcome during repeated-sprint exercise is the inability

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to reproduce power output in subsequent sprints (i.e., fatigue) (Hamilton et al., 1991;

Gaitanos, 1993). However, very little is known about the causes of muscular fatigue

during repeated-sprint exercise.

This is somewhat surprising since research has shown that a ~0.8% enhancement in

sprint speed would give a worthwhile increase in the chance of one player gaining

possession of the ball against an opponent, when both players sprint for the ball (Paton

et al., 2001). Moreover, Mujika and associates (2000) have reported that an average

improvement of 0.02-s over 5 m and 0.03-s over 15 m sprint running, obtained after a

period of creatine supplementation, would translate into distances of 10.3 and 19.3 cm,

respectively; more than enough to outrun an opponent and, for example, attain

possession of the ball. Conversely, when fatigue affects performance, Fitzsimons et al.

(1993) reported an average of a 5.7% speed decrement when a group of subjects

performed six, 40-m sprint separated by 24 s. Such fatigue induced decrements in

muscular performance are likely to influence the final outcome of the game. It follows

that understanding the mechanisms responsible for muscular fatigue during repeated-

sprint efforts maybe crucial.

It has been reported that repeated brief bouts (i.e., 6-s) of maximal exercise with

incomplete recovery periods impose unique physiological and metabolic demands, with

dramatic changes in muscle metabolites and accumulation of metabolic end-products

(Bangsbo, 2000). Such changes within the intramuscular environment can impair the

excitation-contraction coupling process (Allen et al., 1995). As a result, it has been

suggested that fatigue during repeated-sprint exercise is related to factors residing

within the muscle itself (i.e., peripheral fatigue) (Hautier et al., 2000). However, little

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scientific support has been furnished to support this (Bangsbo, 2000). Electrical activity

of the muscle may also be at least partially related to central activation (Orizio et al.,

2000, Suzuki et al., 2002). Moreover, research has shown that during maximal

intermittent exercise both neural activation of the contracting muscles (i.e., central

processes) and peripheral mechanisms can play a role in fatigue development (Michaut

et al., 2003; Taylor et al., 2000; Nordlund et al., 2004). By using an exercise protocol

that has previously been reported to cause large metabolic changes at the muscular level

(Gaitanos et al., 1993), accompanied by surface electromyography (EMG) to chart

muscle activation during the course of fatigue it may be possible to investigate the

central and peripheral factors operating during repeated-sprint exercise.

1. 2. Statement of the problem

The present study was carried out to examine the acute effects of repeated-sprint

exercise, performed on an air-braked cycle ergometer, on neuromuscular fatigue and

recovery in men.

1. 3. Hypothesis

It was hypothesized that both peripheral and central factors would be responsible for the

loss of power observed during repeated “all-out” sprints.

1. 4. Significance / Justification

In light of the worldwide popularity of multiple-sprint sports and the negative effects

that fatigue might have on repeated-sprint exercise performance, it is important to

understand the underlying mechanisms causing muscular fatigue during this mode of

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5

exercise. This study explored some of the potential limiting factors of muscular

performance during repeated-sprint exercise.

1. 5. Limitations

1. Since the patterns of work/rest during actual match play in multiple-sprint sports

is very complex and unpredictable, inferences based on the results of this study

cannot be readily generalized to every game situation.

1. 6. Delimitations

1. The study was restricted to eight well-trained subjects.

2. The setting was a laboratory environment, rather than field based

3. A standing cycle sprint ergometer protocol, not a running protocol, was used as

the mode of exercise.

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CHAPTER 2

Literature review

Factors limiting muscular performance during repeated-sprint exercise

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2.1. Introduction

Repeated-sprint exercise, for the purpose of this review, is defined as maximal or near-

maximal intensity bouts (i.e., at least two) of 10 s or less in duration that are

intermittently reproduced with incomplete recovery periods in between (i.e., recovery is

typically less than 60 s in duration). This mode of exercise represents the movement

pattern of many popular sports such as soccer (Reilly, 1996), basketball (McInnes et al.,

1995), field hockey (Spencer et al., 2004) and tennis (Ferrauti et al., 2001a). These

kinds of sports, which are characterized by intermittent loads, have been classified as

multiple-sprint sports (Hamilton et al., 1991). Athletes engaged in these disciplines are

required to repeatedly produce maximal or near-maximal sprints of short duration (i.e.,

1 to 7 s) interspersed with brief recovery intervals, over an extended period of time (i.e.,

1 to 4 h) (Bishop et al., 2001). Crucial for the final outcome of the game is the ability of

multiple-sprint athletes to reproduce performance in subsequent sprints during the

match (Krustrup et al., 2003). Therefore, an important fitness component of these sports

is what has been termed repeated-sprint ability (RSA) (Fitzsimons et al., 1993; Bishop

et al., 2001).

Because of the social and economical importance of such sporting activities (FIFA,

2004), there has been an increasing interest in studying the physiology of repeated-

sprint exercise. Several research groups, using laboratory (Gaitanos et al., 1991;

Hamilton et al., 1991; Gaitanos et al., 1993; Signorile et al., 1993; Balsom et al., 1994;

Balsom et al., 1995; Dawson et al, 1997; Drust et al., 2000; Preen et al., 2001; Tomlin

and Wenger, 2002; Billaut et al., 2003; Bishop et al., 2003; Bishop et al., 2004a; Bishop

2004b) and field based (Balsom et al., 1992b; Aziz et al., 2000; Mujika et al., 2000;

Nicholas et al., 2000; Wragg et al., 2000; Bishop et al., 2001; Ferrauti et al., 2001b;

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Paton et al., 2001; Krustrup et al., 2003) protocols have contributed to our current

understanding of this mode of exercise. These studies have all shown a decrease in

muscular performance (i.e., fatigue) from the earliest stages of the exercise. For

example, Gaitanos et al. (1993) reported a 33.4% and 26.6% decrement in peak and

mean power output, respectively when eight males completed 10, 6-s all-out sprints

with 30 s recovery periods conducted on a cycle ergometer. Billaut et al. (2003) also

showed that 15 s rest period was not enough to restore power output for two 8-s

maximal cycling sprints. However, little is known about the factors that limit muscular

performance during this type of exercise.

Fatigue has been studied using many different paradigms. Consequently, different

definitions have been used in its study. For the present study, muscular fatigue was

defined as a transient decrease in the force-generating ability of muscle, resulting from

recent contraction (Williams, 1997). There is no doubt that muscle fatigue is an

extremely complex phenomenon. Fatigue may involve alterations in neural activation of

contracting muscles (i.e., central fatigue) and/or changes within the muscle cell itself

(i.e., peripheral fatigue). It is now accepted that rather than a single factor, several

mechanisms are likely to account for the decline in muscular performance. Moreover,

the details of the task will determine the underlying mechanisms and sites associated

with fatigue (i.e., task dependency) (Enoka and Stuart, 1992; Hunter et al., 2004). In the

context of multiple-sprint sports, intense and repeated exercise is performed during a

relatively long period (i.e., 1 to 4 h) (Roetert and Ellenbecker, 1998; Bangsbo, 2000).

Therefore, transient episodes of muscular fatigue may occur after an intense exercise

bout, or after a set of repeated-sprint exercises with incomplete recovery periods

(Bangsbo, 1994). Moreover, as the match progresses, the ability to perform intense

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exercise may be impaired by other types of fatigue for which the underlying

mechanisms may be different (Bangsbo, 1994). As a result of this combination between

repeated sprints and the prolonged pattern of exercise observed in different multiple-

sprint sports, deterioration in performance similar to that occurring during both intense

and long-term exercise may be observed (Bangsbo, 1994).

In this review, a primary objective is to carry out an integrative analysis of the available

literature on both repeated-sprint exercise and muscular fatigue to understand the

possible causes limiting muscular performance during this type of exercise.

Understanding the aetiology of muscular fatigue is arguably the first step in order to

devise effective training and performance enhancement strategies that will delay the

onset of fatigue and eventually improve performance in sports in which repeated-sprint

exercise is the basic movement pattern.

2.2. Performance and muscular fatigue during intermittent exercise

During repeated-sprint exercise, performance can be defined as the ability to perform

maximally after previous exercise, for example as determined by peak power output

during maximal cycling (Bangsbo, 2000). The degree of fatigue experienced during

repeated-sprint exercise performance can vary considerably depending on factors such

as: the intensity and duration of each exercise period; the recovery time; the exercise

intensity during recovery time; and the preceding number of high-intensity exercise

periods (Balsom et al., 1992a; Billaut et al., 2003). During laboratory studies, most of

these variables are controlled and manipulated. However, during actual sport activities,

the changes in the sequence of activity/recovery are more complex and unpredictable.

This variability imposes unique physiological demands and it is likely that the

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mechanisms underlying fatigue may be different (Enoka and Stuart, 1992). Although, in

sports with intermittent load profiles, physiological demands imposed on the athletes

during competition are difficult to duplicate under laboratory conditions, research

performed under controlled laboratory setting forms the basis of our current knowledge

of repeated-sprint exercise physiological responses.

2.3. Possible sites of fatigue during repeated-sprint exercise

Intense muscle contractions impose a major strain on a wide variety of physiological

systems. To generate the high force levels accompanying intense activity, maximal or

near maximal activation of all of the synergistic muscles is a fundamental requirement

(Green, 1997). At the individual muscle level, this maximal activation depends on full

recruitment of all motor units at high firing frequencies (De Luca, 1985). Voluntary

force generation results from a sequence of events (see Figure 1) and each of these is a

potential limiting factor for force (Vøllestad, 1997). The importance of these factors will

depend on the details of the task (Enoka and Stuart, 1992). The first process comprises

all the central factors influencing activation of the motoneurones including motivational

factors as well as integration of sensory information leading to generation of action

potentials in the sarcolemma. Skeletal muscle cells are activated by the alpha-

motoneurons, which release acetylcholine at the neuromuscular junction. Acetylcholine

binds the receptor on the surface membrane of muscle cells, resulting in a local

depolarisation of the surface membrane extending towards and into the T-tubule system

(Westerblad et al., 2000). From the individual muscle cell, a successful response to the

high firing frequencies is critically dependent on the sarcolemma and T-tubule system

being able to regenerate potentials at a high frequency to the interior of the cell.

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INTENTION TO MOVE

Activity in cerebral cortex, basal ganglia and cerebellum

Motor cortex firing

Impulses in descending motor pathways

Motoneuron excitation

Impulse propagation to axon terminals

Neuromuscular transmision

Impulse propagation along muscle fiber plasmalemma

Impulses into T-tubules

Ca2+ release from SR

Engagement of cross-bridges

FORCE OR POWER OUTPUT

Figure 2.1. Schematic illustration of the successive stages involve in generation of muscle force or power

output. Modified from McComas (1996), pp 231.

The ability to sustain an action potential at high frequency is primarily dependent on the

ability to resequester the potassium ions (K+) back into the cell from the interstitial

CENTRAL

PROCESSES

Excitation-contraction coupling

PERIPHERAL

PROCESSES

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space and to expel the excess sodium ions (Na+), which enter during the action

potential, back to the interstitial space (Green, 1997). Once the neural signal reaches the

muscle cell membrane, it must also be rapidly conducted from the T-tubule membrane

to the sarcoplasmic reticulum. Specifically, this relates to the integrity of the calcium

release channels located primarily in the terminal cisternae and in apposition to the T-

tubules (Melzer et al., 1995). Within these channels, the action potential activates two

types of Ca2+ channels in order to release Ca2+ from the sarcoplasmic reticulum

(Westerblad et al., 2000). The dihydropyridine (DHP) receptors/channels are found in

high concentration in the membrane of the T-tubules (McComas, 1996). The DHP

channel appears to act primarily as a voltage sensor that transmits a signal to a second

type of Ca2+ channel, found in the membrane of the SR, the ryanodine receptor (RyR).

The affected RyRs permit the rapid escape of Ca2+ (approximately a 100-fold increase

with respect to the resting values) from the lumen of the sarcoplasmic reticulum into the

cytosol, diffusing to the myofibrils where it binds to troponin C. This results in

movement of tropomyosin from the active site of the actin. The myosin heads can then

bind to actin forming ‘cross-bridges’ allowing contraction to begin (Westerblad et al.,

2000). When activation ceases, SR Ca2+ release stops and the cytosolic concentration of

Ca2+ is rapidly lowered and Ca2+ dissociates from troponin C. This dissociation causes

the cross-bridges to detach from the actin filaments and the muscle cell relaxes

(Westerblad et al., 2000).

During intense and repetitive muscular activity, maximal exercise can only be

maintained for a brief period of time and fatigue quickly develops. For example, during

a maximal cycling sprint, power output peaks during the first 2-3 s and thereafter

declines (Bogdanis et al., 1995). If maximal effort continues to 30 s, power output

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typically falls by about 50% (Beelen and Sargeant, 1991; Bogdanis et al., 1995). Under

such exercise conditions, the inability to maintain or produce power output can be due

to failure at one or more steps in the chain of events leading to muscle contraction. This

review does not intend to cover all the putative factors that have been related with the

development of muscle fatigue, thus only a few selected candidates that have been

suggested to contribute to the loss of muscular performance during repeated-sprint

exercise will be considered. These include factors grouped together describing

peripheral fatigue and those influencing central fatigue.

2.3.1. Peripheral fatigue

2.3.1.1. Muscle excitability

At the peripheral level (i.e., beyond the neuromuscular junction), an inability to

repeatedly generate action potentials at the high frequency required for the intense

contraction activity during sprinting exercise may result in excitation failure or a failure

to fully translate the neural signal to the interior of the fibre, finally resulting in lower

muscle force (Green, 1997). This form of fatigue is often referred to as high-frequency

fatigue (Fitts, 1994; Allen et al., 1995; Green, 1997). The repetitive excitation of the

muscle may cause a progressive reduction in the transmembrane gradients for Na+ and

K+, which may result in a less negative resting membrane potential and decrease muscle

fibre excitability (Stephenson et al., 1998; Fowles et al., 2002). Indeed, muscle fatigue

has been frequently associated with changes in the characteristics of the sarcolemma

action potential (reviewed in Fitts, 1994). However, whether these changes contribute to

the loss of force during volitional intense exercise in humans remains unresolved.

Recently, Fowles et al. (2002) suggested that for an isometric fatiguing exercise,

compromised Na+-K+ pump activity may contribute to reduced sarcolemmal excitability

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and contribute to muscle fatigue in humans. This would suggest that given the high

levels of activation required to perform repeated-sprint exercise, impaired excitability of

the sarcolemma could contribute to the muscular fatigue observed during this mode of

exercise. However, Fowles et al. (2002) also reported that during recovery, muscle

excitability was restored while force was still depressed. This finding supports the

existence of other sites that might be responsible for more prolonged fatigue (Fitts,

1994).

During intense exercise, a large and rapid release of K+ from contracting skeletal muscle

to extracellular fluids is observed, resulting in a decrease in intracellular K+ (Lindinger

et al., 1995). This could result in a concomitant depolarization, reducing excitability and

contractility of the muscle (Clausen et al., 1998). Stimulation of the Na+-K+ pump may

counterbalance this effect. However, the capacity of the Na+-K+ pump to maintain the

ionic balance between intracellular and extracellular fluids might be inadequate even

after a few seconds of intense work if high-frequency stimulations, as during sprint

exercise, are required (Clausen et al., 1998). During recovery from exercise, restoration

of Na+ and K+ ions within the skeletal muscle is rapid (Lindinger et al., 1995). However,

if previous supramaximal exercise (i.e., 30 s maximal cycling sprint) is performed and a

short recovery period is allowed (i.e., 4 min), restoration may not be complete (Spriet et

al., 1989). This means that fatigue related to Na+-K+ disruptions might be a transient

phenomenon (Bangsbo, 2000), which would explain the temporary loss of performance

associated with periods of repeated, intense exercise with incomplete recovery periods

as typically occurs in game sports. However, the role of K+ accumulation as a

contributor to muscle fatigue has been recently challenged (Nielsen et al., 2001;

Pedersen et al., 2003; Mohr et al., 2004). Resembling a closer exercise scenario,

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Pedersen et al. (2003) have reported that the loss of excitability and force by increased

extracellular K+ concentrations was counteracted by simultaneous elevation of muscle

temperature, lactic acid and the presence of catecholamines. Dramatic rises in both

muscle (Gaitanos et al., 1993) and blood (Gaitanos et al., 1991) lactate concentrations

have been reported during repeated-sprint exercise. Similarly, a large increase in

circulating catecholamines (i.e., adrenaline and noradrenaline) have been found (Brooks

et al., 1990; Ferrauti et al., 2001a). While more research is needed, these recent findings

do not seem to support that the accumulation of K+ per se plays a major role in

neuromuscular fatigue during repeated-sprint exercise.

2.3.1.2. Excitation-contraction coupling

There is evidence, mainly from animal studies, using isolated single fibres, that

impaired intracellular calcium (Ca2+) regulation by the sarcoplasmic reticulum (SR) is

implicated in the fatigue process (Gollnick et al., 1991; Allen et al. 1995; Green, 1997;

Williams et al., 1998; Danieli-Beto et al., 2000; Hill et al., 2001; Matsunaga et al., 2002;

Li et al., 2002). Ca2+ is essential for the contractile process. Upon stimulation, Ca2+ is

released by the SR which leads to muscle contraction; the subsequent removal of Ca2+

(calcium uptake) from the contractile proteins back into the SR by the SR Ca2+-ATPase

results in relaxation of the muscle (Hill et al., 2001). All these three parameters of Ca2+

handling (Ca2+ release, Ca2+ uptake and Ca2+-ATPase) of the SR might play an

important role in the development of muscle fatigue. However, the reduction of SR Ca2+

release seems to be an especially interesting aspect to assess during high-intensity

exercise (Favero, 1999). Reduced SR Ca2+ release has been linked to rapid force

reduction (Westerblad et al., 1998) and it has been pointed out that a large reduction in

SR Ca2+ release is likely to be a major contributor to depressed muscle force with

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fatigue (Allen et al., 1995). Indeed, if the amount of Ca2+ released from the SR into the

cytosol decreases, this will in turn attenuate the binding of Ca2+ to troponin C

(Vøllestad, 1997). Fewer cross-bridges will be formed between actin and myosin

molecules and hence a lower force or power is generated. The notion that diminished

SR Ca2+ release may play a critical role in the development of muscle fatigue is

supported by the fact that agents such as caffeine, which increase the opening of the SR

Ca2+ release channels, can reverse much of the decline of force in fatigued mouse

muscle (Lännergren and Westerblad, 1991). However, the mechanisms for impaired SR

Ca2+ release with fatigue remains poorly understood. Some evidence suggests a

metabolic origin of this reduction. Localized substrate depletion (i.e., ATP, glycogen)

(Owen et al., 1996; Chin and Allen, 1997; Lees et al., 2001) and metabolic-end products

such as Pi, H+, lactate or Mg2+ (Westerblad and Allen, 1992; Favero et al., 1995; Favero,

1999) have been shown to reduce SR Ca2+ release in vivo. Moreover, the decline in SR

Ca2+ release occurs later in fibres with high capacity for oxidative metabolism (van der

Laarse et al, 1991) and it occurs more rapidly when fibres are fatigued under anaerobic

conditions (Lännergren and Westerbald, 1991). However, in most of the studies, SR

Ca2+ release activity is assessed in vitro, under conditions resembling the environment

of a resting muscle, suggesting that the direct action of the metabolites does not induce

decrements in SR Ca2+ release activity per se. It is important to note that most of the

current knowledge of SR responses to fatigue has been gained from animal studies

utilizing non-physiological models (i.e. chronic low-frequency electrical stimulation), to

induce repetitive contractile activity. Owing to the fact that the aetiology of muscle

fatigue is dependent on stimulation frequency or muscle activation protocol, and also

probably is species-specific (Danieli-Betto et al., 2000), it seems reasonable to argue

whether or not these recent advances, providing a greater understanding in other

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species, can be extended to the human muscle under physiological models of voluntary

activity.

Studies investigating SR function in exercising human muscle have shown that SR Ca2+

uptake and/or Ca2+-ATPase activity is retarded by prolonged submaximal exercise

(Booth et al., 1997; Green et al., 1998; Tupling et al., 2000) and after intense exercise

(Gollnick et al., 1991; Hargreaves et al., 1998; Hill et al., 2001; Li et al., 2002). Only

recently, the effects of fatigue on SR Ca2+ release in human skeletal muscle have been

investigated. Hill et al. (2001) were the first authors reporting an association between

impaired Ca2+ release and low frequency fatigue in man. They found a 35% depression

in Ca2+ release (P < 0.01) immediately following 7 min of intense, isokinetic, leg

kicking exercise with a 33% reduction in the maximum voluntary isometric torque. Li et

al. (2002) have reported that SR Ca2+ release was markedly reduced with fatigue after

maximal knee extension contractions in three groups of subjects with different training

status and that this reduction was significantly related to the extent of muscle

fatigability. Interestingly, neither Hill et al. (2001) nor Li et al. (2002), despite the

profound alterations in metabolic status following exercise, could find any relationship

between metabolic disturbances and failure of SR Ca2+ release. For example, Hill et al.

(2001) reported a 15-fold increase in muscle lactate and 32% decrease in

phosphocreatine (PCr) values. Although animal studies have pointed out metabolic

disturbances as potential mechanisms responsible for the decrease in the rate of SR Ca2+

release, the results of Hill et al. (2001) and Li et al. (2002) seem to suggest that

impaired SR Ca2+ release regulation is not directly related to muscle metabolic strain in

human muscle. However, with such limited research, evidence-based conclusions are

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not yet possible and further studies eliciting different metabolic responses in exercising

humans are needed.

2.3.1.2. Metabolic energy supply

Repeated-sprint exercise demands extremely high rates of ATP hydrolysis to supply

energy to both contractile (i.e., actomyosin power stroke) and non-contractile (i.e.,

Na+/K+ pump, Ca2+ pump) events. A large fraction of the energy required to fuel periods

of brief, high-intensity exercise is supplied by anaerobic metabolism (i.e., glycolysis

and high-energy phosphates). As mentioned above, such exercise leads to a rapid

decline (few seconds) in force or power output. Therefore, it is believed that some of the

consequences of anaerobic metabolism cause the decline in contractile function

(Westerblad et al., 2002). Alterations in the excitation-contraction coupling and/or in the

contractile apparatus (i.e., peripheral fatigue) as a result of limitations in energy supply

and intramuscular accumulation of selected metabolic by-products such as lactate, H+,

Pi, AMP, ADP, IMP have been traditionally regarded as the primary cause of muscular

fatigue during high-intensity exercise (Allen et al., 1995; Green et al., 1997; Sahlin et

al., 1998).

2.3.1.2.1. PCr availability/Pi accumulation

Although, during a single brief maximal effort of 10 seconds, the anaerobic system

supplies up to 94% of the energy (Gastin, 2001), the energy patterns change radically

when intense bouts of exercise are repeated (Christmass et al., 1999). During brief,

high- intensity exercise most ATP resynthesis is provided by means of phosphocreatine

(PCr) breakdown and muscular glycogen degradation to lactic acid (Spriet et al., 1989;

Hargreaves et al., 1998). Following a brief maximal effort, a decrease in PCr stores has

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been reported (Bogdanis et al., 1995). After such exercise, complete recovery of PCr

stores can last between 3 and 5 min (Tomlin and Wenger, 2001). However, recovery

time between two consecutive efforts in most multiple-sprint sports is usually smaller

(Bangsbo, 1994; Roetert and Ellenbecker, 1998). Thus, PCr stores may only be partially

restored before the next action. Moreover, with repetition of high-intensity efforts, these

stores will progressively depleted (Yoshida and Watari, 1993; Gaitanos et al., 1993;

Stathis et al., 1994). Several studies have reported that during repeated bouts of high

intensity exercise with incomplete recovery phases, the contribution of aerobic

metabolism to energy supply becomes greater. This is matched with a reduction in

anaerobic ATP production (Spriet et al., 1989; Gaitanos et al., 1993; Bogdanis et al.,

1996; Hargreaves et al., 1998; Krustrup et al., 2001). Parallel to these bioenergetic

changes, a reduction in power output has been reported (Gaitanos et al., 1993, Bogdanis

et al., 1996, Hargreaves et al., 1998). The reduced availability of PCr might contribute

to this decrease of anaerobic energy production and exercise performance. Bogdanis et

al. (1996) compared the contribution of aerobic and anaerobic metabolism during two

repeated maximal cycle sprints, separated by 4 min of recovery. They reported that

during sprint 2, anaerobic ATP turnover was reduced up to 41% from sprint 1, whereas

aerobic metabolism provided almost half (49%) of the total amount of energy.

Moreover, they found a high correlation between PCr availability and the power output

during the last sprint. Investigations carried out by the same authors (Bogdanis et al.,

1995) showed that after maximal cycling, peak power output recovered following a

similar time course to that of PCr resynthesis. Similarly, the relationship between

maximal force and muscle PCr levels both during contraction and during the recovery

period has been reported for isometric submaximal contractions to fatigue (Sahlin and

Ren, 1989). This is further supported by research reporting that after creatine

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supplementation, muscular performance can be enhanced (Balsom et al., 1995; Van

Leemputte et al., 1999; Mujika et al., 2000) by augmentation of initial PCr stores

(Hultman et al., 1996).

These results seem to suggest that from an energetics perspective, maintaining a high

power output is highly correlated with PCr availability. However, it is doubtful that low

PCr levels and/or lack of energy per se cause fatigue during intense repeated exercise

(Bangsbo, 2000), since muscle ATP rarely falls below 60% of pre-exercise levels

during exhaustive exercise (Bangsbo et al., 1992). However, there have been single

fibre studies identifying almost complete loss of ATP with fatigue (Karatzaferi et al.,

2001). Moreover, St Clair Gibson et al. (2001a) have pointed out that decrements in

sprinting performance follow a curvilinear rather than a rectilinear trend. The authors

noted that if substrate depletion was the cause of decrements in sprinting performance, a

cumulative effect would be expected, and sprint performance would decrease linearly to

zero through the course of the sprint trial as the participants eventually became

completely exhausted.

PCr breakdown leads to an almost stoichiometric increase in inorganic phosphate (Pi)

within the muscle cell (Sahlin et al., 1998). Several mechanisms have recently

associated the increased level of Pi with the depression of muscular function, suggesting

this to be a major cause of muscle fatigue (Westerblad et al., 2002). Therefore, it is

possible that the observed correlation between power or force output and PCr during

exercise and recovery might be related to the augmented Pi concentration and not energy

deficiency per se (Sahlin et al., 1998). However, the role of Pi accumulation as a

contributor to muscular fatigue in humans is unclear. The increased intramuscular PCr

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stores after a period of creatine supplementation is expected to cause an augmented

release of Pi and therefore an earlier onset on muscle fatigue (Sahlin et al., 1998).

Moreover, Rico-Sanz (2003), using 31P-magnetic resonance spectroscopy, found that

muscular fatigue during repeated, intense isotonic exercise in humans occurred despite

the total Pi accumulation progressively decreasing with the number of bouts. This last

finding, as well with the fact that creatine supplementation increases muscular

performance during repeated-sprint exercise, does not seem to support the role of Pi

accumulation as a major limiting factor for performance during repeated-sprint exercise.

2.3.1.2.2. Muscle glycogen

Although the importance of muscle glycogen on continuous endurance exercise is well

documented (Coggan and Coyle, 1991; Febbraio and Dancey, 1999; Febbraio et al.,

2000; Romijn et al., 2000; Fritzsche et al., 2000), the role that this energetic source

plays in the performance of high intensity intermittent exercise remains unclear. Casey

et al. (1996), found that a reduction in carbohydrate intake in the diet, and consequently

a lower availability of muscular glycogen, resulted in a reduced total work output

during the first three 30-s sprints, but not in the fourth, during a maximal cycle

ergometer exercise. Similar results have been reported by other investigators (Nevill et

al., 1993; Hargreaves et al., 1998; Harmer et al., 2000). In addition, fatigue during

intense short-term intermittent exercise (leading to exhaustion in approximately 15 min)

was unrelated to muscle glycogen levels (Krustrup et al., 2003). Therefore, it seems to

be difficult to change intermittent exercise performance by manipulation of

carbohydrate in the diet.

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However, muscle glycogen might play an important role in intermittent endurance

performance. Rico-Sanz et al. (1999) have reported a high correlation between muscle

glycogen utilization and resting muscle glycogen and a moderate correlation between

the net muscle glycogen utilized and time to exhaustion during a soccer specific

intermittent test. Similar conclusions have been reached by other investigators (Leatt

and Jacobs, 1989; Bangsbo et al., 1992c; Balsom et al., 1999; Nicholas et al., 1999) who

measured muscle glycogen concentrations. Moreover, several studies have positively

correlated carbohydrate-electrolyte drink ingestion with prolonged intermittent

endurance performance (Nicholas et al., 1995; Welsh et al., 2002). Although, it is not

clear what the mechanisms are, these results suggest that low levels of muscle glycogen

may be related to fatigue during prolonged intermittent exercise. However, apart from

any possible metabolic cause, the observation that a high percentage of individual fibres

were full, or partly full, with glycogen at exhaustion (Krustrup et al., 2003), may

suggest that integrated neural mechanisms contribute to performance impairments

(Carter et al., 2004).

2.3.1.2.3. Acidosis

During intense exercise, glycogen is broken down to provide ATP to the contracting

muscle fibre. Intramyocellular accumulation of lactic acid occurs as a consequence of

the degradation of glycogen during high-intensity muscle contraction (Rico-Sanz,

1998). Lactic acid dissociates into lactate and H+. While lactate ions are believed to

have little effect on muscle contractility (Bangsbo et al., 1992a; Posterino et al., 2001),

the increase in H+ (i.e., reduced pH or acidosis) is the classic cause of skeletal muscle

fatigue (Westerblad et al., 2002). Reduced pH is believed to be an important factor in

muscle fatigue for at least three reasons (Bruton et al., 1998). First, low pH inhibits the

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activity of enzymes such as phosphofructokinase (Hermansen, 1981), which are

important for maintenance of the anaerobic energy supply. Second, studies that used

isolated muscle fibres have clearly shown that low pH has an inhibitory effect on

muscle performance, reducing both force output and shortening velocity (Metzger and

Moss, 1987; Westerblad and Allen, 1993). Finally, investigations in human muscle in

situ have shown that there is a good correlation between the decline in pH and the fall in

force as fatigue develops (reviewed by Giannesini et al., 2003). Moreover, a decrease in

action potential conduction velocity along the muscle fibre has been related to reduced

levels of intracellular pH (Brody et al., 1991).

Repeated-sprint exercise is usually associated with elevated anaerobic glycolysis

turnover, and consequently, with a large production and accumulation of lactic acid

(Brooks et al., 1990; Gaitanos et al., 1991; Balsom et al., 1992b; Gaitanos et al., 1993).

Therefore, muscle acidosis has been associated with the decline in power output during

repeated-sprint exercise (Gaitanos et al., 1993; Bishop et al., 2003). However, recent

studies have challenged the role of acidosis as a direct cause of muscle fatigue.

Results for studies using single muscle fibres provide the most direct evidence of the

cellular mechanisms of muscular fatigue. This is because investigators are able to

control the surrounding environment and produce an isolated acidosis without other

metabolic changes (Bruton et al., 1998; Westerblad et al., 2002). Several of these

studies have recently shown that muscle acidosis may have little effect on skeletal

muscle contractile performance at near-physiological temperatures (Westerblad et al.,

2002). Previous evidence linking fatigue and acidosis has come from studies with

isolated fibres performed at lower temperatures (< 15º C) (Westerblad et al., 2002).

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Reduced pH in those studies have shown that acidification may reduce isometric force,

shortening velocity and relaxation time (Allen et al., 1995). Other studies in animals

have shown similar results disassociating H+ from muscle fatigue (Adams et al., 1991;

Cieslar and Dobson, 2001). Similarly, research in humans have shown that the

decrements in muscular performance during intense exercise do not always occur

parallel to a reduced pH (Bangsbo 1992b; Bangsbo et al., 1993; Bangsbo et al., 1996;

Degroot et al., 1993; Rico-Sanz, 2003). Furthermore, the accumulation of intracellular

H+ decreased with repeated bouts of intense isotonic exercise inducing fatigue in

humans (Rico-Sanz, 2003). In addition, muscle pH remains depressed for several

minutes during recovery, while force or peak power output rapidly recovers (Sahlin and

Ren, 1989; Bogdanis et al., 1995) suggesting the existence of other factors in the

development of muscular fatigue during intense exercise.

In the applied repeated-sprint literature, it has also been reported that recovery of power

output can be achieved despite reduced pH (Blonc et al., 1997; Bogdanis et al., 1998).

Bogdanis et al. (1998) found that after a 10-s maximal cycling sprint, pH decreased and

remained unaffected for at least the following 2 min of recovery. However, their

subjects were able to reproduce peak power output in a second 10-s sprint after 2 min of

recovery. Similarly, Blonc et al. (1997) reported that power development during 6-8 s

maximal cycling sprints, intercalated with different recovery durations, were unaffected

from lactate values observed between sprints. Billaut et al. (2003) also found that

subjects could reproduce power output over two 8-s maximal cycling sprints with 30 s

rest. Furthermore, Balsom et al. (1992b) and Gaitanos et al. (1993) showed that a

reduction in performance, during repeated 40-m running sprints (approximately 6 s

duration) and 6-s cycling sprints separated by 30 s, was evident only after the 5th sprint.

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Additionally, Krustrup et al. (2003) reported that the development of fatigue during

intense short-term intermittent exercise (i.e., repeated 20-m running sprints with 10 s

rest at a progressively increased speed performed until exhaustion) was unrelated to low

pH or high muscle lactate concentrations. When combined, these findings suggest that

acidosis is not directly involved, or, it is not the only factor causing fatigue during

repeated-sprint exercise. Rather than being a direct cause of muscular fatigue, the

observed temporal correlation between reduced pH and impaired muscle function may

be coincidental. Thus, a decline in muscle pH reflects the metabolic consequences of

intense exercise exceeding the aerobic and anaerobic system capabilities to buffer ATP

(Westerblad et al., 2002).

2.3.2. Central fatigue

Peak power generation is achieved through the optimal and dynamic interaction

between the muscle (excitation-contraction processes), neural recruitment (type of

motor units, size of motor unit pool, frequency and pattern of discharge), and the

metabolic source of ATP production (anaerobic and aerobic) (Green, 1986). Repetitive

muscular contractions have been shown to affect the optimal synchrony between these

interacting factors. As a result, a decrease in the capacity to generate force or power

output is often observed (i.e. fatigue) (Green, 1986). The decrease in mechanical power

output during repeated-sprint exercise with incomplete recovery periods has been

mainly attributed to peripheral changes within the muscle cell (Bangsbo et al., 2000).

However, part of the fatigue (i.e., decline in power output) during this kind of exercise

might be of central origin.

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Central fatigue can be defined as the loss of force occurring through inadequate

activation of the motoneurons (Taylor et al., 2000). Maximal-intensity sprint exercise

necessitates extremely high levels of neural activation (Ross et al., 2001). Therefore, for

multiple-sprint athletes, any fatigue-induced reductions in neural drive to the

motoneurons would negatively affect muscular performance during repeated-sprint

exercise. Fatigue of central origin has been reported to occur during isometric

(Gandevia et al., 1996) and isokinetic (Newham et al., 1991) maximal efforts.

Moreover, central mechanisms have been found to contribute to the development of

fatigue during maximal intermittent exercise using both isometric (Taylor et al., 2000;

Nordlund et al., 2004) and isokinetic concentric (Michaut et al., 2003) contractions. The

putative mechanisms of central fatigue are numerous. Central fatigue can originate from

a supraspinal site and/or from the spinal level (Gandevia, 2001) whose relative

contribution may be task dependent.

For instance, Taylor et al. (2000), during a series of intermittent isometric maximal

voluntary contractions of the elbow, found an increment in torque evoked by

transcranial magnetic stimulation concomitant with a decline in maximal voluntary

torque. The isometric contraction times ranged between 5 and 30 s. The loss of

voluntary force averaged 7%, but the authors reported losses greater than 25% in

individual trials and individual subjects. This indicated supraspinal fatigue resulting in a

suboptimal output from the motor cortex to the motoneuron pool preventing maximal

activation of the skeletal muscle. Whether such fatigue contributions to fatigue are

present during repeated-sprint exercise is not known. However, if pertinent, this premise

would suggest that during similarly repeated 5-s sprints, sub-optimal output from the

motor cortex to the muscle might potentially limit performance.

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As previously reported, repeated-sprint exercise is associated with dramatic metabolic

disruptions within the muscle cell (Gaitanos et al., 1993). Despite the direct effects that

selected metabolites might have on muscle contractility (Allen et al., 1995), it is also

believed that changes in metabolites may have an indirect action via centrally controlled

mechanisms (Bigland-Ritchie et al., 1986b; Gandevia, 2001). A reflex originated within

the muscle in response to metabolic changes, via group III and IV afferents, has been

proposed (Kaufman et al., 1983; Bigland-Ritchie et al., 1986a; Garland, 1991; Sinoway

et al., 1993). These reflexes possibly contribute to the reduction in voluntary drive

through spinal and supraspinal actions during maximal contractions (Bigland-Ritchie et

al., 1986b, Duchateau and Hainaut 1993, Duchateau et al., 2002; Gandevia, 2001,

Garland and McComas, 1990). Indeed, this feedback loop between intramuscular

receptors and the central nervous system would match the neural drive to the muscle

contractile properties. Thus, in the fatigue state, the reflex may act as a protective

mechanism to preserve force-generating capabilities and possibly to avoid irreversible

cellular damage (Bigland-Ritchie et al., 1986b). As muscle fatigues, contractile

properties, particularly relaxation time becomes slower (Bigland-Ritchie and Woods,

1984). This slowing of relaxation time, accompanied by a reduction in neural drive (i.e.,

firing rates) will allow the muscle group to maintain the same force output levels

(Bigland-Ritchie et al., 1986b). While results of this research have been gained using

mainly sustained isometric contractions and animal models, the possibility of reduced

motoneuron excitability in response to metabolic disruptions remains as potential

fatigue agents that would affect the ability to fully activate the synergistic musculature

during repeated “all-out” sprints. This would ultimately impair performance.

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In the context of the multiple-sprint sports, high-intensity exercise during a match often

consists of rapid accelerations following by deceleration with players rarely achieving

maximum velocity. For example, McInnes et al. (1995) reported that the average

duration of high-intensity running was 1.7 ± 0.2 s for basketball players during

competition, values that are similar to those recorded in male field hockey-players: 1.8

± 0.4 s (Spencer et al., 2004). Often, these high-intensity activities will determine the

final outcome of a game (Reilly, 1997). Thus, the ability to generate explosive power

within a few seconds seems to be an important determinant of performance for multiple-

sprints athletes. Consequently, any factor that reduces the rate of force development

(RFD) would contribute to fatigue during fast movements, such as sprint running, by

decreasing the percent of force that can be achieved in the early phase of muscle

contraction (Fitts, 1994; Aagaard et al., 2002). RFD is influenced by both neural and

peripheral factors (Aagaard et al., 2002). However, neural output to the motoneuron is

likely to be the major contributor to the RFD magnitude (Grimby et al., 1981; Nelson,

1996). During maximal-intensity voluntary exercise, there is a decrease of the

motoneuron firing rate as the contractile properties within the muscle (i.e., shortening-

relaxation rates) become slower in response to change(s) caused by fatigue (Bigland-

Ritchie et al., 1983a; Bigland-Ritchie et al., 1983b). This reduction in motoneuron

discharge frequency would in turn yield a suboptimal efferent neural drive and

therefore, might reduce RFD. Training studies that have demonstrated concurrent

increases in RFD and the efferent neuromuscular drive of skeletal muscle support this

hypothesis (Van Cutsem et al., 1998; Aagaard et al., 2002). Interestingly, a reduction in

motoneuron firing rates has been reported to occur despite the ability to maintain the

same level of mechanical force (De Luca et al., 1996). Moreover, RFD can increase at

stimulation rates higher than to produce maximum force (Grimby et al., 1981; Nelson,

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1996). Thus, suboptimal efferent motoneuron output is likely to affect more the RFD

than maximal contraction force per se. This suggests that neglecting dynamic

parameters such as RFD, by only utilising force as the fatigue variable, might

underestimate the functional impairment of fatigued muscles (Vedsted et al., 2003).

Owing to repeated-sprint exercise requiring brief, fast limb movements, any reduction

in the RFD might limit muscular performance as it will be difficult to reach higher

levels of muscle force within the initial phase of muscle contraction.

Although the critical phases of play during sport games are likely to be dependent on

repeated anaerobic efforts, these are superimposed on a background of largely aerobic

submaximal activities (Reilly, 1996). For instance, it has been reported that soccer

players cover between 8 and 12 km during a game (Reilly 1996). As a result of

repeated-sprint exercise punctual and transient episodes of muscular fatigue may occur

after an intense exercise bout or after a set of repeated sprints. Moreover, due to the

prolonged pattern of exercise, a deterioration in performance similar to that occurring

during long-term exercise, may be observed (Bangsbo, 1995). Indeed, several studies

have reported a reduction in the total distance covered (Bansgbo et al., 1991) and the

amount of high-intensity running (Rebelo et al., 1998; Mohr and Bangsbo, 2001) at the

end of a soccer match that seems to be related to the development of fatigue (Reilly,

1997). The occurrence of this type of fatigue in the latter stages of the match may also

have a central component. Recent studies have demonstrated that central fatigue plays

an important role in the loss of neuromuscular performance during long-duration

repetitive dynamic actions (Lepers et al., 2000; Lepers et al., 2002; Millet et al., 2003,

Place et al., 2004).

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To summarise, a decrease in neural drive to the motoneurons originating from

supraspinal and/or spinal level has been reported to occur during maximal voluntary

activation. As maximal sprint exercise demands high levels of neural drive, failure to

fully activate the contracting musculature will clearly affect force development and

sprint performance. Moreover, the presence of central fatigue associated with long

submaximal efforts might cause further impairments in the ability to develop explosive

power in the later stages of the match. In order to design training protocols aiming to

avoid or delay the appearance of muscular fatigue, further research is needed to

understand its mechanisms and to quantify the relative importance of the neural origin

of losses in performance during repeated-sprint exercise. The in vivo human studies of

repeated-sprint exercise restrict the ability to easily measure motoneuron pool

excitability or changes in sarcolemmal membrane potential. Thus, changes in neural

performance can only be inferred from measures that reflect the overall changes in the

neuromuscular unit.

2.4. Neuromuscular activity monitored by surface electromyography

(EMG)

Electromyography (EMG) signals are the electrical manifestation of the neuromuscular

activation associated with a contracting muscle (Basmajian and De Luca, 1985). As a

consequence, surface EMG signals are frequently used for the non-invasive study of

muscle fatigue. These signals reflect the cumulative effect of the action potentials of the

recruited muscle fibres (Orizio, 2000). EMG recordings can be analysed in the time

domain (amplitude) and in the frequency domain (power spectrum). However, as the

signal is not stationary during dynamic exercise, power spectral analysis is more

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problematic (De Luca, 1997). Therefore, in the following section only the EMG

changes in the time domain are discussed.

2.4.1. EMG changes in the time domain

The amplitude characteristics of EMG are determined using rectified signals, averaged

rectified signals, envelopes of rectified signals or root mean square (RMS) of the EMG

(Basmajian and De Luca, 1985). Expression of the EMG signal in the time domain

allows for evaluation of neuromuscular activation patterns, inasmuch the magnitude of

the EMG amplitude is believed to be determined by the number of active fibres, their

firing rate, their size and the average size of the each fibre action potential (Bigland-

Ritchie, 1981; Suzuki et al., 2002). Moreover, EMG amplitude may be, at least

partially, related to the efferent neural output to the muscle (Aagaard et al., 2002). By

monitoring EMG activity it is possible, therefore, to clarify whether a decrease in power

output is completely attributable to the loss of muscle contractile properties or whether

central drive may contribute to this loss.

During maximal isometric contractions, EMG amplitude decreases progressively

(Bigland-Ritchie et al., 1983a; Kranz et al., 1985; Moritani et al., 1986), and this change

is probably caused by a gradual decline in motor unit excitation rates (Bigland-Ritchie

et al., 1983a). A decrease in EMG amplitude during intermittent, maximal isometric

contractions has also been reported (Gabriel et al., 2001). On the contrary, during

submaximal static contractions, a different pattern occurs and the EMG amplitude

increases (Viitasalo and Komi, 1977; Fallentin et al., 1993; West et al., 1995). This

increase in EMG amplitude has been associated with a decline in motor unit mean firing

rate (De Luca et al., 1996) and the recruitment of more motor units with increasing

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fatigue (Fallentin et al., 1993). During repetitive submaximal isometric contractions, the

same pattern has been reported (Bigland-Ritchie et al., 1986b). From the literature

available, Orizio (2000) stated that during isometric contractions: a) the EMG amplitude

decrease may reflect the derecruitment of motor units, a decrease in motor unit firing

rate and a possible decrease in the amplitude of the motor unit action potential; b) the

EMG amplitude increase may reflect changes in the shape of the muscle fibre action

potential and decrease in conduction velocity and a slowing of the signal, recruitment of

new motor units, and synchronization and grouping of active motor units.

The extrapolation of these findings, during sustained isometric contractions, to dynamic

(concentric, eccentric) exercise is complicated because: a) the EMG signal is non-

stationary; b) relative movement between electrodes and muscle may create signal

artefacts; and c) the motor unit pool may change during movement (Merletti et al.,

2001). However, static contractions are not common in daily life or athletic activities.

Instead, most daily activities consist of dynamic contractions. In order to obtain relevant

data to human performance under more physiological contraction modes, EMG analysis

have been extended to dynamic conditions (Tesch et al., 1983; Bouissou et al., 1989;

Tesch et al., 1990; Potvin and Bent, 1997; Gerdle et al., 2000; Linnamo et al., 2000;

Kay et al., 2001; Scheuermann et al., 2001; St Clair Gibson et al., 2001b; Burnley et al.,

2002; Hunter et al., 2002; Hunter et al., 2003; Karlsson et al., 2003; Nikolopoulos et al.,

2004). By using standardized dynamic contractions (e.g., isokinetic dynamometers or

cycling) and by applying filtering procedures (i.e., high-pass filter) some of the

problems associated with the measurement of EMG activity during dynamic exercise

can be overcome (Conforto et al., 1999; Karlsson et al., 2003). This has extended the

application of monitoring EMG manifestations of muscular fatigue to dynamic

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contractions without violating some of the basic assumptions of the methods used for

analysis (Karlsson et al., 2003).

To date, only one study has examined the electromyographic manifestations of fatigue

during a repeated-sprint task. Hautier et al. (2000) analysed ten males who performed

15 maximal 5-s sprints with a 25-s rest between them on a friction-loaded cycle

ergometer. The EMG activity was obtained from five leg muscles during sprint 1 and

sprint 13. Maximal power output decreased significantly (11.3%) from the first sprint to

the 13th sprint. With reference to the prime mover muscles during cycling (gluteus

maximus and vastus lateralis) EMG amplitude remained unchanged after fatigue (i.e.,

sprint 13). The authors concluded that muscles were maximally activated during the

repeated-sprint exercise and therefore, the loss of power could be attributed mainly to

peripheral fatigue involving neuromuscular transmission and/or impaired excitation-

contraction coupling. However, with such a paucity of data further investigations seem

to be warranted.

2.5. Conclusions

Repeated-sprint ability is an important fitness component of many popular sports.

Several studies have shown that repeated-sprint exercise causes muscle fatigue.

However, the review of the literature highlights that very little is known about the

causes of muscular fatigue during repeated-sprint exercise. The peripheral factors within

the muscle cell, in response to the exaggerated metabolic demands imposed by this type

of exercise have been regarded as the main cause of muscular fatigue, but conclusive

evidence is still not apparent. Moreover, some of the findings from studies of isolated

muscle fibres can not be reconciled with the results obtained from exercising humans.

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Nevertheless, it appears that the aetiology of muscle fatigue during repeated sprint

exercise is a complex phenomenon that might involve neural activation to the

contracting muscles as well as factors within the muscle fibres. More integrative studies

are needed to gain further insights into what are the limiting factors in muscular

performance during repeated-sprint exercise. Therefore, the purpose of this study was to

further examine the time-course of mechanical power output and the associated

neuromuscular activity during fatiguing repeated-sprint exercise and recovery in man. It

was hypothesised that both central and peripheral factors would be responsible for the

loss of power observed during repeated-sprint exercise in man.

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CHAPTER 3

Reproducibility of a 6-s maximal cycling sprint test

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3.1. Introduction

Anaerobic performance (i.e., the ability to generate maximal power during brief,

maximal intensity exercise) is an important fitness component for many athletic events

and daily activities. Although many different tests have been developed to assess

anaerobic performance, currently the most widely used “anaerobic test” is the 30-s

Wingate Anaerobic Cycling Test, in which subjects pedal maximally for 30 s against a

constant resistance applied to the flywheel (Inbar et al., 1996). This relatively simple

test requires minimal levels of skill and provides a highly reliable measure of peak

power, mean power and a fatigue index (Inbar et al., 1996).

Although the Wingate test is used as an index of anaerobic performance, aerobic energy

sources have been estimated to provide 20-30% of the energy utilized throughout the

Wingate test (Withers et al., 1991; Inbar et al., 1996; Calbet et al., 1997). Moreover, the

relative contribution of aerobic and anaerobic metabolism to the power generation

during the Wingate test has been reported to be dependent on the subject’s training

background (Granier et al., 1995; Calbet et al., 1997; Calbet et al., 2003). That is,

endurance-trained athletes obtain a higher fraction of the energy for the Wingate test

from oxidative metabolism than sprint-trained athletes. Despite these results, very little

effort has been directed to the development of anaerobic cycling tests yielding

meaningful data on human muscular power with a minimal aerobic involvement.

Moreover, shorter tests may be required to assess anaerobic performance in specific

athletic, clinical or research populations.

A 6-s maximal cycling test is believed to depend heavily on the muscular functions of

strength and power (Wilson and Murphy, 1996) and on the amount and rate of

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utilization of anaerobic metabolism (Gaitanos et al., 1993). It is now generally accepted

that the ATP turnover to fuel a sprint exercise lasting 6 s or less is almost exclusively

provided by anaerobic metabolism (Boobis et al., 1982; Gaitanos et al., 1993; Parolin et

al., 1999). Therefore, it appears that a 6-s maximal sprint cycling test can provide an

accurate indication of anaerobic power characteristics in humans.

In addition, many athletic activities require participants to repeatedly produce maximal

sprints of short duration (< 6 s) interspersed with brief recovery periods, over an

extended period of time (60 to 90 min) (Spencer et al., 2004). To study the development

of muscle fatigue and other physiological responses during this kind of exercise,

laboratory and field protocols in which repeated maximal sprints (5 to 10) are separated

by a short and fixed recovery period (typically 30 s) have been developed (Fitzsimons et

al., 1993; Gaitanos et al., 1993; Ørtenblad et al., 2000; Bishop et al., 2001; Bishop et al.,

2003; Billaut et al., 2004a; Bishop et al., 2004). To evaluate power decrements induced

by fatigue during repeated-sprint bouts, reliable baseline values seem to be essential.

While some studies have evaluated the reliability of power output during different

repeated-sprint cycling tests (Fitzsimons et al., 1993; Capriotti et al., 1999; Glaister et

al., 2003) using fixed recovery periods, we are not aware of any study assessing the

reproducibility of power output during a single 6-s cycling sprint.

Fitness tests must provide valid, objective and reliable information. Reliability of a test

is defined as “the consistency or reproducibility of performance when someone

performs the test repeatedly” (Hopkins et al., 2001). A test is valid if it measures what it

claims to measure (Australian Sports Commission, 2000). While the validity of any

anaerobic test is somewhat difficult to prove due to the lack of a criterion performance,

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researchers and practitioners should use highly reliable tests, because these are the only

tests that can also have high validity (Hopkins et al., 2001). Therefore, the aim of this

study was to investigate the reproducibility of muscle power output during 6-s maximal

cycling sprint on an air-braked cycle ergometer .

3.2. Methods

3.2.1. Subjects

Eleven healthy males volunteered to participate in this study. The subjects were all

sport-science students and included multiple-sprint sports, middle-distance runners,

sailors and martial art performers. The subjects’ characteristics were as follows (mean ±

SD): age 19 ± 1 y, height 181.5 ± 6.2 cm, mass 76.9 ± 9.5 kg, and O2peak 54.9 ± 6.1

mL·kg-1·min-1. All subjects were unfamiliar with cycle sprinting before participation in

the present study. The exercise protocol and all possible risks and benefits associated

with participation in the study were explained to each subject. Each subject provided

written informed consent prior to participating in the study. Approval for the study’s

procedures was granted by the Human Rights Committee of The University of Western

Australia.

3.2.2. Ergometer

An air-braked front-access cycle ergometer (Repco, Melbourne, Australia) was used to

conduct the tests. The ergometer was interfaced with an IBM-compatible computer

system to allow for the collection of data for the calculation of power generated on each

flywheel revolution and work performed during each individual sprint repetition

(Cyclemax, The University of Western Australia, Perth, Australia). The technical

aspects of the air-braked cycle ergometer have previously been described

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39

(Maxwell et al., 1998). In brief, subjects pedal against air resistance caused by

rectangular vanes attached perpendicular to the axis of rotation of the flywheel. The

power output of the air-braked cycle ergometer is proportional to the cube of the

flywheel velocity. An optical sensor monitored the velocity of the flywheel at a

sampling rate of 80 pulses per pedal revolution. Before testing, the ergometer was

dynamically calibrated on a mechanical rig (Western Australian Institute of Sport,

Perth, Australia) across a range of power outputs (100–2000 W) following the

procedures described elsewhere (Maxwell et al., 1998). Peak power output (PPO) and

mean power output (MPO) were calculated for each maximal 6-s cycling bout.

3.2.3. Procedures

Each subject performed a 6-s standing sprint on the front-access cycle ergometer on

four separate occasions. The testing sessions were separated by a minimum of one day

and a maximum of seven days. Subjects performed their trials at the same time of the

day (± 2 h) with the laboratory conditions being approximately 20º C and 50% relative

humidity during all trials. The subjects were asked to follow their normal diet and to

refrain from any form of intense physical activity for the 24 h prior to testing, nor eat

within 2 h before each testing session. Before each test, subjects performed a

standardized warm-up comprising, 4 min of cycling at 100 to 120 W, followed by 3

bouts of maximal standing start acceleration (approx 2 s) and then 3 min of rest prior to

performing the 6-s sprint. Subjects were instructed to perform an “all out” effort from

the beginning of the test until instructed to stop. All sprints were performed from the

same initial pedal position. Toe clips and heel straps were used to secure the feet to the

pedals. Strong verbal encouragement was provided during each trial.

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3.2.4. Statistical analyses

In order to test for any systematic change in performance from trials 1 to 4, a one-way

analysis of variance (ANOVA) was used to compare the PPO and MPO obtained during

the four trials. When a significant interaction was detected, data were subsequently

analysed using a Newman-Keuls post-hoc test to determine where that difference

occurred. The typical or standard error of measurement (SEM) expressed as a mean

coefficient of variation (CV) was used as a measure of within-subject reproducibility

(Hopkins et al., 2001) for PPO and MPO. The CV was derived from the root mean-

square error (RMSE) (the RMSE is the SD that represents the within-subject variation

from test to test, averaged over all the subjects) by performing a two-way ANOVA on

the natural logarithm of the variable, and then transforming the within-subject SD

(RMSE) with the following formula (Schabort et al., 1998):

CV = 100(eSD – 1)

Confidence intervals (CI) for the CV were calculated using the methods described by

Hopkins (2003).

3.3. Results

Figure 1 displays the PPO (mean ± SD) recorded during the four, 6-s maximal cycling

tests. PPO was significantly higher (4.9%; P < 0.05) in trial 2 compared with trial 1,

whereas there were no significant differences between trials 2, 3 and 4. Similarly, the

MPO (Figure 2) for trial 2 was higher (5.8%; P <0.05) than the MPO in trial 1, but

there were no difference across trials 2, 3 and 4.

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800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

Trial 1 Trial 2 Trial 3 Trial 4

Peak

pow

er o

uptu

t (W

)

* * *

Figure 3.1. Peak power recorded during the four 6-s maximal cycling sprint trials. *Significantly higher

(P < 0.05) compared with trial 1. Values are mean ± SD for the four trials (N = 11).

Table 1 shows the within-subject CV between trials for PPO and MPO. The removal of

data obtained in trial 1 markedly decreased the within-subject CV (4.4 to 2.8% and 4.0

to 2.9% for PPO and MPO respectively). Moreover, lower CV were obtained when only

data for day 3 and 4 were analysed (1.8% and 2.5% for PPO and MPO respectively).

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42

700

800

900

1000

1100

1200

1300

1400

1500

1600

Trial 1 Trial 2 Trial 3 Trial 4

Mea

n po

wer

out

put (

W)

* * *

Figure 3.2. Mean power recorded during the four 6-s maximal cycling sprint trials. *Significantly higher

(P < 0.05) compared with trial 1. Values are mean ± SD for the four trials (N = 11).

Table 1.1. Reliability of performance between trials for peak and mean power expressed as a coefficient

of variation (CV) and confidence limits (95% CL).

Peak Power Mean Power

CV (%) 95% CL (%) CV (%) 95% CL (%) Trials 1, 2, 3, 4 4.4 3.5 – 5.8 4.0 3.2 – 5.4 Trials 2, 3, 4 2.8 2.1 – 4.0 2.9 2.2 – 4.1 Trials 3, 4 1.8 1.3 – 3.2

2.5 1.7 – 4.4

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3.4. Discussion

The main finding of the present study was that the PPO and MPO achieved during the

first 6-s maximal cycling sprint trial was less (P < 0.05) than that achieved during trials

2, 3 and 4. There was no significant difference in power output between trials 2, 3 and 4

suggesting that good reproducibility could be obtained after one familiarization session.

However, reproducibility was improved with a second familiarization trial. The CV

calculated from trials 2, 3 and 4 was 2.8 and 2.9% for PPO and MPO respectively,

while the CV calculated using data from the third and fourth trial was lower: 1.8 and

2.5% for PPO and MPO respectively. However, these results should be interpreted with

caution because the performance of the air-braked cycle ergometer used for testing in

the present study might be influenced by changes in barometric pressure or temperature.

Despite laboratory conditions were similar through the testing sessions (see Methods

section), no corrections for barometric pressure or temperature were conducted. The

inclusion of such corrections might have impacted reliability outcomes reported in the

study.

The CV obtained in the present study are similar to previously reported values for

cycling sprint tests (10 to 30 s) (Hopkins et al., 2001). In a meta analysis investigating

the reliability of power output during various sprint cycling tests, Hopkins et al. (2001)

converted measures of reliability reported in previous studies (i.e., correlation

coefficients) to typical errors expressed as a CV. Typical errors (CV) for peak power

ranged from 1.5 to 3.9% in those studies, which are similar to the CV for peak power

reported in the present study (1.8 – 2.5%). While the reliability of muscular

performance over 5 s has previously been reported (Hopkins et al., 2001), it is important

to note that the peak power was not recorded during a 5-s all-out test, but was obtained

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as an additional measure of performance during a longer test (i.e., 10 and 30-s Wingate

test). Therefore, the present study is the first to provide typical error estimates (CV)

between trials for brief (6-s) maximal cycling exercise.

Knowledge of the learning or practice effects of a particular test is crucial to the

detection of, and valid interpretation of, effects on performance in studies evaluating

training, nutritional or other interventions. A practice or learning effect is defined as any

systematic change in performance scores during the performance of a novel exercise

task distinct from any experimental intervention (Watt et al., 2002). In the present study,

a repeated-measures ANOVA revealed that there was a significant increase (P < 0.05)

in peak power (4.9%) and mean power (5.8%) from trial 1 to 2 suggesting that a

practice effect occurred. Several studies have previously reported the occurrence of this

phenomenon in maximal cycling exercise (Martin et al., 2000; Watt et al., 2002;

Capriotti et al., 1999; Glaister et al., 2003). Moreover, Hopkins et al. (2001), in a meta-

analysis of 30 performance tests measuring power, identified a practice effect between

the first two trials of a test. Consequently, at least one familiarisation session should

precede baseline measurements (Hopkins et al., 2001). However, in our study, when

data from trial 2, 3 and 4 were analysed, the CVs were calculated as 2.8 and 2.9% for

PPO and MPO respectively, while the CVs calculated using data only from trial 3 and 4

were markedly lower: 1.8 and 2.5% for PPO and MPO respectively. Although we could

not find statistical differences between trial 2, 3 and 4, these results indicate that after

two practice trials our subjects were able to produce more stable values. Similar results

in maximal cycling exercise have been reported by Capriotti et al. (1999), using a

protocol of 10, 7-s sprints with 30-s recovery, by Martin et al. (2000) during 4 sprints of

approximately 3.5 s with full recovery between sprints and by Glaister et al. (2003)

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using a protocol consisted of twenty, 5-s sprints separated either by 10 or 20 s of passive

rest. Thus, although one familiarisation trial was able to reduce the CV, results in the

present study and others suggest that two familiarisation trials can further reduce the CV

and provide a more adequate familiarization with maximal cycling exercise tests in

previously unaccustomed subjects. Further studies should examine the number of trials

necessary to elicit stable scores during the performance of different performance tests.

Reducing the variability of performance scores in a particular test, thus improving its

reliability, has important implications when estimating the sample size required to

detect changes in performance during experimental interventions (Hopkins, 2000;

Hopkins et al., 1999). Using the formulae recommended by Hopkins (2000) to estimate

sample sizes from the typical error (i.e., CV), sample size is proportional to the square

of the typical error. This highlights the importance of using tests with a small CV. For

example, using the CV obtained from trials 3 and 4 in the present study (1.8 and 2.5%

for PPO and MPO respectively), a sample size of ~12 subjects would be needed to

detect a reasonably achievable (Wilson and Murphy, 1996) 1.5% change in PPO

performance (for a repeated-measures study without a control group). However, ~22

subjects would be needed to detect the same change in MPO. Clearly, these differences

in sample size will have an important impact on the selection of the dependent variable.

In summary, the results of the present study indicate that reliable power outputs can be

obtained after one familiarization session in subjects unfamiliar with maximal cycling

sprint exercise. However, the inclusion of an extra familiarization session ensured more

stable power outputs. Therefore, two trials should allow adequate familiarization with

the maximal 6-s cycling test. These results will be useful to researchers investigating the

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effects of training interventions, ergogenic aids or fatigue on maximal power output in

humans.

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CHAPTER 4

Mechanical power output, the power-fatigability relationship and EMG activity

during and following recovery from repeated-sprint exercise

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4.1. Introduction

Neuromuscular fatigue can be defined as a transient decrease in the force-generating

ability of muscle, resulting from recent contraction (Williams, 1997). In accordance

with this definition, previous studies have shown that repeated bouts of brief, maximal-

intensity exercise (< 6 s) with incomplete recovery periods (< 60 s) results in muscular

fatigue (i.e., a decrease in power output) (Hamilton et al., 1991; Gaitanos et al., 1991;

Balsom et al., 1992a; Balsom et al., 1992b; Fitzsimons et al., 1993; Gaitanos et al.,

1993; Cherry et al., 1998; Bishop et al., 2003). However, the fatigue mechanisms

limiting muscular performance during intense repeated-sprint exercise, the typical

movement pattern of many popular sports (e.g., soccer, basketball, rugby and tennis),

are poorly understood. While it is believed that in well-motivated subjects, the causes of

fatigue during this kind of exercise are peripheral in origin (i.e., beyond the

neuromuscular junction), little scientific support has been furnished (see Bangsbo, 2000

for review).

During repeated-sprint exercise, large increments in metabolic strain have been reported

(Gaitanos et al., 1993). For example, a dramatic increase in metabolic by-products (i.e.,

lactate, Pi), lowered pH, and decrements in phosphocreatine (PCr) concentration have

been found during repeated-sprint exercise occurring as soon as after the first sprint

(Gaitanos et al., 1993; Bogdanis et al., 1996). These changes in the intracellular

environment of the recruited fibres have been associated with decrements in the power-

generating capacity of the skeletal muscle by altering components of the excitation-

contraction coupling process (Allen et al. 1995; Green, 1997; Westerblad et al., 1998).

Therefore, it could be expected that peripheral fatigue will limit muscular performance

from the earliest stages of this type of exercise. However, studies both in humans and

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animals have shown that decrements in muscular performance during intense exercise

do not always occur in parallel with biochemical changes in the muscle fibres (Degroot

et al., 1993; Bangsbo et al., 1996; Ciesler and Dobson, 2001; Rico-Sanz, 2003).

Moreover, results from Krustrup et al. (2003) suggest that muscular fatigue during

intense intermittent short-term exercise is unrelated to changes in muscle PCr, lactate,

pH or glycogen. This suggests that other factors such as neural control mechanisms may

be important determinants of fatigue development during this exercise mode.

According to the force-fatigability relationship, the greater the force exerted by a

muscle or motor unit during a given task, the more the muscle will fatigue (Enoka and

Stuart, 1992). In the applied repeated-sprint exercise literature, a similar relationship has

been reported. That is, individuals with high initial power levels usually experience the

greatest power decrements (i.e., fatigue) (Hamilton et al., 1991; Bishop et al., 2003).

However, the nature of this relationship remains unknown. This lack of information is

intriguing since the ability of multiple-sprint athletes to reproduce performance in

subsequent sprints during a game is believed to be crucial for both individual

performance and team outcome (Krustrup et al., 2003). Thus, strategies to improve

repeated-sprint performance aim to increase both the initial sprint level and the

maintenance of power output in subsequent sprints, which does not seem to be

consistent with the power-fatigability relationship.

Experiments using electrical stimulation for whole muscle and for single motor units

(reviewed in Enoka and Stuart, 1992) have shown that by changing the activation

pattern, the force-fatigability relationship of the muscles can be altered. Muscle fatigue

can be envisaged as a form of short-term adaptation modifying the contractile properties

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of the muscle to better match the demand (mechanical output) with the supply

(excitation and metabolic limitations) (Sargeant et al., 1994). Therefore, muscle fatigue

might be a potentially valuable tool to study whether acute alterations of the normal

patterns of muscle activation can change the power-fatigability relationship during

repeated-sprint exercise. The inclusion of the recovery period between fatiguing sets of

repeated-sprints exercise would allow subjects to recover at least part of the initial

power output and therefore, a comparison before and after the fatiguing exercise using

similar initial sprints matched for power could be made. For example, it has been

reported that although muscle fatigability (i.e., task failure) is greater for men compared

with women, both genders experienced similar levels of muscle fatigue when they

matched for strength (Hunter et al., 2004). To the author’s knowledge, no previous

investigations have examined the effects of previous short-term fatiguing exercise using

the same exercise model (i.e., repeated sprints) on the power-fatigability relationship

during repeated-sprint exercise.

Surface electromyography (EMG) signals have been extensively used to non-invasively

characterise muscular fatigue during both static and dynamic contractions (Orizio 2000;

Potvin and Bent, 1997). Expression of the EMG signals in the time domain (i.e.,

amplitude) allows for evaluation of neuromuscular activation patterns, since it is

believed to reflect motor-unit activation and the discharge rates of the active motor units

(Suzuki et al., 2002; Farina et al., 2004). Moreover, it may be at least partially related to

the efferent neural output to the muscle (Aagaard et al., 2002). Therefore, the purpose of

this study was to examine the acute effects of repeated-sprint exercise and recovery on

the time-course of mechanical power output, the power-fatigability relationship and

neuromuscular activity in man.

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4.2. Methods

4.2.1. Subjects

Eight healthy males volunteered to participate in this study. The subjects were all sport-

science students and included multiple-sprint sports athletes, sailors and martial art

performers. The subjects’ characteristics were as follows (mean ± SD): age 19.5 ± 0.9 y,

height 183.4 ± 5.4 cm, mass 80.3 ± 8.7 kg, and O2peak 53.9 ± 5.8 mL·kg-1·min-1. The

exercise protocol and all possible risks and benefits associated with participation in the

study were explained to each subject. Each subject provided written informed consent

prior to participating in the study. Approval for the study’s procedures was granted by

the Human Rights Committee of The University of Western Australia.

4.2.2. Protocol

The exercise protocol consisted of 10, 6-s sprints on a cycle ergometer interspersed with

30 s of recovery, followed, after 6 min of passive recovery, by five 6-s sprints also

interspersed by 30 s of recovery. All subjects were familiarised with the test protocol by

completing two sessions before the main trial, with the three sessions completed within

two weeks. It has previously been established that satisfactory reliability of repeated-

sprint cycling tests is achieved after two familiarization sessions (10 × 7-s, with each

sprint separated by 30 s) (Capriotti et al., 1999). Moreover, the subjects were recruited

from a short-term maximal sprint cycling reliability study and had already completed

four sprint cycling sessions (see Chapter 3). Subjects performed their trials at the same

time of the day (± 2 h) with the laboratory conditions being approximately 20º C and

50% relative humidity during all trials. The subjects were asked to follow their normal

diet and to refrain from any form of intense physical activity for the 24 h prior to testing

and to not eat within 3 h before each testing session. Before each test, subjects

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52

performed a standardized warm-up, comprising 4 min of cycling at a power of 100 to

120 W, followed by three bouts of maximal standing-start accelerations (approx 2 s)

followed by a 3-min rest before performing the main trial. Subjects were instructed to

perform an “all-out” effort from the beginning of the test until instructed to stop. Toe

clips and heel straps were used to secure the feet to the pedals. Strong verbal

encouragement was provided during each trial. Peak and mean power were calculated

for each maximal 6-s cycling bout. All of the sprints were performed from the same

initial pedal position. During the subsequent 30-s rest period after each sprint, and

during the 6-min rest period between sprint 10 and 11, subjects remained quietly seated

on the ergometer.

4.2.3. Ergometer

An air-braked front-access cycle ergometer (Repco, Melbourne, Australia) was used to

conduct the tests. The ergometer was connected to an IBM-compatible computer system

to allow for the collection of data for the calculation of power generated on each

flywheel revolution and the work performed during each individual sprint repetition

(Cyclemax, The University of Western Australia, Perth, Australia). The technical

aspects of the air-braked cycle ergometer have previously been described (Maxwell et

al., 1998). In brief, subjects pedal against air resistance caused by rectangular vanes

attached perpendicular to the axis of rotation of the flywheel. The power output of the

air-braked cycle ergometer is proportional to the cube of the flywheel velocity. An

optical sensor monitored the velocity of the flywheel at a sampling rate of 80 pulses per

pedal revolution. Before testing, the ergometer was dynamically calibrated on a

mechanical rig (Western Australian Institute of Sport, Perth, Australia) across a range of

power outputs (100–2000 W) following the procedures described elsewhere (Maxwell

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et al., 1998). Peak power output (PPO) and mean power output (MPO) were calculated

for each maximal 6-s cycling bout.

4.2.4. Muscle EMG

The EMG activity from the vastus lateralis (VL) of the right leg was recorded via

bipolar Ag-AgCl surface electrodes at an interelectrode distance 20 mm. Before placing

the electrodes, the overlying skin was carefully prepared. The hair was shaved, the skin

lightly abraded to remove the outer layer of epidermal cells and thoroughly cleansed

with alcohol to reduce the skin-electrode interface impedance to below 2 K-ohms.

Electrodes were fixed lengthwise, parallel to a line bisecting the proximal and distal

tendons, over the middle of the muscle belly. The electrodes were taped down with

cotton wool swabs to minimise sweat induced interference. The EMG reference

electrode was placed over the right iliac crest. To prevent movement artefact, wires

between the electrodes and the computer were secured to the skin with adhesive tape.

The EMG signal was amplified (x 1000) (P511, Grass Instrument Division, West

Warwick, RI) and sampled at a rate of 2048 Hz using a custom-written data acquisition

program (LabVIEW, National Instruments Corp., Austin, TX). Before sampling, the

EMG signals were analogue band-pass filtered (high-pass 10 Hz, low-pass 1000 Hz) to

remove unwanted noise and possible movement artefacts in the low-frequency region

and to eliminate aliasing and other artefacts in the high-frequency region (Karlsson et

al., 2003). The EMG data were recorded between the onset and the end of each 6-s

sprint. After additional high-pass filtering (at 20 Hz) to eliminate movement artefact,

root mean square (RMS) was calculated from each sprint. For each sprint, the RMS was

normalized to the first sprint value, which was assigned the value of 100%.

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4.2.5. Statistics

Data are presented as mean ± SEM. A one-way (i.e., sprint number) ANOVA with

repeated measures was used to allocate the significant differences in each dependent

variable over time. When significant main effects were found, the means between the

sprints were compared using a paired Student t-test (Ratel et al., 2002). Significance

was accepted when P < 0.05.

4.3. Results

Mechanical data

The PPO and MPO values recorded during each of the 6-s sprints are displayed in

Figure 4.1. The highest PPO (1513.0 ± 71.4 W) and MPO (1225.7 ± 62.6 W) were

recorded during the first sprint. When compared with the first sprint, there was a

significant decrease in PPO (5.8%) and MPO (4.9%) during the second sprint (P <

0.001). The decrease in PPO and MPO after the first five sprints averaged 14.0% and

17.9% (P < 0.001) respectively. Over the first ten sprints PPO and MPO decreased by

24.7% (P < 0.001) and 27.2% (P < 0.001) from the maximal value, respectively.

During sprint 11, following 6 min of passive rest, PPO (1314.7 ± 51.4 W) and MPO

(1070.5 ± 50.0 W) values recovered significantly in relation to those achieved in sprint

10 (P < 0.001), but remained 13.1% and 11.7% (P < 0.005) lower, respectively, than the

values achieved during the first sprint (Figure 1). Similar to the results reported for the

first ten 6-s sprints, PPO (1232.3 ± 52.5 W) and MPO (985.4 ± 43.4 W) during sprint 12

were significantly lower (6.3% and 7.9% respectively; P < 0.001) than the values

achieved in sprint 11. Over the five sprints (11 to 15), PPO and MPO decreased by

17.0% (P < 0.01) and 20.3% (P < 0.001), respectively. At the last sprint (sprint 15), PPO

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and MPO were 27.9% and 30.4% less (P < 0.001) than the maximal value obtained

during the first sprint.

A

0

200

400

600

800

1000

1200

1400

1600

1800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Sprint Num ber

Pow

er O

utpu

t (W

)

B

0

200

400

600

800

1000

1200

1400

1600

1800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Sprint Num ber

Pow

er O

utpu

t (W

)

Figure 4.1. Average peak power output (A) and average mean power output (B) for the entire group over

the 15 sprints. Data are presented as mean ± SEM (n = 8). * Significantly lower (P < 0.01) than values

recorded at the first sprint. ** Significantly lower (P < 0.05) than values recorded at the tenth sprint.

Based on MPO values, performance in sprint 11 (1070.5 ± 50.0 W) was not

significantly different to performance in sprint 4 (1050.0 ± 46.0 W). The decrease in

MPO for the five sprints following sprint 4 (i.e., sprint 4 to 8) averaged 12.6% (P <

* *

* *

**

**

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0.001), which was significantly less than the decrements in MPO for sprint 11 to 15

(20.3%; P < 0.05) (Figure 4.2). Thus, even when matched for initial power outputs,

decrements in power output during five successive sprints were more pronounced when

subjects had already performed a fatiguing repeated-sprint exercise.

700

750

800

850

900

950

1000

1050

1100

1150

1200

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Sprint number

Mea

n po

wer

out

put (

W)

Sprint 4 to 8Sprint 11 to 15

Figure 4.2. Average mean power output changes during five successive sprints when the initial sprints

were matched for power. The rate of decrease in power output for sprints 11 to 15 was greater than for

sprint 4 to 8 (P < 0.05). Data are presented as mean ± SEM (n = 8).

A composite power curve derived from the 1-s averages of power output for sprint 1,

10, 11 and 15 is shown in Figure 4.3. When compared with the first sprint, fatigue

associated with the performance of our protocol led to a shift downward and to the right

in each one second epoch (P < 0.05). That is, power was reduced and peak power

occurred later in the sprint.

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57

0

200

400

600

800

1000

1200

1400

1600

1800

1 2 3 4 5 6

Time (seconds)

Pow

er o

utpu

t (W

)

Sprint 1

Sprint 10

Sprint 11

Sprint 15

Figure 4.3. Mean time course of power output (W) second by second (expressed in absolute values)

during sprints 1, 10, 11 and 15. Performance in all seconds during sprint 1 was significantly higher (P <

0.05) than in sprints 10, 11 and 15. Data are presented as mean ± SEM (n = 8).

EMG activity

The changes in EMG amplitude (RMS) across the repeated sprints are displayed in

Figure 4.4. Normalized RMS values decreased over the ten sprints (14.6%; P < 0.001).

A repeated measures one-way ANOVA revealed a significant main effect for sprint,

with post-hoc analysis showing that by sprint 4 the normalised RMS was significantly

less (P < 0.05) than sprint 1. During the 6 min of passive rest between sprint 10 and

sprint 11, RMS did not show any recovery from sprint 10 (P = 0.4). In fact, RMS

continued to show a slight 3.1 decrement to 17.8% of the initial values recorded during

the first sprint (P < 0.005). During subsequent sprints (11 to 15), there was a further

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significant decrease (P < 0.05) in RMS. Values for sprint 15 were 7.0% (P < 0.05) and

21.3% (P < 0.001) lower than the values achieved in sprint 11 and sprint 1, respectively.

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65

70

75

80

85

90

95

100

105

110

115

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Sprint num ber

Nor

mal

ized

EM

G

Figure 4.4. Normalised EMG amplitude (root mean square) changes of the vastus lateralis muscle for the

entire group over the 15 sprints. Data are presented as mean ± SEM (n = 8). * Significantly lower (P <

0.01) than values recorded at the first sprint. ** Significantly lower (P < 0.05) than values recorded at the

tenth sprint.

Despite no significant difference in MPO values achieved in sprints 4 and 11, RMS

recorded during sprint 11 was 12.0% lower (P < 0.05) than in sprint 4. Over the five

sprints following sprint 4 (sprints 4 to 8), there was an 8.3% decrement (P < 0.05) in

neuromuscular activity (i.e., RMS) which was greater, although non-significantly (P =

0.2), than the drop in RMS registered from sprint 11 to 15 (4.3%; P < 0.05).

4.4. Discussion

The purpose of the present study was to examine the time-course of mechanical power

output and neuromuscular activity during fatiguing repeated-sprint exercise and

**

**

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recovery in man. We found a decline in power output during the fatiguing exercise that

was accompanied by a decrease in EMG amplitude of the vastus lateralis muscle. Six

minutes after the fatiguing exercise, power output during sprint 11 significantly

recovered with respect to values recorded in sprint 10, but remained significantly lower

than that recorded in the initial sprint. Thus, 6 min was insufficient time to fully recover

from the fatiguing repeated-sprint protocol utilised in this study. The main findings in

the present study were that: 1) the partial recovery of power output in sprint 11 was not

accompanied by recovery EMG amplitude; 2) similar mean power outputs were

recorded during sprint 4 and 11 despite a significantly lower EMG activity recorded

during the latter sprint; and 3) despite comparable mean power outputs during sprint 4

and 11, the decrease in power output over the next five sprints was greater for sprints 11

to 15 than for sprints 4 to 8.

10 x 6-s exercise trial

During repeated-sprint exercise, performance can be defined as the ability to perform at

maximum, for example as determined by peak power output during maximal cycling,

after previous exercise (Bangsbo, 2000). Performance in this mode of exercise depends

therefore, on the ability to recover from periods of prior work (Billaut et al., 2003). The

loss of power, observed over the ten, 6-s sprints with 30 s of recovery in the present

study (24.7% and 27.2% for PPO and MPO respectively) demonstrated the occurrence

of muscular fatigue. Gaitanos et al. (1993) using the same exercise protocol (i.e., 10 x

6-s/30-s rest), conducted on a friction-loaded cycle ergometer, reported similar

decrements for MPO (26.6%) and slightly higher decrements for PPO (33.4%) for eight

male physical education students. Differences in the subjects’ training status and the

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different ergometers employed in the two studies might partially explain the higher

fatigue index for PPO reported by Gaitanos et al. (1993).

Decrements in power output during the present study were evident after the first sprint

(Figure 4.1), showing that after only one 6-s sprint, 30 s of recovery was not enough to

reproduce the initial power output in a subsequent sprint. These results have

implications in the context of multiple-sprint sports since short recovery periods, during

short-term intermittent exercise, have been reported not only to decrease muscular

performance (i.e., running speed), but also to negatively affect the quality of specific

sporting skills (Ferrauti et al., 2001b). However, our results contrast with previous

reports showing that a 30-s rest period is enough to restore power output for three or

four consecutive 6-s sprints (Gaitanos et al., 1993; Balson et al., 1992b) or two 8-s

sprints (Billaut et al., 2003). Moreover, the higher initial power output values achieved

by our subjects may explain this as it has been reported that individuals with high initial

force levels usually experience the greatest fatigue decrements (Kanehisa et al., 1997;

Bishop et al., 2003).

Despite repeated-sprint exercise being an important component of many popular athletic

activities, very little is known about the underlying mechanisms related to the

development of muscular fatigue during this form of exercise. Alterations in excitation-

contraction coupling and/or in the contractile apparatus as a result of limitations in

energy supply and intramuscular accumulation of selected metabolic by-products

(lactate, H+, Pi, AMP, ADP, IMP) have been traditionally regarded as the primary cause

of muscular fatigue during high-intensity exercise (Allen et al., 1995; Green et al., 1997;

Sahlin et al., 1998). While metabolic data were not collected during the present study,

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Gaitanos et al. (1993), using the same exercise protocol, reported dramatic changes in

the concentration of muscle metabolites. For instance, the authors reported that

phosphocreatine (PCr) concentration after sprint 10 fell to 16% of the initial values.

There was also an approximately 30-fold increase in muscle lactate concentrations,

which has been associated with large reductions in muscle pH (~6.6) (Gaitanos et al.,

1993). Depletion of PCr stores has frequently been cited as a limiting factor for the

performance of repeated-sprint exercise (Bishop et al., 2004b). This is supported by the

fact that after a 30-s maximal cycling bout, peak power output recovery followed a

similar time course to that of PCr resynthesis (Bogdanis et al., 1995). In addition,

accumulation of H+ (i.e., reduced muscle pH) has been associated with fatigue through

alterations in mechanisms affecting Ca2+ release/uptake, sensitivity of the myofilaments

to Ca2+, and force produced by the crossbridges (Allen et al., 1995). It is possible

therefore, that part of the power decrement observed in the present experiment would be

explained by metabolic changes within the contracting muscles. However, recent studies

have shown that acidosis may have little effect on skeletal muscle contractile

performance at near-physiological temperatures (reviewed in Westerblad et al., 2002).

Support for the hypothesis that fatigue during repeated-sprint exercise might reside

within the muscle itself (i.e., peripheral fatigue) is provided by Hautier et al. (2000).

They examined changes in the EMG signal during brief, maximal intermittent cycling

exercise, using a fatiguing protocol similar to that employed in the present study (15

maximal 5-s sprints with 25-s rest between each sprint). EMG activity (i.e., RMS) of the

prime mover muscles (gluteus maximus and vastus lateralis) remained unchanged over

the repeated-sprints, despite the significant loss of power observed in the last sprint.

These results led the authors to conclude that the causes of fatigue during their repeated-

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sprint exercise task were within the muscles rather than due to decrements in neural

drive.

In contrast, in the present study, we found that a decrement in power output was

accompanied by a decrease in EMG activity of the vastus lateralis during repeated

maximal sprints. Our results are similar to those reported by St Clair Gibson et al.

(2001b) and Kay et al. (2001), who found a parallel fall in neuromuscular activity and

power output during repeated bouts of high-intensity cycling. Similarly, Tesch et al.

(1983) showed a similar decline in EMG activity and the force of the knee extensor

muscles during maximal, repeated concentric contractions. Thus, our results, and those

obtained in previous studies, suggest that reduced neural drive to the motoneurons

might have played a role in the development of muscular fatigue observed in the present

study. However, a decrease in EMG amplitude during maximal contractions might also

be related with changes in sarcolemma excitability (Pasquet et al., 2000), or the signal

itself. With the methodology used in the present study, the precise mechanism

underlying the decrease in EMG activity cannot be ascertained.

It is likely, that with maximal intermittent exercise, both central and peripheral

mechanisms contribute to the development of muscle fatigue (Taylor et al., 2000;

Michaut et al., 2003; Nordlund et al., 2004). Indeed, this scenario might provide a

partial explanation to the differences in the results reported by Hautier et al. (2000) and

those obtained in the present study. The mean peak power output recorded in the last

sprint by Hautier et al. (2000) represented 88.7% of the value achieved during the first

sprint. In our study, values in the last sprint represented 75.3% of the first sprint.

Clearly, the degree of muscle fatigue induced by the protocol used by Hautier et al.

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(2000) was markedly lower than that experienced by our subjects. In the present study,

EMG amplitude was significantly reduced after sprint 4, when mean peak power output

reached 88.6% of the maximal values recorded during the first sprint, which is similar

to the final value reported by Hautier et al. (2000). It might be possible therefore, that

the initial loss of power in both studies would have resulted from changes in the

contractile process at a peripheral level, but when exercise progresses and the severity

of fatigue becomes greater, central mechanisms would have been activated. Such a

time-course of changes in neuromuscular function has previously been reported during

long-duration cycling (Lepers et al., 2002).

Another likely candidate to explain the fall in power output during the present study,

might be the selective fatigue of fast-twitch fibres. These fibres dominate power

production during supramaximal exercise (McCartney et al., 1983), but are fatigued

more easily than slow-twitch fibres (Barclay, 1996), leaving slow-twitch fibres to

govern power output. In our study, comparison of the average second by second power

output between sprint 1 and sprint 10 shows that repeated sprints led to a shift to the

right in time at which peak power occurred (see Figure 4.3) implying fatigue in

mechanisms producing power output. Given that subjects with greater percentage of

fast-twitch fibres are able to generate higher power output (McCartney et al., 1983),

selective fatigue of those fibres might be related with the failure to develop the expected

power output in the later stages of the present study. Furthermore, it has been reported

that subjects possessing muscles with higher percentage of fast-twitch fibres show the

greatest exercise-induced fatigue (Thorstensson and Karlsson, 1976; Viitasalo and

Komi, 1981; Hamada et al., 2003).

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The recovery period and the subsequent 5 x 6-s trial

To the best of our knowledge, this is the first study to examine the recovery of repeated-

sprint ability following previous fatiguing activity using the same exercise model (i.e.,

repeated-sprints). During the last five sprints (11 to 15), subjects experienced a greater

decrement in MPO (20.3%) than during the first five sprints (1 to 5) (17.9%). This

occurred despite the greater power output values recorded during the initial sprint

(sprint 1) compared with sprint 11. Moreover, the most salient observation in the

present study was that although, following the 6-min recovery period, MPO during

sprint 11 was not significantly different from power output recorded during sprint 4,

greater fatigability during successive sprints were evident between sprints 11 to 15

(20.3%) compared with sprints 4 to 8 (12.6%). Therefore, these results seem to suggest

that the recovery of short-term power output and repeated-sprint ability after a fatiguing

maximal intermittent exercise might be mediated by different mechanisms. This is a

novel and interesting finding that reinforces the specificity of the muscular fatigue

induced by repeated-sprint exercise. Furthermore, the findings in the present study

suggest that the power-fatigability relationship during repeated-sprint exercise might

change as a consequence of previous activity, independent of the initial power values

achieved.

The partial recovery of power output in sprint 11, after the 6-min recovery period, was

accompanied by an unexpected further decline in EMG amplitude, compared with

values recorded in sprint 10. Moreover, during sprints 4 and 11, comparable power

output values were achieved despite significantly less EMG activity in the latter.

Consequently, under fatigue conditions similar performance could be achieved with

varying strategies for muscle activation. EMG amplitude can be influenced by both

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intrinsic and extrinsic factors (De Luca, 1997). Extrinsic factors are mainly related with

electrode configuration and location. The intrinsic factors believed to determine the

magnitude of the EMG amplitude are: the number and the firing rate of active fibres, as

well as the size and average size of the each fibre action potential (Bigland-Ritchie,

1981; Suzuki et al., 2002). Therefore, as the surface electrode configuration and

placement was fixed across the sprints, any change in EMG amplitude might be

assumed to be caused primarily by changing intrinsic factors (Suzuki et al., 2002). As

surface EMG is not able to determine the particular intrinsic factors involved, it remains

unresolved as to how our subjects were able to generate comparable power output after

fatigue and recovery with less EMG activity.

One possibility is that other lower-limb muscles that are involved in cycling and were

not examined in the present study, would have been recruited to a greater extent due to

fatigue of the vastus lateralis muscle (Psek and Carafelli, 1993). For example, it has

been reported that under fatigue conditions, biceps femoris, which normally acts as an

antagonist to knee extension movement, increased its activity as a result of fatigue of

the vastus lateralis muscle (Psek and Carafelli, 1993). However, Hautier et al. (2000)

reported a decrease in the activation in antagonist muscles (biceps femoris and

gastrocnemius lateralis muscles) during repeated-cycling sprints. According to these

authors, this would be an adaptation to ensure that the prime power-producing muscles

(i.e., vastus lateralis muscle and gluteus maximus muscle) can efficiently transfer force

to the pedal under fatigue conditions without braking the hip and knee extension power.

Furthermore, they suggested that the results reported by Psek and Cafarelli (1993) might

be due to the low fitness levels and lack of specific training by their subjects. The

subjects involved in the present study were familiarized with repeated-sprint cycling by

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means of two familiarization sessions. In addition, they all initially took part in a

maximal cycling sprint reliability study involving at least another four sessions (see

Chapter 3). Neural changes in individuals beginning a new activity have been reported

to occur even only after one training session (Gabriel et al., 2001). Therefore, it seems

unlikely that the similar power output values achieved in sprint 4 and 11, despite the

less EMG amplitude detected in sprint 11, can be explained by an increase in antagonist

muscle activity. Another possibility is that other agonist muscles were more active than

the vastus lateralis muscle under fatigue conditions. However, this seems also unlikely

since it has been reported that neuromuscular fatigue of one muscle is tightly associated

with the fatigue of the synergistic muscles (Sacco et al., 1997).

It has been postulated that an inhibitory reflex originating from the muscles in response

to fatigue that, as a protective mechanism to avoid membrane excitability and

excitation-contraction failure, would decrease motoneuron firing rates (Bigland-Ritchie

et al., 1986a). This reduction in firing rate has been reported to occur in spite of the

ability to maintain the same level of force (De Luca et al., 1996). As EMG amplitude is

partially dependent on the firing rate of active motor units (Suzuki et al., 2002), the

lower EMG amplitude recorded in sprint 11 compared with sprint 4, despite the similar

power output values, might have been related to decreased motor unit firing rate.

Furthermore, as maximal-intensity sprint exercise necessitates extremely high levels of

neural activation (Ross et al., 2001), firing rate reduction might have limited the

muscular performance in subsequent sprints. This may help to explain the markedly

greater fatigability between sprint 11 to 15 (20.3%) compared with sprint 4 to 8

(12.6%).

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To maintain the same level of force, despite the reduction of EMG activity, it is

believed that some mechanisms within the muscle fibre might partially offset this type

of fatigue (De Luca et al., 1996). In this regard, both prolongation in relaxation time and

a potentiation of the motor unit twitch tension may be of significance (Green, 1986). An

increase in the relaxation rate of the muscle during fatigue has previously been reported

(Bigland-Ritchie and Woods, 1984). The relaxation rate of the muscle declines so that

the lower firing frequency maintains the tetanus and thus optimises maximum force

production. Simultaneously, potentiation of the twitch as a consequence of repetitive

stimulation may partially offset the development of fatigue (Hamada et al., 2000).

Activities recruiting fast-twitch fibres, as is believed to occur in the present exercise

protocol, should induce the highest post-activation potentiation because fast-twitch

fibres have been reported to show the greatest post-activation potentiation (Hamada et

al., 2003). Moreover, the effects of potentiation is more pronounced for dynamic

exercise (concentric contractions) than isometric contractions (Grange et al., 1998).

Thus, the temporal coexistence of fatigue and post-activation potentiation after the

recovery period in the present study might explain, at least partially, the matched power

outputs achieved during sprint 4 and 11 despite the significantly less EMG activity

registered during the latter sprints.

To summarize, we have described changes in mechanical power output and

neuromuscular activity that occur during maximal repeated-sprint exercise and recovery

in young men. The fatiguing protocol and subsequent recovery period used in the

present study significantly altered the relationship between power output and

neuromuscular activity of the vastus lateralis muscle in our group of subjects. This

study also showed that regardless of the same initial power outputs, the power-

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fatigability relationship during repeated-sprint exercise was markedly greater when

subjects had previously performed a fatiguing maximal intermittent protocol. Studies

examining the development of fatigue during normal dynamic activities by

simultaneously assessing function at the various sites along the pathway of force

production are needed to unravel the underlying mechanisms contributing to the

development of muscular fatigue in humans, specially during repeated-sprint activities.

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CHAPTER 5

Summary, conclusions and recommendations for further study

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5.1. Summary

Research investigating factors limiting muscular performance during repeated-sprint

exercise is very limited. Alterations in the excitation-contraction coupling and/or in the

contractile apparatus, as a result of limitations in energy supply and intramuscular

accumulation of selected metabolic by-products (e.g., lactate, H+, Pi, AMP, ADP, IMP)

have traditionally been regarded as the primary cause of muscular fatigue during high-

intensity exercise (Allen et al., 1995). During repeated-sprint exercise, muscle

metabolism is stretched to its limits and consequently power decrements observed

during this mode of exercise have been mainly ascribed to metabolic changes within the

muscle fibre itself (i.e., peripheral fatigue). Further support for the hypothesis that

fatigue during repeated-sprint exercise might reside within the muscle itself was

provided by Hautier et al. (2000). They found no change in amplitude characteristics of

EMG activity (i.e., RMS) of the prime mover muscles (gluteus maximus and vastus

lateralis) over 15 maximal 5-s cycling sprints with 25-s rest between each sprint.

However, with only one study conducted so far, further investigations seem to be

warranted. Therefore, the purpose of the present study was to examine the time-course

of mechanical power output and neuromuscular activity during fatiguing repeated-sprint

exercise and recovery in man. Based on equivocal evidence for the role of

neuromuscular factors it was hypothesised that changes in neuromuscular parameters

(as measured by EMG) would accompany changes in power output during repeated-

sprint exercise.

Prior to the main study, we investigated the reproducibility of power output during a

single 6-s cycling sprint. Eleven healthy moderately trained males performed a 6-s

standing sprint on the front-access cycle ergometer on four separate occasions. The

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results of the analysis showed that reliable power outputs can be obtained after one

familiarization session in subjects unfamiliar with maximal cycling sprint exercise.

However, the inclusion of an extra familiarization session ensured more stable power

outputs. Therefore, two trials should allow adequate familiarization with the maximal 6-

s cycling test.

For the main study of the present thesis, eight young moderately trained adult men

performed an exercise protocol that consisted of ten, 6-s sprints on a wind-braked cycle

ergometer interspersed with 30 s of recovery followed, after 6 min of passive recovery,

by five 6-s sprints, again interspersed by 30 s of recovery. Peak power output (PPO) and

mean power output (MPO) were measured during each sprint and EMG data (i.e., RMS)

from the vastus lateralis muscle were also recorded.

Decrements in power output during the present study were evident after the first sprint,

showing that after only one 6-s sprint, 30 s of recovery was not enough to allow

reproduction of a similar power output in a subsequent sprint. Six minutes after the

fatiguing exercise, power output during sprint 11 significantly recovered with respect to

values recorded in sprint 10, but remained significantly lower than that recorded in the

initial sprint. Thus, 6 min was insufficient to fully recover from the fatiguing repeated-

sprint protocol utilised in this study. The main findings in the present study were that: 1)

the partial recovery of power output in sprint 11 was accompanied by an unexpected

further reduction of EMG amplitude; 2) similar mean power outputs were recorded

during sprint 4 and 11 despite a significantly lower EMG activity recorded during the

latter sprint; and 3) despite comparable mean power outputs during sprint 4 and 11, the

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decrease in power output over the next five sprints was greater for sprints 11 to 15 than

for sprints 4 to 8.

In summary, we have described changes in mechanical power output and neuromuscular

activity that occur during maximal repeated-sprint exercise and recovery in young men.

The fatiguing protocol and subsequent recovery period used in the present study

significantly altered the relationship between power output and neuromuscular activity

of the vastus lateralis muscle in our group of subjects. This study also showed that

regardless of the same initial power outputs, the power-fatigability relationship during

repeated-sprint exercise was markedly greater when subjects had previously performed

a fatiguing maximal intermittent protocol. These results seem to suggest that the

recovery of short-term power output and repeated-sprint ability after a fatiguing

maximal intermittent exercise might be mediated by different mechanisms. This is the

most novel and interesting finding of the present study, which reinforces the specificity

of the muscular fatigue induced by repeated-sprint exercise. These results have

implications in the designing of fitness programs and physiological testing of repeated-

sprint performance for multiple-sprint athletes.

5.2. Conclusions

On the basis of these results and within the limitations of the study, it was concluded

that:

• Even though the presence of other mechanisms cannot be ruled out to explain

the fall of muscle EMG (i.e., RMS) activity, the present study gives some

quantitative support to the hypothesis that the fatigue observed during this mode

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of exercise might be associated, at least partially, with altered levels of neural

drive and/or excitation-contraction coupling to the muscle.

• Muscular fatigue was evident after the first sprint showing that a 30-s rest period

was not enough to restore power output for two consecutive 6-s sprints.

• The rate of power-fatigability during repeated-sprint exercise was markedly

greater when subjects had previously performed a fatiguing, maximal

intermittent protocol, regardless of the same initial mechanical outputs,

• The present study provides qualitative support to the existence of alterations in

the level and/or pattern of muscle activation during repeated-sprint exercise in

the unfatigued and fatigued state, despite subjects being able to achieve similar

performances (i.e., mechanical output).

• Recovery of short-term maximal performance and repeated-sprint ability might

be mediated by different mechanisms.

5.3. Recommendations for further study

The findings from the present study provide the following directions for future research:

• Further integrative studies should identify the various mechanisms contributing

to neuromuscular fatigue during intermittent exercise and their temporal

activation as task proceeds.

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• As in sports with intermittent load profiles, physiological demands imposed on

the athletes during competition are difficult to duplicate under laboratory

conditions, future investigations should extend the study of muscle fatigue to

sport-specific test settings.

• Future investigations should systematically explore the power-fatigability

relationship domain during repeated-sprint exercise with different work/rest

patterns to establish its global nature.

• More studies are needed to assess the effects of acute (i.e., fatigue) and chronic

(i.e., training) perturbations on the power-fatigability relationship to determine

which changes within the motor system might affect this relationship.

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Appendix A

Statement of informed consent

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STATEMENT OF INFORMED CONSENT Sarcoplasmic reticulum Ca2+ release and muscular fatigue following high-intensity

intermittent exercise in humans

Investigators responsibilities – Participant rights

- As a subject you are free to withdraw your consent to participate at any time without prejudice

- The researchers will answer any questions you may have in regard to the study at any time. Questions concerning the study can be directed to:

Alberto Mendez-Villanueva – School of Human Movement & Exercise Science, UWA

Ph: (8) 9328 4842; Email: jamendez@cyllene.uwa.edu.au Dr. David Bishop – School of Human Movement & Exercise Science, UWA

Ph: (8) 9380 7282 Dr. Peter Hamer – School of Human Movement & Exercise Science, UWA

Ph: (8) 9380 2365

I, (print your name)_______________________________________________________________ have read the above statement and information sheet which explains thoroughly the nature, purpose and risks of the study and that any questions I have asked have been answered to my satisfaction. I agree to participate in this investigation, realising that I may withdraw at any time without reason and without affecting my relationship with staff members at The University of Western Australia. I understand that all information obtained is treated as strictly confidential and will not be released by the investigator unless required by law. I have been advised as to what data is being collected, what the purpose is, and what will be done with the data upon completion of the research.

Signature of participant_________________________________Date_____________

The Human Research Ethics Committee at the University of Western Australia requires that all participants are informed that, if they have any complaint regarding the manner, in which a research project is conducted, it may be given to the researcher or, alternatively to the Secretary, Human Research Ethics Committee, Registrar’s Office, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009 (telephone number (8) 9380-3703). All study participants will be provided with a copy of the Information Sheet and Consent Form for their personal records.

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INFORMATION SHEET

Sarcoplasmic reticulum Ca2+ release and muscular fatigue following high-intensity

intermittent exercise in humans

Purpose of the study

For those of you who participate in team sports like hockey or Australian football, you

are probably aware that you are required to repeatedly produce high-intensity sprint

efforts of only a few seconds (interspersed with short recovery periods) over the

duration of a game. Certain individual sports, such as tennis or surfing, also require

short, repetitive, high-intensity efforts over the duration of the match or practise session.

Thus, an important fitness component of these sports is what has been termed “repeated

sprint ability” (RSA). While the importance of RSA is readily acknowledged, little is

known about what limits RSA and what are the best training interventions to improve

RSA. One important component in muscle fatigue is a reduced Ca2+ release during

muscular contractions. However, the effects of repeated sprints on Ca2+ release function

in humans are unknown. Thus, the primary propose of this study is to investigate the

effects of fatigue induced by brief repeated bouts of all-out effort with short recovery

intervals on Ca2+ release activity in human skeletal muscle.

Procedures

Ten healthy, physically active males will be recruited as subjects. The total duration of

the present study will be three weeks. As a subject, you will be required to undertake

four familiarization sessions in the first week (one devoted to Graded Exercise Test

(GXT) and three to the RSA test). Each of these sessions will last about an 30 minutes.

These familiarization session are very important to allow you to be fully confident with

all the study procedures. On week two, you will participate in three separate testing

sessions (one devoted to GXT and two to the RSA test). The first session will require

you to complete (GXT) to voluntary exhaustion on a stationary bike. The approximate

duration of this test is 60 min. The second and third session will involve the RSA test on

a stationary bike (duration approximately 30 min). On week three you will attend one

single testing session. During this session you will carry out the RSA test as on week

two.

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Graded Exercise Test (GXT)

This is a standard laboratory test used throughout the world to provide an indication of a

person’s aerobic (endurance) fitness. The test will require you to cycle on a stationary

cycle ergometer until volitional fatigue, using an incremental exercise procedure. The

entire test will last between 25 and 35 min (7-8 stages of 4 min each) . Capillary blood

samples will be taken from the earlobe at rest (before the test) and at the end of each 4-

min stage. Gas analysis will be undertaken during the test to provide recordings of your

oxygen consumption.

Repeated Sprint Ability Test (RSA)

The RSA test will consist of ten, 6-s maximal sprints with each sprint separated by 30-s.

This test will be undertaken on a stationary bicycle. During the 30-s recovery between

sprints, you will rest completely. Five seconds before starting the next sprint you will be

asked to assume the ready position and await the start of countdown. You will complete

this test on three occasions. During the first two RSA test (on week two) your muscular

activity will be recorded using a surface electromyography (EMG). The third RSA test

will be conducted on week three. On this day, four muscle biopsies will be taken from

the vastus lateralis. The four muscle samples will be taken at the following times: just

before exercise, immediately post-exercise, 6 min after trial completion and 10 min after

trial completion. Therefore, the total number of biopsies for the whole study will be four

and all of them will be taken the same day (last testing session on week third).

EMG

Surface EMG is a procedure commonly used to display the degree of muscle activation

during a contraction. This is completely painless and non-invasive, and involves the

placement of surface electrodes over the belly of the muscle (i.e., vastus lateralis).

Before placement of the electrodes, the skin overlying the vastus lateralis muscle will be

shaved, abraded with sandpaper and cleaned with alcohol to reduce skin impedance.

Two electrodes will be then taped on the leg, covered with cotton swabs to minimize

interference from sweat. A bandage will be applied to the thigh to avoid any

displacement of the electrodes during the testing session.

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Muscle biopsy

This is a common procedure used to determine muscle adaptations to exercise. The

muscle biopsy will involve the administration of a local anaesthetic (Xylocaine) to the

outside part of the upper thigh (vastus lateralis muscle), followed by a small incision

made through the skin and underlying fascia (but not the muscle). A biopsy needle will

then be inserted through the incision and into the muscle, in order to remove a small

piece of muscle tissue (approximately 80-100 mg). Finally, the incision will be closed

with steri strips, without the requirement of stitches. This process may result in slight

discomfort (a mild “cork”) which may last for the following 1-3 days. This may be

accompanied by local temporary bruising (this is quite rare), along with the very small

risk of superficial nerve damage in the skin (due to incision), which if present, may

cause temporary loss of sensation to the area. This is also very unlikely to occur. A

qualified and experienced medical Sports Doctor of the University of Western Australia

will perform the muscle biopsies.

Benefits, risks and safety

The benefits of this study include determining your peak VO2 (maximal oxygen

consumption), lactate threshold and repeated sprint ability. This study requires maximal

effort testing and consequently you will experience feelings of discomfort due to

fatigue.

Every effort will be made to minimise any risk associated with these tests by having all

participants perform familiarization sessions and a thorough warm up and cool down

during each testing session. The minimal risks associated with the muscle biopsy

procedure have been outlined above.

Since some of the tests are completed to fatigue, participants with health problems

(e.g., cardiac diseases, asthma, diabetes, etc) will be excluded.

Confidentiality of data

Personal details and test results will be treated confidentially at all times. Individual

data will not be identifiable but collective results may be published. Prior consent will

be sought for any visual recording of any testing session (photographs) from the subject

involved and these recordings will remain under confidential storage and only published

with the express permission of the subject involved.

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As a subject, you are free to withdraw your consent to participate at any time without

prejudice. Your participation in this study does not prejudice any right to compensation,

which you may have under statute or common law.

Contact details for the researchers

If you have any questions or concerns regarding testing procedures, please do not

hesitate to contact the researchers. Contact details are as follows:

• Alberto Mendez-Villanueva – School of Human Movement & Exercise

Science, UWA Ph: (8) 9328 4842; Email: jamendez@cyllene.uwa.edu.au

• Dr. David Bishop – School of Human Movement & Exercise Science, UWA

Ph: (8) 9380 7282

• Dr. Peter Hamer – School of Human Movement & Exercise Science, UWA Ph:

(8) 9380 2365

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APPENDIX B

Raw data / Statistical analysis (Chapter 3)

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Appendix B1

Descriptive data

Peak VO2 Subject Age (y) Height

(cm) Weight

(kg) L/min ml/kg/min Watts

1 20 183.6 81.2 5031.5 62.0 320 2 20 174.6 62.4 3173.4 50.1 230 3 21 189.5 93.9 4448.7 47.7 320 4 19 187.4 81.8 4898.5 59.45 290 5 20 181 84.7 4079.0 47.93 290 6 21 171.3 69.3 3657.2 53.08 230 7 19 177.6 78.6 4311.4 54.44 320 8 19 184.5 80.5 4069.4 50.12 290 9 19 174.4 72.0 M M M 10 18 189 79.7 M M M 11 18 183.5 62.7 4039.3 64.53 320

Mean 19.5 181.5 77.0 4189.8 54.4 290 SD 1.0 6.2 9.5 576.5 5.3 37

M = Data missing

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Appendix B2

Raw data from the four 6-s maximal sprint trial

Peak Power Output (W) Mean Power Output (W) Subject Trial 1 Trial 2 Trial 3 Trial 4 Trial 1 Trial 2 Trial 3 Trial 4

1 1572.16 1633.47 1534.56 1552.82 1282.15 1341.65 1278.47 1331.61 2 1279.76 1287.77 1236.94 1253.90 1012.25 1073.43 996.96 1008.48 3 1897.48 1876.95 1886.08 1885.80 1562.13 1582.81 1582.45 1576.82 4 1236.58 1279.58 1348.61 1402.40 1016.41 1080.25 1124.17 1143.20 5 1592.56 1614.12 1683.37 1648.00 1333.18 1354.28 1372.49 1368.70 6 1430.75 1439.78 1366.40 1357.89 1157.80 1213.71 1150.18 1135.15 7 1246.02 1305.59 1262.37 1315.40 1049.08 1063.59 1053.03 1097.50 8 1212.40 1375.36 1410.40 1393.80 979.58 1155.58 1194.99 1139.30 9 1196.34 1439.56 1331.37 1412.40 1003.73 1148.98 1080.48 1175.72 10 1147.81 1305.01 1358.09 1385.17 956.66 1077.80 1119.64 1148.95 11 770.89 776.19 776.42 770.10 633.78 638.79 632.12 645.57

Mean 1325.70 1393.94 1381.33 1397.97 1089.70 1157.35 1144.09 1161.00 SD 292.46 276.65 278.49 273.55 241.30 235.38 237.09 232.36

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Appendix B3

Log-transformed x 100 raw scores (power output) from the four 6-s maximal sprint trial

Peak Power Output (W) Mean Power Output (W) Subject Trial 1 Trial 2 Trial 3 Trial 4 Trial 1 Trial 2 Trial 3 Trial 4

1 736.0 739.8 733.6 734.8 715.6 720.2 715.3 719.4 2 715.4 712.0 712.0 713.4 692.0 697.9 690.5 691.6 3 754.8 753.7 754.2 754.2 735.4 736.7 736.7 736.3 4 712.0 715.4 720.7 724.6 692.4 698.5 702.5 704.1 5 737.3 738.6 742.8 740.7 719.5 721.1 722.4 722.2 6 726.6 727.2 722.0 721.4 705.4 710.1 704.8 703.4 7 712.8 717.4 714.1 718.2 695.6 696.9 695.9 700.1 8 710.0 722.6 725.2 724.0 688.7 705.2 708.6 703.8 9 708.7 727.2 719.4 725.3 691.1 704.7 698.5 707.0 10 704.6 717.4 721.4 723.3 686.3 698.3 702.1 704.7 11 664.7 665.4 665.5 664.6 645.2 645.9 644.9 647.0

Mean 716.6 721.5 721.0 722.2 697.0 703.2 702.0 703.6 SD 23.1 22.4 22.3 22.3 23.1 22.8 23.0 22.4

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Appendix B4

Statistical analysis (one-way ANOVA) of peak power output obtained during the four trials

One Way Repeated Measures Analysis of Variance (Log Data Peak Power Output)

Normality Test: Passed (P = 0.1259) Group N Missing Trial 1 11 0 Trial 2 11 0 Trial 3 11 0 Trial 4 11 0 Group Mean Std Dev SEM Trial 1 716.6 23.1 6.97 Trial 2 721.5 22.4 6.77 Trial 3 721.0 22.3 6.72 Trial 4 722.2 22.3 6.71 Source of Variance DF SS MS Between Subjects 10.00 19758.7 1975.9 Between Treatments 3.00 210.7 70.2 Residual 30.00 547.8 18.3 Total 43.00 20517.3 Source of Variance F P Between Subjects Between Treatments 3.85 0.0193 Residual Total The differences in the mean values among the treatment groups are greater than would be expected by chance; there is a statistically significant difference (P = 0.0193). To isolate the group or groups that differ from the others use a multiple comparison procedure. All Pairwise Multiple Comparison Procedures (Student-Newman-Keuls Method) : Comparison Diff of Means p q Trial 4 vs Trial 1 5.600 4 4.346 Trial 4 vs Trial 3 1.236 3 0.960 Trial 4 vs Trial 2 0.709 2 0.550 Trial 2 vs Trial 1 4.891 3 3.796 Trial 2 vs Trial 3 0.527 2 0.409 Trial 3 vs Trial 1 4.364 2 3.387

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Comparison P<0.05 Trial 4 vs Trial 1 Yes Trial 4 vs Trial 3 No Trial 4 vs Trial 2 Do Not Test Trial 2 vs Trial 1 Yes Trial 2 vs Trial 3 Do Not Test Trial 3 vs Trial 1 Yes

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Appendix B5

Statistical analysis (one-way ANOVA) of mean power output obtained during the four trials

One Way Repeated Measures Analysis of Variance (Log Data Mean Power Output).

Normality Test: Passed (P = 0.2616) Group N Missing Trial 1 11 0 Trial 2 11 0 Trial 3 11 0 Trial 4 11 0 Group Mean Std Dev SEM Trial 1 697.0 23.1 6.97 Trial 2 703.2 22.8 6.86 Trial 3 702.0 23.0 6.94 Trial 4 703.6 22.4 6.77 Source of Variance DF SS MS Between Subjects 10.00 20398.4 2039.8 Between Treatments 3.00 305.2 101.7 Residual 30.00 459.9 15.3 Total 43.00 21163.5 Source of Variance F P Between Subjects Between Treatments 6.64 0.00143 Residual Total The differences in the mean values among the treatment groups are greater than would be expected by chance; there is a statistically significant difference (P = 0.00143). To isolate the group or groups that differ from the others use a multiple comparison procedure. All Pairwise Multiple Comparison Procedures (Student-Newman-Keuls Method) : Comparison Diff of Means p q Trial 4 vs Trial 1 6.582 4 5.576 Trial 4 vs Trial 3 1.582 3 1.340 Trial 4 vs Trial 2 0.373 2 0.316 Trial 2 vs Trial 1 6.209 3 5.260 Trial 2 vs Trial 3 1.209 2 1.024 Trial 3 vs Trial 1 5.000 2 4.236

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Comparison P<0.05 Trial 4 vs Trial 1 Yes Trial 4 vs Trial 3 No Trial 4 vs Trial 2 Do Not Test Trial 2 vs Trial 1 Yes Trial 2 vs Trial 3 Do Not Test Trial 3 vs Trial 1 Yes

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Appendix B6

Statistical analysis (two-way ANOVA) on the natural logarithm x 100 of the peak power output obtained for the four trials

Two Way Analysis of Variance

General Linear Model (No Interactions)

Dependent Variable: Peak Power Output (4 trials) Normality Test: Passed (P = 0.1259) Source of Variance DF SS MS Subject 10 1.9759 0.19759 Trial 3 0.0211 0.00702 Residual 30 0.0548 0.00183 Total 43 2.0517 0.04771 Source of Variance F P Subject 108.21 <0.0001 Trial 3.85 0.0193 Residual Total

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Appendix B7

Statistical analysis (two-way ANOVA) on the natural logarithm x 100 of the peak power output obtained for the three trials

Two Way Analysis of Variance

General Linear Model (No Interactions)

Dependent Variable: Peak Power Output (3 trials) Normality Test: Passed (P = 0.3991) Source of Variance DF SS MS Subject 10 1.481084 0.148108 Trial 2 0.000847 0.000423 Residual 20 0.015099 0.000755 Total 32 1.497030 0.046782 Source of Variance F P Subject 196.180 <0.0001 Trial 0.561 0.5795 Residual Total

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Appendix B8

Statistical analysis (two-way ANOVA) on the natural logarithm x 100 of the mean power output obtained for the four trials

Two Way Analysis of Variance

General Linear Model (No Interactions)

Dependent Variable: Mean Power Output (4 trials)

Normality Test: Passed (P = 0.2685) Source of Variance DF SS MS Subject 10 2.0398 0.20398 Trial 3 0.0306 0.01018 Residual 30 0.0464 0.00155 Total 43 2.1168 0.04923 Source of Variance F P Subject 131.94 <0.0001 Trial 6.59 0.0015 Residual Total

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Appendix B9

Statistical analysis (two-way ANOVA) on the natural logarithm x 100 of the mean power output obtained for the three trials

Two Way Analysis of Variance

General Linear Model (No Interactions)

Dependent Variable: Mean Power Output (3 trials) Normality Test: Passed (P = 0.3776) Source of Variance DF SS MS Subject 10 1.53564 0.153564 Trial 2 0.00145 0.000725 Residual 20 0.01590 0.000795 Total 32 1.55299 0.048531 Source of Variance F P Subject 193.136 <0.0001 Trial 0.911 0.4181 Residual Total

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Appendix C

Raw data / Statistical analysis (Chapter 4)

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Appendix C1

Descriptive data

Peak VO2 Subject Age (y) Height

(cm) Weight

(kg) L/min ml/kg/min Watts

1 20 183.6 81.2 5031.5 62.0 320 2 18 189 79.7 4756.7 59.7 290 3 21 189.5 93.9 4448.7 47.7 320 4 19 177.6 78.6 4311.4 54.44 320 5 19 184.5 80.5 4069.4 50.12 290 6 19 187.4 81.8 4898.5 59.45 290 7 20 174.6 62.4 3173.4 50.1 230 8 20 181 84.7 4079.0 47.93 290

Mean 19.5 183.4 80.3 4346.1 53.9 293.75 SD 0.9 5.4 8.7 595.0 5.8 30

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Appendix C2

Raw data (peak power) from the repeated-sprint trial (15 x 6-s / 30-s)

Peak Power (W)

Subject Sprint 1 Sprint 2 Sprint 3 Sprint 4 Sprint 5 Sprint 6 Sprint 7 Sprint 8 Sprint 9 Sprint 10 Sprint 11 Sprint 12 Sprint 13 Sprint 14 Sprint 15

1 1613.4 1534.1 1421.1 1366.8 1359.5 1339.8 1288.5 1131.8 1117 1116.6 1280 1279.8 1196.5 1139.5 1084.9

2 1439.6 1430.5 1421.6 1399.6 1358.5 1340.2 1385 1296.4 1253.7 1279.4 1367.6 1322.6 1296.8 1262.6 1212.2

3 1885.8 1746.3 1663.6 1515.5 1449.1 1340.2 1296.8 1180.4 1203.6 1171.5 1515.5 1439.1 1262.6 1172.2 1092.6

4 1385.2 1340.8 1367 1271.4 1262.9 1171.8 1212.8 1204 1220.8 1179.7 1331.6 1279.6 1262.9 1231.6 1204

5 1393.8 1254.7 1237.5 1254.54 1234.58 1214.62 1171.8 1139.7 1157.3 1187.7 1237.3 1171.5 1124 1154.1 1188

6 1449.1 1358.1 1340.81 1297.1 1237.1 1163.4 1108 1069.7 1010.7 1047.7 1253.7 1131.5 1077.6 1032.4 960.2

7 1253.9 1131.8 1085.1 1077.9 1010.4 1010.5 925.9 925.4 884.9 878.4 1054.8 946.4 832.8 813.8 764.3

8 1683.37 1605.6 1567.5 1540.95 1493.46 1421.6 1398.49 1332 1303.52 1256 1477.1 1288 1237.5 1187.8 1228.6

Mean 1513.0 1425.2 1388.0 1340.5 1300.7 1250.3 1223.4 1159.9 1143.9 1139.6 1314.7 1232.3 1161.3 1124.3 1091.9

SD 201.9 198.0 180.3 150.2 151.2 134.2 156.3 128.1 137.7 128.3 145.5 148.4 152.3 142.9 160.2

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Appendix C3

Raw data (total work) from the repeated-sprint trial (15 x 6-s / 30-s)

Total Work (J)

Subject Sprint 1 Sprint 2 Sprint 3 Sprint 4 Sprint 5 Sprint 6 Sprint 7 Sprint 8 Sprint 9 Sprint 10 Sprint 11 Sprint 12 Sprint 13 Sprint 14 Sprint 15

1 7770.7 7615.2 7154 6897 6517.9 6402.5 6174 5528.3 5349.8 5483 6492.9 6027.7 6038.4 5478.4 5338.6

2 7550.3 7239.4 7033.1 6829.7 6755.7 6576.5 6449.3 6338.2 6021.4 6261.8 6878.6 6651.3 6213.8 6157 5791.1

3 9478.9 8910 7956.7 7008.9 6654.2 6055.2 5991.7 5634 5468.3 5487.8 7405.9 6613.9 5962.1 5631.8 4869.2

4 6663.1 6420.4 6371.5 6030.4 5990.4 5759.4 5643 5593 5611.6 5439.4 6735.9 6102.5 6063 5765.8 5580.1

5 6835.8 6347.8 6059.8 6005.6 5896 5760 5720.7 5439.3 5537.1 5646.4 6015.8 5898 5667.1 5594.5 5768.3

6 7128.1 6730.3 6702.5 6363 5831.8 5647.8 5330.2 5188.8 4865.8 4821.4 6197 5431.9 5175.2 4781.9 4695.2

7 6050.9 5704.1 5098.8 4964.7 4612.3 4457 4204.2 4035.3 3876.8 3862.6 4637 4368.1 3793 3562.7 3222.5

8 8234.9 7876.02 7666.26 7465.32 7015.6 6754.3 6453.7 6276.9 5897.5 5842.3 7020.1 6207.5 5781.8 5409.7 5710

Mean 7354.0 6995.3 6625.2 6299.9 6036.9 5808.3 5644.7 5504.2 5328.5 5355.6 6422.9 5912.6 5586.8 5297.7 5121.9

SD 1062.4 1019.5 917.1 781.4 761.7 721.5 736.8 715.1 683.4 726.8 848.5 735.9 792.5 799.9 872.0

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Appendix C4

Raw data (EMG RMS) from the repeated-sprint trial (15 x 6-s / 30-s)

EMG (RMS)

Subject Sprint 1 Sprint 2 Sprint 3 Sprint 4 Sprint 5 Sprint 6 Sprint 7 Sprint 8 Sprint 9 Sprint 10 Sprint 11 Sprint 12 Sprint 13 Sprint 14 Sprint 15

1 0.556 0.581 0.564 0.535 0.525 0.554 0.516 0.501 0.508 0.507 0.431 0.454 0.464 0.43 0.428 2 0.359 0.296 0.346 0.366 0.346 0.24 0.337 0.34 0.316 0.333 0.257 0.25 0.261 0.243 0.231 3 0.514 0.518 0.427 0.417 0.402 0.369 0.381 0.362 0.346 0.389 0.372 0.377 0.366 0.341 0.347 4 0.472 0.451 0.434 0.415 0.419 0.422 0.416 0.415 0.435 0.394 0.456 0.454 0.44 0.428 0.438 5 0.458 0.432 0.373 0.398 0.394 0.381 0.373 0.337 0.343 0.34 0.288 0.296 0.316 0.312 0.302 6 0.537 0.586 0.55 0.527 0.51 0.496 0.502 0.474 0.479 0.458 0.519 0.501 0.504 0.478 0.469 7 0.399 0.398 0.396 0.415 0.381 0.398 0.375 0.356 0.362 0.352 0.351 0.337 0.331 0.31 0.321 8 0.58 0.583 0.586 0.539 0.534 0.563 0.57 0.537 0.491 0.549 0.51 0.516 0.533 0.477 0.51

Mean 0.484 0.481 0.460 0.452 0.439 0.428 0.434 0.415 0.410 0.415 0.398 0.398 0.402 0.377 0.381 SD 0.08 0.11 0.09 0.07 0.07 0.11 0.08 0.08 0.08 0.08 0.10 0.10 0.10 0.09 0.10

122

122

Appendix C5

Statistical analysis of peak power output

Test of Homogeneity of Variances

PPO

.349 14 105 .985

LeveneStatistic df1 df2 Sig.

ANOVA

PPO

1719189 14 122799.200 5.052 .0002552294 105 24307.5604271483 119

Between GroupsWithin GroupsTotal

Sum ofSquares df Mean Square F Sig.

Paired Samples Test

87.78375 45.89026 16.22466 49.41854 126.14896 5.411 7 .001373.39625 183.91593 65.02410 219.63869 527.15381 5.742 7 .001198.32125 111.19198 39.31230 105.36243 291.28007 5.045 7 .001421.17125 207.65413 73.41682 247.56805 594.77445 5.737 7 .001222.85000 116.34775 41.13514 125.58084 320.11916 5.418 7 .00125.77375 44.17368 15.61776 -11.15637 62.70387 1.650 7 .143

Sprint1 - Sprint2 Pair 1 Sprint1 - Sprint10 Pair 2 Sprint1 - Sprint11 Pair 3 Sprint1 - Sprint15 Pair 4 Sprint11 - Sprint15 Pair 5 Sprint4 - Sprint11 Pair 6

Mean Std. DeviationStd. Error

Mean Lower Upper

95% Confidence Interval of the Difference

Paired Differences

t df Sig. (2-tailed)

123

123

Appendix C6

Statistical analysis of mean power output

Test of Homogeneity of Variances

MPO

.343 14 105 .986

LeveneStatistic df1 df2 Sig.

ANOVA

MPO

55697081 14 3978362.952 5.932 .00070416794 105 670636.1351.26E+08 119

Between GroupsWithin GroupsTotal

Sum ofSquares df Mean Square F Sig.

Paired Samples Test

358.68500 130.71455 46.21457 249.40490 467.96510 7.761 7 .0002108.500 926.02648 327.39980 1334.322 2882.678 6.440 7 .0001041.188 624.29512 220.72166 519.26372 1563.111 4.717 7 .0022342.213 1132.50470 400.40088 1395.415 3289.010 5.850 7 .0011301.025 629.99871 222.73818 774.33290 1827.717 5.841 7 .00122.67750 405.35369 143.31417 -316.207 361.56167 .158 7 .879

Sprint1 - Sprint2Pair 1Sprint1 - Sprint10Pair 2Sprint1 - Sprint11Pair 3Sprint1 - Sprint15Pair 4Sprint11 - Sprint15Pair 5Sprint4 - Sprint11Pair 6

Mean Std. DeviationStd. Error

Mean Lower Upper

95% ConfidenceInterval of the

Difference

Paired Differences

t df Sig. (2-tailed)

124

124

Appendix C7

Statistical analysis of EMG RMS

Test of Homogeneity of Variances

EMG

.392 14 105 .975

LeveneStatistic df1 df2 Sig.

ANOVA

EMG

.201 14 .014 1.935 .030

.780 105 .007

.981 119

Between GroupsWithin GroupsTotal

Sum ofSquares df Mean Square F Sig.

Paired Samples Test

.07137 .03440 .01216 .04261 .10014 5.868 7 .001

.11550 .03460 .01223 .08657 .14443 9.441 7 .000

.12225 .05452 .01928 .07667 .16783 6.342 7 .000

.13837 .04503 .01592 .10073 .17602 8.692 7 .000

.01613 .01901 .00672 .00023 .03202 2.400 7 .643

.05088 .05156 .01823 .00777 .09398 2.791 7 .027

Sprint1 - Sprint4Pair 1Sprint1 - Sprint10 Pair 2Sprint1 - Sprint11 Pair 3Sprint1 - Sprint15 Pair 4Sprint11 - Sprint15Pair 5Sprint4 - Sprint11 Pair 6

Mean Std. DeviationStd. Error

Mean Lower Upper

95% ConfidenceInterval of theDifference

Paired Differences

t df Sig. (2-tailed)