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APPLIED SCIENCES: Physical Fitness and Performance

Fatigue Responses during Repeated Sprints Matched for Initial Mechanical Output


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Medicine & Science in Sports & Exercise: December 2007 - Volume 39 - Issue 12 - p 2219-2225
doi: 10.1249/mss.0b013e31815669dc
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Team sports such as football, hockey, and basketball require participants to perform a number of short sprints, interspersed with periods of rest or low- to moderate-intensity activity. Many of these sprints are separated by rest periods long enough (> 1 min) to allow complete or near-complete recovery, and, therefore, subsequent sprint performance is not significantly impaired. However, recent game analysis has shown that some of these sprints are separated by short rest periods (< 30 s) (29), which have been shown to negatively affect subsequent sprint performance (2). Therefore, one of the fitness requirements of team-sport athletes is the ability to perform short-duration sprints (< 6 s) with a short recovery time (< 30 s), and this has been termed repeated-sprint ability (RSA).

The inability to reproduce performance in subsequent sprints (i.e., fatigue) during repeated-sprint exercise has been mainly ascribed to changes within the muscle itself (i.e., muscular adjustments) (11). In this regard, differences in blood lactate-removal ability (30), H+ accumulation (4), PCr degradation (11), or impairments of excitation-contraction coupling (26) have been previously suggested. The hypothesis that fatigue during repeated sprints is mainly attributable to muscle factors is further supported by the observation of steady levels of neural activation in prime mover muscles, inferred by surface electromyography (EMG) signals (3,15). The unchanged EMG signal amplitude suggests that, despite mechanical performance becoming progressively impaired, the neural system's ability to recruit motor units is not impaired. However, neural adjustments such as a reduction in the central nervous system's drive to the active musculature (7,27), reduced muscle activation (16,27), and alterations in muscle coordination of the contracting musculature (3) have also been linked to fatigue development during repeated-sprint exercise.

To date, it has not been possible to identify the most important factors responsible for muscle fatigue during repeated-sprint exercise. This can largely be attributed to the use of different protocols (e.g., work and rest ratios) and the task-dependent nature of fatigue (20). An alternative would be to compare the performance of the same task under different conditions of fatigue and to identify the mechanisms that limit the performance of the more difficult (i.e., fatiguing) task (20). In the present study, this was accomplished by having the same subjects perform two sets of repeated sprints, separated by a period of passive recovery.

It is well known that the greater the force exerted by a muscle, or motor unit, during a given task, the more the muscle will fatigue (8). Thus, a possible confounding factor when interpreting the literature examining fatigue during repeated-sprint exercise is the initial power level of the subjects (6,14). The current experimental design would allow subjects to recover at least part of the initial mechanical output, and, therefore, a comparison of changes during sets of repeated sprints matched for initial power could be made. Additionally, it has been reported that team-sport athletes are required to carry out several periods of repeated-sprint activity throughout a game (29), and that after a period of high-intensity exercise, the ability to perform repeated sprints is significantly impaired (18). Thus, the current experimental design would allow us to identify some of the potential mechanisms underlying the reduced exercise performance during repeated periods of repeated-sprint activity.

The aim of the present study, therefore, was to compare muscle fatigability during sets of repeated cycling sprints matched for initial mechanical output performed for nonfatigued and fatigued muscle. Strategies used by subjects to perform the tasks were characterized by the measurement of EMG signals. It was hypothesized that previous fatiguing repeated-sprint exercise, followed by a rest period, would exacerbate the rate of fatigue development during subsequent repeated-sprint exercise, regardless of the initial mechanical output. A further hypothesis was that the decline in maximal power output during repeated-sprint exercise might be associated with adjustments in neural activity (i.e., EMG).



Eight healthy males volunteered to participate in this study. The subjects were all sport science students and included multiple-sprint athletes and martial arts performers. The subjects' characteristics were as follows (mean ± SD): age 19.5 ± 0.9 yr, height 183.4 ± 5.4 cm, mass 80.3 ± 8.7 kg, and V˙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 before participating in the study. Approval for the study's procedures was granted by the institutional human rights committee.

Experimental design.

All subjects came to the laboratory for three familiarization sessions and one exercise session. An initial laboratory visit was scheduled to obtain data on physical characteristics. During the second and third visits, the subjects were familiarized with the repeated-sprint exercise protocol, which they completed in full both days. In the last session, they performed the 15 × 6-s test of RSA. At least 48 h separated the last familiarization session to the exercise session. The subjects were asked to follow their normal diet and to refrain from any form of intense physical activity for the 24 h before testing and to not eat within 3 h of each testing session.

Repeated-sprint exercise.

The exercise protocol consisted of 10, 6-s sprints on a front-access, cycle ergometer (Repco, Melbourne, Australia), interspersed with 30 s of passive recovery. This was followed, after 6 min of passive recovery, by five, 6-s sprints, also interspersed by 30 s of recovery. Before the test, subjects performed a standardized warm-up, comprising 4 min of cycling at a power output of 100-120 W, followed by three bouts of maximal standing-start acceleration (approx 2 s) and 3 min of rest before performing the main trial. In a separate session, performance in a 6-s sprint cycling test was recorded and was then used as the criterion score during the main trial. Subjects were instructed to perform an "all-out" effort from the beginning of the test until instructed to stop. During the first sprint, subjects were required to achieve at least 95% of their criterion score, as a check on pacing. All of the subjects satisfied the 95% criteria on the first sprint. Toe clips and heel straps were used to secure the feet to the pedals. Strong verbal encouragement was provided during each trial. All of the sprints were performed from the same initial pedal position with the right crank arm located 45° forward to the vertical axis. During the subsequent 30-s rest period after each sprint, and during the 6-min rest period between sprints 10 and 11, subjects remained quietly seated on the ergometer. Water was provided ad libitum throughout the trials.


An air-braked, front-access cycle ergometer was used to conduct the repeated-sprint exercise test. 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 (Lab-VIEW, National Instruments Corp., Austin, TX). The technical aspects of the air-braked cycle ergometer have previously been described (22). In brief, subjects pedaled 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. Instantaneous power is expressed as the work done during a 0.2-s measuring epoch. Work done is then totaled for the trial period to determine work, which is expressed relative to time to determine power. An optical sensor monitored the velocity of the flywheel at a sampling rate of 128 pulses per flywheel revolution. Before testing, the ergometer was calibrated on a mechanical rig across a range of power outputs (100-2000 W). Peak power output (PPO) and total work (TW) were calculated for each maximal 6-s cycling bout.

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 of 20 mm. We chose the VL muscle because it has been reported that total power output during repeated 6-s cycling sprints separated by 30 s of passive rest (same task as in the present experiment) was significantly correlated with the activation of VL, as evaluated by muscle functional magnetic resonance imaging (1). Before placing the electrodes, the overlying skin was carefully prepared. The hair was shaved, and the skin was 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 kΩ. Electrodes were fixed lengthwise and 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 minimize sweat-induced interference. The EMG reference electrode was placed over the right iliac crest. To prevent movement artifact, wires between the electrodes and the computer were secured to the skin with adhesive tape and leads braided to minimize electromagnetically induced interference. The EMG signal was amplified (× 1000) (P511, Grass Instrument Division, West Warwick, RI) and sampled at a rate of 2048 Hz using a custom-written data-acquisition program (Lab VIEW, National Instruments Corp., Austin, TX). Before sampling, the EMG signals were analog band-pass filtered (high-pass 10 Hz, low-pass 1000 Hz) to remove unwanted noise and possible movement artifacts in the low-frequency region, and to eliminate aliasing and other artifacts in the high-frequency region. The EMG data were recorded between the onset and the end of each 6-s sprint. EMG recording was by a digital trigger, coincident with the start of the 6-s sprint, and data collection was stopped by a digital signal at the end of the sprint. After additional high-pass filtering (at 20 Hz) to eliminate movement artifact, root mean square (RMS) was calculated from each sprint. Additionally, the frequency spectrum was obtained with a Hamming window function and transformed via fast Fourier transformation. The frequency spectrum analysis was restricted to frequencies in the range of 20-500 Hz, as the EMG signal content outside of this range consists mostly of noise. The resulting power spectrum was quantified by calculating the median power frequency (MF) for each epoch of the raw EMG data.


Data are reported as means ± standard deviation (SD). A one-way (sprint number) ANOVA with repeated measures was used to allocate the significant differences in each dependent variable. A two-way ANOVA [sprint number × time (before and after recovery)] with repeated measures was used as a follow-up to the one-way ANOVA, to investigate differences between sets of sprints matched for initial mechanical output. Contrast analysis was used to test for differences when ANOVA revealed a significant main effect or interaction. Statistical significance was established at α = 0.05 a priori, and all multiple comparisons were adjusted using the Bonferroni method. All statistical analyses were performed using SPSS for Windows (version 13.0, Chicago, IL).


Mechanical data.

The PPO and TW values recorded during each of the 6-s sprints are displayed in Figure 1. During the first 10 sprints, PPO and TW decreased by 24.1% (P < 0.001) and 27.2% (P < 0.001) from the maximal value, from 18.8 ± 1.6 to 14.2 ± 1.2 W·kg−1 and from 92.8 ± 6.3 to 66.7 ± 6.7 J·kg−1, respectively. During sprint 11, after 6 min of passive rest, PPO (16.4 ± 0.8 W·kg−1) and TW (79.8 ± 4.8 J·kg−1) values recovered significantly in relation to those achieved in sprint 10 (P < 0.001), but they remained 13.1% and 11.7% (P < 0.005) lower, respectively, than the values achieved during the first sprint. During the last five sprints (11-15), after the recovery period, PPO and TW decreased by 17.0% (P < 0.01) and 20.3% (P < 0.001), respectively. At the last sprint (sprint 15), PPO (13.6 ± 1.6 W·kg−1) and TW (63.7 ± 8.8 J·kg−1) were 27.9% and 30.4% less (P < 0.001) than the maximal value obtained during the first sprint.

Mean peak power output (A) and total work (B) for the entire group during the 15 sprints. Data are presented as means ± SD, expressed as either watts (i.e., peak power output) or joules (i.e., total work) per kilogram of body mass (N = 8). The dotted lines show the two sprints that were matched for mechanical output (i.e., total work).

On the basis of TW values, performance in sprint 11 (79.8 ± 4.8 J·kg−1) was not significantly different from performance in sprint 4 (80.3 ± 5.3 J·kg−1; P = 0.81) (Fig. 1). The decrease in TW for the five sprints after sprint 4 (i.e., sprints 4 to 8) averaged 14.5% (P < 0.001), which was significantly less than the decrement in TW from sprints 11 to 15 (14.5 vs 20.3%; P < 0.05), as displayed in Figure 2. Similarly, PPO in sprint 11 (16.7 ± 0.9 W·kg−1) was not significantly different from PPO in sprint 4 (16.4 ± 0.8 W·kg−1; P = 0.14). PPO decrements between sprint 4 and 8 averaged 13.3% (P < 0.001), which was significantly less than the decrement in PPO from sprints 11 to 15 (13.3 vs 17.0%; P < 0.05), as shown in Figure 2. Thus, even when matched for initial mechanical output, decrements in total work and peak power output during five 4successive sprints were more pronounced when subjects had already performed fatiguing, repeated-sprint exercise.

Changes in peak power output (A) and mean total work (B) during five successive sprints when the initial sprints were matched for mechanical output. Data are presented as means ± SD, expressed as either watts (i.e., peak power output) or joules (i.e., total work) per kilogram of body mass (N = 8). * Significant difference between sprints (P < 0.05).

EMG activity.

The temporal profiles of the EMG amplitude (RMS) and frequency spectrum (MF) across the repeated sprints are shown in Figure 3. The values for both RMS and MF, which were highest during the first sprint, decreased during the 10 sprints by 14.3% (P < 0.01) and 10.6% (P < 0.01), from 0.484 ± 0.03 to 0.415 ± 0.03 V and from 89.9 ± 5.6 to 80.4 ± 5.8 Hz, respectively. After the 6 min of passive rest between sprint 10 and sprint 11, RMS during sprint 11 (0.398 ± 0.03 V) did not show any recovery from sprint 10 but, rather, a slight, nonsignificant decrement (3.4%; P = 0.41), and it was 17.8% lower than the initial values recorded during the first sprint (P < 0.001). However, during the 6-min rest, MF significantly recovered, from sprint 10 (80.4 ± 5.8 Hz) to sprint 11 (89.3 ± 7.2 Hz) (11.9% increase; P < 0.01), and it was not statistically different from MF values recorded during sprint 1 (89.9 ± 5.6 Hz). During subsequent sprints (11-15), there was a further, significant decrease (P < 0.05) in RMS. Values for sprint 15 were 4.4% (P < 0.05) and 21.3% (P < 0.001) lower than the values achieved in sprint 11 and sprint 1, respectively. Similarly, MF for sprint 15 was 6.8% and 8.0% significantly lower (P < 0.05) than the values obtained during sprint 11 and sprint 1, respectively.

Changes in EMG amplitude (A) and median power frequency (B) during five successive sprints when the initial sprints were matched for peak power output and total work. Data are presented as means ± SD, expressed as either volts (i.e., root mean square) or hertz (i.e., median power frequency) (N = 8).

Despite no significant differences in PPO or TW achieved in sprints 4 and 11, RMS recorded during sprint 11 (0.398 ± 0.03 V) was 12.0% lower (P < 0.05) than in sprint 4 (0.452 ± 0.02 V). In contrast, values of MF recorded during sprint 4 (85.5 ± 5.5 Hz) and 11 (89.3 ± 7.2 Hz) were not significantly different (P = 0.33). During the five sprints after sprint 4 (sprints 4-8), there was an 8.3% decrement (P < 0.05) in neuromuscular activity (i.e., RMS), which was not significantly (P = 0.19) different from the drop in RMS registered from sprint 11 to 15 (4.4%; P < 0.05) (Fig. 4). That is, fatigue induced a decrease in RMS in sprint 11 in comparison with sprint 4, but the pattern of decrease in RMS throughout the following five sprints (i.e., 11-15 and 4-8) was not significantly different, despite a greater decrease in PPO and TW from sprint 11 to 15. Between sprints 4 and 8 there was a nonsignificant decrease in MF (5.4%; P = 0.29), whereas from 11 to 15 there was a significant drop in MF (6.8%; P < 0.05). As with RMS values, there was no difference between the decrease in MF from sprints 4 to 8 and from sprints 11 to 15 (P = 0.76) (see Fig. 4).

Changes in EMG amplitude (root mean square) (A) and power frequency spectrum (median power frequency) (B) of the vastus lateralis muscle for the group during the 15 sprints. Data are presented as means ± SD, expressed as either volts (i.e., root mean square) or hertz (i.e., median power frequency) (N = 8). * Significant difference between sprints (P < 0.05).


The purpose of this study was to compare, for the first time, muscle fatigue during two sets of repeated sprints separated by a recovery period. As expected, there was a greater fatigability during the second set of repeated sprints, despite the same initial mechanical output. The greater fatigability in the second set of sprints was accompanied by lower muscle activation in the initial sprint compared with the first set of sprints, yet the rate of decrease in net EMG activity was similar for the two sets.

To the authors' knowledge, this is the first study to examine the recovery of repeated-sprint ability after previous fatiguing activity using the same exercise model (i.e., repeated sprints). A major finding of the present study was that after the 6-min recovery after the 10, 6-s sprints (i.e., in sprint 11), subjects were able to reproduce the mechanical performance (i.e., PPO and TW) achieved during sprint 4, but not RSA (discussed below). This is an important finding because it suggests that recovery of single-sprint performance and RSA, after a fatiguing, maximal, intermittent exercise, might follow different time courses. Presumably, this is related with the different factors that determine performance and, hence, the rate of fatigue, in the two specific tasks. Unfortunately, identification of such factors is beyond the scope of the present study. Nevertheless, this is a novel and interesting finding that reinforces the specificity of the muscular fatigue induced by repeated-sprint exercise.

Previous studies have reported an association between the absolute initial force level and power decrement during repeated sprints with incomplete recovery (6,14). The underlying physiological mechanisms behind this association are unknown, but indirect evidence would suggest that greater absolute forces are associated with decrements in the power-generating capacity of the recruited fibers attributable to greater metabolite-induced disturbances (10). By including a recovery period, we examined fatigability during sets of sprints matched for initial mechanical output, in an attempt to control for the previously reported effect of initial sprint performance on sprint decrement. However, despite being matched for initial sprint power, greater fatigability during successive sprints was evident from sprints 11 to 15 compared with that observed from sprints 4 to 8 (Fig. 2). These results support previous findings reporting marked differences in fatigability during repeated cycling sprints (i.e., 5 × 6-s max sprints every 30 s) in two groups of women who were matched for initial sprint performance (5). The present results indicate that factors other than differences in initial mechanical output per se also contribute to fatigue during repeated-sprint exercise.

These findings raise questions as to the potential physiological mechanisms responsible for individual differences in muscle fatigue during repeated-sprint exercise. We observed differences in the patterns of EMG activity during the two sets of repeated sprints. These adjustments included a 12% lower RMS in sprint 11 than in sprint 4, but no change in the MF of the VL muscle. Despite the lower RMS recorded in sprint 11 than in sprint 4, the rate of decrease in EMG activity (i.e., RMS and MF) was similar for the two sets of repeated sprints (Fig. 3). These results are in disagreement with those reporting a steady level of neural activation for the VL muscle during fatiguing, repeated-sprint exercise (3,15). However, our results are in agreement with those reported previously in which neural adjustments such as a reduction in the central nervous system's drive to the active musculature (7,27), an attenuation of muscle activation (16), and alterations in the activation timing of the contracting musculature (3) have been linked to fatigue development during repeated cycling sprints.

Although caution should be used when inferring motor-control strategies from EMG during dynamic muscle contractions (9), the fall in EMG activity observed in the present study might be attributable to a decrease in motor unit activity (i.e., progressive inhibition of motor units and/or the decrease in motor unit firing rate) (9). This is supported by the observation that MF values, indicative of other neuromuscular changes that have also been proposed to affect RMS (e.g., motor unit action potential amplitude, duration, and shape) (21), were similar in both sprints (i.e., 4 and 11). Furthermore, discharge rate has only minor effects on the power spectrum (19). Thus, the different RMS values at the beginning of the two sets of repeated sprints (i.e., sprint 4 and 11) may primarily be attributable to a difference in the number and/or the discharge rate of the motor neurons that innervate the active fibers.

The similarity between the rates of decrease in RMS indicates that net inhibition of the agonist motor neuron pool increased at the same rate during both tasks and that, at the beginning of the two tasks, modulation of motor unit activity was different. Because surface EMG is not able to distinguish between motor unit recruitment and rate coding, the precise mechanisms underlying the decrease in EMG activity cannot be ascertained. However, regardless of whether the decrease in EMG is attributable to rate coding, motor unit inhibition, or both, our primary finding would be unaffected; the inability to reproduce repeated-sprint performance in subsequent sprints was associated with a change in the ability of the nervous system to increase motor unit activity.

The maintenance of performance in the initial sprint, after the 6-min recovery period (i.e., sprint 11), despite the lower RMS values recorded in sprint 11 in comparison with sprint 4, is an interesting finding. It is possible that a reduction in motor unit activity is only detrimental to muscle performance when combined with other physiological signals that occur as a result of the maintenance of high levels of neural activation to drive the active musculature. One of the main mechanisms suggested to modulate motor unit activity during fatigue is a reflex inhibition that results from the accumulation of metabolites in the muscle fibers that then activate metaboreceptors and induce an inhibition of homonymous motoneurons via small-diameter group III and IV muscle afferents (28). Although metabolic data were not collected during the present study, dramatic changes in muscle and blood metabolism, such as decreased pH and phosphocreatine content and the accumulation of lactate, have been observed during repeated-sprint exercise protocols (4,10). Furthermore, repetitive maximal contractions can induce homeostatic disturbances within the central nervous system itself that can attenuate motor unit activation (24). Therefore, the decrease in EMG amplitude observed under the present experimental conditions might have been related to peripheral feedback and/or central nervous system influences because of the high physiological and metabolic strain imposed by repeated-sprint exercise.

Simultaneously, along with an attenuation of motor unit activity, postactivation potentiation (PAP) of the twitch as a consequence of repetitive stimulation could potentially offset the development of fatigue in the initial sprint (13). It has been reported that PAP may help to maintain voluntary force despite the decline in motor unit activity (i.e., reduced discharge rates) (17). Furthermore, the extent of the decline in discharge rate has been shown to be significantly related to the amount of PAP (17). Activities recruiting fast-twitch fibers, as is believed to occur in the present exercise protocol, should induce the highest PAP because fast-twitch fibers have been reported to show the greatest PAP (13). Moreover, the effects of potentiation are more pronounced for dynamic exercise (concentric contractions), as was the case in the present study, than for isometric contractions (12). Thus, the mechanism(s) causing PAP after the 6-min recovery period might have prevailed over those responsible for fatigue, because PAP is still evident for > 10 min (25). Potentiation of the muscle in sprint 11 may, therefore, have counteracted some of the fatigue effects, resulting in a similar mechanical performance as in sprint 4, despite decreased neuromuscular activation (i.e., less RMS). This combination of variables might not be present during subsequent sprints (i.e., sprints 12-15), because it has been reported that potentiation is progressively overwhelmed by fatigue as exercise continues (13). Thus, although PAP may have allowed subjects to reproduce performance in sprint 11, a persistent reduction in motor unit activity, coupled with a loss of the initial PAP effect, may have contributed to the larger decrement in power output observed in the following sprints (i.e., 11-15 compared with sprints 4-8).

An additional possibility is that other lower-limb muscles that are involved in repeated cycling sprints (1) and that were not examined in the present study were recruited to a greater extent than the VL muscle after fatigue. However, this seems unlikely, because it has been reported that neuromuscular fatigue of one muscle is tightly associated with the fatigue of the synergistic muscles (23). This is further supported by the results of Akima et al. (1), who, using a fatiguing protocol similar to that employed in the present study (10 maximal 6-s cycling sprints with 30 s of rest between each sprint), found a similar activation pattern in the 11 thigh muscles as evaluated by muscle functional magnetic resonance signal change. Moreover, power output during the 10 sprints was significantly correlated with activation patterns of VL muscle, suggesting that mechanical performance during repeated cycling sprints is associated with the activation of the VL muscle (1).

In summary, results from the present study demonstrate that the short-term activation history of the muscle alters the pattern of muscle activation and fatigability during sets of repeated-sprint exercise matched for initial mechanical output. Moreover, these results suggest that the recovery of single-sprint power output and repeated-sprint ability, after a fatiguing, maximal, intermittent exercise, might be mediated by different mechanisms. Lastly, the findings of the present study provide qualitative support for the existence of adjustments at the neural level that were related with both the ability to recover single-sprint performance and the rate of fatigue during repeated-sprint exercise.

This study was supported by a grant from the Gatorade Sports Science Institute. The authors wish to express their sincere gratitude to all the participants for their maximal effort and cooperation.


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