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Medicine & Science in Sports & Exercise:
Applied Sciences: Biodynamics

Effects of steady-state versus stochastic exercise on subsequent cycling performance

PALMER, GARRY S.; NOAKES, TIMOTHY D.; HAWLEY, JOHN A.

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School of Life Science, Kingston University, Kingston Upon Thames, Surrey, ENGLAND, and MRC/UCT Bioenergetics of Exercise Research Unit Department of Physiology, University of Cape Town Medical School, SOUTH AFRICA

Submitted for publication October 1995.

Accepted for publication April 1996.

Address for correspondence: Garry S. Palmer, School of Life Science, Kingston University, Kingston upon Thames, Surrey, KT1 2EE, England. E-mail:G.PALMER@KING.AC.UK.

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Abstract

The aims of this investigation were to evaluate the physiological responses to laboratory based stochastic exercise and to assess the effects of stochastic versus steady-state exercise on subsequent cycling time trial (TT) performance. Six competitive cyclists (peak power output (PPO) 432 ± 39 W (values are mean ± SD) undertook in a random order two 150-min paced rides that were either constant load (58% of PPO) or stochastic in nature (58± 12.2% of PPO). These rides were immediately followed by a 20-km TT performance on an air-braked ergometer. Mean heart rate (HR) responses throughout the 150-min paced rides and during the subsequent TT were not significantly different between trials. Yet, despite the similarities in HR, the mean time for the TT was significantly faster (26:32 ± 1:30 vs 28:08 ± 1:47 min, P < 0.05), and the mean power output was significantly greater (340.3 ± 44.2 vs 302.5 ± 42.3 W; 77.8± 10.2 vs 70.0 ± 9.8% of PPO, P < 0.05) following the steady-state ride. These results demonstrate that following 150 min of steady-state riding, subsequent 20 km TT performance was significantly improved when compared with 150 min of stochastic exercise.

Numerous studies have described the physiological responses of trained and untrained individuals to prolonged, submaximal steady-state exercise (3,4,9). While such steady-state conditions are necessary to determine various metabolic parameters, including the patterns of substrate (i.e., carbohydrate and fat) utilization, they may not be an accurate reflection of actual conditions in the field(10). While near steady-state conditions may prevail during most distance running events (those > 10 km) and cycle time trial(TT) competitions(2,4,5,10,11), it has recently been questioned whether the use of “steady-state” laboratory testing to assess athletes who typically compete in events that produce responses to exercise that randomly vary in intensity and are, therefore, stochastic in nature has ecological validity (10).

Accordingly, the aims of the current investigation were, first, to evaluate the physiological responses of well-trained cyclists to laboratory-based stochastic exercise and, second, to assess the effects of prolonged, submaximal steady-state and stochastic cycling on subsequent cycling time-trial performance.

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METHODS

Subjects. Six highly trained male cyclists who had all been competing in registered cycle road races for at least the two previous seasons participated in this study. The physical characteristics of the subjects are shown in Table 1. All subjects had participated in previous studies in our laboratory and were familiar with the experimental testing apparatus and procedures. Prior to the commencement of the study, each subject was fully informed of the nature of the investigation and gave his written consent to participate in accordance with the guidelines of the American College of Sports Medicine (1).

Table 1
Table 1
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Preliminary testing. Following a 24-h period during which they refrained from all heavy exercise, the subjects reported to the laboratory to undertake a progressive, incremental exercise test to exhaustion on a Kingcycle air braked cycle ergometer (Kingcycle Ltd, High Wycombe, Bucks, UK). This cycle ergometry system has been described in detail previously(11), and, for well trained subjects, has been shown to have a high test re-test reliability for progressive, maximal tests (r = 0.98)(8) and a coefficient of variation of only 1.1 ± 0.9% for repeated 20-km performance trials (11).

Before each test, subjects performed a “run down” calibration during which they accelerated to a work rate of ≈400 W and then immediately stopped pedaling while remaining seated in their normal riding position. During this calibration the support offered to the subjects' bicycles by the Kingcycle ergometry system was adjusted until the necessary rolling resistance was achieved, as indicated by a reference power decay curve previously stored by the Kingcycle (11).

Following this rundown calibration of the ergometer, subjects warmed up at a self -selected intensity for 10-15 min. At the end of this warm-up period, the workload was adjusted to 250 W and thereafter continuously increased by 20 W·min-1 until the subject could no longer maintain the required power output. Strong verbal encouragement was given by the same investigator during all maximal tests. The subject's PPO was taken as the highest average power during any 60-s period of the exercise test (Table 1), while peak oxygen uptake (˙VO2peak) was estimated from the equation of Keen et al. (8). The SEE of this method is 0.15 l for well-trained cyclists. During the maximal test and the subsequently described trials, heart rates (HR) were measured using a Polar Sports Tester heart rate monitor (Polar Electro OY, Kempele, Finland). This monitor consists of an electrode belt worn around the chest, a transmitter, and a wrist mounted receiver. The receiver recorded and stored the instantaneous HR at 5-s intervals during the maximal exercise test and at 15-s intervals during the two experimental trials.

During all experimental trials on the Kingcycle ergometer, power output (W) and pedal cadence (rpm) were monitored continuously and stored by an IBM compatible computer, which was interfaced with the Kingcycle ergometry system.

Experimental trials. All subjects were required to undertake two experimental trials, which were conducted in random order 7-14 d apart. Each trial was conducted at the same time of day, a minimum of 24 h post exercise and 3 h post prandial. All subjects were required to maintain a full training and dietary record for the 72 h prior to their first trial and were then instructed to maintain the same diet and training regimen in the 72 h preceding the second trial.

Each subject was required to undertake two experimental rides. After calibration of the Kingcycle ergometry system and a self-selected, standardized warm-up, subjects completed 150 min of paced cycling on an electrically braked ergometer (Lode, Groningen, The Netherlands) which had been adapted with clip-in pedals and low profile handlebars. On this ergometer power output is independent of pedal frequency between 60-120 rpm. The rides were either steady state or stochastic in nature. During one paced ride the average power output was 58% of PPO (steady state), while for the other trial the same average exercise intensity, as defined by the area under the power versus time curve, was varied within one SD of 12.2% PPO (stochastic ride)(Fig. 1). These paced efforts were immediately followed by a 20-km performance time trial (TT) on the Kingcycle ergometer (described subsequently).

Figure 1-The group m...
Figure 1-The group m...
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The range in power outputs during the stochastic ride were from 35.8 to 82.3% of PPO, or from ≈155 to ≈355 W. Such a range of power outputs was designed to mimic the efforts of cyclists during a previously investigated 105-km mass start cycle race during which we found that there was a random variation in the frequency and amplitude of exercise over time(10).

Such stochastic exercise was reproduced in the present study from the HR measured previously during competition and the previously reported%HRmax = 0.64·%˙VO2max + 36.7 regression measured in the laboratory (10). ˙VO2 values were subsequently converted to power output using the equation of Keen et al.(8).

Exactly 60 s after the subjects had completed the 150 min paced effort, they commenced a 20-km performance TT on the Kingcycle ergometer. During the TT the subjects were instructed to cover 20 km “in the shortest possible time,” and the only feedback they were given was the elapsed distance.

Whilst HR data and power outputs were recorded at 15 and 60 s, respectively, during the TT, these data have been reported at 5% intervals of total TT time. This was undertaken to illustrate subjects' performances with a common end point.

To prevent the onset of hypoglycemia and minimize cardiovascular drift during the 150-min paced rides, subjects consumed a carbohydrate solution (5% maltodextrin (MAXIM, AMS, Ltd., Goole, UK)) at a rate of 10 mL·kg-1·h-1, while during the TT subjects were allowed access to water ad libitum.

Statistical analyses. All results are presented as means ± SD. Where data have been grouped, a pooled estimate of common variance(7) was used to calculate group SDs. Statistical significance between values (P < 0.05) was assessed with a paired Student's t-test.

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RESULTS

Figure 1A illustrates the group mean power outputs (W) and Figure 1B the mean HR (beats·min-1) during the two 150-min paced rides. As described previously, the power output during this section of the trial was 58% PPO for the steady-state ride and 58± 12.2% of PPO for the stochastic effort. The mean HR recorded during the steady-state and stochastic 150 min paced effort were not significantly different (153 ± 6 and 150 ± 11 beats·min-1; 79.6± 3.2% and 77.3 ± 5.9% of HRpeak, respectively).

Table 2 shows the percentage of time spent exercising at different intensities during the 150-min stochastic ride. Riders spent the majority of the time (49.5 ± 15.6% and 31.7 ± 18.5% of total time, respectively) between 71-80 and 81-90% of HRpeak, with only 18.7% of total time spent outside this range (0.5 ± 0.8%, 14.3 ± 24.0%, and 3.9 ± 3.9% of total time between 51-60, 61-70, and 91-100% of HRpeak respectively).

Table 2
Table 2
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Figure 2 illustrates the time each subject took to complete the 20-km performance ride. The group mean time for the TT following the steady-state ride was significantly faster than the times recorded following the stochastic ride (26:32 ± 1:30 min vs 28:08 ± 1:47, respectively, P < 0.05). All subjects improved their performance following the steady-state ride by 1:36 ± 1:18 min or ≈6%.

Figure 2-The time to...
Figure 2-The time to...
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As would be expected from the faster performance ride, the group mean power output (Fig. 3A) following the steady-state effort was also significantly greater than that following the stochastic ride (340.3± 44.2 vs 302.5 ± 42.3 W; 77.8 ± 10.2 vs 70.0 ± 9.8% of PPO, P < 0.05). The mean HR of the six subjects for each 5% section of the TT is illustrated in Figure 3B. Despite the significantly higher power outputs associated with the steady-state ride compared with the stochastic ride, the HR recorded during the two subsequent TT were not significantly different (178 ± 5.0 vs 172 ± 16.3 beats·min-1; 92.5 ± 2.6 vs 89.4 ± 8.5% of HRpeak).

Figure 3-The group m...
Figure 3-The group m...
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DISCUSSION

The primary aim of this investigation was to mimic the responses of elite cyclists observed in the field under standardized laboratory conditions. This aim was achieved, as illustrated by the similar HR responses recorded during the 150-min stochastic ride in the laboratory compared with those recorded previously in the field during an actual competition of approximately the same duration (10). Such similarities indicate that the stochastic workload used in this investigation closely mimicked the physiological demands of bunch or mass start cycle racing.

Hence, the first important finding was that, despite the identical mean power outputs and HR during the initial 150 min of exercise(Fig. 1), there was a significant improvement in the time to complete the 20-km TT following the 150-min fixed intensity ride versus the stochastic ride (an average improvement of 1:36 ± 1:18 min, P< 0.05). Indeed, all riders improved their TT performance(Fig. 2) after the 150-min steady-state ride compared with the stochastic effort. As might be expected, this improvement in cycling performance was also reflected in a greater mean power output following the steady-state ride (340.3 ± 44.2 vs 302.5 ± 42.3 W, P< 0.05; Figure 3A).

Of interest was the finding that both the HR and power outputs recorded for cyclists during the 20 km TT were similar to previous field and laboratory performance rides reported by us (10,11) and others (5,6), suggesting that the athletes were working at approximately the same maximal steady state that they would have been for a time trial event in the field. As this investigation was concerned solely with mimicking the stochastic responses of group cycle racing and assessing the effects of such varying intensities on TT performance, the absence of true steady-state conditions in the stochastic ride precluded any metabolic measurements. Hence, we can only speculate about the mechanisms concerned with reducing performance following stochastic exercise. Our suspicion is that the repeated work jumps during the stochastic ride may have been associated with an increased muscle glycogen utilization, but further work will be required to determine whether this is the case.

Whatever the mechanism, the results of this investigation and that reported by Foster et al. (6) emphasize that exercise physiologists have little knowledge of the optimal pacing strategies to enhance endurance exercise performance.

In conclusion, this is the first study to attempt to replicate and assess the stochastic nature of competitive bunch cycle racing under standard laboratory conditions and its subsequent effect on performance. While further research will be required to determine the metabolic responses and substrate kinetics of the elite athlete during stochastic exercise, the results of this investigation clearly demonstrate that cycling time trial performance following variable intensity exercise was significantly impeded when compared with constant load work of the same absolute intensity and duration. This finding may have practical relevance to the way in which competitive cyclists pace themselves during competition.

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REFERENCES

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