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Original article
Previous experience influences pacing during 20 km time trial cycling
  1. D Micklewright1,
  2. E Papadopoulou1,
  3. J Swart2,
  4. T Noakes2
  1. 1Centre for Sports and Exercise Science, Department of Biological Sciences, University of Essex, Colchester, UK
  2. 2UCT/MRC Research Unit for ESSM, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
  1. Correspondence to Dr D Micklewright, Centre for Sports and Exercise Science, Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK; dpmick{at}essex.ac.uk

Abstract

Objective To investigate how experience and feedback influence pacing and performance during time trial cycling.

Design Twenty-nine cyclists performed three 20 km cycling time trials using a Computrainer. The first two time trials (TT1 and TT2) were performed (1) without any performance feedback (n = 10), (2) with accurate performance feedback (n = 10) or (3) with false feedback showing the speed to be 5% greater than the actual speed (n = 9). All participants received full feedback during the third time trial (TT3), and their performance and pacing data were compared against TT2.

Results Completion time, average power and average speed did not change among the false feedback group, but their pacing strategy did change as indicated by a lower average cadence, 89.2 (SD 5.2) vs 96.4 (6.8) rpm, p<0.05, and higher power during the first 5 km (SMD = 39, 36, 36, 27 and 27 W for 1–5 km respectively). Pacing changed among the blind feedback group indicated by a faster completion time, 35.9 (3.1) vs 36.8 (4.4) min, p<0.05, and power increases during the final 5 km (SMD = 14, 13, 18, 23 and 53 W for 16–20 km respectively). No performance or pacing changes were observed among the accurate feedback group.

Conclusions Pacing is influenced by an interaction between feedback and previous experience. Conscious cognitive processes that lead to ratings of perceived exertion and pacing appear to be influenced by previous experience.

https://doi.org/10.1136/bjsm.2009.057315

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    According to the Central Governor Model, exercise pace is regulated by the brain, so physiological systems are never taxed to failure but are maintained within homeostatic limits, even at the point of exhaustion.1,–,4 The Central Governor Model distinguishes between the sensation of fatigue, described as the conscious perception of changes in body functions,5 and efferent muscular control, described as a subconscious feed-forward mechanism.3 The fatigue sensation experienced during exhaustive exercise is a protective mechanism which over-rides any conscious desire to increase exercise intensity if the increase were to threaten homeostasis.3 Instead of allowing complete muscle recruitment during voluntary exercise,1 2 6 7 the central governor allegedly maintains a substantial motor reserve.

    Gaining access to the motor reserve has significance for performing athletes and has been indicated in studies where improved performance was observed following the ingestion of the psychotropic drug amphetamine.8 9 Chandler and Blair found improved time to exhaustion and anaerobic capacity among runners under the influence of amphetamine which they concluded was due to masked feelings of fatigue.8 Their conclusions have since been supported by Swart et al, who demonstrated that amphetamine ingestion among endurance cyclists caused suppressed perceptions of exertion despite increases in metabolic cost.9 It was also found that feelings of fatigue, heart rate, blood pressure and cerebral blood flow could be manipulated among hypnotised cyclists by suggesting uphill or downhill gradients that did not actually exist.10 These studies show that improved performance is possible by manipulating central psychological mechanisms.

    Perhaps one of the ways in which amphetamine ingestion and hypnotism causes suppressed feelings of fatigue and improved athletic performance is by changing their beliefs. In a recent study, cyclists who believed in the ergogenic effects of caffeine were found to perform better during a 40 km time trial compared with non-believing cyclists, even though they had only taken a non-caffiene placebo.11 Beliefs have been defined as, “… a person's subjective probability judgements concerning some discriminable aspect of his world; they deal with a person's understanding of himself and his environment [which] … at the descriptive end of the continuum are directly tied to the stimulus situation and at the inferential end of the continuum are formed on the basis of these stimuli and past experiences.” (p. 131)12 The stimulus situation in this definition is synonymous with Ulmer's concept of exogenous reference signals which he explains influence the black box calculations responsible for efferent muscular control.13 14 During exercise, sensations of exertion are consciously interpreted by drawing upon mental representations and beliefs that have been constructed and reinforced through similar experiences that have occurred in the past.3 It is only through the appropriate interpretation of effort sensations, which we speculate is partly driven by beliefs, that cyclists will be able to pace themselves effectively without experiencing premature fatigue. This is a feed-forward process which Ulmer originally referred to as teleoanticipation.13 An athlete's performance beliefs can potentially influence their perceptions of exertion, conscious pacing decisions and subconscious central governance of efferent muscular control.

    While the effects of feedback15 16 and experience17 have been studied previously, few studies have investigated whether an interaction between experience and feedback influences pacing and performance during exercise. It was predicted that using successive time trial experiences to restructure a cyclist's beliefs towards better performance would influence their perceived exertion and conscious pacing responses to feedback information.

    Methods

    Subjects

    Twenty-nine male cyclists participated in this study whose mean (SD)) age, height and body mass were 34.3(7.4) years, 177.5(6.1) cm and 78.3(8.7) kg. Subjects were recruited, providing they had at least 2 years' competitive cycling experience and were participating in regular training. Their mean (SD)) competitive cycling experience was 6.1 (5.2) years and during the 6 months preceding the study had engaged in cycle training 3.7 (1.1) times per week with the duration of each session being 90.6 (27.4) min at a modal intensity of hard (five-point scale from very light to very hard). There were no between-group differences in age, F(2,26) = 0.1, p>0.05, body mass, F(2,26) = 0.2, p>0.05, height, F(2,26) = 1.0, p>0.05, or competitive cycling experience, F(2,26) = 0.6, p>0.05. The descriptive characteristics of each group are presented in table 1.

    View this table:
    Table 1

    Group descriptive data for age, body mass, height and competitive cycling experience

    Each subject provided written informed consent to take part in the study. All procedures used were conducted in accordance with the Declaration of Helsinki and were approved by ethics committees at the University of Cape Town and the University of Essex.

    Design

    A 3×3 within- and between-subjects experimental design was used in which subjects performed three 20 km cycling time trials (within-subjects factor) while being provided with three different types of feedback (between-subject factor). Time trials were repeated at the same time of day (±2 h) with a recovery interval varying between 3 and 7 days. Subjects were asked to refrain from training for 24 h before each test.

    Cycling ergometry

    Subjects completed all cycling procedures using their own bike on a Computrainer Pro cycle trainer (RacerMate, Seattle, Washington) that was calibrated according to the manufacturer's instructions. Participants performed a 5 min self-paced warm-up before each 20 km time trial. Power, speed and cadence were continuously recorded during each time trial.

    Subjects were randomly allocated to a blind feedback condition (n = 10), an accurate feedback condition (n = 10) or a false feedback condition. Subjects in each condition were given different types of feedback during the first two time trials (TT1 and TT2).

    Blind feedback condition (TT1BLIND and TT2BLIND)

    Blind feedback subjects received no feedback during TT1 and TT2 about their performance, time elapsed or distance covered as a way of promoting uncertain performance beliefs. Subjects were prevented from seeing the CompuTrainer visual display unit or any other feedback devices such as watches, clocks, heart rate monitors or cycle computers.

    Accurate feedback condition (TT1FEEDBACK and TT2FEEDBACK)

    Accurate feedback subjects were permitted to view the Computrainer visual display unit during TT1 and TT2. This helped to reinforce realistic performance beliefs among these subjects by providing them with continuous accurate feedback regarding the time elapsed, distance elapsed, power output, average power output, cadence, average cadence, speed and average speed.

    False feedback condition (TT1FALSE and TT2FALSE)

    False feedback subjects received continuous inaccurate feedback showing their performance to be 5% better than true values. This was achieved by miscalibrating a Sigma BC1606DTS wireless cycle computer (Sigma Sport, Germany) so that the circumference of the rear cycle wheel was entered as 5% greater than the actual rear wheel circumference causing speed, average speed and distance covered to be displayed as 5% greater than those recorded by the Computrainer Pro. The Sigma cycle computer was not able to display power output. False feedback subjects were not permitted to see any feedback from the Computrainer Pro or other performance devices such as watches, clocks or heart rate monitors. Thus, through successive exposure to false feedback during TT1 and TT2, a genuinely optimistic performance belief was evoked among subjects that they were capable of performing the 20 km time 5% faster than they actually could. Subjects in the false feedback condition were not aware of the deception, and this was confirmed during a debrief interview held at the end of the study.

    Final time trial with accurate feedback (TT3)

    During the third cycling time trial (TT3), all subjects, regardless of condition, were provided with accurate performance feedback via the CompuTrainer visual display about time elapsed, distance elapsed, power output, average power output, cadence, average cadence, speed and average speed. The false feedback group were told that the batteries for the cycle computer had failed and reassured that the Computrainer feedback was exactly the same. Ratings of perceived exertion (RPE) were taken every 4 km during TT3 using the 6–20 Borg scale.18 RPE was not measured during TT1 and TT2 because it may have interfered with reinforcement of beliefs particularly among the blinded cyclists and false feedback cyclists. Prior to performing TT3, subjects in the false feedback condition were reminded of their best time trial completion time and average speed using the false values (+5%) but at this stage were not informed about the deception. A debrief interview, including an explanation of the deception, was conducted once TT3 had been completed. The experimental design is illustrated in fig 1.

    Figure 1
    Figure 1

    Experimental design showing between- and within-subjects factors.

    Statistical analysis

    A power analysis for a 3×3 ANOVA design was performed for sample size estimation using the SAS macro program FPOWER.SAS. The outcome indicated that 24 subjects (eight subjects per condition) would be needed to achieve statistical power of >0.7 assuming a moderate effect size of 0.5.

    Average power, speed and cadence outcomes were calculated for each warm-up, every 1 km segment of each time trial and for the overall 20 km time trial performance. Two-way ANOVAs were used to analyse condition-by-trial differences in 20 km time trial completion time, average power, average speed and average cadence. Three-way ANOVAs were used to compare pacing condition-by-trial pacing profiles for power, speed and cadence. Significant interactions and main effects were followed up with planned post hoc comparisons between TT2 and TT3 using paired-samples t tests for within-group comparisons and independent sample t tests for between-group comparisons. Paired-samples t tests were also used to compare TT3 RPE values. All results are expressed as mean (SD) and effect sizes as partial eta squared (ηp2) or eta squared (η2).

    Results

    Warm up outcomes

    No condition or trial differences were detected during the warm-up for average power, F(4,40) = 0.7, p>0.05, ηp2 = 0.06, average speed, F(4,40) = 0.5, p>0.05, partial ηp2 = 0.05 or average cadence, F(4,40) = 0.7, p>0.05, partial ηp2 = 0.06. Warm-up outcomes are presented in table 2.

    View this table:
    Table 2

    Condition and trial warm-up averages for power, speed and cadence

    Time trial average power, speed and cadence

    Two-way within- and between-subjects ANOVA results for 20 km average power, speed and cadence are presented in table 3. Post hoc comparisons between TT2 and TT3 among the blind feedback group showed increased average power, t(9) = −2.5, p<0.05, η2 = 0.41, increased average cadence, t(8) = −1.9, p<0.05, η2 = 0.31, and increased average speed, t(9) = −2.4, p<0.05, η2 = 0.39. Post hoc comparisons between TT2 and TT3 among the false feedback group showed decreased average cadence, t(9) = 2.6, p<0.05, η2 = 0.43. There were no other significant post hoc comparisons. Overall 20 km time trial averages for completion time, power, speed and cadence are presented in fig 2.

    Figure 2
    Figure 2

    Time-trial average performance outcomes for completion time (A), power output (B), speed (C) and cadence (D).

    View this table:
    Table 3

    Two-way between- and within-subjects ANOVA outcomes for 20 km time trial power, speed and cadence

    Time trial pacing outcomes for power, speed and cadence

    A condition-by-trial-by-segment interaction was detected for power, F(36,468) = 4.4, p<0.0001, ηp2 = 0.25 and speed, F(36,468) = 7.0, p<0.0001, ηp2 = 0.35, but not for cadence, F(36,468) = 0.6, p>0.05, ηp2 = 0.05. Post hoc comparisons between TT2 and TT3 for each 1 km segment are shown on the power, speed and cadence pacing profiles of each condition that are illustrated in figs 3–5.

    Figure 3
    Figure 3

    Power output pacing profile differences between TT2 and TT3 (primary y-axis) with RPE changes (secondary y-axis) for the blind feedback group (A), accurate feedback group (B) and the false feedback group (C).

    Figure 4
    Figure 4

    Speed pacing profile differences between TT2 and TT3 (primary y-axis) with RPE changes (secondary y-axis) for the blind feedback group (A), accurate feedback group (B) and the false feedback group (C).

    Figure 5
    Figure 5

    Cadence pacing profile differences between TT2 and TT3 (primary y-axis) with RPE changes (secondary y-axis) for the blind feedback group (A), accurate feedback group (B) and the false feedback group (C).

    Ratings of perceived exertion

    A condition-by-trial interaction was found for TT3 RPE, F(8,104) = 2.1, p<0.05, ηp2 = 0.14, with a trial main effect, F(4,104) = 69.1, p<0.0001, ηp2 = 0.73, but no condition main effect, F(2,26) = 2.4, p>0.05, ηp2 = 0.16. Post hoc comparisons in RPE between each 4 km segment were made for each condition, and the outcomes are presented on fig 3. RPE profiles without statistical outcomes are also repeated in figs 4, 5, so that visual comparisons can also be made against speed and cadence profiles. Pearson product moment correlation tests revealed a positive association between average RPE and distance cycled during TT3 among the blind feedback condition, r = 0.964, p<0.005, the accurate feedback condition, r = 0.957, p<0.005, and the false feedback condition, r = 0.973, p<0.005.

    False feedback condition performance trial changes

    Due to the +5% performance feedback deception, the false feedback group cycled for a shorter time and distance during TT2 which was on average 1868 (79) s and 18871 (111) m respectively. Consequently, the false feedback group cycled on average for a further 84 (57) s and 1129 (111) m during their TT3 performance compared with TT2. For each subject, amended TT3 performance outcomes were calculated using the exact distance that they covered during TT3. No differences in false feedback group performance were found between TT2 and TT3 (amended) for time, 1868 (79) versus 1848 (52) s, t(8) = 1.1, p>0.05, average power, 259 (24) versus 264 (6), t(8) = −0.9, p>0.05, or average speed, 36.4 (1.5) versus 36.8 (1.1), t(8) = −1.1, p>0.05. However, a reduction in average cadence was found, 96.4 (6.8) versus 89.3 (5.1), t(8) = 2.6, p<0.05.

    Discussion

    This study demonstrates that the time trial pacing strategy is based upon an interaction between previous experience and feedback rather than being influenced by only one of these factors. This was most apparent in the false feedback group who, after being led to believe through successive time trial experiences that they were capable of greater levels of performance, produced a much higher than usual power output and speed during the first 5 km of TT3 (figs 3, 4). Previous studies have found that varying the type of feedback is insufficient on its own to influence pacing,15 16 but what our findings add is that the way feedback affects a cyclist's pacing strategy is mediated by their previous experience. Performance feedback is propositional raw information, yet what appears to be important is how athletes interpret and act upon such information. We suggest that, consistent with multilevel theories of cognition,19 it is experience-derived schematic mental representations of similar cycling experiences that determine how, at a conscious cognitive level, cyclists interpret streams of performance feedback data which in turn continually influences both their perceived exertion and pacing adjustments throughout a race.

    Despite the pacing changes among the false feedback group, their overall performance did not improve (fig 2), and after about 13 km during TT3 their power output and speed fell below levels achieved in TT2. This indicates that, no matter what the conscious intentions of the cyclist are, overall performance for a known distance is subconsciously governed via feed-forward teleo-anticipation mechanisms. The notion that subconscious feed-forward mechanisms will ultimately supersede ambitious pacing deviations is consistent with the central governor rationale which is to avert physiological catastrophe and premature fatigue during exercise.1 2 3 4 Perhaps achieving optimal cycling performance depends upon congruency between a cyclist's conscious performance beliefs and their maximum physiological performance potential so that their pacing strategy is neither too ambitious nor too conservative. Achieving optimal belief-capability congruency is perhaps gradually refined with repeated cycles of relevant experience. Evidence to support this can be found in a recent study20 that showed how cyclists, deprived of any performance feedback, gradually refined their pacing strategy during four successive 4 km time trials.20

    Another interesting finding of our study is that the false feedback group did not notice the additional distance of 1129 m that they cycled during TT3 compared with their TT1 and TT2 performances. Albertus et al also found that 20 km time trial cyclists were unable to detect a 1375 m discrepancy between actual and informed distance.15 Both results suggest that, in terms of completing a race and avoiding premature fatigue, cyclists are fairly impervious to distance feedback that is not entirely accurate. But some distance feedback, even if inaccurate, still seems to help cyclists pace themselves unless, as one study20 has recently established, they become extremely experienced at completing a known distance without feedback. Our results support the usefulness of distance feedback because the end spurt during the final 2 km was only seen during those time trials where feedback was given. In fact, the blind feedback group significantly decreased their completion time during TT3 (fig 2), and this was due to increased power output from 14 km and a noticeable endspurt from 18 km (figs 3, 4).

    The end spurts observed in our study, seen in both power and RPE profiles (fig 3), support an idea recently suggested by Tucker and Noakes that interpretation of afferent signals and subsequent pacing are subject to demand uncertainty at the beginning of a race which is gradually resolved as the endpoint approaches, and the occurrence of premature fatigue becomes less likely.21 It is particularly interesting to note that during TT3, even the false feedback group were able to perform an end spurt despite the fact that, until the 18 km mark, they showed the typical symptoms of fatigue as indicated by falling power output and increasing RPE. This suggests that power output is subconsciously attenuated until a point in the race is reached where unexpected physiologically demanding events become unlikely. Since the unexpected event rarely occurs, performing the end spurt is usually viable.

    The gradual increase in RPE during TT3 among all groups supports the idea that, as the endpoint gets closer, cyclists become more confident in their ability to tolerate higher levels of discomfort without experiencing premature fatigue. Unfortunately, our findings do little to clarify the exact role of RPE in pacing regulation due to the inconsistent relationships that were observed between the RPE trace and patterns of power, speed and cadence for each group (fig 3). However, previous studies have found that athletes sometimes use thoughts dissociated from feelings of fatigue and effort to help pace themselves22,–,24 which demonstrates the influential role of conscious cognition. What our study seems to indicate is that previous experience influences the conscious cognitive processes leading to RPE and subsequent pacing. The apparent willingness of the false feedback group to persist during TT3 with a RPE consistently above 16 (1–2 Borg points higher than the other groups) may have been due to their misleading previous experience distorting how they appraised their chance of success even though they were probably confronted with conflicting sensory afferent signals. In other words a mismatch between their afferent sensations and their expected outcomes caused elevated levels of RPE yet a conscious determination to persevere based upon the knowledge from their previous experience that they can achieve a certain level of performance. Consequently, these data indicate that cyclists may choose to pace themselves according to power or speed feedback, even if this means accepting a higher-than-usual RPE level but only if their experience supports this as a successful strategy. If there is an absence or lack of relevant experience then perhaps pacing strategy becomes more dependent upon the interpretation of sensory afferent feedback or RPE.

    The main conclusion of this study is that pacing is influenced by an interaction between feedback and previous experience. Conscious cognitive processes, like the interpretation of sensory afferent signals and environmental cues, appear to be influenced by schematic mental representations developed during similar previous experiences that in turn lead to certain RPE and pacing outcomes.

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    Footnotes

    • Competing interests None.

    • Provenance and Peer review Not commissioned; externally peer reviewed.

    • Ethics approval Ethics approval was provided by University of Cape Town, South Africa and University of Essex, UK.