David Parry

Cognition and performance: anxiety, mood and perceived exertion among Ironman triathletes

Objective The authors examined the changing patterns of mood before and after an Ironman triathlon, and the relationships between expected performance outcomes, perception of effort and pacing. Design Twelve participants in the 2008 Ironman Austria triathlon competition were studied before, during and after the event. Each participant completed measures of mood, anxiety and perceived exertion, while pacing was calculated from official race timings at various points on the course. Results Positive correlations were found between distance covered and rating of perceived exertion (RPE) during each of the individual disciplines, and also between RPE and the percentage of overall race time completed (r=0.826, p<0.001). A negative correlation was found between average speed and distance covered during the run segment (r=−0.911, p<0.005) with pace gradually declining. Differences occurred in the profile of mood states mood subscales of tension and fatigue between the baseline, prerace and postrace trials. Somatic anxiety was higher before the race compared with baseline measures. Discussion RPE followed a linear progression of RPE during each discipline followed by a re-setting of the perception of effort at the start of the next discipline. The increase in RPE for the entire event followed a linear increase. The linear decline in run pace is consistent with a recent model in which expected RPE is used to modulate pacing. Anxiety and mood responses of participants in this study indicate that the emotional response of athletes before and after ultra-endurance exercise is closely aligned with their conscious thoughts.

Pacing Behavior and Tactical Positioning in 1500-m Short-Track Speed Skating

PURPOSE To gain more insight in pacing behavior and tactical positioning in 1500-m short-track speed skating, a sport in which several athletes directly compete in the same race. METHODS Lap times and intermediate rankings of elite 1500-m short-track- skating competitors were collected over the season 2012-13 (N = 510, 85 races). Two statistical approaches were used to assess pacing behavior and tactical positioning. First, lap times were analyzed using a MANOVA, and for each lap differences between sex, race type, final rankings, and stage of competition were determined. Second, Kendall tau b correlations were used to assess relationships between intermediate and final rankings. In addition, intermediate rankings of the winner of each race were examined. RESULTS In 1500 m (13.5 laps of 111.12 m), correlations between intermediate and final ranking gradually increased throughout the race (eg, lap 1, r = .05; lap 7, r = .26; lap 13, r = .85). Moreover, the percentage of race winners skating in the leading position was over 50% during the last 3 laps. Top finishers were faster than bottom-place finishers only during the last 5 laps, with on average 0.1- to 1.5-s faster lap times of the race winners compared with the others during the last 5 laps. CONCLUSIONS Although a fast start led to faster finishing times, top finishers were faster than bottom-placed finishers only during the last 5 laps. Moreover, tactical positioning at 1 of the foremost positions during the latter phase of the race appeared to be a strong determinant of finishing position.

Risk perception influences athletic pacing strategy

PURPOSE The objective of this study is to examine risk taking and risk perception associations with perceived exertion, pacing, and performance in athletes. METHODS Two experiments were conducted in which risk perception was assessed using the domain-specific risk taking (DOSPERT) scale in 20 novice cyclists (experiment 1) and 32 experienced ultramarathon runners (experiment 2). In experiment 1, participants predicted their pace and then performed a 5-km maximum effort cycling time trial on a calibrated Kingcycle mounted bicycle. Split times and perceived exertion were recorded every kilometer. In experiment 2, each participant predicted their split times before running a 100-km ultramarathon. Split times and perceived exertion were recorded at seven checkpoints. In both experiments, higher and lower risk perception groups were created using median split of DOSPERT scores. RESULTS In experiment 1, pace during the first kilometer was faster among lower risk perceivers compared with higher risk perceivers (t(18) = 2.0, P = 0.03) and faster among higher risk takers compared with lower risk takers (t(18) = 2.2, P = 0.02). Actual pace was slower than predicted pace during the first kilometer in both the higher risk perceivers (t(9) = -4.2, P = 0.001) and lower risk perceivers (t(9) = -1.8, P = 0.049). In experiment 2, pace during the first 36 km was faster among lower risk perceivers compared with higher risk perceivers (t(16) = 2.0, P = 0.03). Irrespective of risk perception group, actual pace was slower than predicted pace during the first 18 km (t(16) = 8.9, P < 0.001) and from 18 to 36 km (t(16) = 4.0, P < 0.001). In both experiments, there was no difference in performance between higher and lower risk perception groups. CONCLUSIONS Initial pace is associated with an individuals perception of risk, with low perceptions of risk being associated with a faster starting pace. Large differences between predicted and actual pace suggest that the performance template lacks accuracy, perhaps indicating greater reliance on momentary pacing decisions rather than preplanned strategy.

Optic flow influences perceived exertion and distance estimation but not running pace.

PURPOSE This study aimed to examine the effect of variations in optic flow on perceived and actual running pace and RPE. METHODS Twelve male moderately trained competitive runners performed a self-paced 5-km running reference time trial followed by a three 5-km running self-paced trials under different optic flow conditions (RNORM = actual running speed, RSLOW = actual speed - 25%, and RFAST = actual speed + 25%). Participants were asked to self-report RPE when they thought they had reached each kilometer. RESULTS There was no difference in the 5-km completion times (P > 0.05) or actual pace (P > 0.05) between the optic flow conditions. A greater distance was covered between the perceived kilometer points during RSLOW compared with RNORM (P < 0.01) and RFAST (P < 0.01). RPE (normalized for running distance) was lower during RSLOW compared with RNORM (P < 0.01) and RFAST (P < 0.01). CONCLUSIONS Different rates of optic flow lead to an apparent compression or elongation of perceived distance during running, with slow optic flow resulting in an increased ratio of perceived to actual running distance. Pacing was not different in the slow optic flow condition, despite lower perceived exertion, which is contrary to the predictions of the RPE template model, hazard scores, and estimated time limits.

The Central Governor Model Cannot be Adequately Tested by Observing its Components in Isolation

In a recent issue of Sports Medicine, a current opinion article was published entitled ‘‘Is it Time to Retire the ‘Central Governor’?’’ in which the author Roy Shephard concluded that, based on a lack of experimental evidence, the Central Governor Model (CGM) should be treated with scepticism. In the opening few pages of the article Shephard provides a concise overview of CGM before claiming that the CGM is ‘‘y hampered by the absence of a systematic and clearly enunciated listing of its inherent correlates.’’ Although this statement is to some extent true, it should be pointed out that the apparent lack of detailed evidence to which Shephard refers is perhaps a reflection of CGM ‘complexity’, which, in fairness to Noakes and his colleagues, is something that they have repeatedly acknowledged. The complexity of CGM, involving feed forward muscular control derived from the integration of numerous afferent peripheral signals, presents considerable methodological challenges that, for the time being at least, leaves Noakes in the rather difficult scientific position of having to defend his model with only partial and inconclusive evidence. Attempts to discredit the CGM, which are summarized in Shephard’s article, have largely been deduced from empirical observations of component physiological systems that, despite being isolated from the CNS, exhibit self-limiting functions. However, we should be cautious about rejecting the CGM based upon such deductions because this approach essentially disregards the complex physiological, neurological and psychological interactions that model proposes and therefore does not constitute a sufficiently rigorous test. For instance, one of the claimsmade by Shephard is that a central governor is not needed because of a study that showed gradual rather than catastrophic reduction in the force production of locally stimulated soleus muscles. In fact, all that can really be interpreted from this study is that locally stimulated muscles gradually fatigue. The study does not provide any definitive evidence to rule out the existence of a central governor further up the neurological chain since the lack of attenuation of efferent command when a locally stimulated muscle becomes fatigued could be explained by the absence of other afferent signals that are usually present during ‘whole body’ exercise to represent changes in temperature, oxygen uptake, substrate availability and pH to name a few. Under such circumstances it is of no surprise that the efferent signal was not attenuated since the failure of single locally stimulated muscle does not really threaten overall homeostasis of the body and therefore does not test what Noakes et al. have proposed is the main purpose of the central governor. In other words, the apparent normality of isolated CGM components should not be used as evidence against the model because it does not reflect the inherent complexity of Noakes’ model that, with time and methodological advances, might yet be proved correct. Another approach to oppose CGM that has been recently adopted is to draw on the evidence of evolutionary studies. Shephard suggests that there is little evidence for selective pressures that would lead to the evolution of a ‘central governor’ in humans. He suggests that hunting skills are acquired during adult life, and that this would mitigate against the genetic transmission of skills since reproduction occurs early in adult life. Of course, no acquired characteristics can be genetically transmitted, irrespective of when in life their acquisition takes place. Abilities are genetically transmitted, not skills, which are always learnt, and in our own recent work we have shown the importance of how experience and learning help athletes to make sense of present events and pace themselves appropriately in order to avoid premature fatigue during exercise. To state that a central governor could not have evolved because the associated phenotype is often not present until later life CORRESPONDENCE Sports Med 2010; 40 (1): 91-94 0112-1642/10/0001-0091/

Information Acquisition Differences between Experienced and Novice Time Trial Cyclists

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Optic Flow Influences Perceived Exertion During Cycling

Purpose To use eye-tracking technology to directly compare information acquisition behavior of experienced and novice cyclists during a self-paced, 10-mile (16.1 km) time trial (TT). Method Two groups of novice (n = 10) and experienced cyclists (n = 10) performed a 10-mile self-paced TT on two separate occasions during which a number of feedback variables (speed, distance, power output, cadence, HR, and time) were projected within their view. A large RPE scale was also presented next to the projected information and participants. Participants were fitted with a head-mounted eye tracker and HR monitor. Results Experienced cyclists performed both TT quicker than novices (F1,18 = 6.8, P = 0.018) during which they primarily looked at speed (9 of 10 participants), whereas novices primarily looked at distance (6 of 10 participants). Experienced cyclists looked at primary information for longer than novices across the whole TT (24.5% ± 4.2% vs 34.2% ± 6.1%; t18 = 4.2; P < 0.001) and less frequently than novices during the last quarter of the TT (49 ± 19 vs 80 ± 32; t18 = −2.6; P = 0.009). The most common combination of primary and secondary information looked at by experienced cyclists was speed and distance, respectively. Looking at 10 different primary–secondary feedback permutations, the novices were less consistent than the experienced cyclists in their information acquisition behavior. Conclusions This study challenges the importance placed on knowledge of the endpoint to pacing in previous models, especially for experienced cyclists for whom distance feedback was looked at secondary to, but in conjunction with, information about speed. Novice cyclists have a greater dependence on distance feedback, which they look at for shorter and more frequent periods than the experienced cyclists. Experienced cyclists are more selective and consistent in attention to feedback during TT cycling.