Original article Br J Sports Med 2012;46:30–35. doi:10.1136/bjsm.2009.064261 30 ABSTRACT Introduction Both mean power output (MPO) and the distribution of the available energy over the race, that is, pacing strategy, are critical factors in performance. The purpose of this study was to determine the relative importance of both pacing strategy and MPO to performance. Methods Six well-trained, regionally competitive cyclists performed four 1500-m ergometer time trials (~2 min). For each subject, the fastest (Fast) and slowest (Slow) time trials were compared and the relative importance of differences in power output and pacing strategy were determined with an energy flow model. Results The difference in final time between Fast and Slow was 4.0 (2.5) s. Fast was performed with a higher MPO (437.8 (32.3) W vs 411.3 (39.0) W), a higher aerobic peak power (295.3 (36.8) vs 287.5 (34.7) W) and a higher anaerobic peak power (828.8 (145.4) W vs 649.5 (112.2) W) combined with a relatively higher, but not statistically different anaerobic rate constant (0.051 (0.016) vs 0.041 (0.009) W). The changes in MPO (63% anaerobic, 37% aerobic) largely explained the differences in final times. Athletes chose a different pacing strategy that was close to optimal for their physiological condition in both Fast and Slow. Conclusion Differences in intraindividual performance were mainly caused by differences in MPO. Athletes seemed to be able to effectively adjust their pacing profile based on their “status of the day”. Keywords modelling performance, energy expenditure, aerobic, anaerobic, sports. Performance in endurance sports is determined by power production in relation to power losses to the environment, which in turn are deter- mined by the chosen pacing strategy. 1 2 Besides the absolute amount of energy that can be pro- duced over a race, the distribution of energy is critical for performance. 1–4 To what extent variations in power production and power losses contribute to day-to-day variations in self-paced middle-distance performance is unknown. Several studies have explored the importance of pacing strategy and a variety of pacing pat- terns has been identified, depending on exer- cise duration, external conditions and sport. 5–15 Modelling techniques can be used to manipulate pacing strategy, with the advantage that numer- ous pacing strategies can be tested under con- stant environmental and internal conditions, 2 3 8 resulting in reasonably accurate predictions of performance in both cycling 1 2 8 16–18 and speed skating. 2 3 19 This study was designed to fur- ther explore this issue by measuring the relative contribution of variations in power production and pacing strategy to performance. Multiple self-paced races in the same athletes were stud- ied and an energy flow model was applied to the self-paced data to determine how close athletes are to their optimal pacing strategy. METHODS Subjects Six well-trained, male cyclists (table 1) par- ticipated. The cyclists were competitive at a regional level and were habituated to cycling time trials in laboratory settings. The testing took place approximately 1 month after the end of the competitive season, during a period of light training (90–120 min at an intensity gen- erally below ventilatory threshold (VT), 3 or 4 days a week). Subjects could drink ad libitum before the test, but not during the test. All pro- vided written informed consent. The protocol was approved by the university human subjects committee. Experimental setup Athletes completed an incremental cycle ergom- eter test followed by four self-paced 1500-m time trials (~ 2 min) with >48 h between trials. Trials were performed on a racing cycle interfaced with a wind load simulator with a heavy flywheel (Findlay Road Machine, Toronto, Canada), which has been shown to provide realistic per- ceptual and power output responses. 9–11 Power output ( P tot ), accumulated distance and elapsed time were recorded every second using an SRM dynamometer (Koningskamp, Germany). Gas exchange data were obtained during the trials using open circuit spirometry (Quinton Q-plex, Seattle, Washington, USA). The fastest (Fast) and slowest (Slow) trials for every subject were com- pared. Relative importance of pacing strategy and mean power output (MPO) to the difference 1 Research Institute MOVE, Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdam, The Netherlands 2 Center for Human Movement Sciences, University Medical Center Groningen/University of Groningen, Groningen, The Netherlands 3 Department of Exercise and Sports Science, University of Wisconsin-LaCrosse, LaCrosse, Wisconsin, USA Correspondence to Florentina J. Hettinga, Center for Human Movement Sciences, University Medical Center Groningen/University of Groningen, Antonius Deusinglaan 1, NL-9713 DK, Groningen, The Netherlands; f.j.hettinga@med.umcg.nl Accepted 3 October 2009 Published Online First 22 October 2009 Relative importance of pacing strategy and mean power output in 1500-m self-paced cycling F J Hettinga, 1,2 J J de Koning, 1,3 M Hulleman, 1 C Foster 3 Table 1 Characteristics of the subjects Characteristics Mean (SD) Age (years) 32.1 (10.8) Height (cm) 177 (6) Body mass (kg) 72.1 (7.2) VT (%VO 2max ) 68 (8) VO 2max (litre/min) 4.1 (0.1) VO 2max (ml/min/kg) 57.1 (5.4) P@VO2 max (W) 354 (17) P@VO2 max , maximal power output at VO 2max ; VO 2max , maximal oxygen consumption; VT, ventilatory threshold. group.bmj.com on September 25, 2014 - Published by bjsm.bmj.com Downloaded from