Variable power pacing decreases finishing time compared to constant pacing in cycling time trials: A systematic review and meta-analysis

Aims This meta-analysis aims to evaluate the effects of variable pacing strategies for outdoor cycling time trials in relation to reducing event time by optimising power distribution in parallel with course characteristics. Secondly, this meta-analysis seeks to compare the proposed time savings of variable pacing strategies to constant pacing during cycling time trails. Methods Pubmed, SportsDiscus and Scopus were used with the academic search engines Google Scholar and ResearchGate. The databases were searched for randomised control trials and repeated measures design studies and any other study type which met the inclusion criteria. Two repeated measures design studies, one randomised control trial and eight mathematical modelling and simulation studies were included after screening a total of 281 studies. Sample sizes range from 1-20 consisting of either mathematically modelled cyclists or well trained racing cyclists. Results Three different variable pacing strategies were explored (5%, 10% and 15% power variation) concerning three different course lengths; 10, 16.1 and 40-km. For all course profiles and conditions where it was possible, a 15% variation in power output resulted in the most significant time savings compared to constant pacing. The standardised mean difference for 15% variability for a 10-km time trial was -77.682 (s). For 40-km the time saving was -63.684(s). For courses where a 15% variation in power was not achievable (e.g courses with long steep uphill segments where a 15% variation would result in a power output significantly above critical power output) a 10% variation power resulted in the biggest time-saving. Conclusion The primary finding of this meta-analysis was that variable pacing strategies applied in parallel to course characteristics will always result in a significant time saving compared to constant pacing. Cyclists should aim to vary power output by 15% to experience maximal time savings. However, for a variation in power to be successful, it needs to be applied in parallel to course characteristics (course profile or wind conditions). A variable pacing strategy based on distance and irrespective of course characteristics was always detrimental to performance.