PeerJ (Jul 2023)

Track cycling sprint sex differences using power data

  • Hamish Ferguson,
  • Chris Harnish,
  • Sebastian Klich,
  • Kamil Michalik,
  • Anna Katharina Dunst,
  • Tony Zhou,
  • J Geoffrey Chase

DOI
https://doi.org/10.7717/peerj.15671
Journal volume & issue
Vol. 11
p. e15671

Abstract

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Objectives Currently, there are no data on sex differences in the power profiles in sprint track cycling. This cross-section study analyses retrospective data of female and male track sprint cyclists for sex differences. We hypothesized that women would exhibit lower peak power to weight than men, as well as demonstrate a different distribution of power durations related to sprint cycling performance. Design We used training, testing, and racing data from a publicly available online depository (www.strava.com), for 29 track sprint cyclists (eight women providing 18 datasets, and 21 men providing 54 datasets) to create sex-specific profiles. R2 was used to describe model quality, and regression indices are used to compare watts per kilogram (W/kg) for each duration for both sexes against a 1:1 relationship expected for 15-s:15-s W/kg. Results We confirmed our sample were sprint cyclists, displaying higher peak and competition power than track endurance cyclists. All power profiles showed a high model quality (R2 ≥ 0.77). Regression indices for both sexes were similar for all durations, suggesting similar peak power and similar relationship between peak power and endurance level for both men and women (rejecting our hypothesis). The value of R2 for the female sprinters showed greater variation suggesting greater differences within female sprint cyclists. Conclusion The main finding shows female sprint cyclists in this study have very similar relationships between peak power and endurance power as men. Higher variation in W/kg for women in this study than men, within these strong relationships, indicates women in this study, had greater inter-athlete variability, and may thus require more personalised training. Future work needs to be performed with larger samples, and at different levels to optimize these recommendations.

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