420
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

Quantification of brake data acquired with a brake power meter during simulated cross-country mountain bike racing

ORCID Icon, , &
Pages 343-353 | Received 02 Nov 2016, Accepted 17 Nov 2017, Published online: 17 Jan 2018
 

Abstract

There is currently a dearth of information describing cycling performance outside of propulsive and physiological variables. The aim of the present study was to utilise a brake power meter to quantify braking during a multi-lap cross-country mountain bike time trial and to determine how braking affects performance. A significant negative association was determined between lap time and brake power (800.8 ± 216.4 W, mean ± SD; r = −0.446; p < 0.05), while the time spent braking (28.0 ± 6.4 s) was positively associated with lap time (314.3 ± 37.9 s; r = 0.477; p < 0.05). Despite propulsive power decreasing after the first lap (p < 0.05), lap time remained unchanged (p > 0.05) which was attributed to decreased brake work (p < 0.05) and brake time (p < 0.05) in both the front and rear brakes by the final lap. A multiple regression model incorporating braking and propulsion was able to explain more of the variance in lap time (r2 = 0.935) than propulsion alone (r2 = 0.826). The present study highlights that riders’ braking contributes to mountain bike performance. As riders repeat a cross-country mountain bike track, they are able to change braking, which in turn can counterbalance a reduction in power output. Further research is required to understand braking better.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 212.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.