Novak Andrew R, Bennett Kyle J M, Fransen Job, Dascombe Ben J
a Applied Sports Science and Exercise Testing Laboratory, School of Environmental and LifeSciences, Faculty of Science and Information Technology , University of Newcastle , Ourimbah , Australia.
b Sport and Exercise Science, Faculty of Health , University of Technology Sydney , Moore Park , Australia.
J Sports Sci. 2018 Feb;36(4):462-468. doi: 10.1080/02640414.2017.1313999. Epub 2017 Apr 13.
This study aimed to cross validate previously developed predictive models of mountain biking performance in a new cohort of mountain bikers during a 4-h event (XC4H). Eight amateur XC4H cyclists completed a multidimensional assessment battery including a power profile assessment that consisted of maximal efforts between 6 and 600 s, maximal hand grip strength assessments, a video-based decision-making test as well as a XC4H race. A multiple linear regression model was found to predict XC4H performance with good accuracy (R = 0.99; P < 0.01). This model consisted of [Formula: see text] relative to total cycling mass (body mass including competition clothing and bicycle mass), maximum power output sustained over 60 s relative to total cycling mass, peak left hand grip strength and two-line decision-making score. Previous models for Olympic distance MTB performance demonstrated merit (R = 0.93; P > 0.05) although subtle changes improved the fit, significance and normal distribution of residuals within the model (R = 0.99; P < 0.01), highlighting differences between the disciplines. The high level of predictive accuracy of the new XC4H model further supports the use of a multidimensional approach in predicting MTB performance. The difference between the new, XC4H and previous Olympic MTB predictive models demonstrates subtle differences in physiological requirements and performance predictors between the two MTB disciplines.
本研究旨在对先前开发的山地自行车骑行表现预测模型进行交叉验证，该验证针对一组新的山地自行车骑行者在一场4小时的赛事（XC4H）中展开。八名业余XC4H自行车骑行者完成了一系列多维评估，包括一项功率曲线评估（该评估由6至600秒之间的最大努力测试组成）、最大手握力评估、一项基于视频的决策测试以及一场XC4H比赛。结果发现，一个多元线性回归模型能够以较高的准确性预测XC4H赛事表现（R = 0.99；P < 0.01）。该模型由相对于总骑行质量（包括比赛服装和自行车质量的体重）的[公式：见原文]、相对于总骑行质量的60秒持续最大功率输出、左手握力峰值以及双线决策得分组成。先前针对奥运距离山地自行车赛事表现的模型显示出一定价值（R = 0.93；P > 0.05），不过细微的变化改善了模型内残差的拟合度、显著性和正态分布（R = 0.99；P < 0.01），凸显了不同赛事之间的差异。新的XC4H模型的高预测准确性进一步支持了在预测山地自行车骑行表现时采用多维方法。新的XC4H模型与先前的奥运山地自行车预测模型之间的差异表明，这两种山地自行车赛事在生理需求和表现预测指标方面存在细微差异。
