Predictors of performance in a 4-h mountain-bike race. Issue 4 (16th February 2018)

• Record Type: Journal Article
• Authors: Novak, Andrew R.; Bennett, Kyle J. M.; Fransen, Job; Dascombe, Ben J.
• Is Part Of: Journal of sports sciences. Volume 36:Issue 4(2018)
• Page Start: 462
• Page End: 468
• Publication Date: 2018-02-16
• Subjects: Cycling -- physiology -- power output -- decision-making -- off-road cycling -- cross-country marathon; Sports -- Periodicals; Sports -- Physiological aspects -- Periodicals; Sports -- Psychological aspects -- Periodicals; 612.044
• DOI: 10.1080/02640414.2017.1313999
• Languages: English
• ISSNs: 0264-0414

Abstract

ABSTRACT: 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 2 = 0.99; P < 0.01). This model consisted of 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 2 = 0.93; P > 0.05) although subtle changes improved the fit, significance and normal distribution of residuals within the model ( R 2 = 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.