Scaling maximal oxygen uptake to predict cycling time-trial performance in the field: a non-linear approach

Abstract

The purpose of the present article is to identify the most appropriate method of scaling ${\dot{\text V}}_{{\text{O}}_2 \max}$ for differences in body mass when assessing the energy cost of time-trial cycling. The data from three time-trial cycling studies were analysed (N=79) using a proportional power-function ANCOVA model. The maximum oxygen uptake-to-mass ratio found to predict cycling speed was ${\dot{\text V}}_{{\text{O}}_2 \max } (m)^{ - 0.32},$ precisely the same as that derived by Swain for sub-maximal cycling speeds (10, 15 and 20 mph). The analysis was also able to confirm a proportional curvilinear association between cycling speed and energy cost, given by ${\dot{(\text V}}_{{\text{O}}_2 \max } (m)^{ - 0.32} )^{0.41} .$ The model predicts, for example, that for a male cyclist (72 kg) to increase his average speed from 30 km h⁻¹ to 35 km h⁻¹, he would require an increase in ${\dot{\text V}}_{{\text{O}}_2 \max}$ from 2.36 l min⁻¹ to 3.44 l min⁻¹, an increase of 1.08 l min⁻¹. In contrast, for the cyclist to increase his mean speed from 40 km h⁻¹ to 45 km h⁻¹, he would require a greater increase in ${\dot{\text V}}_{{\text{O}}_2 \max}$ from 4.77 l min⁻¹ to 6.36 l min⁻¹, i.e. an increase of 1.59 l min⁻¹. The model is also able to accommodate other determinants of time-trial cycling, e.g. the benefit of cycling with a side wind (5% faster) compared with facing a predominatly head/tail wind (P<0.05). Future research could explore whether the same scaling approach could be applied to, for example, alternative measures of recording power output to improve the prediction of time-trial cycling performance.