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 VO2max 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 VO2max(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 (VO2max(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 VO2max 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 VO2max 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 predominately 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.

Optimal power-to-mass ratios when predicting flat and hill-climbing time-trial cycling

“…The methods used during studies 1, 2 and 3 have previously been described in detail (see Nevill et al 2005;Coyle et al 1991;Davison et al 2000, respectively). As such, the following sections provide only a summary.…”

“…In order to establish the most appropriate power-tomass ratio to best reflect cycling speed, the following power-function model (Nevill et al 1992(Nevill et al , 2005) was used to explore the optimal relationship between cycle speed, power output (W) (either W MAP , W VT or W AVG ) and body mass (m),…”

“…As a result, a number of authors have adopted the ratio, _ V O 2 ðmÞ À0:32 as the optimal oxygen-to-mass ratio associated with sub-maximal and maximal cycling performances. However, only recently were Nevill et al (2005) able to confirm a similar association exists when predicting time-trial cycling performances using _ V O 2max and body mass as the predictor variables incorporated in the allometric model described above (Nevill et al 1992). The authors found the optimal _ V O 2max -to-mass ratio to predict average time-trial cycling speed was ð _ V O 2max ðmÞ À0:32 Þ 0:41 : Not only did this power-function ratio confirm that the anticipated optimal oxygen-tomass ratio was given by _ V O 2max ðmÞ À0:32 but also that the association between cycling speed and oxygen cost is curvilinear (being constrained by air resistance), thought to be proportional to the cube root of the energy expended, ð _ V O 2max ðmÞ À0:32 Þ 0:41 : A number of authors (Padilla et al 1999;Lucia et al 2004) have adopted the same ratio structure when using maximal power output (W max ) as a predictor of level/flat cycling performance (i.e.…”

“…(Padilla et al 1999). However, as far as we are aware, no research has directly confirmed/validated these ratios and the curvilinear associations between uphill cycling speed and both mass and power output as predictors, see Nevill et al (2005).…”

“…As the primary resistance to movement whilst cycling is proportional to air resistance, we shall adopt similar proportional allometric or power-function models adopted by Nevill et al (1992Nevill et al ( , 2005). This will not only assess the optimal power-tomass ratios associated with flat and hill-climbing cycling performances, but also accommodate the anticipated proportional curvilinear association between time-trial cycling speeds and energy expenditure.…”