title: 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.
authors.affiliation: Research Institute of Healthcare Sciences, University of Wolverhampton, Walsall, West Midlands, England. a.m.nevill@wlv.ac.uk
authors.forename: A M
authors.initials: AM
authors.lastname: Nevill
authors.forename: S A
authors.initials: SA
authors.lastname: Jobson
authors.forename: G S
authors.initials: GS
authors.lastname: Palmer
authors.forename: T S
authors.initials: TS
authors.lastname: Olds
mesh_terms: D000328:Adult; D000465:Algorithms; D000886:Anthropometry / Q000379:methods*; D001642:Bicycling / Q000502:physiology*; D015992:Body Mass Index; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004734:Energy Metabolism / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D017711:Nonlinear Dynamics; D010101:Oxygen Consumption / Q000502:physiology*; D010807:Physical Endurance / Q000502:physiology*; D005082:Physical Exertion / Q000502:physiology; D013647:Task Performance and Analysis