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Scaling Maximal Oxygen Uptake to Predict Cycling Time-Trial Performance in the field: a Non-Linear Approach

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Scaling Maximal Oxygen Uptake to Predict Cycling Time-Trial Performance in the field: a Non-Linear Approach View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Wolverhampton Intellectual Repository and E-theses Eur J Appl Physiol (2005) 94: 705–710 DOI 10.1007/s00421-005-1321-8 ORIGINAL ARTICLE A. M. Nevill Æ S. A. Jobson Æ G. S. Palmer Æ T. S. Olds Scaling maximal oxygen uptake to predict cycling time-trial performance in the field: a non-linear approach Accepted: 12 January 2005 / Published online: 20 May 2005 Ó Springer-Verlag 2005 Abstract The purpose of the present article is to identify trial cycling, e.g. the benefit of cycling with a side wind _ the most appropriate method of scaling VO2 max for dif- (5% faster) compared with facing a predominatly head/ ferences in body mass when assessing the energy cost of tail wind (P<0.05). Future research could explore time-trial cycling. The data from three time-trial cycling whether the same scaling approach could be applied to, studies were analysed (N=79) using a proportional for example, alternative measures of recording power power-function ANCOVA model. The maximum oxy- output to improve the prediction of time-trial cycling gen uptake-to-mass ratio found to predict cycling speed performance. À0:32 _ was VO2 maxðmÞ ; precisely the same as that derived by Swain for sub-maximal cycling speeds (10, 15 and Keywords Power function Æ Proportional allometric 20 mph). The analysis was also able to confirm a pro- model Æ Cycling speed Æ Body mass Æ Wind resistance portional curvilinear association between cycling speed and energy cost, given by V_ m À0:32 0:41: The ð O2 maxð Þ Þ model predicts, for example, that for a male cyclist (72 kg) to increase his average speed from 30 km hÀ1 to 1 Introduction À _ 35 km h , he would require an increase in VO2 max from 2.36 l minÀ1 to 3.44 l minÀ1, an increase of À1 There is a considerable body of knowledge investigating 1.08 l min . In contrast, for the cyclist to increase his the energy cost of running in both adults (Margaria et al. mean speed from 40 km hÀ1 to 45 km hÀ1, he would 1 1963; Dill 1965; Nevill et al. 1992) and children (Nevill _ À require a greater increase in VO2 max from 4.77 l min to À1 À1 et al. 2004). Indeed, when investigating a group of 6.36 l min , i.e. an increase of 1.59 l min . The model recreationally active adults, men (N=112) and women is also able to accommodate other determinants of time- (N=92), Nevill et al. (1992) were able to confirm that 1 _ À using both VO2 max 1 min and body mass (m) as pre- dictor variables, the best predictor of 5-km running A. M. Nevill Æ S. A. Jobson performance (m sÀ1) was, Research Institute of Healthcare Sciences, University of Wolverhampton, Walsall, À1 _ 1:01 À1:03 West Midlands, England Run speed ðms Þ¼84:3ðVO2 maxÞ ðmÞ : G. S. Palmer With both exponents close to unity but with opposite Sportstest Ltd, Silver End Business Park, signs, the model suggests that the energy cost of 5-km Brierley Hill, West Midlands, England run speeds was almost exactly proportional to (and a linear relationship with) the traditional ratio standard T. S. Olds maximum oxygen uptake (l minÀ1) divided by body Centre for Applied Anthropometry, À1 À1 School of Health Sciences, mass (kg) or (ml kg min ). A similar result was ob- The University of South Australia, tained when investigating the energy cost of running for Underdale, South Australia 12-year-old boys (Nevill et al. 2004). The authors con- A. M. Nevill (&) firmed the best predictor of boys’ 1-mile run speed was School of Sport, Performing Arts and Leisure, the traditional ratio standard, maximum oxygen uptake University of Wolverhampton, Gorway Road, (ml kgÀ1 minÀ1). Walsall, WS1 3BD, England However, relatively little is known about the energy E-mail: [email protected] Tel.: +44-1902-322838 requirements of other sporting events that are weight Fax: +44-1902-322898 supported, such as time-trial cycling, wheel-chair 706 racing, rowing or canoeing. Clearly, further research is required to clarify whether the same linear relationship Methods _ and body size denominator component of VO2 max as observed in running, is likely to be appropriate when Study 1 predicting sporting performance in weight-supported events. Subjects Swain (1994), when investigating the energy cost of Twenty-three male cyclists were recruited from local cycling over relatively level ground at sub-maximal cycling clubs to participate in this investigation (sub- speeds (20, 15 and 10 mph), was able to estimate (using a ject characteristics presented in Table 1). All subjects re-analysis of data from Swain et al. (1987)) that the had previous experience of laboratory-based testing mass exponent for oxygen consumption was only 0.32, and competitive road time-trials. This study was ap- considerably less than the 2/3 value expected on the proved by the Ethics Committee for the School of basis of a surface area law. A similar mass exponent Sport, Performing Arts and Leisure at the University (0.31) was obtained when Swain (1994) re-analysed data of Wolverhampton. Prior to participation in the originally published in McCole et al. (1990). Padilla investigation, subjects were fully informed of the nat- et al. (1999) confirmed the utility of scaling maximal ure and risks of the study, before providing written power output (W ) by dividing W by m0.32 as the max max consent. Subjects were competitive but non-elite cy- most appropriate method of identifying level/flat cycling clists. ability among a group of professional cyclists. Heil (2001) was also able to provide a theoretical explanation for the mass exponent (0.32) based on projected frontal Testing schedule area. These findings suggest that the energy cost of sub- Each subject visited the laboratory for a 2-h test session maximal cycling is the oxygen-to-mass ratio, to determine subject characteristics. On a separate oc- À1=3 _ VO2 ðmÞ : However, what is not clear is whether this casion, subjects performed a competitive 25-mile road relationship is likely to be the same for sub-maximal time-trial. The laboratory session and the time-trial and maximal speeds (i.e. cycling at speeds close to event were completed in a random order separated by no _ VO2 max). Indeed, given that the primary resistance to more than 10 days during the months of May, June and movement whilst cycling is air resistance, is this rela- July. À1=3 _ tionship between cycling speed and VO2 ðmÞ likely to be linear or curvilinear? Pugh (1974) was able to con- firm using polynomial regression that a curvilinear Laboratory testing relationship exists between cycling speed and both 1 1 1 _ À _ À À On arrival at the laboratory, an anthropometric assess- VO2 max(1 min ) and VO2 max(ml kg min ). Unfor- tunately, the polynomial regression methods adopted ment of each subject was performed. Measurements by Pugh were unable to explore whether either method were made by an ISAK qualified practitioner in of recoding oxygen consumption was optimal when accordance with procedures recommended by the predicting cycling speed performance. The author also International Society for the Advancement of Kinan- fails to report details, e.g. the order of the fitted poly- thropometry (ISAK 2001). Participants then completed nomial. a progressive, incremental exercise test to exhaustion on Hence, the purpose of the present article is to identify a Kingcycle air-braked cycle ergometer (Kingcycle Ltd., the most appropriate model to describe and scale/nor- High Wycombe, Buckinghamshire, UK). This system, _ which has previously been described in full (Palmer et al. malise VO2 max (for differences in body mass) when assessing the energy cost of maximal time-trial cycling 1996), allows the subject to exercise on their own bike performance on level ground. Given that the primary against a resistance comparable to that of riding on the resistance to movement whilst cycling is proportional to road. This system has been shown to be both valid and air resistance, we shall adopt a proportional allometric reliable during 20-km, 40-km (Palmer et al. 1996), and or power-function model to predict cycling speed that max-test protocols (Keen et al. 1991). will not only assess the most appropriate oxygen-to- Following standardised calibration procedures (Pal- mass ratio but will also accommodate the anticipated mer et al. 1996), subjects carried out a warm-up at a proportional curvilinear model associated with time- self-selected intensity for 10 min. Immediately following trial cycling speed. This will be achieved using the data this, the maximal test was initiated at a workload of from three studies. Study 1 consists of previously 150–200 W. Thereafter, workload increased at a ramp À1 unpublished data collected from five time-trial events rate of 20 W min (1 W every 3 s). The test was ter- conducted in the West Midlands, UK during 2004. The minated when the subject could no longer maintain the data from two other studies (see Olds et al. 1995; Coyle specified workload, despite strong verbal encourage- et al. 1991) will be used to support further the findings ment, given by the same investigator during all maximal from study 1. trials. 707 For the duration of the maximal test protocol, Road time-trials respiratory gases were recorded on a breath-by-breath basis using an Oxycon Pro automated gas analysis sys- All subjects completed a 26-km time-trial, comprising tem (Erich Jaeger [UK] Ltd., Market Harborough, four lengths of a flat 6.5-km sealed bitumen course.
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