title: Variable versus constant power strategies during cycling time-trials: prediction of time savings using an up-to-date mathematical model.
abstract: Swain (1997) employed the mathematical model of Di Prampero et al. (1979) to predict that, for cycling time-trials, the optimal pacing strategy is to vary power in parallel with the changes experienced in gradient and wind speed. We used a more up-to-date mathematical model with validated coefficients (Martin et al., 1998) to quantify the time savings that would result from such optimization of pacing strategy. A hypothetical cyclist (mass = 70 kg) and bicycle (mass = 10 kg) were studied under varying hypothetical wind velocities (-10 to 10 m x s(-1)), gradients (-10 to 10%), and pacing strategies. Mean rider power outputs of 164, 289, and 394 W were chosen to mirror baseline performances studied previously. The three race scenarios were: (i) a 10-km time-trial with alternating 1-km sections of 10% and -10% gradient; (ii) a 40-km time-trial with alternating 5-km sections of 4.4 and -4.4 m x s(-1) wind (Swain, 1997); and (iii) the 40-km time-trial delimited by Jeukendrup and Martin (2001). Varying a mean power of 289 W by +/- 10% during Swain's (1997) hilly and windy courses resulted in time savings of 126 and 51 s, respectively. Time savings for most race scenarios were greater than those suggested by Swain (1997). For a mean power of 289 W over the "standard" 40-km time-trial, a time saving of 26 s was observed with a power variability of 10%. The largest time savings were found for the hypothetical riders with the lowest mean power output who could vary power to the greatest extent. Our findings confirm that time savings are possible in cycling time-trials if the rider varies power in parallel with hill gradient and wind direction. With a more recent mathematical model, we found slightly greater time savings than those reported by Swain (1997). These time savings compared favourably with the predicted benefits of interventions such as altitude training or ingestion of carbohydrate-electrolyte drinks. Nevertheless, the extent to which such power output variations can be tolerated by a cyclist during a time-trial is still unclear.
authors.affiliation: Research Institute for Sport and Exercise Sciences, Liverpool John Moores University. Liverpool. g.atkinson@ljmu.ac.uk
authors.forename: G
authors.initials: G
authors.lastname: Atkinson
authors.forename: O
authors.initials: O
authors.lastname: Peacock
authors.forename: L
authors.initials: L
authors.lastname: Passfield
mesh_terms: D000054:Acceleration; D001642:Bicycling / Q000502:physiology*; D016552:Ergometry / Q000379:methods* / Q000706:statistics & numerical data; D005080:Exercise Test; D015233:Models, Statistical; D010807:Physical Endurance / Q000502:physiology*; D013997:Time Factors; D006113:United Kingdom; D014919:Wind