Purpose: To examine the ability of a multivariate model to predict maximal oxygen consumption (VO2max) using performance data from a 5-minute maximal test (5MT). Methods: Forty-six road cyclists (age 38 [9] y, height 177 [9] cm, weight 71.4 [8.6] kg, VO2max 61.13 [9.05] mL/kg/min) completed a graded exercise test to assess VO2max and power output. After a 72-hour rest, they performed a test that included a 5-minute maximal bout. Performance variables in each test were modeled in 2 independent equations, using Bayesian general linear regressions to predict VO2max. Stepwise selection was then used to identify the minimal subset of parameters with the best predictive power for each model. Results: Five-minute relative power output was the best explanatory variable to predict VO2max in the model from the graded exercise test (R2 95% credibility interval, .81-.88) and when using data from the 5MT (R2 95% credibility interval, .61-.77). Accordingly, VO2max could be predicted with a 5MT using the equation VO2max = 16.6 + (8.87 × 5-min relative power output). Conclusions: Road cycling VO2max can be predicted in cyclists through a single-variable equation that includes relative power obtained during a 5MT. Coaches, cyclists, and scientists may benefit from the reduction of laboratory assessments performed on athletes due to this finding.
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