Methods to Estimate V˙O2max upon Acute Hypoxia Exposure

Abstract

Introduction: Altitude and an individual's V˙O2max contribute to a decrease in V˙O2max under hypoxic conditions. The purpose of this study was to update previous reviews with recent research in order to quantitatively determine the individual and interacting effects of altitude and baseline V˙O2max on V˙O2max upon acute exposure to hypoxia while developing a statistical model to predict an individual's V˙O2max under hypoxic conditions.

Methods: Meta-regression was conducted on 105 independent groups of participants (n = 958 subjects from 80 different studies). A series of regression models was tested. The final model included altitude, baseline V˙O2max, Alt2, baseline V˙O2max2, and the interaction of altitude with baseline V˙O2max.

Results: A curvilinear model provided the best fit for metadata, explaining almost 80% of the variance in the null model. Nonlinear effects of Alt2 (β = -0.078; 95% confidence interval, -0.15 to -0.002) and baseline V˙O2max2 (β = -0.003; 95% confidence interval, -0.004 to -0.001) showed that V˙O2max decreases as altitude increases and that the decrease is greater in individuals with higher aerobic capacities. The interaction of these effects (β = -0.028; 95% confidence interval, -0.042 to -0.015) also showed that the effects of altitude were augmented with higher baseline aerobic capacities. Furthermore, the predictions of the model were fairly accurate in predicting individual decreases in V˙O2max (root-mean-squared error, 3.9 mL·kg(-1)·min(-1)).

Conclusions: These data provide a robust quantitative framework for the curvilinear and interacting effects of altitude and baseline V˙O2max in determining an individual's effective V˙O2max at altitude. This predictive model is useful for a priori power calculations, design of future experimental studies, and prediction of aerobic capacity declines in applied settings.