A 6-minute sub-maximal run test to predict VO₂ max.

Area of Science:

• Exercise Physiology
• Sports Science
• Biometrics

Background:

• Maximal oxygen uptake (VO2 max) is a vital metric for assessing cardiorespiratory fitness and athletic performance.
• Current maximal exercise testing is accurate but demanding; submaximal tests are less precise.
• Developing accurate submaximal methods for VO2 max prediction is essential for broader application.

Purpose of the Study:

• To propose and evaluate novel methods for predicting maximal oxygen uptake (VO2 max) using submaximal, low-intensity exercise data.
• To compare the predictive accuracy of functional data analysis regression with traditional linear regression models.

Main Methods:

• Collected data from 290 athletes (182 males, 108 females) aged 10-46 years during a maximal incremental exercise test.
• Utilized data from the initial 6 minutes of the test to develop predictive models.
• Applied functional data analysis and traditional linear regression with scalar covariates to predict VO2 max.

Main Results:

• The functional regression model demonstrated superior predictive accuracy (adjusted r² = 0.845, RMSE = 2.8 mL·min⁻¹·kg⁻¹).
• The traditional linear regression model also provided a good fit (adjusted r² = 0.798, RMSE = 3.5 mL·min⁻¹·kg⁻¹).
• Both proposed methods outperformed classical submaximal testing approaches in accuracy.

Conclusions:

• Novel regression models, particularly functional data analysis, can accurately predict maximal oxygen uptake (VO2 max) from short, low-intensity exercise tests.
• These methods offer a more precise alternative to traditional submaximal tests for estimating VO2 max.
• Accurate VO2 max prediction requires oxygen consumption measurement during the submaximal test.