Improved VO_2max Estimation by Combining a Multiple Regression Model and Linear Extrapolation Method.

Area of Science:

• Exercise Physiology
• Cardiorespiratory Fitness Assessment
• Biostatistics

Background:

• Maximal oxygen consumption (VO2max) is a key health indicator.
• Current estimation methods, Multiple Regression Model (MRM) and Linear Extrapolation Method (LEM), have limitations.
• There is a need for improved VO2max estimation techniques.

Purpose of the Study:

• To investigate a novel VO2max estimation method addressing weaknesses of MRM and LEM.
• To compare the accuracy and bias of MRM and LEM for VO2max estimation.
• To identify conditions under which LEM can improve MRM accuracy.

Main Methods:

• 128 adults underwent anthropometric measurements, a physical activity questionnaire, a step test with heart rate (HR) monitoring, and a criterion VO2max treadmill test.
• VO2max was estimated using MRM (including HR, age, sex, BMI, questionnaire scores) and LEM (including HR, VO2 constants, age-predicted maximal HR).
• Bland-Altman plots were used to analyze systematic differences and biases between estimated and measured VO2max.

Main Results:

• MRM had a lower standard error of estimate (4.15 mL·kg-1·min-1) than LEM (5.08 mL·kg-1·min-1).
• LEM exhibited a wider range of 95% limits of agreement compared to MRM.
• MRM demonstrated significant proportional bias, which was mitigated by applying LEM for VO2max estimates ≥45 mL·kg-1·min-1.

Conclusions:

• Substantial proportional bias exists in the MRM for VO2max estimation.
• The LEM can effectively mitigate MRM bias within a specific range of VO2max values (≥45 mL·kg-1·min-1).
• This combined approach offers a more accurate VO2max estimation, particularly for individuals with higher cardiorespiratory fitness.