An accurate VO2max nonexercise regression model for 18-65-year-old adults

Area of Science:

• Exercise Physiology
• Cardiorespiratory Fitness Assessment
• Biostatistics

Background:

• Maximal oxygen uptake (VO2max) is a key indicator of cardiorespiratory fitness.
• Traditional VO2max assessment requires maximal graded exercise testing (GXT), which can be time-consuming and inaccessible.
• Predictive models using nonexercise data can offer a practical alternative for estimating VO2max.

Purpose of the Study:

• To develop and validate a regression equation for predicting VO2max using nonexercise (N-EX) variables.
• To identify which N-EX variables are most effective in predicting VO2max.
• To provide a convenient tool for estimating cardiorespiratory fitness in adults aged 18-65.

Main Methods:

• Recruited 100 participants (ages 18-65) who completed a maximal GXT to determine VO2max.
• Collected N-EX data including age, gender, body mass index (BMI), perceived functional ability (PFA), and physical activity rating (PA-R).
• Utilized multiple linear regression to establish the N-EX prediction equation and cross-validation (PRESS statistics) for model accuracy.

Main Results:

• The developed N-EX regression equation demonstrated high predictive accuracy (R = .93, SEE = 3.45 mL x kg(-1) x min(-1)).
• Cross-validation confirmed minimal shrinkage (R(p) = .91, SEE(p) = 3.63 mL x kg(-1) x min(-1)), indicating good generalizability.
• Perceived functional ability (PFA) was the strongest predictor of VO2max, followed by age, gender, BMI, and physical activity rating (PA-R).

Conclusions:

• A validated N-EX regression model can accurately predict VO2max in adults aged 18-65.
• The model offers a practical and convenient alternative to maximal GXT for estimating cardiorespiratory fitness.
• PFA emerged as the most significant predictor, highlighting its importance in functional capacity assessment.