A Maximal Graded Exercise Test to Accurately Predict VO2max in 18–65-Year-Old Adults

Abstract

The purpose of this study was to develop an age-generalized regression model to predict maximal oxygen uptake (VO2max) based on a maximal treadmill graded exercise test (GXT; George, 1996). Participants (N = 100), ages 18–65 years, reached a maximal level of exertion (mean ± standard deviation [SD]; maximal heart rate [HRmax] = 185.2 ± 12.4 beats per minute (bpm); maximal respiratory exchange ratio [RERmax] = 1.18 ± 0.05; maximal rating of perceived exertion (RPEmax) = 19.1 ± 0.7) during the GXT to assess VO2max (mean ± SD; 40.24 ± 9.11 mL·kg−1·min−1). Multiple linear regression generated the following prediction equation (R = .94, standard error of estimate [SEE] = 3.18 mL·kg−1·min−1, %SEE = 7.9): VO2max (mL·kg−1·min−1) = 13.160 + (3.314 × gender; females = 0, males = 1) − (.131 × age) − (.334 × body mass index (BMI)) + (5.177 × treadmill speed; mph) + (1.315 × treadmill grade; %). Cross validation using predicted residual sum of squares (PRESS) statistics revealed minimal shrinkage (Rp = .93 and SEE p = 3.40 mL·kg−1·min−1); consequently, this model should provide acceptable accuracy when it is applied to independent samples of comparable adults. Standardized β-weights indicate that treadmill speed (.583) was the most effective at predicting VO2max followed by treadmill grade (.356), age (−.197), gender (.183), and BMI (−.148). This study provides a relatively accurate regression model to predict VO2max in relatively fit men and women, ages 18–65 years, based on maximal exercise (treadmill speed and grade), biometric (BMI), and demographic (age and gender) data.