Predicting VO2max in College-Aged Participants Using Cycle Ergometry and Perceived Functional Ability

Abstract

The purpose of this study was to develop a multiple linear regression model to predict treadmill VO2max scores using both exercise and non-exercise data. One hundred five college-aged participants (53 male, 52 female) successfully completed a submaximal cycle ergometer test and a maximal graded exercise test on a motorized treadmill. The submaximal cycle protocol required participants to achieve a steady-state heart rate equal to at least 70% of age-predicted maximum heart rate (220-age), while the maximal treadmill graded exercise test required participants to exercise to volitional fatigue. Relevant submaximal cycle ergometer test data included a mean (±SD) ending steady-state heart rate and ending workrate equal to 164.2 ± 13.0 bpm and 115.3 ± 27.0 watts, respectively. Relevant non-exercise data included a mean (±SD) body mass (kg), perceived functional ability score, and physical activity rating score of 74.2 ± 15.1, 15.7 ± 4.3, and 4.7 ± 2.1, respectively. Multiple linear regression was used to generate the following prediction of (R = .91, standard error of estimates (SEE) = 3.36 ml·kg−1·min−1): VO2max = 54.513 + 9.752 (gender, 1 = male, 0 = female) – .297 (body mass, kg) + .739 (perceived functional ability, 2–26) + .077 (work rate, watts) – .072 (steady-state heart rate). Each predictor variable was statistically significant (p < .05) with beta weights for gender, body mass, perceived functional ability, exercise workrate, and steady-state heart rate equal to .594, –.544, .388, .305, and –.116, respectively. The predicted residual sums of squares (PRESS) statistics reflected minimal shrinkage (RPRESS = .90, SEEPRESS = 3.56 ml·kg−1·min−1) for the multiple linear regression model. In summary, the submaximal cycle ergometer protocol and accompanying prediction model yield relatively accurate VO2max estimates in healthy college-aged participants using both exercise and non-exercise data.