Abstract des Autors

This study examines the viability of utilizing a dynamical system model and heuristic parameter estimation algorithm to make predictions for maximum heart rate (HRmax) and maximal oxygen uptake (VO2max) using data collected from a submaximal testing protocol. VO2max is widely considered to be the best single measurement of overall fitness in humans. When a VO2max assessment is not available, HRmax is often used to prescribe exercise intensities for training and rehabilitation. In the absence of maximal cardiopulmonary exercise testing (CPET), HRmax and VO2max are typically estimated using traditional submaximal prediction methods with well-known limitations and inaccuracies. For this study, 12 regularly exercising healthy young adult males performed a bout of maximal CPET on a cycle ergometer to determine their true HRmax and VO2max. Participants also performed a submaximal bout of exercise at varied intensities. A dynamical system model and heuristic parameter estimation algorithm were applied to the submaximal data to estimate the participants’ HRmax and VO2max. The submaximal predictions were evaluated by computing the coefficient of determination R2 and the standard error of the estimate (SEE) through comparisons with the true maximal values for HRmax (R2 = 0.96, SEE = 2.4 bpm) and VO2max (R2 = 0.93, SEE = 2.1 mL/kg/min). The results from this study suggest that a dynamical system model and heuristic parameter estimation algorithm can provide accurate predictions for HRmax and VO2max using data collected from a submaximal testing protocol.