A dynamical systems approach for the submaximal prediction of maximum heart rate and maximal oxygen uptake

This study examines the viability of utilizing a dynamical system model and heuristic parameter estimation algorithm to make predictions for maximum heart rate (HR max) and maximal oxygen uptake (V ˙ O 2 max) using data collected from a submaximal testing protocol. V ˙ O 2 max is widely considered to be the best single measurement of overall fitness in humans. When a V ˙ O 2 max assessment is not available, HR max is often used to prescribe exercise intensities for training and rehabilitation. In the absence of maximal cardiopulmonary exercise testing (CPET), HR max and V ˙ O 2 max 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 HR max and V ˙ O 2 max. 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’ HR max and V ˙ O 2 max. 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 HR max (R2= 0.96 , SEE = 2.4 bpm) and V ˙ O 2 max (R2= 0.93 , SEE = 2.1 mL kg- 1 min- 1). The results from this study suggest that a dynamical system model and heuristic parameter estimation algorithm can provide accurate predictions for HR max and V ˙ O 2 max using data collected from a submaximal testing protocol.

Duke Scholars

Author Brian Mann Thomas Lord Department of Mechanical Engineering and Materia ...