Estimation of Cardiorespiratory fitness using a using a chest mounted accelerometer

Abstract   Cardiorespiratory fitness expressed as maximal oxygen consumption (V̇O2 max) is a strong predictor of cardiovascular health. However, quantification of cardiorespiratory fitness by cardiopulmonary exercise (CPX) assessment is complex and costly and therefore not suitable in most clinical settings. In the current study we develop and validate an algorithm for estimation of V̇O2 max using seismocardiography (SCG-V̇O2 max). SCG is measurement of precordial vibrations using an accelerometer. Methods SCG recordings and results from ergometer CPX testing of V̇O2 max in 300 subjects from six clinical studies were combined in a database. 83 subjects underwent repeated measurements sessions across several days. SCG was obtained using a sensitive accelerometer, located on the lower sternum at the Xiphoid protrusion and subjects were placed in a supine position. 5 subjects were excluded due to cardiovascular disease, or missing data. A machine learning algorithm was devolved for estimation of V̇O2 max in a training set including 221 subjects. The remaining 74 subjects were included in a test set for validation. Correlation and accuracy between SCG-V̇O2 max and ergometer CPX V̇O2 max assessed and day to day variation was assessed in subjects who underwent multiple ergometer CPX sessions. Results In 144 recordings from 74 test set subjects SCG-V̇O2 max was 45.0±9.3 ml/min/kg which was comparable to the ergometer CPX V̇O2 max at 44.3±10.1 ml/min/kg (p=0.09) and correlation between ergometer CPX V̇O2 max and SCG-V̇O2 max was r=0.875. Mean average percentage error was 8.7%. Day to day variation measured as the within-subjects-standard-deviation was 1.2 ml/min/kg for SCG-V̇O2 max and 1.7 ml/min/kg for ergometer CPX. Conclusions SCG based estimation of VV̇O2 max is a novel, low cost, easy to use and accurate estimation method of CRF with a high level of reproducibility. SCG-V̇O2 max can potentially facilitate that cardiorespiratory fitness becomes an integrated part of modern health assessment. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): VentriJect A/S DK