Cardiorespiratory fitness estimation from heart rate and body movement in daily life.

Low cardiorespiratory fitness (CRF) increases risk of all-cause mortality and cardiovascular events. Periodic CRF assessment can have an important preventive function. The objective of this study was to develop a protocol-free method to estimate CRF in daily life based on heart rate (HR) and body acceleration measurements. Acceleration and HR data were collected from 37 subjects (men = 49%) while they performed a standardized laboratory activity protocol (sitting, walking, running, cycling) and during a 5-day free-living monitoring period. CRF was determined by oxygen uptake (V̇o) during maximal exercise testing. A doubly labeled water-validated equation was used to predict total energy expenditure (TEE) from acceleration data. A fitness index was defined as the ratio between TEE and HR (TEE-pulse). Activity recognition techniques were used to process acceleration features and classify sedentary, ambulatory, and other activity types. Regression equations based on TEE-pulse data from each activity type were developed to predict V̇o. TEE-pulse measured within each activity type of the laboratory protocol was highly correlated with V̇o ( from 0.74-0.91). Averaging the outcome of each activity-type specific equation based on TEE-pulse from the laboratory data led to accurate estimates of V̇o [root mean square error (RMSE): 300 mL O/min, or 10%]. The difference between laboratory and free-living determined TEE-pulse was 3.7 ± 11% ( = 0.85). The prediction method preserved the prediction accuracy when applied to free-living data (RMSE: 367 mL O/min, or 12%). Measurements of body acceleration and HR can be used to predict V̇o in daily life. Activity-specific prediction equations are needed to achieve highly accurate estimates of CRF. This is among the very few studies validating, in free-living conditions, a method to estimate cardiorespiratory fitness using heart rate and body acceleration data. A novel parameter called TEE-pulse, which was defined as the ratio between accelerometer-determined energy expenditure and heart rate, was highly correlated with maximal oxygen uptake (V̇o). Activity classification and the use of activity-selective prediction equations outperformed previously published methods for estimating V̇o from heart rate and acceleration data.