Individual versus Group Calibration of Machine Learning Models for Physical Activity Assessment Using Body-Worn Accelerometers.

Purpose: We sought to determine if individually calibrated machine learning models yielded higher accuracy than a group calibration approach for physical activity intensity assessment.

Methods: Participants (n = 48) wore accelerometers on the right hip and nondominant wrist while performing activities of daily living in a semistructured laboratory and/or free-living setting. Criterion measures of activity intensity (sedentary, light, moderate, vigorous) were determined using direct observation.

Cross-validation and out-of-sample testing of physical activity intensity predictions with a wrist-worn accelerometer.

Wrist-worn accelerometers are gaining popularity for measurement of physical activity. However, few methods for predicting physical activity intensity from wrist-worn accelerometer data have been tested on data not used to create the methods (out-of-sample data). This study utilized two previously collected data sets [Ball State University (BSU) and Michigan State University (MSU)] in which participants wore a GENEActiv accelerometer on the left wrist while performing sedentary, lifestyle, ambulatory, and exercise activities in simulated free-living settings.