A Head-to-Head Comparison of Three Literature Algorithms for Physical Activity Endpoints from a Wrist Accelerometer in Free-Living Conditions

Wrist accelerometers are widely used in clinical trials for continuous assessment of participants’ day-to-day physical activity and function, through derivation of metrics such as time spent in sedentary, non-sedentary, light, and moderate-to-vigorous physical activity (MVPA). Multiple algorithms have been proposed to derive these physical activity endpoints; however, the agreement across the endpoints derived from these algorithms has not been thoroughly explored. In this work, we leverage a publicly available dataset, CAPTURE-24, including wrist accelerometer data and experts’ annotations of various physical activities for 151 healthy adults, monitored at-home for one day, to compare three literature algorithms: Algorithm #1, SciKit Digital Health (SKDH), from Adamowicz et al., Algorithm #2 from Staudenmayer et al., and Algorithm #3 from Montoye et al. Algorithms’ outputs were assessed against experts’ annotations via paired t-tests, Pearson’s correlation (R), Bland–Altman analysis, accuracy, sensitivity, specificity, and F1-score. Algorithm #1 and Algorithm #2 showed stronger correlation and lower bias with experts’ annotations for time in sedentary (Algorithm #1: R = 0.91), and MVPA (Algorithm #1: R = 0.63), compared to Algorithm #3. In addition, Algorithm #1 and Algorithm #3 outperformed Algorithm #2 for time in light activity. Future work using longer monitoring periods and reference calorimetry devices should be performed to confirm the reproducibility of these findings.