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In Medicine and science in sports and exercise

** : Modeling approaches for translating accelerometer data into physical activity metrics are often developed using a group calibration approach. However, it is unknown if models developed for specific individuals will improve measurement accuracy.

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 non-dominant wrist while performing activities of daily living in a semi-structured laboratory and/or free-living setting. Criterion measures of activity intensity (sedentary, light, moderate, vigorous) were determined using direct observation. Data were reintegrated into 30-second epochs, and eight random forest models were created to determine physical activity intensity by using all possible conditions of training data (individual vs. group), protocol (laboratory vs. free-living), and placement (hip vs. wrist). A 2x2x2 repeated-measures analysis of variance was used to compare epoch-level accuracy statistics (% accuracy, kappa [k]) of the models when used to determine activity intensity in an independent sample of free-living participants.

RESULTS : Main effects were significant for the type of training data (group: accuracy = 80%, k = 0.59; individual: accuracy = 74% [p = 0.02], k = 0.50 [p = 0.01]) and protocol (free-living: accuracy = 81%, k = 0.63; laboratory: accuracy = 74% [p = 0.04], k = 0.47 [p < 0.01]). Main effects were not significant for placement (hip: accuracy = 79%, k = 0.58; wrist: accuracy = 75% [p = 0.18]; k = 0.52 [p = 0.18]). Point estimates for mean absolute error were generally lowest for the group training, free-living protocol, and hip placement.

CONCLUSION : Contrary to expectations, individually calibrated machine learning models yielded poorer accuracy than a traditional group approach. Additionally, models should be developed in free-living settings when possible to optimize predictive accuracy.

Montoye Alexander H K, Westgate Bradford S, Clevenger Kimberly A, Pfeiffer Karin A, Vondrasek Joseph D, Fonley Morgan R, Bock Joshua M, Kaminsky Leonard A