Introduction
This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared to a research-grade accelerometry.
Methods
Seventy-five 5–12-year-olds (58% male, 63% White) participated in a 60-minute protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (Cosmed K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity[MVPA]) were estimated via indirect calorimetry (criterion) and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph.
Results
Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95CI = 67.1%, 69.3%), 73.0% (95CI = 71.8%, 74.3%), and 66.6% (95CI = 65.7%, 67.5%), respectively; and weighted specificity = 84.4% (95CI = 83.6%, 85.2%), 82.0% (95CI = 80.6%, 83.4%), and 75.3% (95CI = 74.7%, 75.9%), respectively. Apple Watch produced the lowest mean bias (inactive = -4.0 ± 4.5, light activity = 2.1 ± 4.0) and absolute error (inactive = 4.9 ± 3.4, light activity = 3.6 ± 2.7) for inactive and light physical activity minutes. For MVPA, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent.
Conclusions
Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than a research-grade device, when compared to indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.