Leveraging Accelerometry and Heart Rate Data from Consumer Wearables to Predict Physical Activity in Children: A...

This study examined the potential of a device agnostic approach for predicting physical activity energy expenditure (PAEE) from research-grade and consumer wearable accelerometry and heart rate (HR) raw data compared with indirect calorimetry in children.

Two hundred thirty-one 5- to 12-yr-olds (52.4% male) of diverse skin tone and body weights participated in a 60-min protocol with multiple activities at varying intensities. Children wore two of three consumer wearables (Apple Watch Series 7, Garmin Vivoactive 4S, Fitbit Sense) and a research-grade accelerometer (ActiGraph GT9X) on their nondominant wrist, and a chest-placed, research-grade HR monitor (Actiheart 5, ECG), concurrently. Children also wore a K5 criterion measure of PAEE (i.e., COSMED K5). Cross-sectional time series (CSTS), generalized additive mixed effects model (GAMM), and random forest (RF) were used to estimate minute-by-minute PAEE from features extracted from raw accelerometry and HR data. Variance explained ( R2 ), in addition to other metrics, evaluated agreement between estimated and criterion measurements.

For the research-grade devices (i.e., ActiGraph accelerometry and Actiheart HR), R2 values were 0.74, 0.74, and 0.76 for CSTS, GAMM, and RF, respectively. For Apple, R2 values were 0.77, 0.76, and 0.78; Garmin's values were 0.73, 0.73, and 0.75; and Fitbit's values were 0.63, 0.65, and 0.67 for CSTS, GAMM, and RF, respectively. Across all other evaluation metrics, a similar pattern was observed with Fitbit performing the worst but with little variability between the modeling approaches or the other devices.

Except for Fitbit, accelerometry and HR data from consumer wearables predicted PAEE comparably to research-grade devices, and there was little variability across modeling approach. These outcomes support deploying a consumer wearable device-agnostic approach for PAEE estimation in children.