INTRODUCTION

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 heartrate (HR) raw data compared to indirect calorimetry in children.

METHODS

Two-hundred and thirty-one 5-12-year-olds (52.4% male) of diverse skin tone and body weights participated in a 60-minute 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 non-dominant 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.

RESULTS

For the research grade devices (i.e., ActiGraph accelerometry and Actiheart HR) R2 was 0.74, 0.74, and 0.76 for CSTS, GAMM, and RF, respectively. For Apple R2 was 0.77, 0.76, and 0.78, Garmin's was 0.73, 0.73, and 0.75, and Fitbit's was 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.

CONCLUSIONS

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.