Citation
Weaver, R.G., de Zambotti, M., White, J., Finnegan, O., Nelakuditi, S., Zhu, X., Burkart, S., Beets, M., Brown III, D., Pate, R.R. and Welk, G.J., 2023. Evaluation of a device-agnostic approach to predict sleep from raw accelerometry data collected by Apple Watch Series 7, Garmin Vivoactive 4, and ActiGraph GT9X Link in children with sleep disruptions. Sleep Health.
Abstract
Goal and aims
Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography.
Focus method/technology
Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4.
Reference method/technology
Standard manual PSG sleep scoring.
Sample
Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male).
Design
Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices.
Core analytics
Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography).
Additional analytics and exploratory analyses
Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices).
Core outcomes
Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices.
Important additional outcomes
Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent.
Core conclusion
This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.
