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

Abstract

Goal and aimsEvaluate 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/technologyAutomatic 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/technologyStandard manual PSG sleep scoring. SampleFifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male). DesignParticipants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices. Core analyticsDiscrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography). Additional analytics and exploratory analysesEquivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices). Core outcomesCompared 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 outcomesEquivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent. Core conclusionThis 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.