TY - JOUR
T1 - 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
AU - Weaver, R. Glenn
AU - de Zambotti, Massimiliano
AU - White, James
AU - Finnegan, Olivia
AU - Nelakuditi, Srihari
AU - Zhu, Xuanxuan
AU - Burkart, Sarah
AU - Beets, Michael
AU - Brown, David
AU - Pate, Russ R.
AU - Welk, Gregory J.
AU - Ghosal, Rahul
AU - Wang, Yuan
AU - Armstrong, Bridget
AU - Adams, Elizabeth L.
AU - Reesor-Oyer, Layton
AU - Pfledderer, Christopher
AU - Dugger, Roddrick
AU - Bastyr, Meghan
AU - von Klinggraeff, Lauren
AU - Parker, Hannah
N1 - Publisher Copyright:
© 2023 National Sleep Foundation
PY - 2023/8
Y1 - 2023/8
N2 - 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.
AB - 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.
KW - Ambulatory sleep monitoring
KW - Open source
KW - Sleep tracking
KW - Validation
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U2 - 10.1016/j.sleh.2023.04.005
DO - 10.1016/j.sleh.2023.04.005
M3 - Article
C2 - 37391280
AN - SCOPUS:85163810324
SN - 2352-7218
VL - 9
SP - 417
EP - 429
JO - Sleep Health
JF - Sleep Health
IS - 4
ER -