Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data

Abstract Using polysomnography over multiple weeks to characterize an individual’s habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone inte...

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Autores principales:	Stijn A. A. Massar, Xin Yu Chua, Chun Siong Soon, Alyssa S. C. Ng, Ju Lynn Ong, Nicholas I. Y. N. Chee, Tih Shih Lee, Arko Ghosh, Michael W. L. Chee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/891f6cd7ee184cd4817021da7c0ac051
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spelling	oai:doaj.org-article:891f6cd7ee184cd4817021da7c0ac0512021-12-02T18:24:55ZTrait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data10.1038/s41746-021-00466-92398-6352https://doaj.org/article/891f6cd7ee184cd4817021da7c0ac0512021-06-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00466-9https://doaj.org/toc/2398-6352Abstract Using polysomnography over multiple weeks to characterize an individual’s habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities. Agreement in bed and wake time estimates across modalities was high (rho = 0.81–0.92) and were adrift of one another for an average of 4 min, providing redundant sleep measurement. On the ~23% of nights where discrepancies between modalities exceeded 1 h, k-means clustering revealed three patterns, each consistently expressed within a given individual. The three corresponding groups that emerged differed systematically in age, sleep timing, time in bed, and peri-sleep phone usage. Hence, contrary to being problematic, discrepant data across measurement modalities facilitated the identification of stable interindividual differences in sleep behavior, underscoring its utility to characterizing population sleep and peri-sleep behavior.Stijn A. A. MassarXin Yu ChuaChun Siong SoonAlyssa S. C. NgJu Lynn OngNicholas I. Y. N. CheeTih Shih LeeArko GhoshMichael W. L. CheeNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Stijn A. A. Massar Xin Yu Chua Chun Siong Soon Alyssa S. C. Ng Ju Lynn Ong Nicholas I. Y. N. Chee Tih Shih Lee Arko Ghosh Michael W. L. Chee Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data
description	Abstract Using polysomnography over multiple weeks to characterize an individual’s habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities. Agreement in bed and wake time estimates across modalities was high (rho = 0.81–0.92) and were adrift of one another for an average of 4 min, providing redundant sleep measurement. On the ~23% of nights where discrepancies between modalities exceeded 1 h, k-means clustering revealed three patterns, each consistently expressed within a given individual. The three corresponding groups that emerged differed systematically in age, sleep timing, time in bed, and peri-sleep phone usage. Hence, contrary to being problematic, discrepant data across measurement modalities facilitated the identification of stable interindividual differences in sleep behavior, underscoring its utility to characterizing population sleep and peri-sleep behavior.
format	article
author	Stijn A. A. Massar Xin Yu Chua Chun Siong Soon Alyssa S. C. Ng Ju Lynn Ong Nicholas I. Y. N. Chee Tih Shih Lee Arko Ghosh Michael W. L. Chee
author_facet	Stijn A. A. Massar Xin Yu Chua Chun Siong Soon Alyssa S. C. Ng Ju Lynn Ong Nicholas I. Y. N. Chee Tih Shih Lee Arko Ghosh Michael W. L. Chee
author_sort	Stijn A. A. Massar
title	Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data
title_short	Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data
title_full	Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data
title_fullStr	Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data
title_full_unstemmed	Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data
title_sort	trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/891f6cd7ee184cd4817021da7c0ac051
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Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data

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