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Digital phenotyping by consumer wearables identifies sleep-associated markers of cardiovascular disease risk and biological aging.

Title: Digital phenotyping by consumer wearables identifies sleep-associated markers of cardiovascular disease risk and biological aging.
Authors: Teo, JX
Davila, S
Yang, C
Hii, AA
Pua, CJ
Yap, J
Tan, SY
Sahlén, A
Chin, CW-L
Teh, BT
Rozen, SG
Cook, SA
Yeo, KK
Tan, P
Lim, WK
Item Type: Journal Article
Abstract: Sleep is associated with various health outcomes. Despite their growing adoption, the potential for consumer wearables to contribute sleep metrics to sleep-related biomedical research remains largely uncharacterized. Here we analyzed sleep tracking data, along with questionnaire responses and multi-modal phenotypic data generated from 482 normal volunteers. First, we compared wearable-derived and self-reported sleep metrics, particularly total sleep time (TST) and sleep efficiency (SE). We then identified demographic, socioeconomic and lifestyle factors associated with wearable-derived TST; they included age, gender, occupation and alcohol consumption. Multi-modal phenotypic data analysis showed that wearable-derived TST and SE were associated with cardiovascular disease risk markers such as body mass index and waist circumference, whereas self-reported measures were not. Using wearable-derived TST, we showed that insufficient sleep was associated with premature telomere attrition. Our study highlights the potential for sleep metrics from consumer wearables to provide novel insights into data generated from population cohort studies.
Issue Date: 4-Oct-2019
Date of Acceptance: 9-Sep-2019
URI: http://hdl.handle.net/10044/1/74985
DOI: 10.1038/s42003-019-0605-1
ISSN: 0006-291X
Publisher: Elsevier
Start Page: 1
End Page: 10
Journal / Book Title: Biochemical and Biophysical Research Communications
Volume: 2
Copyright Statement: © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Data integration
Predictive markers
Risk factors
Senescence
Data integration
Predictive markers
Risk factors
Senescence
Publication Status: Published
Conference Place: England
Article Number: 361
Online Publication Date: 2019-10-04
Appears in Collections:National Heart and Lung Institute



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