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Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.

Willetts, M. and Hollowell, S. and Aslett, L.J.M and Holmes, C.C. and Doherty, A. (2018) 'Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.', Scientific reports., 8 (1). p. 7961.

Abstract

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high- intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

Item Type:Article
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1038/s41598-018-26174-1
Publisher statement:Open Access. 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/.
Date accepted:02 May 2018
Date deposited:22 May 2018
Date of first online publication:21 May 2018
Date first made open access:22 May 2018

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