Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants
Autor: | Louis J. M. Aslett, Aiden R. Doherty, Christopher Holmes, Matthew Willetts, Sven Hollowell |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Adult
Male medicine.medical_specialty Adolescent Population lcsh:Medicine Machine learning computer.software_genre Article Machine Learning Young Adult 03 medical and health sciences 0302 clinical medicine Accelerometry Activities of Daily Living medicine Humans 030212 general & internal medicine education Hidden Markov model lcsh:Science Exercise Aged Biological Specimen Banks Aged 80 and over education.field_of_study business.industry Public health Work (physics) lcsh:R 030229 sport sciences Middle Aged Biobank United Kingdom Random forest Phenotype Scale (social sciences) Female lcsh:Q Self Report Sleep (system call) Artificial intelligence Sedentary Behavior Sleep Psychology business computer |
Zdroj: | Scientific Reports, Vol 8, Iss 1, Pp 1-10 (2018) Scientific reports, 2018, Vol.8(1), pp.7961 [Peer Reviewed Journal] Scientific Reports |
ISSN: | 2045-2322 |
Popis: | 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. |
Databáze: | OpenAIRE |
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