PT  - JOURNAL ARTICLE
AU  - Matthew Willetts
AU  - Sven Hollowell
AU  - Louis Aslett
AU  - Chris Holmes
AU  - Aiden Doherty
TI  - Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,609 UK Biobank participants
AID  - 10.1101/187625
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 187625
4099  - http://biorxiv.org/content/early/2017/09/12/187625.short
4100  - http://biorxiv.org/content/early/2017/09/12/187625.full
AB  - 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 Markov confusion matrices, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 84,616 minutes of recorded free-living behaviours from 57 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,609 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.