| Title: | Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants |
| Journal: | Scientific Reports |
| Published: | 21 May 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/29784928/ |
| DOI: | https://doi.org/10.1038/s41598-018-26174-1 |
| URL: | https://www.nature.com/articles/s41598-018-26174-1.pdf |
Publication 2243
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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.5 Authors
- Matthew Willetts
- Sven Hollowell
- Louis Aslett
- Chris Holmes
- Aiden Doherty
1 Application
| Application ID | Title |
|---|---|
| 9126 | Extracting Meaningful Health Information from Raw Accelerometer Data in UK Biobank |
Enabling scientific discoveries that improve human health