Statistical machine learning
of sleep and physical activity
phenotypes from sensor data in
96,220 UK Biobank participants
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.
https://github.com/activityMonitoring/biobankAccelerometerAnalysis https://www.nature.com/articles/s41598-018-26174-1