The project aims to identify genetic variants associated with sleep patterns, and to perform Mendelian randomisation studies to identify the downstream causal consequences of disturbed sleep patterns on metabolic diseases such as obesity and type 2 diatbetes. UK Biobank offers a large and high quality dataset to perform above analyses. Over a hundred thousand individuals wore a wearable movement sensor (accelerometer) on their wrist for the duration of one-week (24/7). Previously, eScience Engineer Vincent van Hees developed and published software to analyse this kind of accelerometer data. This was successfully used in preliminary analyses of UK Biobank. The sleep detection functionality of his software is designed to combine sleep diary information with accelerometer information. However, in UKBiobank there is no sleep diary data. Vincent implemented a possible solution for this but did not publish an article on it. In the project we will evaluate the currently implemented algorithm to detect human sleep from wearable accelerometer data without the aid of sleep diaries. Next, we will attempt to develop a better method, e.g. with machine learning, and release as update to the existing open source software.


University of Exeter
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