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Approved research

Advancing actigraphy-based daily activity and sleep analysis with machine learning to understand baseline biometrics in healthy individuals and those experiencing various medical conditions

Principal Investigator: Dr Jian Wang
Approved Research ID: 45113
Approval date: December 18th 2018

Lay summary

While physical activity and good sleep is beneficial for human health both physically and mentally, most evidence of such claim is based on self-assessed measures of activity. In the UK Biobank study, data from activity monitors are collected 24/7 for 7 days from over 100K participants producing over 15 trillion movement readings. Coupled with extensive demographic and health information, this dataset will give us the first glimpse of baseline biometrics from healthy individuals as well as individuals experiencing various medical conditions. Conventional algorithms to detect sleep from actigraphy data require static sleep diaries or actigraphy marker button to label the boundaries of time in bed. These manual practices add to the uncertainty and participant burden, which we aim to reduce by automating detection of sleep boundaries during both night sleep and day time napping from the data directly. Here, we aim to explore probabilistic models for sleep detection to improve upon the conventional algorithms that only distinguish binary sleep versus waking time. Finally, we aim to translate these innovations into open source software feasible to be re-used by non-domain experts. Collaborating with Dr. Vincent van Hees, the Netherlands eScience Center in Amsterdam, the Netherland, we will begin a two year study to develop the algorithm and apply it in UK Bioback dataset to derive sleep and activity metrics in medical conditions to serve as benchmark for Lilly clinical studies planning to incorporate activity monitor as quality of life assessment tool. Leveraging public datasets collected in the last few years by large observational studies such as the Whitehall II Study (Sabia et al., 2014), UK Biobank (Doherty et al., 2017), and the Newcastle 85+ study (Anderson et al., 2014), the project would advance the activity monitor data open source software development and accelerate public and private efforts to study impact of sleep health and physical activity on healthy living, aging, disease, as well as transition from wellness to illness, and possibly illness back to wellness with the aid of lifestyle adjustment or therapeutics