Defying the Circadian Rhythm: Clustering Participant Telemetry in the UK Biobank Data

The UK Biobank dataset follows over 500,000 volunteers and contains a diverse set of information related to societal outcomes. Among this vast collection, a large quantity of telemetry collected from wrist-worn accelerometers provides a snapshot of participant activity. Using this data, a population of shift workers, subjected to disrupted circadian rhythms, is analysed using a mixture model-based approach to yield protective effects from physical activity on survival outcomes. In this paper, we develop a scalable, standardized, and unique methodology that efficiently clusters a vast quantity of participant telemetry. By building upon the work of Doherty et al. (2017), we introduce a standardized, low-dimensional feature for clustering purposes. Participants are clustered using a matrix variate mixture model-based approach. Once clustered, survival analysis is performed to demonstrate distinct lifetime outcomes for individuals within each cluster. In summary, we process, cluster, and analyse a subset of UK Biobank participants to show the protective effects from physical activity on circadian disrupted individuals.