Using wearable sensors to transform the understanding of cardiovascular disease: Prediction, mechanisms, and patient relevant endpoints. (360G-Wellcome-223100_Z_21_Z)

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality globally. However, current prevention and treatment strategies for CVD are inadequate. In response there is a growing interest in wearable sensors that offer the potential to continuously, noninvasively, and painlessly measure key attributes of relevance to cardiovascular health - such as physical activity, sleep, and atrial fibrillation - in patients’ everyday lives. In this fellowship, I propose a new programme of research to use wearable sensors to provide novel insights into cardiovascular disease (CVD), with respect to its prediction, discovery of target mechanisms, and new methods to prioritise and assess the impact of potential treatments on day-to- day physical activity and sleep. This research will harness the power of reproducible machine learning in large-scale wearable sensor and human genomic datasets to advance the understanding of CVD. My research will provide the basis for new methods, target mechanisms, and tools to reduce the burden of CVD.