Using Digital Health Data To Support Early Diagnosis of Cardiovascular Disease (52:21)

In this webinar, Professor Allan Lawrie explores how digital health data can support earlier diagnosis of rare diseases, specifically in the field of rare cardiovascular disease.

Beginning with the use of electronic health records to identify people at higher risk, his research has expanded to include information collected from smartphones and wearable devices, providing new insights into people's health in everyday life.

Drawing on collaborations with Stanford University through the My Heart Counts app, Allan discusses the potential of combining continuous wearable data with healthcare records. He shares key lessons learned from model development and validation, including why promising models may fail to generalise across different populations and how differences in control groups can influence performance.

Allan also presents current work being conducted in parallel in the United States and the United Kingdom, including the development of a cross-platform digital health research infrastructure that integrates data from Android and iOS devices, as well as wearable technologies such as Oura, Fitbit, and Withings. Through the inclusion of ECG data, raw sensor data and general PPG, this research aims to develop some generalizable, translatable models not just for early detection but also remote monitoring.

Finally, this session also explores the next phase of the platform: using large language models and behavioural science to deliver personalised, adaptive health interventions. By combining prompt engineering, reinforcement learning, and real-time behavioural feedback, the project aims to optimise "just-in-time" digital nudges that encourage healthier behaviours based on an individual's characteristics and responses.

This webinar also includes an update on Health Research from Home's 2nd driver project - Long-term COVID outcomes.