This research uses wearable data and AI to detect disease earlier by analyzing continuous health signals rather than isolated clinical snapshots. By personalizing models to individual baselines, the system identifies subtle changes linked to conditions like infections, heart issues, and mental health crises, enabling earlier intervention and potentially saving lives.

This research uses linked provincial health data to measure the population burden of coeliac disease in Alberta. By identifying diagnosis rates, care gaps, and early-life risk factors, the work informs healthcare planning and policy. The findings highlight rising diagnoses in children and the long-term personal and economic impact of a lifelong, diet-based condition.