The prevalence of digital and wearable technology in daily life is generating an astounding amount of personal health data useful for understanding complex health conditions. These data sources are foundational for developing and evaluating data-driven solutions to support disease prevention and care. Yet, significant gaps exist in delivering actionable insights to individuals, accessing high-quality real-world datasets for data-centric research, and ensuring the reproducibility of algorithmic models developed to support health management.

To bridge these gaps, this talk will present research efforts on the opportunistic use of digital and wearable device data to enable diabetes prevention and management. The presenter will discuss how personal health data from digital technologies can support the discovery of actionable insights, the implementation of lifestyle interventions, and the development of data-driven solutions for diabetes prevention and care. The talk will be organized around three core topics: context-aware sensing for digital health interventions, wearable data analytics and AI for personalized care, and bridging the data gap to accelerate machine learning for health. 

This webinar will be captioned in real time. Individuals needing reasonable accommodations should email [email protected]. Requests should be made at least 5 business days before the event.