‍AI for Digital Intervention Optimization
Susan A. Murphy

Artificial Intelligence (AI) methods can be used in a variety of ways to contribute to digital health intervention optimization. These include online continual updating of predictions of risk and receptivity, creation and updating of digital twins for use in the early stages of intervention optimization and for continually updating decision rules that link an individual's current internal state and external context to intervention options. This presentation will touch on the former two and focus on the continual updating of decision rules with the goal of delivering intervention options when they are most effective, yet managing burden.

Control Systems Engineering for Optimizing "Just-in-Time" Adaptive Behavioral Interventions
Daniel Rivera

Control systems engineering using system identification (i.e., a methodology for estimating dynamic models from data) and model predictive control (MPC; a control design technique for constrained, multivariable dynamical systems) offers a powerful framework for developing “just-in-time” adaptive behavioral interventions. The control optimization trial (COT) formalism combines these methods to create data-driven, personalized interventions that dynamically adjust to an individual's changing needs and context. The ongoing YourMove (NCT05598996), Healthy Mom Zone 2 (NCT05807594), and FLASH (NCT06244888) clinical trials are evaluating this approach for physical activity, gestational weight regulation, and weight loss maintenance, respectively. Benefits, challenges, and some practical considerations involved in effectively conducting COT trials will be illustrated with examples taken from these three interventions.