Designing digital health interventions with causal inference and multi-armed bandits: a review

Recent statistics from the World Health Organization show that non-communicable diseases account for 74% of global fatalities, with lifestyle playing a pivotal role in their development. Promoting healthier behaviors and targeting modifiable risk factors can significantly improve both life expectancy and quality of life. The widespread adoption of smartphones and wearable devices enables continuous, in-the-wild monitoring of daily habits, opening new opportunities for personalized, data-driven health interventions. This paper provides an overview of the advancements, challenges, and future directions in translating principles of lifestyle medicine and behavior change into AI-powered mobile health (mHealth) applications, with a focus on Just-In-Time Adaptive Interventions. Considerations for the design of adaptive interventions that leverage wearable and contextual data to dynamically personalize behavioral change strategies in real time are discussed. Bayesian multi-armed bandits from reinforcement learning are exploited as a framework for tailoring interventions, with causal inference methods used to incorporate structural assumptions about the user's behavior. Furthermore, strategies for evaluation at both individual and population levels are presented, with causal inference tools to further guide unbiased estimates. A running example of a simple real-world scenario aimed at increasing physical activity through digital interventions is used throughout the paper. With input from domain experts, the proposed approach is generalizable to a wide range of behavior change use cases.