Modeling Mobile Health Users as Reinforcement Learning Agents
This work addresses the challenge of personalizing mHealth interventions for patients with impaired decision-making, though it appears incremental as it builds on existing MDP frameworks.
The paper tackles the problem of designing optimal interventions for mobile health users by modeling them as reinforcement learning agents with potentially impaired decision-making, showing that different impairments require different intervention strategies.
Mobile health (mHealth) technologies empower patients to adopt/maintain healthy behaviors in their daily lives, by providing interventions (e.g. push notifications) tailored to the user's needs. In these settings, without intervention, human decision making may be impaired (e.g. valuing near term pleasure over own long term goals). In this work, we formalize this relationship with a framework in which the user optimizes a (potentially impaired) Markov Decision Process (MDP) and the mHealth agent intervenes on the user's MDP parameters. We show that different types of impairments imply different types of optimal intervention. We also provide analytical and empirical explorations of these differences.