Exploring Reinforcement Learning for Fluid Transitions Between Clinical Mental Healthcare and Everyday Wellness Support

2026-06-04

AI Summary

This study addresses the frequent disruption in care continuity between clinical psychological interventions and everyday health support. To bridge this gap, the authors introduce reinforcement learning into the clinical–wellness continuum for the first time, proposing a contextual bandit algorithm that dynamically integrates clinical and wellness-oriented journaling prompts to sustain long-term user engagement. Findings demonstrate that the reinforcement learning–generated personalized prompt sequences significantly enhance sustained participation and reduce dropout due to burnout compared to fixed intervention schedules. Notably, certain beneficial effects persist even after the intervention ends, revealing novel design dimensions such as the “withdrawal phase” and modulation of intervention intensity. These insights offer both theoretical grounding and practical guidance for developing integrated digital mental health systems.

Abstract

Mental health struggles wax and wane, yet clinical and wellness interventions typically operate separately, causing frequent breakdowns at care transitions. We explore reinforcement learning (RL) as a means to build digital health systems that deliver clinical and wellness interventions proactively, as part of a coherent care journey. We ask: what complexities does designing such a system involve? We built a contextual bandit that dynamically selects journaling prompts from clinical and wellness repertoires to optimize for an overarching health goal (sustained journaling) and deployed it in a four-week exploratory study (N=38). We found that, first, many benefits of RL-optimized intervention sequences appeared only after interventions ended, raising the question: Should systems that offer coherent clinical-wellness care journeys include stepping-back periods? If so, when and how? Second, participants most engaged with RL-generated interventions deepened their engagement over time, while those most engaged with a constant intervention tended to burn out and drop out later. It raises the question: When should a system blending clinical and wellness interventions reduce intensity to prevent burnout in versus sustain it to maximize treatment gains?

Problem

• care transitions
• clinical-wellness integration
• mental healthcare
• digital health systems
• intervention continuity

Innovation

• reinforcement learning
• clinical-wellness integration
• contextual bandit
• digital mental health
• adaptive intervention