ArticleJournal of medical systems2021
Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator.
Article in Journal of medical systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Toward Participatory Precision Health With Co-Designed Recommendations: Systematic Review of Just-in-Time Adaptive Interventions in Adolescents and Young Adults.Journal of medical Internet research · 2026Pooled it
- Just-in-Time Adaptive Interventions for Adolescent and Young Adult Substance Use: A Systematic Review.The Journal of adolescent health : official publication of the Society for Adolescent Medicine · 2025Pooled it
- Just-in-Time Adaptive Interventions for Behavior Change in Physiological Health Outcomes and the Use Case for Knee Osteoarthritis: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Trial
- Reinforcement learning for proposing smoking cessation activities that build competencies: Combining two worldviews in a virtual coach.BMC medical informatics and decision making · 2025Trial
- Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial.Journal of medical Internet research · 2024Trial
- Psychological, economic, and ethical factors in human feedback for a chatbot-based smoking cessation intervention.NPJ digital medicine · 2025Article
- Harnessing Causality in Reinforcement Learning With Bagged Decision Times.Proceedings of machine learning research · 2025Article
- Article
- Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview.Journal of medical Internet research · 2024Article
- Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks.Proceedings of the ... International Joint Conference on Autonomous Agents and Multiagent Systems : AAMAS. International Joint Conference on Autonomous Agents and Multiagent Systems · 2024Article
- Mixed-Response State-Space Model for Analyzing Multi-Dimensional Digital Phenotypes.Journal of the American Statistical Association · 2023Article
- Patient Acceptability of Home Monitoring for Neovascular Age-Related Macular Degeneration Reactivation: A Qualitative Study.International journal of environmental research and public health · 2022Article
- Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines.Algorithms · 2022Article
- Just-in-Time Prompts for Running, Walking, and Performing Strength Exercises in the Built Environment: 4-Week Randomized Feasibility Study.JMIR formative research · 2022Article
- Sensor Technology and Intelligent Systems in Anorexia Nervosa: Providing Smarter Healthcare Delivery Systems.BioMed research international · 2022Review
- Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Mobile health (mHealth) intervention systems can employ adaptive strategies to interact with users. Instead of designing such complex strategies manually, reinforcement learning (RL) can be used to adaptively optimize intervention strategies concerning the user's context. In this paper, we focus on the issue of overwhelming interactions when learning a good adaptive strategy for the user in RL-based mHealth intervention agents. We present a data-driven approach integrating psychological insights and knowledge of historical data. It allows RL agents to optimize the strategy of delivering context-aware notifications from empirical data when counterfactual information (user responses when receiving notifications) is missing. Our approach also considers a constraint on the frequency of notifications, which reduces the interaction burden for users. We evaluated our approach in several simulation scenarios using real large-scale running data. The results indicate that our RL agent can deliver notifications in a manner that realizes a higher behavioral impact than context-blind strategies.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.