Adaptive Interventions with User-Defined Goals for Health Behavior Change
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Abstract (arXiv)
Promoting healthy lifestyle behaviors remains a major public health concern, particularly due to their crucial role in preventing chronic conditions such as cancer, heart disease, and type 2 diabetes. Mobile health applications present a promising avenue for low-cost, scalable health behavior change promotion. Researchers are increasingly exploring adaptive algorithms that personalize interventions to each person's unique context. However, in empirical studies, mobile health applications often suffer from small effect sizes and low adherence rates, particularly in comparison to human coaching. Tailoring advice to a person's unique goals, preferences, and life circumstances is a critical component of health coaching that has been underutilized in adaptive algorithms for mobile health interventions. To address this, we introduce a new Thompson sampling algorithm that can accommodate personalized reward functions (i.e., goals, preferences, and constraints), while also leveraging data sharing across individuals to more quickly be able to provide effective recommendations. We prove that our modification incurs only a constant penalty on cumulative regret while preserving the sample complexity benefits of data sharing. We present empirical results on synthetic and semi-synthetic physical activity simulators, where in the latter we conducted an online survey to solicit preference data relating to physical activity, which we use to construct realistic reward models that leverages historical data from another study. Our algorithm achieves substantial performance improvements compared to baselines that do not share data or do not optimize for individualized rewards.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 61 references that are in the map. First posted on arXiv 16 Nov 2023.
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16 of 61 references.
References · 16
- 2017On Kernelized Multi-armed Banditscited by 352
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- 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
- 2021Meta-Thompson Samplingcited by 63
- 2017Efficient Contextual Bandits in Non-stationary Worldscited by 62
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- 2016On Context-Dependent Clustering of Banditscited by 44
- 2016A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patientscited by 38
- 2020Power Constrained Banditscited by 30
- 2018Learning Contextual Bandits in a Non-stationary Environmentcited by 26
- 2020IntelligentPooling: Practical Thompson Sampling for mHealthcited by 24
4 more
- 2017Non-Stationary Bandits with Habituation and Recovery Dynamicscited by 23
- 2017Action Centered Contextual Banditscited by 23
- 2016Latent Contextual Bandits and their Application to Personalized Recommendations for New Userscited by 18
- 2018Coordinated Exploration in Concurrent Reinforcement Learningcited by 17
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- 2025A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial
- 2025Practical considerations when designing an online learning algorithm for an app-based mHealth intervention
- 2026Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions
- 2026A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention
- 2023RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions
- 2025Enhancing Adaptive Behavioral Interventions with LLM Inference from Participant-Described States
- 2025Optimizing Algorithms for Mobile Health Interventions with Active Querying Optimization
- 2018A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions