mlsanityData to 7 Oct 2026

A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial

arXiv 2409.02069 ↗First posted 3 Sep 2024cs.AI cs.HCcited by 11

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Abstract (arXiv)

Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forgetfulness and disengagement. To address this, we developed Oralytics, a mHealth intervention system designed to complement clinician-delivered preventative care for marginalized individuals at risk for dental disease. Oralytics incorporates an online reinforcement learning algorithm to determine optimal times to deliver intervention prompts that encourage oral self-care behaviors. We have deployed Oralytics in a registered clinical trial. The deployment required careful design to manage challenges specific to the clinical trials setting in the U.S. In this paper, we (1) highlight key design decisions of the RL algorithm that address these challenges and (2) conduct a re-sampling analysis to evaluate algorithm design decisions. A second phase (randomized control trial) of Oralytics is planned to start in spring 2025.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 8 of its 30 references that are in the map. First posted on arXiv 3 Sep 2024.

Builds on

8 of 30 references.

References · 8

  1. 2017A Tutorial on Thompson Samplingcited by 420
  2. 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
  3. 2016A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patientscited by 38
  4. 2022Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Carecited by 18
  5. 2024Replicable Bandits for Digital Health Interventionscited by 7
  6. 2023Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resamplingcited by 6
  7. 2023Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Healthcited by 6
  8. 2024Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trialscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 6.

11 papers.

  1. 2026Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directionscited by 5
  2. 2025Reinforcement Learning on Dyads to Enhance Medication Adherencecited by 2
  3. 2025SigmaScheduling: Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventionscited by 1
  4. 2025Reproducible workflow for online AI in digital healthcited by 1
  5. 2025Generalized Linear Markov Decision Processcited by 0
  6. 2025Can we use LLMs to bootstrap reinforcement learning? -- A case study in digital health behavior changecited by 0
  7. 2026Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholdingcited by 0
  8. 2026Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliancecited by 0
  9. 2026Optimizing Digital Therapeutic Interventions: Online Learning under Endogenous Adherencecited by 0
  10. 2026Bandit Simulation for Average Reward Inferencecited by 0
  11. 2026A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Interventioncited by 0