ArticleAlgorithms2022
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines.
Article in Algorithms, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effective Behavior Change Techniques in Digital Health Interventions for the Prevention or Management of Noncommunicable Diseases: An Umbrella Review.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2023Pooled it
- How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial.JMIR mHealth and uHealth · 2023Trial
- Offline Reinforcement Learning for Adaptive Support in AI-Assisted Decision-Making.ACM transactions on computer-human interaction : a publication of the Association for Computing Machinery · 2026Article
- Practical considerations when designing an online learning algorithm for an app-based mHealth intervention.Contemporary clinical trials · 2026Article
- Effective monitoring of online AI decision-making algorithms in just-in-time adaptive interventions.NPJ digital medicine · 2026Article
- Digital Twins for Just-in-Time Adaptive Interventions (JITAIs): Framework for Optimizing and Continually Improving JITAIs.Journal of medical Internet research · 2026Article
- Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?Annual review of psychology · 2026Review
- Unseen pain behind a smile; oral health and psychological distress among Indonesian transgenders (Frontiers in oral health · 2026Article
- Replicable Bandits for Digital Health Interventions.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Personalized game-based digital intervention for relieving depression and anxiety symptoms: a pilot RCT.Npj mental health research · 2025Article
- Reinforcement Learning on Dyads to Enhance Medication Adherence.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2025Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Exploring parental opinions on oral hygiene behavior and knowledge of their young children in Lithuania: a cross-sectional survey study.Frontiers in oral health · 2025Article
- ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use.IJCAI : proceedings of the conference · 2024Article
- Contextual Bandits with Budgeted Information Reveal.Proceedings of machine learning 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
- Optimizing an adaptive digital oral health intervention for promoting oral self-care behaviors: Micro-randomized trial protocol.Contemporary clinical trials · 2024Article
- The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial.NPJ digital medicine · 2024Article
- Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning.Journal of machine learning research : JMLR · 2024Article
- Automated Messaging Delivered Alongside Behavioral Treatment for Weight Loss: Qualitative Study.JMIR formative research · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.
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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.