ArticleBMJ open2021
REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04473326 (Optimizing Message Framing for Healthy Habits for Patients With Type 2 Diabetes - Phase II), which is not on this map. Cited by 14 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.
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.
Optimizing Message Framing for Healthy Habits for Patients With Type 2 Diabetes - Phase II (Pragmatic Trial)
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.Journal of medical Internet research · 2025Pooled it
- Text Messages to Promote Physical Activity in Patients With Cardiovascular Disease: A Micro-Randomized Trial of a Just-In-Time Adaptive Intervention.Circulation. Cardiovascular quality and outcomes · 2024Trial
- Acceptability of a Text Message-Based Mobile Health Intervention to Promote Physical Activity in Cardiac Rehabilitation Enrollees: A Qualitative Substudy of Participant Perspectives.Journal of the American Heart Association · 2024Trial
- Increasing Engagement in the Electronic Framingham Heart Study: Factorial Randomized Controlled Trial.Journal of medical Internet research · 2023Trial
- Effective monitoring of online AI decision-making algorithms in just-in-time adaptive interventions.NPJ digital medicine · 2026Article
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- The application of AI-based interventions in diabetes personalized management: a systematic review and meta-analysis.Diabetology & metabolic syndrome · 2026Review
- Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.Journal of clinical and translational science · 2026Review
- Replicable Bandits for Digital Health Interventions.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Key Elements and Theoretical Foundations for the Design and Delivery of Text Messages to Boost Medication Adherence in Patients With Diabetes, Hypertension, and Hyperlipidemia: Scoping Review.Journal of medical Internet research · 2025Article
- Encouraging the prescribing of SGLT2i and GLP-1RA medications to reduce cardiovascular and renal risk in patients with type 2 diabetes: Rationale and design of a randomized controlled trial.American heart journal · 2025Article
- Importance of Utilizing Non-Communicable Disease Screening Tools; Ward-Based Community Health Care Workers of South Africa Explain.International journal of environmental research and public health · 2024Article
- The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial.NPJ digital medicine · 2024Article
- Optimizing Health Coaching for Patients With Type 2 Diabetes Using Machine Learning: Model Development and Validation Study.JMIR formative research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
introductionAchieving optimal diabetes control requires several daily self-management behaviours, especially adherence to medication. Evidence supports the use of text messages to support adherence, but there remains much opportunity to improve their effectiveness. One key limitation is that message content has been generic. By contrast, reinforcement learning is a machine learning method that can be used to identify individuals' patterns of responsiveness by observing their response to cues and then optimising them accordingly. Despite its demonstrated benefits outside of healthcare, its application to tailoring communication for patients has received limited attention. The objective of this trial is to test the impact of a reinforcement learning-based text messaging programme on adherence to medication for patients with type 2 diabetes. METHODS AND ANALYSIS: In the REinforcement learning to Improve Non-adherence For diabetes treatments by Optimising Response and Customising Engagement (REINFORCE) trial, we are randomising 60 patients with suboptimal diabetes control treated with oral diabetes medications to receive a reinforcement learning intervention or control. Subjects in both arms will receive electronic pill bottles to use, and those in the intervention arm will receive up to daily text messages. The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. The trial's primary outcome is average adherence to medication over the 6-month follow-up period. Secondary outcomes include diabetes control, measured by glycated haemoglobin A1c, and self-reported adherence. In sum, the REINFORCE trial will evaluate the effect of personalising the framing of text messages for patients to support medication adherence and provide insight into how this could be adapted at scale to improve other self-management interventions. ETHICS AND DISSEMINATION: This study was approved by the Mass General Brigham Institutional Review Board (IRB) (USA). Findings will be disseminated through peer-reviewed journals, clinicaltrials.gov reporting and conferences. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov (NCT04473326).
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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.