ArticleNPJ digital medicine2024
The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Personalized Mobile App-Based Messaging and Step Tracking for Improving Walking Behavior in Adults: Randomized Controlled Trial.JMIR mHealth and uHealth · 2026Trial
- Mechanism of an integrated PBL-KAP-AI spiral health education model for breast cancer patients.Frontiers in public health · 2025Trial
- Tailored Text Messaging Intervention to Improve Self-Care in Patients With Heart Failure (Text4HF): Protocol for a Pilot Randomized Controlled Trial.JMIR research protocols · 2026Article
- Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.Journal of materials science. Materials in medicine · 2026Review
- Effective monitoring of online AI decision-making algorithms in just-in-time adaptive interventions.NPJ digital medicine · 2026Article
- Polypharmacy, Medicine Optimisation, and Medication Adherence in Older Cancer Patients.Current oncology reports · 2026Review
- Review of the Use of Digital Therapeutics for Obesity Treatment: Toward a Psychological Phenotyping.Advances in nutrition (Bethesda, Md.) · 2026Review
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Personalizing a mental health texting intervention using reinforcement learning.Npj mental health research · 2025Article
- Can reinforcement learning effectively prevent depression relapse?World journal of psychiatry · 2025Review
- Era of Generalist Conversational Artificial Intelligence to Support Public Health Communications.Journal of medical Internet research · 2025Article
- Achieving clinically meaningful outcomes in digital health: a six-step, cyclical precision engagement framework (ENGAGE).Frontiers in digital health · 2025Article
- A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2025Article
- A Primer on Reinforcement Learning in Medicine for Clinicians.NPJ digital medicine · 2024Review
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
Text messaging can promote healthy behaviors, like adherence to medication, yet its effectiveness remains modest, in part because message content is rarely personalized. Reinforcement learning has been used in consumer technology to personalize content but with limited application in healthcare. We tested a reinforcement learning program that identifies individual responsiveness ("adherence") to text message content and personalizes messaging accordingly. We randomized 60 individuals with diabetes and glycated hemoglobin A1c [HbA1c] ≥ 7.5% to reinforcement learning intervention or control (no messages). Both arms received electronic pill bottles to measure adherence. The intervention improved absolute adjusted adherence by 13.6% (95%CI: 1.7%-27.1%) versus control and was more effective in patients with HbA1c 7.5- < 9.0% (36.6%, 95%CI: 25.1%-48.2%, interaction p < 0.001). We also explored whether individual patient characteristics were associated with differential response to tested behavioral factors and unique clusters of responsiveness. Reinforcement learning may be a promising approach to improve adherence and personalize communication at scale.
Identifiers
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.