Evidence map›Paper›PMID 34862289›Full record

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.

Julie C Lauffenburger, Elad Yom-Tov, Punam A Keller, Marie E McDonnell, Lily G Bessette, Constance P Fontanet, Ellen S Sears, Erin Kim, Kaitlin Hanken, J Joseph Buckley and 3 more

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

NCT04473326 nacompletednot on this map

Optimizing Message Framing for Healthy Habits for Patients With Type 2 Diabetes - Phase II (Pragmatic Trial)

TypeinterventionalSponsorBrigham and Women's HospitalRan2021 to 2022Enrolled60ConditionsDiabetes Mellitus, Type 2, Medication AdherenceArmsReinforcement Learning
3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Replicable Bandits for Digital Health Interventions.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Julie C LauffenburgerCenter for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA jlauffenburger@bwh.harvard.edu.ORCID 0000-0002-4940-4140
Elad Yom-TovMicrosoft Research, Microsoft, Herzeliya, Israel.ORCID 0000-0002-2380-4584
Punam A KellerTuck School of Business, Dartmouth College, Hanover, NH, USA.
Marie E McDonnellEndocrinology, Diabetes and Hypertension, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Lily G BessetteDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Constance P FontanetCenter for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-8253-1598
Ellen S SearsCenter for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Erin KimCenter for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Kaitlin HankenCenter for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
J Joseph BuckleyDivision of Sleep Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Renee A BarlevCenter for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Nancy HaffCenter for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Niteesh K ChoudhryCenter for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Funding

The Roybal Center for TherapeuticOptimization using BehavioralScienceP30AG064199 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI Julie Christine Lauffenburger · 2019 to 2026
$8.3M
Development of a novel medication adherability tool to improve cardiovascular disease managementK01HL141538 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI LAUFFENBURGER, JULIE CHRISTINE · 2018 to 2022
$718k
NHLBI NIH HHS K01 HL141538NIA NIH HHS P30 AG064199
6 · The paper itself

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).

Indexed as

Diabetes Mellitus, Type 2Self-ManagementText MessagingGlycated HemoglobinHumansMedication AdherencePragmatic Clinical Trials as TopicRandomized Controlled Trials as TopicGlycated Hemoglobinclinical trialsdiabetes & endocrinologypublic health

Identifiers

PMID34862289
PMCPMC8647547

What OpenQuestion holds

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LicenceCC BY-NC
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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.