REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial

• Published in: BMJ open Vol. 11; no. 12; p. e052091
• Main Authors: Lauffenburger, Julie C, Yom-Tov, Elad, Keller, Punam A, McDonnell, Marie E, Bessette, Lily G, Fontanet, Constance P, Sears, Ellen S, Kim, Erin, Hanken, Kaitlin, Buckley, J Joseph, Barlev, Renee A, Haff, Nancy, Choudhry, Niteesh K
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 03-12-2021; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Protocol
• Subjects: Algorithms; Behavior; clinical trials; Communication; Diabetes; diabetes & endocrinology; Diabetes Mellitus, Type 2 - drug therapy; Electronic health records; Feedback; Glycated Hemoglobin A; Hemoglobin; Humans; Intervention; Medication Adherence; Patient compliance; Pragmatic Clinical Trials as Topic; Public Health; Questionnaires; Randomized Controlled Trials as Topic; Self-Management; Smartphones; Text Messaging; Web portals; United States > US; clinical trials; public health; diabetes & endocrinology
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2021-052091

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 analysisIn 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 disseminationThis 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 numberClinicaltrials.gov (NCT04473326).