Refinement and Adaption of Reinforcement Learning to Personalize Behavioral Messaging for Healthy Habits (REINFORCE2)

Reinforcement learning is an advanced analytic method that discovers each individual's pattern of responsiveness by observing their actions and then implements a personalized strategy to optimize individuals' behaviors using trial and error. The goal of the proposed research is to refine, adapt and perform efficacy testing of a novel reinforcement learning-based text messaging intervention to support medication adherence for patients with type 2 diabetes within a community health center setting. This study will be a parallel randomized pragmatic trial comparing medication adherence and clinical outcomes for adults in a community setting aged 18-84 with type 2 diabetes who are prescribed 1-3 daily oral medications for this disease. Participants will be randomized to one of two arms for the duration of the study period: (1) a reinforcement learning intervention arm with up to daily, tailored text messages based on time-varying treatment-response patterns; or (2) a control arm with up to daily, un-tailored text messages. Outcomes of interest will be medication adherence, as measured by electronic pill bottles, and HbA1c levels over 6 months.

Study Overview

• Status: Completed
• Conditions: Diabetes Mellitus, Type 2; Medication Adherence
• Intervention / Treatment: Behavioral: Reinforcement Learning

Detailed Description

The goal of the proposed research is to refine, adapt and perform efficacy testing of a novel reinforcement learning-based text messaging intervention to support medication adherence for patients with type 2 diabetes within a community setting. Type 2 diabetes is an optimal condition in which to refine this program, as it is one of the most prevalent chronic conditions in the US adult population and requires most patients to be on daily or twice daily doses of medications. This study will be a parallel randomized pragmatic trial comparing medication adherence and clinical outcomes for adults in a community setting aged 18-84 with type 2 diabetes who are prescribed 1-3 daily oral medications for this disease. Participants will be randomized to one of two arms for the duration of the study period: (1) a reinforcement learning intervention arm with up to daily, tailored text messages based on time-varying treatment-response patterns; or (2) a control arm with up to daily, un-tailored text messages. Outcomes of interest will be medication adherence, as measured by electronic pill bottles, and HbA1c levels over 6 months.

• Study Type: Interventional
• Enrollment (Actual): 28
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Massachusetts
    • Boston, Massachusetts, United States, 02118
      • Boston Medical Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 84 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Diagnosis of Type 2 Diabetes Mellitus (T2DM)
• Prescribed between 1-3 daily oral medications for diabetes
• Most recent HbA1c level of 7% or greater
• Suboptimal adherence, defined by proportion of days covered (PDC) < 0.90 based on chart review
• Must have a smartphone for which they are the sole user
• Must have a basic working knowledge of English or Spanish

Exclusion Criteria:

• Currently using a pillbox and/or not willing to use electronic pill bottles for 6 months
• Receive help at home on a daily basis with taking medications

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Double

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Reinforcement Learning Intervention Arm; Up to daily, tailored text messages.	Behavioral: Reinforcement Learning; Participants in the intervention arm will receive up to daily, tailored text messages based on their electronic pill bottle-measured adherence. Given the participants' baseline characteristics and time-varying responses to the messages, a reinforcement learning algorithm will deliver different text messages and adapt over time to determine which type of messaging works best for each individual participant.
No Intervention: Control Arm; Up to daily, untailored text messages.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diabetes medication adherence	Proportion of correct doses recorded by electronic pill bottles in the 6-month follow-up period, averaged across study medications	6 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Glycemic control	Change between baseline HbA1c used for identification and the 6-month intervention period, using laboratory values in the EHR	6 months

Collaborators and Investigators

• Sponsor: Brigham and Women's Hospital
• Collaborators: National Institute on Aging (NIA); Boston Medical Center

Study record dates

Study Major Dates

• Study Start (Actual): August 23, 2023
• Primary Completion (Actual): December 17, 2025
• Study Completion (Actual): December 17, 2025

Study Registration Dates

• First Submitted: February 15, 2023
• First Submitted That Met QC Criteria: February 23, 2023
• First Posted (Actual): February 24, 2023

Study Record Updates

• Last Update Posted (Actual): March 20, 2026
• Last Update Submitted That Met QC Criteria: March 18, 2026
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Endocrine System Diseases; Metabolic Diseases; Glucose Metabolism Disorders; Diabetes Mellitus; Behavior; Nutritional and Metabolic Diseases; Treatment Adherence and Compliance; Health Behavior; Patient Compliance; Patient Acceptance of Health Care; Diabetes Mellitus, Type 2; Medication Adherence; Machine Learning; Artificial Intelligence; Algorithms; Mathematical Concepts; Reinforcement Machine Learning
• Other Study ID Numbers: 2023P000293; P30AG064199-04 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No