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

NCT05742685 | Completed

• 2 other identifiers: 2023P000293P30AG064199-04
• Study Type: interventional
• Target: 28
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Diabetes Mellitus, Type 2; Medication Adherence

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• Glycemic control

  6 months

Study Arms (2)

Reinforcement Learning Intervention Arm

EXPERIMENTAL

Up to daily, tailored text messages.

Behavioral: Reinforcement Learning

Control Arm

NO INTERVENTION

Up to daily, untailored text messages.

Interventions

Reinforcement Learning BEHAVIORAL

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.

Reinforcement Learning Intervention Arm

Eligibility Criteria

• Age: 18 Years - 84 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Brigham and Women's Hospital: lead
• Boston Medical Center: collaborator
• National Institute on Aging (NIA): collaborator

Study Sites (1)

Boston Medical Center

Boston, Massachusetts, 02118, United States

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 2; Medication Adherence

Interventions

Reinforcement Machine Learning

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Patient Compliance; Patient Acceptance of Health Care; Treatment Adherence and Compliance; Health Behavior; Behavior

Intervention Hierarchy (Ancestors)

Machine Learning; Artificial Intelligence; Algorithms; Mathematical Concepts

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: INVESTIGATOR, OUTCOMES ASSESSOR
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: February 15, 2023
• First Posted: February 24, 2023
• Study Start: August 23, 2023
• Primary Completion: December 17, 2025
• Study Completion: December 17, 2025
• Last Updated: March 20, 2026

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)