Reinforcement Learning in Diabetes Mellitus Trial

NCT04473326 | Completed Results

• 2 other identifiers: 2020P000846P30AG064199-01
• Study Type: interventional
• Target: 60
• 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 this pilot study is to develop and test a novel reinforcement learning-enhanced text messaging program to support medication adherence in patients with type 2 diabetes. Type 2 diabetes is an optimal condition in which to test 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 pilot study will be a parallel randomized pragmatic trial comparing medication adherence and clinical outcomes for adults 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. Our outcomes of interest will be medication adherence, as measured by electronic pill bottles, and HbA1c levels.

Conditions

Diabetes Mellitus, Type 2; Medication Adherence

Outcome Measures

Primary Outcomes (1)

• Medication Adherence

  Medication adherence to type 2 diabetes oral medications (averaged) as measured by the number of dates and times of pillbottle openings in the electronic pill bottles

  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:

• Age between 18-84 years
• Diagnosed with type 2 diabetes mellitus (T2DM) and are prescribed between 1-3 daily oral medications for this disease
• Currently have a smartphone with a data plan or WiFi at home
• HbA1c level ≥7.5%
• Basic working knowledge of English
• Willing and able to set up the platform and adhere to study procedures
• Either not currently using a pillbox or willing to use electronic pill bottles (EDMs) for diabetes medications for the duration of the study

You may not qualify if:

• Patients with active enrollment in another diabetes trial within Mass General Brigham
• Patients who receive daily assistance with taking their medications at home
• Patients who are unable to receive text messages for more than 3 days in a row during the study period

Sponsors & Collaborators

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

Study Sites (1)

Brigham and Women's Hospital

Boston, Massachusetts, 02120, United States

Location

Related Publications (1)

• Lauffenburger JC, Yom-Tov E, Keller PA, McDonnell ME, Bessette LG, Fontanet CP, Sears ES, Kim E, Hanken K, Buckley JJ, Barlev RA, Haff N, Choudhry NK. REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial. BMJ Open. 2021 Dec 3;11(12):e052091. doi: 10.1136/bmjopen-2021-052091.

  PMID: 34862289 DERIVED

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

Results Point of Contact

• Title: Julie Lauffenburger, PharmD, PhD
• Organization: Brigham and Women's Hospital

jlauffenburger@bwh.harvard.edu

617-525-8865

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: No

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: July 13, 2020
• First Posted: July 16, 2020
• Study Start: February 4, 2021
• Primary Completion: January 4, 2022
• Study Completion: January 28, 2022
• Last Updated: February 8, 2023
• Results First Posted: February 8, 2023

Record last verified: 2023-02

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)