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

Brief summary (registry, in English)

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.

What you commit to

• Study type: Interventional
• Participants enrolled: 28
• From 23 Aug 2023 to the primary completion date, 17 Dec 2025
• 1 site in 1 country
• Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Age in the registry: 18 years to 84 years · Sex: All · Does not accept healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (1)

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