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| ID | Type | Description | Link |
|---|---|---|---|
| P30AG064199-01 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
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| National Institute on Aging (NIA) | NIH |
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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.
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. 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, untailored text messages. Our outcomes of interest will be medication adherence, as measured by electronic pill bottles, and HbA1c levels.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Reinforcement Learning Intervention Arm | Experimental | Up to daily, tailored text messages. |
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| Control Arm | No Intervention | Up to daily, untailored text messages. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Glycemic Control | Change in glycated hemoglobin A1c from baseline to end of the 6-month follow-up | 6 months |
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Inclusion criteria:
Exclusion criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Brigham and Women's Hospital | Boston | Massachusetts | 02120 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34862289 | Derived | 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. |
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| ID | Title | Description |
|---|---|---|
| FG000 | Reinforcement Learning Intervention Arm | Up to daily, tailored text messages. 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. |
| FG001 | Control Arm | Up to daily, untailored text messages. |
| Title | Milestones | Reasons Not Completed | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | Reinforcement Learning Intervention Arm | Up to daily, tailored text messages. 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. |
| Units | Counts |
|---|---|
| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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 | Participants were included in the analysis until they no longer participated in the study. | Posted | Mean | Standard Deviation | pill bottle openings | 6 months |
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Adverse events were collected from enrollment through the 6 month study follow-up period.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Reinforcement Learning Intervention Arm | Up to daily, tailored text messages. 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. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Julie Lauffenburger, PharmD, PhD | Brigham and Women's Hospital | 617-525-8865 | jlauffenburger@bwh.harvard.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Apr 1, 2021 | Jan 11, 2023 | Prot_SAP_001.pdf |
| ICF | No | No | Yes | Informed Consent Form | Jun 29, 2020 | Sep 29, 2021 | ICF_000.pdf |
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| D055118 | Medication Adherence |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D000098408 | Reinforcement Machine Learning |
| ID | Term |
|---|---|
| D000069550 | Machine Learning |
| D001185 | Artificial Intelligence |
| D000465 | Algorithms |
| D055641 | Mathematical Concepts |
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|
| BG001 | Control Arm | Up to daily, untailored text messages. |
| BG002 | Total | Total of all reporting groups |
| years |
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| Sex: Female, Male | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
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| Self-reported automaticity of medication-taking, n(%) | Higher scores are better (i.e., higher automaticity indicates that respondents think that medication-taking is more automatic) based on a self-reported measure of automaticity | Count of Participants | Participants |
|
| Baseline HbA1c, mean (sd) | Mean | Standard Deviation | percentage of glycated hemoglobin |
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| Education level, n(%) | Count of Participants | Participants |
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| Number of self-reported physicians, n(%) | Count of Participants | Participants |
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| Number of self-reported medications, n(%) | Count of Participants | Participants |
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| Number of Pillsy bottles, n(%) | Count of Participants | Participants |
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| Level of non-adherence, self-reported number of doses missed in past month, n(%) | Count of Participants | Participants |
|
| OG001 | Control Arm | Up to daily, untailored text messages. |
|
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| Secondary | Glycemic Control | Change in glycated hemoglobin A1c from baseline to end of the 6-month follow-up | Participants were included in the analysis until they no longer participated in the study. Differences were calculated among those who had follow-up HbA1c values recorded. | Posted | Mean | Standard Deviation | percentage glycated hemoglobin | 6 months |
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|
|
| 0 |
| 29 |
| 0 |
| 29 |
| 0 |
| 29 |
| EG001 | Control Arm | Up to daily, untailored text messages. | 1 | 31 | 0 | 31 | 0 | 31 |
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| D004700 | Endocrine System Diseases |
| D010349 | Patient Compliance |
| D010342 | Patient Acceptance of Health Care |
| D000074822 | Treatment Adherence and Compliance |
| D015438 | Health Behavior |
| D001519 | Behavior |