Comparing Mobile Health Strategies to Improve Medication Adherence for Veterans With Coronary Heart Disease (Mobile4Meds): Protocol for a Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Adherence to antiplatelet medications is critical to prevent life threatening complications (ie, stent thrombosis) after percutaneous coronary interventions (PCIs), yet rates of nonadherence range from 21-57% by 12 months. Mobile interventions delivered via text messaging or mobile apps represent a practical and inexpensive strategy to promote behavior change and enhance medication adherence. OBJECTIVE: The Mobile4Meds study seeks to determine whether text messaging or a mobile app, compared with an educational website control provided to all Veterans, can improve adherence to antiplatelet therapy among patients following acute coronary syndrome (ACS) or PCI. The three aims of the study are to: (1) determine preferences for content and frequency of text messaging to promote medication adherence through focus groups; (2) identify the most patient-centered app that promotes adherence, through a content analysis of all commercially available apps for medication adherence and focus groups centered on usability; and (3) compare adherence to antiplatelet medications in Veterans after ACS/PCI via a randomized clinical trial (RCT). METHODS: We will utilize a mixed-methods design that uses focus groups to achieve the first and second aims (N=32). Patients will be followed for 12 months after being randomly assigned to one of three arms: (1) customized text messaging, (2) mobile app, or (3) website-control groups (N=225). Medication adherence will be measured with electronic monitoring devices, pharmacy records, and self-reports. RESULTS: Enrollment for the focus groups is currently in progress. We expect to enroll patients for the RCT in the beginning of 2018. CONCLUSIONS: Determining the efficacy of mobile technology using a Veteran-designed protocol to promote medication adherence will have a significant impact on Veteran health and public health, particularly for individuals with chronic diseases that require strict medication adherence. TRIAL REGISTRATION: ClinicalTrials.gov NCT03022669.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".