Treatment Adherence and Secondary Prevention of Ischemic Stroke Among Discharged Patients Using Mobile Phone- and WeChat-Based Improvement Services: Cohort Study
Bibliographic record
Abstract
BACKGROUND: Real-world studies have indicated that adherence is important for guaranteeing medication effectiveness. Few studies have tested the feasibility and efficacy of WeChat-based improvement services, via mobile phone, in secondary prevention-specific follow-up among discharged stroke patients. OBJECTIVE: We evaluated a quadruple-domain, WeChat-based service for ischemic stroke secondary prevention designed to improve treatment adherence of discharged patients. This service focuses on sending reminders for drug use, blood pressure recording, and glucose recording; it also records medication use. We compared the endpoint event rate between WeChat self-monitoring and traditional monitoring. METHODS: A cohort study was used to determine the feasibility of a physician-assisted, WeChat-based improvement service and follow-up self-monitoring platform for the secondary prevention of ischemic stroke. The platform was developed by the Peking University Third Hospital based on the information-motivation-behavioral skills model. The overall adherence rate was calculated as the proportion of medication doses verified via uploading. The ischemic endpoint event rate and medication noncompliance rate were compared between traditional prevention monitoring and WeChat self-monitoring. Factors influencing adherence were summarized. RESULTS: =1.9, df=1, P=.16). Of the platform registry participants, 89.7% (210/234: 167 hospital-based and 43 community-based participants) adhered to inputting information into WeChat for 8-96 weeks. The average adherence time was 16.54 (SD 0.80, range 2-24) months. The average decrease in adherence was 4 participants (1.1%) per month. Being a member of a community-based population was an influencing factor for good adherence at the 2-year follow-up (OR 2.373, 95% CI 1.019-5.527, P=.045), whereas transient ischemic attack was an influencing factor for poor adherence at the 2-year follow-up (OR 0.122, 95% CI 0.016-0.940, P=.04). CONCLUSIONS: Use of WeChat self-monitoring showed a trend of increasing medication compliance and decreasing ischemic endpoint event rate compared with traditional monitoring. However, there were ceiling effects in the outcomes, and a relatively small sample size was used. Male participants displayed better adherence to WeChat self-monitoring. The community-based population displayed good adherence when using WeChat self-monitoring. TRIAL REGISTRATION: ClinicalTrials.gov NCT02618265; https://clinicaltrials.gov/ct2/show/NCT02618265.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".