A Mobile App for Hypertension Management Based on Clinical Practice Guidelines: Development and Deployment
Bibliographic record
Abstract
BACKGROUND: Hypertension is a chronic and lifestyle-related disease that requires continuous preventive care. Although there are many evidence-based clinical practice guidelines (CPGs) for hypertension management, applying them to daily management can be difficult for patients with hypertension. A mobile app, based on CPGs, could help patients with hypertension manage their disease. OBJECTIVE: To develop a mobile app for hypertension management based on CPGs and evaluate its effectiveness in patients with hypertension with respect to perceived usefulness, user satisfaction, and medication adherence. METHODS: The hypertension management app (HMA) was developed according to the Web-Roadmap methodology, which includes planning, analysis, design, implementation, and evaluation phases. The HMA was provided to individuals (N=38) with hypertension. Medication adherence was measured before and after using the HMA for 4 weeks. The perceived usefulness and user satisfaction were surveyed in the patients who completed the medication adherence survey. RESULTS: Of the 38 study participants, 29 (76%) participated in medical adherence assessment. Medication adherence, as measured by the Modified Morisky Scale, was significantly improved in these patients after they had used the HMA (P=.001). The perceived usefulness score was 3.7 out of 5. The user satisfaction scores, with respect to using the HMA for blood pressure recording, medication recording, data sending, alerting, recommending, and educating about medication were 4.3, 3.8, 3.1, 3.2, 3.4, and 3.8 out of 5, respectively, in the 19 patients. CONCLUSIONS: This study showed that a mobile app for hypertension management based on CPGs is effective at improving medication adherence.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".