Digital health technology and hypertension management: a qualitative analysis of patient and specialist provider preferences on data tracking
Why this work is in the frame
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Bibliographic record
Abstract
Aim: Digital health for hypertension management holds potential for improving the quality of care but requires long-term patient engagement to track health data. We explored patient and hypertension specialist perceptions of clinical utility for data tracking including standardized patient-reported outcome measures (PROMs), home blood pressure (BP) measurement, and other health metrics. Methods: Participants reviewed general health status, patient satisfaction, and hypertension-specific PROMs. Semi-structured focus groups (n = 15) with nine patients with hypertension and six hypertension specialists were audio-recorded and thematically analyzed. Results: Key themes identified from patients included: (1) comfort and appreciation of home BP monitoring but only during important periods of hypertension care; (2) preference for tracking new symptoms and medication side effects; (3) patients perceived tracking other health measures including general PROMs, diet and exercise as less relevant to their care; and (4) visually represented BP trends evaluating associations with changes in other health parameters were perceived as useful. Key themes identified by hypertension specialists included: (1) concerns about patient digital literacy; (2) utilizing visual representations of long-term BP data trends for patient empowerment; and (3) unclear relevance of tracking medication adverse effects, PROMs, and other non-BP health metrics. Conclusion: Patients and hypertension specialists had similar perspectives for most aspects of data monitoring but differed in preference for a few aspects that were germane to patients, including monitoring medication adverse effects and symptoms. Including views on data tracking from both patients and providers are essential for designing digital tools to optimize hypertension management.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it