Digital health technology and hypertension management: a qualitative analysis of patient and specialist provider preferences on data tracking
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".