Patient and Health Professional Perceptions of Telemonitoring for Hypertension Management: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Hypertension is the most prevalent and important risk factor for cardiovascular disease, affecting nearly 50% of the US adult population; however, only 30% of these patients achieve controlled blood pressure (BP). Incorporating strategies into primary care that take into consideration individual patient needs, such as remote BP monitoring, may improve hypertension management. OBJECTIVE: From March 2018 to December 2018, Stanford implemented a precision health pilot called Humanwide, which aimed to leverage high-technology and high-touch medicine to tailor individualized care for conditions such as hypertension. We examined multi-stakeholder perceptions of hypertension management in Humanwide to evaluate the program's acceptability, appropriateness, feasibility, and sustainability. METHODS: We conducted semistructured interviews with 16 patients and 15 health professionals to assess their experiences with hypertension management in Humanwide. We transcribed and analyzed the interviews using a hybrid approach of inductive and deductive analysis to identify common themes around hypertension management and consensus methods to ensure reliability and validity. RESULTS: A total of 63% (10/16) of the patients and 40% (6/15) of the health professionals mentioned hypertension in the context of Humanwide. These participants reported that remote BP monitoring improved motivation, BP control, and overall clinic efficiency. The health professionals discussed feasibility challenges, including the time needed to analyze BP data and provide individualized feedback, integration of BP data, technological difficulties with the BP cuff, and decreased patient use of remote BP monitoring over time. CONCLUSIONS: Remote BP monitoring for hypertension management in Humanwide was acceptable to patients and health professionals and appropriate for care. Important challenges need to be addressed to improve the feasibility and sustainability of this approach by leveraging team-based care, engaging patients to sustain remote BP monitoring, standardizing electronic medical record integration of BP measurements, and finding more user-friendly BP cuffs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".