Self-Measured Blood Pressure–Guided Pharmacotherapy: A Systematic Review and Meta-Analysis of United States-Based Telemedicine Trials
Bibliographic record
Abstract
BACKGROUND: The optimal approach to implementing telemedicine hypertension management in the United States is unknown. METHODS: We examined telemedicine hypertension management versus the effect of usual clinic-based care on blood pressure (BP) and patient/clinician-related heterogeneity in a systematic review/meta-analysis. We searched United States-based randomized trials from Medline, Embase, CENTRAL, CINAHL, PsycINFO, Compendex, Web of Science Core Collection, Scopus, and 2 trial registries. We used trial-level differences in BP and its control rate at ≥6 months using random-effects models. We examined heterogeneity in univariable metaregression and in prespecified subgroups (clinicians leading pharmacotherapy [physician/nonphysician], self-management support [pharmacist/nurse], White versus non-White patient predominant trials [>50% patients/trial], diabetes predominant trials [≥25% patients/trial], and White patient predominant but not diabetes predominant trials versus both non-White and diabetes patient predominant trials]. RESULTS: Thirteen, 11, and 7 trials were eligible for systolic and diastolic BP difference and BP control, respectively. Differences in systolic and diastolic BP and BP control rate were -7.3 mm Hg (95% CI, -9.4 to -5.2), -2.7 mm Hg (-4.0 to -1.5), and 10.1% (0.4%-19.9%), respectively, favoring telemedicine. Greater BP reduction occurred in trials where nonphysicians led pharmacotherapy, pharmacists provided self-management support, White patient predominant trials, and White patient predominant but not diabetes predominant trials, with no difference by diabetes predominant trials. CONCLUSIONS: Telemedicine hypertension management is more effective than clinic-based care in the United States, particularly when nonphysicians lead pharmacotherapy and pharmacists provide self-management support. Non-White patient predominant trials achieved less BP reduction. Equity-conscious, locally informed adaptation of telemedicine interventions is needed before wider implementation.
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.043 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".