Adopting Self-Measured Blood Pressure Monitoring Among Underserved Communities (ASPIRE): A Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Addressing barriers to self-measured blood pressure (SMBP) engagement through tailored implementation strategies is critical for improving hypertension-related outcomes. OBJECTIVE: To evaluate the feasibility of implementing the ASPIRE Clinical Integration Package, a multifaceted intervention designed to support SMBP adoption and engagement in under-resourced primary care settings. DESIGN: This randomized trial was conducted in 2024 at one large primary care clinic serving racially and ethnically diverse populations. PARTICIPANTS: Patients were eligible if they had hypertension, were prescribed ≥1 blood pressure-lowering medication, and presented to the clinic with an elevated blood pressure reading. APPROACH: Patients were randomized to receive a free SMBP device (control; n=25) or a free SMBP device and the ASPIRE Clinical Integration Package (intervention; n=25) which included 6 components; 1. Cuff sizing, 2. Training on accurate readings, 3. ASPIRE log, 4. Reminders/support for sharing readings, 5. Social needs screening, 6. Clinic workflow for SMBP documentation. The primary outcomes included feasibility metrics (referral, recruitment, and retention) and fidelity described in terms of the proportion of patients who received each of the 6 ASPIRE components. Secondary outcomes included SMBP engagement (1+ reading documented in the electronic health record) and change in systolic blood pressure. KEY RESULTS: In total, 50 patients were randomized and included in analyses. Referral (60.0%), recruitment (60.2%), and retention (90.0%) targets were met. Fidelity evaluation revealed that 100% of patients received components 1 - 4, 96% and 93% received components 5 and 6, respectively. At 6-months the difference in SMBP engagement was 52.0% (95% confidence interval [CI] 29.3%-74.7%) favoring the intervention arm, and the difference in change in systolic blood pressure was -11.9mmHg (95% CI -21.7, -2.1) favoring the intervention arm. CONCLUSIONS: The ASPIRE Clinical Integration Package demonstrates feasibility and acceptability in promoting SMBP adoption in under-resourced primary care settings. These findings lay the groundwork for a larger trial to assess effectiveness in improving hypertension control and reducing disparities. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT06175793.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".