Long-Term Results of a Digital Hypertension Self-Management Program: Retrospective Cohort Study
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Bibliographic record
Abstract
Background Digital health programs that incorporate frequent blood pressure (BP) self-monitoring and support for behavior change offer a scalable solution for hypertension management. Objective We examined the impact of a digital hypertension self-management and lifestyle change support program on BP over 12 months. Methods Data were analyzed from a retrospective observational cohort of commercially insured members (n=1117) that started the Omada for Hypertension program between January 1, 2019, and September 30, 2021. Paired t tests and linear regression were used to measure the changes in systolic blood pressure (SBP) over 12 months overall and by SBP control status at baseline (≥130 mm Hg vs <130 mm Hg). Results Members were on average 50.9 years old, 50.8% (n=567) of them were female, 60.5% (n=675) of them were White, and 70.5% (n=788) of them had uncontrolled SBP at baseline (≥130 mm Hg). At 12 months, all members (including members with controlled and uncontrolled BP at baseline) and those with uncontrolled SBP at baseline experienced significant mean reductions in SBP (mean –4.8 mm Hg, 95% CI –5.6 to –4.0; –8.1 mm Hg, 95% CI –9.0 to –7.1, respectively; both P<.001). Members with uncontrolled SBP at baseline also had significant reductions in diastolic blood pressure (–4.7 mm Hg; 95% CI –5.3 to –4.1), weight (–6.5 lbs, 95% CI –7.7 to –5.3; 2.7% weight loss), and BMI (–1.1 kg/m2; 95% CI –1.3 to –0.9; all P<.001). Those with controlled SBP at baseline maintained within BP goal range. Additionally, 48% (418/860) of members with uncontrolled BP at baseline experienced enough change in BP to improve their BP category. Conclusions This study provides real-world evidence that a comprehensive digital health program involving hypertension education, at-home BP monitoring, and behavior change coaching support was effective for self-managing hypertension over 12 months.
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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 | 0.000 | 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