Abstract FR445: Use of Digital Health Interventions to Control High Blood Pressure: A Systematic Review of Efficacy
Bibliographic record
Abstract
Introduction: Improving BP control is now often done with digital tools, including apps, telemonitoring, wearables, and online coaching. They provide patients with new tools for self-care and give clinicians constant data about their progress. Yet, it is challenging to thoroughly review the complete results of digital health approaches for hypertension management. We systematically reviewed studies to see how digital health interventions affected blood pressure and patient outcomes. Hypothesis: Patients with hypertension who receive digital health interventions will experience greater improvements in blood pressure and control rates compared to those who receive usual care. Additionally, those using telemonitoring will have particularly favorable outcomes. Methods: According to the PRISMA guidelines, we searched online for studies that tested digital health tools in hypertension. The review analyzed 22 studies, with 15 carried out as randomized controlled trials and 7 done through observational means, all related to smartphone apps, telehealth, and remote blood pressure monitoring. Information on BP decrease, hypertension management success, and how many patients were involved was collected. Biases were assessed using the Cochrane tool for RCTs and the Newcastle-Ottawa tool for observational studies. Results: Most RCTs showed that digital health interventions, when used to control their BP, helped people control their BP better than they would with usual care or education alone. On average, reducing BP was more effective for those receiving an intervention than for those in the usual care group. The outcomes were the best when telemonitoring was closely followed by feedback or counseling. Several studies have found that digital health groups were more likely to follow their medication plans and manage their conditions. Researchers found that these programs made patients happy and maintained their BP levels in regular practice settings. The risk of bias was rarely high in RCTs as a whole. Conclusions: This review, which focused on an important area of hypertension care, showed that digital health tools can help manage blood pressure more effectively. What makes these interventions special is their ability to offer care outside a clinic setting. Our research suggests that using valid digital strategies can help update hypertension management guidelines, but additional research is needed to see their long-term impact.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".