Advancing Digital Health Interventions as a Clinically Applied Science for Blood Pressure Reduction: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background Behavioural counselling via internet- or mobile-based digital platforms is recommended for hypertension; however, outcome heterogeneity is problematic in trials of this digital intervention. Our objective was to assess how therapeutic outcome was optimized in digital trials for hypertension, according to key features of the intervention design and protocol. Methods We identified randomized controlled digital trials for systolic blood pressure (SBP) reduction in taskforce guideline and policy statements, systematic reviews, and meta-analyses published since 2010, by searching the EMBASE, Cochrane Library, psycINFO, and PubMed databases. This search was updated to January 2019. Trials included patients with elevated cardiovascular risk or cardiovascular disease. We classified digital trials by the number of components of the intervention, and whether the protocol was organized by an explicit model of behavioural change or counselling. The influence of these features was evaluated for treatment efficacy and heterogeneity of SBP outcomes. Results Seventeen trials met inclusion criteria: pooled n = 5780, 33% female, 93% taking antihypertensive medications. SBP reduction was −7.3 mm Hg for digital counselling (95% confidence interval: −7.0 to −7.5) vs −3.6 mm Hg for control (95% confidence interval: −3.4 to −3.9), P < 0.0001, with high-moderate heterogeneity (I 2 = 67%). Trials with multiple behavioural intervention components and an organized theoretical framework of behaviour change or counselling demonstrated optimal SBP reduction with low-moderate heterogeneity (I 2 = 49%). Conclusions Digital health interventions optimize the efficacy of medical therapy for SBP reduction. There is opportunity to promote a disruptive change in clinical science that accompanies technological developments in digital health promotion.
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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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".