O86 EFFECTIVENESS OF A MHEALTH (MOBILE-BASED) INTERVENTION FOR THE MANAGEMENT OF CARDIOVASCULAR DISEASES IN SOUTH ASIAN COUNTRIES: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Background: Cardiovascular disease (CVDs) is the leading cause of preventable death and disability, accounting for 18.6 million deaths and 393 million DALYs in 2019. The effectiveness of mobile health (mHealth) interventions for managing CVD in South Asia is poorly documented. This systematic review evaluates the impact of mHealth on cardiovascular health outcomes in this region. Methods: We conducted a systematic search of MEDLINE, EMBASE, CINAHL, Web of Science, and Scopus, covering the period from January 2000 to December 2023. Our search focused on studies employing mobile-based interventions, such as apps, SMS, video conferencing, or phone calls, to improve cardiovascular outcomes, including blood pressure, medication adherence, and physical activity. Data were synthesised using a random-effects meta-analysis, estimating summary hazard ratios (HRs) and 95% confidence intervals (CIs). This review was registered in PROSPERO (CRD42022340907). Results: We identified 1483 studies, of which 14 met our inclusion criteria, encompassing 30,939 participants (17,240 intervention; 14,984 control). Participants ages ranged from 46.4 to 60.6 years. Six studies reported improvement in systolic blood pressure (SBP), one reported improvement in cholesterol, two reported change in self-reported physical activity, and three reported improvement in medical adherence. Meta-analysis revealed a significant reduction in systolic blood pressure (SBP) by -2.32 (95% CI: -4.25 to -0.40; P=0.02) in the intervention arm. No significant changes were observed in diastolic blood pressure (DBP) or medication adherence, with DBP showing a reduction of -1.13 (95% CI: -1.69 to 1.42; P=0.87) and medication adherence an effect size of 0.44 (95% CI: -0.06 to 0.94; P=0.08). Conclusions: mHealth interventions in South Asia show promise in reducing SBP among individuals with CVD, though effects on DBP and medication adherence are less pronounced. The observed benefits are constrained by high heterogeneity and a limited number of studies, indicating the need for further research to consolidate these findings and explore additional CVD outcomes.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".