Assessing the Effectiveness of mHealth Interventions for Diabetes and Hypertension Management in Africa: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Mobile health (mHealth) interventions are effective in improving chronic disease management, mainly in high-income countries. However, less is known about the efficacy of mHealth interventions for the reduction of cardiovascular risk factors, including for hypertension and diabetes, which are rapidly increasing in low- and middle-income countries. Objective: This study aimed to assess the efficacy of mHealth interventions for diabetes and hypertension management in Africa. Methods: We searched PubMed, Cochrane Library, Google Scholar, African Journals Online, and Web of Science for relevant studies published from inception to July 2022. The main outcomes of interest were changes in hemoglobin A1c (HbA1c), systolic blood pressure, and diastolic blood pressure. The random or fixed effect model was used for the meta-analysis, and the I2 statistic was used to gauge study heterogeneity. Z tests and P values were used to evaluate the effect of mHealth interventions on HbA1c and blood pressure levels. Results: This review included 7 studies (randomized controlled trials) with a total of 2249 participants. Two studies assessed the effect of mHealth on glycemic control, and 5 studies assessed the effect of mHealth on blood pressure control. The use of mHealth interventions was not associated with significant reductions in HbA1c levels (weighted mean difference [WMD] 0.20, 95% CI -0.40 to 0.80; P=.51) among patients with diabetes and systolic blood pressure (WMD -1.39, 95% CI -4.46 to 1.68; P=.37) and diastolic blood pressure (WMD 0.36, 95% CI -1.37 to 2.05; P=.69) among patients with hypertension. After conducting sensitivity analyses using the leave-one-out method, the Kingue et al study had an impact on the intervention, resulting in a 2 mm Hg reduction in systolic blood pressure (WMD -2.22, 95% CI -3.94 to -0.60; P=.01) but was nonsignificant for diastolic blood pressure and HbA1c levels after omitting the study. Conclusions: Our review provided no conclusive evidence for the effectiveness of mHealth interventions in reducing blood pressure and glycemic control in Africa among persons with diabetes and hypertension. To confirm these findings, larger randomized controlled trials are required.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.045 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".