Implementation and impact of mhealth in the management of diabetes mellitus in Africa: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) has proposed the concept of mobile health to support healthcare systems delivery worldwide. Mobile health (mHealth) involves using Information and Communication Technology (ICT) for health care provision or delivery services. In the context of Africa, a region that has witnessed a significant increase in mobile phone availability and usage in the last decade and a corresponding rise in the incidence and prevalence of diabetes mellitus, this study has global implications. We conducted a systematic review on the extent of mHealth implementation in managing diabetes mellitus in Africa. We estimated its impact on achieving desired glycemic targets, sustained control, and preventing complications in the past decade. METHODS AND ANALYSIS: The studies assessing the utilization of mHealth in managing patients with diabetes mellitus in Africa were considered based on the PICO method: Population, Intervention, Comparator, and Outcomes. MEDLINE, PubMed, SCOPUS, and the Pan African Clinical Trials Registry were searched. Two authors, independent of each other, screened titles and abstracts retrieved using the search strategy, retrieved the full-text articles, and assessed them for eligibility, extracting data after that. A third independent reviewer was brought in to resolve disagreements between the two authors by discussion. The revised Cochrane Collaboration Risk of Bias Tool was used to assess the quality of included studies. A narrative synthesis of extracted data was done due to the paucity of eligible studies, and the results were summarized in a meta-analysis. RESULTS: None of the six included studies measured the mean FPG or percentage changes as primary outcomes. Five measured the percentage change in HbA1c from baseline to the end of the study. The percentage change in HbA1c from the baseline ranged from 3.6% to 20.53%, achieving significance in three studies. In the meta-analysis the overall WMD (95% CI) was 0.992 (0.48, 1.50). This, in combination with a high z score of 3.822, p <0.001 suggests a statistically significant overall effect that is not likely due to chance. However, a considerable heterogeneity (I2 = 63.9%, p = 0.026) was present implying that the observed effect may not be generalizable to all the studies due to differences in study characteristics in this case most likely sample size and duration of study. None of the studies addressed the secondary outcomes of measuring the direct relationships between these mHealth interventions and the prevention or early detection of diabetes complications. CONCLUSION: Overall, there was a statistically significant reduction in HbA1c levels among individuals living with type 2 diabetes in Africa following mHealth interventions. Few studies were included in the meta-analysis with significant heterogeneity. Therefore, we recommend more well-designed randomized controlled trials to investigate the implementation and efficacy of mHealth in the management of diabetes mellitus in Africa. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021218674.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".