Effectiveness of mHealth Apps for Maternal Health Care Delivery: Systematic Review of Systematic Reviews
Bibliographic record
Abstract
BACKGROUND: Globally, the use of mobile health (mHealth) apps or interventions has increased. Robust synthesis of existing systematic reviews on mHealth apps may offer useful insights to guide maternal health clinicians and policy makers. OBJECTIVE: This systematic review aims to assess the effectiveness or impact of mHealth apps on maternal health care delivery globally. METHODS: We systematically searched Scopus, Web of Science (Core Collection), MEDLINE or PubMed, CINAHL, and Cochrane Database of Systematic Reviews using a predeveloped search strategy. The quality of the reviews was independently assessed by 3 reviewers, while study selection was done by 2 independent raters. We presented a narrative synthesis of the findings, highlighting the specific mHealth apps, where they are implemented, and their effectiveness or outcomes toward various maternal conditions. RESULTS: A total of 2527 documents were retrieved, out of which 16 documents were included in the review. Most mHealth apps were implemented by sending SMS text messages with mobile phones. mHealth interventions were most effective in 5 areas: maternal anxiety and depression, diabetes in pregnancy, gestational weight management, maternal health care use, behavioral modification toward smoking cessation, and controlling substance use during pregnancy. We noted that mHealth interventions for maternal health care are skewed toward high-income countries (13/16, 81%). CONCLUSIONS: The effectiveness of mHealth apps for maternity health care has drawn attention in research and practice recently. The study showed that research on mHealth apps and their use dominate in high-income countries. As a result, it is imperative that low- and middle-income countries intensify their commitment to these apps for maternal health care, in terms of use and research. TRIAL REGISTRATION: PROSPERO CRD42022365179; https://tinyurl.com/e5yxyx77.
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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 | 0.031 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| 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".