Effects of Social Media and Mobile Health Apps on Pregnancy Care: Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The use of social media and mobile health (mHealth) apps has been increasing in pregnancy care. However, the effectiveness of these interventions is still unclear. OBJECTIVES: We conducted a meta-analysis to examine the effectiveness of these interventions with regard to different health outcomes in pregnant and postpartum women and investigate the characteristics and components of interventions that may affect program effectiveness. METHOD: We performed a comprehensive literature search of major electronic databases and reference sections of related reviews and eligible studies. A random effects model was used to calculate the effect size. RESULTS: Fifteen randomized controlled trial studies published in and before June 2018 that met the inclusion criteria were included in the meta-analysis. The interventions were effective in promoting maternal physical health including weight management, gestational diabetes mellitus control, and asthma control with a moderate to large effect size (d=0.72). Large effect sizes were also found for improving maternal mental health (d=0.84) and knowledge about pregnancy (d=0.80). Weight control interventions using wearable devices were more effective. CONCLUSION: Social media and mHealth apps have the potential to be widely used in improving maternal well-being. More large-scale clinical trials focusing on different health outcomes are suggested for future studies.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.049 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".