WeChat-Based Nursing Interventions in Women’s Mobile Health: Systematic Review (Preprint)
Bibliographic record
Abstract
Background: Mobile health (mHealth) technology offers new approaches to improve women's health by providing personalized monitoring and real-time guidance. As one of the most widely used social media platforms in China, WeChat has shown great potential in mHealth practice, yet systematic evidence on its application in women's health care remains insufficient. Objective: This study aims to systematically review WeChat-based nursing interventions in women's mHealth in order to clarify the application status, intervention modalities, target populations, and effectiveness outcomes. Methods: Searches were conducted in IEEE Xplore, Web of Science, PubMed, Scopus, ACM Digital Library, and Cochrane Central Register of Controlled Trials between January 2011 and December 2024. Two independent reviewers screened studies; extracted data on study design, intervention forms, target diseases, and outcome indicators; and assessed methodological quality. The Cohen κ coefficient was used to evaluate interreviewer agreement. Publication trends, institutional collaborations, author contributions, and research hotspots were analyzed using VOSviewer (Leiden University) and InCites (Clarivate) for bibliometric analysis. Results: A total of 31 eligible studies published from 2014 to 2024 were included. Most studies were randomized controlled trials (n=27). Intervention modalities mainly included WeChat groups (n=22), official accounts (n=18), applets (n=4), and private chats (n=9), mostly used in combination. The top focused health issues were prenatal care (n=5), breast cancer (n=5), gynecological cancer (n=5), and gestational diabetes mellitus (n=4). Six studies adopted multidisciplinary teams. Cohen κ was 0.71, indicating substantial agreement. Publications grew rapidly after 2018, peaking in 2021 and 2024. A total of 40 institutions participated, with Xi'an Jiaotong University having the highest citation impact. Most studies were at high risk of bias due to a nonblinding design. Conclusions: WeChat-based nursing interventions improve personalized health information access, self-management ability, treatment compliance, and real-time doctor-patient communication for women. This is the first systematic review to evaluate WeChat mHealth interventions in women's health, filling the research gap. Future research should focus on improving methodological quality, exploring cross-cultural adaptability, conducting long-term follow-up, and integrating wearable devices and electronic health records to further optimize WeChat-based women's health services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".