This review was registered in the PROSPERO International Prospective Register of Systematic Reviews (CRD420251051789) and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines closely in its conduct and reporting (Checklist 1) [24].

This systematic review aimed to explore the research question: How effective are WeChat-based nursing interventions in enhancing health outcomes for women at various life stages, and what is their current implementation status? The design of the review adhered to the PICO framework:

An extensive search plan was formulated by using free-text words and subject headings. The following databases were explored from January 2011 to December 2024: IEEE Xplore, Web of Science, PubMed, Scopus, ACM Digital Library, the Cochrane Central Register of Controlled Trials, and the China National Knowledge Infrastructure (CNKI). An effective retrieval strategy was established using a 2-step process. The first step involved collecting and organizing relevant search terms from related systematic reviews and meta-analyses [25,26]. Afterward, 3 authors (RL, YW, and TW) carefully examined and improved these terms to guarantee thorough inclusion of all essential terms. Using Boolean logic operators, the concluding search strategy was designed: (“wechat” or “weixin” or “official account” or “public account” or “applet” or “mini program”) and (medic* OR illness* OR disease* OR health* OR pharma* OR drug* OR therap*) and (woman OR women OR girl OR female). The full search strategy for each database is detailed in Table 1. Additionally, we did not impose any restrictions on health-related keywords in the Cochrane Central Register of Controlled Trials database, because we assumed that all documents in this database are related to human health by default, so that we can cover a wider range of potential literatures. Since numerous relevant studies have been published in Chinese, we specifically included CNKI. Other ways to identify studies involved expert recommendations and manually searching the references of included studies and pertinent systematic reviews.

Retrieved records were first imported into EndNote X21. Duplicate records were identified using the automatic deduplication tool, and a manual check was conducted to remove any duplicates left due to differences in author names, publication formats, or metadata entries.

The titles and abstracts were independently reviewed by two reviewers (JX and RL) following a step-by-step process using the established inclusion and exclusion criteria (Table 2). Any discrepancies were resolved through discussion until consensus; if unresolved, a third senior reviewer (TW) adjudicated the final decision. Cohen κ was used to assess the agreement between the reviewers.

The Cochrane risk-of-bias tool [27] and Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) [28] were used to assess the quality of randomized controlled trials (RCTs) and quasi-experiments, respectively. Both instruments encourage assessment of internal and external validity and aid in evaluating methodological quality and clarity in reporting. Two independent reviewers (JX and RL) conducted the quality assessment for all studies. Any disagreements were resolved through discussion or by involving a third reviewer (TW).

To support subsequent bibliometric analysis, records from all databases were standardized and manually adjusted to align with the Web of Science format. For studies with missing bibliometric information (eg, incomplete affiliation, citation counts, or journal metadata), supplementary information was manually retrieved from the original full text.

Before bibliometric analysis, data were systematically cleaned and standardized using the bibliometrix R packages. To ensure consistency, author names were standardized in terms of abbreviations, initials, and sequence. For example, different versions like “Zhang, San,” “Zhang, S.,” and “San Zhang” were unified into a consistent format. Synonyms, aliases, and different singular/plural versions of keywords were also unified. For example, “mini-program,” “mini program,” and “applet” were unified into a single term. Other core bibliographic information, such as first/corresponding authors, countries, affiliations, and journals, were also standardized. Ultimately, the cleaned bibliographic records were converted into a structured data frame, making them ready for further bibliometric analysis.

Given the diverse women’s health conditions, the small number of studies per condition, and clinical heterogeneity across included studies, a narrative descriptive synthesis was prespecified as the primary outcome analysis method. One reviewer (JX) independently extracted all data from each included study using the predefined form. Extracted items included author, publication year, country, study setting, study design, objectives, sample size, participant characteristics, intervention details, outcome measures, and key findings. A second reviewer (RL) independently verified all extracted data for accuracy and completeness. Discrepancies between the two reviewers (JX and RL) were resolved through discussion and consensus. If no agreement was reached, a third reviewer (TW) made the final decision. For missing study-level data, we only extracted and analyzed available valid data. No imputation or statistical filling was performed for missing data. The review adhered strictly to the registered protocol, with no deviations from the study plan.

VOSviewer (Leiden University) and InCites (Clarivate) were used to quantify the knowledge structure and emerging research trends. VOSviewer 1.6.18 was used to cluster institutions and researchers. To standardize terminological variations, a custom thesaurus file was used. Association strength normalization was applied, with attraction and repulsion values set at 3 and −1. Clustering was conducted using a resolution parameter of 1.0 and a minimum cluster size of 1, resulting in a network with 21 items, 103 links, and a total link strength of 106. The total citation impact was divided by the number of years since publication to determine the yearly citation impact. The H-index signifies that an author has H articles cited no less than H times.

Figure 1 presents a PRISMA flow diagram summarizing the screening process and exclusions. The electronic databases yielded 3391 records, and an extra 31 were found by manually searching journals, bringing the total initial number of records to 3422. After excluding nonoriginal articles and duplicates, the remaining 1946 records proceeded to title and abstract assessment. Of these, 1632 were excluded at this stage, and 314 full-text articles were assessed for eligibility. Finally, a set of 31 studies was included in the data extraction. Cohen κ was calculated to be 0.71, which suggests a substantial level of agreement.

The review systematically included 31 studies that appeared in publications between 2014 and 2024 (Table 3). Overall, 17 provinces participated in this research discourse, with the majority of publications originating from Beijing (n=5) [29-33], Shanghai (n=5) [34-38], Shanxi (n=3) [39-41], Liaoning (n=3) [42-44], and Zhejiang (n=3) [45-47]. Regarding the research type, 27 were RCTs and 4 were quasi-experiments [34,48-50], respectively.

In most studies, the control group received standard care, which included routine patient education and health management. For pregnant women, standard care typically involves regular classes at the maternity school and scheduled telephone follow-ups (Figure 2) [49]. The mHealth group comprises standard care plus WeChat interventions, which fall into 4 categories: WeChat group, official account, private chats, and applet. Twenty-two studies established WeChat groups, and participants could freely share experiences, discuss difficulties, and interact with peers and researchers at any time [30-33,37-40,43-48,50-56,58]. In these groups, all inquiries and consultations concerning the intervention were promptly handled by researchers. Nine studies used private chats for personalized follow-up communication, often in conjunction with group-based activities [33,35,38,40,42,44,54,56,59]. Eighteen studies delivered health-related information via official accounts to enhance the knowledge of diseases [29,32,34,35,37,39,41,42,44,47,48,50-52,54,55,58,59]. Some of these studies released original content, while others shared and reposted existing credible popular science articles from other sources. Four studies were developed to achieve more individual interaction [33,36,49,57]. An applet usually contains several modules, including a symptom assessment instrument and a health education knowledge base. They could also provide educational videos and check-in tasks [36]. Notably, 19 of the 31 studies used 2 or more intervention components in combination, indicating that combined WeChat-based strategies are common in mHealth interventions.

The most studied diseases include prenatal care (n=5) [34,37,42,48,51], breast cancer (n=5) [32,38,39,47,52], and gynecological cancer (n=5) [33,40,41,46,53], followed by gestational diabetes mellitus (n=4) [30,31,49,54], breastfeeding (n=2) [29,55], polycystic ovary syndrome (n=2) [36,56], pregnant with psychological distress (n=2) [45,57], and postpartum care (n=2) [35,58]. There is only one study for gynecological inflammatory diseases [43], primary dysmenorrhea [44], pelvic organ prolapse [50], and infertility [59].

Six studies established multidisciplinary teams for mHealth initiatives [31,39,40,45,49,51]. In addition to physicians and nurses, midwives were typically included to support pregnant women. Psychologists or psychological counselors are incorporated in mindfulness interventions [45]. Within a diet and exercise program, nutritionists or dietitians would calculate the energy requirements for each pregnant woman, while exercise specialists provided tailored advice as necessary [31]. Pharmacists were tasked with developing medication plans for patients. To assess the quality of life in patients with cervical cancer, one study included a sex counselor [40]. When medication was required, researchers collaborated with endocrinologists to achieve optimal glucose control [49].

The total number of publications shows a fluctuating trend. Prior to 2018, only a small number of articles were released annually [42,44,59]. From 2018 to 2024, there were at least 3 articles published each year, and the number of articles published was the highest in 2021 and 2024 (n=9 both). In terms of research fields, the majority of documents were categorized under obstetrics and gynecology (n=9), health care sciences services (n=7), general internal medicine (n=5), and medical informatics (n=5). Overall, this analysis emphasizes the interdisciplinary nature of the application of WeChat in women’s mHealth, which has attracted increasing interest and experienced substantial growth in recent years.

Analyzing institutional publications and citations offers a thorough insight into the scientific ecosystem. Altogether, 40 institutions participated in this field (Table 4). Multiple top-tier Chinese universities, including Peking University [32], Fudan University [37], Zhejiang University [45], and Shandong University [57], participated actively, alongside international institutions such as the University of Oxford [29], the National University of Singapore [38], and the University of Texas System [57]. Notably, Xi’an Jiaotong University distinguished itself as a productive contributor to the research with the highest citations (n=109) [39-41].

Among the included studies, several publications emerged as highly cited and representative works in this field. Zhou et al [39] conducted the most frequently referenced study, which assessed a WeChat-based multimodal nursing program for women recovering from breast cancer surgery, and found notable enhancements in quality of life. Another work demonstrated that an 8-week WeChat-based mindfulness intervention effectively reduced anxiety and depression in pregnant women, and established a feasible model for psychological care via WeChat [45]. Wu et al [29] demonstrated that WeChat official account-based health education significantly improved exclusive breastfeeding rates. Tian et al [30] showed that WeChat group-mediated health education and lifestyle support yielded better glycemic control than standard prenatal care among women with gestational diabetes mellitus. Collectively, these highly cited studies represented key milestones and laid the methodological foundation for WeChat-based mHealth interventions in women’s health.

In 20 studies, the randomization process was judged to be at low risk of bias, whereas 7 studies presented an unclear risk of bias in this domain (Figure 3 and Table 5) [40,43,47,52-55]. Blinding participants and personnel to group assignments was not feasible in all studies except one due to the interventions’ characteristics [45]. Lack of blinding may have altered participants’ health information-seeking behaviors in the control group, potentially biasing the outcome assessment. With regard to missing outcome data, all included studies were considered to have a low risk of bias. Regarding the measurement of outcomes and selection of the reported result, all studies were rated as having a high risk of bias, primarily because blinding of outcome assessors was not performed, and the reporting of outcomes was insufficiently transparent. Overall, the risk of bias across studies was predominantly high, which should be considered when interpreting the findings.