Obesity Prevention and Reduction in China Using the Social Media Platform WeChat: Scoping Review
Bibliographic record
Abstract
Background: Digital interventions for obesity have demonstrated efficacy in obesity prevention and management. The emergence of smartphones and ubiquitous apps such as WeChat represents potential modality to enhance the reach, sustainability, and cost-effectiveness of such interventions. By the end of the first quarter of 2024, WeChat had approximately 1.36 billion monthly active users, accounting for 96.5% of China's population. The use of this platform for obesity interventions has been validated in multiple Chinese trials, most published in Chinese language journals. Objective: We aim to synthesize the existing evidence on obesity interventions delivered through WeChat to generate implications for future intervention design and development, thereby reaching an international audience. Methods: We conducted a scoping review of PubMed and China National Knowledge Infrastructure using search terms including "WeChat," "obesity," "weight," "BMI," "waist circumference," "hip circumference," "waist-to-hip ratio," "body fat," "skin fold thickness," and these Chinese equivalents "weixin," "feipang," "tizhong," "tizhongzhishu," "yaowei," "tunwei," "yaotunbi," "tizhi," and "pizhehoudu." We included only original research studies, theses, or dissertations with measurable outcomes that used WeChat functions as intervention strategies. Study quality was assessed using the National Institutes of Health Quality Assessment Tool, with specific tools selected based on study design. Descriptive statistics were applied, with categorical variables summarized as frequencies and percentages (n, %) to report study distribution. Results: Our scoping review based on PubMed and China National Knowledge Infrastructure identified 665 initial records, among which 43 studies met eligibility criteria and were included for data extraction to characterize intervention details. Results indicated effectiveness in 86.0% (37/43) of studies, with WeChat-assisted obesity interventions achieving significant short- and long-term weight loss measured by objective outcomes (body weight, BMI, waist circumference, hip circumference, waist-to-hip ratio, and body fat percentage). However, formative research informing intervention design was insufficient. Common methodological limitations included lack of randomization and blinding (42/43, 97.7%) and unreported intervention compliance metrics (39/43, 92.0%). Functionally, interventions primarily used "WeChat group" and "Official Account"-public accounts that provide health education, diet or physical activity logging, and other features. Conclusions: Overall, WeChat represents a promising platform for obesity interventions; however, current apps fail to leverage its full features (eg, online payment and live streaming). Key limitations include methodological heterogeneity and cultural specificity, which were addressed through narrative synthesis stratified by study types. Future research should incorporate the formative phase and use more rigorous methodologies such as randomized controlled trials to optimize intervention design and delivery via this modality.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".