ArticleJournal of medical Internet research2020
The Relationship Between Images Posted by New Mothers on WeChat Moments and Postpartum Depression: Cohort Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- #Fitmom: an experimental investigation of the effect of social media on body dissatisfaction and eating and physical activity intentions, attitudes, and behaviours among postpartum mothers.BMC pregnancy and childbirth · 2022Trial
- Perceptions and Attitudes Regarding Postpartum Depression on Reddit: Inductive Content Analysis.Online journal of public health informatics · 2026Article
- A review of the application of digital phenotyping in predicting peripartum depressive symptoms.NPJ digital medicine · 2026Article
- Using YouTube Comments Data to Explore Postpartum Depression in Social Media: An Infodemiology Study.International journal of environmental research and public health · 2024Article
- A longitudinal study of a mHealth app and regional policies on the uptake of postpartum depression referral after positive screening in Shenzhen.BMC pregnancy and childbirth · 2024Article
- Latent profile analysis for health-related quality of life, sleep quality, morning and evening type, and internet addiction among medical students.Scientific reports · 2023Observational
- Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review.Journal of medical Internet research · 2023Article
- Association between using social media WeChat and depressive symptoms among middle-aged and older people: findings from a national survey.BMC geriatrics · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs social media posts reflect users' emotions, WeChat Moments, the most popular social media platform in China, may offer a glimpse into postpartum depression in the population.
objectiveThis study aimed to investigate the features of the images that mothers posted on WeChat Moments after childbirth and to explore the correlation between these features and the mothers' risk of postpartum depression.
methodsWe collected the data of 419 mothers after delivery, including their demographics, factors associated with postpartum depression, and images posted on WeChat Moments. Postpartum depression was measured using the Edinburgh Postnatal Depression Scale. Descriptive analyses were performed to assess the following: content of the images, presence of people, the people's facial expressions, and whether or not memes were posted on WeChat Moments. Logistic regression analyses were used to identify the image features associated with postpartum depression.
resultsCompared with pictures of other people, we found that pictures of their children comprised the majority (3909/6887, 56.8%) of the pictures posted by the mothers on WeChat Moments. Among the posts showing facial expressions or memes, more positive than negative emotions were expressed. Women who posted selfies during the postpartum period were more likely to have postpartum depression (P=.003; odds ratio 2.27, 95% CI 1.33-3.87).
conclusionsThe vast majority of mothers posted images conveying positive emotions during the postpartum period, but these images may have masked their depression. New mothers who have posted selfies may be at a higher risk of postpartum depression.
trial registrationInternational Clinical Trials Registry Platform ChiCTR-ROC-16009255; http://www.chictr.org.cn/showproj.aspx?proj=15699.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.