Background: As 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.
Objective: This 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.
Methods: We 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.
Results: Compared 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).
Conclusions: The 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 Registration: International Clinical Trials Registry Platform ChiCTR-ROC-16009255; http://www.chictr.org.cn/showproj.aspx?proj=15699.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7735903 | PMC |
http://dx.doi.org/10.2196/23575 | DOI Listing |
Front Psychol
November 2024
School of Marxism, Guizhou Medical University, Guiyang, China.
Background: The study aims to investigate the wellbeing of mid-achieving university students on campus and the factors affecting it. Given that this group represents a large yet often overlooked portion of higher education, the study endeavors to analyze the specific factors affecting their wellbeing to provide insights to foster a more comprehensive and inclusive educational environment.
Methodology: The study adopted a qualitative research method.
Front Psychol
October 2024
School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu, China.
Many social networking services (SNSs) have features that highlight the common friends of pairs of users. Previous research has examined recommendation systems that use mutual friend metrics, but few scholars have studied how the existence of features related to mutual friends affects users in SNSs. To explore this issue further, we conducted interviews with 22 users of WeChat Moments to investigate how certain rules involving mutual friends affect users and how they deal with the issues that arise due to these rules.
View Article and Find Full Text PDFFront Public Health
September 2024
College of Public Administration, China Resources & Environment and Development Academy (REDA), Nanjing Agricultural University, Nanjing, China.
Background: The process of population aging in China is currently undergoing rapid acceleration. Simultaneously, the swift advancement of digitalization is fundamentally transforming individuals' lifestyles. The usage of the internet and mobile internet tools by the older adults population is relatively inadequate.
View Article and Find Full Text PDFPsychol Res Behav Manag
July 2024
Institute for Advanced Studies in Humanities and Social Science, Chongqing University, Chongqing, People's Republic of China.
Purpose: With the development of information technology and various social media, recommendation algorithms have increasingly more influence on users' social media usage. To date, there has been limited research focused on analyzing the impact of recommendation algorithms on social media use and their corresponding role in the development of problematic behaviors. The present study analyzes the impact of recommendation algorithms on college students' information sharing and internalizing, externalizing problem behaviors to address the aforementioned shortcomings.
View Article and Find Full Text PDFBMC Public Health
July 2024
NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!