Author
Listed:
- Junling Gao
- Pinpin Zheng
- Yingnan Jia
- Hao Chen
- Yimeng Mao
- Suhong Chen
- Yi Wang
- Hua Fu
- Junming Dai
Abstract
Huge citizens expose to social media during a novel coronavirus disease (COVID-19) outbroke in Wuhan, China. We assess the prevalence of mental health problems and examine their association with social media exposure. A cross-sectional study among Chinese citizens aged≥18 years old was conducted during Jan 31 to Feb 2, 2020. Online survey was used to do rapid assessment. Total of 4872 participants from 31 provinces and autonomous regions were involved in the current study. Besides demographics and social media exposure (SME), depression was assessed by The Chinese version of WHO-Five Well-Being Index (WHO-5) and anxiety was assessed by Chinese version of generalized anxiety disorder scale (GAD-7). multivariable logistic regressions were used to identify associations between social media exposure with mental health problems after controlling for covariates. The prevalence of depression, anxiety and combination of depression and anxiety (CDA) was 48.3% (95%CI: 46.9%-49.7%), 22.6% (95%CI: 21.4%-23.8%) and 19.4% (95%CI: 18.3%-20.6%) during COVID-19 outbroke in Wuhan, China. More than 80% (95%CI:80.9%-83.1%) of participants reported frequently exposed to social media. After controlling for covariates, frequently SME was positively associated with high odds of anxiety (OR = 1.72, 95%CI: 1.31–2.26) and CDA (OR = 1.91, 95%CI: 1.52–2.41) compared with less SME. Our findings show there are high prevalence of mental health problems, which positively associated with frequently SME during the COVID-19 outbreak. These findings implicated the government need pay more attention to mental health problems, especially depression and anxiety among general population and combating with “infodemic” while combating during public health emergency.
Suggested Citation
Junling Gao & Pinpin Zheng & Yingnan Jia & Hao Chen & Yimeng Mao & Suhong Chen & Yi Wang & Hua Fu & Junming Dai, 2020.
"Mental health problems and social media exposure during COVID-19 outbreak,"
PLOS ONE, Public Library of Science, vol. 15(4), pages 1-10, April.
Handle:
RePEc:plo:pone00:0231924
DOI: 10.1371/journal.pone.0231924
Download full text from publisher
Citations
Citations are extracted by the
CitEc Project, subscribe to its
RSS feed for this item.
Cited by:
- Rohitash Chandra & Ayush Jain & Divyanshu Singh Chauhan, 2022.
"Deep learning via LSTM models for COVID-19 infection forecasting in India,"
PLOS ONE, Public Library of Science, vol. 17(1), pages 1-28, January.
- Xie, Tingting & Yuan, Ye & Zhang, Hui, 2023.
"Information, awareness, and mental health: Evidence from air pollution disclosure in China,"
Journal of Environmental Economics and Management, Elsevier, vol. 120(C).
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0231924. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.