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Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Sep 19, 2018
Open Peer Review Period: Sep 22, 2018 - Nov 17, 2018
Date Accepted: May 14, 2019
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Factors Influencing User Engagement of Health Information Disseminated by Chinese Provincial Centers for Disease Control and Prevention on WeChat: Observational Study

Zhang Y, Xia T, Huang L, Yin M, Sun M, Huang J, Ni Y, Ni J

Factors Influencing User Engagement of Health Information Disseminated by Chinese Provincial Centers for Disease Control and Prevention on WeChat: Observational Study

JMIR Mhealth Uhealth 2019;7(6):e12245

DOI: 10.2196/12245

PMID: 31250833

PMCID: 6620885

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Factors Influencing User Engagement of Health Information Disseminated by Chinese Provincial Centers for Disease Control and Prevention on WeChat: Observational Study

  • Yan Zhang; 
  • Tingsong Xia; 
  • Lingfeng Huang; 
  • Mingjuan Yin; 
  • Mingwei Sun; 
  • Jingxiao Huang; 
  • Yu Ni; 
  • Jindong Ni

Background:

Social media is currently becoming a new channel for information acquisition and exchange. In China, with the growing popularity of WeChat and WeChat official accounts (WOAs), health promotion agencies have an opportunity to use them for successful information distribution and diffusion online.

Objective:

We aimed to identify features of articles pushed by WOAs of Chinese provincial Centers for Disease Control and Prevention (CDC) that are associated with user engagement.

Methods:

We searched and subscribed to 28 WOAs of provincial CDCs. Data for this study consisted of WeChat articles on these WOAs between January 1, 2017 and December 31, 2017. We developed a features frame containing title type, article content, article type, communication skills, number of marketing elements, and article length for each article and coded the data quantitatively using a coding scheme that assigned numeric values to article features. We examined the descriptive characteristics of articles for every WOA and generated descriptive statistics for six article features. The amount of reading and liking was converted into the level of reading and liking by the 75% position. Two-category univariate logistic regression and multivariable logistic regression were conducted to explore associations between the features of the articles and user engagement, operationalized as reading level and liking level.

Results:

All provincial CDC WOAs provided a total of 5976 articles in 2017. Shanghai CDC articles attracted the most user engagement, and Ningxia CDC articles attracted the least. For all articles, the median reading was 551.5 and the median liking was 10. Multivariable logistic regression analysis revealed that article content, article type, communication skills, number of marketing elements, and article length were associated with reading level and liking level. However, title type was only associated with liking level.

Conclusions:

How social media can be used to best achieve health information dissemination and public health outcomes is a topic of much discussion and study in the public health community. Given the lack of related studies based on WeChat or official accounts, we conducted this study and found that article content, article type, communication skills, number of marketing elements, article length, and title type were associated with user engagement. Our study may provide public health and community leaders with insight into the diffusion of important health topics of concern.


 Citation

Please cite as:

Zhang Y, Xia T, Huang L, Yin M, Sun M, Huang J, Ni Y, Ni J

Factors Influencing User Engagement of Health Information Disseminated by Chinese Provincial Centers for Disease Control and Prevention on WeChat: Observational Study

JMIR Mhealth Uhealth 2019;7(6):e12245

DOI: 10.2196/12245

PMID: 31250833

PMCID: 6620885

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