Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Advertisement

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 27.06.19 in Vol 7, No 6 (2019): June

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/12245, first published Sep 19, 2018.

This paper is in the following e-collection/theme issue:

    Original Paper

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

    1Guangdong Medical University, Dongguan, China

    2Baoan District Shajing Health Supervision Office, Shenzhen, China

    3Beijing Jiaotong University, Beijing, China

    *these authors contributed equally

    Corresponding Author:

    Jindong Ni, PhD

    Guangdong Medical University

    No. 1 Xincheng Road, Songshan Lake

    Dongguan,

    China

    Phone: 86 769 2289 6570

    Fax:86 769 2289 6570

    Email: nijd-gw@gdmu.edu.cn


    ABSTRACT

    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.

    JMIR Mhealth Uhealth 2019;7(6):e12245

    doi:10.2196/12245

    KEYWORDS



    Introduction

    Acquisition and dissemination of health information play a significant role in promoting positive health behavior change [1]. Social media is currently becoming a new channel for information acquisition and exchange [2,3]. Nearly one-third of the world’s population uses social media for entertainment, study, work, and socializing [4]. Compared with traditional types of print and broadcast media, social media has a unique advantage in facilitating two-way communication, allowing organizations to personalize content and interact with the public [5]. With the popularity of social media in the public, the use of these tools by health education organizations will have many opportunities to influence and change health behaviors [6].

    WeChat, a free mobile app released in 2011, has become the most widely and frequently used social media platform in China [2]. WeChat has many functions, including instant messaging, free phone calls, mobile payments, etc [7]. In addition, a new functional module of WeChat called WeChat official accounts (WOAs) can be freely used by governments, companies, and organizations to provide information called pushed articles to the public; individuals can freely read these articles and communicate with others via these official accounts [3,8]. According to the latest data, the number of monthly active users of WeChat has reached 650 million and the number of WOAs exceeds 10 million. Nearly 80% of WeChat users have subscribed to the WOA [9]. Studies have found that WeChat can successfully encourage health improvement and behavior change [10]. For example, Wei et al [3] used WOAs to improve malaria health literacy. Cao et al [8] found that giving additional education and instruction via WOAs can improve therapy outcomes of patients. A weight loss intervention campaign based on a WOA was found to be effective for males [10]. With the growing popularity of WeChat and WOA in health knowledge acquisition, their roles in health education and health intervention are gradually receiving more attention [11].

    The widespread public engagement with WeChat creates a ready platform for health promotion agencies for successful information distribution and diffusion online [12]. The China Centers for Disease Control and Prevention (CDC) are professional institutions that conduct health education and health promotion work for the public. In order to broaden health communication and make it easier for users to obtain information, the China CDC opened the Chinese disease control dynamics WOA in April 2014 [13]. Most of the provincial CDCs have now opened WOAs [14]. Generating user engagement is vital for effective information diffusion and health promotion. Identifying predictors of social media engagement can guide the development of content and use of features that have high appeal for the public [15]. User engagement was defined as users reacting to (ie, reading, liking) any content [16,17]. Past research has identified strategies for successful user engagement on Facebook and Twitter, including using multimedia content, highlighting celebrity involvement, using humor or shock appeals, etc [17,18]. However, there is little evidence establishing the best ways to engage with the public using WeChat, highlighting the importance of further exploration of this area. The literature base exploring user engagement through the WOAs of CDCs is even more limited. CDCs have the potential to enable broad dissemination of health information and messaging online and promote healthy behaviors, contributing to the development of social health [5].

    In this study, we reviewed the use of WOAs by Chinese provincial CDCs. The study aim was to identify features of their articles that are associated with user engagement, which we defined as the level of reading and liking. Ultimately, we sought to formulate predictors of user engagement that would inform health education of public health organizations, so they can make use of WOAs to engage their target market and increase the effectiveness of changing health-related behaviors.


    Methods

    Data Source

    We used the mobile WeChat app to search the official accounts by the name of the province and the key words “Centers for Disease Control and Prevention” and “CDC.” We found 28 WOAs of provincial CDCs on January 15, 2018, and subscribed to them. Data for this study consisted of WeChat articles found on these WOAs between January 1, 2017, and December 31, 2017.

    Main Indicators and Article Features Frame

    For each article, we recorded the code of official accounts, push time, and amounts of article reading and liking. We developed a features frame for each article, referring to the research of Kite et al [17]. Specifically, we added article content, title type, and article length, which are features that may affect the effectiveness of the WOAs. Next, we conducted a presurvey on 100 articles. Based on the results of the survey, we have merged some of the less frequently used categories into other and made some modifications during iterative testing to make the features frame more relevant to public health communication. The final features framework with definitions is shown in Table 1. Then we invited six experts in relevant fields to evaluate the content validity of the features frame and calculated the content validity index (CVI). Experts used a 4-point Likert scale to assess the degree of consistency between the content of each item and the corresponding article features. Very unrelated was counted as 1 point, comparatively unrelated was counted as 2 points, comparatively related was counted as 3 points, and very relevant was counted as 4 points. The CVI of the item level was 0.83 to 1.00, and the CVI of the entire framework was 0.98.

    Table 1. Final features frame with definitions.
    View this table

    Coding Method

    We coded the data quantitatively using a coding scheme that assigned numeric values to article features. Title type, article content, article type, communication skills, and article length contained mutually exclusive categories, and they were coded with the corresponding number. However, multiple marketing elements are possible in a single article, so not all categories were mutually exclusive. Each marketing element was coded as 0 or 1 (0 represents not using, 1 represents using). Next we tested the interrater reliability between the two coders. After receiving training and getting familiar with the content, LFH and MJY independently coded the same subset of articles (n=300) from 28 WOAs. Any disagreement between the coders was resolved by discussion. Once interrater reliability reached 80%, LFH and MJY then individually coded half of the WOAs each.

    Statistical Analyses

    For every WOA, we examined the descriptive characteristics of articles, including the number and percentage of articles pushed in a year, median reading, and median liking per article. Next we generated descriptive statistics for each title type, article content, article type, communication skill, marketing element, and article length. We then investigated associations between the features of the articles and user engagement, operationalized as reading and liking. Since the data distribution is skewed, we categorized the amount of reading and liking by the 75% position, defining an amount of reading and liking less than the 75% position as low-level reading and liking and above the 75% position as high-level reading and liking. Given that the marketing elements were multichoice, we converted the features of marketing elements into the number of marketing elements. Next, we applied logistic regression analyses to assess associations between the features of the articles and the user engagement with the level of reading and the level of liking as the outcome variables and six features as categorical independent variables and used the P value to represent the result of the hypothesis test, which decided whether to reject the null hypothesis (the regression coefficient is equal to 0). We conducted a series of two-category univariate logistic regression analyses to perform the initial screening of variables. Variables that gave P<.10 in the univariate analyses were evaluated further using multivariable logistic regression. For the multivariable regression, P<.05 was considered the statistically significant level. Risks were expressed as adjusted odd ratios with 95% confidence intervals. EpiData 3.1 software (EpiData Association) was used to establish the database; SPSS Statistics version 15.0 (IBM Corp) was used for the statistical analysis.


    Results

    Characteristics of WeChat Official Accounts and Article Features

    Overall, 28 provincial CDC WOAs pushed a total of 5976 articles in 2017 (Table 2). Guangdong CDC pushed the most articles (565/5976, 9.45%), and Hainan CDC pushed the fewest articles (4/5976, 0.07%). Shanghai CDC articles attracted the most user engagement (median reading: 3777, median liking: 37), and Ningxia CDC articles attracted the least (median reading: 21, median liking: 0). For all articles, the median reading was 551.5 and the median liking was 10.

    As described in Table 3, article titles were usually declarative sentences (2358/5976, 39.45%). Most articles were related to healthy lifestyle (1355/5976, 22.67%), and text with pictures (3686/5976, 61.68%) was the most common article type. The most common communication skill was informative or instructive (3096/5976, 51.81%). Only 60.66% (3625/5976) of the articles contained any marketing elements, and the most articles used only one kind of marketing element (2543/5976, 42.55%). The article length was usually 1000 words or fewer (3615/5976, 60.49%).

    Table 2. Characteristics of the 28 included WeChat official accounts (N=5976).
    View this table
    Table 3. Frequencies by category of six article features (n=5976).
    View this table

    Association Between Article Features and Level of Reading and Liking

    As shown in Table 4, univariate logistic regression analysis revealed that title type, article content, article type, communication skills, number of marketing elements, and article length were significantly related to reading level (P<.001). Liking level displayed a similar pattern. All article features were evaluated further using multivariable logistic regression.

    Articles for which the title type was an exclamation or emphatic sentence were less likely to obtain a high level of liking than those whose title types were declarative sentences. With regard to article content, articles about infectious diseases, vaccination, food safety and nutrition, and healthy lifestyle were more likely to receive high-level reading and liking than those about other topics. Articles related to health education activities or national health policy conferences were less likely to obtain high-level reading and liking. Environmental and occupational health–related articles were more likely to obtain high-level reading, unlike articles describing research progress, but both showed no effect for level of liking. For article type, articles containing text, pictures, and links were 5.21 times and 5.64 times more likely to receive high-level reading and liking, respectively, than text-only articles. Those containing text and pictures, similar to those containing text, pictures, and videos, were also more likely to receive high-level reading. With regard to communication skills, when compared with informative or instructive articles, those using traits of humor, questioning, fear appeal, and other were more likely to receive high-level reading and liking. Articles with positive emotional appeal were more likely to obtain a high-level liking, but this had no effect observed for level of reading. Articles using three or more kinds of marketing elements were more likely to obtain high-level reading and liking than those using none, while articles using one and two kinds were both less likely to obtain high levels of reading and liking. Compared with articles containing 1000 words or fewer, those containing 1000 to 1500 words and 1500 to 2000 words were more likely to obtain high-level reading and liking.

    Table 4. Univariate and multivariable logistic regression analysis.
    View this table

    Discussion

    Principal Findings

    To our knowledge, this is the first study to investigate the current status of WOAs of CDCs for health information dissemination. Our analysis identified several features of articles that were associated with better information dissemination, which we defined as high-level user engagement. In the field of public health research and practice, WeChat represents a convenient and accessible tool for health education in China, which historically has been difficult using traditional methods [2,19]. Health promotion organizations should be aware of strategies to engage their target audience. The results presented in this paper may provide these organizations with some guidance on how to improve engagement with WeChat users.

    According to our results, the content of articles was associated with user engagement. Indeed, article content was identified as an essential factor in determining whether WeChat users forward or share articles with friends [20]. Research on other social media platforms has also revealed that the content of posts seems to have a significant effect on user engagement [16,21]. Our findings further showed that what the public liked to read and praise were articles about infectious diseases; vaccination; food safety and nutrition; and healthy lifestyles, which reflects that they seem to be interested in popular science articles about daily health knowledge. In contrast, the public was relatively less interested in articles related to research progress and health education activities of institutions or national health policy, which may seem to be far away from daily life.

    As the results showed, article type was an important covariate of user engagement. This is consistent with previous research. Research found that article type is an important indicator that alters the effectiveness of article dissemination on WOAs [22]. Among studies based on Facebook, the effect of the post type is also displayed. One found higher engagement to be associated with pictures, videos, and links [16]. Another showed that video posts were more attractive post types than picture posts [17]. Our study seems to give another possibility related to the combination of article types. A combination of text, pictures, and links was the most engaging article type, 5 times more popular than text only. Although the combination of text, pictures, and videos and the combination of text and pictures were also more likely to get a high-level reading and liking, the effect was far lower. This seems to indicate that links play a more important role in the combination of article types for increasing the user engagement. The effect of the links can be easily found in the advertising field. Ads may work better if the users can directly access the relevant pages through the links in the social media website pages instead of being forced to visit the external website [23]. And in the field of health promotion using networking platforms, relevant links are also identified as a key strategy for successful user engagement [24]. However, only 26.4% of all articles we coded included the combination of text, pictures, and links and the combination of text and pictures accounted for 61.7%, which suggested that public health organizations were trailing behind marketers in advertising field. The administrators of the CDC official accounts should probably add links to the traditional combination of text and pictures.

    The communication skills used were also related to user engagement. Compared with the use of a peaceful tone to convey health knowledge, articles using humor were more likely to receive high levels of reading and liking. This result is similar to the findings of previous research: Klassen et al [25] found that using an optimistic tone was associated with more interactions on Facebook, and posts from health promotion organizations that were more serious in tone had minimal engagement from fans. This may reflect the effect of emotions [26]. Some researchers think that emotion is an important motivator of social sharing, and when participants experience positive emotions viewing a post on social media, they are more likely to engage with that post than are those who do not experience positive emotions [27,28]. In addition, articles using questioning skills were also nearly two times more likely to achieve high-level reading and liking than those using informative or instructive skills. Some surveys have found that many respondents believe that internet health information is not reliable [2,29]. Indeed, there are potential dangers associated with using social media for health communication, such as sharing of misleading or inaccurate information [5]. As professional health organizations, CDCs use questioning expressions to correct this information, which may attract readers’ attention. According to our results, articles with fear appeal were likely to show similar but slightly lower results than those using the above two skills. In the past, fear has been employed by many health promotion organizations to induce behavior change [25]; however, Soames Job [30] and Keller [31] think that fear is only likely to work under particular circumstances. Therefore, the effectiveness of fear appeal may require further research.

    The number of marketing elements was associated with the level of user engagement. Articles that used one or two marketing elements were less likely to obtain high-level reading and liking than those that used none. However, articles using three or more kinds of element obtained the opposite result. This showed that some marketing elements were associated with lower levels of engagement and some with higher engagement. Previous research has discussed the impact of some marketing elements on user engagement in other social media. The effect of celebrities is usually positive. Chapman [32] believed that celebrity involvement in public health campaigns delivers long-term benefit. Similarly, Kite [17] found that the use of celebrities and athletes led to higher average engagement. However, the effect of authoritative people is still controversial. One study found that, compared with Facebook posts that have no marketing elements at all, using authoritative people reduced likes, shares, and comments [17]. But Preece et al [21] thought that using charismatic leaders or respected authorities increased the chances of user engagement. Therefore, which marketing methods can play positive roles in increasing the user engagement on articles of WOAs remains to be further discussed.

    The results of this study showed that article length was also a covariate of user engagement: articles containing 1000 to 1500 words and 1500 to 2000 words were more likely to obtain high-level reading and liking. A study of WeChat articles considered that text above 1500 words can be considered as a long text [33]. Our study showed that users seem to have a preference for articles of moderate length.

    In our study, title type was negatively associated with user engagement. When the title was an exclamation or an emphatic sentence or phrase, the articles were less likely to receive high-level liking compared with titles involving declarative sentences. Zhang [34] analyzed the influence of WeChat article titles on people’s willingness to open the full text and found that the content of the title could affect the dissemination of the article. However, we do not have sufficient evidence to demonstrate what type of title was more popular with readers. Therefore, the link between title type and user engagement seems to present an interesting direction for future research.

    Limitations

    Our findings should be interpreted with consideration of some study limitations. First, the study involved a short time frame of data collection. Evaluating a longer period may identify time differences and improve strategies. Second, this study used reading and liking as indicators of user engagement, and we further transformed them into two-category variables: high-level and low-level. We may thus be obscuring the effects of the original variables. Future analyses should expand our findings by quantitatively evaluating the indicators associated with engagement. Last, other factors that we have not focused on in this study may also shape user engagement such as individual-level factors. These individual factors and article features should be analyzed comprehensively in future research.

    Conclusions

    Social media involves technologies that facilitate opportunities for engaging with the audience [35]. How social media can best be used to 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 the article content, article type, communication skills, the number of marketing elements, and article length were associated with reading level and liking level. However, title type was only associated with liking level. Our results may provide public health and community leaders with insight into the diffusion of important health topics of concern. Because this study was focused primarily on user engagement and article features, future studies might improve our understanding of other factors that contribute to the dissemination of specific key themes. For example, the agency must identify what audience they are trying to reach, how that audience uses WeChat, and what goals and objectives are most appropriate.

    Acknowledgments

    We thank International Science Editing (www.internationalscienceediting.com) for editing this manuscript. Funding was provided by grants from the social science and technology development project of Dongguan, Guangdong, China (grant number 201650715000528), Guangdong Higher Education Teaching Reform Project (grant number 4G17154), and Guangdong Medical University Teaching Reform Research Project (grant number 4SG17143).

    Conflicts of Interest

    None declared.

    References

    1. Adam M, McMahon SA, Prober C, Bärnighausen T. Human-centered design of video-based health education: an iterative, collaborative, community-based approach. J Med Internet Res 2019 Jan 30;21(1):e12128-e12163 [FREE Full text] [CrossRef] [Medline]
    2. Zhang X, Wen D, Liang J, Lei J. How the public uses social media wechat to obtain health information in china: a survey study. BMC Med Inform Decis Mak 2017 Jul 05;17(Suppl 2):66 [FREE Full text] [CrossRef] [Medline]
    3. Li W, Han LQ, Guo YJ, Sun J. Using WeChat official accounts to improve malaria health literacy among Chinese expatriates in Niger: an intervention study. Malar J 2016 Nov 24;15(1):567 [FREE Full text] [CrossRef] [Medline]
    4. Mejova Y, Weber I, Fernandez-Luque L. Online health monitoring using Facebook advertisement audience estimates in the United States: evaluation study. JMIR Public Health Surveill 2018 Mar 28;4(1):e30 [FREE Full text] [CrossRef] [Medline]
    5. Alber JM, Bernhardt JM, Stellefson M, Weiler RM, Anderson-Lewis C, Miller MD, et al. Designing and testing an inventory for measuring social media competency of certified health education specialists. J Med Internet Res 2015 Sep 23;17(9):e221 [FREE Full text] [CrossRef] [Medline]
    6. Korda H, Itani Z. Harnessing social media for health promotion and behavior change. Health Promot Pract 2013 Jan;14(1):15-23. [CrossRef] [Medline]
    7. Man L. Characteristics and skills of text editing under WeChat public platform. Editorial Transactions (Chinese) 2017:116-120.
    8. Cao Y, Lin S, Zhu D, Xu F, Chen Z, Shen H, et al. WeChat public account use improves clinical control of cough-variant asthma: a randomized controlled trial. Med Sci Monit 2018 Mar 14;24:1524-1532 [FREE Full text] [CrossRef] [Medline]
    9. Liang X, Yang Y. An experimental study of Chinese tourists using a company-hosted WeChat official account. Electronic Commerce Research and Applications 2018 Jan;27:83-89. [CrossRef]
    10. He C, Wu S, Zhao Y, Li Z, Zhang Y, Le J, et al. Social media-promoted weight loss among an occupational population: cohort study using a wechat mobile phone app-based campaign. J Med Internet Res 2017 Dec 23;19(10):e357 [FREE Full text] [CrossRef] [Medline]
    11. Hudnut-Beumler J, Po'e E, Barkin S. The use of social media for health promotion in hispanic populations: a scoping systematic review. JMIR Public Health Surveill 2016 Jul 11;2(2):e32 [FREE Full text] [CrossRef] [Medline]
    12. Lim MS, Hare JD, Carrotte ER, Dietze PM. An investigation of strategies used in alcohol brand marketing and alcohol-related health promotion on Facebook. Digit Health 2016 Apr 29;2:2055207616647305 [FREE Full text] [CrossRef] [Medline]
    13. Huan Z, Ming W, Wei W. Practice analysis of disease control organizations using WeChat for health communication. China Pub Health Manag (Chinese) 2017:605-607. [CrossRef]
    14. Mengyuan Z. Opportunities and challenges brought by WeChat public account for health science popularization. New Media Res (Chinese) 2017;3(5):51-52. [CrossRef]
    15. Andrade EL, Evans WD, Barrett N, Edberg MC, Cleary SD. Strategies to increase latino immigrant youth engagement in health promotion using social media: mixed-methods study. JMIR Public Health Surveill 2018 Dec 19;4(4):e71 [FREE Full text] [CrossRef] [Medline]
    16. Card KG, Lachowsky N, Hawkins BW, Jollimore J, Baharuddin F, Hogg RS. Predictors of Facebook user engagement with health-related content for gay, bisexual, and other men who have sex with men: content analysis. JMIR Public Health Surveill 2018 Apr 06;4(2):e38 [FREE Full text] [CrossRef] [Medline]
    17. Kite J, Foley BC, Grunseit AC, Freeman B. Please like me: Facebook and public health communication. PLoS One 2016 Sep;11(9):e0162765 [FREE Full text] [CrossRef] [Medline]
    18. Veale HJ, Sacks-Davis R, Weaver ER, Pedrana AE, Stoové MA, Hellard ME. The use of social networking platforms for sexual health promotion: identifying key strategies for successful user engagement. BMC Public Health 2015 Feb 06;15(1):85 [FREE Full text] [CrossRef] [Medline]
    19. Capurro D, Cole K, Echavarría MI, Joe J, Neogi T, Turner AM. The use of social networking sites for public health practice and research: a systematic review. J Med Internet Res 2014 Mar;16(3):e79 [FREE Full text] [CrossRef] [Medline]
    20. Ji H, Cai D. Analysis of influencing factors of WeChat public information information transmission. J Jimei University (Chinese) 2016;19(4):107-115. [CrossRef]
    21. Preece J, Shneiderman B. The reader-to-leader framework: motivating technology-mediated social participation. AIS Trans HCI 2009 Mar 31;1(1):13-32. [CrossRef]
    22. Zheng B, Ding Y. Analysis of the factors affecting the spreading effect of the WeChat public account Technology Daily. AV (Chinese) 2018:112-115. [CrossRef]
    23. Freeman B, Potente S, Rock V, McIver J. Social media campaigns that make a difference: what can public health learn from the corporate sector and other social change marketers? Pub Health Res Pract 2015 Mar 30;25(2):e2521517 [FREE Full text] [CrossRef] [Medline]
    24. Veale HJ, Sacks-Davis R, Weaver ER, Pedrana AE, Stoové MA, Hellard ME. The use of social networking platforms for sexual health promotion: identifying key strategies for successful user engagement. BMC Public Health 2015 Feb 06;15(1):85 [FREE Full text] [CrossRef] [Medline]
    25. Klassen KM, Borleis ES, Brennan L, Reid M, McCaffrey TA, Lim MS. What people “like”: analysis of social media strategies used by food industry brands, lifestyle brands, and health promotion organizations on facebook and instagram. J Med Internet Res 2018 Jun 14;20(6):e10227 [FREE Full text] [CrossRef] [Medline]
    26. Dunlop SM, Perez D, Cotter T. The natural history of antismoking advertising recall: the influence of broadcasting parameters, emotional intensity and executional features. Tob Control 2014 May;23(3):215-222. [CrossRef] [Medline]
    27. So J, Prestin A, Lee L, Wang Y, Yen J, Chou WS. What do people like to “share” about obesity? A content analysis of frequent retweets about obesity on twitter. Health Commun 2016;31(2):193-206. [CrossRef] [Medline]
    28. Smith S. Conceptualising and evaluating experiences with brands on Facebook. Int J Market Res 2013 May;55(3):357-374. [CrossRef]
    29. Hou S, Gan Y, Bai Q. Present situation of using health information technologies in residents of Beijing. Chinese J Med Libr Inform Sci (Chinese) 2016;25(4):41-44. [CrossRef]
    30. Soames Job RF. Effective and ineffective use of fear in health promotion campaigns. Am J Public Health 1988 Feb;78(2):163-167. [CrossRef] [Medline]
    31. Keller PA, Block LG. Increasing the persuasiveness of fear appeals: the effect of arousal and elaboration. J Consum Res 1996 Mar;22(4):448. [CrossRef]
    32. Chapman S. Does celebrity involvement in public health campaigns deliver long term benefit? Yes. BMJ 2012 Sep 25;345(sep25 2):e6364-e6364. [CrossRef] [Medline]
    33. Zhang Y, Li W. Which WeChat articles are easier to be forwarded—an exploratory research based on content analysis. AV (Chinese) 2016:117-118. [CrossRef]
    34. Lu Z. Analysis of the influence of WeChat Tweet title on people's willingness to open. Southeast Propagation (Chinese) 2017:64-67. [CrossRef]
    35. Thackeray R, Neiger BL, Smith AK. Adoption and use of social media among public health departments. BMC Pub Health 2012;12:242 [FREE Full text] [CrossRef] [Medline]


    Abbreviations

    CDCs: Centers for Disease Control and Prevention
    CVI: content validity index
    WOA: WeChat official account


    Edited by G Eysenbach; submitted 19.09.18; peer-reviewed by A Jadhav, J Lei, C Allen; comments to author 07.01.19; revised version received 28.02.19; accepted 14.05.19; published 27.06.19

    ©Yan Zhang, Tingsong Xia, Lingfeng Huang, Mingjuan Yin, Mingwei Sun, Jingxiao Huang, Yu Ni, Jindong Ni. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 27.06.2019.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.