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eHealth Literacy / Digital Literacy (351) Demographics of Users, Social & Digital Divide (677) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1552)

Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19134, first published .
Associations of Health Literacy, Social Media Use, and Self-Efficacy With Health Information–Seeking Intentions Among Social Media Users in China: Cross-sectional Survey

Associations of Health Literacy, Social Media Use, and Self-Efficacy With Health Information–Seeking Intentions Among Social Media Users in China: Cross-sectional Survey

Associations of Health Literacy, Social Media Use, and Self-Efficacy With Health Information–Seeking Intentions Among Social Media Users in China: Cross-sectional Survey

Authors of this article:

Zhaomeng Niu1 Author Orcid Image ;   Jessica Willoughby2 Author Orcid Image ;   Rongting Zhou3 Author Orcid Image
Article Authors Cited by (25) Tweetations (4) Metrics

    Original Paper

    1Section of Behavioral Sciences, Division of Medical Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States

    2The Edward R Murrow College of Communication, Washington State University, Pullman, WA, United States

    3School of Humanities and Social Sciences, University of Science and Technology of China, Hefei, China

    Corresponding Author:

    Rongting Zhou, PhD

    School of Humanities and Social Sciences

    University of Science and Technology of China

    Jinzhai Road 96

    Hefei, 230026

    China

    Phone: 86 63600495

    Email: rongting@ustc.edu.cn


    Background: Empirical research has demonstrated that people frequently use social media for gathering and sharing online health information. Health literacy, social media use, and self-efficacy are important factors that may influence people’s health behaviors online.

    Objective: We aimed to examine the associations between health literacy, health-related social media use, self-efficacy, and health behavioral intentions online.

    Methods: We conducted a cross-sectional survey of adults 18 years and older (n=449) to examine predictors of health-related behavioral intentions online including health literacy, social media use, and self-efficacy in China using 2 moderated mediation models. Mediation and moderation analyses were conducted.

    Results: Self-efficacy mediated the effects of health literacy (Bindirect=0.213, 95% CI 0.101 to 0.339) and social media use (Bindirect=0.023, 95% CI 0.008 to 0.045) on health behavioral intentions on social media. Age moderated the effects of health literacy on self-efficacy (P=.03), while previous experience moderated the effects of social media use on self-efficacy (P<.001).

    Conclusions: Health literacy and health-related social media use influenced health behavioral intentions on social media via their prior effects on self-efficacy. The association between health literacy and self-efficacy was stronger among younger respondents, whereas the association between health-related social media use and self-efficacy was stronger among those who previously had positive experiences with health information on social media. Health practitioners should target self-efficacy among older populations and increase positive media experience related to health.

    J Med Internet Res 2021;23(2):e19134

    doi:10.2196/19134

    Keywords

    behavioral intention; health literacy; self-efficacy; social media 


    The remarkably fast growth of the internet has made it a major source for information sharing and acquisition. In the late 1990s, the internet became a main source for health information [1]. Advantages of using the internet for health information include (1) it is the most convenient and comprehensive source; (2) the information seekers remain anonymous; and (3) it helps reduce inequalities and eliminate barriers (eg, distance) [2]. A study [3] found that, in China, 76.3% of computer-based users and 68.8% of mobile-based (eg, smartphone) users sought health information on the internet [3]. Furthermore, the advent of social media has enabled more possibilities such as connecting people with similar health concerns or social support groups of patients [2,4].

    With the rapid growth of social networking sites in China, the number of users and the variety of information on such sites have increased tremendously. As of 2018, there were 317 million active users of Weibo and 1 billion users of WeChat, which are the 2 main social media sites in China [5]. Weibo, a platform for microblogging, is often seen as the Chinese version of Twitter. WeChat is an instant messaging app that is similar to WhatsApp or Facebook Messenger, but it has more technological functions, such as free video or voice calls, group chat, public information sharing, mobile payments, and the ability to post pictures or videos [6].

    Generally, extensive use of social media has been associated with informational or emotional need, professional development, social status, self-expression, and social interaction [7]. With an increasing awareness of health among the general public, a growing number of people in China are using social media for seeking and sharing health information [2,6]. Social media provide health information through multimedia affordances instead of solely text, which can increase the understanding of health information among populations with low health literacy [8]. Health literacy entails people's knowledge, motivation, and competency to access, understand, appraise, and apply health information in order to make judgments and decisions in everyday life concerning health care, disease prevention, and health promotion to maintain or improve their quality of life [9].

    Additionally, a variety of health information and knowledge that used to be exclusive to health care providers are now available to health information seekers on social media [10]. Moreover, the user-generated nature of social media enables the sharing of health information and experiences, which provide views on health care from a patient’s perspective and increase patient empowerment [11]. Approximately, 40% of the individuals who sought health information on social media also shared their personal health experiences [12]. Furthermore, previous studies [13,14] have shown that use of social media has positive effects on health behaviors.

    The health impact of social media in China has not been sufficiently studied and understood. For instance, research has focused on an examination of prominent health topics on social media [15] and how the general public views the impact of social media on health information acquisition [6]. Some studies have investigated the benefits and barriers [2], constructs of the theory of planned behaviors [16], cultural determinants, and doctor-patient communication [17] of health information intentions in China. Many studies in China have focused on health literacy among older adults or regionally [18-21]. However, no previous studies have comprehensively examined the potential relationships between health literacy, social media use, self-efficacy, and health behavioral intentions on social media in China based on the health literacy skills framework [22] and the social-cognitive perspective [23]. It is important to assess these constructs because studies have shown that health literacy levels in mainland China (the People's Republic of China excluding the special administrative regions of Hong Kong and Macau) remain low, which warrants further research regarding health literacy and related risk factors [24].

    According to the health literacy skills framework, demographic factors, such as age, would moderate the development of health literacy while potential mediators, such as patient-provider communication [25] and knowledge [26], would mediate the effects of health literacy on health-related outcomes [22]. A number of studies [27-30] have found support for the mediating role of self-efficacy on health-related behavioral intentions [27,28]. Self-efficacy is the capacity to have positive effects on an individual’s health [29]. Lee et al [30] found that self-efficacy mediated the effects of health literacy on health behaviors.

    It has also been documented that social media use plays a positive role in health behaviors [14]. Previous studies [16,31] in China also found the association between social media use and health behavioral intentions. In addition, previous research has identified the association between media use and self-efficacy [32]. Social-cognitive theory posits that one’s involvement with a subject grows over time through positive personal experiences, as one increases self-efficacy [33]. One study [34] found previous online health information seeking experience moderated seeking more of such information online. Therefore, health-related social media use and previous experience with using social media for health purposes could potentially influence self-efficacy and, consequently, impact health behavioral intentions.

    The goal of this study was to examine the role of health literacy, social media use, and self-efficacy on health behavioral intentions on social media in China. Based on the literature, we proposed the following hypotheses (a conceptual model of the study is shown in Figure 1):

    ‎
    Figure 1. Conceptual framework.
    View this figure
    • Hypothesis 1: Self-efficacy will mediate the effects of health literacy on health behavioral intentions on social media.
    • Hypothesis 2: Self-efficacy will mediate the effects of health-related social media use on health behavioral intentions on social media.
    • Hypothesis 3: Age will moderate the effects of health literacy on self-efficacy.
    • Hypothesis 4: Previous experience with using social media for health will moderate the effects of health-related social media use on self-efficacy.

    Recruitment

    We disseminated an online survey on Sina Weibo (Sina Corporation) in China using a paid advertisement service. Participants were required to be social media users and be at least 18 years of age. The first page of the survey was an online consent form including study information. After reading the consent form, indicating that they agreed to participate and were 18 years or older, respondents were allowed to proceed. After completing the survey, respondents were entered in a raffle to win CNY ¥300 (approximately US $42.86). The study was reviewed by the university institutional review board and was approved as an exempt study.

    Measures

    Health-related social media use was measured by one question asking how frequently they have used social media for health information before on a scale ranging from 1 (never) to 7 (multiple times a day) (mean 4.01, SD 1.86). An English instructor at a Chinese university translated the questionnaire and used back-translation to ensure consistency in wording between English and Chinese versions of the survey.

    Health literacy was measured by a scale adapted from Chinn and McCarthy [35] for health literacy measurement. This scale included seven 3-point items (rarely, sometimes, often), such as “When you talk to a doctor or nurse, do you give them all the information they need to help you?” and “Are you someone who likes to find out lots of different information about your health?” (mean 2.14, SD 0.40; Cronbach α=.71).

    Self-efficacy was captured by the self-efficacy scale from Lee et al [29]. Five 7-point Likert-type items measured the degree of agreement with statements regarding self-efficacy in managing one’s health, such as “I have been able to meet the goals I set for myself to improve my health” and “I am confident I can have a positive effect on my health” (mean 5.04, SD 1.04; Cronbach α=.85).

    Previous experience was measured by asking the respondents whether they found the health information on social media useful on three 7-point Likert scale items such as “In the past three months, health advice offered on social media sites has been useful to me” (mean 4.01, SD 1.58; Cronbach α=.91).

    Behavioral intention was measured from an adapted multidimensional scale [36]. Three 7-point Likert-type items asked the extent to which respondents agree with the statements about their behavioral intention regarding health information on social media including “I will act upon the advice that is offered in the message in the near future,” “I will forward the message to my online acquaintances,” and “I will recommend the advice I read in the message to another person” (mean 3.84, SD 1.42; Cronbach α=.90).

    We also measured demographic variables including age, sex, education, and family yearly income. Sex, education, and family yearly income were measured with 3 categorical questions while age was measured by asking participants to indicate their age in numbers (Multimedia Appendix 1).

    Statistical Analysis

    To test the hypothesized associations, we used estimated direct and indirect effects in mediation and moderation models using SPSS statistical software (version 25.0, IBM Corp; PROCESS macro [37]). Mediation models (PROCESS model 4) were used for hypothesis 1 and hypothesis 2, whereas PROCESS model 7 was used to test the moderated mediation effect. Age, sex, education, and family yearly income were controlled as covariates for hypotheses 1, 2, and 4, whereas sex, education, and family yearly income were controlled as covariates for hypothesis 3.


    User Statistics

    A total of 608 respondents began the questionnaire; however, 127 were removed due to declining to participate or incomplete participation (defined as more than 50% of the survey not completed), and 32 were excluded due to missing data. We had a final sample size of 449 (women: n=345; men: n=104; age: mean 25.23 years, SD 5.23, range 18-66). There were 242 participants aged from 23 to 30 years old (242/449, 53.9%). The education level of the sample was high, with 52.6% of the respondents (236/449) reporting having a bachelor’s degree, and 53.7% of the respondents (241/449) reported their family annual income was between ¥50,000 (approximately US $7150) to ¥200,000 (approximately US $28,600) (Table 1).

    Table 1. Descriptive statistics.
    VariableValue
    Behavioral intentions, mean (SD)3.84 (1.42)
    Health literacy, mean (SD)2.16 (.42)
    Self-efficacy, mean (SD)5.04 (1.04)
    Social media use, mean (SD)3.80 (1.48)
    Previous experience, mean (SD)3.99 (1.60)
    Gender, n (%)

    		Female345 (76.8)

    		Male104 (23.2)
    Age (years), mean (SD)25.23 (5.23)
    Education, n (%)

    		High school degree or lower16 (3.6)

    		College degree or some college236 (52.6)

    		Graduate degree or higher197 (43.8)
    Family yearly income (CNYa), n (%)

    		0-50,000124 (27.6)

    		50,001-100,000112 (25.0)

    		100,001-200,000129 (28.7)

    		>200,00184 (18.7)

    aAn approximate exchange rate of 1 CNY to US $0.143 is applicable.

    Evaluation Outcomes

    According to the results of model 4, self-efficacy mediated the effects of health literacy on health behavioral intentions on social media (Bindirect=0.213, SE 0.060, 95% CI 0.101 to 0.339). Thus, hypothesis 1 was supported. The participants who had higher health literacy also had higher self-efficacy and then would be more likely to intend to perform health behaviors based on information acquired on social media (eg, use the health advice they found). Self-efficacy mediated the effects of health-related social media use on health behavioral intentions on social media (Bindirect=0.023, SE 0.009, 95% CI 0.008 to 0.045), indicating hypothesis 2 was supported—the more the participants used social media for health, the higher their self-efficacy, and they would be more likely to have greater intentions to perform health behaviors on social media.

    Moderated Mediation

    The results of moderated mediation models are shown in Table 2 and Table 3. According to the results of model 7, age moderated the effects of health literacy on self-efficacy (B=–0.041, SE 0.019, P=.03). The interaction had a negative effect on self-efficacy. Thus, hypothesis 3 was supported. The moderated mediation model accounted for 10.7% variance in health behavioral intention. Health literacy had a direct effect (B=0.345, SE 0.141, P=.02) as well as an indirect effect (Table 4) on health behavioral intention.

    Previous experience with using social media for health moderated the effects of health-related social media use on self-efficacy (B=0.058, SE 0.015, P<.001). The interaction had a positive effect on self-efficacy. Therefore, hypothesis 4 was supported. This moderated mediation model accounted for 31.3% variance in health behavioral intention. Health-related social media use had a direct effect (B=0.315, SE 0.0309, P<.001) as well as an indirect effect (Table 5) on health behavioral intention.

    Table 2. Regression results for effects of self-efficacy, age, and health literacy on health behavioral intention (R2=0.107, P<.001).
    VariableBa (SE)P value95% CI
    Sex0.156 (0.152).30–0.142 to 0.454
    Education0.214 (0.086).010.046 to 0.382
    Family yearly income0.141 (0.043).0010.058 to 0.225
    Health literacy0.345 (0.141).010.068 to 0.621
    Health literacy × age on self-efficacy–0.041 (0.019).03–0.077 to–0.004
    Self-efficacy0.318 (0.069)<.0010.183 to 0.453
    Direct effect of health literacy on health behavioral intention0.345 (0.141).010.068 to 0.621
    Index of moderated mediation: age–0.013 (0.006)N/Ab–0.025 to –0.001

    aUnstandardized final model coefficients.

    bN/A: not applicable.

    Table 3. Regression results for effects of self-efficacy, previous experience, and health-related social media use on health behavioral intention (R2=0.313, P<.001)
    VariableBa (SE)P value95% CI
    Age0.052 (0.011)<.0010.029 to 0.074
    Sex0.138 (0.136).31–0.128 to 0.405
    Education0.259 (0.078).0010.106 to 0.412
    Family yearly income0.145 (0.037)<.0010.071 to 0.218
    Health-related social media use0.315 (0.031)<.0010.254 to 0.376
    Health-related social media use × previous experience on self-efficacy0.058 (0.015)<.0010.029 to 0.087
    Self-efficacy0.250 (0.057)<.0010.137 to 0.363
    Direct effect of health-related social media use on health behavioral intention0.315 (0.031)<.0010.254 to 0.376
    Index of moderated mediation: previous experience0.015 (0.005)N/Ab0.006 to 0.026

    aUnstandardized final model coefficients.

    bN/A: not applicable.

    Table 4. Conditional indirect effects of health literacy on health behavioral intentions by age.
    Age (years)EffectSE95% CI
    200.2940.0790.145 to 0.452
    250.2260.0620.109 to 0.351
    300.1580.0590.061 to 0.290
    Table 5. Conditional indirect effects of health-related social media use on health behavioral intentions by previous experience.
    Previous experience (score)EffectSE95% CI
    mean – 1 SD (=2.414)–0.0170.012–0.044 to 0.002
    mean (=4.005)0.0060.0070.009 to 0.020
    mean + 1 SD (=5.597)0.0290.0100.011 to 0.050

    Moderation Effects

    Conditional indirect effects of health literacy on health behavioral intentions by age are shown in Table 4, and conditional indirect effects of health-related social media use on health behavioral intentions by previous experience are shown in Table 5. The positive indirect relationship between health literacy on health behavioral intentions was stronger among the younger segment of our sample (point estimate 0.294, SE 0.079, 95% CI 0.145 to 0.452). Additionally, the positive indirect effect of health-related social media use on health behavioral intentions was stronger among those participants who previously had positive experience with health information on social media, who were at one standard deviation above the mean (point estimate 0.029, SE 0.010, 95% CI 0.011 to 0.050). The moderated mediation results also revealed that self-efficacy remained a significant mediator no matter whether the participants were at 20, 25, or 30 years old. However, self-efficacy was only a significant mediator when the participants had the mean score of previous experience with health information on social media or one standard deviation above the mean score.


    Principal Findings

    A substantial number of studies have examined health information on social media in China [6,16-18,28,38]; however, no previous studies have examined health behavioral intentions on a Chinese social media site from both the health literacy skills framework [22] or social-cognitive perspectives [23]. And there has been no study to comprehensively investigate how health information with different features on social media influenced the trust in such health information. Some studies [39,40] have examined the role of past experience and social media use individually, and some studies [18,20,21] have tested the associations between self-efficacy and other health outcomes; however, there are few empirical studies examining the relationship among health literacy, past experience, health-related social media use, and health behavioral intention on social media.

    Our findings indicate that health literacy and health-related social media use influenced health behavioral intentions both directly and through their prior effects on self-efficacy in managing one’s health. Individuals with higher levels of health literacy had greater self-efficacy in managing their health and then, consequently, had greater health behavioral intentions on social media such as using the health information they found or sharing health information with others from the internet. Part of this finding is also consistent with those from previous studies [9,41,42] suggesting that health literacy is positively associated with information sharing. We also found that individuals who used social media for health purposes more frequently were more likely to report higher self-efficacy in managing their health and greater health behavioral intentions on social media. Social media usage for information could improve people’s psychological state and increase confidence and motivations to cope with uncertainties [43]. Therefore, people who use social media for health more frequently would be able to learn new information, cases, health experiences of others, and avoid potential risks, which could lead to a higher confidence in managing one’s own health. Our finding regarding the positive association between self-efficacy and health behavioral intentions is consistent with the social-cognitive theory perspective and previous empirical studies [44,45] examining effects of self-efficacy on different health behaviors and behavioral intentions.

    Another important finding from our study pertains to the moderated mediation effects. Higher health literacy was associated with greater self-efficacy in health, which in turn was related to higher health behavioral intentions on social media. This relationship between health literacy and self-efficacy was moderated by age, suggesting that health literacy increased self-efficacy among younger social media users and eventually promoted their health behavioral intentions on social media. Younger social media users who had greater health literacy tended to have higher confidence in managing their own health and consequently had greater intentions to perform health behaviors. Among the older segment of social media users, no matter their health literacy level, their confidence in managing, improving, and generating positive effects on their health was lower than those of the younger groups. A number of studies [46-48] have found the negative association between age and self-efficacy. In one study [49], older adults who had lower incomes and lower education had relatively low self-efficacy, which was similar to our findings regarding the results of socioeconomic status and self-efficacy.

    The positive relationship between health-related social media use and self-efficacy was stronger among those who had previously benefited from using social media for health. When people seek health information online, they usually not only experience increases in health knowledge but also find social support and help from people in similar situations [50]. Therefore, individuals with prior positive experiences using social media for health would have greater efficacy in exerting positive effects on one’s health. Previous experience with applying health advice found on social media in real life that resulted in good health results would improve their confidence in continuing to seek and use health information online.

    Implications

    With the rapid growth of social media use, this study has important implications for health practitioners. A framework for health behavioral intentions was constructed based on the components of the health literacy skills framework [22] and the social-cognitive perspective [23].

    Health literacy influenced self-efficacy and health behavioral intentions, which highlights the importance of health literacy level in China. The concept of health literacy is not popular in China and the quality of medical services provided in China varies significantly based on areas. This makes it important to improve the health literacy level in China so that people can have the ability to take effective and accurate actions related to health. Given the moderating role of age, participants between the ages of 25 and 30 years in our sample require more customized interventions, such as including carefully evaluated digital elements [51,52], to improve their self-efficacy in managing their health in health interventions.

    Greater health-related use of social media was associated with higher self-efficacy and health behavioral intentions, indicating the importance of social media in understanding health behavioral intentions. Since the association between health-related social media use and self-efficacy was increased by positive experience with social media for health, health practitioners and scholars should aim to improve users’ experiences with social media regarding health information.

    The findings are important for health scholars interested in understanding the factors that influence the intent to use health information on social media sites. This study also provides insights for health message designers who want to build effective health campaigns and distribute accurate and credible health information on social media platforms. Future studies should explicitly investigate how to improve health literacy levels and users’ experience with social media, such as by developing health literacy education programs [53].

    Limitations

    Limitations in this study should be considered. First, we used a convenience sample on social media. The service used to advertise the survey link claimed to spread the survey post randomly, however, those who were interested in this study might share some similar traits (such as being in a younger population group or interested in this topic). Therefore, the sample was not truly representative of social media users in general, which might limit generalizability to other populations. Our sample was biased toward younger populations. Future studies could use different means to distribute the survey in order to reach a more diverse audience.

    Second, while we asked people if they would act on the health information, we cannot verify or assess the potential accuracy of information that would be obtained. Future work should also consider credibility of the sources and information presented as part of the findings in terms of whether acting on information would be beneficial for health [54], especially in the online environment in which health misinformation may be rampant.

    Finally, this study focused on investigating whether or not social media use could predict health-related behavioral intentions. Although previous research suggested that social media use could be influenced by cognition and behaviors [55], we did not test the reinforcing spiral framework of social media use in this study. This framework indicates that media use can influence attitudinal or behavioral outcomes, which can in turn affect habits of using media. According to this framework, media use can be an outcome of psychological processing and behaviors and also can influence psychological and behavioral results. Future studies should examine the reinforcing role of social media use in predicting health-related behavioral intentions on social media.

    Conclusions

    Health literacy and health-related social media use in China participants influenced health behavioral intentions on social media via their prior effects on self-efficacy in health. The association between health literacy and self-efficacy was stronger among younger respondents, whereas the association between health-related social media use and self-efficacy was stronger among those who previously had positive prior experience with health information on social media. Our results provide insights for health practitioners and researchers and increase understanding of the mechanisms behind using social media for health.

    Acknowledgments

    The authors would like to thank Dr Bo Li and Dr Ze Li for their insightful feedback on the manuscript.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Variables.

    DOCX File , 29 KB
    1. Cotten SR, Gupta SS. Characteristics of online and offline health information seekers and factors that discriminate between them. Soc Sci Med 2004 Nov;59(9):1795-1806. [CrossRef] [Medline]
    2. Li Y, Wang X, Lin X, Hajli M. Seeking and sharing health information on social media: a net valence model and cross-cultural comparison. Technological Forecasting and Social Change 2018 Jan;126:28-40. [CrossRef]
    3. Di Z, Junsheng G, Ruosi S. Clustering analysis of health information acquisition channels: active acquisition and passive receiving. J Int Commun 2015;5:81-93.
    4. Oh S, Syn SY. Motivations for sharing information and social support in social media: a comparative analysis of Facebook, Twitter, Delicious, YouTube, and Flickr. J Assn Inf Sci Tec 2015 Apr 21;66(10):2045-2060. [CrossRef]
    5. Zhu Y. Social media engagement and Chinese international student recruitment: understanding how UK HEIs use Weibo and WeChat. Journal of Marketing for Higher Education 2019 Jun 23;29(2):173-190. [CrossRef]
    6. 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]
    7. Zhang L, Pentina I. Motivations and usage patterns of Weibo. Cyberpsychol Behav Soc Netw 2012 Jun;15(6):312-317. [CrossRef] [Medline]
    8. Roberts M, Callahan L, O’Leary C. Social media: a path to health literacy. ISU 2017 Jun 26;37(2):177-187. [CrossRef]
    9. Sørensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, (HLS-EU) Consortium Health Literacy Project European. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health 2012 Jan 25;12(1):80 [FREE Full text] [CrossRef] [Medline]
    10. Lambert SD, Loiselle CG. Health information seeking behavior. Qual Health Res 2007 Oct;17(8):1006-1019. [CrossRef] [Medline]
    11. Verhoef LM, Van de Belt TH, Engelen LJ, Schoonhoven L, Kool RB. Social media and rating sites as tools to understanding quality of care: a scoping review. J Med Internet Res 2014 Feb 20;16(2):e56 [FREE Full text] [CrossRef] [Medline]
    12. Fox S, Duggan M. Health online 2013. Pew Research Center. Washington DC   URL: https://www.ordinedeimedici.com/documenti/Docs7-cybercondria-PIP-HealthOnline.pdf [accessed 2018-06-06]
    13. Kamel Boulos MN, Wheeler S. The emerging Web 2.0 social software: an enabling suite of sociable technologies in health and health care education. Health Info Libr J 2007 Mar;24(1):2-23 [FREE Full text] [CrossRef] [Medline]
    14. Laranjo L, Arguel A, Neves A, Gallagher A, Kaplan R, Mortimer N, et al. The influence of social networking sites on health behavior change: a systematic review and meta-analysis. J Am Med Inform Assoc 2015 Jan;22(1):243-256 [FREE Full text] [CrossRef] [Medline]
    15. Wang S, Paul M, Dredze M. Exploring health topics in Chinese social media: an analysis of Sina Weibo. 2014 Presented at: Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence; Jun 18; Québec, Canada.
    16. Niu Z, Willoughby JF. Examining cultural identity and media use as predictors of intentions to seek mental health information among Chinese. Asian Journal of Communication 2018 Feb 17;28(4):360-376. [CrossRef]
    17. Wu T, Deng Z, Zhang D, Buchanan PR, Zha D, Wang R. Seeking and using intention of health information from doctors in social media: the effect of doctor-consumer interaction. Int J Med Inform 2018 Jul;115:106-113. [CrossRef] [Medline]
    18. Liu Y, Liu L, Li Y, Chen Y. Relationship between health literacy, health-related behaviors and health status: a survey of elderly Chinese. Int J Environ Res Public Health 2015 Aug 18;12(8):9714-9725 [FREE Full text] [CrossRef] [Medline]
    19. Wu Y, Wang L, Cai Z, Bao L, Ai P, Ai Z. Prevalence and risk factors of low health literacy: a community-based study in Shanghai, China. Int J Environ Res Public Health 2017 Jun 12;14(6):628 [FREE Full text] [CrossRef] [Medline]
    20. Liu Y, Xue L, Xue H, Hou P. Health literacy, self-care agency, health status and social support among elderly Chinese nursing home residents. Health Education Journal 2017 Nov 17;77(3):303-311. [CrossRef]
    21. Yang Y, Zhang B, Meng H, Liu D, Sun M. Mediating effect of social support on the associations between health literacy, productive aging, and self-rated health among elderly Chinese adults in a newly urbanized community. Medicine 2019;98(16):e15162. [CrossRef]
    22. Squiers L, Peinado S, Berkman N, Boudewyns V, McCormack L. The health literacy skills framework. J Health Commun 2012 Oct;17 Suppl 3(sup3):30-54. [CrossRef] [Medline]
    23. Bandura A. Social cognitive theory of mass communication. In: Media Effects. New York: Routledge; 2009:110-140.
    24. Wang C, Li H, Li L, Xu D, Kane RL, Meng Q. Health literacy and ethnic disparities in health-related quality of life among rural women: results from a Chinese poor minority area. Health Qual Life Outcomes 2013 Sep 11;11(1):153 [FREE Full text] [CrossRef] [Medline]
    25. Schillinger D. Improving the quality of chronic disease management for populations with low functional health literacy: a call to action. Disease Management 2001 Sep;4(3):103-109. [CrossRef]
    26. Lee SD, Arozullah AM, Cho YI. Health literacy, social support, and health: a research agenda. Social Science & Medicine 2004 Apr;58(7):1309-1321. [CrossRef]
    27. Cao W, Zhang X, Xu K, Wang Y. Modeling online health information-seeking behavior in China: the roles of source characteristics, reward assessment, and internet self-efficacy. Health Commun 2016 Sep 09;31(9):1105-1114. [CrossRef] [Medline]
    28. Deng Z, Liu S. Understanding consumer health information-seeking behavior from the perspective of the risk perception attitude framework and social support in mobile social media websites. Int J Med Inform 2017 Sep;105:98-109. [CrossRef] [Medline]
    29. Sun Young Lee, Hwang H, Hawkins R, Pingree S. Interplay of negative emotion and health self-efficacy on the use of health information and its outcomes. Communication Research 2008 Apr 14;35(3):358-381. [CrossRef]
    30. Lee JY, Divaris K, Baker AD, Rozier RG, Vann WF. The relationship of oral health literacy and self-efficacy with oral health status and dental neglect. Am J Public Health 2012 May;102(5):923-929. [CrossRef]
    31. Niu Z, Willoughby JF, Mei J, Li S, Hu P. A cross-cultural comparison of an extended planned risk information seeking model on mental health among college students: cross-sectional study. J Med Internet Res 2020 May 11;22(5):e15817 [FREE Full text] [CrossRef] [Medline]
    32. Kushin M, Yamamoto M. Did social media really matter? college students' use of online media and political decision making in the 2008 election. Mass Communication and Society 2010 Oct 29;13(5):608-630. [CrossRef]
    33. Bandura A. Self-efficacy and health behaviour. Cambridge Handbook of Psychology, Health and Medicine 1997:160-162.
    34. Jiang S, Street RL. Pathway linking internet health information seeking to better health: a moderated mediation studyPathway Linking Internet Health Information Seeking to Better Health: A Moderated Mediation Study. Health Commun 2017 Aug 27;32(8):1024-1031. [CrossRef] [Medline]
    35. Chinn D, McCarthy C. All Aspects of Health Literacy Scale (AAHLS): developing a tool to measure functional, communicative and critical health literacy in primary healthcare settings. Patient Educ Couns 2013 Feb;90(2):247-253. [CrossRef] [Medline]
    36. Hu Y, Shyam Sundar S. Effects of online health sources on credibility and behavioral intentions. Commun Res 2009 Nov 25;37(1):105-132. [CrossRef]
    37. Hayes A. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York: Guilford Publications; 2017.
    38. Lee JY, Sundar SS. To tweet or to retweet? that is the question for health professionals on twitter. Health Commun 2013 Aug 08;28(5):509-524. [CrossRef] [Medline]
    39. Ho SS, Detenber BH, Rosenthal S, Lee EWJ. Seeking information about climate change. Sci Commun 2014 Feb 07;36(3):270-295. [CrossRef]
    40. Hovick SR, Kahlor L, Liang M. Personal cancer knowledge and information seeking through PRISM: the planned risk information seeking model. J Health Commun 2014 Apr 16;19(4):511-527. [CrossRef] [Medline]
    41. Crook B, Stephens KK, Pastorek AE, Mackert M, Donovan EE. Sharing health information and influencing behavioral intentions: the role of health literacy, information overload, and the internet in the diffusion of healthy heart information. Health Commun 2016 Feb 10;31(1):60-71. [CrossRef] [Medline]
    42. Ledford CJW, Cafferty LA, Russell TC. The influence of health literacy and patient activation on patient information seeking and sharing. J Health Commun 2015;20 Suppl 2:77-82. [CrossRef] [Medline]
    43. Hu S, Gu J, Zhang S. Social media usage, self-efficacy and cultural intelligence: a longitudinal empirical research in China. 2017 Presented at: Wuhan International Conference on e-Business. Association For Information Systems; May 26; Wuhan, China.
    44. Schwarzer R, Schüz B, Ziegelmann JP, Lippke S, Luszczynska A, Scholz U. Adoption and maintenance of four health behaviors: theory-guided longitudinal studies on dental flossing, seat belt use, dietary behavior, and physical activity. Ann Behav Med 2007 Jun;33(2):156-166. [CrossRef]
    45. Gao Z, Xiang P, Lee AM, Harrison L. Self-efficacy and outcome expectancy in beginning weight training class: their relations to students' behavioral intention and actual behavior. Res Quart Exerc Sport 2008 Mar 01;79(1):92-100. [CrossRef]
    46. Clark DO. Age, socioeconomic status, and exercise self-efficacy. Gerontologist 1996 Apr 01;36(2):157-164. [CrossRef] [Medline]
    47. Baker DW, Gazmararian JA, Sudano J, Patterson M. The association between age and health literacy among elderly persons. J Gerontol B Psychol Sci Soc Sci 2000 Nov 01;55(6):S368-S374. [CrossRef] [Medline]
    48. Kobayashi LC, Wardle J, Wolf MS, von Wagner C. Aging and functional health literacy: a systematic review and meta-analysis. J Gerontol B Psychol Sci Soc Sci 2016 May;71(3):445-457 [FREE Full text] [CrossRef] [Medline]
    49. Gecas V. The social psychology of self-efficacy. Annu Rev Sociol 1989 Aug;15(1):291-316. [CrossRef]
    50. Niu Z, Jeong DC, Willoughby JF. Friends over doctors? the influences of source and perceived customization on college drinking. Health Commun 2020 Jan 10:1-11. [CrossRef] [Medline]
    51. Rosenberg D, Depp CA, Vahia IV, Reichstadt J, Palmer BW, Kerr J, et al. Exergames for subsyndromal depression in older adults: a pilot study of a novel intervention. The American Journal of Geriatric Psychiatry 2010 Mar;18(3):221-226. [CrossRef]
    52. Seifert A, Reinwand DA, Schlomann A. Designing and using digital mental health interventions for older adults: being aware of digital inequality. Front Psychiatry 2019;10:568. [CrossRef] [Medline]
    53. Kim H, Xie B. Health literacy in the eHealth era: a systematic review of the literature. Patient Educ Couns 2017 Jun;100(6):1073-1082. [CrossRef] [Medline]
    54. Braithwaite DO, Waldron VR, Finn J. Communication of social support in computer-mediated groups for people with disabilities. Health Commun 1999 Apr;11(2):123-151. [CrossRef] [Medline]
    55. Slater MD. Reinforcing spirals: the mutual influence of media selectivity and media effects and their impact on individual behavior and social identity. Commun Theory 2007 Aug;17(3):281-303. [CrossRef]

    Edited by G Eysenbach; submitted 10.06.20; peer-reviewed by N Daniëls, JR Bautista, C Mather; comments to author 01.07.20; revised version received 04.09.20; accepted 26.10.20; published 25.02.21

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    ©Zhaomeng Niu, Jessica Willoughby, Rongting Zhou. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.02.2021.

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