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Winter 2023

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The Role of Social Media in Family Health Promotion & Health Interventions -Implications for Behavioral Medicine

Biswadeep Dhar, PhD, MEd, MS1; Riya Chakraborty PhD Candidate/ABD2, MEd, MA, Priyanshi Sharma, BA3; Shahmir H. Ali, PhD4; Child and Family Health SIG

Why Social Media & How is it Beneficial?

With increasing advancements in the scope and functionalities of digital technologies, the field of behavioral medicine has embraced digital health technologies to design innovative health interventions. This article will primarily focus on social media interventions on family health and highlight motivations and barriers to using social media. Similar to other domains of behavioral medicine, scientists can conduct theoretically informed research on the health-related utilization of social media by exploring how platforms on social media can be used to influence health-related knowledge, beliefs, attitudes, and behaviors (Arigo et al., 2019). Social media platforms are a new frontier for health behavioral studies (Pagoto et al., 2016). With the recent rise in weight loss and fitness apps, participants can not only digitally follow each other’s health and social developments, but also can interact and engage with each other though discussion forums (Conroy et al., 2014).

On the other hand, social media apps like Facebook, X, and Instagram also create powerful opportunities for recruiting participants into health studies (Ali et al., 2020). Recruitment on such platforms can help significantly save time and, if used effectively, improve outreach to diverse and vulnerable populations (Iribarren et al., 2018). In addition, digital health interventions through social media minimize barriers to research participation, such as transportation costs, conflicts in scheduling, family services, and/or daycare conflicts, where participants can get engaged from anywhere and at any time (Arigo et al., 2018). It should be noted that recruitment through social media presents challenges, such as misrepresentation of the sample, especially when financial incentives are offered. For example, malicious bots exploit financial rewards, which adversely impacts the accuracy and consistency of survey data. Hence, despite several advantages of social media recruitment, increasing awareness is required to mitigate the fraudulent responders in online surveys. A few preventive strategies could be the use of bot detection questions with security checks like CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) into the survey or with the use of additional authentication in Facebook, Google and/or other systems (Goodrich et al., 2023).   

Barriers to Using Social Media

Besides multiple uses of social media in health interventions, several social and technological barriers exist to its accessibility and effective utilization, with a wide gap between the design and the needs of the diverse users (Ancker et al., 2014). For example, older adults, people with disabilities, and other minoritized populations might face barriers in being able to read and/or comprehend social media-based health promotional resources (Zibrik et al., 2015). Indeed, promoting health information on social media may face some of the same existing barriers in traditional health promotion, such as linguistic or attitudinal barriers to the comprehensibility and impact of these materials connected with socioeconomic status or cultural background. It is also known that around a quarter of rural communities’ face internet accessibility issues (Anderson, 2018). Along with these barriers, unconscious biases may disregard candidates with whom the recruiter shares the most significant differences, whether in terms of background or interests. So, to mitigate these biases, conventional modes can be used along with social-media recruitment to ensure a representative sample in the survey. Communities in rural settings with remote healthcare systems may face barriers to accessing social media-based health education and health promotion (Hyman et al., 2022).

Equity & Accessibility Needs

Even though there exist many contemporary challenges in social media accessibility (e.g., those related to age, geographical location, and socioeconomic status), social media nonetheless, has the enormous potential to promote equitable health literacy and sustainable health behavior change. However, to reduce the barriers, social media-based health interventions should integrate equitable, accessible, adaptable, and culturally tailored approaches in the design of both observational and interventional studies involving social media to optimize the inclusivity and impact of social media research (e.g., through community-based participatory research) (Rivera-Romero et al., 2022).

Implications for Behavioral Medicine -Leveraging novel social media functionalities for health promotion

There has been a dramatic rise in innovation when it comes to the modalities and functionalities of social media applications. These innovations have enormous potential to be leveraged by public health professionals to develop more effective, engaging ways of harnessing such platforms to promote health. One such example is social media chatbots, which is rules-based or AI-powered tool that provides automated, tailored texts on social media platforms based on user input or needs. Our team recently employed this functionality to develop a social media chatbot (named “Roti”) that administered multiple 5-10-minute nutrition educational modules via text, tailored to the health and diets of South Asian young adults (Ali et al., Under review ). Specifically, recruited South Asian young adults completed a short screening survey which was used to determine which of the 4 “Roti” lessons would be most relevant to their individualized dietary knowledge, attitudes, and behavioral needs.  Ultimately, 168 South Asian young adults participated in the intervention on either Facebook or Instagram. Preliminary analyses revealed that participants reported high agreement that the social media chatbot educational intervention felt relevant, was helpful, and that they learned something new; analyses are currently underway to explore improvements in knowledge and attitudes as a result of this intervention.

Affiliations:

  1. Department of Human Ecology, University of Maryland, Eastern Shore, Princess Anne, MD, USA
  2. College of Education, University of Florida, Gainesville, FL, USA
  3. College of Computing, Data Science, and Society, University of California, Berkeley, CA, USA; Graduate School of Education, Stanford University, Stanford, CA, USA
  4. Saw Swee Hock School of Public Health, National University of Singapore, Singapore

References:

  1. Ali, S. H., Foreman, J., Capasso, A., Jones, A. M., Tozan, Y., & DiClemente, R. J. (2020). Social media as a recruitment platform for a nationwide online survey of COVID-19 knowledge, beliefs, and practices in the United States: methodology and feasibility analysis. BMC Medical Research Methodology, 20(1), 116. https://doi.org/10.1186/s12874-020-01011-0
  2. Ancker, J. S., Miller, M. C., Patel, V., Kaushal, R., & HITEC Investigators (2014). Sociotechnical challenges to developing technologies for patient access to health information exchange data. Journal of the American Medical Informatics Association : JAMIA, 21(4), 664–670. https://doi.org/10.1136/amiajnl-2013-002073
  3. Anderson, M. (2018). About a quarter of rural Americans say access to high-speed internet is a major problem. https://www. pewresearch.org/fact-tank/2018/09/10/about-a-quarter-of-ruralamericans-say-access-to-high-speed-internet-is-a-major-problem/
  4. Arigo, D., Pagoto, S., Carter-Harris, L., Lillie, S. E., & Nebeker, C. (2018). Using social media for health research: Methodological and ethical considerations for recruitment and intervention delivery. Digital Health, 4, 1–15
  5. Arigo, D., Jake-Schoffman, D. E., Wolin, K., Beckjord, E., Hekler, E. B., & Pagoto, S. L. (2019). The history and future of digital health in the field of behavioral medicine. Journal of Behavioral Medicine, 42(1), 67–83. https://doi.org/10.1007/s10865-018-9966-z
  6. Conroy, D. E., Yang, C. H., & Maher, J. P. (2014). Behavior change techniques in top-ranked mobile apps for physical activity. American Journal of Preventive Medicine, 46, 649–652
  7. Goodrich, B., Fenton, M., Penn, J., Bovay, J., & Mountain, M. (2023). Battling bots: Experiences and strategies to mitigate fraudulent responses in online surveys. Applied Economic Perspectives and Policy, 45, 762-784. https://doi.org/10.1002/aepp.13353
  8. Hyman, A., Stacy, E., Mohsin, H., Atkinson, K., Stewart, K., Novak Lauscher, H., & Ho, K. (2022). Barriers and Facilitators to Accessing Digital Health Tools Faced by South Asian Canadians in Surrey, British Columbia: Community-Based Participatory Action Exploration Using Photovoice. Journal of Medical Internet Research, 24(1), e25863. https://doi.org/10.2196/25863
  9. Iribarren, S. J., Ghazzawi, A., Sheinfil, A. Z., Frasca, T., Brown, W., Lopez-Rios, J., et al. (2018). Mixed-method evaluation of social media-based tools and traditional strategies to recruit high-risk and hard-to-reach populations into an HIV prevention intervention study. AIDS and Behavior, 22, 347–357.
  10. Pagoto, S., Waring, M. E., May, C. N., Ding, E. Y., Kunz, W. H., Hayes, R., et al. (2016). Adapting behavioral interventions for social media delivery. Journal of Medical Internet Research, 18, e24
  11. Rivera-Romero, O., Gabarron, E., Miron-Shatz, T., Petersen, C., & Denecke, K. (2022). Social Media, Digital Health Literacy, and Digital Ethics in the Light of Health Equity. Yearbook of Medical Informatics, 31(1), 82–87. https://doi.org/10.1055/s-0042-1742503
  12. Zibrik L, Khan S, Bangar N, Stacy E, Lauscher H, & Ho K. (2015). Patient and community centered eHealth: exploring eHealth barriers and facilitators for chronic disease self-management within British Columbia’s immigrant Chinese and Punjabi seniors. Health Policy Technol 4(4):348-356. https://doi.org/10.1016/j.hlpt.2015.08.002