Using Social Media for Health: National Data from HINTS 2019

Abstract

Social media (SM) have fundamentally changed the way we exchange information, including how we communicate about health. The goal of this study was to describe current prevalence and predictors of SM use by analyzing nationally representative data from the 2019 Health Information National Trends Survey (HINTS). Multivariate logistic regression models examined the odds of engaging in four SM activities: visiting social networking sites, sharing health information on SM, participating in online support groups, and watching health-related videos. In 2019, approximately 86% of Internet users reported engaging in at least one SM activity. Younger age and female gender were associated with higher likelihood of engaging in all SM activities. No significant ethnic/racial disparities were observed for most SM activities, but Hispanics were found to be more likely to report watching health-related videos. Additionally, those with regular health care access were more likely to participate in online support groups. Previous HINTS survey cycles were also used to examine change in SM use over time, showing that general SM use has increased substantially since 2007, but the use of SM for health-related purposes has not increased to the same extent. The dynamic and evolving nature of SM makes systematic assessment vital. Knowledge of current SM use patterns could make health communication efforts more effective and equitable.

Introduction

The use of social media (hereafter SM) has evolved dramatically over the past decade, with new platforms, functionalities, and use patterns constantly emerging. Characterized by web-based applications that enable users to create and share content or to participate in social networking, SM is altering the way we communicate, which necessitates up-to-date, systematic examinations of its role in various aspects of life.

Compared to a decade ago, SM use in the United States (U.S.) has grown from an estimated 27% in 2009 to 86% in 2019 according to the National Cancer Institute’s (NCI) Health Information National Trends Survey (HINTS) (Chou et al., 2009). Based on Pew Research Center data from 2019, the most popular platforms among U.S. adults are YouTube (with 73% of adults reporting use), Facebook (69%), Instagram (37%), Pinterest (28%), LinkedIn (27%), Snapchat (24%), and Twitter (22%) (Perrin & Anderson, 2019). There are noted sociodemographic differences in use of these SM platforms. For example, those between the ages of 18–29 tend to use Instagram instead of Facebook, more women are on Pinterest and Instagram than men, and a greater percentage of those with a college education use LinkedIn compared to those with less education (Perrin & Anderson, 2019). SM use has been increasing over the past decade, and is now regularly used by people across all age groups, although penetration remains highest among young adults (Perrin & Anderson, 2019). SM use is also highly prevalent regardless of income, education, or race/ethnicity (Social Media Fact Sheet, 2019). This suggests that the digital divide may be less pronounced once people have reliable Internet access. However, a recent analysis showed individuals who are older or of a lower socioeconomic status to be less likely to use SM for health-related purposes, suggesting that the digital divide may persist in the context of health (Calixte, Rivera, Oridota, Beauchamp, & Camacho-Rivera, 2020).

SM use has been shown to impact a variety of health-related behaviors, attitudes, and outcomes, in both positive and negative ways (Korda & Itani, 2013). A 2013 systematic review identified several key benefits of SM use for health, including increased accessibility and widening access to health information, greater social and emotional support from peers, and increased opportunities for public health surveillance–but also highlighted a number of important concerns about the use of SM, including issues surrounding information quality, the potential for information overload, and risks related to data security (Moorhead et al., 2013). Additional research also supports the idea that SM presents both opportunities and challenges for health improvement. For example, the literature suggests that social networking can enhance peer connection (Allen, Ryan, Gray, McInerney, & Waters, 2014; Boyd & Ellison, 2007; Ellison, Steinfield, & Lampe, 2007), improve the efficacy of eHealth interventions (Parikh & Huniewicz, 2015), and provide more equitable access to information and sources of support (Ahn, 2011; Correa, 2016; Kontos, Blake, Chou, & Prestin, 2014). Similarly, a review by Chou, Prestin, Lyons, and Wen (2013) noted the benefits of SM platforms for health promotion efforts, including improved reach, increased interactivity, lower cost, and the capacity to tailor messages to recipients.

However, research also demonstrates that SM use could pose a risk to health and well-being. For example, studies show that greater SM use is associated with mental health challenges among adolescents (Kelly, Zilanawala, Booker, & Sacker, 2018) and young adults (Primack et al., 2017). Additionally, there is concern that some content on these platforms might promote, glorify, or normalize harmful behaviors, such as eating disorders (Bert, Gualano, Camussi, & Siliquini, 2016), self-harm (Arendt, Scherr, & Romer, 2019), tobacco product use (Albarracin, Romer, Jones, Jamieson, & Jamieson, 2018; Phua, 2019), and alcohol consumption (Alhabash, McAlister, Quilliam, Richards, & Lou, 2015). In recent years, the capacity of SM to facilitate the spread of health misinformation has also become increasingly apparent, with falsehoods about the COVID-19 pandemic, rumors about vaccines, and myriad unproven treatments and medical hoaxes circulating on SM platforms (Castillo, Mendoza, & Poblete, 2011; Chou, Oh, & Klein, 2018; Donzelli et al., 2018; Menczer, 2016). The growing use of SM for news and other information also presents additional challenges, such as the potential for confusion, apathy, and overall erosion of trust in scientific and health experts (Anderson & Rainie, 2017; Chou, Gaysynsky, & Cappella, 2020; Huber, Barnidge, Gil De Zuniga, & Liu, 2019).

One important benefit of SM is its potential to facilitate more equitable access to information, suggesting that SM could be an important channel for health communication interventions that target underserved communities (Chou et al., 2009; Hanson, West, Thackeray, Barnes, & Downey, 2014; Heldman, Schindelar, & Weaver, 2013; Kontos et al., 2014). SM-based interventions for vulnerable populations have been used to promote physical activity, smoking cessation, and cancer screening, among other health behavior targets (Hudnut-Beumler, Po’e, & Barkin, 2016; Joseph, Keller, Adams, & Ainsworth, 2015). However, as SM use patterns evolve, the notion of equitable access becomes more nuanced. For example, if health information on SM platforms is inaccurate or of poor quality, ease of information access could have a negative impact – especially for populations that may lack the requisite health and media literacy skills to critically evaluate the information they encounter. Therefore, evaluating SM use patterns in different communities is vital to understanding communication inequalities.

Additionally, research suggests that SM uses and gratifications differ by individual characteristics, such as gender (Kircaburun, Alhabash, Tosuntaş, & Griffiths, 2020), making it important to assess differences in SM use by select sociodemographic characteristics in order to inform targeting and tailoring efforts. Uses and Gratification Theory posits that individuals are active and goal-oriented consumers of media who are aware of their motives for using certain media (e.g. information, personal relationships, etc.) and select media based on those needs and expectations (Alhabash & Ma, 2017). Investigating patterns in SM use can shed light on the different needs that users gratify by engaging in SM (such as information sharing or seeking out others with similar medical conditions), which can inform the design and delivery of campaigns and interventions.

Building on a seminal publication that used 2007 HINTS survey data on social media use (Chou et al., 2009), this study aims to provide an update on sociodemographic and health-related factors associated with the use of SM in general, and for health purposes specifically. Using HINTS 2019 data, this study presents a timely snapshot of general and health-related SM use among U.S. adults and identifies key factors associated with SM use. Specifically, the following research questions drive this analysis: 1) how has the use of SM—generally and for health purposes—changed over time? 2) how do the sociodemographic characteristics of internet users and non-internet users compare? 3) how do the sociodemographic characteristics of SM users and non-users compare? and 4) what are the most consistent and salient factors that predict the different uses of social media in 2019? Answering these questions through data from a nationally representative sample can help ensure that critical health communication efforts utilize SM in an effective and up-to-date manner.

Methods

Data Source

Data for this study were drawn from HINTS 5 Cycle 3 (2019), a nationally representative cross-sectional postal survey of non-institutionalized American adults whose households were randomly selected using address-based sampling. Comprehensive reports on the conceptual framework and sample design of HINTS are published elsewhere (Hesse, Moser, Rutten, & Kreps, 2006; Nelson et al., 2004). To increase response rates and inform future data collection efforts, HINTS 5 Cycle 3 included a pilot study to test a “push-to-web” response option, wherein an additional random sample of the address-based postal sample was encouraged to respond using a web-based platform rather than the paper-pen survey instrument. Within the push-to-web pilot study, respondents were further randomized to either a “web option” condition, where they were encouraged to respond via web, but not provided with an incentive to do so, or to a “web bonus” condition, where they received an additional incentive if they completed the survey online rather than on paper. Responses from three conditions (paper only, web option, and web bonus) were combined (N = 5,438). Response rates were not significantly different across the conditions: 30.2% for the postal mode and 30.6% for the push-to-web mode - 29.6% for the web option and 31.5% for the web bonus condition.

Outcome and Predictor Variables

This study includes five outcome measures. The first is overall Internet use, measured by the question: “Do you ever go online to access the Internet or World Wide Web, or to send and receive an email?” General and health-specific SM use was assessed by responses to the following four questions: “In the past 12 months, have you used the Internet for any of the following reasons? 1) To visit a social networking site (SNS), such as Facebook or LinkedIn; 2) To share health information on social networking sites, such as Facebook or Twitter; 3) To participate in an online forum or support group for people with a similar health or medical issue; or 4) To watch a health-related video on YouTube. These four items fit our a priori operational definition of SM use: the utilization of Internet-based websites or applications that enable users to create and share content or to participate in social networking (Leonardi, Huysman, & Steinfield, 2013). Although “social media” and “social networking sites” are often used interchangeably, we use “social media” in this paper as it is a broader term that encompasses the use of social networking sites, interactive apps, as well as other platforms that enable the development and exchange of user-generated content (Pham, 2014).

Covariates were selected through an iterative process, which included a combination of a priori determination (e.g., standard sociodemographic variables) and review of related literature (e.g., impact of regular source of health care and online support group engagement (Bartlett & Coulson, 2011)), and identification of significant bivariate relationships between covariates and outcome variables using chi-square tests. Consequently, the following sociodemographic variables were included: 1) age, grouped into six categories: 18–24, 25–34, 35–44, 45–54, 55–64, 65+; 2) gender (male or female); 3) education, categorized as high school degree or less, some college, and college graduate or more; and 4) race/ethnicity, grouped into four categories: non-Hispanic White, non-Hispanic Black (Black/African American), Hispanic, and non-Hispanic other. Additionally, a proxy measure for healthcare access was included: “Not including psychiatrists and other mental health professionals, is there a particular doctor, nurse, or other health professional that you see most often?” (yes/no). We also ascertained health communication in one’s personal social network: “Do you have friends or family members that you talk to about your health?” (yes/no). The final covariate pertained to health information seeking: “The most recent time you looked for information about health or medical topics, where did you go first?” Respondents were instructed to select one option from a list that included a variety of sources, such as “Internet”, “family”, “doctor or healthcare provider”, and “newspapers”. This variable was dichotomized as “Internet” vs. “other” to assess the relationship between SM use and use of the Internet for health-information seeking.

Data Analysis

To accommodate the complex sampling design of HINTS, analyses were conducted in SAS version 9.4 using final sample weights to obtain population-level point estimates and a set of 50 replicate weights to compute accurate variance estimates. Details on sample calculation and weighting can be found in the HINTS 5 Cycle 3 Methodology Report (Westat, 2019). Missing or incorrect responses (e.g., non-applicable, completed in error) were recoded as “missing”. Further, we tested for survey mode effects on all the outcome variables and found an effect for only one outcome variable (“participating in an online health forum or support group”). Therefore, mode was controlled for in the analysis of the “online health forum or support group” variable, but was not included as a control variable in analyses for the remaining outcome variables.

In order to assess change in SM activity over time, the prevalence of each SM activity among Internet users was calculated for HINTS 5 Cycle 3 (2019), as well as previous cycles of the survey: HINTS 3 (2007), HINTS 4 Cycle 1 (2011), HINTS 4 Cycle 3 (2013), HINT FDA (2015), HINTS 5 Cycle 1 (2017), and HINTS 5 Cycle 2 (2018).

An in-depth analysis was then conducted using the most recent HINTS data (2019), to assess characteristics of current Internet users and SM users. Frequencies were first calculated to describe the study sample of Internet users (compared to non-Internet users). To analyze the characteristics of SM users, a new variable was created to capture Internet users who reported engaging in any of the four SM activities assessed in the survey. Demographics of SM users were compared with those of non-users.

Chi-square tests were used to estimate associations between the covariates and each type of SM use behavior at the bivariate level (data available upon request). Separate multivariate logistic regression models were then conducted to examine the odds of engaging in each of the four SM activities. Each model included the same set of predictors. Finally, given the significant contribution of age in the models, each SM activity was also examined in a series of age-stratified analyses where a separate logistic regression model was conducted for each of three age categories: 18–34, 35–54, and 55 and above. Significant predictors of each SM activity are summarized below. All results reported were significant at P < .05.

Results

While the use of SM in general has increased substantially since 2007 (from 34% to 79%), the use of SM for health-related purposes has not increased to the same extent, with online support group participation growing from 5% to 9%, sharing health information on social media remaining below a quarter of Internet users in all years, and watching YouTube health videos ranging from a low of 28% in 2015 to 41% in 2019 (Figure 1). Further analysis of the most recent data revealed that approximately 84% of American adults reported using the Internet in 2019. We explored factors associated with Internet use by comparing characteristics of Internet users with those who do not report Internet use (Table 1). Bivariate analyses suggested that Internet users were more likely to be younger, male, educated, and non-Hispanic White or “other” race. Internet users were also more likely to have a regular health care provider and friends/family to talk to about health. Approximately 86% of Internet users reported engaging in any of the four SM activities measured in the study. SM users tended to be younger, female, and have higher educational attainment (Table 2). Those using SM were also significantly more likely to report having friends and family members to talk to about health and to report visiting the Internet first the last time they looked for health information. Visiting an SNS was the most commonly reported use of SM, with 79% of Internet users reporting this activity. Among health-related uses of SM, the most popular activity was watching health-related YouTube videos (41%), followed by sharing health information on an SNS (17%), with online support group participation being the least commonly reported activity (9%).

Figure 1.

Changes in social media use, 2007–2019 Note: HINTS 3 (2007) did not include items on “sharing health information on social media” or “watching a YouTube Video”; HINTS 4 Cycle 1 (2011) did not include items on “visiting a social networking site” or “watching a YouTube Video”; and HINTS FDA (2015) did not include an item on “visiting a social networking site”.

Table 1.

Weighted sample characteristics: proportion of Internet and Non-Internet users

Characteristic	Internet Users (N = 4322, 84.39%)	Non-Internet Users (N = 1074, 15.61%)
Age	P <.0001
18–24	97.15	2.85
25–34	96.86	3.14
35–44	95.67	4.33
45–54	85.85	14.15
55–64	82.04	17.96
65+	65.02	34.98
Gender	P = 0.0453
Male	86.06	13.94
Female	83.88	16.12
Education	P <.0001
High school or less	67.37	32.63
Some college	90.22	9.78
College graduate or more	96.05	3.95
Race/ethnicity	P <.0001
Non-Hispanic White	89.40	10.60
Black/African American	78.63	21.37
Hispanic	80.08	19.92
Non-Hispanic other a	90.61	9.39
Have regular healthcare provider	P =0.0060
Yes	86.59	13.41
No	81.97	18.03
Have friends/family to talk to about health	P = 0.0018
Yes	86.64	13.36
No	78.17	21.83
Went to the Internet first when looking for health information most recently	P < .0001
Yes	97.01	2.99
No	75.42	24.58

^aOther includes American Indian, Asian American, Pacific Islander, Native Hawaiian, Alaskan Native, and multiple races.

Table 2.

Weighted sample characteristics of Internet users (N = 4322, 84.4% of US population) who do and do not use social media (defined as reporting any of the four SM activities measured)

Characteristic	Social Media Users (N = 3495, 85.56%)	Social Media Non-Users (N = 792, 14.44%)
Age	P <.0001
18–24	96.31	3.69
25–34	96.14	3.86
35–44	90.30	9.70
45–54	86.48	13.52
55–64	77.38	22.62
65+	70.36	29.64
Gender	P < .0001
Male	82.12	17.88
Female	89.06	10.95
Education	P = .0007
High school or less	81.45	18.55
Some college	85.95	14.05
College graduate+	88.90	11.10
Race/ethnicity	P = .5382
Non-Hispanic White	85.28	14.73
Black/African American	86.75	13.25
Hispanic	87.60	12.40
Other a	89.22	10.78
Have regular health care provider	P = .0599
Yes	84.58	15.41
No	87.88	12.12
Have friends/family to talk to about health	P = .0040
Yes	87.36	12.65
No	79.14	20.87
Went to the Internet first when looking for health information most recently	P = .0053
Yes	88.54	11.46
No	81.20	18.79

^aOther includes American Indian, Asian American, Pacific Islander, Native Hawaiian, Alaskan Native, and multiple races mentioned.

After associations between predictor variables and SM outcome variables in the HINTS 5 Cycle 3 (2019) data were examined through a series of weighted bivariate analyses (results not shown), we conducted four separate logistic regression models to estimate the odds of engaging in each SM activity (Table 3). Among Internet users, visiting an SNS was predicted by age, gender, and education. Compared to those age 65 or older, those aged 18–24 were almost 10 times more likely to visit an SNS (OR = 9.75, 95%CI (3.51–27.10), p < .0001), and the odds of visiting an SNS decreased with age. Females were approximately twice as likely to visit an SNS compared to males (OR = 1.68, 95%CI (1.26–2.24), p = .0007). Lastly, compared to those with at least a college degree, those with a high school education or less (OR = 0.46, 95% CI (0.28–−0.75), p = .0022) and those with some college education (OR = 0.67, 95%CI (0.47–0.95), p = .0241) were less likely to visit an SNS.

Table 3.

Multivariate logistic regressions of four types of social media use among those who are online

	Odds of visiting a social networking site		Odds of sharing health information on social networking sites		Odds of watching a health video on YouTube		Odds of participating in an online health support group
Characteristic	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
Age	-	<.0001	-	<.0001	-	<.0001	-	0.0008
18–24	9.75 (3.51 –27.10)	<.0001	5.68 (2.53 –12.74)	<.0001	4.22 (2.09– 8.55)	0.0002	2.28 (0.67–7.78)	0.1849
25–34	5.34 (1.78 –16.03)	0.0035	4.93 (2.51 –9.70)	<.0001	3.40 (2.12– 5.43)	<.0001	6.22 (2.55–15.21)	<.0001
35^14	3.51 (2.15 –5.72)	<.0001	3.94 (2.16–7.20)	<.0001	2.48 (1.72– 3.56)	<.0001	4.26 (2.03–8.92)	0.0002
45–54	2.52(1.61–3.95)	0.0001	3.49 (1.92 –6.35)	0.0001	2.45 (1.68– 3.57)	<.0001	4.50 (2.09–9.70)	0.0002
55–64	1.30 (0.94 –1.79)	0.1123	1.47 (0.80 –2.70)	0.2088	1.85 (1.44–2.37)	<.0001	2.37 (0.90–6.26)	0.0818
65+	1.00	-	1.00	-	1.00	-	1.00	-
Gender	-	0.0007	-	0.0013	-	0.0027	-	0.0012
Male	1.00	-	1.00	-	1.00	-	1.00	-
Female	1.68 (1.26–2.24)	0.0007	2.02 (1.34 –3.05)	0.0013	1.53 (1.17–2.01)	0.0027	2.74 (1.50–4.99)	0.0012
Education	-	0.0077	-	0.1564	-	0.5304	-	0.3201
High school or less	0.46 (0.28– 0.75)	0.0022	0.95 (0.51 –1.77)	0.8630	0.84 (0.57–1.23)	0.3597	0.58 (0.28–1.18)	0.1326
Some college	0.67(0.47–0.95)	0.0241	1.43 (0.96 –2.12)	0.0754	0.91 (0.70–1.16)	0.4305	0.88 (0.54–1.43)	0.5894
College graduate or more	1.00	-	1.00	-	1.00	-	1.00	-
Race/ethnicity	-	0.6443	-	0.2537	-	0.0051	-	0.3460
Non-Hispanic White	1.00	-	1.00	-	1.00	-	1.00	-
Black/African American	0.85(0.48–1.51)	0.5716	1.12 (0.61 –2.04)	0.7187	1.48 (0.96–2.27)	0.0726	1.19 (0.54–2.61)	0.6673
Hispanic	1.22 (0.73–2.02)	0.4406	1.42 (0.79 –2.56)	0.2386	1.88 (1.19–2.95)	0.0074	0.64 (0.33–1.27)	0.1996
Other a	1.29 (0.58–2.83)	0.5263	2.09 (0.94 –4.66)	0.0712	1.94 (0.97–3.88)	0.0620	1.61 (0.66–3.90)	0.2898
Have regular healthcare provider	-	0.8689	-	0.2057	-	0.1667	-	0.0207
Yes	1.03 (0.70 –1.51)	0.8689	1.40 (0.83 –2.39)	0.2057	1.21 (0.92–1.60)	0.1667	1.86 (1.10–3.16)	0.0207
No	1.00	-	1.00	-	1.00	-	1.00	-
Have friends/family to talk to about health	-	0.2381	-	0.0037	-	0.1430	-	0.5927
Yes	1.00	-	1.00	-	1.00	-	1.00	-
No	0.73 (0.44 –1.24)	0.2381	0.53 (0.35–0.81)	0.0037	0.69 (0.42–1.14)	0.1430	0.83 (0.42–1.65)	0.5927
Went to the Internet first when looking for health information most recently	-	0.0598	-	0.1255	-	0.1687	-	0.9957
Yes	1.00	-	1.00	-	1.00	-	1.00	-
No	0.67 (0.44 –1.02)	0.0598	1.47 (0.90–2.40)	0.1255	0.80 (0.57–1.11)	0.1687	1.00 (0.54–1.83)	0.9957

^aOther includes American Indian, Asian American, Pacific Islander, Native Hawaiian, Alaskan Native, and multiple races.

Health-related SM engagement was predicted by multiple sociodemographic and health-related factors. Those in younger age groups were more likely to share health information online—for example, compared to those age 65 or older, those 18–24 were almost six times more likely to share health information on an SNS (OR = 5.68, 95%CI (2.53–12.74), p < .0001). Females were twice as likely to share health information on an SNS compared to males (OR = 2.02, 95%CI (1.34–3.01), p = .0013). Results also showed that those who did not have family and friends to talk to about health concerns were less likely to share health information on an SNS compared to those who had family and friends to talk to about health concerns (OR = 0.53, 95%CI (0.35–0.81), p = .0037). Each younger age group was successively more likely to watch health videos on YouTube compared to those in older age categories, and females were 1.5 times as likely as males to watch a health video on YouTube (OR = 1.53, 95%CI (1.17–2.01), p = .0027). Race/ethnicity also emerged as a significant predictor for watching health videos on YouTube, with those identifying as Hispanic being more likely to report watching health videos on YouTube compared to non-Hispanic Whites (OR = 1.88, 95%CI (1.19–2.95), p = .0074).

In contrast to younger age consistently predicting higher likelihood of visiting an SNS, sharing health information on an SNS, or watching a YouTube video, the findings regarding age and participation in online health forums or support groups were more nuanced. While individuals aged 25–34 were six times more likely to participate in online health support groups compared to those aged 65 or older (OR = 6.22, 95%CI (2.54–15.21), p = .0002), there was no significant difference between the youngest age group (18–24) and the oldest group (65 or older) in online support group participation. The pattern regarding gender and participation in online forums or support groups, however, was consistent with the pattern observed for other SM activities, with females being nearly three times as likely to participate in online health support groups compared to males (OR = 2.74, 95%CI (1.50–4.99), p = .0012). Notably, individuals who had a regular health care provider were also more likely to participate in online health support groups compared to those who did not have a regular provider (OR = 1.86, 95%CI (1.10–3.16), p = .0207).

Stratified analysis for the three separate age strata (18–34, 35–54, and 55 and above) revealed one interesting finding. In the oldest age group, education was a significant factor for all SM behaviors, meaning older individuals with higher educational attainment were more likely to engage in each of the four SM activities compared to those with less education. In the two younger age strata, however, educational attainment was not a consistent predictor of SM activity (results not shown).

Discussion

Compared to the findings of Chou et al. (2009), the 2019 HINTS data indicate that self-reported SM use – in general and for health-specific purposes – has grown across all segments of the population. However, while general SM use has increased from 34% to 79% among Internet users, the use of SM for health-related purposes increased only modestly, with online support group participation growing from 5% to 9% and sharing of health information on SM remaining relatively stable over the years. The analysis also indicates that (younger) age and (female) gender are persistent predictors of SM engagement. Overall, the age differences observed are not as pronounced as they were in Chou et al. (2009), suggesting that age-related differences in health-related SM use have decreased over the last decade. However, a persistent linear trend regarding age was still observed, with each younger age group reporting a higher likelihood of using SM in general and for health specifically than the next older group. The only exception to this trend was in online support group participation: it was most prevalent among those between the ages 25–34, but less frequent among young adults aged 18–24. While those in the youngest group may have fewer health issues and therefore less need for health-focused support groups, it is also possible that younger people may not use “traditional” online support groups devoted to health topics, and instead may be connecting with others through SM platforms such as Instagram, Snapchat, TikTok, or mobile apps.

Females were more likely than males to participate in SM activities, in line with previous research (Greenwood, Perrin, & Duggan, 2016). Females were more likely than males to engage in online support groups, which is consistent with past observations that women are more engaged in healthcare-related activities and conversations than their male counterparts, both online and offline (Krizek, Roberts, Ragan, Ferrara, & Lord, 2001; Mo, Malik, & Coulson, 2009; Thompson et al., 2016). It is also possible that certain female-specific health topics or concerns have more active online communities (e.g., pregnancy, motherhood, breast cancer).

The lack of significant differences in SM use across racial/ethnic groups is revealing: consistent with Chou et al. (2009) racial-ethnic minority groups in the U.S. were found to have consistently high use of SM. The analysis provides additional evidence of a narrowing digital divide in terms of race/ethnicity, as well as educational attainment, in the context of SM. This suggests that by facilitating health information access and sharing, SM could reduce information inequities and health disparities in racial/ethnic minority communities. However, while equitable information access may be a positive goal for public health, not all information access is equally health promoting. The volume of falsehoods, inaccurate health information, and misinformation online (e.g., about the COVID-19 pandemic, vaccinations, disease prevention and “miracle cures”) is alarmingly high (Okagbue et al., 2020; Sharma et al., 2020; Trethewey, 2020). Consequently, the risk of being exposed to health misinformation on SM is also high, and possibly poses a greater threat to those with limited health literacy and those who are more vulnerable to misinformation (Chou et al., 2020).

While few differences in race/ethnicity were observed, one finding concerning race/ethnicity is worth noting: Hispanics were more likely to report watching health videos on YouTube compared to non-Hispanic Whites. Linguistic preference and cultural identity may play critical roles in people’s choice of platforms and media consumption patterns generally. It is possible that on platforms such as YouTube, Spanish-language or culturally tailored content is more accessible. However, accessing health videos may also carry certain risks, particularly if the information is inaccurate or false. For instance, research has documented misinformation in YouTube videos on specific health topics such as cardiopulmonary resuscitation and prostate cancer (Liu, Haukoos, & Sasson, 2014; Loeb et al., 2019). Considering this finding about Hispanics’ self-reported use of YouTube for health content, future research should investigate the impact of YouTube viewing and SM use on health-related attitudes and behaviors and develop high-quality, language-concordant, culturally-tailored content on platforms that are popular among target populations. In addition to promoting health literacy, these efforts can fill any “information void” and mitigate the risk of exposure to misinformation on the same platforms.

Educational attainment appears to be associated with general SM use, but not engagement in health-related activities on SM platforms. This education effect in general SM use has been previously observed for specific platforms such as LinkedIn (Perrin, 2018). Regarding the lack of significant association between educational attainment and health-related SM use, there are many plausible explanations, given other unmeasured factors such as different interests and information needs, different levels of trust in various sources of health information, and diverse reasons for going on SM in the first place (Thackeray, Crookston, & West, 2013). Regardless of the reasons, when it comes to offering credible health information on SM, the opportunity to reach and engage people across a wide range of educational levels remains promising. Health communication practitioners must ensure that their SM efforts are accessible to those with limited education and/or health literacy by following best practices (e.g., writing in plain language) and incorporating the innovative use of visuals and videos when presenting information.

Having a regular health care provider (a proxy measure of having regular access to health care) was found to be associated with online support group participation. There are a few possible explanations. People with significant health challenges may be more likely to access both online support groups and clinical care because these individuals are more engaged with their health care overall (i.e., as “activated patients” (Wagner, 1998)). Providers might also be recommending or offering access to certain online resources, including online support groups. This finding may suggest that online support and connection doesn’t replace in-person clinical care and may complement or even augment one’s health care. Similarly, having family/friends to talk to about health was found to be associated with sharing health information on SM, again pointing to the fact that health communication is cross-channel. It may be that certain people generally talk more about health with their social networks, both on and offline, and that SM platforms may offer an opportunity to connect with one’s network and provide an additional way to obtain social support.

This analysis has several implications for health communication practice. First, while SM in general is embraced across all population segments (nearly 80% of respondents reported visiting an SNS), many people still do not utilize SM for health purposes, possibly due to preference for in-person communication, skepticism about health information exchanged on SM (Vraga & Tully, 2019), or simply a lack of perceived need. Consequently, health communication efforts need to continue to employ all available channels, including traditional mass media and in-person health education. Second, of the health-related SM uses examined, watching health-related YouTube videos was the most commonly reported activity. Health care organizations and local, state, and federal public health entities should continue to enhance their SM presence, including having information available in video formats on YouTube and similar platforms, to ensure that accurate and high-quality information is accessible on popular SM sites. In light of the high rates of YouTube use among Hispanic individuals, organizations should pay particular attention to developing evidence-based health videos that are language concordant and culturally sensitive and relevant. Finally, since age remains the most critical factor in SM use, practitioners should also be cognizant of the fact that some older adults may not have a presence on SM or feel comfortable navigating information on these platforms. Older individuals may be more effectively reached through traditional channels (e.g., mass media and small media), but may benefit from health communication efforts that foster digital literacy and assistance in obtaining high-quality, accurate information and social support through SM.

This study has a few limitations. First, HINTS data are cross-sectional, preventing causal inferences from being drawn about the observed relationships between variables. Second, the survey’s response rate, though comparable with other national surveys of similar scope (Maitland et al., 2017), is relatively low. Third, respondents’ self-report of sociodemographic variables, Internet and SM use, and other behaviors may not be accurate and depend on recall as well as correct interpretation of the survey questions and response options. It is possible that respondents have differential understanding of questions related to SM activities. Finally, the survey only asked about a few specific uses of SM, therefore the data do not provide a comprehensive picture of SM use for health-related purposes. For instance, individuals might be turning to SM to find tips and motivation for achieving health goals such as weight loss, and this health-related use is not captured in the current iteration of the survey. Despite these limitations, this study provides an important and timely update regarding SM use generally and for health-related purposes among American adults and suggests important implications for health communication research and practice.

Conclusion

To be effective, public health communication efforts, including those needed to confront crises such as the COVID-19 pandemic, will need to adapt to the dynamic and evolving nature of the SM environment. Iterative and timely assessments of Americans’ SM use behaviors can provide critical baseline data to inform these efforts. Consistent with prior data, no significant ethnic/racial disparities in SM use were observed. In fact, minority populations may be more likely to engage in certain types of health-related SM use such as watching YouTube health videos by those self-identified as Hispanic. Similarly, and consistent with prior data, age remains an important predictor of SM use, with younger individuals being more likely to use SM compared to those who are older – but the magnitude of these differences has decreased over the past decade. Being cognizant of these demographic trends in SM use could help public health practitioners develop more effective and more equitable health communication efforts.