Social influence impacts health behavior change (Berkman et al., 2000). For example, offline social networks have the capacity to influence health behaviors of individuals who subscribe to these networks (Laranjo et al., 2014). But social influence does not depend on face-to-face interactions to impact health behavior (Berkman et al., 2000). Online social media platforms are a haven for social influence, unbound by geographic barriers and other potential hurdles (Keller et al., 2014; Ventola, 2014). As online social media becomes more accessible, mirroring mobile device ownership trends (Anderson, 2015), public health professionals become increasingly interested in learning how to leverage social media to disseminate information and promote healthy behaviors (Laranjo et al., 2014).
Current research confirms that social media may be effective at eliciting health behavior
change, but suggests that different groups of people respond differently to information supplied via social media (e.g. Maher et al., 2014; Williams et al., 2014). It’s easy to make some generalizations about who is more likely to respond to information they see over social media. For example, one might expect that a Millennial would be more likely to be inspired by a clever meme (e.g. image to the right) to make a lifestyle change, than a Baby Boomer who may be less familiar with the reference and less likely to see the meme to begin with. With these initial considerations in mind, it’s worth taking a look at how specific variables may impact the public and make certain populations more or less likely to take action when exposed to information over social media.
Repeat exposure. The first variable that might make someone more or less likely to act on online information is exposure frequency. In other words, how often have they been exposed to a specific piece of information? In 1885, Thomas Smith reflected on the number of times an individual has to be exposed to something before they will take action (Smith, 1885). In his work titled Successful Advertising, Smith noted that the first time someone sees a piece of information, they probably won’t notice it. By the seventh time they’ve been exposed, they might be irritated by it. And finally, by the twentieth time, they might act on the information (Smith, 1885). This is called “effective frequency.” While Smith was speaking specifically to the world of advertising, this sentiment holds true in the context of health information over social media. Social media content often impacts knowledge and behavior discordantly, where knowledge may not produce behavior change at first. As a result, it stands to reason that any demographic variables that impact the amount of time an individual spends on social media, will also impact the number of times they are exposed to a particular message over social media.
To exemplify this point, I’d like to draw on personal experience. I am a frequent Facebook user, particularly during the wee hours of the night when my toddler is “sleeping.” Seattle Children’s Hospital recently launched an ad campaign over Facebook, recruiting families of young children with sleep disturbances (ad to the right). I myself noted this ad several times, typically around 2am, before I clicked to learn more. It I was an infrequent Facebook user, I probably wouldn’t have ever even noted this research endeavor was taking place. But because I often found myself on social media, I benefited from repeat exposure, and eventually took action to learn more.
Perceived efficacy of technology. Repeat exposure isn’t always sufficient on its own. An individual’s perception of technology also plays strongly into whether they will act on information supplied over social media. For example, users who actively look for health information online presumably see value in this technology, but they are also more likely to engage in health activities compared to those who do not look for health information online (CHIRr, 2007; Dutta-Bergman, 2004). There are a number of factors that may impact perception of technology. Some of these factors are outlined by the Technology Acceptance Model (TAM), including the following: 1) how useful an individual feels the technology is, or perceived usefulness; 2) how easy they feel it is to use, or perceived ease of use; and 3) their attitude toward technology use (Ahadzadeh et al., 2015). Perceived usefulness is defined as the belief that using technology will benefit the user, whereas perceived ease of use targets the effort involved with using said technology (Ahadzadeh et al., 2015; Wong et al., 2012). Any demographic variable that impacts these dimensions may subsequently affect whether an individual is willing to change a behavior in response to information encountered over social media. For example, if technology is too difficult to use, action is less likely. On the flip side, if the individual feels that the information supplied over social media is relevant and useful to them, they are more likely to act on it. While there are a variety of variables the may impact the perceived usefulness and ease of use of technology, one of the most significant is age.
Age of user. Current research suggests that age is a strong predictor of social media use and impact (Greenwood et al., 2016). A 2016 Pew Research Report found that 88% of all internet users between the ages of 18-29 have a Facebook account, compared to 62% of internet users over the age of 65 (Greenwood et al., 2016). The difference observed between these age demographics is consistent across “dose” — younger internet users are more likely to spend more time on social media than their older counterparts. In other words, younger social media users are more likely have repeat exposure to information posted to social media. A factor that, as Smith pointed out in 1885, is critical to catalyzing action.
Age may also predict how likely someone is to act on health information over social media. For example, individuals between the ages of 18 and 24 are more likely to share and trust health information over social media when compared to older demographics (Health Research Institute, 2012). Millennials are also more likely to engage with health information, where engagement is defined as viewing or performing health-related activities over social media (Health Research Institute, 2012). People over the age of 65, on the other hand, place less trust in online resources and perceive these resources to be less useful in general (Agree et al., 2015; Health Research Institute, 2012). With these perceptions in mind, exposure to health information over social media is more likely to impact the health behaviors of Millennials, over other generations. The disparity observed between generations is partially due to an individual’s ability to obtain, process, and understand health information from the internet, with older adults exhibiting lower tech skills (Agree et al., 2015).
Perceived risk and health status. Health information seeking depends on personal health-related motivations (Kwon et al., 2011). Someone with a greater perceived health risk is more likely to seek health information and is subsequently more likely to engage in disease prevention behaviors (Kwon et al., 2011). Similarly, an individual’s health status may predict how knowledge gained via social media may impact behavior change. Someone in poorer health may be willing to engage with, and act on, health-related information acquired over social media. One study reported that millennials who are in poor health are more likely to engage with health information over social media than their healthy counterparts (Agree et al., 2015).