20
Media Eﬀects and Health
Jessica G. Myrick
Since the beginning of the formal study of message eﬀects on audiences, health information and
health-related outcomes have played a crucial role in advancing research on media eﬀects. For
instance, public concern in the 1930s that movies were harming children’s health led psychologists
to design special beds that measured how much children tossed and turned at night after
watching diﬀerent genres of ﬁlms (Southwell & Thorson, 2015). In the 1950s, psychologists at
Yale used messages about oral health in their early tests of the interplay between emotional
responses to messages and subsequent behavior change (Janis & Feshbach, 1953). These are
only a couple of examples of how issues of health have long been intertwined with the study of
media use and its eﬀects on users and on society. As such, it is worthwhile to explore the theoretical
perspectives, message features, psychological processes, and individual diﬀerences associated
with health media eﬀects given that insights into this area can beneﬁt the ﬁeld as a whole.
Despite their wide relevance to the development of media eﬀects theory and empirical ﬁndings,
health-related media eﬀects are also notable for the ways in which the context diﬀers from
other subﬁelds. First, there may not be an area of media eﬀects research with a more practical
link to shaping how humans go about their individual and collective lives. A real applied need
exists to generate ﬁndings to identify aspects of media that may harm health, as well as to
better understand how media can help individuals live healthier, happier lives. Second, healthrelated
media eﬀects deal directly with conceptual issues of personal relevance, uncertainty, and
risk more so than some other media eﬀects contexts. If someone does not see the latest blockbuster
movie or misses the broadcast of a big sports game, the consequences are not as grave as
missing the news story about a new infectious disease in one’s geographic area. Perhaps its closest
cousins—environment and science-related media eﬀects—also deal with issues of uncertainty
and risk, but often do not generate the same level of perceived personal relevance (that is,
unless they emphasize the health implications of their topics).
Given that the topic of health has relevance across media eﬀects subﬁelds, as well as unique
attributes that separate it from other areas, researchers have a lot to consider when trying to
grasp the nature of health-related media eﬀects. What follows is an examination of some of the
important issues, typically applied theories, notable studies, current controversies, and emerging
trends facing researchers interested in this area of media eﬀects scholarship and its applications
308
Media Eﬀects and Health • 309
to the real world. By reviewing and engaging with these topics, future work in this area can
advance our understanding of the interplay between health and media.
Types of Health Media and Their Eﬀects
Not all health messages share the same goals. Strategic messages aim to persuade audiences to
change their health behaviors. Such health messages often come in the form of public communication
campaigns, which are
purposive attempts to inform or inﬂuence behaviors in large audiences within a speciﬁed
time period using an organized set of communication activities and featuring an array of
mediated messages in multiple channels generally to produce noncommercial beneﬁts to
individuals and society.
(Atkin & Rice, 2013, p. 3)
Advertisements are another, more speciﬁc form of strategic health messaging, and are not
always noncommercial in nature. For example, the U.S. and New Zealand are two of very
few countries in the world that allow direct-to-consumer advertisements for pharmaceuticals
(Ventola, 2011). As such, research suggests that advertisements by for-proﬁt pharmaceutical
companies can lead audiences to ask their healthcare providers for potentially unnecessary
prescriptions (Mintzes et al., 2003).
News reporting typically does not aim to persuade consumers to adopt particular health behaviors,
but instead to inform them. Journalists strive to inform audiences about various health topics,
and health/medicine has become a common beat at major news outlets across the globe (Myrick,
2014). Besides informing the public about health topics, news coverage of health issues can also
inﬂuence public support for health-related policies (Coleman, Thorson, & Wilkins, 2011). How
eﬀectively health news informs individuals about health topics and health policies is a debatable
issue (Schwitzer, 2014), but recent evidence suggests that the quality of coverage of medical interventions
(as judged by standards of completeness, accuracy, and usefulness) has generally improved
(Walsh-Childers, Braddock, Rabaza, & Schwitzer, 2018).
In addition to strategic campaigns, advertisements, and news reporting about health, entertainment
media also serve as a form of health communication. When content creators purposefully
integrate health topics into entertainment messages, the practice is known as
“entertainment education” and relies on social modeling and narrative persuasion theories to
subtly inﬂuence audience health behaviors (Singhal & Rogers, 1999). Entertainment media can
also shape public opinion when health topics are introduced into a storyline or featured in
celebrity news coverage, even if there was no intention to change the public’s health-related
behaviors. For instance, Quick (2009) analyzed viewers of the popular television drama Grey’s
Anatomy. Based on tenets of cultivation theory, he found that heavy viewers of the show perceived
the program as more credible than lighter viewers, and that credibility perceptions were
positively associated with viewing real-world doctors as courageous.
Across these diﬀerent types of health messages, the eﬀects on audiences can vary in
a number of important ways. Health message eﬀects can be either personal or public (i.e.,
focused on individual-level or society-level change), they can be either intended and purposeful
or unintended and accidental, and they can be beneﬁcial or damaging to health (Brown &
Walsh-Childers, 2009). Any one health message could potentially have multiple types of eﬀects.
310 • Jessica G. Myrick
For instance, a news story informing readers about the importance of their own dietary choices
could also persuade legislators to develop policies encouraging fruit and vegetable consumption,
thereby having both personal and public inﬂuence. Furthermore, one person could read the
same news article and start eating more vegetables—a beneﬁcial change to her health—whereas
another could read the same article, dismiss the content, and decide to eat fewer vegetables just
to spite the people who insist they’re so important—a harmful behavioral change. The actions
of the person who dismisses the news story were likely not an intended outcome of the journalist
who wrote it, whereas educating the individual who did change, and maybe even educating
the legislator, could have been desired, intentional outcomes. Across the various health message
types and their eﬀects, theories have helped researchers better understand and predict outcomes
associated with health messages. Below, theories popular in this area of research are reviewed.
Theoretical Perspectives
Much of the theory that is applied to health message eﬀects research did not originate with
communication scholars. That is, social psychological and health behavior theories form an
important constituency of the conceptual frameworks applied to health-related media eﬀects
studies. Of these theories, three in particular stand out. First, Bandura’s social cognitive theory
(SCT), an outgrowth of social learning theory, has a number of components arguing that personal,
behavioral, and environmental factors are interrelated and reciprocally inﬂuence each
other to determine human behavior (Bandura, 1986; see Chapter 7 in this volume). However,
the aspects of SCT typically applied to health media eﬀects research are the propositions that
(a) one’s self-eﬃcacy, or conﬁdence in one’s ability to perform a behavior, and (b) one’s belief
that more positive than negative outcomes will occur (outcome expectations) both predict the
likelihood of performing a behavior (Bandura, 2004a). SCT has also been applied to contexts
where real-life celebrities or ﬁctional characters model health-related behavior via messages, and
when viewers see that behavior as rewarded, they may be more likely to copy the behavior than
when they see it being punished (Bandura, 2004b).
Another prominent health behavior theory applied to media eﬀects research is the health
belief model (HBM) (Janz & Becker, 1984; Rosenstock, 1974). In this model, the likelihood of
engaging in a health-related behavior is predicted by multiple variables: perceived beneﬁts of
and barriers to enacting the behavior, perceived threat, and perceived self-eﬃcacy. Additionally,
the HBM posits that additional “cues to action” can motivate behavior, one such cue being
media. Work in health communication applying the HBM typically assumes that messages that
emphasize the threat, beneﬁts for enacting a behavior, ways to overcome barriers to that behavior,
and self-eﬃcacy information for enacting the behavior will persuade audiences to take
action. However, it is less clear how each of these message components should be ordered in
a message, and, conceptually, if perceptions of these four variables all mediate, or if some moderate,
eﬀects on post-message attitudes, intentions, and behaviors (Jones et al., 2015).
The third health behavior theory frequently applied to explanations of health message eﬀects is
actually a group of theories with similar variables and proposed interrelationships. Originally conceptualized
as the theory of reasoned action (Ajzen, 2011; Ajzen & Fishbein, 1980), scholars now
often apply the “reasoned action approach” (RAA) to their work, with this framework including
the theory of planned behavior (TPB) and the integrated behavioral model (IM) (Head & Noar,
2013). The most recent iteration of these related theories, the IM argues that although behavioral
intentions are the most immediate precursor to behavior, the intentions–behavior link can be
Media Eﬀects and Health • 311
moderated by environmental factors such as skills, abilities, and/or environmental constraints
(Fishbein, 2008). Moreover, the TPB and IM state that attitudes, self-eﬃcacy, and social norms
(both injunctive and descriptive) each predict intentions. Fishbein and Yzer (2003) state that in
order to eﬀectively apply the IM to health interventions, researchers must ﬁrst identify the target
population’s beliefs that undergird their attitudes, self-eﬃcacy, and social norms. Once appropriate
beliefs are identiﬁed, messages can help change those beliefs and, through their impacts on attitudes,
self-eﬃcacy, and social norms, shift intentions that should inﬂuence behavior (depending
on audience abilities and their environment).
Together, these three health behavior theories (SCT, HBM, and IM) are frequently applied to
the crafting of persuasive health messages and campaigns. Yet, additional theories from communication
scholars have also inﬂuenced the ﬁeld. One of note is the extended parallel process
model (EPPM). Witte (1992) proposed the EPPM as a merger of two previous theories related
to fear appeal eﬀects: the parallel process model (Leventhal, 1971) and protection motivation
theory (Rogers, 1975). Whereas previous models focused on cognitive responses to fear appeals,
the EPPM also included the emotion of fear. According to the EPPM, after audiences assess the
topic of the message as threatening (i.e., the audience perceives a severe threat to which they
are susceptible), they then experience fear.
A crucial component of the EPPM is determining if the threat appraisals of severity and susceptibility
are more or less dominant than corresponding eﬃcacy appraisals of self-eﬃcacy to
perform a behavior and response-eﬃcacy that the target behavior will actually work. When
threat appraisals are stronger than eﬃcacy appraisals, the model predicts that individuals will
seek to diminish their fear through maladaptive behaviors, such as source denigration or rationalization
of unhealthy behaviors. However, when eﬃcacy appraisals are stronger than threat
appraisals, the EPPM posits that individuals will feel capable of addressing the threat and will
then enact adaptive, danger-control behaviors (for a detailed overview of the EPPM and comparisons
to prior fear appeal theories as they relate to health messages, see Myrick & Nabi,
2017). The full EPPM comprises 12 propositions, but existing work has yet to support most of
them, including a consistent interaction between perceived threat and perceived eﬃcacy
(Popova, 2012). Nonetheless, multiple meta-analyses have found a positive and signiﬁcant linear
relationship between fear arousal and persuasive outcomes such as attitudes and behavior
change (Mongeau, 1998; Tannenbaum et al., 2015; Witte & Allen, 2000), and the EPPM, as well
as extensions of it, continue to form the conceptual basis for many message eﬀects studies.
Another notable theory applied to studies of health message eﬀects is exempliﬁcation theory
(Zillmann, 2006). This theory posits that the use of exemplars in messages has a stronger
impact on audience risk perceptions than do statistics because exemplars activate heuristics
related to risk perception. Plus, exemplars are more likely to evoke emotions than are numbers
alone. Exempliﬁcation theory has helped to eﬀectively predict audiences’ risk perceptions about
a number of health topics, such as insect-borne illnesses (Gibson & Zillmann, 2000) and skin
cancer (Zillmann & Gan, 1996), graphic labels on tobacco products (Bigman, Nagler, & Viswanath,
2016), and to digital weight-loss messages (Knobloch-Westerwick & Sarge, 2013).
Additional Theoretical Perspectives, Notable Programs of Research, and
Important Studies
As the broad ﬁeld of health communication has grown, scholars focusing on the role of the media
in shaping health behavior have established important, theoretically informed programs of research.
312 • Jessica G. Myrick
In some cases, individual studies stand out as points where innovative ideas or compelling empirical
evidence caused others to start investigating and theorizing in a speciﬁc area of health-related media
eﬀects. In other cases, the body of research accrued over time, building a convincing argument for
a new way of examining how media inﬂuence health behaviors. Below, a few examples of notable
programs of theory-based research in this area are described.
Emotions and Health
Emotional appeals are increasingly of interest in health media eﬀects research. Notably, many
public communication campaigns rely heavily on emotional appeals, especially fear appeals, in their
attempts to sway the public’s health behavior. Witte’s (1992) work on the EPPM, mentioned above,
forms the foundation for much of the health-related fear appeal research. Additional work by Dillard
and Peck (2000) helped health communication researchers integrate research on dual-process
models of attitude formation with the burgeoning work on the role of emotions in health message
eﬀects. This work also examined unique eﬀects for diﬀerent emotions, motivating increased study
of discrete emotions as important responses to health messages. Furthermore, Nabi’s conceptualization
of the cognitive functional model (Nabi, 1999) and the emotions-as-frames model (Nabi, 2003)
nudged the ﬁeld to think about how diﬀerent types of emotions impact health information processing
and other message outcomes. By emphasizing the ability of emotions to persuade and motivate,
these researchers have advanced health media research into an era where message designers know it
is not enough to simply inform audiences about health threats, as many already know they exist.
Instead, understanding the diﬀerent types of emotions that lead to selective exposure, message elaboration,
and motivation to pursue often diﬃcult health behavior changes is an important development
in health media research that continues to grow in popularity (Myrick, 2015).
Adolescents and Sexuality
Another example of a program of research that inﬂuenced our understanding of health-related
media eﬀects is Brown and her colleagues’ work on how adolescents’ media use shapes their sexual
behaviors. Brown (2000) developed the Media Practice Model with three central features: (1) media
use is active; (2) a reciprocal (i.e., non-linear) relationship exists between media use and eﬀects; and
(3) identity, sense of self, and aspects of one’s psychological development inﬂuence media selection,
media interaction, and application of lessons from media to one’s own life. Brown and her colleagues
found in multiple studies that exposure to sexual content in music, movies, television, and
magazines accelerated adolescents’ sexual activity, with some diﬀerences based on demographics
(Brown et al., 2006; Pardun, L’Engle, & Brown, 2005). Additional work found that mass media were
just as inﬂuential on adolescents’ sexual behavior as were other socialization factors, such as family,
religion, school, and peers (L’Engle, Brown, & Kenneavy, 2006). This body of work contributed
a great deal of empirical evidence about the nature of mediated portrayals of health-related behaviors,
as well as the role of media use, in socializing adolescents and shaping their views on what is
and is not normal, acceptable, or even expected sexual behavior.
Entertainment
Research on the eﬀects of entertainment media on health outcomes is another important area of
study. Slater and Rouner (2002) developed an extended version of the elaboration likelihood
Media Eﬀects and Health • 313
model (E-ELM) to explain how entertainment messages inﬂuence viewers beyond modeling of
rewarded behaviors or avoidance of punished behaviors. The E-ELM focuses on the absorption
potential of a narrative and posits that entertainment messages can be particularly inﬂuential
when audiences are immersed in a narrative, thereby making them less likely to counter-argue
and more likely to shift their attitudes than if they were consuming non-entertainment media.
The entertainment overcoming resistance model (EORM) has also been proposed as a conceptual
framework for understanding how entertainment messages inﬂuence attitudes and behavioral
outcomes, including health-related outcomes (Moyer-Gusé, 2008). The EORM integrates multiple
theoretical perspectives on audience involvement (including the E-ELM and social cognitive
theory) to provide a comprehensive explanation as to why entertainment messages can shift
behaviors. Together, this work linking entertainment media to health outcomes helped spur
increased interest in entertainment education and the eﬀects of narratives related to health.
Social Norms
In the mid-2000s, a conceptual article (Lapinski & Rimal, 2005) and an empirical test of the theory of
normative social behavior (Rimal & Real, 2005) turned many health communication scholars’ attention
to the role of social norms in shaping health message eﬀects. Rimal and his colleagues demonstrated
that descriptive norms, or our beliefs about how common a behavior is in our social network,
inﬂuence behavior, but are also moderated by norms of social approval, outcome expectations, and
group identity. In a later update to their initial theorizing about the role of social norms, Rimal and
Lapinski (2015) noted that additional moderators may impact the role of social norms on health
behaviors (e.g., involvement, group proximity) and that studying speciﬁc attributes of target behaviors
is crucial for continuing to advance our understanding of how social dynamics shape message eﬀects.
Psychological Reactance
Another concept that became prominent in the health communication literature during the mid-
2000s was that of reactance. Dillard and Shen (2005) published a multi-study paper describing the
nature of reactance and how reactance theory can be applied to health communication contexts.
Building on work by Brehm (1966) deﬁning reactance as a threat to one’s freedom, Dillard and
Shen demonstrated that both anger as well as negative cognitions were important components of
reactance. They found that messages that included threats to freedom evoked greater anger and
negative cognitions, which in turn inﬂuenced attitudes and intentions (related to ﬂossing and
alcohol consumption). Shen (2010) later investigated ways to mitigate reactance, ﬁnding that
inducing state empathy is a helpful tactic. Together, this work on reactance helped advance the
ﬁeld’s understanding of when and why persuasive health messages may backﬁre with some audience
members. Since health messages typically focus on threats, the research on reactance reminds
scholars that persuasion and post-message health behavior change is not as simple as telling audiences
that a threat exists. Instead, audience resistance to the message and the messenger, as well
as to the target behavior, must also be considered when investigating health message eﬀects.
Limitations and Controversies
The programs of research and notable studies mentioned above are all evidence of the rich
quality of scholarship in this area of media eﬀects research. However, the subﬁeld is not without
314 • Jessica G. Myrick
its limitations and ongoing controversies. More broadly, health communication scholarship has
been criticized for its frequent lack of attention to or advancement of theory (Beck et al., 2004;
Freimuth, Massett, & Meltzer, 2006). One contributing factor is the interdisciplinary nature of
much of the work on health media eﬀects, whereby communication scholars partner with
researchers from public health and medical sciences who are often less concerned with theory
than they are with practical outcomes and securing grants to achieve those outcomes (Hannawa
et al., 2013). However, even when theory is applied, it is sometimes done incompletely or without
careful attention to the dynamics of media and audiences.
For example, most studies that apply SCT focus on the role of self-eﬃcacy in predicting message
outcomes while ignoring the role of outcome expectations, another central part of the
theory. When researchers do add outcome expectations to work applying SCT in health contexts,
the explanatory power of their models increases (Noar et al., 2015), making a case for the
need to more thoroughly apply existing theories in order to improve our ability to predict
health message eﬀects. In another example, Nabi and Clark (2008) found that some audiences
may still model negatively reinforced behaviors performed by mediated characters, even though
SCT would predict that people would not want to try behaviors they saw as punishable. That is,
audience expectations related to scripted television dramas resulted in an outcome opposite to
the original tenets of SCT, which should motivate additional work in this area to better understand
when and why SCT does or does not predict health message eﬀects.
O’Keefe (2012) made another case for using caution when applying outside theories to
research about the eﬀects of health messages on audiences. He noted how the application of
prospect theory (Kahneman & Tversky, 1979) to health-related media eﬀects has been problematic.
Speciﬁcally, this approach is based on the argument that audiences are more likely to be
persuaded to perform disease-preventing behaviors if they see a loss-framed message (i.e., one
that emphasizes potential costs) because prevention contexts are low risk. However, audiences
will be more likely to be persuaded by gain-framed messages (i.e., ones that emphasize potential
beneﬁts) when the target behavior is about detecting a disease, which is a higher risk context
than prevention (Rothman & Salovey, 1997). Yet, these suppositions have not been supported
by meta-analyses, which ﬁnd almost no relationships between gain or loss message framing and
the type of target behavior (prevention or detection) on message outcomes (O’Keefe & Jensen,
2007, 2009). O’Keefe (2012) suggests that the lack of support for this application of a theory
from another ﬁeld to the health message eﬀects context is likely due to diﬀerent deﬁnitions of
key terms: the behavioral economists who developed prospect theory conceptualized risk as
uncertainty (with more uncertainty of an outcome indicating greater economic risk), and not as
the likelihood of a bad outcome occurring, which is how risk is typically applied in health communication
contexts.
Moreover, theoretical propositions as to the role of risk perceptions themselves in shaping
health message outcomes have received mixed support. Fishbein and Yzer (2003) note that
across applications of diﬀerent health behavior theories, perceived risk is not a consistent immediate
predictor of behavioral outcomes. One potential explanation for the lack of direct connection
between perceived risk and behavioral outcomes is that simply putting risk-related
information in a message is not necessarily enough to change risk perceptions, and that more
work is needed to understand the interplay between message factors and audience identities,
beliefs, and personality traits in shaping risk.
Some theory does exist trying to link features of health narratives with risk perceptions. So
and Nabi (2013) proposed and tested their risk convergence model, ﬁnding that in addition to
Media Eﬀects and Health • 315
perceived personal relevance of a health threat, common psychological processes related to narratives—character
identiﬁcation, parasocial interaction, perceived realism, and transportation—
can each inﬂuence audiences’ social distance with mediated personae facing health threats.
Lower social distance, in turn, is positively related to audiences’ personal risk perceptions.
Future work could apply this model in multiple domains and continue to test the ways in
which diﬀerent message features and psychological processes associated with them impact risk
perceptions, and then how risk predicts behavioral change. Additional work could advance our
understanding of the role of risk in health message eﬀects by testing risk perceptions as both
moderators and mediators of behaviors, not just message outcomes.
Given their connection to risk perceptions, another notable controversy in the ﬁeld of
health-related media eﬀects has been how best to utilize emotional appeals to promote behavior
change. As noted above, meta-analyses of EPPM and fear-related message eﬀects have not supported
the predicted interactions between perceived threat and perceived eﬃcacy (Popova,
2012). However, meta-analytic evidence does support a positive linear relationship between fear
arousal and persuasion (Tannenbaum et al., 2015). Yet, researchers still do not fully understand
the moderators or boundary conditions as to when and for whom fear-arousing messages lead
to attitude and behavior change. Recently, Dillard and his colleagues have returned to earlier
theorizing on fear appeal eﬀects to argue for the importance of looking at within-person shifts
in fear arousal to understand its persuasive power, with the fear curve being a better predictor
of persuasion than end-message fear (Dillard, Li, Meczkowski, Yang, & Shen, 2017). Although
this within-subjects research points to the persuasiveness of fear in laboratory settings, more
work is needed to understand the best placement of threat components in a message in order
to evoke the ideal fear curve (or curves) for persuasion, and how these shifts in fear compare to
messages that do not evoke fear.
Even if fear is positively related to persuasion, ethical concerns arise when using scare tactics
to persuade audiences to change their health behaviors, especially when targeting high-risk
populations who may not have the resources to eﬀectively deal with the fear aroused by
a health message (Hastings, Stead, & Webb, 2004). If fear causes distrust of a message source or
if audiences become desensitized to fear-arousing messages, then fear appeals could easily backﬁre
and cause more harm than good. Studying audiences’ multiple emotional responses (Dillard,
Plotnick, Godblod, Freimuth, & Edgar, 1996), mixed emotional responses (Myrick & Oliver,
2015), coactive aﬀective states (Keene & Lang, 2016), and their shifts in emotional responses
across message components (Nabi, 2015) are also encouraging strategies for overcoming current
limitations in understanding the role of emotions in health-related media eﬀects.
The above examples all demonstrate the need for media eﬀects researchers interested in
health-related message eﬀects to develop deeper understandings of the other ﬁelds from which
they draw theory and to then critically analyze the role of media and media aﬀordances in applying
those theories. Health messages have had unintended negative eﬀects on audiences, potentially
due to a lack or misspeciﬁcation of theory. For instance, multiple anti-drug public service
announcements have been found to be perceived as ineﬀective by the target audience (Fishbein,
Hall-Jamieson, Zimmer, von Haeften, & Nabi, 2002); others have even associated anti-drug campaign
exposure with lower intentions to avoid drugs (Hornik, Jacobsohn, Orwin, Piesse, &
Kalton, 2008). Increased critical application of theory could help health communication scholars
better explain the role of media and audience factors in shaping health outcomes. Moreover,
theory could help prevent the dissemination of messages that inadvertently harm public health.
The task of better integrating theory into health message design will likely require media
316 • Jessica G. Myrick
researchers to be persuasive advocates for theory’s ability to improve predictive power with both
their interdisciplinary colleagues and with funding organizations at early stages of these projects.
Perhaps more than in other subﬁelds of media eﬀects research, message eﬀects on actual
behaviors (and not just on attitudes or behavioral intentions) are crucial in the context of health
messages. If attitudes or intentions shift but not behaviors, then public investments in large-scale
health campaigns may be unwarranted. As noted above, existing theory in health media eﬀects
posits a speciﬁc relationship between attitudes, intentions, and behaviors, whereby attitudes are
one predictor of intentions, which then predict behavior (Fishbein & Yzer, 2003). However, the
message design formula suggested by the integrated model may need additional testing and further
reﬁnement in order to better encapsulate the dynamic interplay of these variables. For
example, Nabi and Myrick (2019) found that feelings of hope and of fear after viewing sun-safety
messages predicted both intentions and actual behavior, but neither emotion predicted or even
correlated with attitudes toward sun-safety behaviors. They posited that when messages target
unpleasant behaviors (e.g., the time-consuming task of putting on sunscreen, which many people
also ﬁnd uncomfortable), attitudes may be less important than motivational factors (like emotions)
in shaping behavioral outcomes. Additional research is needed to better assess how audience and
message factors determine the interrelationships between attitudes, intentions, and behaviors.
Opportunities for Advancement
The above limitations and controversies oﬀer a number of opportunities for advancement of
health-related media eﬀects research. Additionally, many other possibilities exist for researchers
to advance this area of work. For instance, health communication as a ﬁeld has long included
both mass communication and interpersonal scholars, making it an area of research that fosters
interconnections between these two areas (Kreps, Bonaguro, & Query, 1998). Research on the
eﬀectiveness of public health campaigns has recognized the role interpersonal interactions about
media messages can have in either promoting or preventing desired message outcomes. Southwell
and Yzer (2007) argue that interpersonal communication can directly shape media campaign
outcomes, can mediate campaign outcomes by facilitating subsequent behaviors, and can
moderate campaign outcomes. Other researchers have applied social network theories and analysis
techniques from interpersonal communication contexts to better understand how health
interventions disseminate through schools via social talk (Choi, Hecht, & Smith, 2017) and how
people support each other, share information, and seek information related to health via online
support groups (Kim et al., 2012; Kim, Shah, Namkoong, McTavish, & Gustafson, 2013) and
social media platforms (Myrick, Holton, Himelboim, & Love, 2016).
Another area of advancement for health-related media eﬀects research is in increasing our
understanding of how messages in one’s existing media environment shape responses to subsequent
health messages. Hypothetically, while a laboratory experiment may ﬁnd a particular type
of message highly persuasive in convincing the public to get vaccinated against inﬂuenza, if the
vaccination campaign launches at the same time as news reports about the low levels of eﬀectiveness
of the vaccine, then that campaign may not be eﬀective. Additionally, research on the
eﬀects of competing political frames and counter-frames could provide guidance to health communication
researchers interested in understanding this real-world phenomenon (e.g., Chong &
Druckman, 2013). Gaining deeper insights into inter-media eﬀects and the dynamics of exposure
to diﬀerent types of health media content will require creative, multi-method, and longitudinal
designs in future research.
Media Eﬀects and Health • 317
Understanding social determinants of health and their interrelationships with message eﬀects
was an additional advancement in the ﬁeld that took place during the mid-2000s and continues
to oﬀer opportunities for advancement of media eﬀects research. Viswanath and colleagues
demonstrated how social determinants such as race/ethnicity, social class, and neighborhood
location or structure could impact the eﬀectiveness of health messages (Viswanath & Emmons,
2006). Niederdeppe, Bu, Borah, Kindig, and Robert (2008) suggest purposeful message framing,
use of narratives, and visual images are all promising message design strategies for raising
awareness of social determinants of health.
Implications of Newer Communication Technology
The aﬀordances of newer communication technologies oﬀer many additional research opportunities
for scholars interested in health-related media eﬀects. Thanks to these technologies, it is now
easier than ever for algorithms to create and deliver health messages that are speciﬁcally designed
for particular individuals. Health communication researchers have repeatedly found such message
tailoring to be an eﬀective health behavior-change communication strategy (Kreuter, Farrell, Olevitch,
& Brennan, 2000; Rimal & Adkins, 2003). Research suggests that tailored messages are
more persuasive than generic ones because tailored messages increase the perceived personal relevance
of the content (Jensen, King, Carcioppolo, & Davis, 2012). Notably, though, simply tailoring
health messages on demographics or previous behavior may not be the most persuasive approach
to message tailoring. A meta-analysis of the eﬀects of print tailored health messages found that
tailoring messages based on theory-related variables resulted in stronger eﬀects sizes than did
messages tailored on behavior alone (Noar, Benac, & Harris, 2007). This ﬁnding accentuates the
need to apply theory when studying health media eﬀects and when crafting algorithms to supply
tailored health information to individuals.
Another aﬀordance of communication technology is the ability of individuals to search
online for health information themselves anytime and anywhere, all without having to visit
a trained healthcare professional. Online health information seeking is associated with important
health outcomes, from an increased likelihood of making an appointment with a healthcare
provider (Eastin & Guinsler, 2006) to gains in knowledge, improved social support, enhanced
coping abilities, and stronger self-eﬃcacy (Galarce, Ramanadhan, & Viswanath, 2011; MorahanMartin,
2004; Shim, Kelly, & Hornik, 2006). In an experimental study of the eﬀects of online
health information seeking, a number of important mediators of post-search attitudes and
behaviors were found, including how relevant the search seemed to the user and how scared as
well as how hopeful they felt once the search was over (Myrick, 2017). Additional work is
needed to understand how and why online health information motivates some people to take
action while it causes others to avoid information.
While online health information seeking is associated with many positive outcomes, as mentioned
above, it has a darker side. The internet is ﬁlled with inaccurate health information,
myths, and falsehoods, which can spread across social networks faster than ever due to digital
technologies and the popularity of social media platforms (Southwell & Thorson, 2015). For
instance, even though medical science has repeatedly disavowed the falsely reported link
between vaccinations and autism, many websites continue to warn parents away from vaccinating
their children (Kata, 2012). In 2014, dangerous untrue rumors spread in west Africa via the social
media platform Twitter among communities aﬀected by the Ebola virus (Oyeyemi, Gabarron, &
Wynn, 2014). Fortunately, preliminary work suggests that posting corrections to health
318 • Jessica G. Myrick
misinformation on social media can help correct users’ misbeliefs about health threats (Bode &
Vraga, 2018). Additional work testing theoretical mechanisms of correction eﬀects and how best
to counteract health misinformation, perhaps with tailored counter-messages, is needed.
Furthermore, the aﬀordance of interactivity makes understanding the eﬀects of websites,
video games, wearable health/ﬁtness trackers, and mobile applications an important developing
area of research. Interactivity can be deﬁned as receiving information or feedback that is contingent
upon the previous message or action (Rafaeli, 1988). Users have rated interactive health
websites more favorably, and as having more comprehensible material, than websites with fewer
interactive features (Lustria, 2007). While each of the aforementioned platforms—websites,
games, trackers, and mobile applications—can be interactive, they may diﬀer in other facets that
shape how they impact users. For instance, narratives can be interwoven into exergames, which
are games designed to increase physical activity (Lu, 2015), whereas it may be more diﬃcult to
make an informational website or ﬁtness tracker into a narrative experience. Another consideration
is that social cues and design features allow users to compare their tracked exercise with
others or seek social support, resulting in a mix of human-computer and computer-mediated
health communication. Additional work is needed to continue assessing the psychological
mechanisms of interactivity’s role in health message design and eﬀects.
Conclusion
We know a great deal about the ways in which media aﬀect individual and public health. We
know that there are a number of potential theoretical frameworks to explain message eﬀects in
this area, many of them dealing with the role of psychological processes such as threat and eﬃcacy
perceptions, emotions, and social norms, to name only a few. We know that newer communication
technologies may potentially increase the spread of health misinformation, but they
also oﬀer great promise for reaching diﬀerent populations and motivating sustained behavior
change. We know that there is a great deal of impressive research in this area of media eﬀects,
but also that much remains to be done. If societies want to use media to combat growing
health crises related to poor diets, lack of clean water, increased stress levels, infectious diseases,
and other serious threats to health and well-being, then improved application of theory and
advances in methodology, as well as creative integrations of the two, are needed to continue to
advance health-related media eﬀects research.
References
Ajzen, I. (2011). The theory of planned behaviour: Reactions and reﬂections. Psychology & Health,
26, 1113–1127. doi:10.1080/08870446.2011.613995
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood
Cliﬀs, NJ: Prentice-Hall.
Atkin, C. K., & Rice, R. E. (2013). Theory and principles of public communication campaigns. In
R. E. Rice & C. K. Atkin (Eds.), Public communication campaigns (4th ed., pp. 3–20). Los
Angeles, CA: Sage.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood
Cliﬀs, NJ: Prentice-Hall.
Bandura, A. (2004a). Health promotion by social cognitive means. Health Education and Behavior,
31, 143–164. doi:10.1177/1090198104263660
Media Eﬀects and Health • 319
Bandura, A. (2004b). Social cognitive theory for personal and social change by enabling media. In
A. Singhal, M. J. Cody, E. M. Rogers, & M. Sabido (Eds.), Entertainment-education and social
change: History, research, and practice (pp. 75–96). Mahwah, NJ: Lawrence Erlbaum Associates.
Beck, C. S., Benitez, J. L., Edwards, A., Olson, A., Pai, A., & Torres, M. B. (2004). Enacting “health
communication”: The ﬁeld of health communication as constructed through publication in
scholarly journals. Health Communication, 16, 475–492. doi:10.1207/s15327027hc1604_5
Bigman, C. A., Nagler, R. H., & Viswanath, K. (2016). Representation, exempliﬁcation, and risk:
Resonance of tobacco graphic health warnings across diverse populations. Health Communication,
31, 974–987. doi:10.1080/10410236.2015.1026430
Bode, L., & Vraga, E. K. (2018). See something, say something: Correction of global health
misinformation on social media. Health Communication, 33, 1131–1140. doi:10.1080/
10410236.2017.1331312
Brehm, J. W. (1966). A theory of psychological reactance. New York, NY: Academic Press.
Brown, J. D. (2000). Adolescents’ sexual media diets. Journal of Adolescent Health, 27, 35–40.
doi:10.1016/S1054-139X(00)00141-5
Brown, J. D., Engle, K. L., Pardun, C. J., Guo, G., Kenneavy, K., & Jackson, C. (2006). Sexy media
matter: Exposure to sexual content in music, movies, television, and magazines predicts black
and white adolescents’ sexual behavior. Pediatrics, 117, 1018. doi:10.1542/peds.2005-1406
Brown, J. D., & Walsh-Childers, K. (2009). Eﬀects of media on personal and public health. In
J. Bryant & M. B. Oliver (Eds.), Media eﬀects: Advances in theory and research (3rd ed., pp.
469–489). New York, NY: Routledge.
Choi, H. J., Hecht, M., & Smith, R. A. (2017). Investigating the potential impact of social talk on
prevention through social networks: The relationships between social talk and refusal
self-eﬃcacy and norms. Prevention Science, 18, 459–468. doi:10.1007/s11121-017-0764-6
Chong, D., & Druckman, J. N. (2013). Counterframing eﬀects. The Journal of Politics, 75, 1–16.
doi:10.1017/S0022381612000837
Coleman, R., Thorson, E., & Wilkins, L. (2011). Testing the eﬀect of framing and sourcing
in health news stories. Journal of Health Communication, 16, 941–954. doi:10.1080/
10810730.2011.561918
Dillard, J. P., Li, R., Meczkowski, E., Yang, C., & Shen, L. (2017). Fear responses to threat appeals:
Functional form, methodological considerations, and correspondence between static and
dynamic data. Communication Research, 44, 997–1018. doi:10.1177/0093650216631097
Dillard, J. P., & Peck, E. (2000). Aﬀect and persuasion: Emotional responses to public service
announcements. Communication Research, 27, 461–495. doi:10.1177/009365000027004003
Dillard, J. P., Plotnick, C. A., Godblod, L. C., Freimuth, V. S., & Edgar, T. (1996). The multiple
aﬀective outcomes of AIDS PSAs: Fear appeals do more than scare people. Communication
Research, 23, 44–72. doi:10.1177/009365096023001002
Dillard, J. P., & Shen, L. (2005). On the nature of reactance and its role in persuasive health
communication. Communication Monographs, 72, 144–168. doi:10.1080/03637750500111815
Eastin, M. S., & Guinsler, N. M. (2006). Worried and wired: Eﬀects of health anxiety on
information-seeking and health care utilization behaviors. Cyberpsychology, Behavior and
Social Networking, 9, 494–498. doi:10.1089/cpb.2006.9.494
Fishbein, M. (2008). A reasoned action approach to health promotion. Medical Decision Making,
28, 834–844. doi:10.1177/0272989X08326092
Fishbein, M., Hall-Jamieson, K., Zimmer, E., von Haeften, I., & Nabi, R. L. (2002). Avoiding the
boomerang: Testing the relative eﬀectiveness of antidrug public service announcements before
a national campaign. American Journal of Public Health, 92, 238–245. doi:10.2105/AJPH.92.2.238
Fishbein, M., & Yzer, M. C. (2003). Using theory to design eﬀective health behavior interventions.
Communication Theory, 13, 164–183. doi:10.1111/j.1468-2885.2003.tb00287.x
320 • Jessica G. Myrick
Freimuth, V. S., Massett, H. A., & Meltzer, W. (2006). A descriptive analysis of 10 years of
research published in the Journal of Health Communication. Journal of Health Communication,
11, 11–20. doi:10.1080/10810730500461042
Galarce, E. M., Ramanadhan, S., & Viswanath, K. (2011). Health information seeking. In
T. L. Thompson, R. Parrott, & J. F. Nussbaum (Eds.), The Routledge handbook of health communication
(2nd ed., pp. 167–180). New York, NY: Routledge.
Gibson, R., & Zillmann, D. (2000). Reading between the photographs: The inﬂuence of incidental
pictorial information on issue perception. Journalism & Mass Communication Quarterly, 77,
355–366. doi:10.1177/107769900007700209
Hannawa, A. F., Kreps, G. L., Paek, H.-J., Schulz, P. J., Smith, S., & Street, R. L. (2013). Emerging
issues and future directions of the ﬁeld of health communication. Health Communication, 29,
955–961. doi:10.1080/10410236.2013.814959
Hastings, G., Stead, M., & Webb, J. (2004). Fear appeals in social marketing: Strategic and ethical
reasons for concern. Psychology and Marketing, 21, 961–986. doi:10.1002/mar.20043
Head, K. J., & Noar, S. M. (2013). Facilitating progress in health behaviour theory development
and modiﬁcation: The reasoned action approach as a case study. Health Psychology Review, 8,
34–52. doi:10.1080/17437199.2013.778165
Hornik, R., Jacobsohn, L., Orwin, R., Piesse, A., & Kalton, G. (2008). Eﬀects of the National Youth
Anti-Drug media campaign on youths. American Journal of Public Health, 98, 2229–2236.
doi:10.2105/AJPH.2007.125849
Janis, I. L., & Feshbach, S. (1953). Eﬀects of fear-arousing communications. The Journal of Abnormal
and Social Psychology, 48, 78–92. doi:10.1037/h0060732
Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health Education
Quarterly, 11, 1–47. doi:10.1177/109019818401100101
Jensen, J. D., King, A. J., Carcioppolo, N., & Davis, L. (2012). Why are tailored messages more
eﬀective? A multiple mediation analysis of a breast cancer screening intervention. Journal of
Communication, 62, 851–868. doi:10.1111/j.1460-2466.2012.01668.x
Jones, C. L., Jensen, J. D., Scherr, C. L., Brown, N. R., Christy, K., & Weaver, J. (2015). The Health
Belief Model as an explanatory framework in communication research: Exploring parallel,
serial, and moderated mediation. Health Communication, 30, 566–576. doi:10.1080/
10410236.2013.873363
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica,
47, 263–291. doi:10.2307/1914185
Kata, A. (2012). Anti-vaccine activists, Web 2.0, and the postmodern paradigm—An overview of
tactics and tropes used online by the anti-vaccination movement. Vaccine, 30, 3778–3789.
doi:10.1016/j.vaccine.2011.11.112
Keene, J. R., & Lang, A. (2016). Dynamic motivated processing of emotional trajectories in
public service announcements. Communication Monographs, 83, 1–18. doi:10.1080/
03637751.2016.1198040
Kim, E., Han, J. Y., Moon, T. J., Shaw, B., Shah, D. V., McTavish, F. M., & Gustafson, D. H.
(2012). The process and eﬀect of supportive message expression and reception in online breast
cancer support groups. Psycho-Oncology, 21, 531–540. doi:10.1002/pon.1942
Kim, S. C., Shah, D. V., Namkoong, K., McTavish, F. M., & Gustafson, D. H. (2013). Predictors of
online health information seeking among women with breast cancer: The role of social support
perception and emotional well-being. Journal of Computer-Mediated Communication, 18, 98–
118. doi:10.1111/jcc4.12002
Knobloch-Westerwick, S., & Sarge, M. A. (2013). Impacts of exempliﬁcation and eﬃcacy as characteristics
of an online weight-loss message on selective exposure and subsequent weight-loss
behavior. Communication Research, 42, 547–568. doi:10.1177/0093650213478440
Media Eﬀects and Health • 321
Kreps, G. L., Bonaguro, E. W., & Query, J. L. (1998). The history and development of the ﬁeld of
health communication. In L. D. Jackson & B. K. Duﬀy (Eds.), Health communication research:
Guide to developments and directions (pp. 1–15). Westport, CT: Greenwood Press.
Kreuter, M. W., Farrell, D., Olevitch, L., & Brennan, L. (2000). Tailoring health messages:
Customizing communication with computer technology. Mahwah, NJ: Lawrence Erlbaum
Associates.
L’Engle, K. L., Brown, J. D., & Kenneavy, K. (2006). The mass media are an important context for
adolescents’ sexual behavior. Journal of Adolescent Health, 38, 186–192. doi:10.1016/j.
jadohealth.2005.03.020
Lapinski, M. K., & Rimal, R. N. (2005). An explication of social norms. Communication Theory,
15, 127–147. doi:10.1111/j.1468-2885.2005.tb00329.x
Leventhal, H. (1971). Fear appeals and persuasion: The diﬀerentiation of a motivational construct.
American Journal of Public Health, 61, 1208–1224.
Lu, A. S. (2015). Narrative in exergames: Thoughts on procedure, mechanism, and others. Games
For Health Journal, 4, 19–24. doi:10.1089/g4h.2014.0090
Lustria, M. L. A. (2007). Can interactivity make a diﬀerence? Eﬀects of interactivity on the comprehension
of and attitudes toward online health content. Journal of the American Society for
Information Science and Technology, 58, 766–776. doi:10.1002/asi.20557
Mintzes, B., Barer, M. L., Kravitz, R. L., Bassett, K., Lexchin, J., Kazanjian, A., … Marion, S. A.
(2003). How does direct-to-consumer advertising (DTCA) aﬀect prescribing? A survey in primary
care environments with and without legal DTCA. Canadian Medical Association Journal,
169, 405.
Mongeau, P. A. (1998). Another look at fear arousing messages. In M. Allen & R. Preiss (Eds.),
Persuasion: Advances through meta-analysis (pp. 330–375). Cresskill, NJ: Hampton Press.
Morahan-Martin, J. M. (2004). How internet users ﬁnd, evaluate, and use online health information:
A cross-cultural review. Cyberpsychology, Behavior and Social Networking, 7, 497–510.
doi:10.1089/cpb.2004.7.497
Moyer-Gusé, E. (2008). Toward a theory of entertainment persuasion: Explaining the persuasive
eﬀects of entertainment-education messages. Communication Theory, 18, 407–425.
doi:10.1111/j.1468-2885.2008.00328.x
Myrick, J. G. (2014). Journalism and health. In T. L. Thompson (Ed.), Encyclopedia of health communication
(pp. 605–608). Thousand Oaks, CA: Sage.
Myrick, J. G. (2015). The role of emotions in preventative health communication. Lanham, MD:
Lexington Books.
Myrick, J. G. (2017). The role of emotions and social cognitive variables in online health information
seeking processes and eﬀects. Computers in Human Behavior, 68, 422–433. doi:10.1016/j.
chb.2016.11.071
Myrick, J. G., Holton, A. E., Himelboim, I., & Love, B. (2016). #Stupidcancer: Exploring
a typology of social support and the role of emotional expression in a social media community.
Health Communication, 31, 596–605. doi:10.1080/10410236.2014.981664
Myrick, J. G., & Nabi, R. L. (2017). Fear arousal and health and risk messaging. In R. Parrott
(Ed.), Oxford research encyclopedia of health and risk message design and processing. New York,
NY: Oxford University Press.
Myrick, J. G., & Oliver, M. B. (2015). Laughing and crying: Mixed emotions, compassion, and the
eﬀectiveness of a YouTube PSA about skin cancer. Health Communication, 30, 820–829.
doi:10.1080/10410236.2013.845729
Nabi, R. L. (1999). A cognitive-functional model for the eﬀects of discrete negative emotions on
information processing, attitude change, and recall. Communication Theory, 9, 292–320.
doi:10.1111/j.1468-2885.1999.tb00172.x
322 • Jessica G. Myrick
Nabi, R. L. (2003). Exploring the framing eﬀects of emotion. Communication Research, 30, 224–
247. doi:10.1177/0093650202250881
Nabi, R. L. (2015). Emotional ﬂow in persuasive health messages. Health Communication, 30,
114–124. doi:10.1080/10410236.2014.974129
Nabi, R. L., & Clark, S. (2008). Exploring the limits of social cognitive theory: Why negatively
reinforced behaviors on TV may be modeled anyway. Journal of Communication, 58, 407–427.
doi:10.1111/j.1460-2466.2008.00392.x
Nabi, R. L., & Myrick, J. G. (2019). Uplifting fear appeals: Considering the role of hope in fear-based
persuasive messages. Health Communication, 34, 463–474. doi:10.1080/10410236.2017.1422847
Niederdeppe, J., Bu, Q. L., Borah, P., Kindig, D. A., & Robert, S. A. (2008). Message design strategies
to raise public awareness of social determinants of health and population health
disparities. Milbank Quarterly, 86, 481–513. doi:10.1111/j.1468-0009.2008.00530.x
Noar, S. M., Benac, C. N., & Harris, M. S. (2007). Does tailoring matter? Meta-analytic review of
tailored print health behavior change interventions. Psychological Bulletin, 133, 673–693.
doi:10.1037/0033-2909.133.4.673
Noar, S. M., Myrick, J. G., Zeitany, A., Kelley, D., Morales-Pico, B., & Thomas, N. E. (2015). Testing
a social cognitive theory-based model of indoor tanning: Implications for skin cancer prevention
messages. Health Communication, 30, 164–174. doi:10.1080/10410236.2014.974125
O’Keefe, D. J. (2012). From psychological theory to message design: Lessons from the story of
gain-framed and loss-framed persuasive messages. In H. Cho (Ed.), Health communication
message design: Theory and practice (pp. 3–20). Los Angeles, CA: Sage.
O’Keefe, D. J., & Jensen, J. D. (2007). The relative persuasiveness of gain-framed loss-framed messages
for encouraging disease prevention behaviors: A meta-analytic review. Journal of Health
Communication, 12, 623–644. doi:10.1080/10810730701615198
O’Keefe, D. J., & Jensen, J. D. (2009). The relative persuasiveness of gain-framed and loss-framed
messages for encouraging disease detection behaviors: A meta-analytic review. Journal of Communication,
59, 296–316. doi:10.1111/j.1460-2466.2009.01417.x
Oyeyemi, S. O., Gabarron, E., & Wynn, R. (2014). Ebola, Twitter, and misinformation:
A dangerous combination? BMJ: British Medical Journal, 349. doi:10.1136/bmj.g6178
Pardun, C. J., L’Engle, K. L., & Brown, J. D. (2005). Linking exposure to outcomes: Early adolescents’
consumption of sexual content in six media. Mass Communication and Society, 8, 75–91.
doi:10.1207/s15327825mcs0802_1
Popova, L. (2012). The extended parallel process model: Illuminating the gaps in research. Health
Education and Behavior, 39, 455–473. doi:10.1177/1090198111418108
Quick, B. L. (2009). The eﬀects of viewing Grey’s Anatomy on perceptions of doctors and patient
satisfaction. Journal of Broadcasting & Electronic Media, 53, 38–55. doi:10.1080/
08838150802643563
Rafaeli, S. (1988). Interactivity: From new media to communication. In R. P. Hawkins,
J. M. Wiemann, & S. Pingree (Eds.), Advancing communication science: Merging mass and
interpersonal processes (pp. 110–134). Newbury Park, CA: Sage.
Rimal, R. N., & Adkins, A. D. (2003). Using computers to narrowcast health messages: The role of
audience segmentation, targeting, and tailoring in health promotion. In T. L. Thompson,
A. M. Dorsey, K. I. Miller, & R. Parrott (Eds.), Handbook of health communication (pp. 497–
513). Mahwah, NJ: Lawrence Erlbaum Associates.
Rimal, R. N., & Lapinski, M. K. (2015). A re-explication of social norms, ten years later. Communication
Theory, 25, 393–409. doi:10.1111/comt.12080
Rimal, R. N., & Real, K. (2005). How behaviors are inﬂuenced by perceived norms: A test of the
theory of normative social behavior. Communication Research, 32, 389–414. doi:10.1177/
0093650205275385
Media Eﬀects and Health • 323
Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. The
Journal of Psychology, 91, 93–114. doi:10.1080/00223980.1975.9915803
Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education and
Behavior, 2, 328–335. doi:10.1177/109019817400200403
Rothman, A. J., & Salovey, P. (1997). Shaping perceptions to motivate healthy behavior: The role
of message framing. Psychological Bulletin, 121, 3–19. doi:10.1037/0033-2909.121.1.3
Schwitzer, G. (2014). A guide to reading health care news stories. JAMA Internal Medicine, 174,
1183–1186. doi:10.1001/jamainternmed.2014.1359
Shen, L. (2010). Mitigating psychological reactance: The role of message-induced empathy in
persuasion. Human Communication Research, 36, 397–422. doi:10.1111/j.1468-2958.2010.01381.x
Shim, M., Kelly, B., & Hornik, R. (2006). Cancer information scanning and seeking behavior is
associated with knowledge, lifestyle choices, and screening. Journal of Health Communication,
11(S1), 157–172. doi:10.1080/10810730600637475
Singhal, A., & Rogers, E. M. (1999). Entertainment-education: A communication strategy for social
change. Mahwah, NJ: Lawrence Erlbaum Associates.
Slater, M. D., & Rouner, D. (2002). Entertainment-education and elaboration likelihood: Understanding
the processing of narrative persuasion. Communication Theory, 12, 173–191.
doi:10.1111/j.1468-2885.2002.tb00265.x
So, J., & Nabi, R. L. (2013). Reduction of perceived social distance as an explanation for media’s
inﬂuence on personal risk perceptions: A test of the risk convergence model. Human Communication
Research, 39, 317–338. doi:10.1111/hcre.12005
Southwell, B. G., & Thorson, E. A. (2015). The prevalence, consequence, and remedy of misinformation
in mass media systems. Journal of Communication, 65, 589–595. doi:10.1111/jcom.12168
Southwell, B. G., & Yzer, M. (2007). The roles of interpersonal communication in mass media
campaigns. In C. S. Beck (Ed.), Communication yearbook (Vol. 31, pp. 420–462). New York,
NY: Lawrence Erlbaum Associates.
Tannenbaum, M. B., Hepler, J., Zimmerman, R. S., Saul, L., Jacobs, S., Wilson, K., &
Albarracín, D. (2015). Appealing to fear: A meta-analysis of fear appeal eﬀectiveness and
theories. Psychological Bulletin, 141, 1178–1204. doi:10.1037/a0039729
Ventola, C. L. (2011). Direct-to-consumer pharmaceutical advertising: Therapeutic or toxic? Pharmacy
and Therapeutics, 36(10), 669–684. Retrieved from www.ncbi.nlm.nih.gov/pmc/articles/
PMC3278148/
Viswanath, K., & Emmons, K. M. (2006). Message eﬀects and social determinants of health: Its
application to cancer disparities. Journal of Communication, 56(S1), S238–S264. doi:10.1111/
j.1460-2466.2006.00292.x
Walsh-Childers, K., Braddock, J., Rabaza, C., & Schwitzer, G. (2018). One step forward, one step
back: Changes in news coverage of medical interventions. Health Communication, 33, 174–187.
doi:10.1080/10410236.2016.1250706
Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model.
Communication Monographs, 12, 329–349. doi:10.1080/03637759209376276
Witte, K., & Allen, M. (2000). A meta-analysis of fear appeals: Implications for eﬀective
public health campaigns. Health Education and Behavior, 27, 591–615. doi:10.1177/
109019810002700506
Zillmann, D. (2006). Exempliﬁcation eﬀects in the promotion of safety and health. Journal of
Communication, 56, S221–S237. doi:10.1111/j.1460-2466.2006.00291.x
Zillmann, D., & Gan, S. (1996). Eﬀects of threatening images in news programs on the perception
of risk to others and self. Medienpsychologie: Zeitschrift Für Individual Und Massenkommunikation,
8, 288–305.