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. Author manuscript; available in PMC: 2020 Sep 16.
Published in final edited form as: J Health Commun. 2019 Sep 16;24(9):700–710. doi: 10.1080/10810730.2019.1664676

Cognitive and affective responses to mass-media based genetic risk information in a socio-demographically diverse sample of smokers

Erika A Waters 1, Nicole Ackerman 1, Courtney S Wheeler 1
PMCID: PMC6900866  NIHMSID: NIHMS1541943  PMID: 31525122

Abstract

Many individuals receive information about genomics from the mass media. When media reports are about conditions that are considered behavioral, such as smoking, they may negatively affect certain health-promoting cognitions. We examined how informing adult smokers about the genetic basis for nicotine addiction influences smoking-related health cognitions and affect and whether responses vary by socio-demographics or genetics beliefs. We recruited 392 smokers (Mage=44.5, 52.8% African American, 51.3% no college experience, 66.2% women) from public locations in a mid-sized Midwestern city. They were randomly assigned to read a news article describing either a pharmacy’s decision to stop selling tobacco (n=78) or the discovery of a gene associated with increased risk of nicotine addiction and lung cancer (n=314). Participants also completed a survey assessing socio-demographics, health cognitions (quit intentions, self-efficacy, response efficacy, perceived risk), affect (worry, anticipated regret), genetic determinism, and other genetics beliefs. ANOVAs revealed no statistically significant main effects of genetic information on any health cognitions or affects. Linear regressions revealed that socio-demographics and genetics beliefs moderated very few effects. This suggests that concerns that mass media-based dissemination of genetic discoveries may have detrimental effects on smoking-related cognitions and affects are likely unwarranted.

Keywords: smoking, genetics, health cognitions, affect, gene-environment interaction

Introduction

Cigarette smoking causes considerable morbidity and premature mortality in the United States, but nicotine dependence is an intractable problem for many people. Some smokers might have difficulty quitting because they inherited a genetic variant that predisposes them to have severe nicotine dependence (Bierut & Tyndale, 2018). Thus, it might be possible to either tailor smoking cessation treatment by genotype or attempt to motivate smokers to quit by informing them that they have a high-risk genotype (Bierut & Tyndale, 2018). However, meta-analyses suggest that informing smokers that they have a high-risk genotype will likely not promote smoking cessation (Smerecnik, Grispen, & Quaak, 2012). In addition, limited access to affordable healthcare in the United States and elsewhere suggests that many people may not be able to participate in personalized genetic testing. To increase the reach of genomic medicine, it may be useful for the mass media to disseminate information about the role of genetics in smoking and health and to encourage smoking cessation.

The assumption underlying the idea that providing genetic risk information – whether personalized or not – will motivate smoking cessation is based on several common health behavior theories (Conner & Norman, 1995). The theories assert that informing people that they are at high risk for a negative outcome will increase their perceived risk of that outcome, which in turn will increase intentions to change behavior. Increased intentions, together with other social and contextual factors, will support behavior change. Meta-analyses support these theoretical assertions (Sheeran, Harris, & Epton, 2014). Behavioral interventions that also increase other health-promoting cognitions and affects (i.e., self-efficacy, response efficacy, worry, anticipated regret, intentions) are also effective (Brewer, DeFrank, & Gilkey, 2016; Hay, McCaul, & Mangan, 2006; Sheeran et al., 2014).

One factor that may hamper the extent to which the public may benefit from genetic information presented by the mass media stems from the public’s perception of the nature of genes and genetics. The public uses cultural sources such as documentaries, news media, films, and history to develop and discuss their understanding of genetics (Bates, 2005). Although they do so critically (Bates, 2005), acknowledge the limitations of their sources, and in general endorse the principle that some health problems are caused by both genetics and the environment (Waters, Muff, & Hamilton, 2014), they remain subject to information content of varying quality. For example, the mass media’s depiction of genetics sometimes promotes genetic-based stigma or discrimination (Nwulia et al., 2011), genetic determinism (“your fate is in your genes”) (Horwitz, 2005), and genetic essentialism (“you are your genes”) (Dar-Nimrod & Heine, 2011). Even in cases in which a gene’s activity depends on an individual’s behavior or environment, the media often places undue emphasis on the genetic component and insufficient emphasis on the environmental or behavioral component (Horwitz, 2005).

Deficits in the media’s portrayal of genetics is problematic because, despite the public’s willingness to engage in critical evaluation of the information (Bates, 2005), they are also subject to a variety of challenges that limit their ability to either evaluate the information or apply the information to the fullest extent possible (Condit, 2010). In other words, they may be aware of genetics as a concept and its relation to heredity (Condit, 2010), but they may not have enough knowledge to know how to use it or understand the underlying principles that lead to nuanced interpretations necessary for using it in making medical decisions (Condit & Shen, 2011; Smerecnik, Mesters, de Vries, & de Vries, 2008). Other factors that constrain people’s ability to gain advanced understanding of genetics include limitations in probabilistic thinking, a lack of a cognitive model for integrating the interactive role of behavior and genetics in health outcomes, and a tendency to preferentially attribute health outcomes to behavior (or genetics) depending on which is most favored by their personal goals and context (Condit, 2010, 2011).

Thus, to the extent that (1) genetics beliefs are shaped by the mass media, (2) the media promotes the idea that one’s genetic “fate” is inescapable, and (3) the public’s ability and motivation to evaluate the media’s claims about complex gene-by-environment and gene-by behavior interactions are constrained, one would expect that providing genetic information – personalized or not – would alter health cognitions and affect in ways that inhibit behavior change (Condit, 2011; Conner & Norman, 1995).

Support for this idea has been reported in experimental studies that inform people that there is a genetic basis for a health condition commonly viewed as having a behavioral etiological nature and observational studies that assess people’s etiological beliefs about various health conditions. For both methodologies, attributing a health condition to genetic rather than behavioral causes is associated with lower levels of key health-promoting cognitions, including: perceived controllability (Dar-Nimrod, Cheung, Ruby, & Heine, 2014; Jeong, 2007) (Dar-Nimrod, Zuckerman, & Duberstein, 2013), perceived susceptibility, and perceived disease severity (Smerecnik, Mesters, de Vries, & de Vries, 2009). Genetic attributions were also associated with more genetic determinism (Dar-Nimrod et al., 2014) and less restricted eating behavior. Studies that provide personalized test results have reported similar reports of detrimental changes in health cognitions and affects such as response efficacy (Sanderson et al., 2009) and worry about negative health outcomes (Lerman & Schwartz, 1993).

However, little of the above research examined cigarette smokers (but see Dar-Nimrod, Zuckerman, & Duberstein, 2014; Sanderson et al., 2009). Unlike many of the other health problems that have behavioral and genetic causes that were assessed in prior research (e.g., hypertension, alcohol consumption), cigarette smoking is socially stigmatized and an addictive behavior. Thus, smoking-related health cognitions and affects may be particularly susceptible to influence from information about a genetic basis for nicotine addiction. The only study that examined this experimentally asked undergraduate cigarette smokers to read a news article about the genetic basis for nicotine addiction (Waters, Kincaid, et al., 2014). It found no effects on any of the health behavior constructs assessed (i.e., quitting self-efficacy, worry, or feelings of risk). However, participants in that study were young (Mage = 19 years), relatively light smokers (41% smoked less than 1 cigarette daily), and racially homogenous (less than 20% racial minority), raising concerns about the extent to which the results would generalize to other populations of smokers. Thus, almost nothing is known about the effect of informing smokers in the general U.S. population that there is a genetic basis for nicotine addiction.

Objectives and Hypotheses

Our primary objective was to test whether and how informing a socio-demographically diverse sample of adult smokers about the genetic basis for nicotine addiction influences smoking-related health cognitions and affect. We chose to provide non-personalized information in the form of a news article because it more accurately reflects the current state of genomic medicine; that is, potentially exciting scientific discoveries have been found, but few have been translated into a medical technology or service that can be implemented in widespread clinical practice. Yet, the information is disseminated through traditional and social mass media sources, likely shaping the public’s attitudes about the implications of the findings for their lives (Pidgeon, Kasperson, & Slovic, 2003). We pose the following hypotheses and research questions:

  1. Based on the literature describing the relationship between genetic attributions and health behavior theory constructs (Dar-Nimrod et al, 2013; Dar-Nimrod et al, 2014; Jeong, 2007; Smerecnik et al, 2009; Sanderson et al., 2009; Lerman &Schwartz, 1993), we hypothesized that smokers who read an article about the genetic basis for nicotine addiction would display less favorable smoking cessation-related cognitions (i.e., lower self-efficacy of quitting, lower perceived risk, lower intentions to quit) and less negative affect (i.e., less worry and anticipated regret).

  2. Based on the literature suggesting that people process and interpret new information in ways that are consistent with their existing beliefs (Chen & Chaiken, 1999; Petty & Cacioppo, 1996), we hypothesized that the effect of the genetic article on smoking-related cognitions and affect would be moderated by their existing beliefs about genetic determinism and perceived controllability of a behavior that has a genetic vs. environmental etiology. Specifically, we expected that the detrimental effect of learning about a genetic basis for nicotine addiction would be stronger as people endorsed higher genetic determinism and lower perceived controllability.

  3. There is ample research describing the public’s concern with genetic stigma and discrimination (e.g., Nwulia, Hipolito, Aamir, et al., 2011). Therefore, we also conducted exploratory, hypothesis-generating analyses asking whether other genetic beliefs (e.g., stigma and discrimination concerns) moderated any of the main effects.

People from racial minority backgrounds, with less formal education, and with limited health literacy or numeracy may respond differently to genetic information compared to people from majority racial backgrounds, with more formal education, and with adequate health literacy and numeracy (Diaz, Mainous, Gavin, & Wilson, 2014; Hensley Alford et al., 2010; Hurle et al., 2013). Therefore, our secondary objective was to determine whether race, education, health literacy, or numeracy moderated the effects of the articles on cognitive and affective outcomes. Due to a dearth of research in the area that precludes directional hypotheses, this objective was considered exploratory.

Materials and Methods

Design and Setting

To achieve the study objectives, we conducted secondary analysis of data collected during a randomized controlled trial whose goal was to test whether two communication interventions, alone or in combination, reduced rejection of a news media article about the genetic basis for nicotine addiction compared to an article that did not include the communication intervention. Study procedures were conducted in the Saint Louis, Missouri, USA metropolitan area from July 2014 – May 2015. Analyses for the parent study yielded no statistically significant differences among the five conditions (unpublished data). There were also no significant interactions among the five experimental conditions and demographics, indicating that the communication intervention was ineffective for all audience segments.

Participants

Inclusion criteria were: age 18–74, be a current smoker, able to read and speak English, have African American or Caucasian racial background, and have a basic knowledge of genetics but not self-identify as an expert in genetics. We recruited participants using multiple strategies including print advertisements, face-to-face recruitment at local businesses and community centers, a participant registry, and word of mouth. Because one of the goals of the parent study was to develop messages that were equally effective for smokers with less formal education and/or who were African American, recruitment was stratified so that approximately 50% of the sample was African American and 50% had no college experience.

Of the 524 individuals who were screened, 434 (83%) were eligible and agreed to enroll in the study. Of the 434 enrollees, 419 (96.5%) returned the baseline survey (354 by mail and 65 online). Eighteen individuals were excluded from analysis because they did not complete the consent process or they took the survey multiple times. Another 7 participants were excluded due to not being a current smoker, leaving a final sample size of 392 (i.e., 90.3% of enrollees).

Procedure

For the parent study, eligible participants were randomly assigned in a 1:1 ratio to one of five experimental conditions in which they read either a news article either pertaining to a pharmacy’s decision to stop selling tobacco products or one of four articles describing the discovery of a gene for nicotine addiction and lung cancer (described below). Randomization was executed by computer for participants electing to complete the study online. Sequentially lettered envelopes were used to both randomize participants who preferred completing the study by mail and to blind the research assistant who was responsible for mailing the study materials.

Participants first completed a pre-intervention survey, then read the article to which he or she was randomly assigned, and then completed a post-intervention survey. Participants were sent a follow-up survey 3 months after completion of the initial surveys and intervention; those data will not be analyzed in this manuscript due to a very low response rate. Participants received one $20 gift card to a local grocery store for returning the baseline survey and another $20 gift card for returning the follow-up survey.

Stimuli

All stimuli were tested and refined using cognitive interviewing with 20 participants from the target population. The text for the five conditions can be found in the online supplementary materials Appendix A. In brief, the stimulus for the “no genetic information” control article was an abbreviated version of a CNN article describing the decision made by CVS pharmacies to stop selling tobacco products (hereafter referred to as “pharmacy control,” Landau, 2014). The stimulus for the “authentic control” condition was an abbreviated version of an article describing the discovery of a genetic variant associated with more severe nicotine addiction and a higher risk of developing lung cancer (Associated Press, 2008).

The original goal of the remaining three conditions was to reduce rejection of a news media story that described the discovery of a genetic basis for nicotine addiction. The content of those conditions was created based on factors identified in focus groups of adult smokers that seemed to be associated with smokers’ rejection of the news story (Waters, Ball, Carter, & Gehlert, 2014; Waters, Ball, & Gehlert, 2017). Specifically, the goal of the “agentic” article was to prevent the genetic information from reducing quitting self-efficacy. It included a short paragraph telling participants that, although quitting smoking is difficult, it can be done with effort and assistance. In contrast, the “analogy” article sought to increase acceptance of the genetic information by linking a novel gene-by-behavior risk (i.e., genetics and nicotine addiction) to a gene-by-behavior risk that the focus group members found credible (i.e., genetics and alcohol dependence. It included a paragraph emphasizing the similarity between the research reported in the smoking article with research about the genetics of alcoholism. The last article included both the agentic and analogy paragraphs.

Measures

See Table 1 for the wording of all items relevant for the analyses presented here. See the online supplementary materials Appendix B for details about the confirmatory and exploratory factor analyses (CFA and EFA, respectively) that we used to obtain the scales are listed below and described in Table 1. The full survey can be obtained from the corresponding author.

Table 1.

Item wording and internal consistency for health cognitions, affects, and possible moderators included in these analyses

Pre-Intervention Survey Item wording [Response Options]
Genetic Determinism I think that the fate of each person is in his or her genes [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Post-Intervention Survey
Intentions to Quit Smoking (α = 0.85) Which of the following best describes you? [1-I do not want to stop smoking, 2-I think I should stop smoking but do not really want to, 3-I want to stop smoking but have not thought about when, 4-I really want to stop smoking but I donť know when I will, 5-I want to stop smoking and hope to soon, 6-I really want to stop smoking and intend to in the next 3 months, and 7-I really want to stop smoking and intend to in the next month]
Are you seriously considering quitting smoking in the next 3 months [yes/no]
Overall, on a scale from 1 to 10 where 1 is NOT AT ALL interested and 10 is EXTREMELY interested, how interested are you in quitting smoking in the next 3 months?
Self-efficacy (α = 0.92) Overall, how confident are you that you will try to quit smoking in the next 3 months? [1-Not at all confident, 2-A little confident, 3-Somewhat confident, 4-Very confident, and 5-Completely confident]
Overall, how confident are you that you will actually quit smoking in the next 3 months? [1-Not at all confident, 2-A little confident, 3-Somewhat confident, 4-Very confident, and 5-Completely confident]
Response-efficacy (α = 0.88) Do you think quitting smoking would reduce your chances of getting lung cancer? [definitely would not, probably would not, maybe, probably would, definitely would]
How much do you think quitting smoking would reduce your chances of getting lung cancer? [not at all, a little, somewhat, a lot, a huge amount]
Worry about getting lung cancer (α = 0.86) How worried are you about getting lung cancer? [1-Not at all worried, 2-A little worried, 3-Somewhat worried, 4-Very worried, 5-Extremely worried]
How often do you worry about getting lung cancer? [1-Never, 2-Sometimes, 3-Occasionally, 4-Often, 5-Always]
Worry about being unable to quit smoking (α = 0.73) How worried are you about having a gene that makes it harder to quit smoking? [1-Not at all worried, 2-A little worried, 3-Somewhat worried, 4-Very worried, 5-Extremely worried]
How often do you worry about not being able to quit smoking?” [1-Never, 2-Sometimes, 3-Occasionally, 4-Often, 5-Always]
Perceived risk of getting lung cancer (α = 0.75) How likely do you think it is that you will get lung cancer in your lifetime if you do not quit smoking? [1-Very Unlikely, 2-A Little Unlikely, 3-Neither likely nor unlikely, 4-Liklely, 5-Very Likely, 6-I do not know]
Compared to other smokers, how likely do you think you are to get lung cancer in your lifetime if you do not quit smoking? [1-Much less likely, 2-Less likely, 3-About the same, 4-More likely, 5-Much more likely, 6-I do not know]
Select one answer that best represents your opinion about the statement: “I feel like I could easily get lung cancer in my lifetime if I do not quit smoking.” [1-Much less likely, 2-Less likely, 3-About the same, 4-More likely, 5-Much more likely, 6-I do not know]
Perceived risk of having a gene making it harder to quit smoking (α = 0.87) How likely do you think it is that you have a gene that makes it harder for you to quit smoking? [1-Very Unlikely, 2-A Little Unlikely, 3-Neither likely nor unlikely, 4-Likely, 5-Very Likely, 6-I do not know]
Compared to other smokers, how likely do you think you are to have a gene that makes it harder for you to quit smoking? [1-Much less likely, 2-Less likely, 3-About the same, 4-More likely, 5-Much more likely, 6-I do not know]
Select one answer that best represents your opinion about the statement: “I feel like I have a gene that makes it harder for me to quit smoking.” [1-Much less likely, 2-Less likely, 3-About the same, 4-More likely, 5-Much more likely, 6-I do not know]
Anticipated regret (α = 0.75) I would be mad at myself if I got lung cancer because I didn’t quit smoking [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
I would regret not quitting if I got lung cancer because I didn’t quit smoking [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
If I had it to do over again, I would never have started smoking cigarettes [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Perceived Control Despite Genetics (α = 0.82) In your opinion, how much control does a smoker have over smoking when he or she has a gene related to nicotine addiction? [1 = No control, 2 = very little control, 3 = a good amount of control, 4 = a lot of control, and 5 = complete control]
In your opinion, how much control does a smoker have over smoking when he or she has a genetic makeup that is linked with nicotine addiction? [1 = No control, 2 = very little control, 3 = a good amount of control, 4 = a lot of control, and 5 = complete control]
In your opinion, how much control does a smoker have over smoking when he or she has a gene that is very common among heavy smokers? [1 = No control, 2 = very little control, 3 = a good amount of control, 4 = a lot of control, and 5 = complete control]
Perceived Control Despite Environment (α = 0.81) In your opinion, how much control does a smoker have over smoking when his or her friends are all smokers? [1 = No control, 2 = very little control, 3 = a good amount of control, 4 = a lot of control, and 5 = complete control]
In your opinion, how much control does a smoker have over smoking when he or she loves to go out to parties and smoke? [1 = No control, 2 = very little control, 3 = a good amount of control, 4 = a lot of control, and 5 = complete control]
In your opinion, how much control does a smoker have over smoking when his or her lifestyle is linked with smoking? [1 = No control, 2 = very little control, 3 = a good amount of control, 4 = a lot of control, and 5 = complete control]
Stigma Concerns (α = 0.87) Nonsmokers will think that smokers who have a gene linked to nicotine addiction are flawed. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Nonsmokers will think that smokers who have a gene linked to nicotine addiction are weak. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Nonsmokers will look down on smokers who have a gene linked to nicotine addiction. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Discourage/understand smoking (α = 0.71) Kids who learn that they have a gene linked to nicotine addiction before they start smoking will be less interested in starting smoking in the first place. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Smokers who hear about a gene linked to nicotine addiction will be more interested in quitting smoking. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Knowing that there is a gene linked to nicotine addiction will help smokers understand why they have such a hard time quitting smoking. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Discrimination concerns (α = 0.74) Getting health insurance will be harder for smokers who have a gene linked to nicotine addiction. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Rental housing will be harder to get for smokers who have a gene linked to nicotine addiction. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Getting jobs will be harder for smokers who have a gene linked to nicotine addiction. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Perceived absolution about smoking (α = 0.55) Smokers who hear about a gene linked to nicotine addiction will use the information as an excuse to not quit smoking. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Smokers who hear about a gene linked to nicotine addiction will not take personal responsibility for their behavior. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Smokers who hear about a gene linked to nicotine addiction will feel comforted because they finally know why they have a hard time quitting. [1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree]
Health Literacy How confident are you filling out forms by yourself? [1 = not at all, 2 = a little, 3 = somewhat, 4 = very, 5 = extremely]
Numeracy Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin would come up heads in 1,000 flips? (correct answer = 500)
Imagine that the chance of getting a disease is 1%. If there were 1,000 people, about how many would be expected to get the disease? (correct answer = 10)
Imagine that the chance of getting an infection is 1 in 1,000. What percent of people would be expected to get the disease? (correct answer = 0.1)
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Pre-Intervention Survey

Genetic Determinism

A modified version of the Beliefs in Genetic Determinism Scale (Keller, 2005) was included, but CFA showed that the scale did not perform well in our sample. Thus, we included only the single item assessing beliefs about genetic “fate.”

Tobacco Use History, Genetics Benefits and Drawbacks

Smoking status, nicotine dependence, cessation history, use of alternative tobacco products (e.g., electronic cigarettes), and perceived benefits and drawbacks of genetics research were assessed but were not included in these analyses. Therefore, we do not discuss them further.

Post-Intervention Survey

Information Recall

To obtain a rough indication of whether or not participants attended to the news article, we asked them, “Think about the news article you just read. What was it trying to tell people? [The article said that people with a certain gene are more likely to have a harder time quitting smoking than people without the gene / The article said that people with a certain gene are less likely to get lung cancer than people without the gene / The article said that CVS pharmacies were going to stop selling cigarettes / I do not remember what the article said]. Responses were coded as correct or incorrect/don’t remember.

Health Cognitions and Affects

We used or adapted the following measures from prior research: (1) intentions to quit smoking (Hyland et al., 2006), (2) self-efficacy of quitting smoking (Etter, Bergman, Humair, & Perneger, 2000), (3) response efficacy of quitting smoking in reducing lung cancer risk (Sanderson et al., 2009), (4) worry about getting lung cancer (Köblitz et al., 2009), (5) worry about being unable to quit smoking (Köblitz et al., 2009), (6) perceived risk of getting lung cancer (adapted from Weinstein et al., 2007), (7) perceived risk of having a gene making it harder to quit smoking (adapted from Weinstein et al., 2007), and (8) anticipated regret (Weinstein et al., 2007). “I don’t know” responses for the perceived risk items were coded as missing. Average scores were created for each construct and re-scored from 0 to 4, except for intentions to quit smoking, which was standardized.

Genetics Beliefs

Several genetic beliefs were assessed: perceived control over smoking despite having a gene that increases nicotine addiction (three items, modified from Dar-nimrod, Zuckerman, & Duberstein, 2013), perceived control over smoking despite being in a smoking-conducive environment (3 items, modified from Dar-Nimrod et al., 2013), “stigma concerns” (a belief that genetic testing related to nicotine addiction will exacerbate the stigma that smokers already experience), “discrimination concerns” (a belief that genetic testing related to nicotine addiction will prompt others to discriminate against them due to their genetic status), “discourage/understand smoking” (a belief that knowing about a gene linked to nicotine addiction will discourage smoking and help smokers understand why they have trouble quitting), and “perceived absolution about smoking” (a belief that hearing about a gene linked to nicotine addiction will make smokers believe that the information provides an excuse for not quitting). The latter four concepts were developed out of prior focus group research on this topic (Waters, Ball, et al., 2014; Waters et al., 2017).

Socio-Demographics

We assessed sex, age, race/ethnicity, and education using standard measures. We also assessed health literacy (one item, modified from the Single Item Literacy Scale, Morris, MacLean, Chew, & Littenberg, 2006) and numeracy (three items, modified from Schwartz, Woloshin, Black, & Welch, 1997). Health literacy was categorized as limited (1 to 3) or adequate (4 or 5) (Morris et al., 2006). Numeracy was a sum of the number of items answered correctly (0 to 3). We categorized participants as having limited (0 or 1 correct) or adequate (2 or 3 correct) numeracy.

Other

The following constructs were assessed but were not included in these analyses because they were beyond the scope of the research question: perceived message clarity and personal relevance, interest in taking a genetic test, affect about taking a genetic test, perceived genetic knowledge, and medical mistrust.

Statistical Analysis

Confirmatory factor analysis was performed to verify the items that purportedly assessed perceived control despite genetics and the environment (Dar-Nimrod et al., 2013) adequately represented the factors in our sample. Exploratory factor analysis was performed on the genetics beliefs survey items developed by the researcher based on previous focus groups (Waters, Ball, et al., 2014; Waters et al., 2017). The resulting factor scores for each perceived control and genetic belief subscale were used in subsequent analyses. See online Appendix B for more details.

A dichotomous variable was created representing all four genetic articles combined vs. the pharmacy control article. ANOVA was used to test the main effects of the dichotomous intervention variable on smoking-related cognitive and affective outcomes. Linear regression models were used to test for interactive effects between the intervention variable and potential moderators on the outcomes. The pharmacy control article was the reference group for all analyses. To investigate any statistically significant interactions, least-squares means were calculated and post-hoc tests with a Tukey-Kramer correction were performed on categorical moderators. For continuous moderators, simple slopes analysis was performed based on points that were one standard deviation above and below the mean value for the moderator. If the simple slopes posthoc analyses were not significant, the effects were not examined further. All statistical analysis was done in SAS ® version 9.4.

Post-hoc power analysis indicates that 394 participants allocated in a 4:1 ratio will permit detecting a small-to-moderate sized effect (Cohen’s d=.36), assuming α=.05 and Power=.80. Missing data were handled using listwise deletion.

Results

Participants were distributed evenly across the five articles: pharmacy control (N=78), authentic control (N=80), analogy (N=78), agentic (N=77), and agentic & analogy (N=79). After combining the intervention conditions, 314 participants received a version of the genetic article and 78 received the pharmacy control. Socio-demographic characteristics and potential moderators were distributed evenly between the two groups (ps > .05, see Table 2). As planned, approximately half of the sample self-reported being either African American (52.8%) or having no college experience (51.3%). The vast majority of the sample (n=318, 81%) correctly recalled the information presented in the news article. Table 3 contains the outcome descriptives and intercorrelations.

Table 2.

Socio-demographic characteristics and genetics beliefs by experimental condition

Pharmacy Control Genetic Story
n (%) n (%)
Sex Male 26 (33.8%) 109 (34.9%)
Female 51 (66.2%) 203 (65.1%)
Race White 41 (52.6%) 166 (52.9%)
Minority 37 (47.4%) 148 (47.1%)
Formal Education Vocational or lower 39 (50.0%) 162 (51.6%)
Some college or higher 39 (50.0%) 152 (48.4%)
Health Literacy Limited 12 (15.4%) 64 (20.4%)
Adequate 66 (84.6%) 250 (79.6%)
Numeracy Limited 53 (68.0%) 213 (68.3%)
Adequate 25 (32.0%) 99 (31.7%)
Mean (SD) Mean (SD)
Age 45.0 (13.1) 44.3 (13.2)
Genetic Determinism 1.58 (1.15) 1.70 (1.13)
Perceived Control Despite Environment 1.45 (0.91) 1.53 (0.85)
Perceived Control Despite Genetics 1.24 (0.84) 1.40 (0.78)
Stigma Concerns 2.04 (1.05) 1.96 (0.96)
Discrimination Concerns 2.26 (0.99) 2.13 (0.93)
Discourage/Understand Smoking 2.47 (0.84) 2.54 (0.75)
Perceived Absolution about Smoking 2.28 (0.75) 2.34 (0.73)
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Note. Higher mean values indicate stronger endorsement of the construct.

Table 3.

Descriptives and intercorrelations among cognitive and affective outcomes.

Correlation Coefficient (p-value)
Intentions (standardized) Self-efficacy Response-efficacy Worry – Lung Cancer Worry – Quit Smoking Perceived Risk – Lung Cancer Perceived Risk – Smoking Gene Anticipated Regret
Intentions (standardized) - 0.74 (<0.001) 0.19 (<0.001) 0.32 (<0.001) 0.29 (<0.001) 0.06 (0.21) −0.01 (0.77) 0.31 (<0.001)
Self-efficacy - 0.21 (<0.001) 0.24 (<0.001) 0.20 (<0.001) 0.02 (0.68) −0.02 (0.67) 0.19 (<0.001)
Response-efficacy - 0.24 (<0.001) 0.17 (0.001) 0.20 (<0.001) 0.06 (0.25) 0.39 (<0.001)
Worry about getting lung cancer - 0.53 (<0.001) 0.47 (<0.001) 0.11 (0.03) 0.34 (<0.001)
Worry about being unable to quit smoking - 0.29 (<0.001) 0.38 (<0.001) 0.36 (<0.001)
Perceived risk of getting lung cancer - 0.12 (0.02) 0.26 (<0.001)
Perceived risk of having a gene making it harder to quit - 0.05 (0.31)
Anticipated Regret -
Averaged scale mean (SD) 0.002 (0.91) 1.89 (1.27) 3.12 (1.00) 2.14 (1.10) 2.17 (1.11) 2.32 (1.00) 2.11 (1.13) 3.16 (0.92)
Averaged scale minimum, maximum −2, 1 0, 4 0, 4 0, 4 0, 4 0, 4 0, 4 0, 4
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Primary Objective and Hypotheses

There were no direct effects of intervention condition on any of the cognitive or affective outcomes (Table 4). With the few exceptions described below, the vast majority of the interactions were not statistically significant (Table 5 and Appendix Tables C.1 and C.2). The results did not differ when the sample was restricted to individuals who correctly remembered the contents of the article (data available from first author).

Table 4.

Main effects of intervention condition on cognitive and affective outcomes

Outcome F df p Eta-Squared Pharmacy Control Mean (SD) Genetic Story Mean (SD)
Intentions (standardized) 2.03 1, 389 0.16 0.0052 −0.13 (0.93) 0.04 (0.9)
Self-efficacy 0.27 1, 389 0.60 0.0007 1.82 (1.20) 1.90 (1.28)
Response-efficacy 0.11 1, 389 0.74 0.0003 3.15 (1.06) 3.11 (0.98)
Worry about getting lung cancer 0.27 1, 390 0.60 0.0007 2.08 (1.24) 2.15 (1.06)
Worry about being unable to quit smoking 1.17 1, 390 0.28 0.0030 2.29 (1.19) 2.14 (1.09)
Perceived risk of getting lung cancer 1.21 1, 379 0.27 0.0032 2.43 (0.97) 2.29 (1.01)
Perceived risk of having a gene making it harder to quit 0.72 1, 380 0.40 0.0019 2.01 (1.18) 2.13 (1.12)
Anticipated Regret 0.19 1, 390 0.67 0.0005 3.12 (1.01) 3.17 (0.89)
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Table 5.

Summary of statistically significant exploratory moderation effects

Moderators Outcomes
Intentions Self-Efficacy Response-Efficacy Worry – Lung Cancer Worry – Quit Smoking Perceived Risk – Lung Cancer Perceived Risk – Smoking Gene Anticipated Regret
Race X* X ns ns ns ns ns ns
Education ns ns ns ns ns ns ns ns
Health Literacy X ns ns ns ns ns ns ns
Numeracy ns ns ns ns ns ns ns X
Genetic Perceived Control ns ns ns ns ns ns ns ns
Environmental Perceived Control ns ns ns X ns X* ns ns
Stigma Concerns X* X X* ns ns ns ns X
Discourage/Understand Smoking ns ns ns ns ns ns ns ns
Discrimination Concerns ns ns X* ns ns ns ns ns
Perceived Absolution ns ns ns ns ns ns ns X
Genetic Determinism ns ns ns ns ns ns ns ns
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An ‘X’ indicates a statistically significant interaction. A * indicates a that the simple effects analysis of the interaction was statistically significant (p<0.05). A ‘ns’ indicates a non-significant interaction.

Secondary Objective and Exploratory Analyses

Only a few socio-demographic characteristics were identified as potential moderators, and the effects were not consistent across health cognitions or affect. Race moderated the effect of intervention condition on intentions (p=0.005, η2=0.02) and self-efficacy (p=0.03, η2=0.01) in a cross-over pattern. Post-hoc tests indicated that the genetic story article (versus pharmacy control) elicited higher intentions in minority (p=.02) but not white participants (p=.73). None of the post-hoc comparisons related to self-efficacy were significant.

Health literacy moderated the effect of the genetic story on intentions (p=0.004, η2=0.02) in a cross-over pattern, and numeracy moderated the effect of the genetic story on anticipated regret (p=0.03, η2=0.01) in a cross-over pattern. However, none of the post-hoc comparisons were significant.

Environmental perceived control moderated the effect of intervention condition on lung-cancer-related worry (p=0.04, η2=0.01) and perceived risk (p=0.04, η2=0.01). Simple slopes analyses found that, among people with lower environmental perceived control, the genetic article elicited lower perceived risk (p=.03). No other post-hoc comparisons were significant.

Three of the four genetics beliefs subscales were identified as moderators, but the results were not consistent across outcomes. Stigma concerns moderated the effect on self-efficacy (p=0.019, η2=0.01), response-efficacy (p=0.02, η2=0.01), anticipated regret (p=0.02, η2=0.01), and quit intentions (p=0.03, η2=0.01). However, only two post-hoc analyses were statistically significant. First, the genetic story elicited higher intentions for people with lower but not higher stigma concerns (p=0.01). Second, the genetic story elicited lower response-efficacy for people with higher but not lower stigma concerns (p=0.04). Discrimination concerns moderated the relationship between intervention condition and response-efficacy (p=0.008, η2=0.02). The genetic story elicited lower response-efficacy for those with lower discrimination concerns (p=.01) but had no effect on those with higher discrimination concerns. Perceived absolution about smoking moderated the relationship between intervention condition and anticipated regret (p=0.02, η2=0.01). However, the post-hoc analyses were not statistically significant.

Education, perceived genetic control, discourage/understand smoking, and genetic determinism did not moderate any effects.

Discussion

We investigated how providing socio-demographically diverse adult smokers with information about the genetic basis for nicotine addiction influenced smoking-related cognitions and affect. We also explored whether such effects might be moderated by socio-demographic characteristics or genetic beliefs. We draw two primary conclusions. First, providing information about the genetic basis for nicotine addiction did not directly affect smoking-related cognitions or affect. Second, with only a small number of exceptions, individual differences in demographics or genetic opinions did not moderate the effects. Furthermore, the few statistically significant interactions that were present did not generally provide useful information after probing them further (i.e., small effect sizes and non-significant simple effects analyses).

These results are consistent with research examining the effect of similar information on the cognitions and beliefs of young and mostly white undergraduate smokers (Waters, Kincaid, et al., 2014). Yet, it conflicts with experimental research reporting detrimental effects of genetic attributions on health-related cognitions and affect in a variety of behavioral domains (Dar-Nimrod et al., 2014; Jeong, 2007; Smerecnik et al., 2009). One possible explanation for the discrepancy is that, because this study was not conducted under supervised laboratory conditions, participants may not have read the article diligently. However, an effect that is only present under tightly controlled conditions is unlikely to have a major impact in more ecologically valid contexts. Thus, concerns about the detrimental impact of news stories about genetic discoveries may not be necessary.

It could also be that the idea of having a high-risk gene may be a larger threat to smokers’ social well-being or sense of autonomy than their physical well-being, thereby prompting them to reject the information (Waters et al., 2017). Such rejection would prevent the information from influencing smoking-related cognitions and affects. This could explain discrepancies between our work and others’ work because hypertension (Smerecnik et al., 2009) and alcohol use (Dar-Nimrod et al., 2013) are not as stigmatized as tobacco use. However, it would not explain the difference between our findings and studies related to obesity (Jeong, 2007). Another possibility is that the participants accepted the message as generally true, but rejected the possibility that it applied directly to them. If this is the case, then it would be reasonable to assume that people’s responses to genetic information disseminated via the mass media may differ compared to people’s responses from personal genetic test results. Alternatively, smokers may believe that they “already know” that smoking is harmful and, therefore, the genetic information is insufficiently threatening compared to more graphic anti-tobacco campaigns (Centers for Disease Control and Prevention, 2013; Waters, Ball, et al., 2014).

It was surprising that genetic determinism did not moderate the effect of the genetic article on self-efficacy, because one would expect that smokers who believe that genes determine one’s future would be discouraged upon learning about a gene that makes it more difficult to quit. However, others have noted that people have complicated views about genetic determinism, and occasionally “deploy” it only when it serves their goals (Condit, 2011). Thus, smokers’ assertion of autonomy over their ability to quit (Waters, Ball, et al., 2014) may override countervailing effects of deterministic beliefs. The sporadic occurrence of significant interactions that also yielded significant simple effects results are difficult to explain. For example, the genetic story produced higher intentions among non-whites and among those with low stigma concerns, but there was little consistency in results across moderators or across health-related cognitions/affect.

Limitations and Future Directions

These results should be considered in light of several limitations. First, participants were recruited from the Midwestern United States; future studies should recruit individuals from different geographic areas to increase generalizability beyond one location. Second, the original study design had five conditions, which we collapsed to two categories. This resulted in a much higher sample size in the genetic than control group, and may have reduced our ability to find significant interactions and simple effects. Future research should achieve a more balanced distribution of participants across groups. It may also be useful to examine the effects of an article that asserts that there is no genetic basis for nicotine addiction. Although research with college students has found that a “non-genetic” etiological explanation does not affect people’s beliefs any more than a genetic explanation (Waters, Kincaid, et al., 2014), it would be interesting to see the effects in a more demographically diverse population. Third, we assessed health literacy with the Single Item Literacy Screener (Morris et al., 2006). Although it performs moderately well in ruling out people with limited literacy, a more comprehensive measure might have greater variability and yield more useful information.

Finally, our investigation into differences in responses to mass media-based genetic information among racial/ethnic minorities and people with different levels of perceived environmental control, stigma concerns, and discrimination concerns was highly exploratory. Relatedly, our investigation of several moderators – many of which did not have directional hypotheses and none of which showed consistent results across outcomes – increases the likelihood of type I error. We did not perform a Bonferroni correction because the analyses were hypothesis-generating. However, if one had been performed, the corrected alpha would be p<.0005, resulting in no significant results. This suggests caution in interpreting the moderation findings and the need to replicate them in future research designed specifically for that purpose.

Conclusions

Socio-demographically diverse adult smokers do not seem to interpret news articles about the discovery of a genetic basis for nicotine addiction and lung cancer in a way that could discourage them from attempting to quit. Furthermore, smokers’ reactions are largely similar across socio-demographic groups and opinions about genetics more generally. Thus, concerns about possible detrimental effects of the mass media disseminating genetic discoveries on smoking-related cognitions and affects are likely unwarranted.

Supplementary Material

Supplementary Material

Acknowledgments

Funding Acknowledgements

This research was supported by a Mentored Research Scholar Grant awarded to Erika Waters by the American Cancer Society (ACS), MRSG-11-214-01-CBBP, by the Washington University Institute of Clinical and Translational Sciences grant UL1TR000448 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH), and the Barnes Jewish Hospital Foundation.

Footnotes

Informed consent: Informed consent was obtained from all individual participants included in the study.

Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee for Washington University in Saint Louis and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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