Effect of e-cigarette advertisement exposure on intention to use e-cigarettes in adolescents

Addictive Behaviors 82 (2018) 1–6

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Effect of e-cigarette advertisement exposure on intention to use e-cigarettes in adolescents Andrea M. Stroup, Steven A. Branstetter

T

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Pennsylvania State University, Department of Biobehavioral Health, 219 Biobehavioral Health Building, University Park, PA 16802, USA

H I G H L I G H T S exposure increased the intent to use e-cigarettes among non-smokers. • Ad exposure did not increase the intent to use e-cigarettes among smokers. • Ad exposure moderated the relation between perceived barriers and intent to use. • Ad exposure moderated the relation between perceived benefits and intent to use. • Ad • Ads may attract new users more than prompting switching among current smokers.

A R T I C L E I N F O

A B S T R A C T

Keywords: E-cigarette Adolescence Media exposure Health belief model Intention to use

Introduction: With the growth of electronic cigarettes use, curiosity about and experimentation with these products has increased among adolescents. The purpose of the present study was to evaluate the moderating effect of e-cigarette advertisement (ad) exposure on the relation between perceptions of use and intentions to use in youth. Methods: Multiple regression analyses utilizing data from the 2014 National Youth Tobacco Survey (N = 17,286) were used to evaluate the effect of ad exposure, perceived harmfulness, barriers, and benefits of ecigarette use on intentions to use among youth who had never used e-cigarettes. Results: Models for non-smokers accounted for 15.5% of the variance in intention to use (R2 = 0.155, F (15) = 187.0, p < 0.001). Results demonstrate that an increase in the number of exposures to e-cigarette ads was associated with an increase in intent to use (b = 0.039, t = 7.4, p < 0.001). Models also demonstrated significant interactions between ad exposure and perceptions of use on future intention to use. For smokers, models explained 11.1% of the variance in intention to use (R2 = 0.111, F (15) = 3.1, p < 0.001). Ad exposure had a non-significant effect on intention to use e-cigarettes (b = −0.010, t = −0.2, p = 0.859). In smokers, ad exposure did not significantly affect the association between perceptions of use and intention to use. Conclusions: Ads are most effective at attracting non-smoking youth as new users rather than promoting product switching in young cigarette smokers.

1. Introduction Electronic cigarette (e-cigarette) use has become a growing trend among current and former users of traditional combustible cigarettes (Delnevo et al., 2016). Additionally, e-cigarettes have emerged as the most commonly used tobacco/nicotine product by high school (13.4%) and middle school (3.9%) students (Arrazola et al., 2015). The number of youth who currently use e-cigarettes has tripled from 2013 to 2014, resulting in a total of 2.4 million users (Arrazola et al., 2015). > 250,000 youth in grades 6–12, who have never tried traditional combustible cigarettes have used e-cigarettes (Centers for Disease ⁎

Control and Prevention, 2014). Those who regularly use e-cigarettes are more likely to also smoke traditional combustible cigarettes and are less likely to quit smoking (Dutra & Glantz, 2014). Historically, industry advertisements have targeted youth through a variety of media advertisements and promotions (Coombs, Bond, Van, & Daube, 2011). Consistent with that trend, there are currently industry-funded campaigns publicizing e-cigarette use (McCarthy, 2014; United States Congress House and Senate, 2014). Advertisement strategies have included sponsorship of youth-oriented events and offering free samples of ecigarettes (Campaign for Tobacco-Free Kids, 2017). These campaigns may increase the positive perceptions of using e-cigarettes and may

Corresponding author. E-mail addresses: [email protected] (A.M. Stroup), [email protected] (S.A. Branstetter).

https://doi.org/10.1016/j.addbeh.2018.02.021 Received 11 December 2017; Received in revised form 26 January 2018; Accepted 13 February 2018 Available online 14 February 2018 0306-4603/ © 2018 Published by Elsevier Ltd.

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2015). A reduction in perceived harmfulness of e-cigarettes has shown to be associated with initiation of use and future intention to use (Berg, Barr, Stratton, Escoffery, & Kegler, 2014; Etter & Bullen, 2011; Pearson, Richardson, Niaura, Vallone, & Abrams, 2012; Rutten et al., 2015). In addition to perceiving e-cigarettes as being less harmful, many smokers, including teenagers, perceive that e-cigarettes may be an effective method to assist in cessation (Hilton, Weishaar, Sweeting, Trevisan, & Katikireddi, 2016). In recent years, there have been several randomized control trials (RCT) that have tested the effectiveness of nicotinic ecigarettes as a cessation tool. One study found that nicotinic e-cigarettes compared to placebo (non-nicotinic) e-cigarettes assisted smokers in abstaining from smoking for at least six months (Hartmann-Boyce et al., 2016). Although there is growing evidence of the effectiveness of ecigarettes in cessation, medical professionals and policymakers are hesitant to recommend switching to e-cigarettes because there are still many unknowns surrounding the safety and efficacy of the product. The majority of youth and young adults are aware of e-cigarettes as an alternative to conventional cigarettes; nearly half of those were first made aware of these products through media channels (Duke et al., 2014). Youth may be exposed to e-cigarette advertisements through media such as internet websites (i.e., YouTube, Facebook, and Twitter), retail outlets, magazines or TV and movies. Not surprisingly, there is evidence that as e-cigarette advertising increases, so does the use of ecigarettes (Nunez-Smith et al., 2010). It is well-established that ad exposure is associated with smoking behavior, including the initiation of smoking and the use of new products (Emery, Vera, Huang, & Szczypka, 2014). Ads for e-cigarettes may be particularly appealing to adolescents as the products are often depicted as fashionable, trendy, and socially acceptable (Ayers, Ribisl, & Brownstein, 2011; Kong, Morean, Cavallo, Camenga, & Krishnan-Sarin, 2015). Approximately 24 million youth were exposed to television e-cigarette advertisements in 2013 (Wills, Knight, Williams, Pagano, & Sargent, 2015). Given that adolescents have approximately 6 and a half hours per week of exposure to media including television, computers, and other electronic devices (U.S. Department of Health and Human Services, 2016), there is a clear need to further understand how youth perceive media regarding e-cigarettes and how this exposure may influence intent to use in the future. The current study sought to understand if ad exposure and perceptions of use influence intention to use e-cigarettes in youth smokers and non-smokers who have never tried e-cigarettes. The results of this study may inform policy efforts to regulate e-cigarettes and their advertisement strategies. It may also inform future research by understanding the mechanisms by which e-cigarette advertisements may influence adolescents' intention to use e-cigarettes.

impact the prevalence of use in youth. The current study evaluates how e-cigarette advertisement exposure may influence perceptions of e-cigarettes and ultimately influence youth intention to use these products. There are approximately 466 brands and 7764 different flavors of ecigarettes (Zhu et al., 2014), varying levels of nicotine in e-cigarettes and a variety of electronic nicotine delivery systems (ENDS). Because of the range of product types and factors influencing nicotine delivery, there is little consensus on the potential health effects of these products. Indeed, there are limited data on the overall safety and long-term health impact of e-cigarettes. Nevertheless, the data that are available suggest that increasing rates of e-cigarette use may become a major public health concern. Several short-term health outcomes associated with exposure to e-cigarettes and e-liquids have been reported including elevated diastolic blood pressure (Farsalinos, Tsiapras, Kyrzopoulos, Savvopoulou, & Voudris, 2014) and cardiac developmental deficiencies (Palpant, Hofsteen, Pabon, Reinecke, & Murry, 2015). Additional studies have found that e-liquid aerosol exposure increased respiratory resistance (Vardavas et al., 2012) and increase the risk of respiratory infection (Dinakar & O'Connor, 2016). When inhaled, e-cigarette aerosol and its components may pose a greater heath effect compared to the physical ingredients of e-liquids (Dinakar & O'Connor, 2016). E-cigarettes have shown to produce chemical emissions of carbonyl compounds, metals, NNK (a carcinogen associated with smokingrelated cancers), and propylene glycol (Goniewicz et al., 2014; Offermann, 2015). The varying flavors of e-cigarettes are also cause for concern, particularly in terms of being appealing to youth. Although popular, flavoring chemicals have been deemed potentially unsafe and detrimental to health (Allen et al., 2016; Farsalinos & Polosa, 2014). Allen et al. (2016) found that of the 51 e-cigarette flavors tested, 39 contained diacetyl, 23 contained 2,3-pentanedione, and 46 contained acetoin. Specifically, diacetyl exposure as low as 0.2 ppm has been associated with bronchiolitis obliterans, also known as “popcorn lung” (Allen et al., 2016). In addition to these potential carcinogens and toxins, one key function of e-cigarettes is to deliver nicotine; research has demonstrated that e-cigarettes deliver sufficient nicotine to significantly increase plasma nicotine and heart rate within 5 min of the first puff (Vansickel & Eissenberg, 2013). Thus, in addition to potential health concerns there is strong potential for addiction among users of electronic cigarettes (National Institute on Drug Abuse, 2017). This is of particular concern because a majority of smokers begin use and establish dependence in adolescence (U.S. Department of Health and Human Services [DHHS], 2012). In one study, mice exposed to nicotine in adolescence showed an increased likelihood of nicotine self-administration later in life (Adriani et al., 2003). This suggests that exposure in early development may increase their susceptibility to becoming addicted as adults. (Doura, Gold, Keller, & Perry, 2008) determined that nicotine exposure during adolescence results in the upregulation of α4β2-containing and α7 receptors, similar to what occurs in adults. However, research showed that in adolescents compared to adults, upregulation was more persistent (i.e., longer duration), had more binding in the midbrain and striatum, and occurred at lower doses of nicotine (Melroy-Greif, Stitzel, & Ehringer, 2016). More binding of nicotine in the midbrain and striatum results in nAChR desensitization, a decrease in dopamine release during non-reward times, and an increase in dopamine release during reward times (Rice & Cragg, 2004). These effects of binding may result in addiction in youth. Despite the potential risks associated with e-cigarette use, individuals have become more willing to experiment based on positive perceptions of use (Ambrose et al., 2014; Amrock, Zakhar, Zhou, & Weitzman, 2015; Barrington-Trimis et al., 2015). Nearly half of adolescent e-cigarette users report they did not believe there were any health risks (Barrington-Trimis et al., 2015). Additionally, smokers often report using e-cigarettes as a substitute to conventional combustible cigarettes in an effort to reduce health risks (Ambrose et al., 2014; Amrock et al., 2015; Chapman & Wu, 2014; Roditis & Halpern-Felsher,

2. Materials & methods 2.1. Data Data for this study come from the 2014 National Youth Tobacco Survey (NYTS), a nationally representative stratified, three-cluster representative sample of middle school (grades 6–8) and high school students (grades 9–12; (Centers for Disease Control and Prevention, 2016). Detailed information about the study design is publicly available (Centers for Disease Control and Prevention, 2016). Of 22,007 completed questionnaires, the present analysis included only participants who reported never using e-cigarettes (N = 17,286) in order to best evaluate future intention to use these products (Table 1). (See Table 2.) 2.2. Measures Participants completed the NYTS survey at selected schools, via pencil-and-paper, self-administered questionnaire booklets. There were 81 questions in the survey that assessed current tobacco use, perceptions of a range of tobacco products, as well as exposure to different media types. 2

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2.2.4. Advertisement (ad) exposure In the current study, ad exposure is thought to represent the HBM construct of “cues to action” because it promotes the use of e-cigarettes by providing awareness of the product and its possible benefits. Ad exposure was measured by using four questionnaire items: 1) “When you are using the Internet, how often do you see ads or promotions for electronic cigarettes or e-cigarettes?,” 2) “When you read newspapers or magazines, how often do you see ads or promotions for electronic cigarettes or e-cigarettes?,” 3) “When you go to a convenience store, supermarket, or gas station, how often do you see ads or promotions for electronic cigarettes or e-cigarettes?,” and 4) “When you watch TV or go to the movies, how often do you see ads or promotions for electronic cigarettes or e-cigarettes?” For each of these questions, responses options identified were: 1 = never, 2 = rarely, 3 = sometimes, 4 = most of the time, and 5 = always. These five items were averaged to reflect a single “advertisement exposure” variable. These items were averaged because they were highly correlated, with r ranging from 0.42 to 0.53. Those who reported they did not have exposure to these ads were excluded from analyses using the ad exposure variable.

Table 1 Sample demographics.

Age Gender Male Female Ethnicity Caucasian Black or African American Native Hawaiian/Other Pacific Islander Asian Hispanic American Indian/Alaska Native

Smokers

Non-smokers

N = 17,286

15.7 (SD = 2.0)

14.3 (SD = 2.1)

14.3 (SD = 2.1)

165 (56.9%) 123 (42.4%)

8232 (49.0%) 8478 (50.4%)

8494 (49.1%) 8674 (50.2%)

157 (54.1%) 88 (30.3%)

9735 (57.9%) 3854 (22.9%)

9971 (57.7%) 3994 (23.1%)

13 (4.5%)

478 (2.8%)

496 (2.9%)

11 (3.8%) 92 (31.7%) 21 (7.2%)

1257 (7.5%) 5107 (30.4%) 1302 (7.7%)

1282 (7.4%) 5296 (30.6%) 1336 (7.7%)

Table 2 Standardized regression coefficients predicting youth intention to use e-cigarettes. Variables

Cigarette smoking status Smokers

Age Sex (male) Perceived benefits Perceived barriers Perceived harmfulness Ad exposure Benefits × Ad Exposure Barriers × Ad Exposure Harmfulness × Ad Exposure

2.2.5. Perceived barriers The ability to buy tobacco products, as measured by the item, “How easy do you think it is for kids your age to buy tobacco products in a store?” was used to reflect the HBM construct of perceived barriers. Response options were 1 = easy, 2 = somewhat easy, and 3 = not easy at all.

Non-smokers

b

β

p

b

β

p

−0.023 −0.020 0.200 0.059 −0.191

−0.049 −0.010 0.193 0.048 −0.154

0.456 0.867 0.003⁎⁎ 0.461 0.022⁎⁎⁎

−0.006 0.032 0.173 −0.047 −0.186

−0.020 0.026 0.207 −0.054 −0.276

0.013⁎⁎⁎ 0.001⁎ 0.000⁎ 0.000⁎ 0.000⁎

−0.010 0.053

−0.011 0.263

0.859 0.347

0.039 0.020

0.056 0.101

0.000⁎ 0.002⁎⁎

0.034

0.118

0.656

0.026

0.125

0.000⁎

0.094

0.354

0.201

−0.026

−0.160

0.000⁎

2.2.6. Perceived benefits The perceived number of friendships was used to reflect the HBM construct of perceived benefits. This measure was chosen because of the impact social relationships and peers have on youth smoking. It was measured by the item, “Do you think young people who use electronic cigarettes or e-cigarettes have more friends?” Response options included 1 = definitely yes, 2 = probably yes, 3 = probably not, and 4 = definitely not. 2.2.7. Perceived harmfulness The HBM construct of perceived harmfulness was assessed by the items: 1) “How much do you think people harm themselves when they use ecigarettes some days but not every day?” with response options of 1 = no harm, 2 = little harm, 3 = some harm, and 4 = a lot of harm, 2) “Do you believe that e-cigarettes are less harmful, equally harmful, or more harmful than cigarettes?” with response options of 1 = less harmful, 2 = equally harmful, and 3 = more harmful, and 3) “Do you believe that e-cigarettes are less addictive, equally addictive, or more addictive than cigarettes?” with response options of 1 = less addictive, 2 = equally addictive, and 3 = more addictive. These three items were averaged to reflect a single “perceived harmfulness” variable due to high correlation between the items, r's ranging from 0.35 to 0.80. Respondents who chose either of the response options, “I do not know enough about the product” and “I have never heard of e-cigarettes” for any of the three perceived harm questions were excluded from those particular analyses.

⁎

p < 0.001. p < 0.01. ⁎⁎⁎ p < 0.05. ⁎⁎

2.2.1. E-cigarette use Use of e-cigarettes was measured using the item: “Have you ever tried an electronic cigarette or e-cigarette such as Blu, 21st Century Smoke or NJOY?” with response options of 1 = yes and 2 = no. Those that responded “Yes” were defined as experienced users of e-cigarettes and were excluded from analyses.

2.2.2. Current cigarette smoking Smoking status was defined using the item: “During the past 30 days, on how many days did you smoke cigarettes?” Respondents who selected “zero days” were categorized as non-smokers in the analyses. Respondents who chose any other option were considered to be smokers.

2.3. Analysis Multiple regression analyses were conducted using IBM SPSS Version 21 (IBM Corp., 2012). Data were weighted to reflect initial selection probabilities, nonresponse adjustments, weight trimming, and post-stratification to U.S. student population estimates (Centers for Disease Control and Prevention, 2016). Each model included covariates known to be associated with smoking behaviors, including age, race/ ethnicity and gender. Given that we conducted several separate models, we controlled for Type I error using the False Discovery Rate (FDR) method (Benjamini & Hochberg, 1995). The FDR method has the advantage of increased power, efficiency, and less risk of Type II errors than Bonferroni procedure. In the present study we present FDR

2.2.3. Behavioral intention Intention to use e-cigarettes was measured using the items: 1) “Have you ever been curious about using an e-cigarette such as Blu, 21st Century Smoke or NJOY?” and 2) “Do you think that you will try an e-cigarette soon?” Response options included 1 = definitely yes, 2 = probably yes, 3 = probably not, and 4 = definitely not. These two items were averaged to reflect a single “intention to use e-cigarettes” variable. These items were combined due to high correlation between them (r = 0.650, p < 0.001). 3

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stores. Results of this study show that the positive portrayal of e-cigarettes in ads is able to persuade non-smokers to have greater intentions to try e-cigarettes in the future. In contrast, e-cigarette ad exposure did not significantly predict intent to use e-cigarettes in smokers in this study. Adolescent smokers who view industry-funded ads may not be convinced that there are reduced risks or added benefits associated with using e-cigarettes compared to cigarettes. This study also hypothesized that ad exposure would significantly moderate the associations between perceptions of use and intent to use. This is one of the first known studies to investigate the interaction effects of ad exposure and constructs of the HBM relative to intention to use e-cigarettes in youth. In non-smokers, ad exposure significantly moderated the relations between perceived barriers, benefits, and harmfulness and intent to use e-cigarettes. These significant interactions imply that this group may be more influenced by e-cigarette ads compared to their smoking counterparts. Supportive literature shows that adolescents exposed to e-cigarette ads report a reduction in perceived harmfulness compared to cigarettes and that they are safe. (Farrelly et al., 2015; Reinhold, Fischbein, Bhamidipalli, Bryant, & Kenne, 2017). There is little question that non-smoking youth are increasingly exposed to e-cigarette ads (Duke et al., 2014); this increased exposure, in conduction with evidence that these non-smokers are more vulnerable to the effects of ads on intent to use, suggests policy and regulations may look to increasingly examine e-cigarette advertising. Smokers showed non-significant interactions among ad exposure and the relationships between perceptions of use and intent to use. Results show that varying levels of ad exposure have no effect on their perceptions of use or their intent to use. It is suspected that ads did not affect smokers because they may not be interested in changing products or “stepping down” to e-cigarettes. It is also plausible that the content of the ads were not tailored specifically to appeal to smokers. National data show that adolescent e-cigarette use has now surpassed combustible cigarette smoking and dual-use of these products is on the rise (Arrazola et al., 2015; Dutra & Glantz, 2014). Although many current smokers may end up using e-cigarettes during their youth, the present study demonstrated that various types of ad exposure may not trigger smokers' intention to use e-cigarettes or affect their perceptions of use. According to Rosen and Maurer (2008), exposing adolescents to antismoking ads help prevent initiation of smoking and inspires current smokers to attempt to quit. These findings, in conjunction with the current study findings, indicate that ads may be effective in youth cessation but not effective in motivating youth smokers to transition to a non-combustible product. Since this is the first known study identifying the effect of e-cigarette ads on perceptions of use and intentions to use e-cigarettes in youth smokers, it would be beneficial to conduct an experimental study to test these findings. In the present study, results supported the hypothesis that perceived barriers (i.e., ability to purchase e-cigarettes) would have a significant negative association with intention to use in non-smokers, but not in smokers. Non-smokers may believe it is hard to acquire e-cigarettes and that the repercussions of violating rules on purchasing these products may outweigh the benefits of using e-cigarettes. It is likely that adolescent smokers have already have found ways to obtain cigarettes, therefore they may not feel limited in their ability to purchase e-cigarettes. Literature also shows that adolescents who have previously accessed age-restricted substances unlawfully and more likely to access e-cigarettes (Hughes et al., 2015). Historically, peer relationships have played a significant role in youth behavior change and decision-making. If youth are considering trying e-cigarettes, their final decision is likely to be influenced by peers. In this study, the results supported the hypothesis that perceived benefits of using e-cigarettes (i.e., having more friends) would significantly predict intent to use in both smokers and non-smokers. Similarly, Song et al. (2009) found that adolescents with higher levels of perceived benefits of smoking were nearly three times more likely to initiate use. Non-smokers specifically, may be more willing to try e-

adjusted p values for all results. 3. Results 3.1. Sample characteristics The average age of non-smokers was 14.3 (SD = 2.1) years old and in smokers was 15.7 (SD = 2.0) years old. There were 8232 (49.0%) male and 8478 (50.4%) female non-smokers and 165 (56.9%) male and 123 (42.4%) female smokers. In non-smokers and smokers, respectively, 57.9% and 54.1% reported to be Caucasian, 30.4% and 31.7% Hispanic, 22.9% and 30.3% Black or African American, 7.7% and 7.2% American Indian/Alaska Native, 7.5% and 3.8% Asian, and 2.8% and 4.5% Native Hawaiian or Other Pacific Islander. 3.2. Hypotheses and findings Hypothesis 1. E-cigarette advertisement exposure will have an independent significant positive association with intention to use in both smokers and non-smokers. Non-smokers. The overall regression model for non-smokers was significant, R2 = 0.155, F(15) = 187.0, p < 0.001. Results demonstrate that ad exposure had a significant effect on intention to use ecigarettes such that increases in the number of exposures to e-cigarette ads was associated with an increase in intent to use, b = 0.039, t = 7.4, p < 0.001. Smokers. Similarly, the regression model for smokers was significant, R2 = 0.111, F(15) = 3.1, p < 0.001. Results demonstrate that the relation between ad and intention to use e-cigarettes was not significant, b = −0.010, t = −0.2, p = 0.859, even when controlling for smoking history. Hypothesis 2. High levels of ad exposure will display independent positive associations among perceived ability to purchase e-cigarettes and perceived benefits of use, and intent to use. High levels of exposure to the positive information provided by advertisements will decrease the perceived harmfulness of e-cigarettes and increase intention to use. Non-smokers. The interaction between ad exposure and perceived barriers to purchase e-cigarettes was significant (b = 0.026, p < 0.001), indicating that as ad exposure increased, the effect of perceived barriers to purchase on intention to use became more positive. There was also a significant interaction effect (b = 0.020, p = 0.002) showing that as ad exposure levels increased, perceived benefits increased, resulting in increased intention to use among nonsmokers. Examination of the interaction of ad exposure by perceived harmfulness revealed a significant interaction effect (b = −0.026, p < 0.001), suggesting that as ad exposure levels increased, the perceived harm decreased and intention to use increased. Smokers. There was a non-significant interaction between ad exposure and perceived barriers to purchase e-cigarettes on intention to use (b = 0.034, p = 0.656). At different levels of ad exposure, the relationship between perceived benefits and intention to use did not vary (b = 0.053, p = 0.347). Similarly, a non-significant interaction was demonstrated between ad exposure and the effect of perceived harmfulness on intention to use (b = 0.094, p = 0.201). 4. Conclusion This study hypothesized that e-cigarette advertisement exposure would have a significant positive association with intention to use in youth smokers and non-smokers. In accordance with previous smoking findings, this study demonstrated that tobacco industry ads are significantly associated with non-smokers' intention to use e-cigarettes (Farrelly et al., 2015). Non-smokers may be attracted to the ad content such as having a variety of flavors available, being the “safer” option compared to cigarettes, and being widely available for purchase at local 4

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cigarettes to fit into a group given that e-cigarettes are socially acceptable and are the most commonly used tobacco product by high school and middle school students (Arrazola et al., 2015). Current smokers may be less likely to use e-cigarettes as a tool for social acceptance as their use of conventional cigarettes may have already earned them acceptance among a specific group of peers. In conclusion, this study has found that despite the potential risks associated with e-cigarette use, nonsmokers exhibited intentions to try e-cigarettes when exposed to ads, as well as allow their perceptions of use be influenced by level of ad exposure. Nonsmokers should be addressed directly in anti-smoking campaigns focusing on e-cigarette experimentation and use. Limiting overall youth exposure to ads on an individual level and monitoring the content and claims within those ads on a regulatory level would be beneficial.

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4.1. Limitations The findings of the present study should be viewed in light of its limitations. Most importantly, data included in this study were crosssectional and therefore we could not directly infer casual relationships. It is plausible that non-smoking youth who have already determined that they will try e-cigarettes sought out and intentionally increased their exposure to e-cigarette ads. Further, all data were collected using self-report measures. These measures may be subject to recall bias, social desirability or other biases that could influence the results. Specifically, one should be cautious when interpreting the measurement of past 30 day cigarette use in youth because of the variation in types of use within those 30 days (i.e., a few days of experimentation compared to several weeks of smoking multiple cigarettes per day). Despite these potential limitations, the models in this study represent plausible explanations and should spur further longitudinal examinations of the influence ads on the development of e-cigarette use in youth. Role of funding sources This work was supported, in part, by a fellowship grant awarded by FDA/NIH/P50-DA-036107 and by 1R21CA181962-01A1. The FDA and NIH had no role in the analysis or interpretation of these data, writing the manuscript, or the decision to submit the paper for publication. Contributors Andrea Stroup conducted literature searches and provided summaries of previous research studies, conducted initial statistical analysis, and created initial drafts of the manuscript. Steven Branstetter provided overall direction for the project and analytical approach, provided assistance with initial statistical analysis, conducted additional analyses, and made ongoing and final edits of the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest Andrea Stroup reports no conflicts of interest. Steven Branstetter reports no conflicts of interest. Data statement The data used in this secondary data analysis is the publically available, 2014 National Youth Tobacco Survey dataset. The dataset can be found at the following link: https://www.cdc.gov/tobacco/data_ statistics/surveys/nyts/index.htm. References Adriani, W., Spijker, S., Deroche-Gamonet, V., Laviola, G., Le Moal, M., Smit, A. B., &

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