Data
We used data from a nationally representative longitudinal online survey of US adults aged ≥18 years administered by GfK Custom Research. Participants were recruited from a probability sample of residential postal addresses covering approximately 95% of all U.S. households. Invitation letters were mailed to all sampled households and contained website links and passwords to enable the selected household to access the survey. The probability of selection was known for all participants and participants could not volunteer for study enrollment. Those who were not Internet-enabled were provided additional study incentive payments to complete the survey in public locations with Internet access, such as libraries.
The survey was conducted in two waves: April 12 to June 30, 2014 (baseline) and September 11 to November 17, 2014 (follow-up). Non cigarette smokers were defined as respondents who never smoked or who reported smoking at least 100 cigarettes in their lifetime, but smoked “not at all” at baseline. Non e-cigarette users were persons who reported that they used e-cigarettes “not at all” at baseline. All baseline non users of cigarettes or e-cigarettes who participated at baseline (n = 3123) were re-contacted for follow-up approximately 5 months later; a longitudinal retention rate of 74.6% was achieved. All analyses reported in this study are based on the longitudinal cohort of n = 2191 persons who neither smoked cigarettes nor used e-cigarettes at baseline and who completed both survey waves.
Measures
Exposure to e-cigarette advertisements at baseline
To measure exposure to e-cigarette advertisements, respondents were shown one of 5 popular e-cigarette advertisements (three Blu and two Njoy advertisements) at random via a video stream within the survey. Those unable to view the video stream were shown a storyboard of images from the advertisement. Using this protocol to cue recall, participants were then asked to indicate whether they had seen the e-cigarette advertisement on either television or online in the past 3 months. Respondents who reported having seen an advertisement in the past 3 months were defined as having being exposed to the e-cigarette advertisement they viewed.
Receptivity to e-cigarette advertisements at baseline
Receptivity to e-cigarette advertisements among those who reported being exposed was measured with a multi-item scale similar to those used in previous research [13]. After viewing each advertisement in the survey, each respondent was asked whether he or she agreed or disagreed with the following statements: (1) “this ad was worth remembering”; (2) “this ad grabbed my attention”; (3) “this ad was powerful”; (4) “this ad was informative”; (5) “this ad was meaningful”; and (6) “this ad was convincing”. Each item was assessed on a scale from 1 (strongly disagree) to 5 (strongly agree). Item-specific responses were averaged for each advertisement, and then averaged across advertisements, to obtain a single value (range 1–5).
Smoking history and awareness of tips advertisements
Cigarette smoking history of baseline non-users of cigarettes and e-cigarettes was explored using a lifetime threshold of 100 cigarettes; respondents were classified as never smokers (smoked <100 cigarettes in lifetime) or former smokers (smoked ≥100 cigarettes in a lifetime but were not smokers at the time of the survey).
The 2014 wave of the Centers for Disease Control and Prevention’s national tobacco education campaign Tips From Former Smokers (Tips) aired in two 9-week phases that overlapped with the study period (Phase 1: February 3–April 6, 2014; Phase 2: July 7–September 7, 2014) [14]. Therefore, we assessed exposure to Tips advertisements (“yes” or “no”) as a potential confounder.
Current e-cigarette use at follow-up
Current e-cigarette use at follow-up was defined as using e-cigarettes “some days” or “every day” (vs. “not at all”).
Statistical analysis
Subgroup differences in exposure and receptivity were assessed using χ2 and Wald tests. Based on prevalence of e-cigarette use at Wave 2 by advertisement exposure at Wave 1 among baseline non-users of cigarettes and e-cigarettes, we estimated the attributable risk percentage (among those exposed) and the population attributable risk percentage (among the entire population).
Multivariable logistic regression was used to measure the association between receptivity to e-cigarette advertisements and e-cigarette use at follow-up among baseline non-users of cigarettes and e-cigarettes, controlling for sex, age, race/ethnicity, awareness of Tips advertisements, cigarette smoking history, educational attainment, and presence of a smoker in the household. We controlled for regional variation in e-cigarette consumption by including region fixed effects. Data were weighted, and corresponding population totals were calculated for select estimates; statistical significance was ascertained using a threshold of p < 0.05.
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