E-cigarette use has increased rapidly among U.S. adults. However, reasons for use among adults are unclear. We assessed reasons for e-cigarette use among a national sample of U.S. adults. Data were collected via online surveys among U.S. adults aged 18 or older from April through June 2014. Descriptive and multivariate regression analyses were conducted to assess reasons for e-cigarette use among 2448 current e-cigarette users, by sociodemographic characteristics and product type. Assessed reasons included cessation/health, consideration of others, convenience, cost, curiosity, flavoring, and simulation of conventional cigarettes. Among current e-cigarette users, 93% were also current cigarette smokers. The most common reasons for e-cigarette use were cessation/health (84.5%), consideration of others (71.5%), and convenience (56.7%). The prevalence of citing convenience (adjusted prevalence ratio [aPR] = 1.49) and curiosity (aPR = 1.54) as reasons for e-cigarette use were greater among current cigarette smokers than nonsmokers (P < 0.05). The prevalence of citing flavoring as a reason for use was greater among adults aged 18 to 24 (aPR = 2.02) than 55 or older (P < 0.05). Tank use was associated with greater prevalence of citing every assessed reason except convenience and curiosity. Cessation- and health-related factors are primary reasons cited for e-cigarette use among adults, and flavorings are more commonly cited by younger adults. Efforts are warranted to provide consumers with accurate information on the health effects of e-cigarettes and to ensure that flavoring and other unregulated features do not promote nicotine addiction, particularly among young adults.

A user's topography affects his or her exposure to HPHCs. As this study demonstrates, user characteristics, such as level of smoking, can influence topography. Thus, it is crucial to understand the topography profiles of different user types to assess the potential for population harm and to identify potentially vulnerable populations. This study only looked at topography of cigarette smokers using disposable e-cigarettes. Further research is needed to better understand potential variation in e-cigarette topography and resulting exposures to HPHCs among users of different e-cigarette devices and liquids.

AimsA potential unintended consequence of legalizing recreational marijuana is increased marijuana-related driving impairment. Some states where recreational marijuana is legal have begun implementing interventions to mitigate driving under the influence (DUI) of marijuana, including media campaigns to increase knowledge about DUI laws. However, little is known about the associations between knowledge of DUI laws and marijuana DUI behavior. In this study, we provide new data from a survey of marijuana users in Colorado and Washington to examine associations between marijuana drugged driving and two potential behavioral precursors of marijuana DUI. We also explore other factors that may influence marijuana DUI.MethodsData are from an online survey of marijuana users in Colorado and Washington. Respondents who reported any marijuana use in the past 30 days (n = 865) served as the analytic sample. We examined prevalence of two behavioral outcomes: (1) any driving of a motor vehicle while high in the past year and (2) driving a motor vehicle within 1 hour of using marijuana 5 or more times in the past month. Additional outcomes measuring willingness to drive while high were also assessed. Logistic regressions were used to estimate each outcome as a function of two multi-item scales measuring knowledge of the legal consequences of driving high and perceptions that driving while high is not safe. Additional covariates for potential confounders were included in each model.ResultsPrevalence of past-year driving while under the influence of marijuana was 43.6% among respondents. The prevalence of driving within 1 hour of using marijuana at least 5 times in the past month was 23.9%. Increased perception that driving high is unsafe was associated with lower odds of past-year marijuana DUI (OR = 0.31, P < 0.01) and lower past-month odds of driving 5 or more times within 1 hour of using marijuana (OR = 0.26, P < 0.01). Increased knowledge of marijuana DUI laws was also associated with lower odds of each of these outcomes (OR = 0.63, P < 0.01, OR = 0.69, P = 0.02, respectively). Post-estimation Wald tests confirmed the negative associations with marijuana DUI were greater in magnitude for safety perceptions than knowledge of DUI laws. Increased perceptions that driving while high is unsafe was associated with significantly lower willingness to drive after using marijuana while increased knowledge of marijuana DUI laws was not associated with these outcomes.ConclusionsDespite recent interventions targeting public awareness of the legal consequences of marijuana DUI, our results suggest that knowledge of these laws is a weaker predictor of DUI behavior than perceptions that driving high is unsafe. In addition, safety perceptions predict decreased openness to driving high while knowledge of DUI laws was not associated with openness. These findings suggest that interventions for reducing the incidence of marijuana DUI are likely to be more successful by targeting safety perceptions related to marijuana DUI rather than knowledge of...

BackgroundE-cigarettes have rapidly increased in popularity in recent years, driven, at least in part, by marketing and word-of-mouth discussion on Twitter. Given the rapid proliferation of e-cigarettes, researchers need timely quantitative data from e-cigarette users and smokers who may see e-cigarettes as a cessation tool. Twitter provides an ideal platform for recruiting e-cigarette users and smokers who use Twitter. Online panels offer a second method of accessing this population, but they have been criticized for recruiting too few young adults, among whom e-cigarette use rates are highest.ObjectiveThis study compares effectiveness of recruiting Twitter users who are e-cigarette users and smokers who have never used e-cigarettes via Twitter to online panelists provided by Qualtrics and explores how users recruited differ by demographics, e-cigarette use, and social media use.MethodsParticipants were adults who had ever used e-cigarettes (n=278; male: 57.6%, 160/278; age: mean 34.26, SD 14.16 years) and smokers (n=102; male: 38.2%, 39/102; age: mean 42.80, SD 14.16 years) with public Twitter profiles. Participants were recruited via online panel (n=190) or promoted tweets using keyword targeting for e-cigarette users (n=190). Predictor variables were demographics (age, gender, education, race/ethnicity), e-cigarette use (eg, past 30-day e-cigarette use, e-cigarette puffs per day), social media use behaviors (eg, Twitter use frequency), and days to final survey completion from survey launch for Twitter versus panel. Recruitment method (Twitter, panel) was the dependent variable.ResultsAcross the total sample, participants were recruited more quickly via Twitter (incidence rate ratio=1.30, P=.02) than panel. Compared with young adult e-cigarette users (age 18-24 years), e-cigarette users aged 25 to 34 years (OR 0.01, 95% CI 0.00-0.60, P=.03) and 35 to 44 years (OR 0.01, 95% CI 0.00-0.51, P=.02) were more likely to be recruited via Twitter than panel. Smokers aged 35 to 44 years were less likely than those aged 18 to 24 years to be recruited via Twitter than panel (35-44: OR 0.03, 95% CI 0.00-0.49, P=.01). E-cigarette users who reported a greater number of e-cigarette puffs per day were more likely to be recruited via Twitter than panel compared to those who reported fewer puffs per day (OR 1.12, 95% CI 1.05-1.20, P=.001). With each one-unit increase in Twitter usage, e-cigarette users were 9.55 times (95% CI 2.28-40.00, P=.002) and smokers were 4.91 times (95% CI 1.90-12.74, P=.001) as likely to be recruited via Twitter than panel.ConclusionsTwitter ads were more time efficient than an online panel in recruiting e-cigarette users and smokers. In addition, Twitter provided access to younger adults, who were heavier users of e-cigarettes and Twitter. Recruiting via social media and online panel in combination offered access to a more diverse population of participants.

Objectives: In this study, we examined visual attention of a warning label on a sugar-sweetened beverage (SSB) and its effects on visual attention to SSB product descriptors and perceptions of SSB using eye tracking technology. Methods: We had 180 young adults view an image of a generic soda can with or without a text warning on a computer monitor. Results: Participants spent less time looking at marketing elements on the can in the "Warning" condition compared to the "No warning" (control) condition. Compared to the control, participants in the "Warning" condition viewed the sugar-sweetened beverage as less healthy (1.78 warning vs 2.21 control, p < .01) and believed that drinking SSBs contributed to diabetes (5.70 warning vs 5.27 control, p < .01). Visual attention to warning label was associated with correct recall of the warning and opting out of purchasing the can. Conclusions: Textual warning on SSB reduced visual attention to marketing elements on the can. Although there were few statistically significant differences between the conditions on most