An Investigation of Age-Differentiated Conversations About Electronic Nicotine Delivery Systems on Reddit.

Introduction: This study analyzes age-differentiated Reddit conversations about ENDS. Methods: This study combines 2 methods to (1) predict Reddit users' age into 2 categories (13-20 years [underage] and 21-54 years [of legal age]) using a machine learning algorithm and (2) qualitatively code ENDS-related Reddit posts within the 2 groups. The 25 posts with the highest karma score (number of upvotes minus number of downvotes) for each keyword search (i.e., query) and each predicted age group were qualitatively coded. Results: Of 9, the top 3 topics that emerged were flavor restriction policies, Tobacco 21 policies, and use. Opposition to flavor restriction policies was a prominent subcategory for both groups but was more common in the 21-54 group. The 13-20 group was more likely to discuss opposition to minimum age laws as well as access to flavored ENDS products. The 21-54 group commonly mentioned general vaping use behavior. Conclusions: Users predicted to be in the underage group posted about different ENDS-related topics on Reddit than users predicted to be in the of-legal-age group.

JUUL's 2019 Removal of Mint-Flavored Pods and Changes to the Retail Environment of Electronic Nicotine Delivery Systems in Florida and the United States.

INTRODUCTION: The increase in youth electronic nicotine delivery system (ENDS) use coincided with JUUL's rapid rise, which prompted investigations and lawsuits aimed at this leading brand. In response, JUUL discontinued sweet flavors in late 2018, followed by mint flavors in November 2019. We assessed ENDS sales and prices at both the state and national level before and after JUUL's removal of mint flavors. AIMS AND METHODS: Nielsen retail sales data on ENDS products from convenience and food stores in 4-week aggregates were analyzed between January 2019 and January 2020 in Florida and the United States. Standardized units were created. Unit market share and growth rates were calculated for top brands and flavors in the periods before and after JUUL's mint removal. Average prices within brand and product type were calculated. RESULTS: Following JUUL's removal of mint in November 2019, JUUL's market share dropped from over 66% in Florida and the United States to 37.1% (Florida) and 55.1% (United States). In January 2020, the second leading brands were Puff Bar (15.0%) in Florida and Vuse (18.1%) in the United States. Mint market share decreased and share of all other flavor categories increased, particularly menthol and concept. Total ENDS sales increased in Florida but decreased in the United States. Average prices of ENDS devices decreased. CONCLUSIONS: While JUUL's actions led to a decline in its sales, Puff Bar emerged and menthol and concept flavors experienced growth. Findings also demonstrate how changes by influential brands differentially affect purchase patterns at the national and state level. IMPLICATIONS: These findings support the growing body of evidence that tobacco industry self-regulation, with selective flavor removal by the leading ENDS brand in this case, is insufficient to reduce total ENDS sales, including sales of flavored products which are preferred by youth. Results suggest that brand and flavor substitution compensated for the removal of mint JUUL pods. Understanding changes to the ENDS market in response to industry actions, at both the state and national level, can inform future regulation and interventions. These findings can also inform efforts to prevent and reduce youth ENDS use.

Identifying Electronic Nicotine Delivery System Brands and Flavors on Instagram: Natural Language Processing Analysis.

BACKGROUND: Electronic nicotine delivery system (ENDS) brands, such as JUUL, used social media as a key component of their marketing strategy, which led to massive sales growth from 2015 to 2018. During this time, ENDS use rapidly increased among youths and young adults, with flavored products being particularly popular among these groups. OBJECTIVE: The aim of our study is to develop a named entity recognition (NER) model to identify potential emerging vaping brands and flavors from Instagram post text. NER is a natural language processing task for identifying specific types of words (entities) in text based on the characteristics of the entity and surrounding words. METHODS: NER models were trained on a labeled data set of 2272 Instagram posts coded for ENDS brands and flavors. We compared three types of NER models-conditional random fields, a residual convolutional neural network, and a fine-tuned distilled bidirectional encoder representations from transformers (FTDB) network-to identify brands and flavors in Instagram posts with key model outcomes of precision, recall, and F1 scores. We used data from Nielsen scanner sales and Wikipedia to create benchmark dictionaries to determine whether brands from established ENDS brand and flavor lists were mentioned in the Instagram posts in our sample. To prevent overfitting, we performed 5-fold cross-validation and reported the mean and SD of the model validation metrics across the folds. RESULTS: For brands, the residual convolutional neural network exhibited the highest mean precision (0.797, SD 0.084), and the FTDB exhibited the highest mean recall (0.869, SD 0.103). For flavors, the FTDB exhibited both the highest mean precision (0.860, SD 0.055) and recall (0.801, SD 0.091). All NER models outperformed the benchmark brand and flavor dictionary look-ups on mean precision, recall, and F1. Comparing between the benchmark brand lists, the larger Wikipedia list outperformed the Nielsen list in both precision and recall. CONCLUSIONS: Our findings suggest that NER models correctly identified ENDS brands and flavors in Instagram posts at rates competitive with, or better than, others in the published literature. Brands identified during manual annotation showed little overlap with those in Nielsen scanner data, suggesting that NER models may capture emerging brands with limited sales and distribution. NER models address the challenges of manual brand identification and can be used to support future infodemiology and infoveillance studies. Brands identified on social media should be cross-validated with Nielsen and other data sources to differentiate emerging brands that have become established from those with limited sales and distribution.

Can social media monitoring help identify the next EVALI? An examination of Reddit posts about vitamin E acetate and Dank Vapes.

BACKGROUND: Scientists identified vitamin E acetate (VEA) and "Dank Vapes" (a fake brand of tetrahydrocannabinol [THC] vaping products) as contributors to the 2019-2020 outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI). On social media, people who post about vaping or THC discussed the causes of EVALI. We examined whether Reddit conversations may have served as early signals of the outbreak. METHODS: We collected Reddit posts from March 2018 to February 2020 on vaping- and THC-related subreddits that mentioned VEA or Dank Vapes. We identified peaks in post volume, examined post content, and used natural language processing to identify terms most characteristic of posts. RESULTS: There were almost no posts about VEA before EVALI. Subsequently, there were two peaks, both referencing media coverage of scientific findings that linked VEA to EVALI. Discussion regularly referenced concerns about the legitimacy of Dank Vapes before EVALI; peaks in posts were largely unrelated to scientific findings or media coverage of those findings. The terms most characteristic of VEA posts were EVALI-related; those most characteristic of Dank Vapes posts were about quality or legitimacy. CONCLUSIONS: Although posts about VEA and Dank Vapes did not predict the outbreak, the public health community could use social media to encourage people who vape or use THC to report future health concerns (e.g., through FDA's Safety Reporting Portal). Researchers and regulators could also use social media to see if potentially problematic products, such as Dank Vapes, have a history of concern among individuals who use those products.

Correction: Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation.

[This corrects the article DOI: 10.2196/25807.].

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation.

BACKGROUND: Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users' demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. OBJECTIVE: We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. METHODS: This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users' age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. RESULTS: The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. CONCLUSIONS: We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users' posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

An Experimental Study of Nicotine Warning Statements in E-cigarette Tweets.

INTRODUCTION: It is unclear whether warnings on electronic cigarette (e-cigarette) advertisements required by the US Food and Drug Administration (FDA) will apply to social media. Given the key role of social media in marketing e-cigarettes, we seek to inform FDA decision making by exploring how warnings on various tweet content influence perceived healthiness, nicotine harm, likelihood to try e-cigarettes, and warning recall. METHODS: In this 2 × 4 between-subjects experiment participants viewed a tweet from a fictitious e-cigarette brand. Four tweet content versions (e-cigarette product, e-cigarette use, e-cigarette in social context, unrelated content) were crossed with two warning versions (absent, present). Adult e-cigarette users (N = 994) were recruited via social media ads to complete a survey and randomized to view one of eight tweets. Multivariable regressions explored effects of tweet content and warning on perceived healthiness, perceived harm, and likelihood to try e-cigarettes, and tweet content on warning recall. Covariates were tobacco and social media use and demographics. RESULTS: Tweets with warnings elicited more negative health perceptions of the e-cigarette brand than tweets without warnings (p < .05). Tweets featuring e-cigarette products (p < .05) or use (p < .001) elicited higher warning recall than tweets featuring unrelated content. CONCLUSIONS: This is the first study to examine warning effects on perceptions of e-cigarette social media marketing. Warnings led to more negative e-cigarette health perceptions, but no effect on perceived nicotine harm or likelihood to try e-cigarettes. There were differences in warning recall by tweet content. Research should explore how varying warning content (text, size, placement) on tweets from e-cigarette brands influences health risk perceptions. IMPLICATIONS: FDA's 2016 ruling requires warnings on advertisements for nicotine-containing e-cigarettes, but does not specify whether this applies to social media. This study is the first to examine how e-cigarette warnings in tweets influence perceived healthiness and harm of e-cigarettes, which is important because e-cigarette brands are voluntarily including warnings on Twitter and Instagram. Warnings influenced perceived healthiness of the e-cigarette brand, but not perceived nicotine harm or likelihood to try e-cigarettes. We also saw higher recall of warning statements for tweets featuring e-cigarettes. Findings suggest that expanding warning requirements to e-cigarette social media marketing warrants further exploration and FDA consideration.

Effect of menthol cigarette and other menthol tobacco product bans on tobacco purchases in the RTI iShoppe virtual convenience store.

OBJECTIVE: To test how a potential US ban of menthol products or replacement with 'green' products and ads could influence tobacco purchases. METHODS: US adult menthol smokers (N=1197) were recruited via an online panel and randomly assigned to complete a shopping task in one of four versions (experimental conditions) of the RTI iShoppe virtual store: (1) no ban, (2) replacement of menthol cigarettes and ads with green replacement versions, (3) menthol cigarette ban and (4) all menthol tobacco product ban. Logistic regressions assessed the effect of condition on tobacco purchases. RESULTS: Participants in the menthol cigarette ban (OR=0.67, 95% CI 0.48 to 0.92) and all menthol product ban conditions (OR=0.60, 95% CI 0.43 to 0.83) were less likely to purchase cigarettes of any type than participants in the no ban condition. Participants in the green replacement (OR=1.74, 95% CI 1.13 to 2.70), menthol cigarette ban (OR=3.40, 95% CI 2.14 to 5.41) and all menthol product ban conditions (OR=3.14, 95% CI 1.97 to 5.01) were more likely to purchase a cigarette brand different from their usual brand than participants in the no ban condition. CONCLUSIONS: Our findings suggest that menthol bans could have great public health impact by reducing cigarette purchases. However, tobacco marketing strategies, such as creating green (or other replacement) versions of menthol cigarettes, may undermine public health benefits of a menthol ban by prompting purchases of non-menthol cigarettes. Our findings highlight the importance of taking tobacco marketing tactics into consideration in tobacco product regulation.

Antismoking Advertisements and Price Promotions and Their Association With the Urge to Smoke and Purchases in a Virtual Convenience Store: Randomized Experiment.

BACKGROUND: Point of sale (POS) advertising is associated with smoking initiation, current smoking, and relapse among former smokers. Price promotion bans and antismoking advertisements (ads) are 2 possible interventions for combating POS advertising. OBJECTIVE: The purpose of this analysis was to determine the influence of antismoking ads and promotions on urges to smoke and tobacco purchases. METHODS: This analysis examined exposure to graphic (graphic images depicting physical consequences of tobacco use) and supportive (pictures of and supportive messages from former smokers) antismoking ads and promotions in a virtual convenience store as predictors of urge to smoke and buying tobacco products among 1200 current cigarette smokers and 800 recent quitters recruited via a Web-based panel (analytical n=1970). We constructed linear regression models for urge to smoke and logistic regression models for the odds of purchasing tobacco products, stratified by smoking status. RESULTS: The only significant finding was a significant negative relationship between exposure to supportive antismoking ads and urge to smoke among current smokers (beta coefficient=-5.04, 95% CI -9.85 to -0.22; P=.04). There was no significant relationship between graphic antismoking ads and urge to smoke among current smokers (coefficient=-3.77, 95% CI -8.56 to 1.02; P=.12). Neither relationship was significant for recent quitters (graphic: coefficient=-3.42, 95% CI -8.65 to 1.81; P=.15 or supportive: coefficient=-3.82, 95% CI -8.99 to 1.36; P=.20). There were no significant differences in urge to smoke by exposure to promotions for current smokers (coefficient=-1.06, 95% CI -4.53 to 2.41; P=.55) or recent quitters (coefficient=1.76, 95% CI -2.07 to 5.59; P=.37). There were also no differences in tobacco purchases by exposure to graphic (current smokers: coefficient=0.93, 95% CI 0.67 to 1.29; P=.66 and recent quitters: coefficient=0.73, 95% CI 0.44 to 1.19; P=.20) or supportive (current smokers: coefficient=1.05, 95% CI 0.75 to 1.46; P=.78 and recent quitters: coefficient=0.73, 95% CI 0.45 to 1.18; P=.20) antismoking ads or price promotions (current smokers: coefficient=1.09, 95% CI 0.86 to 1.38; P=.49 and recent quitters: coefficient=0.90, 95% CI 0.62 to 1.31; P=.60). CONCLUSIONS: The results of this analysis support future research on the ability of supportive antismoking ads to reduce urges to smoke among current cigarette smokers. Research on urges to smoke has important tobacco control implications, given the relationship between urge to smoke and smoking cigarettes, time to next smoke, and amount smoked.

Warning Labels on Sugar-sweetened Beverages: An Eye Tracking Approach.

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 measures of product appeal or risk perception, warnings increased some perceived risks of SSBs indicating that warning labels on SSBs might be a promising strategy in informing consumers, particularly young adults, about risks of added sugars.

Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure.

Social media data are increasingly used by researchers to gain insights on individuals' behaviors and opinions. Platforms like Twitter provide access to individuals' postings, networks of friends and followers, and the content to which they are exposed. This article presents the methods and results of an exploratory study to supplement survey data with respondents' Twitter postings, networks of Twitter friends and followers, and information to which they were exposed about e-cigarettes. Twitter use is important to consider in e-cigarette research and other topics influenced by online information sharing and exposure. Further, Twitter metadata provide direct measures of user's friends and followers as opposed to survey self-reports. We find that Twitter metadata provide similar information to survey questions on Twitter network size without inducing recall error or other measurement issues. Using sentiment coding and machine learning methods, we find Twitter can elucidate on topics difficult to measure via surveys such as online expressed opinions and network composition. We present and discuss models predicting whether respondents' tweet positively about e-cigarettes using survey and Twitter data, finding the combined data to provide broader measures than either source alone.

Deception and Shopping Behavior Among Current Cigarette Smokers: A Web-Based, Randomized Virtual Shopping Experiment.

BACKGROUND: Virtual stores can be used to identify influences on consumer shopping behavior. Deception is one technique that may be used to attempt to increase the realism of virtual stores. OBJECTIVE: The objective of the experiment was to test whether the purchasing behavior of participants in a virtual shopping task varied based on whether they were told that they would receive the products they selected in a virtual convenience store (a form of deception) or not. METHODS: We recruited a US national sample of 402 adult current smokers by email from an online panel of survey participants. They completed a fully automated randomized virtual shopping experiment with a US $15 or US $20 budget in a Web-based virtual convenience store. We told a random half of participants that they would receive the products they chose in the virtual store or the cash equivalent (intervention condition), and the other random half simply to conduct a shopping task (control condition). We tested for differences in demographics, tobacco use behaviors, and in-store purchases (outcome variable, assessed by questionnaire) by experimental condition. RESULTS: The characteristics of the participants (398/402, 99.0% with complete data) were comparable across conditions except that the intervention group contained slightly more female participants (103/197, 52.3%) than the control group (84/201, 41.8%; P=.04). We did not find any other significant differences in any other demographic variables or tobacco use, or in virtual store shopping behaviors, including purchasing any tobacco (P=.44); purchasing cigarettes (P=.16), e-cigarettes (P=.54), cigars (P=.98), or smokeless tobacco (P=.72); amount spent overall (P=.63) or on tobacco (P=.66); percentage of budget spent overall (P=.84) or on tobacco (P=.74); number of total items (P=.64) and tobacco items purchased (P=.54); or total time spent in the store (P=.07). CONCLUSIONS: We found that telling participants that they will receive the products they select in a virtual store did not influence their purchases. This finding suggests that deception may not affect consumer behavior and, as a result, may not be necessary in virtual shopping experiments.

Smokers' Attention to Point-of-Sale Antismoking Ads: An Eye-tracking Study.

OBJECTIVES: We used eye-tracking to examine smokers' visual attention in one of 4 antismoking ad contexts (alone, next to cigarette ad, tobacco display, or cooler). Participants viewed 4 ad types (graphic, intended emotive, and benefits of quitting-graphic ads, and benefits of quitting-informational ads), each with 3 areas of interest (AOI) (anti-ad image, anti-ad text, and other text). METHODS: Current smokers (N = 153) viewed ads for 10 seconds each. Multivariable random effect linear regressions with post-test comparisons (with sidak-adjusted p-values) were used to test for differences in fixations and dwell time by ad context and type while adjusting for covariates. Visual attention was adjusted by percentage of anti-ad area taken up by each AOI. RESULTS: Adjusting for covariates, there were no differences by ad context (p > .05). Fixations and dwell time were greatest for the image of the benefits of quitting-graphic ad, the text of the graphic ad, and the other text of the intended emotive ad (all ps < .005). Conclusions: Visual attention to antismoking ads did not vary by ad context but varied significantly by ad type.

Classification of Twitter Users Who Tweet About E-Cigarettes.

BACKGROUND: Despite concerns about their health risks, e­cigarettes have gained popularity in recent years. Concurrent with the recent increase in e­cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e­cigarettes. Monitoring the trends in e­cigarette-related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e­cigarette-related content on Twitter. OBJECTIVE: The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e­cigarette-related topics into distinct categories. METHODS: We collected approximately 11.5 million e­cigarette-related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e­cigarettes. Trained human coders examined the handles' profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users' metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types. RESULTS: Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e­cigarettes (average F1 score=83.3%). Accuracy varied by user type, with F1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance-an overall F1 score gain of 10.6%-beyond metadata features alone. CONCLUSIONS: This study provides a method for classifying five different types of users who tweet about e­cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e­cigarettes online to help inform public health surveillance, education, and regulatory efforts.

Predicting age groups of Twitter users based on language and metadata features.

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles' metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen's d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as "school" for youth and "college" for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.

Risk Factors for Youth E-Cigarette "Vape Trick" Behavior.

PURPOSE: Some adolescent users of e-cigarettes and other electronic vaping products (EVPs) report performing "vape tricks" (exhaling aerosol to make shapes). However, little is known about this behavior. We examined the frequency of performing and watching vape tricks and the characteristics of those most likely to perform vape tricks among a sample of adolescent EVP users. METHODS: We used social media ads to recruit a national convenience sample of U.S. adolescents (n = 1,729) to participate in an online survey in September 2016. Inclusion criteria required participants to be aged 15-17 years and to have used EVPs at least once in the past 30 days. RESULTS: The majority of EVP-using adolescents reported trying (77.8%) and watching vape tricks in person (83.7%) or online (74.0%). Risk factors for performing tricks included using advanced vaping devices, vaping every day, white race, moderate levels of seeing and sharing vaping information on social media, and believing that EVP use is more normative among peers. Likelihood of trying vape tricks decreased as beliefs about the harmfulness of EVPs increased. CONCLUSIONS: Vape tricks pose a potential threat to adolescent health if they encourage nonusers to initiate or current EVP users to use more frequently or switch to advanced devices that produce more harmful chemical emissions. Further research should examine the possible health effects of performing vape tricks, and future public health campaigns should be informed by an understanding of the appeal of this activity for adolescents.

Antismoking Ads at the Point of Sale: The Influence of Ad Type and Context on Ad Reactions.

Efforts are underway to educate consumers about the dangers of smoking at the point of sale (POS). Research is limited about the efficacy of POS antismoking ads to guide campaign development. This study experimentally tests whether the type of antismoking ad and the context in which ads are viewed influence people's reactions to the ads. A national convenience sample of 7,812 adult current smokers and recent quitters was randomized to 1 of 39 conditions. Participants viewed one of the four types of antismoking ads (negative health consequences-graphic, negative social consequences-intended emotive, benefits of quitting-informational, benefits of quitting-graphic) in one of the three contexts (alone, next to a cigarette ad, POS tobacco display). We assessed participants' reactions to the ads, including perceived effectiveness, negative emotion, affective dissonance, and motivational reaction. Graphic ads elicited more negative emotion and affective dissonance than benefits of quitting ads. Graphic ads elicited higher perceived effectiveness and more affective dissonance than intended emotive ads. Antismoking ads fared best when viewed alone, and graphic ads were least influenced by the context in which they were viewed. These results suggest that in developing POS campaigns, it is important to consider the competitive pro-tobacco context in which antismoking ads will be viewed.

Comparing Twitter and Online Panels for Survey Recruitment of E-Cigarette Users and Smokers.

BACKGROUND: E-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. OBJECTIVE: This 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. METHODS: Participants 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. RESULTS: Across 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. CONCLUSIONS: Twitter 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.

Does Digital Video Advertising Increase Population-Level Reach of Multimedia Campaigns? Evidence From the 2013 Tips From Former Smokers Campaign.

BACKGROUND: Federal and state public health agencies in the United States are increasingly using digital advertising and social media to promote messages from broader multimedia campaigns. However, little evidence exists on population-level campaign awareness and relative cost efficiencies of digital advertising in the context of a comprehensive public health education campaign. OBJECTIVE: Our objective was to compare the impact of increased doses of digital video and television advertising from the 2013 Tips From Former Smokers (Tips) campaign on overall campaign awareness at the population level. We also compared the relative cost efficiencies across these media platforms. METHODS: We used data from a large national online survey of approximately 15,000 US smokers conducted in 2013 immediately after the conclusion of the 2013 Tips campaign. These data were used to compare the effects of variation in media dose of digital video and television advertising on population-level awareness of the Tips campaign. We implemented higher doses of digital video among selected media markets and randomly selected other markets to receive similar higher doses of television ads. Multivariate logistic regressions estimated the odds of overall campaign awareness via digital or television format as a function of higher-dose media in each market area. All statistical tests used the .05 threshold for statistical significance and the .10 level for marginal nonsignificance. We used adjusted advertising costs for the additional doses of digital and television advertising to compare the cost efficiencies of digital and television advertising on the basis of costs per percentage point of population awareness generated. RESULTS: Higher-dose digital video advertising was associated with 94% increased odds of awareness of any ad online relative to standard-dose markets (P<.001). Higher-dose digital advertising was associated with a marginally nonsignificant increase (46%) in overall campaign awareness regardless of media format (P=.09). Higher-dose television advertising was associated with 81% increased odds of overall ad awareness regardless of media format (P<.001). Increased doses of television advertising were also associated with significantly higher odds of awareness of any ad on television (P<.001) and online (P=.04). The adjusted cost of each additional percentage point of population-level reach generated by higher doses of advertising was approximately US $440,000 for digital advertising and US $1 million for television advertising. CONCLUSIONS: Television advertising generated relatively higher levels of overall campaign awareness. However, digital video was relatively more cost efficient for generating awareness. These results suggest that digital video may be used as a cost-efficient complement to traditional advertising modes (eg, television), but digital video should not replace television given the relatively smaller audience size of digital video viewers.