JUUL on Twitter: Analyzing Tweets About Use of a New Nicotine Delivery System

Online Interest in Elf Bar in the United States: Google Health Trends Analysis

BACKGROUND

Despite the popularity of JUUL e-cigarettes, other brands (eg, Elf Bar) may be gaining digital attention.

OBJECTIVE

This study compared Google searches for Elf Bar and JUUL from 2022 to 2023 using Google Health Trends Application Programming Interface data.

METHODS

Using an infodemiology approach, we examined weekly trends in Google searches (per 10 million) for "Elf Bar" and "JUUL" at the US national and state levels from January 1, 2022, to December 31, 2023. Joinpoint regression was used to assess statistically significant trends in the search probabilities for "Elf Bar" and "JUUL" during the study period.

RESULTS

Elf Bar had less online interest than JUUL at the beginning of 2022. When the US Food and Drug Administration denied JUUL marketing authority on June 23, 2022, JUUL searches peaked at 2609.3 × 107 and fell to 83.9 × 107 on September 3, 2023. Elf Bar searches surpassed JUUL on July 10, 2022, and steadily increased, reaching 523.2 × 107 on December 4, 2022. Overall, Elf Bar's weekly search probability increased by 1.6% (95% CI 1.5%-1.7%; P=.05) from January 2022 to December 2023, with the greatest increase between May 29 and June 19, 2022 (87.7%, 95% CI 35.9%-123.9%; P=.001). Elf Bar searches increased after JUUL's suspension in Pennsylvania (1010%), Minnesota (872.5%), Connecticut (803.5%), New York (738.1%), and New Jersey (702.9%).

CONCLUSIONS

Increasing trends in Google searches for Elf Bar indicate that there was a growing online interest in this brand in the United States in 2022.

Public Response to Federal Electronic Cigarette Regulations Analyzed Using Social Media Data Through Natural Language Processing: Topic Modeling Study

BACKGROUND

e-Cigarette (electronic cigarette) use has been a public health issue in the United States. On June 23, 2022, the US Food and Drug Administration (FDA) issued marketing denial orders (MDOs) to Juul Labs Inc for all their products currently marketed in the United States. However, one day later, on June 24, 2022, a federal appeals court granted a temporary reprieve to Juul Labs that allowed it to keep its e-cigarettes on the market. As the conversation around Juul continues to evolve, it is crucial to gain insights into the sentiments and opinions expressed by individuals on social media.

OBJECTIVE

This study aims to conduct a comprehensive analysis of tweets before and after the ban on Juul, aiming to shed light on public perceptions and sentiments surrounding this contentious topic and to better understand the life cycle of public health-related policy on social media.

METHODS

Natural language processing (NLP) techniques were used, including state-of-the-art BERTopic topic modeling and sentiment analysis. A total of 6023 tweets and 22,288 replies or retweets were collected from Twitter (rebranded as X in 2023) between June 2022 and October 2022. The encoded topics were used in time-trend analysis to depict the boom-and-bust cycle. Content analyses of retweets were also performed to better understand public perceptions and sentiments about this contentious topic.

RESULTS

The attention surrounding the FDA's ban on Juul lasted no longer than a week on Twitter. Not only the news (ie, tweets with a YouTube link that directs to the news site) related to the announcement itself, but the surrounding discussions (eg, potential consequences of this ban or block and concerns toward kids or youth health) diminished shortly after June 23, 2022, the date when the ban was officially announced. Although a short rebound was observed on July 4, 2022, which was contributed by the suspension on the following day, discussions dried out in 2 days. Out of the top 50 most retweeted tweets, we observed that, except for neutral (23/45, 51%) sentiment that broadcasted the announcement, posters responded more negatively (19/45, 42%) to the FDA's ban.

CONCLUSIONS

We observed a short life cycle for this news announcement, with a preponderance of negative sentiment toward the FDA's ban on Juul. Policy makers could use tactics such as issuing ongoing updates and reminders about the ban, highlighting its impact on public health, and actively engaging with influential social media users who can help maintain the conversation.

Characterizing Twitter chatter about temporary alcohol abstinence during "Dry January"

With roots as a public health campaign in the United Kingdom, "Dry January" is a temporary alcohol abstinence initiative encouraging participants to abstain from alcohol use during the month of January. Dry January has become a cultural phenomenon, gaining increasing news media attention and social media engagement. Given the utility of capturing naturalistic discussions around health topics on social media, we examined Twitter chatter about Dry January and associated temporary abstinence experiences. Public tweets were collected containing the search terms "dry january" or "dryjanuary" posted between 15 December and 15 February across 3 years (2020-2). A random subsample stratified by year (n = 3145) was pulled for manual content analysis by trained coders. Final codebook accounted for user sentiment toward Dry January, user account type, and themes related to Dry January participation. Engagement metadata (e.g. likes) were also collected. Though user sentiment was mixed, most tweets expressed positive or neutral sentiment toward Dry January (74.7%). Common themes included encouragement and support for Dry January participation (14.1%), experimentation with and promotion of nonalcoholic drinks (14.0%), and benefits derived from Dry January participation (10.4%). While there is promise in the movement to promote positive alcohol-related behavior change, increased efforts to deliver the campaign within a public health context are needed. Health communication campaigns designed to inform participants about evidence-based treatment and recovery support services proven to help people quit or cut down on their drinking are likely to maximize benefits.

Miscommunication about the US federal Tobacco 21 law: a content analysis of Twitter discussions

OBJECTIVE

Tobacco 21 is a law that sets the minimum legal sales age of tobacco products to 21. On 20 December 2019, the USA passed a federal Tobacco 21 law. The objective of this study is to explore Twitter discussions about the federal Tobacco 21 law in the USA leading up to enacted.

METHODS

Twitter messages about Tobacco 21 posted between September and December 2019 were collected via RITHM software. A 2% sample of all collected tweets were double coded by independent coders using a content analysis approach.

RESULTS

Findings included three content categories of tweets (news, youth and young adults and methods of avoiding the law) with eight subcodes. Most news tweets incorrectly described the law as a purchase law (54.7%). However, Tobacco 21 is in fact a sales law-it only includes penalties for tobacco retailers who sell to under-age purchasers. About one-fourth (27%) of the tweets involved youth and young adults, with some claiming the law would reduce youth smoking and others doubting its ability to limit youth access to tobacco products. Few tweets (2.5%) mentioned methods of circumventing the policy, such as having an older peer purchase tobacco.

CONCLUSIONS

As several countries explore raising their age of sale of tobacco laws to 21, they should couple policy enactment with clear and accurate communication about the law. Compliance agencies at all levels (eg, local, regional, national) can use social media to identify policy loopholes and support vulnerable populations throughout the policy implementation process.

Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis

BACKGROUND

On December 20, 2019, the US "Tobacco 21" law raised the minimum legal sales age of tobacco products to 21 years. Initial research suggests that misinformation about Tobacco 21 circulated via news sources on Twitter and that sentiment about the law was associated with particular types of tobacco products and included discussions about other age-related behaviors. However, underlying themes about this sentiment as well as temporal trends leading up to enactment of the law have not been explored.

OBJECTIVE

This study sought to examine (1) sentiment (pro-, anti-, and neutral policy) about Tobacco 21 on Twitter and (2) volume patterns (number of tweets) of Twitter discussions leading up to the enactment of the federal law.

METHODS

We collected tweets related to Tobacco 21 posted between September 4, 2019, and December 31, 2019. A 2% subsample of tweets (4628/231,447) was annotated by 2 experienced, trained coders for policy-related information and sentiment. To do this, a codebook was developed using an inductive procedure that outlined the operational definitions and examples for the human coders to annotate sentiment (pro-, anti-, and neutral policy). Following the annotation of the data, the researchers used a thematic analysis to determine emergent themes per sentiment category. The data were then annotated again to capture frequencies of emergent themes. Concurrently, we examined trends in the volume of Tobacco 21-related tweets (weekly rhythms and total number of tweets over the time data were collected) and analyzed the qualitative discussions occurring at those peak times.

RESULTS

The most prevalent category of tweets related to Tobacco 21 was neutral policy (514/1113, 46.2%), followed by antipolicy (432/1113, 38.8%); 167 of 1113 (15%) were propolicy or supportive of the law. Key themes identified among neutral tweets were news reports and discussion of political figures, parties, or government involvement in general. Most discussions were generated from news sources and surfaced in the final days before enactment. Tweets opposing Tobacco 21 mentioned that the law was unfair to young audiences who were addicted to nicotine and were skeptical of the law's efficacy and importance. Methods used to evade the law were found to be represented in both neutral and antipolicy tweets. Propolicy tweets focused on the protection of youth and described the law as a sensible regulatory approach rather than a complete ban of all products or flavored products. Four spikes in daily volume were noted, 2 of which corresponded with political speeches and 2 with the preparation and passage of the legislation.

CONCLUSIONS

Understanding themes of public sentiment-as well as when Twitter activity is most active-will help public health professionals to optimize health promotion activities to increase community readiness and respond to enforcement needs including education for retailers and the general public.

Viewer Reactions to EVALI Storylines on Popular Medical Dramas: A Thematic Analysis of Twitter Messages

Previous research has found an association between awareness of e-cigarette, or vaping, product-use associated lung injury (EVALI) and lower intention to use e-cigarettes among young people. This study utilized Twitter data to evaluate if the January 2020 depiction of EVALI on New Amsterdam, Chicago Med, and Grey's Anatomy-three popular primetime medical dramas-could be a potential innovative avenue to raise awareness of EVALI. We obtained tweets containing e-cigarette-related search strings from 1/21/2020 to 02/18/2020 and filtered these with storyline-specific keywords, resulting in 1,493 tweets for qualitative coding by two trained human coders. Content codes were informed by prior research, theories of narrative influence, and e-cigarette related outcomes. Of 641 (42.9%) relevant tweets, the most frequent content codes were perceived realism (n = 292, 45.6%) and negative response (n = 264, 41.2%). A common theme among these tweets was that storylines were unrealistic because none of the characters with EVALI used THC-containing products. Approximately 12% of tweets (n = 78) mentioned e-cigarette knowledge and 28 (4.4%) mentioned behavior, including quitting e-cigarettes because of viewing the storylines. Implications for health communication research utilizing social media data and maximizing the achievement of positive health-related outcomes for storylines depicting current health topics are discussed.

#TurntTrending: a systematic review of substance use portrayals on social media platforms

AIMS

There is a growing body of literature exploring the types of substance-related content and their portrayals on various social media platforms. We aimed to summarize how content related to substances is portrayed on various social media platforms.

METHODS

This systematic review was pre-registered on PROSPERO (ref: CRD42021291853). A comprehensive search was conducted in the databases of PubMed, Scopus, PsycINFO and Web of Science in April 2021. Original qualitative studies published post-2004 that included thematic and sentiment analyses of social media content on tobacco, alcohol, psychostimulant, e-cigarette, cannabis, opiate, stimulant/amphetamine, inhalant and novel psychoactive substance were included. Social media platforms were defined as online web- or application-based platforms that allowed users to generate content and interact via 'liking', comment or messaging features. Only studies that included summative and/or thematic content analyses of substance-related social media content were included.

RESULTS

A total of 73 studies, which covered 15 905 182 substance-related posts on Twitter, YouTube, Instagram, Pinterest, TikTok and Weibo, were identified. A total of 76.3% of all substance-related content was positive in its depiction of substance use, with 20.2% of content depicting use negatively. Sentiment regarding opiate use however was commonly negative (55.5%). Most studies identified themes relating to Health, Safety and Harms (65.0%) of substance use. Themes relating to Promotions/Advertisements (63.3%), Informative content (55.0%) and Use behaviours (43.3%) were also frequently identified.

CONCLUSIONS

Substance-related content that promotes engagement with substance use or actively depicts use appears to be widely available on social media. The large public presence of this content may have concerning influences on attitudes, behaviours and risk perceptions relating to substance use, particularly among the most vulnerable and heaviest users of social media-adolescents and young adults.

#BlueGirl: A study of collective trauma on Twitter

Collective trauma is the outcome of traumatic incidents that necessitate collective self-reflection and create a need for public discourse. Access to social media allows individuals to shape event coverage beyond what is put forth by mainstream media through hashtag-based communities. The death of Sahar Khodayari, or "Blue Girl," an Iranian woman who committed suicide after being sentenced to prison for appearing in a sports stadium dressed as a man, is one such event that became a collective trauma. The current study sought to examine the collective trauma reaction among Farsi-speaking Twitter users in response to Khodayari's death as represented in public tweets. The overall pool of data consisted of 8,653 tweets containing at least one of four hashtags (i.e., #BlueGirl, #Blue_Girl,, # #), posted September 9-24, 2019, as well as 603 tweets posted on the anniversary of the event 1 year later. Using thematic analysis, we identified three main themes within these public tweets representing different responses to Khodayari's death: social justice, awareness, and process. In undemocratic countries, like Iran, where there are limited free physical spaces for public gatherings, social media provides a platform for people to come together after tragic events to express emotions and discuss thoughts and actions. The present findings demonstrate that Twitter allowed Farsi-speaking users to demand social justice, share awareness about #BlueGirl, and process their emotions and thoughts accordingly. Therefore, social media may provide a window into the collective trauma experience, and it may also serve as a new mode of studying trauma on new platforms.

Using Natural Language Processing to Explore "Dry January" Posts on Twitter: Longitudinal Infodemiology Study

BACKGROUND

Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Though Dry January has become a global phenomenon, there has been limited investigation into Dry January participants' experiences. One means through which to gain insights into individuals' Dry January-related experiences is by leveraging large-scale social media data (eg, Twitter chatter) to explore and characterize public discourse concerning Dry January.

OBJECTIVE

We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multiple years of tweets (2020-2022)? (2) Do unique themes or patterns emerge in Dry January 2021 tweets after the onset of the COVID-19 pandemic? and (3) What is the association with tweet composition (ie, sentiment and human-authored vs bot-authored) and engagement with Dry January tweets?

METHODS

We applied natural language processing techniques to a large sample of tweets (n=222,917) containing the term "dry january" or "dryjanuary" posted from December 15 to February 15 across three separate years of participation (2020-2022). Term frequency inverse document frequency, k-means clustering, and principal component analysis were used for data visualization to identify the optimal number of clusters per year. Once data were visualized, we ran interpretation models to afford within-year (or within-cluster) comparisons. Latent Dirichlet allocation topic modeling was used to examine content within each cluster per given year. Valence Aware Dictionary and Sentiment Reasoner sentiment analysis was used to examine affect per cluster per year. The Botometer automated account check was used to determine average bot score per cluster per year. Last, to assess user engagement with Dry January content, we took the average number of likes and retweets per cluster and ran correlations with other outcome variables of interest.

RESULTS

We observed several similar topics per year (eg, Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content over time. Although there was overlap in themes across multiple years of tweets, unique themes related to individuals' experiences with alcohol during the midst of the COVID-19 global pandemic were detected in the corpus of tweets from 2021. Also, tweet composition was associated with engagement, including number of likes, retweets, and quote-tweets per post. Bot-dominant clusters had fewer likes, retweets, or quote tweets compared with human-authored clusters.

CONCLUSIONS

The findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemplating, preparing for, or actively pursuing attempts to quit or cut down on their drinking.

Analyzing Twitter Chatter About Tobacco Use Within Intoxication-related Contexts of Alcohol Use: "Can Someone Tell Me Why Nicotine is So Fire When You're Drunk?"

INTRODUCTION

Alcohol and tobacco are commonly used together. Social influences within online social networking platforms contribute to youth and young adult substance use behaviors. This study used a sample of alcohol- and tobacco-related tweets to evaluate: (1) sentiment toward co-use of alcohol and tobacco, (2) increased susceptibility to tobacco use when consuming alcohol, and (3) the role of alcohol in contributing to a failed attempt to quit tobacco use.

METHODS

Data were collected from the Twitter API from January 1, 2019 through December 31, 2019 using tobacco-related keywords (e.g., vape, ecig, smoking, juul*) and alcohol-related filters (e.g., drunk, blackout*). A total of 78,235 tweets were collected, from which a random subsample (n = 1,564) was drawn for coding. Cohen's Kappa values ranged from 0.66 to 0.99.

RESULTS

Most tweets were pro co-use of alcohol and tobacco (75%). One of every ten tweets reported increased susceptibility to tobacco use when intoxicated. Non-regular tobacco users reported cravings for and tobacco use when consuming alcohol despite disliking tobacco use factors such as the taste, smell, and/or negative health effects. Regular tobacco users reported using markedly higher quantities of tobacco when intoxicated. Individuals discussed the role of alcohol undermining tobacco cessation attempts less often (2.0%), though some who had quit smoking for prolonged periods of time reported reinitiating tobacco use during acute intoxication episodes.

CONCLUSIONS

Tobacco cessation interventions may benefit from including alcohol-focused components designed to educate participants about the association between increased susceptibility to tobacco use when consuming alcohol and the role of alcohol in undermining tobacco cessation attempts.

IMPLICATIONS

Sentiment toward co-use of alcohol and tobacco on Twitter is largely positive. Individuals reported regret about using tobacco, or using more than intended, when intoxicated. Those who had quit smoking or vaping for prolonged periods of time reported reinitiating tobacco use when consuming alcohol. While social media-based tobacco cessation interventions like the Truth Initiative's "Ditch the Juul" campaign demonstrate potential to change tobacco use behaviors, these campaigns may benefit from including alcohol-focused components designed to educate participants about the association between increased susceptibility to tobacco use when consuming alcohol and the role of alcohol in undermining tobacco cessation attempts.

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series

Background

Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use.

Objective

This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques.

Methods

We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting.

Results

When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account.

Conclusions

e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.

Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets

BACKGROUND

Puff Bars are e-cigarettes that continued marketing flavored products by exploiting the US Food and Drug Administration exemption for disposable devices.

OBJECTIVE

This study aimed to examine discussions related to Puff Bar on Twitter to identify tobacco regulation and policy themes as well as unanticipated outcomes of regulatory loopholes.

METHODS

Of 8519 original tweets related to Puff Bar collected from July 13, 2020, to August 13, 2020, a random 20% subsample (n=2661) was selected for qualitative coding of topics related to nicotine dependence and tobacco policy.

RESULTS

Of the human-coded tweets, 2123 (80.2%) were coded as relevant to Puff Bar as the main topic. Of those tweets, 698 (32.9%) discussed tobacco policy, including flavors (n=320, 45.9%), regulations (n=124, 17.8%), purchases (n=117, 16.8%), and other products (n=110, 15.8%). Approximately 22% (n=480) of the tweets referenced dependence, including lack of access (n=273, 56.9%), appetite suppression (n=59, 12.3%), frequent use (n=47, 9.8%), and self-reported dependence (n=110, 22.9%).

CONCLUSIONS

This study adds to the growing evidence base that the US Food and Drug Administration ban of e-cigarette flavors did not reduce interest, but rather shifted the discussion to brands utilizing a loophole that allowed flavored products to continue to be sold in disposable devices. Until comprehensive tobacco policy legislation is developed, new products or loopholes will continue to supply nicotine demand.

Content and trend analysis of user-generated nicotine sickness tweets: A retrospective infoveillance study

INTRODUCTION

Exposure to pro-tobacco and electronic nicotine delivery system (ENDS) social media content can lead to overconsumption, increasing the likelihood of nicotine poisoning. This study aims to examine trends and characteristics of nicotine sickness content on Twitter between 2018–2020.

METHODS

Tweets were collected retrospectively from the Twitter Academic Research Application Programming Interface (API) stream filtered for keywords: ‘nic sick’, ‘nicsick’, ‘vape sick’, ‘vapesick’ between 2018–2020. Collected tweets were manually annotated to identify suspected user-generated reports of nicotine sickness and related themes using an inductive coding approach. The Augmented Dickey-Fuller (ADF) test was used to assess stationarity in the monthly variation of the volume of tweets between 2018–2020.

RESULTS

A total of 5651 tweets contained nicotine sickness-related keywords and 18.29% (n=1034) tweets reported one or more suspected nicotine sickness symptoms of varied severity. These tweets were also grouped into five related categories including firsthand and secondhand reports of symptoms, intentional overconsumption of nicotine products, users expressing intention to quit after ‘nic sick’ symptoms, mention of nicotine product type/brand name that they consumed while ‘nic sick’, and users discussing symptoms associated with nicotine withdrawal following cessation attempts. The volume of tweets reporting suspected nicotine sickness appeared to increase throughout the study period, except between February and April 2020. Stationarity in the volume of ‘nicsick’ tweets between 2018–2020 was not statistically significant (ADF= -0.32, p=0.98) indicating a change in the volume of tweets.

CONCLUSIONS

Results point to the need for alternative forms of adverse event surveillance and reporting, to appropriately capture the growing health burden of vaping. Infoveillance approaches on social media platforms can help to assess the volume and characteristics of user-generated content discussing suspected nicotine poisoning, which may not be reported to poison control centers. Increasing volume of user-reported nicotine sickness and intentional overconsumption of nicotine in twitter posts represent a concerning trend associated with ENDS-related adverse events and poisoning.

Policy and Behavior: Comparisons between Twitter Discussions about the US Tobacco 21 Law and Other Age-Related Behaviors

To combat the e-cigarette epidemic among young audiences, a federal law was passed in the US that raised the minimum legal sales age of tobacco to 21 years (commonly known as Tobacco 21). Little is known about sentiment toward this law. Thus, the purpose of our study was to systematically explore trends about Tobacco 21 discussions and comparisons to other age-restriction behaviors on Twitter. Twitter data (n = 4628) were collected from September to December of 2019 that were related to Tobacco 21. A random subsample of identified tweets was used to develop a codebook. Two trained coders independently coded all data, with strong inter-rater reliability (κ = 0.71 to 0.93) found for all content categories. Associations between sentiment and content categories were calculated using χ2 analyses. Among relevant tweets (n = 955), the most common theme—the disjunction between ages for military enlistment and tobacco use—was found in 17.8% of all tweets. Anti-policy sentiment was strongly associated with the age of military enlistment, alcohol, voting, and adulthood (p < 0.001 for all). Opposition to Tobacco 21 propagates on social media because the US federal law does not exempt military members. However, the e-cigarette epidemic may have fueled some support for this law.

Stealth Vaping Among College Students on Four Geographically Distinct Tobacco-Free College Campuses: Prevalence and Practices

INTRODUCTION

To offset tobacco use among college students, many universities have implemented tobacco-free campus policies. Given how easily e-cigarette users can discreetly vape in places where it is prohibited without being detected (ie, stealth vape), it is important to assess whether students are subverting these policies. This study examined the prevalence of stealth vaping on-campus among students at multiple universities, including frequency and methods for stealth vaping.

AIMS AND METHODS

A convenience sample of college students was obtained from four geographically distinct, large, public universities with established policies prohibiting all forms of tobacco use on campus. Participants (N = 863 current e-cigarette users) reported on demographics and e-cigarette use. Those who previously stealth vaped on-campus (n = 422) completed additional questions, including common locations, techniques, and devices/e-liquids used for stealth vaping.

RESULTS

Nearly half (48.9%) of past 30-day e-cigarette users reported previously stealth vaping on-campus. Among stealth vapers, 48.1% owned a smaller device for stealth vaping and 38.9% used e-liquids with low visibility vapors. Common on-campus stealth vaping locations included bathrooms, libraries, parking garages, and classrooms. Techniques used for stealth vaping included deep inhale, blowing a hit into one's clothes, and swallowing a hit.

CONCLUSIONS

A large percentage of respondents commonly violated campus tobacco-free policies by stealth vaping. For such policies to be effective at reducing on-campus e-cigarette use, there is a need for enhanced monitoring and enforcement. More research is needed to better understand factors influencing stealth vaping behaviors and public health implications.

IMPLICATIONS

This sample of college student e-cigarette users commonly violated campus smoke- and tobacco-free policies by stealth vaping on campus. Respondents used a range of methods (eg, smaller device and low visibility e-liquids) and locations (eg, bathrooms, libraries, and parking garages) for stealth vaping. If campus policies are to be effective at reducing on-campus e-cigarette use, there is a need for enhanced monitoring and enforcement. E-cigarette devices specifically designed and marketed to facilitate stealth vaping (eg, resembling USB flash drives and pens) may require regulatory action. More research is needed to better understand factors influencing stealth vaping behaviors and public health implications.

Association between Inclusion of E-Cigarettes in Statewide Comprehensive Smoke-Free Indoor Air Laws and Vaping Behaviors: Results from a Longitudinal Population Study

Several states have included e-cigarettes in their comprehensive smoke-free indoor air laws (i.e., aerosol-free policies), prohibiting the use of e-cigarettes--in addition to combustible tobacco products--in workplaces, restaurants, and bars. However, whether these policies contribute to reductions in e-cigarette use remains largely unknown. This study utilized a nationally representative longitudinal sample to examine the association between the implementation of statewide aerosol-free policies and e-cigarette use behaviors. Methods: This longitudinal retrospective study implemented a quasi-experimental design. Waves 1-4 (2013-2018) restricted-use data from adult participants (weighted N = 22,838,787; unweighted N = 8,663) in the Population Assessment of Tobacco and Health (PATH) study were analyzed. The generalized difference-in-difference approach along with weighted hurdle and multinomial logistic regressions were conducted to examine the associations between aerosol-free policies and three measures of e-cigarette use (past 30-day e-cigarette abstinence status and number of use days, and past-12-month use frequency) pre- and post-policy implementation. Results indicated there were not statistically significant differences in e-cigarette use behaviors between participants living in states with and without the aerosol-free policies (ps ranged from 0.301 to 0.831), considering pre- and post-policy implementation. Findings indicated that the effectiveness of the aerosol-free policies on e-cigarette use behaviors was not supported by longitudinal national data. States that have implemented aerosol-free policies should make pertinent efforts to enhance the awareness of these policies and to strengthen their enforcement. Future investigation into e-cigarette use in places where it is prohibited and implications with regard to effectiveness of aerosol-free policies is warranted.

Conceptual model for the evaluation of attractiveness, addictiveness and toxicity of tobacco and related products: The example of JUUL e-cigarettes

Many new tobacco and related products (nTRP) have emerged on the market, with unknown health risks. Here, we present a conceptual model containing the factors and relations between them that contribute to the nTRP's health effects. Factors that determine attractiveness, addictiveness and toxicity of nTRP were defined based on previous assessments, literature, and expert discussions. Our model will aid in identifying key risk factors contributing to increased risk of adverse health effects for a product in a qualitative manner. Additionally, it can gauge attractiveness for specific user groups, as a determinant for population prevalence of use. Our model can be used to identify aspects of nTRP that require attention for public information or product regulation. As an example, we applied this to JUUL, a popular e-cigarette in the US. Aspects of concern for JUUL are its attractive and discrete shape, user-friendly prefilled pods, flavors, high aerosol nicotine levels, and liquids containing nicotine salts instead of free-based nicotine. The addictiveness and especially attractiveness are sufficiently high to have a large potential impact on population health due to its contribution to use and hence exposure. Products and their use can change over time; therefore market research and monitoring are crucial.

Surveillance of U.S. Corporate Filings Provides a Proactive Approach to Inform Tobacco Regulatory Research Strategy

The popularity of electronic cigarettes in the United States and around the world has led to a startling rise in youth nicotine use. The Juul® e-cigarette was introduced in the U.S. market in 2015 and had captured approximately 13% of the U.S. market by 2017. Unlike many other contemporary electronic cigarette companies, the founders behind the Juul® e-cigarette approached their product launch like a traditional high-tech start-up company, not like a tobacco company. This article presents a case study of Juul's corporate and product development history in the context of US regulatory actions. The objective of this article is to demonstrate the value of government-curated archives as leading indicators which can (a) provide insight into emergent technologies and (b) inform emergent regulatory science research questions. A variety of sources were used to gather data about the Juul® e-cigarette and the corporations that surround it. Sources included government agencies, published academic literature, non-profit organizations, corporate and retail websites, and the popular press. Data were disambiguated, authenticated, and categorized prior to being placed on a timeline of events. A timeline of four significant milestones, nineteen corporate filings and events, twelve US regulatory actions, sixty-four patent applications, eighty-seven trademark applications, twenty-three design patents and thirty-two utility patents related to Juul Labs and its associates is presented, spanning the years 2004 through 2020. This work demonstrates the probative value of findings from patent, trademark, and SEC filing literature in establishing a premise for emergent regulatory science research questions which may not yet be supported by traditional archival research literature. The methods presented here can be used to identify key aspects of emerging technologies before products actually enter the market; this shifting policy formulation and problem identification from a paradigm of being reactive in favor of becoming proactive. Such a proactive approach may permit anticipatory regulatory science research and ultimately shorten the elapsed time between market technology innovation and regulatory response.

Young adults' intention to quit using JUUL

BACKGROUND

With research indicating some young audiences may desire to quit using JUUL, a high-nicotine e-cigarette, we sought to explore factors that may motivate them to quit.

METHODS

This sequential, mixed methods study included a cross-sectional online survey of college students (n = 631) followed by in-person interviews (n = 51) with survey participants. Data were collected March-April 2019. The survey asked about intention to quit using JUUL. A latent class analysis (LCA) identified participant groups who would quit for various reasons. Participants were also asked 'Can you be too old to JUUL?' during the survey. During the interviews, participants were provided preliminary survey findings and asked about their perceptions of the data. Interview participants were also asked about their expectations for future use of JUUL.

RESULTS

Four classes emerged from the LCA, indicating costs to self (i.e., harm to lungs/brain, price; 46.8%), financial costs (36.6%), all costs (e.g., social, monetary, health; 9.3%), and harm to self (7.3%) may have influenced our sample's decision to quit using JUUL. Interviewees affirmed desires to quit using JUUL, especially after leaving college. Only 27.19% of survey participants reported an age threshold for using JUUL (M = 31.8 years, SD = 10.0); however, several interviewees explained that although someone could not be too old to JUUL, it would be 'immature' or 'childish' for adults who were not trying to quit smoking to use JUUL socially.

DISCUSSION

Comprehensive tobacco control strategies such as taxing e-cigarettes, marketing campaigns, and nicotine cessation programs are needed to help nicotine dependent young adults quit using high-nicotine e-cigarettes.

Parents Are Unaware of Their Youths' Tobacco Use: Results from the Population Assessment of Tobacco and Health Study

BACKGROUND

In this study, we determined the prevalence of and factors associated with parent unawareness of youth tobacco use.

METHODS

We used data from waves 1, 2, and 3 (2013-2016) of the Population Assessment of Tobacco and Health study, a nationally representative study of 13,650 US youth ages 12 to 17 and their parents. We conducted weighted multivariate analyses comparing parent unawareness of youth-reported ever use and associations between parents' unawareness of youth use and covariates.

RESULTS

Youth ever tobacco use ranged from 21.8% in 2013-2014, to 24.1% in 2014-2015, to 23.4% in 2015-2016. Parent unawareness ranged from 57.6% in 2013-2014, to 61.9% in 2014-2015, and 64.5% in 2015-2016. Factors associated with higher parent unawareness of youth tobacco use in 2015-2016 were youth being female, black, or Hispanic (adjusted odds ratio [AOR] = 1.24-2.61; all ps < .05). Youth with lower academic performance, relatives who used tobacco, tobacco available in the home, or past 30-day use, were less likely to have parents unaware of their use (AORs 0.33-0.56; all ps < .05).

CONCLUSIONS

Among youth who reported ever using tobacco, most had parents who were unaware of their use. School-based efforts targeting specific sociodemographic factors could increase parent awareness of youth tobacco use.