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IntroductionIn 2018, the United States Food and Drug Administration (FDA) required that electronic cigarette (e-cigarette) manufacturers, packagers, importers, distributors, and retailers display an addictive or alternate warning statement on e-cigarette visual advertisements. Few studies have investigated the FDA-mandated and other warnings on social media. This study examined the prevalence and content of warning statements in e-cigarette-related YouTube videos.MethodsIn 2019, The Virginia Commonwealth University Center for the Study of Tobacco Products conducted bi-monthly (February-June) YouTube searches by relevance and view count to identify e-cigarette-related videos. Overall, 178 videos met the inclusion criteria. Staff coded each video for the presence of a visual/verbal warning statement, warning statement type (eg, FDA-mandated, addiction/tobacco, safety/toxic exposure, health effects), sponsorship, and tobacco product characteristics. A data extraction tool collected the video URL, title, upload date, and number of views, likes/dislikes, and comments.ResultsOnly 5.1% of videos contained FDA-mandated and 21.9% contained non-mandated warnings. All videos with FDA-mandated and 46.2% of non-mandated warnings were represented visually. Only 13.1% of industry-sponsored videos uploaded after the mandate effective date had an FDA-mandated warning statement and videos with FDA-mandated and non-mandated (v. no) warnings had significantly fewer views, likes, dislikes, and comments. Among all non-mandated warnings, 31.3% featured an addiction/tobacco, 18.8% a safety/toxic exposure, and 37.5% a health effects warning.ConclusionsThe prevalence of FDA-mandated warning statements in e-cigarette related YouTube videos was low. FDA enforcement of the warning statement mandate on YouTube could increase the public's understanding of the addictive nature of nicotine in e-cigarettes.ImplicationsThe FDA has the authority to regulate the advertisement and promotion of e-cigarettes on the Internet. These data can inform future FDA requirements related to the language content and visual representation of addiction/tobacco, safety/exposure, and health effects warning statements that appear in YouTube videos and other visual social media popular among young people. Such data would help consumers make informed decisions about purchasing e-cigarette products, using e-cigarettes, and avoiding unintentional harm related to e-cigarettes. In addition, these data may help social media platforms make decisions on whether they will prohibit advertisements that promote or facilitate the sale of tobacco products.

Objective: This study is the first to examine the influence of e-cigarette emission phrasing on perceived harm of secondhand exposure, and whether harm perception was associated with support for a tobacco-free campus policy. Participants: In the fall 2018 and spring 2019 semesters, 52 sections of a college English course (N = 791 students) were cluster randomized to one of three conditions ("vapor," "aerosol," or "chemicals") assessing harm of secondhand exposure to e-cigarette emissions. Methods: Regression models adjusted for demographic characteristics, tobacco use, and other potential confounders. Results: Compared to the "vapor" condition, "chemicals" and "aerosol" conditions were associated with increased odds of perceiving secondhand exposure to e-cigarettes to be harmful/very harmful (AOR = 2.0, p < 0.01). Greater perceived harm of secondhand e-cigarette exposure was associated with increased odds of supporting a tobacco-free campus policy (AOR = 2.22, p < 0.001). Conclusions: Health campaigns should use accurate terminology to describe e-cigarette emissions, rather than jargon that conveys lower risk.

Objective: To examine the association between fraternity/sorority membership and athlete status with tobacco use by tobacco product type. Participants: Undergraduate college students who participated in the Spring 2017 National College Health Assessment-II Survey (n = 47,821). Methods: Prevalence of current (past-30 day) cigarette smoking, waterpipe tobacco smoking (WTS), and e-cigarette (ECIG) use was examined. Logistic regressions examined associations between fraternity/sorority membership and participation in collegiate athletics with tobacco use. Results: Fraternity/sorority members had the highest current use rates for cigarettes, waterpipe tobacco, and ECIGs and were nearly twice as likely to report cigarette smoking, WTS, and ECIG use relative to non-fraternity/sorority members. Relative to non-varsity athletes, varsity athletes were less likely to report cigarette smoking and WTS, but not ECIG use. Conclusions: Fraternity/sorority members appear to be at risk for use of multiple tobacco products. Varsity athlete status may be protective for some tobacco products, but does not appear no for ECIG use. ECIGs may appeal to groups typically at low risk for other tobacco products.

Flavor additives are commonly used in combustible tobacco products to mask harshness and increase appeal. However, research on the availability of flavored waterpipe tobacco (WT) is lacking, yet is important to support implementation of policies. We completed a comprehensive online search in 2020 to identify WT brands and flavors sold online in the USA. We conducted a descriptive content analysis categorizing flavors as explicit (i.e., clear taste/flavor) or concept (i.e., no clear taste/flavor); and coded for 23 flavor descriptors (e.g., fruit, mint/menthol, tobacco). Flavor names were double-coded and discrepancies were resolved by a third coder. We identified 66 WT brands with 118 product lines (i.e., sub-brands). We found 2953 flavors, including 1871 unique flavors. Brands averaged 45 flavors (range: 1–183). Almost three quarters (73.5%, n = 2171) used explicit flavor names and 26.5% (n = 782) used concept flavor names. Concept flavors varied widely, and included names such as 1001 Nights and California Dream. The most common flavor descriptors were fruit (54.1%) and mint/menthol (12.5%). Tobacco was rarely (0.2%) used as a flavor descriptor. Flavor descriptors also included location (10.7%), color (11.1%), candy (6.3%), cool/ice (5.3%), and alcohol (5.5%). WT is available in 1871 unique flavors, likely contributing to product appeal and use. Like other tobacco products, fruit and mint/menthol are common flavors. Given the significant health consequences associated with WT smoking and the role of flavors in product use, regulatory action specifically targeting WT flavors is urgently needed. [ABSTRACT FROM AUTHOR]

Introduction Electronic cigarettes (ECIGs) heat a nicotine-containing liquid to produce an inhalable aerosol. ECIG power (wattage) and liquid nicotine concentration are two factors that predict nicotine emission rate ("flux"). These factors can vary greatly across devices and users. Aims and Methods The purpose of this study was to examine ECIG device and liquid heterogeneity in "real world" settings and the association with predicted nicotine flux, nicotine yield, and total particulate matter (TPM) emissions. Past 30-day ECIG users (n = 84; mean age = 23.8 years [SD = 9.6]) reported device and liquid characteristics. Device power was measured via multimeter, device display screens, or obtained via labeling. Liquid nicotine concentration was obtained via labeling or through chemical analysis. Predicted nicotine flux, nicotine yield, and TPM associated with 10 4-second puffs were calculated for participants' primary devices. Results Participants' primary devices were box mods (42.9%), disposable vapes (20.2%), and pod mods (36.9%). Most participants (65.5%) reported not knowing their primary device wattage. Rebuildable/box mods had the greatest power range (11.1–120.0 W); pod mod power also varied considerably (4.1–21.7 W). Unlike device wattage, most participants (95.2%) reported knowing their liquid nicotine concentration, which ranged from 3.0 to 86.9 mg/ml (M = 36.0, SD = 29.3). Predicted nicotine flux varied greatly across products (range =12.0–160.1 μg/s, M = 85.6 μg/s, SD = 34.3). Box mods had the greatest variability in wattage and predicted nicotine flux, nicotine yield, and TPM yield. Conclusions ECIG device and liquid heterogeneity influence nicotine and other toxicant emissions. Better measurement of ECIG device and liquid characteristics is needed to understand nicotine and toxicant emissions and to inform regulatory policy. Implications ECIG device and liquid heterogeneity cause great variability in nicotine flux and toxicants emitted. These data demonstrate the need to examine device and liquid characteristics to develop empirically informed, health-promoting regulatory policies. Policies may include setting product standards such that ECIG products cannot (1) have nicotine fluxes much greater than that of a cigarette to decrease the risk of dependence, (2) have nicotine fluxes that are very low and thus would have minimal appeal to cigarette smokers and may serve as starter products for youth or nontobacco users, and (3) emit large amounts of particulate matter and other toxicants. [ABSTRACT FROM AUTHOR]

Background: How nicotine dependence will be affected when current smokers initiate electronic cigarette (e-cig) use to reduce cigarette smoking is unknown. This study evaluated cigarette, e-cig, and total nicotine dependence over 6 months among smokers reducing cigarette consumption by replacing with e-cigs.Methods: Adult cigarette smokers were randomized to one of four conditions [36mg/ml e-cig, 8mg/ml e-cig, 0mg/ml e-cig, or cigarette-substitute (CS) (provided at no cost)] and instructed to reduce their cigarette smoking by 75% at 1 month. Participants completed follow-up at 1, 3, and 6 months. The Penn State Nicotine Dependence Index (PSNDI) measured dependence on cigarettes (PSCDI) and e-cigs (PSECDI). Urine cotinine measured total nicotine exposure. Linear mixed effects models for each outcome were conducted and included interaction terms between visit and condition.Results: Participants (n=520) were 58.8% female, 67.3% White, and 48.0 years old. At baseline, the median number of cigarettes smoked per day was 17.3 and the mean PSCDI score was 13.4, with no significant differences between conditions. Participants in the e-cig conditions reported significantly lower PSCDI scores, compared with baseline, and with the CS condition at all follow-up visits. Those in the 36mg/ml e-cig condition reported greater PSECDI scores at 6 months, compared with baseline and the 0mg/ml and 8mg/ml conditions. At all follow-up visits, there were no differences in total nicotine exposure compared to baseline, nor between any conditions.Conclusion: E-cig use was associated with reduced cigarette dependence, compared to the CS, without significant increases in total nicotine exposure.Implications: Initiation of electronic cigarette use while continuing to smoke could potentially increase nicotine dependence. In this randomized trial aimed at helping smokers to reduce their cigarette intake, we found that use of an e-cig was associated with a reduction in cigarette dependence and an increase in e-cig dependence (in the condition with the highest nicotine concentration only), with no long term increase in total nicotine dependence or nicotine exposure. [ABSTRACT FROM AUTHOR]

Indoor electronic cigarette (e-cigarette) use exposes bystanders to airborne PM2.5 (particulate matter 2.5 µm and smaller) and volatile organic compounds (VOCs). To understand potential risks for bystanders, measuring real-time e-cigarette exposure is important. However, no study evaluates the performance of low-cost sensors for real-time measurements of indoor e-cigarette aerosol concentrations. Thirty-two low-cost GeoAir2 monitors were used to detect e-cigarette-generated PM2.5 and VOCs inside a controlled laboratory exposure chamber. The GeoAir2 was compared to an OPC-N3 low-cost monitor and the MiniWRAS and MiniRae reference instruments. Three e-cigarette devices (JUUL, NJOY Daily, and VOOPOO DRAG 2) were used to generate emissions using a diaphragm pump programmed to puff based on e-cigarette-user puffing behaviors. The GeoAir2 PM2.5 concentrations were similar to the raw MiniWRAS PM2.5 concentrations for different e-cigarettes. For the JUUL, PM2.5 measurements were close or on the 1-1 line, for the NJOY Daily measurements were on the 1-1 line for concentrations lower than 200 µg/m3, and PM2.5 measurements were overestimated for the VOOPOO DRAG 2 (mean correlation ≥ 0.97). The OPC-N3 significantly underestimated concentrations compared to the raw and filter-corrected MiniWRAS concentrations (mean correlation ≥ 0.98). The GeoAir2 VOC concentrations compared to the MiniRAE were on the 1-1 line for the VOOPOO DRAG 2, overestimated for the JUUL and NJOY Daily (mean correlation ≥ 0.98). These results show that GeoAir2 is an effective tool for examining e-cigarette use in indoor settings, such as homes, vehicles, workplaces, vape shops, or other locations, to better understand bystander exposures to e-cigarette aerosols. Copyright © 2022 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]