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Introduction E-cigarettes are increasingly being marketed as containing tobacco-free nicotine (TFN). There is no data examining use of TFN e-cigarettes by young adults and how use may differ from non-TFN e-cigarettes. The current study aims to characterize young adult TFN e-cigarette use and examine differences between those who report using TFN and non-TFN e-cigarettes. Method U.S. young adults (18–25) with lifetime e-cigarette use (n = 927) were recruited via online panels in Fall 2021 and answered questions about TFN and non-TFN e-cigarettes. Participants were categorized by lifetime TFN e-cigarette use status (yes vs. no; 34% yes). Bivariate comparisons examined differences in e-cigarette characteristics (device type, flavors, nicotine concentration) between the TFN and non-TFN groups. Binary logistic regression models examined associations between lifetime frequency of vaping ( Results In multivariable adjusted models, lifetime TFN e-cigarette use was associated with younger age, greater lifetime vaping, and nicotine pouch use. Young adults who used TFN e-cigarettes were more likely to report fruit, mint, menthol, and beverage flavors and know their e-liquid nicotine concentration compared with those who used non-TFN e-cigarettes. Conclusion Among young adults who have used e-cigarettes, more frequent e-cigarette use and use of nicotine pouches, which can also contain TFN, were associated with TFN e-cigarette use. Understanding behaviors and characteristics of those using TFN e-cigarettes is critical to regulation of TFN containing products. Implications This study characterizes young adults who report experience with tobacco free nicotine (TFN) e-cigarettes and compares them to those who have not used TFN e-cigarettes. Young adults with more frequent e-cigarette use and use of nicotine pouches are more likely to report TFN e-cigarette use.

IntroductionRestricting available e-cigarette flavours to only tobacco and menthol may reduce appeal among youth; it is unknown how flavour restrictions impact adults using e-cigarettes to quit smoking cigarettes.MethodsOnline US survey data were collected in summer 2021 from 857 adults who reported using e-cigarettes in a recent attempt to quit smoking. Survey items assessed e-cigarette flavours used during their quit attempt, whether e-cigarette flavour bans restricted access to flavours they like, and what impact the restrictions had on e-cigarette behaviour. Multivariable logistic regression models were used to examine the associations of flavour bans with success quitting smoking for 1 month or longer.Results30.2% (N=259) reported restricted access to e-cigarette flavours they like. During their quit attempt, 64.9% (N=168) used tobacco or menthol-flavoured e-cigarettes, and 90.7% (N=235) used another flavour that could be affected by restrictions, most commonly fruit, mint, and candy/dessert. Responses to flavour restrictions included switching devices to continue using preferred flavours (39.4%), using the same device only with available flavours (35.9%), buying preferred flavours elsewhere (eg, online) (19.3%), making flavours (3.5%) and ‘other’ (eg, no longer using e-cigarettes) (1.9%). The odds of quitting smoking for 1 month or longer were not significantly different between those experiencing flavour restrictions (vs not), preferring tobacco/menthol (vs restricted) flavour, or switching flavours in response to the bans (vs finding another way to obtain restricted flavours) (p>0.11).ConclusionExperiencing e-cigarette flavour restrictions was not associated with success quitting smoking among adults using e-cigarettes to try to quit.

Introduction E-cigarette liquid nicotine concentrations typically are labeled as mg/mL or percent, which poorly convey nicotine strength to users. We evaluated whether four novel nicotine concentration labels better convey information about nicotine strength and addictiveness. Methods Adolescents and young adults (N = 826) completed an online survey in 2020. Participants rated nicotine concentrations (3, 6, 18, 30, 40, and 50 mg/mL) from “no nicotine” to “very high nicotine” communicated using current market labels (mg/mL, percent) and four new labels (text-based, caution sign-shaped, horizonal stoplight, vertical thermometer) which used color, symbols, and verbal strength descriptors. Participants reported on perceived addictiveness for all labels viewed and rank-ordered labels on perceived ability to convey information accurately. Results Participants ranked the vertical (77%) and horizontal (70%) labels in first or second place and mg/mL (59.1%) and percent (47.2%) in last or second-to-last place. All new labels conveyed nicotine strength more accurately than did market labels (M[SD] correct of 6: percent = 1.50[1.08]; mg/mL = 2.14[1.52]; caution = 5.23[1.37]; vertical thermometer = 5.28[1.51]; text = 5.33[1.36]; horizontal stoplight = 5.47[1.14]), with the horizontal label also outperforming the thermometer and caution labels. Underestimating nicotine strength was uncommon among all new labels (4.7–6.8%). The new labels also were associated with increased perceived addictiveness at higher concentrations (30, 40, and 50 mg/mL), although the thermometer label underperformed the others. When considering perceived nicotine strength, rates of strength underestimates, perceived addictiveness, and rank ordering, the horizontal stoplight label performed best. Conclusions Novel labeling could improve understanding of nicotine strength and e-cigarette-related risk. Implications Extending prior research showing that adolescents and young adults who use Electronic Nicotine Delivery Systems (ENDS) have difficulty understanding nicotine concentrations labeled using mg/mL and percent nicotine, the current study demonstrates that novel nicotine concentration labels can improve understanding of nicotine strength and influence perceptions of addictiveness among young ENDS users. While four novel labels were tested, each outperforming current market labeling, the novel label that resembles a horizontal stoplight performed best. The study provides proof-of-concept that creating an easy-to-understand nicotine concentration label is possible and that new labeling better conveys information about nicotine strength and addictiveness.

BACKGROUND Family history of alcohol use disorder; AUD (FH +) and impulsivity-related traits are known risk factors for problem drinking that have been investigated in predominately White samples. This cross-sectional study examined whether these risk factors vary by sex in the overall, majority White sample and in a Black subsample. METHOD A model building regression procedure was used to investigate the combined effect of FH + and impulsivity-related traits on alcohol quantity, frequency, and problems by sex (overall sample: N = 757, 50% female, 73% White, agemean = 33.74, SD = 11.60; Black subsample: n = 138, 47% female, agemean = 33.60, SD = 9.87). RESULTS Overall Sample. No sex differences were found in the compounding effects of FH + and impulsivity-related traits on alcohol outcomes. Males reported more physical, social, and overall alcohol-related problems than females. FH + was positively associated with all alcohol-related consequences. Poor self-regulation was the only trait associated with all alcohol outcomes. Black Subsample: A three-way interaction suggested a negative association between inhibition and frequency of alcohol use among FH + males only. A two-way interaction also suggested impulse control was associated with more interpersonal alcohol-related problems among males only. Main effects were also found in the expected direction such that higher impulsivity and FH + were associated with poorer alcohol outcomes. CONCLUSION These findings suggest no sex differences in the overall sample in the interactive effects of established risk factors for AUD on alcohol outcomes, and that poor self-regulation may be key for personality-targeted alcohol prevention and intervention programs. Preliminary findings of sex differences in the Black subsample should be replicated. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

This study determined whether young adults perceptions of how tobacco free nicotine (TFN) compared to tobacco-derived nicotine (TDN) were associated with curiosity to try or use TFN e-cigarettes. U.S. young adults aged 18-25 years (n = 1176) completed an online survey in October 2021. Survey questions assessed TFN awareness, curiosity, use, and perceptions of how flavor, taste, ease of access, cost, and harm compared between TFN and TDN. Participants were categorized into 3 groups: not curious to try TFN (n = 511, 43.5%), curious to try TFN (n = 348, 29.6%), and ever used TFN (n = 317, 27%). Multinomial logistic regression measured associations between perceptions and TFN curiosity and use (vs. not curious to try), with total number tobacco products used and demographics as covariates. Compared to young adults who were not curious to try TFN e-cigarettes, those who were curious perceived TFN as less addictive than TDN (aOR = 2.27; 95% CI =1.34-3.86). Those who had used TFN e-cigarettes perceived TFN as having flavors that tasted better (aOR = 1.80; 95% CI = 1.63-1.99), "smoother" (aOR = 1.88; 95% CI =1.18-2.99), and less like tobacco (aOR 1.94; 95% CI 1.23-3.05). Adjusted models did not show differences in perceptions between those who had tried TFN and those who were curious to try TFN. Young adults who were curious to try or had used TFN had more positive perceptions of TFN. As it is currently unclear whether synthetic nicotine carries the same health risks as TDN, ongoing surveillance of TFN uptake among young adults is warranted.

INTRODUCTION: E-cigarettes are marketed on brand-sponsored Facebook profile pages despite Facebook’s policy to prohibit e-cigarette marketing on their platform. We aimed to identify e-cigarette marketing strategies used and products marketed on brand-sponsored Facebook profiles and assess whether these strategies and featured products differ by user engagement. METHODS: We examined up to 10 recent Facebook posts on 26 e-cigarette brand-sponsored profile pages uploaded between August 1 and November 31, 2016. We identified positive engagement (i.e. “like,”“love”), negative engagement (i.e. “sad,” “angry”), post type (e.g. link, photo), type of sales promotion (e.g. giveaways, discounts), non-sales promotion (e.g. event promotion without product marketing), and marketed products (e.g. e-cigarettes, e-liquids). RESULTS: We examined 225 Facebook posts. Engagement was modest (e.g. Median “likes” = 8 [Min = 0, Max = 591]). The most common post types were photos (52.6%) and links (35.5%). Of all links, 83.7% were links to online shops. Of all posts, 35.0% had overt sales promotional content, 32.0% featured non-sales promotional content, and 68.0% featured an e-cigarette product. The most commonly featured product was an e-cigarette device (50.6%). Posts with more positive engagement included giveaways (versus sales [p = 0.009]) and posts marketing vaping devices (versus e-liquids [p = 0.004)]). Negative engagement was not associated with marketing strategies or products. Of the brand-sponsored profile pages, 42.3% could be accessed by underage users. DISCUSSION: E-cigarettes are marketed on Facebook using a variety of promotional strategies. Importantly, underage youth often can access this marketing content. Comprehensive tobacco control policies that restrict e-cigarette marketing on social media are needed urgently.

Prior research suggests that cannabis expectancies are related to cannabis misuse and problems. Although there are established measures of cannabis expectancies, existing measures have psychometric limitations and/or are lengthy. Existing measures typically have a two-factor structure of positive and negative expectancies, but recent conceptualizations of alcohol expectancies support a valence- (positive vs. negative) and arousal-based (high vs. low arousal) structure. Thus, the present study sought to test a similar structure for cannabis. Cannabis expectancy items underwent 2 preliminary studies, assessing item valance/arousal (n = 233) and relevance to cannabis (n = 124). A final pool of 76 items underwent exploratory factor analysis (n = 303), and remaining items underwent confirmatory factor analysis in a separate sample (n = 469). Lastly, an additional sample (n = 435) examined validity. Results suggested a 3-factor structure (general positive, high arousal negative, low arousal negative) for the 17-item Anticipated Effects of Cannabis Scale (AECS), which was invariant across cannabis use frequency, sex, and race/ethnicity. Positive expectancies were strongly associated with cannabis use, whereas low arousal negative expectancies were protective against cannabis frequency; high arousal negative expectancies were strongly associated with more negative consequences and dependence symptoms. In addition, the proposed interpretation of AECS test scores showed evidence of incremental validity relative to another abbreviated measure. The current study provides initial support for the AECS, a brief, psychometrically sound cannabis expectancies measure. The AECS captures the full range of cannabis effects and may be suited to test discrepancies between cannabis expectancies and subjective response. Additional research is needed to validate its structure and predictive utility. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Introduction E-cigarette e-liquid nicotine concentrations typically are labeled as mg/mL or percent nicotine. We examined whether these metrics accurately convey nicotine strength to young e-cigarette users and if youth can compare concentrations presented in mg/mL and percent nicotine. Aims and Methods Eight hundred and twenty-one adolescent and young adult e-cigarette users participated in the survey. Participants rated nicotine concentration strengths presented as mg/mL (0–60 mg/mL) and percent nicotine (0%–6%) from “no nicotine” to “very high nicotine.” Participants also viewed pairs of nicotine concentrations (eg, 18 mg/mL vs. 5%) and indicated which concentration was stronger or if the concentrations were equivalent. Results On average, participants correctly identified 5.92 (2.68) of 18 nicotine strengths, correctly identifying strengths labeled as mg/mL (3.47 [2.03]) more often than percent nicotine (2.45 [1.38], p < .001). Excluding nicotine-free, participants rated concentrations presented as mg/mL as stronger, more addictive, and more harmful than equivalent concentrations presented as percent nicotine. Participants seldom correctly identified that one concentration was stronger or that both were equivalent (7.58 [5.88] of 19 pairings), although they more often correctly identified the stronger concentration when it was presented in mg/mL (4.02 [SD = 3.01]) than in percent nicotine (2.53 [2.73], p < .001). The most consistent predictor of correct answers on these tasks was familiarity with using both products labeled as mg/mL and labeled as percent nicotine. Conclusions Young e-cigarette users had difficulty understanding nicotine concentrations labeled using the most common metrics, raising concerns about inadvertent exposure to high nicotine levels and suggesting that a more intuitive labeling approach is needed. Implications This study extends prior work showing that young e-cigarette users often are uncertain whether the e-liquids they use contain nicotine by demonstrating that adolescents and young adults have difficulty understanding nicotine concentrations labeled using the two most common metrics (mg/mL and percent nicotine). Errors generally underestimated nicotine strength, and users were not able to accurately compare nicotine concentrations presented as mg/mL and percent nicotine. Difficulty understanding labeling metrics persisted even after accounting for user characteristics like age and vaping experience, suggesting that a novel, easy to understand labeling system is needed to convey information about nicotine strength accurately.

Background Nicotine pouches containing synthetic nicotine or tobacco-derived nicotine (TDN) are available in the United States. Synthetic nicotine pouches are often marketed as “tobacco-free nicotine” (TFN), which may alter risk perceptions and product appeal. This study examined young adults’ perceptions of TFN versus TDN pouches and the associations between product perceptions and TFN pouch awareness, susceptibility, and use, respectively. Aims and Methods In total 630 young adults (18–25 years) completed an online Qualtrics panels survey in 2021. Participants were informed that TFN pouches contain synthetic nicotine as opposed to TDN. Participants reported on comparative risk perceptions for TFN versus TDN pouches and on TFN pouch awareness, susceptibility, and use. Unadjusted between-group comparisons and adjusted binary logistic regressions were run to examine relationships between product perceptions and TFN pouch awareness, susceptibility, and use. Results Participants were aware of (37.3%), susceptible to (29.2%), or had used TFN pouches (3.8%). In unadjusted comparisons, TFN pouch awareness, susceptibility, and use were associated with disproportionately perceiving TFN pouches as less harmful or otherwise better than TDN pouches. In adjusted models, relationships between favorable perceptions and both TFN pouch awareness and susceptibility remained significant. Conclusions The descriptor “tobacco-free” may impact risk perceptions and the appeal of nicotine pouches among young adults. While no direct relationship was observed between TFN perceptions and TFN pouch use in the adjusted model, perceptions remained related to product awareness and susceptibility, which may be linked to future use. Continued surveillance is needed to fully determine how the term “tobacco-free” on product packaging and advertising impacts longitudinal public health outcomes. Implications Nicotine pouches originally contained TDN. Today, numerous brands of synthetic nicotine pouches, which are often marketed as “tobacco-free,” are available on the market. We informed participants that “tobacco-free nicotine” pouches contain synthetic nicotine and examined comparative risk perceptions (i.e. tobacco-free vs. TDN pouches) and TFN pouch awareness, susceptibility, and use. Perceiving tobacco-free nicotine pouches as less harmful than tobacco-derived pouches was associated with product awareness, susceptibility, and use in unadjusted models and with awareness and susceptibility in adjusted models. Restricting the term “tobacco-free” may become necessary if the term inaccurately reduces product risk perceptions or increases product appeal.

Given high youth e-cigarette use, it is important to investigate how traits, like impulsivity, may be associated with youth e-cigarette use behaviors. The study aim is to determine if impulsivity is associated with trying more e-cigarette flavors and device types, and greater frequency of e-cigarette use.Cross sectional survey data from CT high schoolers (n = 4875, 6 schools) were collected in 2019. Lifetime (ever) e-cigarette users (n = 2313) completed the Brief Barrett Impulsivity Scale, which contains two subscales; behavioral impulsivity and impaired self-control. Among lifetime users, associations between impulsivity subscales and number of e-cigarette flavors tried, e-cigarette devices tried, and past 30-day frequency of e-cigarette use were examined using regression models. Additionally, associations of impulsivity and use frequency were examined among only current e-cigarette users (≥1 day of use in past 30; n = 1327). School, age, race/ethnicity, vaping initiation age, other tobacco product use, and sex were included as covariates in models.Higher behavioral impulsivity was associated with greater number of e-cigarette flavors tried (AOR: 1.06, 95% CI: 1.02, 1.11, p .008) and higher frequency of past 30-day use both among ever and current e-cigarette users (AOR: 1.26, 95%CI:1.10,1.44, p .001; AOR: 1.12, 95%CI:1.02,1.22, p .02), but not number of e-cigarette devices tried. Impaired self-control was not associated with any outcomes.Youth with higher behavioral impulsivity may be more at risk for using more e-cigarette flavors and using e-cigarettes more frequently. Regulations aimed at reducing flavor availability among youth and interventions targeting impulsive behavior may be important for this population.

The Modified Cigarette Evaluation Questionnaire (MCEQ) measures self-reported, subjective reinforcing and aversive effects of smoking. The MCEQ is proposed as comprising five factors. However, its latent factor structure has been confirmed in only one published study, and key psychometric properties like measurement invariance and test-criterion validity have not been formally examined.We examined the latent structure of the MCEQ using confirmatory and exploratory factor analysis and evaluated internal consistency, measurement invariance, between-group differences, and relationships with smoking-related characteristics in a sample of 764 adults who smoke cigarettes. As a secondary test of construct validity, we fit identified latent factor structures to a sample of 131 adults from a prior trial who were biochemically-confirmed to currently smoke.A four-factor structure that recently was validated for an e-cigarette-adapted version of the MCEQ outperformed the originally-proposed five-factor structure in several ways: it uniquely evidenced good model fit both within the primary and replication samples, reached scalar invariance for all participant subgroups tested, was sensitive to detecting numerous between-groups differences in subsamples of interest, and each of its subscales was associated with at least one smoking characteristic assessed.The four-factor MCEQ structure evidenced stronger psychometric properties in our sample than did the original five-factor MCEQ. However, until additional research is conducted on the utility of the five- and four-factor solutions in other samples, neither the five- nor the four-factor MCEQ structure should be assumed. In the meantime, researchers should examine the MECQ's latent structure in their data prior to conducting subsequent analyses.

Introduction E-cigarette dependence measures largely focus on e-cigarette use (“vaping”) that is linked to nicotine use, and measures assessing sensory aspects of vaping that may influence use (e.g., taste) are limited in scope. Thus, we developed the novel Sensory E-cigarette Expectancies Scale (SEES). Methods In Summer 2017, 610 adult e-cigarette users (48.7% male, 84.9% White, 37.41[±12.15] years old) completed an online survey that included 23 SEES items. Psychometric analyses included evaluating latent structure, internal consistency, measurement invariance, mean differences, and test-criterion relationships. Results Factor analyses supported a 9-item, 3-subscale structure (taste/smell, pleasure/satisfaction, vapor cloud production). Subscales evidenced internal consistency and scalar invariance by sex, race, smoking status (current/not), vaping status (daily/not), e-liquid nicotine content (yes/no), and device type (cig-a-likes/vape-pens/Advanced Personal Vaporizers [APVs]/Mods). Women and daily e-cigarette users reported stronger SEEs for taste/smell and pleasure than their counterparts. Non-white participants reported stronger SEEs for cloud production than White participants. Cig-a-like users reported the weakest SEEs for taste/smell and weaker SEEs linked to cloud production than APV/mod users. SEES scores evidenced convergence with nicotine dependence (mean r = .36). Finally, SEES scores predicted vaping frequency and habitual vaping concurrently and incrementally beyond nicotine dependence. Conclusions The SEES evidenced good psychometric properties, suggesting that the measure can be used to assess sensory vaping expectancies in adults. Importantly, SEES scores indicated that sensory expectancies are related, yet distinct, from nicotine dependence. Future research should evaluate how SEEs relate to product characteristic preferences and patterns of vaping including the development and maintenance of addiction.

Rates of e-cigarette use are high among youth, and there is little known about the long-term health effects of e-cigarettes. Since peer influence is a powerful determinant of tobacco use, we aimed to identify message themes about e-cigarettes that youth would use to encourage or discourage peers from using e-cigarettes. We conducted 10 focus groups (N = 69) with non-smokers and smokers from one middle school, high school and college in Connecticut. Participants engaged in a discussion about e-cigarettes, and each participant created one written message to encourage e-cigarette use and one to discourage use among peers. We content-analyzed the messages and identified three main themes and 12 encouraging and discouraging sub-themes. Encouraging themes included health benefits of e-cigarettes relative to cigarettes, attractive product characteristics (e.g. flavors) and social advantages (e.g. positive social image) of using e-cigarettes. Discouraging messages included health risks of e-cigarettes relative to cigarettes, unattractive product characteristics (e.g. cost) and social disadvantages (e.g. negative social image of using an e-cigarette). Overall, there were no differences by sex, age group or smoking status in generation of encouraging and discouraging message themes. This study identified youth-generated themes that may aid in e-cigarette prevention among youth.

Measures of medical cannabis (MC) use are lacking. This study details the development and psychometric evaluation of The Medical Cannabis Expectancy Questionnaire (MCEQ), a novel measure of positive and negative expectations associated with using combustible, vaporizable, and edible MC. 333 adult MC users completed a 30-minute online survey in Spring 2017 (64.0% female, 82.3% White, mean age 32.77[±10.19] years). Participants reported on demographics, product preference, MCEs, frequency of MC use, quality of life, and negative cannabis use consequences. Psychometric analyses included evaluations of latent factor structure, measurement invariance, between-groups differences in MCEs, and test-criterion relationships with MC outcomes. The 27-item MCEQ evidenced a 2-factor structure (positive/negative). MCEs were scalar invariant by product type, sex, and reasons for MC use (medical only vs medical/recreational). Participants held more positive MCEs for combustibles than for vaporizables or edibles and more negative MCEs for combustibles and edibles than for vaporizables. MCEs did not differ by sex. Participants who also used cannabis recreationally reported stronger positive MCEs for all MC products. MCEs also differed by product preference. Additionally, preference for and more positive MCEs associated with using a specific product were associated with more frequent use of that product. Positive MCEs for all products also were associated with increased quality of life, but these relationships failed to reach statistical significance after accounting for covariates. Finally, negative MCEs for combustibles and edibles were associated with more negative consequences. The MCEQ is the first psychometrically promising measure of MC expectancies, and it uniquely distinguishes among expectations associated with using combustible, vaporizable, and edible MC. As MC use continues to proliferate, having measures dedicated to MC (versus recreational cannabis) may better inform research and clinical efforts. Further, differentiating between product types is important given established differences among them (e.g., duration of effect onset).

Introduction E-liquid flavor is typically presented by flavor category (e.g. menthol, mint, fruit, dessert). Cooling sensations produced by flavor additives such as menthol enhance appeal of e-cigarettes among youth, but not all e-liquids that produce cooling sensations are labeled as menthol. Sensory experiences produced by flavors may allow for a new way to capture e-cigarette flavor use. This study aims to examine use of flavors that produce cooling sensations among youth and its association with e-cigarette use behaviors. Methods A 2019 survey of high school students (n = 4875) examined use of e-cigarette flavors that produced cooling sensations (cooling flavors) among past 30-day e-cigarette users. E-cigarette use behaviors (flavor use, nicotine use, frequency of use) were examined between those who did and did not use cooling flavors. A binary logistic regression was used to examine associations between vaping frequency, nicotine (vs. non-nicotine) use, and vaping cooling flavors while controlling for demographics, number of flavors vaped in the past month, and vaping age of onset. Results 51.6% (n = 473/916) of the analytic sample endorsed vaping cooling flavors. There were no demographic differences by vaping cooling flavors. Vaping cooling flavors was associated with vaping more frequently (AOR:1.04,95% CI:1.03,1.05) and vaping nicotine (AOR:2.37,95% CI:1.53,3.67). Conclusion Vaping cooling flavors was associated with greater nicotine vaping and frequency of e-cigarette use. Assessing sensory experience, such as cooling, in addition to flavor category may more fully capture e-cigarette flavor use and its impacts on youth e-cigarette use behaviors.

Introduction JUUL, a closed system e-cigarette with disposable pods, is popular among youth, with positive attributes of this product linked to current use by youth. However, many youth try JUUL and do not continue using; understanding differences in the appeal of this device between current users and those who chose not to continue use can inform regulation and prevention efforts. The aim of the current study is to compare JUUL appeal in youth among past users (ie, used, but not in past month) and current users (ie, used in past month). Methods A cross-sectional survey was conducted in four Connecticut high schools in Spring 2018. This survey assessed JUUL use and reasons for liking/disliking JUUL, including its' pharmacological effects (eg, nicotine “buzz”), product characteristics (eg, flavors), peer influence, appeal compared to other e-cigarettes, and concealability. Logistic regressions were conducted to examine differences in liking/disliking JUUL by use status (past vs. current). Results Among JUUL users (N = 1374; 43% of total sample), 30.4% were past users and 69.6% were current users. Compared to current users, past users were less likely to like JUUL for positive pharmacological effects (eg, nicotine “buzz”), product characteristics (eg, flavors), and peer use and more likely to dislike JUUL for the adverse pharmacological effects (eg, headache), product characteristics (eg, flavors), and for “other” reasons (open-ended response; eg, perceived harm). Conclusions Findings suggest that altering JUUL appeal through regulating nicotine content and flavors may be key in policy aimed at shifting youth to become past JUUL users. Implications This study investigates how current and past youth JUUL users differ in their report of the appeal of JUUL. Past users are less likely to report experiencing positive pharmacological effects and product characteristics of JUUL and are less likely to report appeal due to peer use. Understanding how appeal of JUUL may differ among past and current users can aid in our understanding of how to regulate these products so that they are less appealing to current youth users.

Objectives We evaluated the appeal and use of e-cigarette product features in a sample of high school students in Connecticut. Methods In 2015, we conducted school-wide surveys in 8 high schools and analyzed data from 1765 students (877 susceptible tobacco-naive never-e-cigarette users, 315 susceptible tobacco-exposed never-e-cigarette users, and 656 current e-cigarette users). Students were asked: "What is cool about e-cigarettes?" Response options included e-liquid ingredients (eg, flavors, nicotine content, propylene glycol/vegetable glycerin ratio [ie, PG/VG), the heating source (eg, battery), and physical design (eg, color, shape, appearance). Students were asked: "How do you customize your e-cigarette?" (Change flavors, voltage, PG/VG levels, temperature, skin/tank color, other) and use of nicotine in the e-liquid (yes/no). Multivariable regression assessed associations between current e-cigarette users' customization and e-cigarette use frequency (#days/month). Results Flavors were the most appealing product feature among all groups. Among current users, using nicotine e-liquid was more prevalent than changing flavors (44.2% vs 29.6%). Using nicotine e-liquids and changing PG/vG ratios were associated with more frequent e-cigarette use among current users. Conclusions These findings can inform tobacco regulatory actions that put forth evidence-based recommendations to manufacture e-liquid flavors, nicotine concentrations and PG/VG ratios that prevent e-cigarette initiation and escalation of use.

BACKGROUND: Electronic cigarette (e-cigarette) use is common among youth, and there are concerns that e-cigarette use leads to future conventional cigarette use. We examined longitudinal associations between past-month cigarette and e-cigarette use to characterize the stability and directionality of these tobacco use trajectories over time. METHODS: High school students (N = 808, 53% female) completed surveys across 3 waves (2013, 2014, and 2015) in 3 public schools in Connecticut. Using autoregressive cross-lagged models, we examined bidirectional relationships between past-month cigarette and e-cigarette use over time. Models were adjusted for covariates related to tobacco use (ie, sex, race/ethnicity, socioeconomic status, and use of other tobacco products). RESULTS: Past-month e-cigarette use predicted future cigarette use (wave 1–2: odds ratio [OR] = 7.08, 95% confidence interval [CI] = 2.34–21.42; wave 2–3: OR = 3.87, 95% CI = 1.86–8.06). However, past-month cigarette use did not predict future e-cigarette use (wave 1–2: OR = 2.02, 95% CI = 0.67–6.08; wave 2–3: OR = 1.90, 95% CI = 0.77–4.71). Additionally, frequency of cigarette and e-cigarette use increased over time. By wave 3, 26% of cigarette users and 20.5% of e-cigarette users reported using 21–30 days out of the past month. CONCLUSIONS: E-cigarette use was associated with future cigarette use across 3 longitudinal waves, yet cigarette use was not associated with future e-cigarette use. Future research needs to examine mechanisms through which e-cigarette use leads to cigarette use. E-cigarette regulation and prevention programs may help prevent future use of cigarettes among youth.

In recent years, the tobacco use landscape among youth has changed greatly, underscoring a need to understand current tobacco product initiation patterns. This study aimed to examine if adolescents’ first tobacco product tried differs by race/ethnicity and socioeconomic status (SES). Cross-sectional survey data were collected from 6 Connecticut high schools in Spring 2019 (N = 4875). The analytic sample comprised ever users of tobacco products who reported on the first tobacco product they tried (N = 2530; 53.6% female). Of ever tobacco product users, 64.6% reported that their first tobacco product tried was an e-cigarette, 24.4% reported blunts, 6.5% reported another tobacco product, and 4.5% reported cigarettes. A multinomial logistic regression model examined race/ethnicity and SES as predictors of the first tobacco product tried with cigarettes as the reference group. Compared to Non-Hispanic (NH) White students, NH Black students (OR = 0.38, 95%CI = 0.17–0.82), Hispanic students (OR = 0.44, 95%CI = 0.27–0.71), and students of other NH races (OR = 0.30, 95%CI = 0.18–0.52) were less likely to report initiating tobacco product use with e-cigarettes. Additionally, compared to NH White students, NH Black students were more likely to report using blunts as their first tobacco product (OR = 3.01, 95%CI = 1.38–6.56). Compared to low SES, middle SES (OR = 1.62, 95%CI = 1.04–2.52) was associated with greater odds of reporting e-cigarettes as the first tobacco product. These findings suggest that youth of different backgrounds initiate tobacco use with different tobacco products and tailored prevention strategies are needed.

Objectives In November 2018, JUUL, Inc. restricted sales of mango, fruit medley, crème brûlée, and cucumber pods to its website. Physical/online sales of tobacco, menthol, and mint pods continued. We examined rates of adolescent JUUL device and flavored pod use before and after restrictions were implemented to examine their potential impact. Methods Students from 4 Connecticut high schools completed cross-sectional surveys in 2018 (N = 3170) and 2019 (N = 3074). Results Compared to 2018, current (past-month) JUUL use decreased in 2019 (30.2–25.6%). Among current JUUL users, restricted flavor use (mango [62.8–36.9%]; cucumber [27.7–11.9%]; fruit [23.5–11.4%]; crème brûlée [12.3–5.0%]) and the total number of flavors used (2.09[SD = 1.71]-1.51[SD = 1.33]) decreased (p-values < .001), while mint pod use increased (62.0–68.6%, p < .01). Tobacco and menthol pod use remained stable. Conclusions Following voluntary sales restrictions implemented by JUUL, Inc., restricted flavor use and the total number of flavors used decreased among JUUL users while mint use increased. Results suggest flavor restrictions may impact adolescents’ e-cigarette use. While 2020 Food and Drug Administration restrictions on mint pods may further limit youth JUUL use, research is needed to determine where youth acquire restricted flavors and if restrictions prompt increased popularity of unrestricted devices/e-liquids.

Ordinal data (eg, "low," "medium," "high"; graded response on a Likert scale) with an additional "don't know" category are frequently encountered in the medical, social, and behavioral science literature. The handling of a "don't know" option presents unique challenges as it often "destroys" the ordinal nature of the data. Commonly, nominal models are employed which ignore the partial ordering and have a complicated interpretation, especially in situations with repeatedly measured outcomes. We propose two-part models that easily accommodate longitudinal partially ordered (semiordinal) data. The most easily interpretable formulation consists of a random effect logistic submodel for "don't know" vs all the other categories combined, and a random effect ordinal submodel for the ordered categories. Correlated random effects account for statistical dependence within individual. An extension allowing for nonproportionality of odds for the predictor effects in the ordinal submodel is also considered. Maximum likelihood estimation is performed using adaptive Gaussian quadrature in SAS PROC NLMIXED. A simulation study is performed to evaluate the performance of the estimation algorithm in terms of bias and efficiency, and to compare the results of joint and separate models of the two parts, and of proportional and nonproportional model formulations. The methods are motivated and illustrated on a dataset from a study of adolescents' perceptions of nicotine strength of JUUL e-cigarettes. Using the proposed approach we show that adolescents perceive 5% nicotine content as relatively low, a misconception more pronounced among past month nonusers than among past month users of JUUL e-cigarettes.

Introduction E-cigarettes appeal to adolescents because of alternative uses, such as dripping (i.e., applying e-liquid directly on the atomizer) and conducting vape tricks (i.e., creating shapes from exhaled aerosol). However, little is known about these behaviors and adolescents who engage in these behaviors. Methods Using cross-sectional surveys from 4 high schools in Connecticut in 2017 (N = 2945), we assessed the frequency of dripping and conducting vape tricks, product characteristics (e.g., nicotine, flavor) used for these behaviors, and where adolescents learn about these behaviors. We also conducted multinomial logistic regression analysis to assess whether demographics, age of e-cigarette use onset, past-month-use of e-cigarettes, and lifetime use of other tobacco products were associated with dripping and/or vape tricks. Results Among ever e-cigarette users (N = 1047), 20.5% ever dripped and 54.9% ever conducted vape tricks. The most frequently endorsed 1) flavors used for both behaviors were fruit, candy, and mint, 2) nicotine concentrations used for dripping was 3 mg and for vape tricks was 0 mg, and 3) the top source for learning these behaviors were friends. The multinomial model showed that earlier age of e-cigarette use onset, past-month-use of e-cigarettes, and lifetime use of other tobacco products were associated with dripping and vape tricks. Discussion Engaging in dripping and vape tricks was associated with risky tobacco use behaviors (e.g., earlier age of onset, other tobacco use), and involved exposure to nicotine and flavors. Reducing appeal of dripping and vape tricks and preventing product characteristics that facilitate these behaviors may reduce harm to adolescents.

Background Adolescent e-cigarette use (i.e., “vaping”) likely confers risk for developing nicotine dependence. However, there have been no studies assessing e-cigarette nicotine dependence in youth. We evaluated the psychometric properties of the 4-item Patient-Reported Outcomes Measurement Information System Nicotine Dependence Item Bank for E-cigarettes (PROMIS-E) for assessing youth e-cigarette nicotine dependence and examined risk factors for experiencing stronger dependence symptoms. Methods In 2017, 520 adolescent past-month e-cigarette users completed the PROMIS-E during a school-based survey (50.5% female, 84.8% White, 16.22[1.19] years old). Adolescents also reported on sex, grade, race, age at e-cigarette use onset, vaping frequency, nicotine e-liquid use, and past-month cigarette smoking. Analyses included conducting confirmatory factor analysis and examining the internal consistency of the PROMIS-E. Bivariate correlations and independent-samples t-tests were used to examine unadjusted relationships between e-cigarette nicotine dependence and the proposed risk factors. Regression models were run in which all potential risk factors were entered as simultaneous predictors of PROMIS-E scores. Results The single-factor structure of the PROMIS-E was confirmed and evidenced good internal consistency. Across models, larger PROMIS-E scores were associated with being in a higher grade, initiating e-cigarette use at an earlier age, vaping more frequently, using nicotine e-liquid (and higher nicotine concentrations), and smoking cigarettes. Conclusion Adolescent e-cigarette users reported experiencing nicotine dependence, which was assessed using the psychometrically sound PROMIS-E. Experiencing stronger nicotine dependence symptoms was associated with characteristics that previously have been shown to confer risk for frequent vaping and tobacco cigarette dependence.

Background Identifying risk factors for youth e-cigarette use is critical, given high rates of e-cigarette use and unknown health effects of long-term use. The current study examined whether an early age of onset of e-cigarette use mediates the association between impulsivity and e-cigarette frequency. Methods Cross-sectional survey data of e-cigarette users (n = 927) were collected from 8 high schools in southeastern Connecticut. The sample was 44.7% female (mean age 16.2 [SD = 1.2], mean age of e-cigarette onset 14.7 [SD = 1.6]). Two domains of self-reported, trait impulsivity were assessed using the abbreviated Barratt Impulsiveness Scale: impaired self-regulation (e.g., problems with concentration or self-control) and behavioral impulsivity (e.g., doing things without thinking). Mediation was tested with Mplus, and the model included school as a cluster variable and controlled for covariates related to e-cigarette use (i.e., sex, age, race, peer use, and other tobacco products ever tried). Results The hypothesized mediation was supported for both domains of impulsivity (impaired self-regulation a 1 b = 0.09, SE = 0.02, 95%CI [0.03–0.14], p = .002; behavioral impulsivity a 2 b = 0.07, SE = 0.03, 95%CI [.01–.14], p = 0.03). Specifically, impaired self-regulation ( B = −0.33, SE = 0.06, p 0.001) and behavioral impulsivity ( B = −0.26, SE = 0.11, p = 0.02) predicted trying e-cigarettes at an earlier age, and earlier initiation was associated with more days of e-cigarette use in the past month ( B = −0.28, SE = 0.08, p 0.001). Conclusions Adolescents who endorse aspects of impulsivity, such as acting without thinking, are at greater risk for more frequent e-cigarette use through an early age of e-cigarette initiation. Further research is needed to evaluate these relationships longitudinally and to develop targeted e-cigarette interventions for impulsive youth.

Background Vaping is the second most common modality of using cannabis following smoking. We examined differences in demographics and substance use behaviors between adolescent cannabis vapers and those exclusively using other cannabis modalities. Methods In 2019, 4875 students from six Connecticut high schools completed school-wide, online surveys. Past-month cannabis users (n = 931; 52.8% female, 16.38(1.27) years old, 44.9% non-Hispanic White) reported on cannabis modalities used (e.g., combustible, vaporizable, edible) and were classified as cannabis vapers or non-vapers. Cannabis vapers reported on device type used to vape cannabis: a cannabis-specific device or modified/hacked e-cigarette. Unadjusted and adjusted relationships were examined to identify demographic and substance-related differences between cannabis vapers and non-vapers. Results 56.3% of past-month cannabis users reported vaping cannabis. Cannabis vapers reported using both cannabis-specific vaporizers (91.9%) and modifying/hacking e-cigarettes (23.7%). Cannabis vapers, relative to other cannabis users, were more likely to be male, White, initiate cannabis use at a younger age; endorse past-month use of all cannabis modalities, alcohol, and e-cigarettes; and report smoking and dabbing cannabis more frequently; consuming more drinks per drinking day; and using e-cigarettes at a younger age and more frequently. Conclusions Relative to other cannabis modalities, vaping was associated with increased use of several cannabis products and other substances. Adolescent past-month cannabis users reported modifying e-cigarettes to vape cannabis. Findings suggest that regulations should be implemented to prevent e-cigarettes from being modifiable for use with cannabis and highlight the importance of assessing different cannabis use modalities, as vaping was associated with distinct substance-related risks.

Background The United States' 2019 outbreak of e-cigarette or vaping-associated lung injuries (EVALI) was linked to an additive most common in informally-sourced vaporizable marijuana concentrates. This study estimates how states' recreational and medical marijuana policies related to their 2019 EVALI incidence and residents' likelihood of vaping as their primary mode of marijuana use. Methods Multivariable negative binomial regressions estimated associations between states' total 2019 EVALI cases and marijuana policies: recreational legalization, medical legalization only, and whether medical-only policies allowed home cultivation, prohibited combustible use, or had operational dispensaries. Logistic regressions used survey data from the Behavioral Risk Factor Surveillance System's 2016-2019 marijuana supplements to assess how these policies related to past-30-day marijuana users' selection of vaping as their primary mode of use. Results EVALI incidence was 42% lower in recreational marijuana states (95%CI=0.339,0.993), versus a positive but statistically insignificant association with medical legalization alone. Adjusting for policy attributes revealed heterogeneity: among medical-marijuana-only states, EVALI incidences were > 60% lower where laws allowed home cultivation (aIRR=0.374; 95%CI=0.196, 0.715). Similarly, among past-30-day marijuana users, odds of vaping as one's primary mode of use were > 40% lower in medical-only states where home cultivation was allowed versus prohibited (aOR=0.588; 95%CI=0.365,0.946). Conclusions Marijuana policy attributes linked to lower EVALI incidences were also associated with reduced likelihoods of vaping as one's primary mode of use. As additives in informally-sourced vaping concentrates could drive future EVALI cases, marijuana policy design should account for effects on mode of use in licit and illicit markets, to limit the scope of future outbreaks.

Objective The primary study aim was to examine whether rates of using e-cigarettes (“vaping”) to lose weight are elevated among American adults who self-report having an eating disorder (ED). Method Adult vapers who reported either currently having an ED (n = 178; 72.5% women, 83.7% White, mean age 33.26 [SD = 8.35] years) or no ED history (n = 433; 54.7% women, 83.4% White, 35.98 [SD = 11.71] years) completed an anonymous survey. Results Relative to participants reporting no ED history, participants who reported currently having an ED were more likely to report vaping to lose/control weight, because it can be concealed from others, and due to the availability of sweet flavors. Participants reporting a current ED also were more likely to vape daily, to use higher nicotine concentrations, and to vape to relieve negative affect than were individuals without EDs. Discussion Individuals who self-reported currently having an ED endorsed vaping motives that are consistent with eating pathology (e.g., substance-induced weight loss, hiding compensatory behaviors from others). They also were more likely to vape daily and to use higher nicotine concentrations, raising health concerns. The findings suggest that treatment providers should screen for e-cigarette use in clients seeking ED treatment.

The Biphasic Alcohol Effects Scale (BAES) is widely used to assess stimulant and sedative alcohol effects. This paper reviews (a) recent measurement developments, (b) behavioral and physiological correlates, and (c) the role of the BAES in refining theories of SR and pharmacological interventions. An abbreviated scale (B-BAES) and a comprehensive measures of alcohol effects (SEAS) demonstrate strong psychometric properties and use of the BAES has helped refine the Differentiator Model of SR. Importantly, both BAES stimulation and sedation robustly predict risk for later alcohol problems, and the BAES has demonstrated utility in examining mechanisms of pharmacotherapy effects. The BAES is the most widely used measure of SR and has informed both theory and practice. Recent findings point to important future directions including the need to (a) examine developmental influences, (b) refine our understanding of FH effects, and (c) consider expansion to capture novel aspects of SR.

Objective We examined the associations between socioeconomic status (SES) and adolescent polytobacco use profiles (including e-cigarettes and blunts). Methods Adolescents (N = 1932) completed surveys conducted in 2014 in 3 Connecticut high schools. Within a Latent Class Analysis (LCA) framework, logistic regressions examined associations between SES and polytobacco use profiles comprising never, ever, and current e-cigarette, blunt, cigarette, cigar, cigarillo, hookah, and smokeless tobacco use. Results We identified 5 classes: (1) current polytobacco users; (2) ever polytobacco users; (3) current e-cigarette, blunt, and cigarette users; (4) ever e-cigarette and blunt users; and (5) never users. Low SES, relative to high SES, was associated with greater likelihood of being: (1) an ever polytobacco user; (2) a current e-cigarette, blunt, and cigarette user; and (3) an ever e-cigarette and blunt user, relative to a never user. Conclusions Low SES is associated with membership in distinct polytobacco use latent classes. Regulatory initiatives that focus exclusively on cigarette use may miss the opportunity to influence adolescent use of other products, which may be especially relevant to low-income individuals. Future research should examine whether targeting a broader range of products reduces SES-related tobacco use disparities.

Objectives We examined sources of e-cigarette acquisition among youth, and changes in these sources, between 2014 and 2015. We also assessed whether youth were ever refused the sale of e-cigarettes. Methods Anonymous, cross-sectional surveys conducted in five high schools in 2014 and 2015 in Connecticut assessed demographics, e-cigarette and cigarette use, and e-cigarette acquisition sources (friends/boyfriends/girlfriends, tobacco shops, siblings, online, parents/adult family members, other). We restricted analyses to adolescents younger than 18 years old who had used e-cigarettes in the past month (2014: N = 400, 2015: N = 390). Results Top sources of e-cigarette acquisition were friends (2014: 50.2%, 2015: 45.4%), tobacco shops (2014: 17.5%, 2015: 12.6%), and online shops (2014: 9.8%, 2015: 10.5%). A multilevel model, controlling for sex, age, and cigarette smoking status, while clustering by schools showed a decrease in the proportion of youth obtaining e-cigarettes from friends (AOR = .84) between 2014 and 2015. In 2015, 69.8% and 85.8% purchased e-cigarettes from a physical store and an online store, respectively. Conclusions Peers were the most popular source of e-cigarette acquisition. Many adolescents were able to purchase e-cigarettes from commercial sources. Future studies should continue to conduct surveillance of where adolescents obtain e-cigarettes to inform prevention strategies.

Introduction Most tobacco use is initiated during adolescence and susceptibility is an important predictor of initiation. Research is needed to understand susceptibility across tobacco products among youth to inform prevention efforts. Aims and Methods We surveyed 2945 students from four Southeastern Connecticut high schools in 2017. We assessed susceptibility to future use of seven different tobacco products (ie, cigarettes, e-cigarettes, hookah, cigars, cigarillos, blunts, and smokeless tobacco) among youth who had never used a tobacco product. Susceptibility was coded as any response other than “definitely not” to three items assessing (1) curiosity, (2) willingness to experiment with the product in the future, and (3) willingness to try if offered by a friend. Latent class analysis was used to identify patterns of susceptibility across multiple tobacco products and sociodemographic predictors of these classes. Results Among n = 1246 youth who had never tried a tobacco product, 51.2% (n = 638) were susceptible to at least one product and 29.1% (n = 363) were susceptible to two or more products (average among susceptible youth: M = 2.4 products, SD = 1.7, range 1–7). Latent class analysis identified three classes: youth who were: (1) susceptible to all products (5.6%), (2) susceptible to e-cigarettes, hookah, and blunts (23.6%), and (3) mostly non-susceptible to all products (70.8%). In a multivariable model, demographic variables and peer tobacco use predicted class membership. Conclusions We observed high rates of susceptibility to non-traditional tobacco products among youth, and many youth were susceptible to more than one product. Targeting susceptibility across multiple tobacco products may be important for reducing youth tobacco use. Implications Patterns of polytobacco use susceptibility were identified among youth who had never previously used tobacco products. Many youth were susceptible to more than one product and we observed three distinct classes of susceptibility among youth: those who were mostly non-susceptible, those who were susceptible to alternative or non-traditional tobacco products (e-cigarettes, hookah, and blunts), and those who were susceptible to all tobacco products. These findings suggest the importance of addressing susceptibility across multiple tobacco products.

Introduction JUUL is a closed-system e-cigarette that uses disposable pods with high concentrations of nicotine. JUUL use among youth arose exponentially since 2015, thus, it is essential to understand youths’ reasons for liking/disliking JUUL to inform the regulation of the product. Methods A cross-sectional survey was conducted in 4 high schools in Connecticut in 2018 (N = 3170). The survey assessed JUUL use in the past month and reasons for liking/disliking JUUL, which included pharmacological effects (e.g., subjective nicotine and/or flavor effects), product characteristics (e.g., flavors, nicotine, shape), peer influence (e.g., friends’ use), comparisons to other e-cigarettes (e.g., lower harm), and concealability (e.g., hide from authority). Results 30.2% (N = 956) were past-month users who used a JUUL on an average of 13.6 days (SD = 11.7; 25% reported daily use). The top reasons for liking JUUL were: “it gives me a buzz” (52%), “I like the flavors” (43%), and “my friends use it” (36%). The top reasons for disliking JUUL were: “the pods are expensive” (57%), “nicotine is too high” (20%), and “it gives me a headache” (18%). Regression models indicated that liking JUUL because of the buzz, ability to help concentrate, nicotine level, and ease of hiding it from teachers were associated with more days of use in the past month, while peer influence was negatively associated. Furthermore, disliking the high cost of pods was associated with more frequent use. Conclusions Comprehensive policies that regulate device characteristics that appeal to youth (e.g., nicotine level, flavors) are needed to prevent JUUL use among youth.

Background Extensive research indicates that having a positive family history of alcohol use disorder (FHP) and impulsivity are 2 risk factors for problem drinking. To our knowledge, no study has investigated which facets of impulsivity interact with family history to increase risk for problem drinking. The goal of this study was to: (i) examine whether FHP individuals with higher levels of impulsivity are more likely to engage in problematic drinking, and (ii) identify which facets of impulsivity interact with FHP to increase risk for problems. Methods The data consisted of a combined sample of 757 participants (50% female, 73% White, mean age = 32.85, SD = 11.31) drawn from the Transdisciplinary Tobacco Use Research Center and the Center for the Translational Neuroscience of Alcohol. Analyses of covariance and cumulative logistic regression models investigated the association of family history and impulsivity-related traits with drinking quantity, frequency, and alcohol-related problems. Models were adjusted for age, sex, race, ethnic group, education level, and data source. Results Significant interactions between impulsivity and family history were found for measures of alcohol-related problems. Specifically, there was a stronger positive association of Barratt Impulsiveness Scale (BIS) poor self-regulation with interpersonal, F(1, 504) = 6.27, p = 0.01, and impulse control alcohol-related problems, F(1, 504) = 6.00, p = 0.01, among FHP compared to FHN individuals. Main effects of family history and impulsivity on alcohol quantity and frequency of use and problems were also found. Conclusions These findings suggest that having both a family history of AUD and high BIS poor self-regulation is more strongly associated with alcohol-related consequences in the interpersonal and impulse control domains. Given the heterogeneity of impulsivity, these findings highlight the need for additional research to examine which facets of impulsivity are associated with which alcohol outcomes to narrow phenotypic risk for alcohol misuse.

Background A short delay to first intoxication confers alcohol-related risk, but risk factors for a short delay have yet to be examined. Methods 230 high school students (55.7% male; age 16.52 [1.19] years; 70.9% White) were surveyed about alcohol use. We examined whether sex, race, parental history of alcohol problems, age of onset, type of alcohol consumed, drinking company, and subjective response to alcohol were associated with 1) delay to first binge episode and 2) binge drinking status (i.e., never bingers, individuals who binge drank on their first drinking occasion, and individuals who binge drank at a later date). Finally, we examined whether first-occasion bingers reported heavier drinking and alcohol-related problems than later-occasion and never bingers. Results Overall, a shorter delay was associated with being male an older age of onset, and, during one's first drinking experience, consuming liquor, drinking with friends or alone, and experiencing high arousal negative alcohol effects. First-occasion bingers were more likely to be male, consume liquor, and experience stronger high arousal positive and negative alcohol effects than never bingers and to have a later age of onset, experience stronger high arousal negative, and weaker low arousal negative alcohol effects than later-occasion bingers. First-occasion bingers also reported heavier current drinking and more alcohol-related problems. Conclusions Characteristics of underage drinkers that confer risk for a shorter delay and first-occasion binging may provide fruitful targets for intervention, as efforts to delay binge drinking may mitigate alcohol-related risk associated with underage alcohol use., Highlights • A short delay to drinking to intoxication confers alcohol-related risk among youth. • We examined predictors of a short delay from alcohol onset to first binge episode. • Male sex, an older age of onset, and drinking liquor predicted a shorter delay. • Drinking with friends/alone and high arousal negative alcohol effects predicted delay. • Adolescents who binge drank at drinking onset were at risk for heavier alcohol use.

Background and aims To estimate progression to polytobacco use (PTU) over 1 year among a sample of US youth. Design Prospective survey with two waves 1 year apart: wave 1 (2013-14) and wave 2 (2014-15). We conducted latent transition analysis (LTA) to identify latent class transitions and examine socio-demographic predictors of transition types. Setting United States. Participants A total of 11, 996 people who were aged 12-17 years at wave 1. Measurements Publicly available data were used from the Population Assessment of Tobacco and Health (PATH) study, a nationally representative sample of US civilian, non-institutionalized population aged 12 years and older. Tobacco use status was assessed and classified in terms of: never use, non-current (not in the past 30 days) and current (past 30-day) use of cigarettes, cigars, e-cigarettes, hookah and smokeless tobacco. Other nicotine products were excluded because rates of use were either too low to model (e.g. pipe) or the product was not assessed in the PATH youth sample (e.g. nicotine replacement products). Findings We identified three distinct patterns: class 1, non-use (wave 1 prevalence = 86%; wave 2 prevalence = 78%); class 2, ever use of cigarettes and e-cigarettes (wave 1 prevalence = 11%; wave 2 prevalence = 14%); and class 3, current PTU (wave 1 prevalence = 4%; wave 2 prevalence = 7%). Probability of progression from non-use to ever use of cigarettes and e-cigarettes was 0.06 and ever use of cigarettes and e-cigarettes to current PTU was 0.32. Non-users were more likely to transition to ever use of cigarettes and e-cigarettes if they were older (versus younger), white (versus non-white) or if their parental education level was high school or less (versus more than high school); and ever users of cigarettes and e-cigarettes to current PTU if they were older, male or white. Conclusions US youth who had previously tried e-cigarettes and cigarettes at wave 1 (2013-14) had a 32% chance of transitioning to current use of two or more tobacco products within 1 year.

IMPACT: The findings from this study will inform the development of an FDA-approved patient-rated outcome measure of drug and alcohol craving that can be used in clinical trials aimed at developing or testing effective treatments for substance use disorder. OBJECTIVES/GOALS: Craving is a potential target of investigative medications to reduce drug use due to the strong link between craving and drug use. We will identify all existing craving measures as the first step for developing an FDA-approved patient-rated outcome measure for use in clinical trials. METHODS/STUDY POPULATION: Following PRISMA guidelines, we will update Rosenberg’s (2009) craving review by conducting a systematic review of all existing published and unpublished measures of craving for alcohol, nicotine, cannabis, opioid, and stimulant use. Electronic database (i.e., Ovid MEDLINE, Embase, PsycINFO, Web of Science, Cochrane), forward, backward, and author searches will be conducted. We will also request unpublished craving measures on major listservs (e.g., Research Society on Alcoholism, the Collaborative Perspectives on Addiction, and the College on Problems of Drug Dependence). All papers included in Rosenberg’s (2009) review through September 2020 will be included. RESULTS/ANTICIPATED RESULTS: The findings from this review will provide a comprehensive summary of the construct of craving and its hypothesized and tested domains. This review will elucidate whether the literature suggests there are components of craving unique to alcohol, nicotine, cannabis, opioid, and/or stimulant use, and whether there are key elements of craving common across the disorders. Therefore, these findings will inform whether a single patient-rated outcome measure of craving can be developed for use across substances or if unique patient-rated outcome measures of craving need to be developed for each substance. DISCUSSION/SIGNIFICANCE OF FINDINGS: While many different measures of craving exist, none have gone through the developmental steps required to qualify as an FDA-approved patient-rated outcome measure on which drug treatment labeling can be based. Completing this systematic review is the first step in this process.

BACKGROUND: Adolescent e-cigarette use has increased recently; however, little is known about trends in use of specific devices by youth. This study aims to 1) compare rates of e-cigarette device use over time, 2) examine changes in frequency of device use, and 3) identify predictors of device use. METHODS: Cross-sectional surveys were distributed school-wide across 4 diverse Connecticut high-schools in 2017, 2018, 2019 and assessed current (i.e., past-30-day) use of various e-cigarette devices: disposables/cig-a-likes, vape pens, mods, JUULs, and other rechargeable pod devices (added in 2018 and 2019). Analyses compared rates of device use and frequency (i.e., number of days used in past 30) over time. Multivariable logistic regression models examined demographic and tobacco use characteristics (e.g., age first trying e-cigarettes) as predictors of current use of each device type in 2019. RESULTS: From 2017–2019, rates of using JUUL, disposables/cig-a-likes, and vape pens increased significantly, while use of mods and other pod devices decreased (ps46%) and JUUL (28% to >35%) devices. In multivariable models, first trying e-cigarettes at a younger age was associated with current use of disposable/cig-a-like, vape pens, mods, and other rechargeable pod devices. CONCLUSIONS: From 2017–2019, JUUL, disposable/cig-a-like, and vape pens increased in popularity and were used frequently. Tobacco regulations designed to reduce youth use should consider various device types.

Background Effective electronic cigarette (e-cigarette) prevention messages are needed to combat the rising popularity/uptake of e-cigarettes among youth. We examined preferences for e-cigarette prevention messages that either emphasized gains (e.g., You save money by not using e-cigarettes) or losses (e.g., You spend money by using e-cigarettes) among adolescents and young adults. Methods Using surveys in two middle schools, four high schools, and one college in CT ( N = 5405), we assessed students' preferences for gain- or loss-framed e-cigarette prevention messages related to four themes: financial cost, health risks, addiction potential, and social labeling as a smoker. We also assessed whether preferences for each message framing theme differed by sex, school level, cigarette-use status, and e-cigarette use-status. We also examined whether preference for message framing differed by cigarette and e-cigarette susceptibility status among never e-cigarette users. Results Overall, loss-framing was preferred for message themes related to health risks, addiction potential, and social labeling as a smoker, whereas gain-framing was preferred for message themes related to financial cost. Logistic regression analyses showed that 1) females preferred loss-framed messages for all themes relative to males, 2) lifetime e-cigarette users preferred loss-framed health risks and social labeling messages relative to never users, and 3) high school students preferred gain-framed social labeling messages relative to college students. The preference for message framing did not differ by cigarette or e-cigarette susceptibility. Conclusions Preference for message framing differed by themes and individual characteristics. This formative research could inform the construction of persuasive e-cigarette prevention messages.

Introduction Although limited in empirical support, Alcohol Expectancy (AE) theory posits that AEs may overestimate subjective response (SR) to the positive effects of alcohol, which, in turn, confers alcohol-related risk (e.g., Darkes & Goldman, 1993). The recent development of the Anticipated Effects of Alcohol Scale (AEAS; Morean, Corbin, & Treat, 2012) and the Subjective Effects of Alcohol Scale (SEAS; Morean, Corbin, & Treat, 2013) now permits direct AE–SR comparisons using psychometrically sound assessments designed for this purpose. Methods We ran secondary data analyses (Morean et al., 2012, 2013) evaluating measurement invariance of AEs and SR; AE–SR relationships; the accuracy of AEs; and relations between AE–SR discrepancies and binge drinking, driving after drinking, and alcohol-related problems in a sample of 102 young adults (mean age 22.81 [2.25]; 74.5% male; 76.5% Caucasian) who consumed alcohol in a simulated bar setting (target blood alcohol level = .08 g/dL). Results The AEAS and SEAS were scalar measurement invariant and that AEs generally overestimated SR (mean Cohen's d = .48). Relative to SR, inflated high arousal negative AEs (e.g., aggressive) were associated with frequent binge drinking and alcohol-related problems, whereas exaggerated low arousal negative AEs (e.g., woozy) served protective functions. As blood alcohol levels rose, inflated low arousal positive AEs (e.g., relaxed) and low arousal negative AEs (e.g., wobbly) were associated with less frequent driving after drinking. Conclusions Challenging AE–SR discrepancies for high arousal effects may have utility in treatment and prevention efforts, whereas maintaining overestimates of low arousal effects may serve protective functions.

Introduction Preliminary evidence suggests adolescents use e-cigarettes in school. However, little is known about the types of devices that are used in schools, where they are used, and who uses them. Knowledge about these issues is critical to inform school regulations. Methods Cross-sectional surveys were conducted in 6 Connecticut high schools in 2019. Adolescents reported on current use (past 30-day use) of the following e-cigarette devices inschool: JUUL, any pod system other than JUUL, vape pens, disposables, mods, and on deviceuse in different locations: class, bathroom, hallways, outside on school grounds, and other school locations. Separate binary logistic regressions investigated predictors of use (demographics and past month use frequency of each device) in school for each device. Results Overall, 45.0% of current users (N = 1447) used e-cigarettes at school. Among users of each device, prevalence of current use at school varied by device with 45.7% reporting JUUL use, 41.3% other pod use, 34.6% vape pen use, 38.3% disposables use and 27.3% mod use. Current users used devices in bathrooms (75.1%), followed by outside on school grounds (52.2%), classrooms (45.7%), hallways (38.8%) and other school locations (11.7%). Greater e-cigarette past month use frequency for each device was associated with device use in school. Conclusions This study is the first to examine use of specific e-cigarette devices in schools and demonstrates that e-cigarette use frequency predicts school use. Despite rules against vaping, e-cigarette use remains prevalent in many school locations, suggesting alternative strategies such as prevention and e-cigarette education are needed.

Background There are thousands of e-liquid flavors available, and some are named after alcohol beverages (e.g., “pina colada”). It is unclear whether use of e-liquids with alcohol beverage names is associated with adolescent alcohol use. E-cigarettes and alcohol are co-used in adolescents; therefore, it is important to investigate these associations. Methods Eight Southeastern Connecticut high schools were surveyed in Spring 2015 (N = 7045). We examined the association between preference for using e-liquid flavors and alcohol drinking status (i.e., no past month alcohol use, past month alcohol use but no binge drinking, and past month binge drinking) in ever e-cigarette users (N = 1311). Results Among ever e-cigarette users who preferred using e-liquids with alcohol beverage names (N = 111), 30.6 % had no past month alcohol use, 19.8 % had past month alcohol use but did not binge drink in the past month, and 49.5 % binge drank in the past month. Multinomial logistic regression (controlling for demographics and including other e-cigarette flavors that were highly endorsed, i.e., fruit and candy) revealed that the preferences of alcohol beverage-named-e-liquid (OR: 2.84, CI: 1.70–4.75) and fruit flavored e-liquids (OR: 1.55, CI: 1.14–2.11), but not candy flavored e-liquids was associated with past-month binge drinking compared to no past-month alcohol use. Conclusion This evidence suggests that the preference for using alcohol beverage- and fruit-named e-liquid flavors is associated with past-month binge drinking among adolescents. Understanding the associations between alcohol beverage-named e-liquids and alcohol use in adolescents may help inform tobacco regulatory strategies that aim to decrease the use/appeal of e-cigarettes.

BACKGROUND. Although weight-related reasons for smoking and vaping have been examined in adults, research in adolescents is lacking. Thus, we examined the prevalence and correlates of using flavored e-liquids for appetite control or weight loss in high school adolescents. METHODS. The analytic sample included 529 students who completed a school-based survey in Connecticut in Spring 2017 (50.6% female, 79.5% White, mean age 16.27 [SD=1.18], range 13–19 years). Inclusion criteria were past-30-day vaping, using ≥1 flavored e-liquid (past month), and having non-missing data on flavored e-liquid use for appetite control and weight loss. Participants reported on sex, age, race, past-30-day vaping and smoking frequency, nicotine e-liquid use, flavored e-liquid use (e.g., tobacco, mint, fruit, candy), and flavored e-liquid use for appetite control and/or weight loss. RESULTS. Adolescent e-cigarette users (past 30-days) reported vaping flavored e-liquids for appetite control (13.8%) and weight loss (9.3%). Using flavored e-liquids for appetite control or weight loss, respectively, was associated with more frequent vaping (OR = 1.21; 1.21) and using more flavored e-liquids (OR = 1.33; 1.28, p-values < .01). Vaping candy-flavored e-liquids (OR = 1.16, p = 0.02) uniquely was associated with vaping for appetite control. CONCLUSIONS. A subset of adolescents reported using flavored e-liquids for weight-related reasons. These adolescents reported vaping more frequently than their counterparts, raising concerns about increased nicotine exposure. Research is needed to understand where adolescents learn about weight-motivated vaping (e.g., friends, social media) and whether weight-related motives promote e-cigarette initiation among e-cigarette naïve individuals or continued/escalating use among current users.