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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).

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.

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.

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.

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.

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.

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.

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 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.

Background E-cigarette use rates are high among youth, but there is limited information on the types of e-cigarette devices that are used by youth. Methods During Spring 2017, students from 4 high schools completed surveys on use of e-cigarette devices (cig-a-like, vape/hookah pen, modified devices or mods, and JUUL). Among youth who endorsed ever (lifetime) use of an e-cigarette and of at least one device (n = 875), we assessed 1) prevalence rates of ever and current (past-month) use of each device, 2) use of nicotine in each device, and 3) predictors [age, sex, race, socioeconomic status (SES), other tobacco use] of ever use of each device and of use of single versus multiple devices. Results Cig-a-likes were used least frequently (Ever use: cig-a-likes: 25.4%; vape/hookah pens: 60.6%; JUUL: 64.2%; mods: 71.2%; Current use: cig-a-likes: 7.3%; vape/hookah pens; 18.7%; mods: 33.1%; JUUL: 47.1%;). Nicotine use was highest for JUUL (JUUL: 80.3%; mods: 56.3%; cig-a-likes: 51.4%; vape/hookah pens: 46.8%). Among ever users of single devices, use of JUUL was highest (JUUL: 43%; mods: 32%; vape/hookah pens: 21%; cig-a-likes: 4%). Ever use of all devices, except JUUL, was associated with other tobacco product use. Ever use of JUUL was associated with higher SES. Ever use of multiple devices (two: 34.7%; three: 25.8%; four: 11.7%) compared with a single device (27.8%) was associated with other tobacco product use. Conclusions Targeted regulatory and prevention efforts that consider the use of multiple e-cigarette devices are needed to lower youth e-cigarette use rates.

Background Rates of legal medical cannabis (MC) use are increasing, but little is known about the prevalence and correlates of recreational cannabis (RC) use among medical users (MC/R). Methods 348 MC users who resided in a state in which MC is legal and had medical authorization to use MC legally completed an anonymous survey in Spring 2017 (64.1% female, 82.8% White, mean age 33.03[±10.37] years). Rates of endorsing MC/R and the following potential correlates of MC/R were examined: the legal status of RC in participants' states of residence, sex, age, race, primary medical condition, MC product(s) used, MC expectancies, features of MC sought out (e.g., high tetrahydrocannabinol [THC] content), and negative cannabis use consequences. Results 55.5% of MC users engaged in MC/R. MC/R was associated with residing in a state in which RC is legal, being female, using MC for pain or mental health conditions, vaping MC concentrates, holding positive expectancies for combustible MC, and seeking out MC products with high THC concentrations. Preferring MC products with high cannabidiol (CBD) concentrations protected against MC/R. Conclusions More than half of MC users endorsed MC/R, which is considerably higher than rates of misuse observed for other prescription medications. Findings raise concerns about circumvention of RC laws in states where RC remains illegal and could be used to inform MC regulatory efforts (e.g., reducing THC content, increasing CBD content). Findings also suggest that prevention/intervention efforts to reduce MC/R are needed, especially among high-risk populations of MC users (e.g., women, pain patients, psychiatric patients).

Sexual misconduct occurs with disproportionate frequency on college campuses, and alcohol is involved in most sexual assaults. Importantly, collegiate athletes are at risk for both heavy drinking and sexual misconduct. Thus, the current study evaluated the efficacy of a novel, 2.5-hr, peer-facilitated, interactive, group-based bystander intervention program for student athletes that integrated information on sexual misconduct and risky drinking (Preventing and Responding to Sexual Misconduct [PRSM]). In all, 205 athletes completed 25-min surveys immediately before and after the training, and 76 (of 94 invited) completed a 3-month follow-up. Participating in the workshop was associated with significant increases in acknowledgment that sexual misconduct is a problem on campus, knowledge of where to get help if sexual misconduct occurs, knowledge about the college’s procedures for addressing sexual misconduct, confidence that the college’s procedures for addressing sexual misconduct are fair, bystander confidence, and engagement in a range of bystander activities. A significant decrease in rape myths also was observed. Participating in the workshop also produced changes in alcohol-related beliefs and behaviors. After participating in PRSM, athletes reported increased expectations that drinking alcohol can produce negative effects including aggression and acute intoxication. Participating in the workshop also was associated with significant reductions in drinking frequency, the total number of drinks consumed per month, the maximum drinks consumed in 24 hours, the frequency of binge-drinking episodes, and the experience of alcohol-related problems. In sum, the PRSM program evidenced preliminary efficacy as a program designed to increase prosocial bystander behavior and decrease high-risk drinking among collegiate athletes; changes in beliefs and behaviors consistent with reducing risk for sexual misconduct and problem drinking were observed after workshop participation. Future research should evaluate whether the PRSM program is effective for use with other high-risk populations like fraternity members or more diverse institutions of higher education including large universities.

Introduction The 4-item Patient-Reported Outcomes Measurement Information System Nicotine Dependence Item Bank is a psychometrically sound measure for assessing cigarette (PROMIS) and e-cigarette dependence (PROMIS-E). We evaluated whether dual-users of cigarettes and e-cigarettes self-report experiencing different levels of dependence on each product. We subsequently examined whether cigarette and e-cigarette dependence are associated with the frequency of using each product in dual-users. Methods Dual-users completed an online survey in Summer 2017 (n = 326; 49.7% male, 85.3% White, mean age 38.17 [13.08] years). Measurement invariance of the PROMIS and PROMIS-E was evaluated. Mean differences in cigarette and e-cigarette dependence then were examined. The correlation between cigarette and e-cigarette dependence also was examined. Finally, one-way MANOVA was used to evaluate how cigarette and e-cigarette dependence relate to past-month frequency of e-cigarette use and cigarette smoking. Results The PROMIS and the PROMIS-E were scalar measurement invariant, and, on average, dual-users reported stronger dependence on cigarettes than on e-cigarettes. Cigarette and e-cigarette dependence were related, yet distinct constructs (r = 0.35), suggesting that dual-users can discriminate between dependence on each product. Stronger cigarette dependence predicted more frequent past-month smoking and less frequent past-month vaping. Stronger e-cigarette dependence predicted more frequent past-month vaping and less frequent smoking. Conclusions Overall, dual-users reported stronger dependence on cigarettes than on e-cigarettes. However, dependence on each product was associated with increased use of each respective product and with less frequent use of the other product. Future research using the PROMIS can evaluate how potential FDA regulations could reduce nicotine dependence across products.

Introduction Psychometrically sound measures of e-cigarette dependence are lacking. Methods We modified the PROMIS Item Bank v1.0—Smoking: Nicotine Dependence for All Smokers for use with e-cigarettes and evaluated the psychometrics of the 22-, 8-, and 4-item adapted versions, referred to as The E-cigarette dependence scale (EDS). Adults (1009) who reported using e-cigarettes at least weekly completed an anonymous survey in summer 2016 (50.2% male, 77.1% White, mean age 35.81 [10.71], 66.4% daily e-cigarette users, 72.6% current cigarette smokers). Psychometric analyses included confirmatory factor analysis, internal consistency, measurement invariance, examination of mean-level differences, convergent validity, and test-criterion relationships with e-cigarette use outcomes. Results All EDS versions had confirmable, internally consistent latent structures that were scalar invariant by sex, race, e-cigarette use (nondaily/daily), e-liquid nicotine content (no/yes), and current cigarette smoking status (no/yes). Daily e-cigarette users, nicotine e-liquid users, and cigarette smokers reported being more dependent on e-cigarettes than their counterparts. All EDS versions correlated strongly with one another, evidenced convergent validity with the Penn State E-cigarette Dependence Index and time to first e-cigarette use in the morning, and evidenced test-criterion relationships with vaping frequency, e-liquid nicotine concentration, and e-cigarette quit attempts. Similar results were observed when analyses were conducted within subsamples of exclusive e-cigarette users and duals-users of cigarettes and e-cigarettes. Conclusions Each EDS version evidenced strong psychometric properties for assessing e-cigarette dependence in adults who either use e-cigarette exclusively or who are dual-users of cigarettes and e-cigarettes. However, results indicated little benefit of the longer versions over the 4-item EDS, which provides an efficient assessment of e-cigarette dependence. Implications The availability of the novel, psychometrically sound EDS can further research on a wide range of questions related to e-cigarette use and dependence. In addition, the overlap between the EDS and the original PROMIS that was developed for assessing nicotine dependence to cigarettes provides consistency within the field.

IntroductionMany e-cigarette users find the variety of e-cigarette flavors appealing. We examined whether preferences for e-liquid flavors and the total number of flavors preferred differed between samples of adolescent and adult e-cigarette users. We also examined whether these preferences were associated with e-cigarette use frequency for adolescents or adults, respectively.Materials and methodsThe analytic samples comprised 1) 396 adolescent, past-month e-cigarette users from 5 Connecticut high schools who completed an anonymous, school-based survey in Fall 2014 (56.1% male; 16.18 [1.18] years; 42.2% past-month smokers), and 2) 590 adult, past-month e-cigarette users who completed an anonymous, MTurk survey in Fall 2014 (53.7% male; 34.25 [9.89] years; 51.2% past-month smokers).ResultsCompared to adults, a larger proportion of adolescents preferred fruit, alcohol, and "other"-flavored e-liquids, whereas adults disproportionately preferred tobacco, menthol, mint, coffee, and spice-flavored e-liquids (p-values < .05). Adults also preferred a greater total number of flavors compared to adolescents and used e-cigarettes more frequently (p-values < .001). Flavor preferences uniquely were associated with frequency of e-cigarette use within the adolescent sample; the total number of flavors preferred was associated with more days of e-cigarette use (ηp2 = 0.04), as were preferences for fruit (ηp2 = 0.02), dessert (ηp2 = 0.02), and alcohol-flavored (ηp2 = 0.02) e-liquids.ConclusionsFlavor preferences differed between adolescent and adult samples. While youth reported less frequent e-cigarette use overall, their preferences for specific flavors and the total number of flavors preferred were associated with more days of e-cigarette use, indicating that flavor preferences may play an important role in adolescent e-cigarette use.

BACKGROUND AND OBJECTIVES: Electronic cigarette (e-cigarette) use is increasing rapidly among high school (HS) students. Of concern, e-cigarettes can be used to vaporize cannabis, although use rates among adolescents are unknown. We evaluated lifetime rates of using e-cigarettes to vaporize cannabis among all lifetime e-cigarette users (27.9%), all lifetime cannabis users (29.2%), and lifetime users of both e-cigarettes and cannabis (18.8%); common means of vaporizing cannabis including hash oil, wax infused with Δ-9-tetrahydrocannabinol (THC), and dried cannabis; and demographic predictors of using e-cigarettes to vaporize cannabis. METHODS: In the spring of 2014, 3847 Connecticut HS students completed an anonymous survey assessing e-cigarette and cannabis use. RESULTS: Vaporizing cannabis using e-cigarettes was common among lifetime e-cigarette users, lifetime cannabis users, and lifetime dual users (e-cigarette 18.0%, cannabis 18.4%, dual users 26.5%). Students reported using e-cigarettes to vaporize hash oil (e-cigarette 15.4%, cannabis 15.5%, dual users 22.9%) and wax infused with THC (e-cigarette 10.0%, cannabis 10.2%, dual users 14.8%) and using portable electronic vaporizers to vaporize dried cannabis leaves (e-cigarette 19.6%, lifetime cannabis 23.1%, lifetime dual users 29.1%). Binary logistic regression indicated that male students (odds ratio [OR] = 2.05), younger students (OR = 0.64), lifetime e-cigarette users (OR = 5.27), and lifetime cannabis users (OR = 40.89) were most likely to vaporize cannabis using e-cigarettes. Rates also differed by HS attended. CONCLUSIONS: Rates of vaporizing cannabis using e-cigarettes were high. These findings raise concerns about the lack of e-cigarette regulations and the potential use of e-cigarettes for purposes other than vaping nicotine.

Military service is associated with increased rates of heavy drinking. Widely used clinical practices (e.g., motivational interviewing) indicate that addressing both negative and positive drinking consequences is essential to effective treatment. However, research on effectively assessing positive drinking consequences in a clinical population is absent.The current study (1) evaluated the utility of the Positive Drinking Consequences Questionnaire (PDCQ), a measure previously validated in an undergraduate sample, for use with treatment-seeking veterans, and (2) evaluated relationships between positive drinking consequences and alcohol expectancies, pre-treatment alcohol use, and alcohol-related problems.Ninety-seven veterans seeking treatment for alcohol problems completed an anonymous survey (97.9% male; mean age = 49.76[11.40], 67.0% Caucasian).The PDCQ evidenced a single factor latent structure and internal consistency (Cronbach's alpha = .90). Positive drinking consequences and expectancies were related yet distinct constructs. After controlling for demographic factors, experiencing more positive drinking consequences at program intake was associated with heavier pretreatment drinking (ηp(2) = .10, p = .003) and alcohol-related problems (ηp(2) = .18, p.001). Further, PDCQ scores evidenced incremental validity in accounting for pretreatment alcohol use (ηp(2) = .12, p = .002) and alcohol-related problems (ηp(2) = .11, p = .003) when expectancies also were included in the model.Positive drinking consequences are assessed reliably by the PDCQ in a clinical sample and appear to play an important role in the drinking behavior of veterans seeking alcohol treatment.

Impulsive adolescents have difficulty quitting smoking. We examined if treatments that provide behavioral incentives for abstinence improve treatment outcomes among impulsive adolescent smokers, who have been shown to be highly sensitive to reward.We ran secondary data analyses on 64 teen smokers (mean age=16.36 [1.44]; cigarettes/day=13.97 [6.61]; 53.1% female; 90.6% Caucasian) who completed a four-week smoking cessation trial to determine whether impulsive adolescents differentially benefit from receiving cognitive behavioral therapy (CBT), contingency management (CM), or the combination of the two (CM/CBT). Indices of treatment efficacy included self-report percent days abstinent and end of treatment biochemically-confirmed 7-day point prevalence abstinence (EOT abstinence). We assessed self-reported impulsivity using the Brief Barratt Impulsiveness Scale. We used univariate Generalized Linear Modeling to examine main effects and interactions of impulsivity and treatment condition as predictors of self-reported abstinence, and exact logistic regression to examine EOT abstinence.CM/CBT and CM were comparably effective in promoting abstinence, so analyses were conducted comparing the efficacy of CBT to treatments with a CM component (i.e., CM and CM/CBT). CBT and deficient self-regulation predicted lower self-reported abstinence rates within the total analytic sample. Treatments containing CM were more effective than CBT in predicting 1) self-reported abstinence among behaviorally impulsive adolescents (% days abstinent: CM 77%; CM/CBT 81%; CBT 30%) and 2) EOT point prevalence abstinence among behaviorally impulsive adolescents and adolescents with significant deficits in self-regulation.CM-based interventions may improve the low smoking cessation rates previously observed among impulsive adolescent smokers.

Introduction Research has shown that adults perceive that electronic cigarettes (e-cigarettes) are effective for smoking cessation, yet little is known about adolescents and young adults' perceptions of e-cigarettes for quitting cigarette smoking. This study describes middle, high school, and college students' beliefs about, and experiences with, e-cigarettes for cigarette smoking cessation. Methods We conducted 18 focus groups (n = 127) with male and female cigarette smokers and nonsmokers in 2 public colleges, 2 high schools, and 1 middle school in Connecticut between November 2012 and April 2013. Participants discussed cigarette smoking cessation in relation to e-cigarettes. Verbatim transcripts were analyzed using thematic analysis. Results All participants, regardless of age and smoking status, were aware that e-cigarettes could be used for smoking cessation. College and high school participants described different methods of how e-cigarettes could be used for smoking cessation: (a) nicotine reduction followed by cessation; (b) cigarette reduction/dual use; and (c) long-term exclusive e-cigarette use. However, overall, participants did not perceive that e-cigarette use led to successful quitting experiences. Participants described positive attributes (maintenance of smoking actions, "healthier" alternative to cigarettes, and parental approval) and negative attributes (persistence of craving, maintenance of addiction) of e-cigarettes for cessation. Some college students expressed distrust of marketing of e-cigarettes for smoking cessation. Conclusions Adolescent and young adult smokers and nonsmokers perceive that there are several methods of using e-cigarettes for quitting and are aware of both positive and negative aspects of the product. Future research is needed to determine the role of e-cigarettes for smoking cessation in this population.

Among adolescents, low socioeconomic status (SES) is associated with greater exposure to tobacco cigarette advertising and cigarette use. However, associations among SES, e-cigarette advertising and e-cigarette use are not well understood. This study examined exposure to e-cigarette advertisements as a mediator of the relationship between SES and adolescent e-cigarette use. Adolescents (N = 3473; 51% Female) from 8 high schools in Connecticut completed an anonymous survey in Spring 2015. Mediation analysis was used to examine whether the total number of sources of recent e-cigarette advertising exposure (e.g., TV, radio, billboards, magazines, local stores [gas stations, convenience stores], vape shops, mall kiosks, tobacco shops, social media) mediated the association between SES (measured by the Family Affluence Scale) and past-month frequency of e-cigarette use. We clustered for school and controlled for other tobacco product use, age, sex, race/ethnicity and perceived social norms for e-cigarette use in the model. Our sample recently had seen advertisements via 2.1 (SD = 2.8) advertising channels. Mediation was supported (indirect effect: β = 0.01, SE = 0.00, 95% CI [0.001, 0.010], p = 0.02), such that higher SES was associated with greater recent advertising exposure, which, in turn, was associated with greater frequency of e-cigarette use. Our study suggests that regulations to reduce youth exposure to e-cigarette advertisement may be especially relevant to higher SES youth. Future research should examine these associations longitudinally and evaluate which types of advertisements target different SES groups.

BACKGROUND: Electronic cigarettes (e-cigarettes) electrically heat and vaporize e-liquids to produce inhalable vapors. These devices are being used to inhale vapors produced by dripping e-liquids directly onto heated atomizers. The current study conducts the first evaluation of the prevalence rates and reasons for using e-cigarettes for dripping among high school students. METHODS: In the spring of 2015, students from 8 Connecticut high schools (n = 7045) completed anonymous surveys that examined tobacco use behaviors and perceptions. We assessed prevalence rates of ever using e-cigarettes for dripping, reasons for dripping, and predictors of dripping behaviors among those who reported ever use of e-cigarettes. RESULTS: Among 1080 ever e-cigarette users, 26.1% of students reported ever using e-cigarettes for dripping. Reasons for dripping included produced thicker clouds of vapor (63.5%), made flavors taste better (38.7%), produced a stronger throat hit (27.7%), curiosity (21.6%), and other (7.5%). Logistic regression analyses indicated that male adolescents (odds ratio [OR] = 1.64), whites (OR = 1.46), and those who had tried multiple tobacco products (OR = 1.34) and had greater past-month e-cigarette use frequency (OR = 1.07) were more likely to use dripping (Ps < .05). CONCLUSIONS: These findings indicate that a substantial portion (∼1 in 4) of high school adolescents who had ever used e-cigarettes also report using the device for dripping. Future efforts must examine the progression and toxicity of the use of e-cigarettes for dripping among youth and educate them about the potential dangers of these behaviors.

Quickly progressing from initiating alcohol use to drinking to intoxication recently was identified as a novel risk factor for hazardous drinking in college students (ME Morean et al. [2012] Alcohol Clin Exp Res, 36, 1991-1999). The current study evaluated the risk associated with age of onset (AO) and delay to first intoxication (Delay) in a high school sample.Adolescent drinkers (N = 295, age 16.29 [1.14], 55.3% female, 80.3% Caucasian, AO = 13.51 [2.29] years, Delay = 0.80 [1.43] years) completed an anonymous survey about their substance use in February of 2010. Self-report questions assessed AO and age of first intoxication (AI) (i.e., "How old were you the first time you tried alcohol/got drunk?") and past-month alcohol use/binge drinking (i.e., How often did you drink alcohol/drink ≥5 drinks?).Bivariate correlations indicated that AO was positively correlated with AI and inversely correlated with Delay, the frequency of any drinking, and the frequency of binge drinking. When considered alone, Delay was not significantly correlated with either alcohol use outcome. In contrast, hierarchical regression analyses indicated that when considered in concert, an earlier AO and a shorter Delay were each associated with heavier drinking (any drinking adjusted R(2) = 0.08; binge drinking R(2) = 0.06, p-values0.001) beyond demographic characteristics. Two-way interactions among study variables were nonsignificant, suggesting that AO and Delay conferred risk similarly by racial/ethnic status, gender, and grade in high school.When considered simultaneously, both an early AO and a quick progression to drinking to intoxication appear to be important determinants of high school student drinking. In addition to continuing efforts to postpone AO, efforts designed to delay intoxication may modulate alcohol-related risk associated with early drinking.

Self-reported impulsivity confers risk factor for substance abuse. However, the psychometric properties of many self-report impulsivity measures have been questioned, thereby undermining the interpretability of study findings using these measures. To better understand these measurement limitations and to suggest a path to assessing self-reported impulsivity with greater psychometric stability, we conducted a comprehensive psychometric evaluation of the Barratt Impulsiveness Scale-11 (BIS-11), the Behavioral Inhibition and Activation Scales (BIS/BAS), and the Brief Self-Control Scale (BSCS) using data from 1,449 individuals who participated in substance use research. For each measure, we evaluated (a) latent factor structure, (b) measurement invariance, (c) test-criterion relationships between the measures, and (d) test-criterion relations with drinking and smoking outcomes. Notably, we could not replicate the originally published latent structure for the BIS, BIS/BAS, or BSCS or any previously published alternative factor structure (English language). Using exploratory and confirmatory factor analysis, we identified psychometrically improved, abbreviated versions of each measure: 8-item, 2-factor BIS-11 (root-mean-square error of approximation [RMSEA] = .06, comparative fit index [CFI] = .95); 13-item, 4-factor BIS/BAS (RMSEA = .04, CFI = .96); and 7-item, 2-factor BSCS (RMSEA = .05, CFI = .96). These versions evidenced (a) stable, replicable factor structures, (b) scalar measurement invariance, ensuring our ability to make statistically interpretable comparisons across subgroups of interest (e.g., sex, race, drinking/smoking status), and (c) test-criterion relationships with each other and with drinking/smoking. This study provides strong support for using these psychometrically improved impulsivity measures, which improve data quality directly through better scale properties and indirectly through reducing response burden.

Background JUUL e-cigarettes are popular among youth. However, it is unknown whether adolescents understand that 5% JUUL pods contain a high nicotine concentration or consider JUULs to be e-cigarettes. Method 3170 students from 4 Connecticut high schools completed a school-based survey (May–October 2018). Students reported on lifetime and past-month JUUL use and perceived JUUL nicotine strength (low/medium/high/don’t know) when no information about nicotine concentration was provided and, subsequently, when informed JUULs contain 5% nicotine. Students reported whether they believe JUULs are e-cigarettes (no/yes/don’t know). Results Students were never JUUL users (56.6%), ever users (13.2%), and past-month users (30.2%). When no information was provided, students reported that JUULs contain low (10.5%), medium (26.9%), or high nicotine levels (31.1%); 31.4% did not know. When informed JUULs contain 5% nicotine, students were more likely to believe JUUL’s nicotine strength was low (29.5%) or medium (29.3%) than high (21.3%) and less likely to report not knowing (19.9%). 39% of students believed JUULs are not e-cigarettes or did not know. Discussion Most students were unaware of JUUL’s high nicotine concentration, with more believing that JUULs contain low or medium nicotine concentrations when informed JUULs contain 5% nicotine. Thus, youth may misinterpret the nicotine concentration printed on JUUL pod packaging, raising concerns about inadvertent exposure to high nicotine levels and dependence risk. Further, 39% of adolescents believed JUULs are not e-cigarettes or were unsure. Regulatory efforts are needed to establish understandable nicotine concentration labels, require products to be labeled accordingly, and clarify what products constitute e-cigarettes.

Psychometrically sound measures of e-cigarette dependence are lacking.We modified the PROMIS Item Bank v1.0-Smoking: Nicotine Dependence for All Smokers for use with e-cigarettes and evaluated the psychometrics of the 22-, 8-, and 4-item adapted versions, referred to as The E-cigarette dependence scale (EDS). Adults (1009) who reported using e-cigarettes at least weekly completed an anonymous survey in summer 2016 (50.2% male, 77.1% White, mean age 35.81 [10.71], 66.4% daily e-cigarette users, 72.6% current cigarette smokers). Psychometric analyses included confirmatory factor analysis, internal consistency, measurement invariance, examination of mean-level differences, convergent validity, and test-criterion relationships with e-cigarette use outcomes.All EDS versions had confirmable, internally consistent latent structures that were scalar invariant by sex, race, e-cigarette use (nondaily/daily), e-liquid nicotine content (no/yes), and current cigarette smoking status (no/yes). Daily e-cigarette users, nicotine e-liquid users, and cigarette smokers reported being more dependent on e-cigarettes than their counterparts. All EDS versions correlated strongly with one another, evidenced convergent validity with the Penn State E-cigarette Dependence Index and time to first e-cigarette use in the morning, and evidenced test-criterion relationships with vaping frequency, e-liquid nicotine concentration, and e-cigarette quit attempts. Similar results were observed when analyses were conducted within subsamples of exclusive e-cigarette users and duals-users of cigarettes and e-cigarettes.Each EDS version evidenced strong psychometric properties for assessing e-cigarette dependence in adults who either use e-cigarette exclusively or who are dual-users of cigarettes and e-cigarettes. However, results indicated little benefit of the longer versions over the 4-item EDS, which provides an efficient assessment of e-cigarette dependence.The availability of the novel, psychometrically sound EDS can further research on a wide range of questions related to e-cigarette use and dependence. In addition, the overlap between the EDS and the original PROMIS that was developed for assessing nicotine dependence to cigarettes provides consistency within the field.

Alcohol expectancy (AEs) research has enhanced our understanding of how anticipated alcohol effects confer risk for heavy drinking and alcohol-related problems. However, extant AE measures have limitations within 1 or more of the following areas: assessing a comprehensive range of effects, specifying the hypothetical number of drinks consumed, assessing AEs by limb of the blood alcohol curve (BAC), and/or not having undergone psychometric evaluation. Building upon the strengths of existing measures, we employed conceptual and statistical advances in measurement development to create the novel, psychometrically sound Anticipated Effects of Alcohol Scale (AEAS). Unique to this study, pilot data ensured that the AEAS comprised a comprehensive sampling of effects that varied in valence (positive/negative) and arousal (low/high) and were identified as plausible outcomes of drinking. The AEAS specified the number of drinks individuals imagined consuming (adjusted for sex) and the hypothetical drinking episode length (2 hr). AEs were also assessed separately by BAC limb. For validation purposes, the AEAS was included in several survey studies of young adults (ages 18-30). The validity argument for the proposed interpretation of AEAS test scores was based upon the following: (a) exploratory and confirmatory factor analyses (N = 546) identified a 22-item, 4-factor internal structure, categorizing alcohol effects into quadrants (high/low arousal crossed with positive/negative valence); (b) scalar measurement invariance was established for BAC limb, sex, and binge drinking status; (c) convergence/divergence was observed with alternative AEs measures and mood; and (d) test-criterion relationships were observed with several alcohol-related outcomes. The reliability argument was based on test-retest and internal consistency coefficients.

Introduction Some traditional cigarette smokers are motivated to smoke to lose weight or control their weight. The current study evaluated whether a subset of adult e-cigarette users reported vaping to lose or control their weight and examined potential predictors of vaping for weight management. Methods Adult e-cigarette users (n = 459) who reported wanting to lose weight or maintain their weight completed an anonymous online survey. Participants reported on demographics, vaping frequency, e-cigarette nicotine content, cigarette smoking status, preferred e-cigarette/e-liquid flavors, current weight status (i.e., overweight, underweight), use of dieting strategies associated with anorexia and bulimia, lifetime history of binge eating, self-discipline, and impulse control. Binary logistic regression was used to examine whether vaping for weight loss/control was associated with the aforementioned variables. Results Participants who reported vaping for weight loss/control (13.5%) were more likely to vape frequently (adjOR = 1.15; 95% CI [1.00, 1.31]); be overweight (adjOR = 2.80; [1.33, 5.90]); restrict calories (adjOR = 2.23; [1.13, 4.42]); have poor impulse control (adjOR = 0.59; [0.41, 0.86]); and prefer coffee- (adjOR = 2.92; [1.47, 5.80]) or vanilla-flavored e-liquid (adjOR = 7.44; [1.56, 36.08]). Conclusions A subset of adult e-cigarette users reported vaping for weight loss/control, raising concerns about expanded, scientifically unsubstantiated uses of e-cigarettes. Identifying where individuals obtain information about vaping for weight loss (e.g., e-cigarette ads, Internet) and whether weight-related motives promote e-cigarette initiation among e-cigarette naive individuals is important to informing regulatory efforts. Further research also is needed to better understand the link between e-liquid flavors and weight loss motivations.

The Anticipated Effects of Alcohol Scale (AEAS), the Subjective Effects of Alcohol Scale, and the Positive Drinking Consequences Questionnaire (PDCQ) are psychometrically sound measures of alcohol expectancies (expectancies), subjective response to alcohol, and positive drinking consequences, respectively, for use with adults. Prior research using these measures suggests that expectancies, subjective response, and positive drinking consequences are related yet distinct determinants of drinking. The current study presents psychometric evaluations of these measures for use with adolescents including confirmatory factor analyses (CFA) of the previously identified latent structures, internal consistency, and test-criterion relationships. Legally, alcohol cannot be administered to adolescents, so we assessed retrospective subjective response (during the first drinking episode ever [SEAS First] and the most recent drinking episode [SEAS Recent]). The sample comprised 248 Connecticut high school students (53.6% male; mean age 16.50 [1.19] years; 71.4% White) who completed an anonymous survey. CFA confirmed the latent factor structures for each measure. The AEAS, SEAS First, SEAS Recent and the PDCQ were internally consistent (mean α AEAS = .83; SEAS First = .88; SEAS Recent = .89, PDCQ = .87). AEAS subscales evidenced moderate overlap with corresponding SEAS First subscales (mean = .36) and SEAS Recent subscales (mean = .46) and modest overlap with the PDCQ (mean = .17). Expectancies, subjective response, and positive drinking consequences also accounted for significant variance in monthly drinking, lifetime maximum number of drinks consumed, and alcohol-related problems. In sum, the AEAS, the retrospective SEAS, and the PDCQ are psychometrically sound measures for use with adolescents.

Objective E-cigarettes are popular among youth, but little is known about the nicotine concentrations of e-liquids used by adolescents. Materials and method In Spring, 2014, we conducted cross-sectional surveys in four Connecticut high schools and two middle schools. Among past-30-day e-cigarette users (n = 513, 45% female, mean age 15.9 [SD = 1.4]), we examined what nicotine concentration adolescents typically used in their e-cigarettes (range 0–30 mg/mL and “I don’t know”). We first examined whether age, sex, smoking status, e-cigarette use frequency, and/or e-cigarette acquisition source were associated with using nicotine-free e-liquid, nicotine e-liquid, or not knowing the e-liquid nicotine concentration. Among nicotine users (n = 185), we then examined whether the aforementioned variables were associated with using higher nicotine concentrations. Results Adolescents reported using nicotine-free e-liquid (28.5%), nicotine e-liquid (37.4%), or not knowing their e-liquid nicotine concentration (34.1%). Nicotine users comprised more smokers and heavier e-cigarette users compared to nicotine-free e-liquid users and those who did not know their nicotine concentration. Nicotine users also comprised more males and were more likely to purchase e-cigarettes online or from tobacco shops compared to those who did not know their nicotine concentration. Among nicotine users, cigarette smoking, male sex, and purchasing e-cigarettes from tobacco shops predicted using higher nicotine concentrations. Conclusions Adolescents reported using e-liquids with variable nicotine concentrations. Smokers, males, and those who purchased their own e-cigarettes reported using the highest nicotine levels. Of concern, many adolescents were unaware of the nicotine concentration in their e-liquid, raising concerns about inadvertent nicotine exposure among youth.

BACKGROUND: Using alcohol at an early age is a well-documented risk factor for heavy drinking and the experience of a range of negative social and health consequences. However, it remains unclear if early consumption of any alcohol or early drinking to intoxication confers the greatest risk. METHODS: Age of onset (AO) and delay to first intoxication (delay) were examined as independent predictors of heavy drinking and the experience of alcohol-related problems (problems) in a sample of incoming college freshmen (n = 1,160) who completed biannual assessments from the summer following senior year of high school through senior year of college. The sample included more women (66%) than men and a majority were Caucasian (59%). We employed latent growth curve modeling to examine self-reported AO and delay to self-defined first intoxication as predictors of the frequency of heavy drinking and problems during year 4 of college, as well as changes in these alcohol-related outcomes from high school through college. RESULTS: An earlier AO and/or a shorter delay was associated with increased frequency of heavy drinking and problems during senior year of college. Although individuals with a later AO and/or a longer delay were at lower risk overall, they showed larger increases in heavy drinking and problems over time. CONCLUSIONS: The findings highlight the importance of distinguishing between AO and delay to first intoxication. Delay accounted for unique variance in drinking outcomes relative to AO, and the effects of AO were stronger when delay was included as a predictor variable. Results are discussed with regard to potential mechanisms through which an early age of intoxication may confer unique risk relative to AO. The implications for cross-cultural differences in risk for problems attributable to early drinking are also discussed. Language: en

Human laboratory studies have a long and rich history in the field of alcoholism. Human laboratory studies have allowed for advances in alcohol research in a variety of ways, including elucidating of the neurobehavioral mechanisms of risk, identifying phenotypically distinct sub-types of alcohol users, investigating of candidate genes underlying experimental phenotypes for alcoholism, and testing mechanisms of action of alcoholism pharmacotherapies on clinically-relevant translational phenotypes, such as persons exhibiting positive-like alcohol effects or alcohol craving. Importantly, the field of human laboratory studies in addiction has progressed rapidly over the past decade and has built upon earlier findings of alcohol's neuropharmacological effects to advancing translational research on alcoholism etiology and treatment. To that end, the new generation of human laboratory studies has focused on applying new methodologies, further refining alcoholism phenotypes, and translating these findings to studies of alcoholism genetics, medication development, and pharmacogenetics. The combination of experimental laboratory approaches with recent developments in neuroscience and pharmacology has been particularly fruitful in furthering our understanding of the impact of individual differences in alcoholism risk and in treatment response. This review of the literature focuses on human laboratory studies of subjective intoxication, alcohol craving, anxiety, and behavioral economics. Each section discusses opportunities for phenotype refinement under laboratory conditions, as well as its application to translational science of alcoholism. A summary and recommendations for future research are also provided.

Research has consistently identified a family history of alcoholism as a risk factor for alcohol-related problems, and global positive expectancies have been found to moderate this association. High rates of comorbidity between alcohol use and anxiety disorders suggest that a family history of anxiety disorders may also increase risk. Further, expectations of negative reinforcement (e.g., tension reduction) have been found to moderate the influence of anxiety-related traits. The current study sought to extend previous research by examining the influence of parental history of alcoholism, anxiety disorders, and the combination, as predictors of alcohol-related problems. Expectancies of global positive changes and tension reduction were hypothesized to moderate the influence of parental history of alcoholism and anxiety, respectively.Direct interviews with parents assessed their history of alcoholism and anxiety for 144 offspring (ages 18-32; 53.5% male) creating four groups: those with a parental history of alcoholism (27.80%), anxiety (22.20%), both alcoholism and anxiety (33.30%), and no history of psychopathology (16.70%). Established measures assessed the offsprings'alcohol expectancies, alcohol use, and alcohol-related problems.Although expected interactions between parental alcoholism and global positive expectancies and between parental anxiety and tension-reduction expectancies were not found, global positive expectancies were associated with alcohol-related problems among the group with parental history of both alcoholism and anxiety.The results suggest that the relation between parental history of alcoholism and global positive expectancies observed in previous studies may be strongest among individuals with a comorbid parental history of alcohol and anxiety disorders. Incorporating expectancies into interventions targeting individuals with a comorbid parental history of alcohol and anxiety disorders may have utility.