Vaping Frequency Research Articles

The Vaping Teenager: Understanding the Psychographics and Interests of Adolescent Vape Users to Inform Health Communication Campaigns.

Background:Adolescent vaping continues to rise, yet little is known about teen vape users beyond demographics. Effective intervention requires a deeper understanding of the psychographics and interests of adolescent vape users to facilitate targeted communication campaigns.Methods:We analyzed the 2017-2018 weighted cross-sectional online survey data from Virginia high school students (N = 1594) to identify and describe subgroups of adolescents who vaped. Participants reported 30-day vape use, identification with 5 peer crowds (Alternative, Country, Hip Hop, Mainstream, Popular), social prioritization, agreement with personal values statements, social media and smartphone use, and television and event preferences. We compared vaping rates and frequency by peer crowd using a chi-square analysis with follow-up testing to identify higher-risk crowds and confirmed associations using binary and multinomial logistic regression models with peer crowd scores predicting vaping, controlling for demographics. We then used chi-square and t tests to describe the psychographics, media use, and interests of higher-risk peer crowds and current vape users within those crowds.Results:Any current vaping was the highest among those with Hip Hop peer crowd identification (25.4%), then Popular (21.3%). Stronger peer crowd identification was associated with increased odds of any current vaping for both crowds, vaping on 1 to 19 days for both crowds, and vaping on 20 to 30 days for Hip Hop only. Compared with other peer crowds and non-users, Hip Hop and Popular youth and current vape users reported greater social prioritization and agreement with values related to being social and fashionable. Hip Hop and Popular youth and current vape users reported heavy Instagram and Snapchat use, as well as unique television show and event preferences.Conclusions:Hip Hop and Popular adolescents are most likely to vape and should be priority audiences for vaping prevention campaigns. Findings should guide the development of targeted health communication campaigns delivered via carefully designed media strategies.

Associations between vaping and relapse to smoking: preliminary findings from a longitudinal survey in the UK

BackgroundMost smokers attempting to quit relapse. There is little evidence whether the use of e-cigarettes (‘vaping’) increases or decreases relapse. This study aimed to assess 1) whether vaping predicted relapse among ex-smokers, and 2) among ex-smokers who vaped, whether vaping characteristics predicted relapse.MethodsLongitudinal web-based survey of smokers, recent ex-smokers and vapers in the UK, baseline in May/June 2016 (n = 3334), follow-up in September 2017 (n = 1720). Those abstinent from smoking ≥ 2 months at baseline and followed up were included. Aim 1: Relapse during follow-up was regressed onto baseline vaping status, age, gender, income, nicotine replacement therapy use and time quit smoking (n = 374). Aim 2: Relapse was regressed onto baseline vaping frequency, device type, nicotine strength and time quit smoking (n = 159).ResultsOverall, 39.6% relapsed. Compared with never use (35.9%), past/ever (45.9%; adjOR = 1.13; 95% CI, 0.61–2.07) and daily vaping (34.5%; adjOR = 0.61; 95% CI, 0.61–1.89) had similar odds of relapse, for non-daily vaping evidence of increased relapse was inconclusive (65.0%; adjOR = 2.45; 95% CI, 0.85–7.08). Among vapers, non-daily vaping was associated with higher relapse than daily vaping (adjOR = 3.88; 95% CI, 1.10–13.62). Compared with modular devices (18.9% relapse), tank models (45.6%; adjOR = 3.63; 95% CI, 1.33–9.95) were associated with increased relapse; evidence was unclear for disposable/cartridge refillable devices (41.9%; adjOR = 2.83; 95% CI, 0.90–8.95). Nicotine strength had no clear association with relapse.ConclusionRelapse to smoking is likely to be more common among ex-smokers vaping infrequently or using less advanced devices. Research into the effects of vaping on relapse needs to consider vaping characteristics.

Flavored E-cigarette Use and Progression of Vaping in Adolescents.

Electronic cigarettes (e-cigarettes) are available in nontraditional flavors (eg, fruit and candy) that are banned in combustible cigarettes in the United States. Whether adolescent use of e-cigarettes in nontraditional flavors prospectively predicts continuation of vaping and progression to more frequent vaping is unknown. High school students in Los Angeles, California, completed 5 semiannual surveys (2014-2017 [10th grade to 12th grade]). Among past-6-month e-cigarette users at survey waves 1 to 4 (N = 478), e-cigarette flavor (or flavors) used was coded into 2 mutually exclusive categories at each wave (use of ≥1 nontraditional flavors [fruit, candy, sweet or dessert, buttery, blends or combinations, and other] versus exclusive use of tobacco, menthol or mint, or flavorless). Flavor used during waves 1 to 4 was modeled as a time-varying, time-lagged regressor of vaping status and frequency outcomes 6 months later at waves 2 to 5. Across waves 1 to 4, there were 739 (93.8%) observations of nontraditional-flavor use and 49 (6.2%) observations of exclusive use of tobacco, mint or menthol, or flavorless e-cigarettes. Use of e-cigarettes in nontraditional flavors (versus only tobacco, mint or menthol, or flavorless) was positively associated with vaping continuation (64.3% vs 42.9%; adjusted odds ratio = 3.76 [95% confidence interval 1.20 to 10.31]) and past-30-day number of puffs per nicotine vaping episode (mean: 3.1 [SD 5.5] vs 1.5 [SD 3.8]; adjusted rate ratio = 2.41 [95% confidence interval 1.08 to 5.92]) 6 months later. Flavor used was not associated with the subsequent number of past-30-day vaping days or episodes per day. Adolescents who vaped e-cigarettes in nontraditional flavors, compared with those who exclusively vaped tobacco-flavored, mint- or menthol-flavored, or flavorless e-cigarettes, were more likely to continue vaping and take more puffs per vaping occasion 6 months later.

High school students’ use of flavored e-cigarette e-liquids for appetite control and weight loss

BackgroundAlthough 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. MethodsThe 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. ResultsAdolescent 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 < 0.01). Vaping candy-flavored e-liquids (OR = 1.16, p = 0.02) uniquely was associated with vaping for appetite control. ConclusionsA 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.

E-cigarette users commonly stealth vape in places where e-cigarette use is prohibited

Introduction‘Stealth vaping’ is the practice of vaping discreetly in places where electronic cigarette (e-cigarette) use is prohibited. While anecdotal evidence suggests that stealth vaping is common, there have been no...

Fine and ultrafine particles concentrations in vape shops

Vape shops are widespread due to the popularity of electronic cigarettes (e-cigs) as an alternative to tobacco cigarettes. In this study, sixty-seven Southern California vape shops were randomly surveyed for building characteristics, ventilation, and business patterns. Based on the survey results, six representative shops were recruited for real-time measurements of indoor and outdoor fine and ultrafine particles concentrations on a busy and less busy day. Occupancy, vaping frequency, and opening and closing of doors were recorded, and shop air exchange rate was determined. Indoor CO2, relative humidity, and temperature were also recorded. In addition, simultaneous measurements were taken at increasing distances away from a vaping area to assess the mixing and spatial profiles of particle levels inside the shops. During active vaping, real-time indoor particle number concentration and gravimetric-corrected PM2.5 mass concentration across the six vape shops varied from 1.3 × 104 to 4.8 × 105 particles/cm3 and from 15.5 to 37,500 μg/m3, respectively. The spatial profiles of particle number and mass were more uniformly mixed than expected in an indoor environment. Total vaping frequency was the main predictor of particle concentrations inside the vape shops when indoor-outdoor particle mass transfer is minimal (doors closed).

Clinical periodontal status and gingival crevicular fluid cytokine profile among cigarette-smokers, electronic-cigarette users and never-smokers

ObjectiveThere are no studies that have compared the clinical (plaque index [PI], bleeding on probing [BOP], probing depth [PD], clinical attachment loss [AL] and number of missing teeth [MT]) and radiographic (marginal bone loss [MBL]) periodontal parameters and gingival crevicular fluid (GCF) levels of proinflammatory cytokines among cigarette-smokers and individuals using electronic-cigarettes. The aim was to compare the clinical periodontal status and gingival crevicular fluid (GCF) cytokine profile among cigarette-smokers (Group-1), electronic-cigarette users (Group-2) and never-smokers (Group-3). MethodsDemographic data including age, gender, duration and reason for smoking and vaping and daily frequency and duration of smoking and vaping was collected using a questionnaire. Clinical (PI, BOP, PD and clinical AL) and radiographic (MBL) periodontal parameters were recorded. The volume of collected GCF was determined and levels of interleukin (IL) 1β, IL-6, tumour-necrosis-factor-alpha (TNF-α), matrix metalloproteinase (MMP)-8 and interferon-gamma (IFN-γ) were investigated. ResultsForty-six cigarette-smokers, 44 electronic-cigarette users and 45 never-smokers were included in groups 1–3, respectively. Mean scores of PI (P < 0.05), PD (P < 0.05) and clinical AL (P < 0.05) were significantly higher among individuals in Group-1 than Group-3. Compared with groups 1 (P < 0.05) and 2 (P < 0.05), BOP was more often manifested among patients in Group-3. Compared with Group-3, MBL was significantly higher in groups 1 (P < 0.01) and 2 (P < 0.01). GCF volume was significantly higher in Group-1 compared with groups 2 and 3. The concentrations of IL-1β, IL-6, IFN-γ, TNF-α and MMP-8 were significantly higher in the GCF samples of individuals in Group-1 (P < 0.05) than groups 2 and 3. ConclusionPeriodontal status is poorer and GCF levels of proinflammatory cytokines are higher in cigarette-smokers compared with electronic-cigarette smokers and never-smokers. However, the probability of increased periodontal inflammation and GCF proinflammatory cytokine levels in electronic-cigarette users than never-smokers cannot be annulled.

Dependence motives of young adult users of electronic nicotine delivery systems

IntroductionNicotine dependence contributes to changes in tobacco use among young adults. However, research examining salient dependence motives in young adult users of electronic nicotine delivery systems (ENDS) is limited. This study examined the association of dependence motives with ENDS use or lifetime quit attempt, and tests sex moderation in these relationships. MethodsYoung adults (N = 304; age 18–24) self-identifying as regular ENDS users and self-reporting vaping within a week of data collection completed an online survey. They reported demographics, past 30-day vaping and smoking days and frequency, and lifetime quit attempt. Dependence motives were measured with the 14-item Wisconsin Inventory for Smoking Dependency Motives. Backward-stepwise models regressed ENDS use behaviors or lifetime quit attempt onto dependence motives, and separately onto the interactions between motives and sex. ResultsTolerance was positively associated with daily ENDS use frequency (b = 0.34, p < .001) past 30-day vaping days (b = 1.50, p < .001), and negatively associated with lifetime quit attempt (OR = 0.58, p = .005). Loss of control was negatively associated with daily ENDS use frequency (b = −0.24, p < .001) and past 30-day vaping days (b = −1.48, p < .001), and positively associated with lifetime quit attempt (OR = 1.56, p = .002). Craving was positively associated with lifetime quit attempt among males (OR = 1.83, p = .006) but not females (OR = 0.84, p = .51). ConclusionTolerance and loss of control were uniquely associated with ENDS use behaviors across all analytic models, although effects for loss of control were counter-intuitive. Understanding the relationship between dependence motives and ENDS use behaviors or attempts to quit may provide targets for preventive interventions against increased or continued ENDS use.

154. Vaping Flavors Associated With Vaping Dependence Symptoms Among Adolescents: A Pilot Study Using Vape Factor Fast Find (VF3)

Health risks associated with vaping depend on the vaporized substance, of which thousands of different vaping liquids, oils, and waxes are commercially available. Some vaping materials contain nicotine and others contain psychoactive ingredients such as tetrahydrocannabinol. Most vaping materials contain flavorings which may range from strawberries and cream to bubble gum. The presence of nicotine, THC, and various flavorings in vaping materials may present differential risk for developing symptoms of dependence. This study prototyped a new survey instrument –Vape Factor Fast Find (VF3) –designed to record the specific materials vaped by adolescents. We assessed the association between specific vaping materials and symptoms of vaping dependence. Our study population was an online convenience sample of 225 youth ages 16-18 who used vaping devices. Participants were invited to participate through postings in online gaming sites. Participants completed a 12-minute survey of vaping and smoking behaviors and selected their specific brand and model of vaping device from a list of 613 devices using a rapid-indexing cascading list. They selected their specific brand and flavor of vaping liquid from a list of more than 3000 liquids using a similar method. Participants completed the 10-question Penn State Vaping Dependence Scale. We calculated the average completion time for the VF3 questions and reported the most frequently used devices and liquids. We assessed the association between the count of vaping dependence symptoms and specific vaping products using poisson regression, adjusting for tobacco smoking behavior, age, sex, race/ethnicity, the nicotine content of the vaping liquid, and vaping frequency. We used a jackknife procedure to assess this association with specific vaping flavors one-by-one. As a contrast, we used broad vaping liquid flavor categories such as those from the PATH Study to assess the association with dependence symptoms. The Boston University Medical Center IRB approved all study procedures. The study sample was aged 17.5±0.7 (mean±sd), 50% female, 10.5% Black, 17.8% Hispanic, 2.3% Asian, and 60.3% non-Hispanic White. 78.4% of participants found their vaping device in 25.6±26.3 seconds (median±IQR) and 71.6% found their vaping liquid brand and flavor in 22.7±18.3 seconds. Top device brands were Snow Wolf, BLU, and JUUL. Top vaping liquid brands were 180 Vape, The Flavor Apprentice, and Blackbeard Vapor. The materials vaped most frequently were liquids containing nicotine (46.2% of participants), liquids without nicotine (29.8%) and marijuana (15.6%). Vaping liquid flavors that were associated with increased vaping dependence symptoms included Ice Cream (rate ratio 2.53, 95% confidence interval 1.43-4.50), Spice (e.g., clove) (1.72, 1.20-2.45), Sugar (1.61, 0.81-3.19), Vanilla (1.58, 1.21-2.06), and Cake (1.48, 1.00-2.19). The broad flavor category “Candy, Desserts or Sweets” did not reveal an association with dependence symptoms, rate ratio 1.04 (0.91-1.20). Scientists require precise information about the vaping devices and materials used by adolescents to assess the health impacts of vaping. In this pilot study, the Vape Factor Fast Find (VF3) method showed promise as an efficient means of collecting precise information to avoid misclassification of the vaping exposures in future research studies.

Impact of cigarette smoking and vaping on the outcome of full-mouth ultrasonic scaling among patients with gingival inflammation: a prospective study.

There are no studies that have assessed the oral soft tissue response to full-mouth ultrasonic scaling (FMUS) among cigarette-smokers (CS) (group 1), individuals vaping electronic-cigarettes (E-cigs) (group 2), and never-smokers (NS) (group 3). The aim was to assess the impact of cigarette smoking and vaping on periodontal tissues following FMUS. In a clinical prospective study, 89 male individuals were divided into three groups: CS (group 1), E-cig users (group 2), and NS (group 3). A questionnaire was used to gather demographic data and information regarding duration and daily frequency of CS and vaping. Full-mouth plaque index (PI), bleeding on probing (BOP), clinical attachment loss (AL), and probing depth (PD) were measured at baseline and 3 and 6months after FMUS (without root surface debridement). Numbers of missing teeth (MT) were also recorded. In groups 1, 2, and 3, 30, 28, and 31 individuals, respectively were included. In group 1, there was no statistically significant difference in mean PI and PD and numbers of sites with PD ≥ 4mm at 6 months' follow-up compared with baseline and 3 months' follow-up. In groups 2 and 3, there was no significant difference in PI, BOP, and PD at 3 months' (P > 0.05) and 6-months' (P > 0.05) follow-up. There were no pockets with PD ≥ 4mm at 3 and 6months' follow-up in groups 2 and 3. There was no difference in the numbers of MT and none of the individuals exhibited clinical AL in all groups. Following FMUS, gingival inflammation is worse in CS compared with individuals vaping E-cigs and NS. Periodontal inflammatory parameters are worse in cigarette-smokers than individuals vaping electronic cigarettes and never-smokers following FMUS. However, these findings should be interpreted with extreme caution as a number of factors may have influenced the present results.

Development and psychometric validation of a novel measure of sensory expectancies associated with E-cigarette use

IntroductionE-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). MethodsIn 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. ResultsFactor 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. ConclusionsThe 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.

Assessing nicotine dependence in adolescent E-cigarette users: The 4-item Patient-Reported Outcomes Measurement Information System (PROMIS) Nicotine Dependence Item Bank for electronic cigarettes

BackgroundAdolescent 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. MethodsIn 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. ResultsThe 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. ConclusionAdolescent 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.

Psychometric Evaluation of the E-cigarette Dependence Scale.

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.

Associations of Electronic Cigarette Nicotine Concentration With Subsequent Cigarette Smoking and Vaping Levels in Adolescents

Research indicates that electronic cigarette (e-cigarette) use (vaping) among adolescents is associated with the initiation and progression of combustible cigarette smoking. The reasons for this association are unknown. To evaluate whether use of e-cigarettes with higher nicotine concentrations is associated with subsequent increases in the frequency and intensity of combustible cigarette smoking and vaping. In this prospective cohort study involving students from 10 high schools in the Los Angeles, California, metropolitan area, surveys were administered during 10th grade in the spring (baseline) and 11th grade in the fall (6-month follow-up) of 2015 to students who reported using e-cigarettes within the past 30 days and the nicotine concentration level they used at baseline. Self-report of baseline e-cigarette nicotine concentration of none (0 mg/mL), low (1-5 mg/mL), medium (6-17 mg/mL), or high (≥18 mg/mL) typically used during the past 30 days. Frequency of combustible cigarette smoking and e-cigarette use within the past 30 days (0 days [none], 1-2 days [infrequent], or ≥3 days [frequent]) and daily intensity of smoking and vaping (number of cigarettes smoked per day, number of vaping episodes per day, and number of puffs per vaping episode) at the 6-month follow-up. The analytic sample included 181 students (96 boys [53.0%] and 85 girls [47.0%]; mean [SD] age, 16.1 [0.4] years). Each successive increase in nicotine concentration (none to low, low to medium, and medium to high) vaped was associated with a 2.26 (95% CI, 1.28-3.98) increase in the odds of frequent (vs no) smoking and a 1.65 (95% CI, 1.09-2.51) increase in the odds of frequent (vs no) vaping at follow-up after adjustment for baseline frequency of smoking and vaping and other relevant covariates. Use of e-cigarettes with high (vs no) nicotine concentration was associated with a greater number of cigarettes smoked per day at follow-up (adjusted rate ratio [RR], 7.03; 95% CI, 6.11-7.95). An association with a significantly greater number of vaping episodes per day was found with use of low (adjusted RR, 3.32; 95% CI, 2.61-4.03), medium (adjusted RR, 3.32; 95% CI, 2.54-4.10), and high (adjusted RR, 2.44; 95% CI, 1.63-3.24) nicotine concentrations (vs no nicotine) at baseline. Similar results were found for the number of puffs per vaping episode for low (adjusted RR, 2.05; 95% CI, 1.41-2.70), medium (adjusted RR, 3.39; 95% CI, 2.66-4.11), and high (adjusted RR, 2.23; 95% CI, 1.42-3.03) nicotine concentrations. The results of this study provide preliminary evidence that use of e-cigarettes with higher nicotine concentrations by youths may increase subsequent frequency and intensity of smoking and vaping.

Predictors of Adult E-Cigarette Users Vaporizing Cannabis Using E-Cigarettes and Vape-Pens

ABSTRACTBackground: Given limited extant research, we assessed the use of portable, battery-powered cannabis vaporizers by adult e-cigarette users. Methods: 522 adult vapers completed an online survey. Demographics; lifetime and past-month cannabis vaporization via e-cigarettes/vape-pens; preferences for hash oil, D-9-tetrahydrocannabinol (THC) wax, or dried buds; and cannabis vaporization beliefs and motives were examined. Demographics, age of e-cigarette onset, e-cigarette use frequency, state-level legal status of cannabis, cannabis vaping beliefs/motives, and impulsivity were examined as predictors of lifetime cannabis vaporization, past-month cannabis vaporization, and cannabis vaping frequency. Results: E-cigarette users reported lifetime (17.8%) and past-month (11.5%) cannabis vaporization. Vapers preferred hash oil (LT/PM 45.5/47.5%), THC wax (15.2/32.2%), and dried buds (39.4/35.6%). Motivations to vape cannabis included: it tastes better (39.3/37.9%), is healthier (42.9/39.7%), is easier to conceal/hide (35.7/46.6%), does not smell as strong (42.9/39.7%), is more convenient (42.9/27.6%), and produces a stronger/better high (58.1/40.7%) than smoking cannabis. Lifetime and past-month cannabis vaporization, respectively, were associated with initiating e-cigarette use at an earlier age (odds ratio (OR) = 0.09/0.88), being impulsive (OR = 2.25/3.23), having poor self-control (OR = 2.23/1.85), and vaporizing cannabis because it is easier to conceal/hide (OR = 2.45/2.48) or is more convenient than smoking cannabis (OR = 5.02/2.83). Frequency of vaping cannabis was associated with heavier e-cigarette use (ηp2 = 0.10) and impulsivity (ηp2 = 0.09). Conclusions: Adult e-cigarette users are vaporizing cannabis using e-cigarettes/vape-pens. Efforts to curb cannabis vaporization may benefit from targeting impulsivity in users and regulating device features that facilitate or promote convenient, inconspicuous cannabis use.

Vaping to lose weight: Predictors of adult e-cigarette use for weight loss or control

IntroductionSome 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. MethodsAdult 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. ResultsParticipants 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]). ConclusionsA 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 naïve 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 Short Form Vaping Consequences Questionnaire: Psychometric Properties of a Measure of Vaping Expectancies for Use With Adult E-cigarette Users.

E-cigarettes are popular in the United States, but psychometrically sound measures of vaping beliefs and behaviors are lacking. We evaluated the psychometrics of the Short Form Vaping Consequences Questionnaire (S-VCQ), a modified version of the Short Form Smoking Consequences Questionnaire that assesses expectancies for negative consequences, positive reinforcement, negative reinforcement, and appetite/weight control associated with vaping. Adult, past-month e-cigarette users completed an anonymous survey in Fall 2015 (N = 522, 50.4% female; 71.5% white; 34.10 [SD = 9.66] years). Psychometric analyses included confirmatory factor analysis, internal consistency, measurement invariance, t tests, correlations, and test-criterion relationships with vaping outcomes. The S-VCQ evidenced a four-factor latent structure (Bentler's Comparative Fit Index = .95, Root Mean Square Error of Approximation = .05, Standardized Root Mean Square Residual = .06), and subscales evidenced internal consistency (mean α = 0.89). S-VCQ scores were scalar invariant for sex and smoking status; women reported stronger appetite/weight control than men and dual cigarette/e-cigarette users (n = 309) reported stronger negative vaping consequences and negative reinforcement than nonsmokers. Among dual users, vaping and smoking expectancies also were scalar invariant; dual users reported stronger positive reinforcement associated with vaping than smoking but stronger negative consequences, negative reinforcement, and appetite/weight control associated with smoking than vaping. Correlations indicated that vaping and smoking expectancies were related, yet distinct constructs. Univariate general linear models indicated that vaping frequency and dependence were associated with positive reinforcement (ηp2 = .02/.02), negative reinforcement (ηp2 = .02/.08), and appetite/weight control (ηp2 = .02/.02) from vaping. The S-VCQ evidences solid psychometrics as a measure of adult e-cigarette users' vaping expectancies. The current study provides evidence for the reliability and validity of the S-VCQ, the first measure of vaping expectancies that has been validated for use with adult e-cigarette users. Results indicated that the S-VCQ comprises four subscales that evidence internal consistency, scalar measurement invariance for important groups of interest, and test-criterion relationships with vaping outcomes. Researchers are encouraged to consider using this measure for assessing vaping expectancies in adult e-cigarette users.

Self-reported reasons for vaping among 8th, 10th, and 12th graders in the US: Nationally-representative results

ObjectiveThe study describes the most common reasons for using vaporizers (such as e-cigarettes) among US adolescents and investigates how reasons for use differ by grade, lifetime cigarette use, frequency of vaporizer use, gender, race/ethnicity, and parent education. MethodData were collected from 4066 students in the 8th, 10th, and 12th grades in 2015 as part of the Monitoring the Future study, a cross-sectional and nationally representative US survey. ResultsCommon reasons for vaporizer use reported by respondents who had ever used a vaporizer were experimentation (53.0%), taste (37.2%), boredom (23.5%), having a good time (22.4%), and relaxation (21.6%). Reasons differed little across grades or parent education; reasons differed by lifetime use of regular cigarettes, frequency of vaping, gender, and race/ethnicity. ConclusionsOverall, results suggest that decisions to vape are based on curiosity, taste, and pleasure, rather than for reasons such as quitting regular cigarettes or substituting for regular cigarette smoking.