Search

Introduction: Self-reports of electronic cigarette (ECIG) device and liquid characteristics are not always accurate or consistent with characteristics as measured by researchers. Two methods for measuring ECIG characteristics were compared: user self-reports and rater-coded pictures. Methods: Exclusive ECIG users (N = 321) reported on device (disposable, refillable, adjustable power, brand) and liquid (nicotine concentration, formulation, flavor) characteristics. To measure device type, they chose the term that best described their device ("cig-alike," "vape pen," "mod," "pod," "don't know") and the picture that best resembled their device (cig-alike, vape pen, box mod, USB-shaped pod, teardrop-shaped pod, none). Respondents uploaded device and liquid pictures, and independent raters coded these same features. Agreement between methods was examined with Cohen's kappa and intra-class correlations, including with "don't know" responses included and excluded from analyses. Results: Regardless of how "don't know" responses were treated, agreement was highest for disposable (95.3–97.7%), refillable (96.3%), adjustable power (83.6–88.7%), and brand (77.9–80.4%), and lower for nicotine concentration (72.7%), nicotine formulation (58.6–79.4%), and flavor (66.2%). For device type, agreement was moderate using both term-based (67.9–78.8%) and picture resemblance-based (71.7%) items. For terms, the greatest discrepancy was for devices classified as "vape pens" by self-reports; of these, 70.6% were classified as "pods" by raters. For picture resemblance, ∼13% of users reported that their device resembled none of the pictures; raters classified these devices as USB-shaped pods (50.0%) and mods (23.8%). Conclusions: Self-reports may be sufficient for measuring some characteristics (brand, disposable, refillable, adjustable power), but not others (nicotine concentration and formulation, and some flavor). [ABSTRACT FROM AUTHOR]

Aims: This study aims to identify adolescent patterns of polytobacco use and measure transitions between patterns over time. Design: Longitudinal analysis using data derived from waves 1–4 (2013–18) of the Population Assessment of Tobacco and Health (PATH) study. Transitions in tobacco use patterns were examined via latent transition analysis, and then, socio‐demographic characteristics were used to predict transitions via logistic regression. Setting: United States. Participants: Participants included 975 adolescents who used at least one tobacco product at any wave (W1 mean age = 13.29, standard deviation = 0.86; W4 54.2% male; 54.5% white, 25.9% Hispanic). Measurements Measurements included past 30‐day use of cigarettes, electronic cigarettes (e‐cigs), traditional cigars, cigarillos, filtered cigars, snus, smokeless tobacco (SLT) or hookah. Findings Six latent classes were identified. Cigarette users (43.5–58.8%) and SLT users (50.8–79.6%) tended to persist in their use over time. E‐cig users began to probably transition to non‐users (80.0%), but became more likely to persist in this use over time (31.1%). Non‐users at a given wave were most likely to transition to e‐cig users (8.5–43.7%) or cigarette users (6.7–28.6%). Cigarillo/poly‐users and hookah/poly‐users displayed more variable transition patterns. Adolescents were more likely to transition to non‐use (versus become/remain e‐cig users) if they were older (cigarette users, SLT users), younger (e‐cig users), other race (SLT users), male (SLT users) or had less‐educated parents (SLT users) compared with their counterparts. Hispanic (versus white) cigarette users were more likely to transition to non‐users than to persist in this use. Conclusions: Among adolescents in the United States, patterns of tobacco use characterized by the use, mainly, of single, specific products appear to be stable, particularly by late adolescence. In contrast, patterns characterized by polytobacco use appear to be more variable and may represent experimentation without specialization. [ABSTRACT FROM AUTHOR]

Introduction: Extant electronic cigarette (ECIG) dependence measures are largely adapted from those designed for cigarette smoking, though few have been evaluated for their psychometric properties.Methods: Never-smoking ECIG users (N=134) participating in an online survey completed four dependence measures: Penn State Electronic Cigarette Dependence Index (PSECDI), E-Cigarette Dependence Scale (EDS-4), Diagnostic and Statistical Manual for Tobacco Use Disorder (DSM-5), and Glover Nilsson Behavioral Questionnaire (GNBQ). They also reported on their ECIG use characteristics (e.g. behaviors, reasons).Results: Internal consistency was highest for the EDS-4 (Cronbach's α = .88) followed by the GNBQ (α = .75), PSECDI (α = .72), and DSM (α = .71). Confirmatory factor analyses revealed a single-factor structure for the PSECDI, EDS-4, and GNBQ. For the DSM-5, however, two items did not load significantly (ECIG use interferes with responsibilities; reduce/give up activities because of ECIG use). Significant correlations were observed between all measures and the number of ECIG use days/week and/or years using ECIGs, as well as between DSM-5 scores and the number of ECIG quit attempts and initiation age. Endorsement of using ECIGs because "I like flavors" was correlated positively with DSM-5 and GNBQ scores.Conclusions: All dependence measures evaluated herein demonstrated adequate reliability and construct validity. Future work should focus on determining which aspects of dependence are those unique to ECIG use, and subsequently developing a more comprehensive measure of ECIG dependence.Implications: The measures assessed herein - PSECDI, EDS-4, DSM-5, and GNBQ - demonstrated adequate to good reliability and construct validity among a sample of never-smoking ECIG users. The dependence domains covered across measures were related yet distinct. Findings demonstrate the need for future evaluation of these different domains to determine which are the most salient characteristics of ECIG dependence. [ABSTRACT FROM AUTHOR]

• Internal consistency was acceptable for all four dependence measures. • CFA demonstrated a single factor structure for all four dependence measures. • Most dependence scores were correlated with ECIG initiation age or quit attempts. • Dependence scores were correlated significantly with few reasons for ECIG use. Measures designed to assess electronic cigarette (ECIG) dependence are largely adapted from those developed for cigarette smoking, and have been evaluated mainly among dual ECIG-cigarette users. The present study evaluated the psychometric properties of four dependence measures among former-smoking ECIG users. Respondents (N = 187) were daily ECIG users who had formerly smoked cigarettes regularly. As part of an online survey, they completed four dependence measures: Penn State Electronic Cigarette Dependence Index (PSECDI); E-Cigarette Dependence Scale (EDS-4); Glover Nilsson Behavioral Questionnaire (GNBQ); and Diagnostic and Statistical Manual 5th Edition (DSM-5). Confirmatory factor analysis revealed a single factor structure for all four measures, and fit indices were adequate for all models. Most inter-item correlations (mean r 's = 0.21 to 0.45) and all item-total correlations (mean r 's = 0.53 to 0.80) were significant for all four measures. Internal consistency was highest for the GNBQ (α = 0.86) followed by the EDS-4 (α = 0.81), PSECDI (α = 0.76), and DSM-5 (α = 0.75). Age of ECIG initiation was correlated negatively with scores on the EDS-4 and DSM-5, while number of ECIG quit attempts was correlated positively with scores on all measures except the GNBQ. Convergent validity was highest for comparisons between the EDS-4 and PSECDI (r = 0.75) though all comparisons were significant (p 's < 0.001). All four measures demonstrated acceptable reliability and validity in this sample of former-smoking ECIG users. [ABSTRACT FROM AUTHOR]

Introduction: ECIGs differ in their ability to deliver nicotine to the user and, consequently, they may differ in their ability to produce dependence. This study examined individual device characteristics, device type, and user behaviors as predictors of ECIG dependence in a sample of never-smoking ECIG users.Methods: Participants (N = 134) completed an online survey that assessed demographics, ECIG use behavior, and ECIG dependence as measured via the Penn State Electronic Nicotine Dependence Index (PSECDI) and E-cigarette Dependence Scale (EDS-4). Participants uploaded a picture of their personal ECIG device/liquid, which was coded by raters to identify product features. Multivariable linear regressions examined device characteristics (e.g., adjustable power, nicotine concentration) and device type (e.g., vape pen, mod, pod, modern disposable) as predictors of dependence controlling for demographics and user behaviors (e.g., ECIG use duration and frequency, other tobacco use).Results: Longer durations of ECIG use and more use days/week were associated significantly with higher PSECDI (β's = 0.91 and 1.90, respectively; p's < 0.01) and EDS-4 scores (β's = 0.16 and 0.28, respectively; p's < 0.01). Higher nicotine concentrations were associated with higher PSECDI scores only (β = 0.07, p =.011). Dependence scores did not differ as a function of ECIG device types after controlling for covariates.Conclusions: ECIG dependence was observed among the never-smoking ECIG users in this sample, regardless of their ECIG device/liquid features. Findings suggest that regulatory efforts aimed at reducing the dependence potential of ECIGs in never smokers should focus on overall nicotine emissions rather than product features. [ABSTRACT FROM AUTHOR]

Introduction: Electronic cigarettes (ECIGs) vary greatly in their ability to deliver nicotine, which suggests they may also vary in their ability to produce dependence. This study examined individual and combined ECIG device features, and also user behaviors, as predictors of dependence in never-smoking ECIG users. Methods Data were collected from 711 current ECIG users who had smoked <100 cigarettes in their lifetime at Wave 4 of the Population Assessment of Tobacco and Health (PATH) Study. Multivariable linear regressions examined individual (e.g., contains nicotine, uses a tank, flavor preference) and combined (e.g., refillable tanks, refillable mods) device features, and user behaviors (e.g., uses/day) as predictors of dependence, withdrawal, and craving after accounting for demographic variables. Results Results for ECIG dependence and craving showed a similar pattern; higher levels were observed for older age, more frequent past 30-day use, using an ECIG containing nicotine (vs no nicotine), and using a non-refillable cartridge or refillable tank style (vs disposables). Higher withdrawal levels were observed for higher education levels and individual device features of tank (vs no tank), cartridge (vs no cartridge), refillable (vs non-refillable), and "other" flavor preference (vs tobacco flavor). Lower withdrawal levels were associated with a preference for sweet/fruit flavor(s) (vs tobacco flavor). Conclusions Few use behaviors and device features, whether examined alone or in combination, predicted dependence-related outcomes. Findings underscore the challenge with regulating ECIG products in the current marketplace, which is inundated with a myriad of device types. [ABSTRACT FROM AUTHOR]

Introduction: Despite increases in adolescents' polytobacco use, little work has utilized recent national data to examine transitions in polytobacco use over time or predictors of such transitions.Methods: Data derived from the Population Assessment of Tobacco and Health (PATH) study. Participants used at least one tobacco product (cigarettes, electronic cigarettes [ECIGs], traditional cigars, cigarillos, filtered cigars, snus, smokeless tobacco [SLT], hookah) at Wave 3 (W3; 2015-2016) or 4 (W4; 2016-2018) and had Wave 1 (W1) data (N = 1072; Mage= 13.71, SD =1.71, 46.4% female; 56.6% White, 23.2% Hispanic). Latent transition analysis (LTA) examined probabilities of transitioning between classes across waves and sociodemographic correlates of transitions.Results: Five latent classes were identified: Low Users (n = 372, 34.7% W3; n = 249, 23.3% W4), ECIG Users (n = 256, 23.8% W3; n = 286, 26.7% W4), Cigarette Users (n = 215, 20.1% W3; n = 293, 27.3% W4), SLT Users (n = 91, 8.5% W3; n = 92, 8.6% W4), and Polytobacco Users (n = 138, 12.9% W3; n = 152, 14.1% W4). Cigarette Users and SLT Users displayed the highest probabilities for class stability. Low Users were most likely to transition to ECIG Users or Cigarette Users, whereas Polytobacco Users were most likely to transition to Low Users. ECIG Users were likely to transition to Low Users or Cigarette Users. Older and White adolescents were more likely to display riskier transitions.Conclusions: Although Cigarette Users and SLT Users remained largely stable in membership, findings demonstrate greater movement across polytobacco use classes relative to previous research and suggest that ECIGs may be the most common entry point to tobacco use. [ABSTRACT FROM AUTHOR]