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 ( = 321) reported on device (disposable, refillable, adjustable power, brand) and liquid (nicotine concentration, formulation, flavor) characteristics.
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.
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).
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; M= 13.
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.
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