Dataset Information

Measuring E-cigarette dependence: Initial guidance.

ABSTRACT: E-cigarette use rates are increasing among youth and adults, despite limited knowledge about the safety, risks, and potential for this product in substituting for or reducing other tobacco use. Understanding how to characterize and assess e-cigarette dependence will be important for evaluating the public health impact of e-cigarettes and considering prevention and intervention strategies. To provide an initial review of constructs to consider when assessing e-cigarette dependence, a content expert group within the Tobacco Center for Regulatory Science (TCORS) Measurement Workgroup engaged in a review of published manuscripts and 12 tobacco dependence measures, followed by review of suggested dependence domains by a 10-person external subject-matter expert panel. The final domains selected to be considered in the development of a measure of e-cigarette dependence included: 1) Quantity and frequency of use, 2) Tolerance, 3) Perceived benefits, 4) Withdrawal symptoms, 5) Craving/urge to use, 6) Use despite harm, 7) Impaired control, 8) Automaticity, 9) Preferred over competing rewards, and 10) Sensory dependence. Similarities and differences in potential features of e-cigarette dependence compared with dependence on other tobacco products is discussed. Future work will evaluate these dependence items and constructs in a sample of e-cigarette users with a goal of developing a valid, brief, standardized measure of e-cigarette dependence.

SUBMITTER: Bold KW

PROVIDER: S-EPMC5807200 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Measuring E-cigarette dependence: Initial guidance.

Bold Krysten W KW Sussman Steve S O'Malley Stephanie S SS Grana Rachel R Foulds Jonathan J Fishbein Howard H Krishnan-Sarin Suchitra S

Addictive behaviors 20171122

E-cigarette use rates are increasing among youth and adults, despite limited knowledge about the safety, risks, and potential for this product in substituting for or reducing other tobacco use. Understanding how to characterize and assess e-cigarette dependence will be important for evaluating the public health impact of e-cigarettes and considering prevention and intervention strategies. To provide an initial review of constructs to consider when assessing e-cigarette dependence, a content expe ...[more]

PMID: 29174664

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Project description:IntroductionThe majority of e-cigarette vaping youth use nicotine when vaping. Some then become dependent on the nicotine, which can result in subsequent health effects. There has been limited evaluation of convergent validity of e-cigarette dependence measures for use specifically in youth. The aim of this study was to investigate and validate various e-cigarette dependence measures for use in youth populations.Aims and methodsOne thousand two hundred and five Canadian youth aged 16-24 who completed a cross-sectional online survey reported vaping at least monthly and were thus included in the analysis. E-cigarette dependence was assessed using a modified Penn State Electronic Cigarette Dependence Index (PS-ECDI), the E-Cigarette Dependence Scale (EDS), a self-perceived vaping dependence question, and time to first vape after waking. Internal consistency, convergent validity, and concurrent validity of the measures were assessed.ResultsBoth the PS-ECDI and the EDS exhibited a good degree of internal consistency (α = 0.8472 and 0.8405, respectively). All measures exhibited convergent validity against each other and against time to first vape upon waking (p < .001), as well as concurrent validity against vaping frequency and nicotine concentration (p < .001). The PS-ECDI was inferior to the EDS, self-perceived measure, and time from waking when predicting daily vaping frequency, but, along with the self-perceived measure, was superior to the EDS and time from waking when predicting monthly vaping.ConclusionsAll measures exhibit convergent and concurrent validity, as well as internal consistency. Depending on the needs of the study, it would be appropriate to use any of these measures when assessing e-cigarette dependence in adolescent and young-adult populations.ImplicationsThe PS-ECDI and the self-perceived measure are equally effective in predicting monthly vaping, but the self-perceived measure was superior in predicting daily vaping. Thus, the one-item self-perceived measure of dependence is appropriate for use and preferable to the 11-item PS-ECDI or the 4-item EDS in situations of limited time or where subjects are at risk of respondent fatigue, and is superior to time to first vape after waking to predict vaping frequency.

Project description:BACKGROUND:Electronic cigarettes (e-cigarettes) have drastically changed the nicotine and tobacco product landscape. However, their potential public health impact is still unclear. A reliable and valid measure of e-cigarette dependence would likely advance assessment and prognostication of the public health impact of e-cigarettes. The aim of this research was to examine the internal consistency, structure, and validity of three e-cigarette dependence scales. METHODS:Adult dual users (smokers who also vape, N = 256) enrolled in an observational cohort study (45.1% women, 70.7% white). At baseline, participants completed the e-cigarette Fagerström Test of Cigarette Dependence (e-FTCD), the e-cigarette Wisconsin Inventory of Smoking Dependence Motives (e-WISDM), and the Penn State Electronic Cigarette Dependence Index (PS-ECDI). All participants provided a urine sample for cotinine analysis and reported e-cigarette use at 1 year. RESULTS:The e-WISDM subscales had the highest internal consistency (? = .81-.96), then the PS-ECDI (? = .74) and e-FTCD (? = .51). A single-factor structure for the e-FTCD and an 11-factor structure for the e-WISDM were supported, but the PS-ECDI did not have a single-factor structure. All three e-cigarette dependence scales were highly correlated with validation criteria including continued e-cigarette use at 1 year, but not with e-liquid nicotine concentration or cotinine. CONCLUSIONS:The e-WISDM and PS-ECDI had stronger internal consistency than did the e-FTCD, despite the e-FTCD's single-factor structure, but all 3 measures appear to be valid measures of e-cigarette dependence as suggested by their significant relations with self-perceived addiction, heavy use, early use after overnight deprivation, and continued use over time. IMPLICATIONS:This research provides empirical support for three e-cigarette dependence measures: the e-FTCD, the PS-ECDI, and the e-WISDM among dual users of e-cigarettes and combustible cigarettes. The PS-ECDI and e-WISDM are more reliable, but all three measures were strongly correlated with key dependence constructs such as heavy use and continued use over time.

Project description: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.

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Measuring E-cigarette dependence: Initial guidance.

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Measuring E-cigarette dependence: Initial guidance.

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