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IntroductionIt is uncertain whether e-cigarettes facilitate smoking cessation in the real world. We aimed to understand whether and how transitions among cigarette, e-cigarette, and dual use are associated with sociodemographics, dependence measures, and biomarkers.Aims and methodsWe followed 380 adult daily cigarette users and dual users every 2 months for up to 2 years. We estimated transition rates between noncurrent, cigarette-only, e-cigarette-only, and dual use states using a multistate transition model. We estimated univariable hazard ratios (HR) for demographics, dependence measures for cigarettes and e-cigarettes, biomarkers, spousal or partner behaviors, and other measures.ResultsWe estimated that participants transitioned from cigarette-only to e-cigarette-only through a period of dual use. Dual users ceased smoking (transitioning to e-cigarette-only use) at a greater rate than cigarette-only users did (HR 2.44, 95% CI: 1.49, 4.02). However, of the 60% of dual users estimated to transition to single product use in 1 year, 83% would transition to cigarette-only use and only 17% to e-cigarette-only use. E-cigarette dependence measures were generally associated with reduced e-cigarette cessation rather than enhanced cigarette cessation. E-cigarette users motivated by harm or toxicity reduction or because of restrictions on where or when they could smoke had reduced rates of smoking relapse. Cigarette dependence and spousal smoking were barriers to cigarette cessation for dual users, while using e-cigarettes first in the morning, motivation to quit smoking, and sensory, social, and emotional enjoyment of e-cigarettes (secondary dependence motives) were facilitators of smoking cessation among dual users.ConclusionsTobacco control policy and interventions may be informed by the barriers and facilitators of product transitions.ImplicationsAlthough e-cigarettes have the potential to promote smoking cessation, their real-world impact is uncertain. In this cohort, dual users were more likely to quit smoking than cigarette-only users, but the overall impact was small because most dual users returned to cigarette-only use. Moreover, e-cigarette dependence promoted continued dual use rather than smoking cessation. Yet, high motivation to quit smoking and the sensory, social, and emotional enjoyment of e-cigarettes facilitated smoking cessation in dual users. Better understanding the barriers and facilitators of transitions can help to develop regulations and interventions that lead to more effective use of e-cigarettes for smoking cessation.

Tobacco smoking imposes a staggering burden on public health, underscoring the urgency of developing a deeper understanding of the processes that maintain addiction. Clinical and experience-sampling data highlight the importance of anxious withdrawal symptoms, but the underlying neurobiology has remained elusive. Mechanistic work in animals implicates the central extended amygdala (EAc)-including the central nucleus of the amygdala and the neighboring bed nucleus of the stria terminalis-but the translational relevance of these discoveries remains unexplored. Here we leveraged a randomized trial design, well-established threat-anticipation paradigm, and multidimensional battery of assessments to understand the consequences of 24-hour nicotine abstinence. The threat-anticipation paradigm had the expected consequences, amplifying subjective distress and arousal, and recruiting the canonical threat-anticipation network. Abstinence increased smoking urges and withdrawal symptoms, and potentiated threat-evoked distress, but had negligible consequences for EAc threat reactivity, raising questions about the translational relevance of prominent animal and human models of addiction. These observations provide a framework for conceptualizing nicotine abstinence and withdrawal, with implications for basic, translational, and clinical science.

Objective: The aim of this study was to examine dependence on combustible and e-cigarettes among users of both products (dual users), which may provide important insights into long-term use patterns. Method: Dual users (smoking daily for 3 months, using e-cigarettes at least once/week for the past month; N = 256; 45% women, 71% White, M age 39.0 years) not interested in quitting either product participated in a longitudinal, 2-year, observational study. At baseline, participants completed measures of combustible and e-cigarette dependence (Fagerström Test of Cigarette Dependence [FTCD], e-FTCD, Wisconsin Inventory of Smoking Dependence Motives [WISDM], e-WISDM, Penn State Cigarette Dependence Index, and Penn State E-Cigarette Dependence Index) and carried a study smartphone for 2 weeks to record cigarette and e-cigarette use events. Results: Most measures of dependence were product specific (e.g., FTCD and e-FTCD were not correlated, r = -0.003) and predicted product-specific outcomes (e.g., long-term use of that product). However, individuals used the two products for some of the same secondary dependence motives (e.g., weight control, cognitive and affective enhancement). These secondary, or instrumental, motives predicted use of both products at 1 year. Which product was used first in the morning was strongly related to product dependence scores and likelihood of continued product use at 1 year. Conclusions: Among dual users of combustible and e-cigarettes, measures of e-cigarette and cigarette dependence tended to be unrelated to one another, but dual users tended to use both products for the same instrumental motives. Which product is used first in the morning may serve as a valuable measure of relative dependence on the two products. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

BackgroundThe changing prevalence and patterns of tobacco use, the advent of novel nicotine delivery devices, and the development of new biomarkers prompted an update of the 2002 Society for Research on Nicotine and Tobacco (SRNT) report on whether and how to apply biomarker verification for tobacco use and abstinence.MethodsThe SRNT Treatment Research Network convened a group of investigators with expertise in tobacco biomarkers to update the recommendations of the 2002 SNRT Biochemical Verification Report.ResultsBiochemical verification of tobacco use and abstinence increases scientific rigor and is recommended in clinical trials of smoking cessation, when feasible. Sources, appropriate biospecimens, cutpoints, time of detection windows and analytic methods for carbon monoxide, cotinine (including over the counter tests), total nicotine equivalents, minor tobacco alkaloids, and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol are reviewed, as well as biochemical approaches to distinguishing cigarette smoking from use of electronic nicotine delivery devices (ENDS).ConclusionsRecommendations are provided for whether and how to use biochemical verification of tobacco use and abstinence. Guidelines are provided on which biomarkers to use, which biospecimens to use, optimal cutpoints, time windows to detection, and methodology for biochemical verifications. Use of combinations of biomarkers is recommended for assessment of ENDS use.ImplicationsBiochemical verification increases scientific rigor, but there are drawbacks that need to be assessed to determine whether the benefits of biochemical verification outweigh the costs, including the cost of the assays, the feasibility of sample collection, the ability to draw clear conclusions based on the duration of abstinence, and the variability of the assay within the study population. This paper provides updated recommendations from the 2002 SRNT report on whether and how to use biochemical markers in determining tobacco use and abstinence.

BackgroundElectronic 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.MethodsAdult 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.ResultsThe e-WISDM subscales had the highest internal consistency (α = .81-.96), then the PS-ECDI (α = .74) and e-FTCD (α = .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.ConclusionsThe 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.ImplicationsThis 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.

BackgroundDual use of combustible and electronic cigarettes (e-cigarettes) is a growing use pattern; more than half of e-cigarette users are dual users. However, little is known regarding the course of dual use; for example, the likelihood of discontinuation of either combustible or e-cigarettes or both.MethodsAdult daily smokers and dual users (daily smokers who also vaped at least once per week) who did not intend to quit use of either product in the next 30 days participated in a longitudinal, observational study (N = 322, 51.2% women, 62.7% white, mean age = 42.27 [SD = 14.05]). At baseline, participants completed demographics and smoking and vaping history assessments. They also reported daily cigarette and e-cigarette use via timeline follow-back assessment and provided a breath sample for carbon monoxide assay at 4-month intervals for 1 year.ResultsOf those who completed the year 1 follow-up, 1.9% baseline smokers and 8.0% dual users achieved biochemically confirmed seven-day point-prevalence abstinence from combustible cigarettes (χ2 = 4.57, p = .03). Of initial dual users, by 1 year 43.9% were smoking only, 48.8% continued dual use, 5.9% were vaping only, and 1.4% abstained from both products. Among baseline smokers, 92.3% continued as exclusive smokers. Baseline dual users who continued e-cigarette use were more likely to be white and report higher baseline e-cigarette dependence.ConclusionsIn this community sample, the majority of dual users transitioned to exclusive smoking. A higher percentage of dual users quit smoking than smokers, but attrition and baseline differences between the groups compromise strong conclusions. Sustained e-cigarette use was related to baseline e-cigarette dependence.ImplicationsThis research suggests that dual use of combustible and e-cigarettes is not a sustained pattern for the majority of dual users, but it is more likely to be a continued pattern if the user is more dependent on e-cigarettes. There was evidence that dual users were more likely to quit smoking than exclusive smokers, but this may be due to factors other than their dual use.

IntroductionThe availability of electronic cigarettes (e-cigarettes) has profoundly changed the tobacco product landscape. In the United States, almost 6 million adults use both combustible and e-cigarettes (ie, dual users). The goal of this study was to understand how smokers and dual users differ in terms of demographics, cigarette dependence, and exposure to carcinogens.MethodsAn observational cohort (smokers, n = 166, ≥5 cigarettes/day for 6 months and no e-cigarette use in 3 months; dual users, n = 256, smoked daily for 3 months and used e-cigarettes at least once/week for the past 3 months) completed baseline assessments of demographics, tobacco use, and dependence. They also provided breath samples for carbon monoxide (CO) assay and urine samples for cotinine, 3-hydroxycotinine, and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) assays.ResultsCompared to smokers, dual users (mean e-cigarette use = 5.5 days/week [SD = 1.9]) were significantly younger and more likely to be white, have more education, report a history of psychiatric co-morbidity, and smoke fewer cigarettes per day. There were no differences in CO, cotinine, or 3-hydroxycotinine levels; however, dual users had significantly lower levels of NNAL than did smokers. Most smokers and dual users had no plans to quit smoking within the next year; 91% of dual users planned to continue using e-cigarettes for at least the next year.ConclusionsIn this community sample, dual users are supplementing their smoking with e-cigarette use. Dual users, versus smokers, smoked fewer cigarettes per day and delayed their first cigarette of the day, but did not differ in quitting intentions.ImplicationsThis comparison of a community sample of established dual users and exclusive smokers addresses key questions of dependence and health risks of dual use in real-world settings. Dual users were more likely to be white, younger, have more than a high school education and have a psychiatric history. Dual users also smoked significantly fewer cigarettes and had lower levels of NNAL (a carcinogen), but they did not differ from exclusive smokers in CO or cotinine levels, suggesting that they supplemented their nicotine intake via e-cigarettes.

Introduction The current study sought to examine trends in indicators of dependence for youth vaping and smoking during a period of rapid evolution in the e-cigarette market. Aims and Methods Data are from repeat cross-sectional online surveys conducted between 2017 and 2022 among youth aged 16–19 in Canada, England, and the United States (US). Participants were 23 145 respondents who vaped and/or smoked in the past 30 days. Four dependence indicators were assessed for smoking and vaping (perceived addiction, frequent strong urges, time to first use after waking, days used in past month) and two for vaping only (use events per day, e-cigarette dependence scale). Regression models examined differences by survey wave and country, adjusting for sex, age, race, and exclusive/dual use. Results All six indicators of dependence increased between 2017 and 2022 among youth who vaped in the past 30 days (p  < .001 for all). For example, more youth reported strong urges to vape at least most days in 2022 than in 2017 (Canada: 26.5% to 53.4%; England: 25.5% to 45.4%; US: 31.6% to 50.3%). In 2017, indicators of vaping dependence were substantially lower than for smoking; however, by 2022, youth vaping was associated with a greater number of days used in the past month (Canada, US), shorter time to first use (all countries), and a higher likelihood of frequent strong urges (Canada, US) compared to youth smoking. Conclusions From 2017 to 2022, indicators of vaping dependence increased substantially. By 2022, vaping dependence indices were comparable to those of smoking. Implications Indicators of vaping dependence among youth have increased substantially since 2017 to levels that are comparable to cigarette dependence among youth who smoke. Future research should examine factors underlying the increase in dependence among youth who vape, including changes to the nicotine profile and design of e-cigarette products. [ABSTRACT FROM AUTHOR]

Introduction The Fagerström test for cigarette dependence (FTCD) and Brief Wisconsin index of smoking dependence motives (WISDM) are widely used measures of smoking dependence. The FTCD was previously found to have 1-factor and 2-factor structures and Brief WISDM has been found to have an 11-factor and 11-factor hierarchical structure. As such, the current study sought to further investigate the psychometric properties of the FTCD and Brief WISDM with a novel criminal-legal system-involved sample using both a factor-analytic and an item response theory (IRT) approach. Aims and Methods Data from 517 criminal-legal system-involved adults (ie, 18 years of age or older) who smoke from Alabama, USA were analyzed. Confirmatory factor analyses (CFA) were conducted on 1-factor and 2-factor structures of the FTCD and 1-factor, 11-factor, and 11-factor hierarchical structures of the Brief WISDM. IRT analyses investigating item discrimination and threshold parameters were also conducted on the brief WISDM. Results The CFA showed poor fit for a single-factor structure and mixed results for two 2-factor results for the FTCD. CFA also showed poor fit for a single-factor and mixed results for the 11-factor model. Initial IRT investigations using the 11-factor model showed strong item discrimination, but non-ordered threshold parameters. Conclusions Two-factor structures for the FTCD and the 11-factor model for the Brief WISDM were partially supported in a criminal-legal population, suggesting continued support for the multidimensional structure of the measures. Additionally, exploratory IRT analyses suggested good discrimination across the use spectrum for the Brief WISDM. Implications The Fagerström Test of Cigarette Dependence (FTCD) and Brief Wisconsin index of smoking dependence motives (WISDM) are two widely used measures of nicotine dependence, though previous research has shown mixed results for their internal consistency and factor structure. The current study used a unique sample of criminal-legal-involved participants who generally have moderate to high levels of nicotine dependence. The current study found that the FTCD displayed poor internal consistency, a poor fit for a single-factor model, but mixed support for two two-factor models. The Brief-WISDM was found to have strong internal consistency, a poor fit for a single-factor model, but mixed fit for an 11-factor model and good item discrimination. [ABSTRACT FROM AUTHOR]

The prevalence of e‐cigarette use among young adults in the USA is high (14%). Although the majority of users plan to quit vaping, the motivation to make a quit attempt is low and available support during a quit attempt is limited. Using wearable sensors to collect physiological data (eg, heart rate) holds promise for capturing the right timing to deliver intervention messages. This study aims to fill the current knowledge gap by proposing statistical methods to (1) de‐noise beat‐to‐beat interval (BBI) data from smartwatches worn by 12 young adult regular e‐cigarette users for 7 days; and (2) summarize the de‐noised data by event and control segments. We also conducted a comprehensive review of conventional methods for summarizing heart rate variability (HRV) and compared their performance with the proposed method. The results show that the proposed singular spectrum analysis (SSA) can effectively de‐noise the highly variable BBI data, as well as quantify the proportion of total variation extracted. Compared to existing HRV methods, the proposed second order polynomial model yields the highest area under the curve (AUC) value of 0.76 and offers better interpretability. The findings also indicate that the average heart rate before vaping is higher and there is an increasing trend in the heart rate before the vaping event. Importantly, the development of increasing heart rate observed in this study implies that there may be time to intervene as this physiological signal emerges. This finding, if replicated in a larger scale study, may inform optimal timings for delivering messages in future intervention. [ABSTRACT FROM AUTHOR]

Objective: E-cigarette use among college students has increased drastically in recent years. This study aims to inform development of a comprehensive measure of e-cigarette consumption for this population. Participants and Methods: This mixed-method study collected both quantitative and qualitative data from a sample of 43 experienced e-cigarette users from two college campuses, using a self-reported questionnaire and a semi-structured interview, in March-September of 2017. Results: College student users found some of the consumption questions in national surveys difficult to answer. Switching nicotine levels, mixing flavors, co-using with alcohol and marijuana, using with others who were vaping or drinking, and vaping in a car or indoor space were all common. The participants defined "regular user" based on ownership of a device rather than on quantity/frequency. Conclusions: A comprehensive e-cigarette consumption measure should cover the complex characteristics that are potentially associated with negative health consequences such as flavorings, co-use and social contexts.

Aim: To compare effects of three post‐relapse interventions on smoking abstinence. Design: Sequential three‐phase multiple assignment randomized trial (SMART). Setting: Eighteen Wisconsin, USA, primary care clinics. Participants: A total of 1154 primary care patients (53.6% women, 81.2% White) interested in quitting smoking enrolled from 2015 to 2019; 582 relapsed and were randomized to relapse recovery treatment. Interventions: In phase 1, patients received cessation counseling and 8 weeks nicotine patch. Those who relapsed and agreed were randomized to a phase 2 relapse recovery group: (1) reduction counseling + nicotine mini‐lozenges + encouragement to quit starting 1 month post‐randomization (preparation); (2) repeated encouragement to quit starting immediately post‐randomization (recycling); or (3) advice to call the tobacco quitline (control). The first two groups could opt into phase 3 new quit treatment [8 weeks nicotine patch + mini‐lozenges plus randomization to two treatment factors (skill training and supportive counseling) in a 2 × 2 design]. Phase 2 and 3 interventions lasted ≤ 15 months. Measurements The study was powered to compare each active phase 2 treatment with the control on the primary outcome: biochemically confirmed 7‐day point‐prevalence abstinence 14 months post initiating phase 2 relapse recovery treatment. Exploratory analyses tested for phase 3 counseling factor effects. Findings Neither skill training nor supportive counseling (each on versus off) increased 14‐month abstinence rates; skills on versus off 9.3% (14/151) versus 5.2% (8/153), P = 0.19; support on versus off 6.6% (10/152) versus 7.9% (12/152), P = 0.73. Phase 2 preparation did not produce higher 14‐month abstinence rates than quitline referral; 3.6% (8/220) versus 2.1% [3/145; risk difference = 1.5%, 95% confidence interval (CI) = −1.8−5.0%, odds ratio (OR) = 1.8, 95% CI = 0.5–6.9]. Recycling, however, produced higher abstinence rates than quitline referral; 6.9% (15/217) versus 2.1% (three of 145; risk difference, 4.8%, 95% CI = 0.7–8.9%, OR = 3.5, 95% CI = 1.0–12.4). Recycling produced greater entry into new quit treatment than preparation: 83.4% (181/217) versus 55.9% (123/220), P < 0.0001. Conclusions: Among people interested in quitting smoking, immediate encouragement post‐relapse to enter a new round of smoking cessation treatment ('recycling') produced higher probability of abstinence than tobacco quitline referral. Recycling produced higher rates of cessation treatment re‐engagement than did preparation/cutting down using more intensive counseling and pharmacotherapy. [ABSTRACT FROM AUTHOR]