Statistical methods for predicting e-cigarette use events based on beat-to-beat interval (BBI) data collected from wearable devices.

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.

Psychometric properties of the FTCD and Brief WISDM: support for validity in a legal-system-involved sample.

INTRODUCTION: The Fagerstrom Test of 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- 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. METHODS: Data from 517 criminal-legal system-involved adults (i.e., 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 Fagerstrom 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, 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, poor fit for a single-factor model, but mixed fit for an 11-factor model and good item discrimination.

The Impact of Three Alternate Nicotine-Delivery Products on Combusted Cigarette Use: A Randomized Controlled Trial.

INTRODUCTION: Smoking cessation is a critical public health goal. This study examined the ability of e-cigarettes and very low nicotine cigarettes (VLNCs) to serve as cigarette substitutes and whether a substitution was supported by steady-state nicotine from a nicotine patch. AIMS AND METHODS: This mixed design experiment with study product (between-subjects) and patch (within-subjects) factors recruited adults smoking cigarettes daily and not motivated to quit (N = 160). Participants were randomized to 4 weeks of: (1) VLNCs; (2) e-cigarettes; or (3) no product. During two switch weeks, one with an active nicotine patch and one with a placebo patch (in a double-blind and counterbalanced fashion), participants were told to not smoke their usual cigarettes. RESULTS: During the switch weeks, participants in the VLNC (M = 2.88, SD = .65) and e-cigarette (M = 3.20, SD = .63) groups smoked fewer of their own cigarettes per day than did no product group participants who continued to smoke their own cigarettes (M = 5.48, SD = .63); the VLNC and e-cigarette groups did not differ. There was no main effect of patch on mean usual brand cigarettes smoked per day (P = .09), nor was there a product × patch interaction (P = .51). There was a product × age interaction (P = .03); smokers aged 60-74 smoked more of their own cigarettes if they were randomized to no product group. CONCLUSIONS: VLNCs and e-cigarettes appear to reduce usual brand cigarettes smoked per day to a similar degree, regardless of patch condition. Behavioral factors, in addition to nicotine dependence, play an important role in sustaining smoking behavior and need to be addressed in smoking cessation treatment. IMPLICATIONS: This study found that behavioral substitutes for cigarettes, whether or not they delivered nicotine, reduced the number of usual brand cigarettes smoked. Specifically, both e-cigarettes delivering nicotine and VLNCs equally reduce usual brand cigarettes smoked among adults who smoke daily and do not want to quit.

Comparison of indicators of dependence for vaping and smoking: Trends between 2017 and 2022 among youth in Canada, England and the United States.

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. METHODS: Data are from repeat cross-sectional online surveys conducted between 2017 and 2022 among youth aged 16-19 in Canada, England, and the USA. 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%; USA: 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, USA), shorter time to first use (all countries), and a higher likelihood of frequent strong urges (Canada, USA) 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.

Subtypes of Dual Users of Combustible and Electronic Cigarettes: Longitudinal Changes in Product Use and Dependence Symptomatology.

INTRODUCTION: Cross-sectional surveys found behavioral heterogeneity among dual users of combustible and electronic cigarettes. Yet, prior classification did not reflect dynamic interactions between cigarette and e-cigarette consumption, which may reveal changes in product-specific dependence. The contexts of dual use that could inform intervention were also understudied. METHODS: This study conducted secondary analysis on 13 waves of data from 227 dual users who participated in a 2-year observational study. The k-means method for joint trajectories of cigarette and e-cigarette consumption was adopted to identify the subtypes of dual users. The time-varying effect model was used to characterize the subtype-specific trajectories of cigarette and e-cigarette dependence. The subtypes were also compared in terms of use contexts. RESULTS: The four clusters were identified: light dual users, predominant vapers, heavy dual users, and predominant smokers. Although heavy dual users and predominant smokers both smoked heavily at baseline, by maintaining vaping at the weekly to daily level the heavy dual users were able to considerably reduce cigarette use. Yet, the heavy dual users' drop in cigarette dependence was not as dramatic as their drop in cigarette consumption. Predominant vapers appeared to engage in substitution, as they decreased their smoking and increased their e-cigarette dependence. They were also more likely to live in environments with smoking restrictions and report that their use of e-cigarettes reduced cigarette craving and smoking frequency. CONCLUSIONS: Environmental constraints can drive substitution behavior and the substitution behavior is able to be sustained if people find the substitute to be effective. IMPLICATIONS: This study characterizes subtypes of dual users based on the dynamic interactions between cigarette use and e-cigarette use as well as product-specific trajectories of dependence. The subtypes differ in not only sociodemographic characteristics but also contexts of cigarette and e-cigarette use. Higher motivation to use e-cigarettes to quit smoking and less permissive environment for smoking may promote substitution of cigarettes by e-cigarettes.

Retrospective and Real-time Measures of the Quantity of E-cigarette Use: An Ecological Momentary Assessment Study.

INTRODUCTION: Quantifying e-cigarette use is challenging because of the wide variety of products and the lack of a clear, objective demarcation of a use event. This study aimed to characterize the difference between retrospective and real-time measures of the quantity of e-cigarette use and identify the covariates that may account for discrepancies between the two types of measures. METHODS: This study analyzed data from 401 college student e-cigarette users in Indiana and Texas who responded to a web survey (retrospective) and 7-day ecological momentary assessments (EMA) (real-time) on their e-cigarette use behavior, dependence symptomatology, e-cigarette product characteristics, and use contexts from Fall 2019 to Fall 2021. Generalized linear mixed models were used to model the real-time measures of quantity offset by the retrospective average quantity. RESULTS: Although the number of times using e-cigarettes per day seems to be applicable to both retrospective and real-time measures, the number reported via EMA was 8.5 times the retrospective report. E-cigarette users with higher e-cigarette primary dependence motives tended to report more daily nicotine consumption via EMA than their retrospective reports (ie, perceived average consumption). Other covariates that were associated with discrepancies between real-time and retrospective reports included gender, nicotine concentration, using a menthol- or fruit-flavored product, co-use with alcohol, and being with others when vaping. CONCLUSIONS: The study found extreme under-reporting of e-cigarette consumption on retrospective surveys. Important covariates identified to be associated with higher than average consumption may be considered as potential targets for future vaping interventions. IMPLICATIONS: This is the first study that characterizes the direction and magnitude of the difference between retrospective and real-time measures of the quantity of e-cigarette use among young adults-the population most likely to use e-cigarettes. An average retrospective account of vaping events per day may significantly underestimate e-cigarette use frequency among young adults. The lack of insight into the degree of consumption among users with heavy primary dependence motives illustrates the importance of incorporating self-monitoring into cessation interventions.

Associations of Demographics, Dependence, and Biomarkers With Transitions in Tobacco Product Use in a Cohort of Cigarette Users and Dual Users of Cigarettes and E-cigarettes.

INTRODUCTION: It 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 METHODS: We 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. RESULTS: We 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. CONCLUSIONS: Tobacco control policy and interventions may be informed by the barriers and facilitators of product transitions. IMPLICATIONS: Although 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.

A Causal Approach to Functional Mediation Analysis with Application to a Smoking Cessation Intervention.

The increase in the use of mobile and wearable devices now allows dense assessment of mediating processes over time. For example, a pharmacological intervention may have an effect on smoking cessation via reductions in momentary withdrawal symptoms. We define and identify the causal direct and indirect effects in terms of potential outcomes on the mean difference and odds ratio scales, and present a method for estimating and testing the indirect effect of a randomized treatment on a distal binary variable as mediated by the nonparametric trajectory of an intensively measured longitudinal variable (e.g., from ecological momentary assessment). Coverage of a bootstrap test for the indirect effect is demonstrated via simulation. An empirical example is presented based on estimating later smoking abstinence from patterns of craving during smoking cessation treatment. We provide an R package, funmediation, available on CRAN at https://cran.r-project.org/web/packages/funmediation/index.html, to conveniently apply this technique. We conclude by discussing possible extensions to multiple mediators and directions for future research.

Estimation and inference for the mediation effect in a time-varying mediation model.

BACKGROUND: Traditional mediation analysis typically examines the relations among an intervention, a time-invariant mediator, and a time-invariant outcome variable. Although there may be a total effect of the intervention on the outcome, there is a need to understand the process by which the intervention affects the outcome (i.e., the indirect effect through the mediator). This indirect effect is frequently assumed to be time-invariant. With improvements in data collection technology, it is possible to obtain repeated assessments over time resulting in intensive longitudinal data. This calls for an extension of traditional mediation analysis to incorporate time-varying variables as well as time-varying effects. METHODS: We focus on estimation and inference for the time-varying mediation model, which allows mediation effects to vary as a function of time. We propose a two-step approach to estimate the time-varying mediation effect. Moreover, we use a simulation-based approach to derive the corresponding point-wise confidence band for the time-varying mediation effect. RESULTS: Simulation studies show that the proposed procedures perform well when comparing the confidence band and the true underlying model. We further apply the proposed model and the statistical inference procedure to data collected from a smoking cessation study. CONCLUSIONS: We present a model for estimating time-varying mediation effects that allows both time-varying outcomes and mediators. Simulation-based inference is also proposed and implemented in a user-friendly R package.

Time-Varying Mediation of Pharmacological Smoking Cessation Treatments on Smoking Lapse via Craving, Cessation Fatigue, and Negative Mood.

INTRODUCTION: The addictive nature of nicotine makes smoking cessation an extremely challenging process. With prolonged exposure, tobacco smoking transforms from being a positive reinforcer to a negative one, as smoking is used to mitigate aversive withdrawal symptoms. Studying the variations in withdrawal symptoms, especially during their peak in the first week of a quit attempt, could help improve cessation treatment for the future. The time-varying mediation model effectively studies whether altering withdrawal symptoms act as mediators in the pathway between treatment and cessation. AIMS AND METHODS: This secondary data analysis of a randomized clinical smoking cessation trial of three pharmacotherapy regimens (nicotine patch, varenicline, and nicotine patch + mini-lozenge) analyzes ecological momentary assessment (EMA) data from the first 4 weeks post-target quit day (TQD). We assess whether withdrawal symptoms (eg, negative mood, cessation fatigue, and craving) mediate the pathway between pharmacotherapy and daily smoking status and whether this effect varies over time. RESULTS: We found a statistically significant time-varying mediation effect of varenicline on smoking status through craving, which shows decreasing risk of lapse via reduction in craving. We did not find significant time-varying mediation effects through negative mood and cessation fatigue. CONCLUSIONS: This study supports the importance of craving suppression in the smoking cessation process. It also helped identify specific timepoints when withdrawal symptoms increased that would likely benefit from targeted cessation intervention strategies. IMPLICATIONS: This study aimed to understand the underlying dynamic mechanisms of the smoking cessation process using a new analytical approach that capitalizes on the intensive longitudinal data collected via EMAs. The findings from this study further elucidate the smoking cessation process and provide insight into behavioral intervention targets and the timing of such interventions through the estimation of time-varying mediation effects.

Reporting Treatment Fidelity in Behavioral Tobacco Treatment Clinical Trials: Scoping Review and Measurement Recommendations.

INTRODUCTION: Adoption of rigorous standards for reporting treatment fidelity is essential for advancing discovery, validation, and implementation of behavioral treatments. Whereas the NIH Behavior Change Consortium (BCC) developed an assessment tool to assess the quality of reporting and monitoring of treatment fidelity across health behavior change interventions, it has not yet been applied specifically to treatment fidelity in behavioral tobacco treatment trials. AIMS AND METHODS: We conducted a scoping review of peer-reviewed, clinical trials of behavioral adult tobacco treatment interventions published in English between 2006 and 2018. Using the BCC treatment fidelity checklist, articles were coded for the presence or absence of various treatment fidelity strategies within each of 5 domains: Design, Training, Delivery, Receipt, and Enactment. Eligible articles (N = 755) were coded by two independent coders. RESULTS: The proportion of reporting strategies varied within the fidelity domains, ranging from 5.2% to 96.3% in Design, 1.9% to 24.9% in Training, 2.6% to 32.3% in Delivery, 5.2% to 44.3% in Receipt, and 6.7% to 43.2% in Enactment. The mean proportion of adherence to treatment fidelity strategies within each domain was: Design (68%), Training (14%), Delivery (15%), Receipt (16%), and Enactment (25%). Only 11 studies achieved ≥80% reporting across >1 fidelity domain. There was no evidence for improvement in fidelity reporting across the 13-year time frame from the initial BCC publication to the present. CONCLUSIONS: These findings illustrate the lack of consistency in fidelity reporting in tobacco treatment trials and underscore the challenges faced in evaluating rigor and reproducibility, as well as interpretation and dissemination of findings. Recommendations are made for improving fidelity reporting in tobacco treatment trials. IMPLICATIONS: The SRNT Treatment Research Network sponsored a scoping review to summarize the current state of reporting treatment fidelity and make recommendations for best practices in reporting fidelity in tobacco treatment trials. The review identified a lack of consistency in fidelity reporting, illustrating the challenges faced in evaluating rigor, and reproducibility, as well as interpretation and dissemination of findings.

A new statistical model for longitudinal ecological momentary assessment data on dual use of electronic and combustible cigarettes.

Background: Existing studies of dual use of electronic and combustible cigarettes either collected longitudinal data with long gaps in between waves or conducted ecological momentary assessment (EMA) over a short period of time. In recent years, the measurement burst design that embeds an EMA protocol in each wave assessment of a traditional longitudinal study has become more popular and yet conventional generalized linear mixed models (GLMM) have important limitations for handling data from this design.Objectives: This study proposed a new statistical method to analyze data from the measurement burst design.Methods: This new statistical method was designed to model the short-term (within-wave) as well as long-term (between-wave) changes and was validated by a simulation study. Secondary analysis was conducted to analyze data from 205 dual users (52% male) and 146 exclusive smokers (50% male) who participated in a recent study using the measurement burst design.Results: The simulation study shows that the proposed method can handle the gap between waves well and is also robust to nonlinear changes across waves. Although no short-term change in smoking was found, dual users reported a long-term reduction in cigarette use that was more rapid compared to exclusive smokers (߈=-0.0127,p=.0167). Vaping more was associated with smoking less (߈=-0.0058,p=.0054).Conclusion: The proposed method is highly applicable as it can be easily implemented by substance use researchers and the results can be straightforwardly interpreted. The results suggest that e-cigarette use may play a role in promoting a long-term reduction in smoking among dual users.

Cost-effectiveness of stop smoking incentives for medicaid-enrolled pregnant women.

Maternal smoking increases mortality and morbidity risks for both mother and infant. The First Breath Wisconsin study examined the cost-effectiveness of providing incentives to pregnant women who smoked to engage in stop smoking treatment. Participants (N = 1014) were Medicaid-enrolled pregnant women recruited from September 2012 to April 2015 through public health departments, private, and community health clinics in Wisconsin. The incentive group (n = 505) could receive $460 for completing pre-birth visits ($25 each), post-birth home visits ($40, $25, $25, $40 for 1-week, 2-month, 4-month and 6-month visits), monthly smoking cessation phone calls post-birth ($20 each), and biochemically-verified tobacco abstinence at 1-week ($40) and 6-months ($40) post-birth. The control group (n = 509) received up to $80 for 1-week ($40) and 6-month ($40) post-birth assessments. Intervention costs included incentive payments to participants, counselor and administrative staff time, and smoking cessation medications. Cost-effectiveness analysis calculated the incremental cost-effectiveness ratio (ICER) per one additional smoker who quit. The incentive group had higher 6-month post-birth biochemically-confirmed tobacco abstinence than the control group (14.7% vs. 9.2%). Incremental costs averaged $184 per participant for the incentive group compared to controls ($317 vs $133). The ICER of financial incentives was $3399 (95% CI $2228 to $8509) per additional woman who was tobacco abstinent at 6 months post-birth. The ICER was lower ($2518 vs $4760) for women who did not live with another smoker. This study shows use of financial incentives for stop smoking treatment is a cost-effective option for low-income pregnant women who smoke.

Validating E-Cigarette Dependence Scales Based on Dynamic Patterns of Vaping Behaviors.

INTRODUCTION: Existing e-cigarette dependence scales are mainly validated based on retrospective overall consumption or perception. Further, given that the majority of adult e-cigarette users also use combustible cigarettes, it is important to determine whether e-cigarette dependence scales capture the product-specific dependence. This study fills in the current knowledge gaps by validating e-cigarette dependence scales using novel indices of dynamic patterns of e-cigarette use behaviors and examining the association between dynamic patterns of smoking and e-cigarette dependence among dual users. METHODS: Secondary analysis was conducted on the 2-week ecological momentary assessment data from 116 dual users. The Smoothly Clipped Absolute Deviation penalty (SCAD) was adopted to select important indices for dynamic patterns of consumption or craving and estimate their associations with e-cigarette dependence scales. RESULTS: The fitted linear regression models support the hypothesis that higher e-cigarette dependence is associated with higher levels of e-cigarette consumption and craving as well as lower instability of e-cigarette consumption. Controlling for dynamic patterns of vaping, dual users with lower e-cigarette dependence tend to report higher day-to-day dramatic changes in combustible cigarette consumption but not higher average levels of smoking. CONCLUSIONS: We found that more stable use patterns are related to higher levels of dependence, which has been demonstrated in combustible cigarettes and we have now illustrated in e-cigarettes. Furthermore, the e-cigarette dependence scales may capture the product-specific average consumption but not product-specific instability of consumption. IMPLICATIONS: This study provides empirical support for three e-cigarette dependence measures: PS-ECDI, e-FTCD, and e-WISDM, based on dynamic patterns of e-cigarette consumption and craving revealed by EMA data that have great ecological validity. This is the first study that introduces novel indices of dynamic patterns and demonstrates their potential applications in vaping research.

Indicators of dependence and efforts to quit vaping and smoking among youth in Canada, England and the USA.

OBJECTIVE: The current study examined indicators of dependence among youth cigarette smokers and e-cigarette users in Canada, England and the USA, including changes between 2017 and 2019. METHODS: Data are from repeated cross-sectional online surveys conducted in 2017, 2018 and 2019 with national samples of youth aged 16-19 years, in Canada (n=12 018), England (n=11 362) and the USA (n=12 110). Measures included perceived addiction to cigarettes/e-cigarettes, frequency of experiencing strong urges to smoke/use an e-cigarette, plans to quit smoking/using e-cigarettes and past attempts to quit. Logistic regression models were fitted to examine differences between countries and changes over time. RESULTS: The proportion of ever-users who vaped frequently was significantly higher in 2019 compared with 2017 for all outcomes in each country. Between 2017 and 2019, the proportion of past 30-day vapers reporting strong urges to vape on most days or more often increased in each country (Canada: 35.3%, adjusted OR (AOR) 1.69, 95% CI 1.20 to 2.38; England: 32.8%, AOR 1.55, 1.08 to 2.23; USA: 46.1%, AOR 1.88, 1.41 to 2.50), along with perceptions of being 'a little' or 'very addicted' to e-cigarettes (Canada: 48.3%, AOR 1.99, 1.44 to 2.75; England: 40.1%, AOR 1.44, 1.03 to 2.01; USA: 53.1%, AOR 1.99, 1.50 to 2.63). Indicators of dependence among smokers were consistently greater than e-cigarette users, although differences had narrowed by 2019, particularly in Canada and the USA. CONCLUSIONS: Prevalence of dependence symptoms among young e-cigarette users increased between 2017 and 2019, more so in Canada and the USA compared with England. Dependence symptom prevalence was lower for e-cigarettes than smoking; however, the gap has narrowed over time.

Effects of Combined Varenicline With Nicotine Patch and of Extended Treatment Duration on Smoking Cessation: A Randomized Clinical Trial.

Importance: Smoking cessation medications are routinely used in health care. Research suggests that combining varenicline with the nicotine patch, extending the duration of varenicline treatment, or both, may increase cessation effectiveness. Objective: To compare combinations of varenicline plus the nicotine or placebo patch vs combinations used for either 12 weeks (standard duration) or 24 weeks (extended duration). Design, Settings, and Participants: Double-blind, 2 × 2 factorial randomized clinical trial conducted from November 11, 2017, to July 9, 2020, at 1 research clinic in Madison, Wisconsin, and at 1 clinic in Milwaukee, Wisconsin. Of the 5836 adults asked to participate in the study, 1251 who smoked 5 cigarettes/d or more were randomized. Interventions: All participants received cessation counseling and were randomized to 1 of 4 medication groups: varenicline monotherapy for 12 weeks (n = 315), varenicline plus nicotine patch for 12 weeks (n = 314), varenicline monotherapy for 24 weeks (n = 311), or varenicline plus nicotine patch for 24 weeks (n = 311). Main Outcomes and Measures: The primary outcome was carbon monoxide-confirmed self-reported 7-day point prevalence abstinence at 52 weeks. Results: Among 1251 patients who were randomized (mean [SD] age, 49.1 [11.9] years; 675 [54.0%] women), 751 (60.0%) completed treatment and 881 (70.4%) provided final follow-up. For the primary outcome, there was no significant interaction between the 2 treatment factors of medication type and medication duration (odds ratio [OR], 1.03 [95% CI, 0.91 to 1.17]; P = .66). For patients randomized to 24-week vs 12-week treatment duration, the primary outcome occurred in 24.8% (154/622) vs 24.3% (153/629), respectively (risk difference, -0.4% [95% CI, -5.2% to 4.3%]; OR, 1.01 [95% CI, 0.89 to 1.15]). For patients randomized to varenicline combination therapy vs varenicline monotherapy, the primary outcome occurred in 24.3% (152/625) vs 24.8% (155/626), respectively (risk difference, 0.4% [95% CI, -4.3% to 5.2%]; OR, 0.99 [95% CI, 0.87 to 1.12]). Nausea occurrence ranged from 24.0% to 30.9% and insomnia occurrence ranged from 24.4% to 30.5% across the 4 groups. Conclusions and Relevance: Among adults smoking 5 cigarettes/d or more, there were no significant differences in 7-day point prevalence abstinence at 52 weeks among those treated with combined varenicline plus nicotine patch therapy vs varenicline monotherapy, or among those treated for 24 weeks vs 12 weeks. These findings do not support the use of combined therapy or of extended treatment duration. Trial Registration: ClinicalTrials.gov Identifier: NCT03176784.

Changes in Use Patterns Over 1 Year Among Smokers and Dual Users of Combustible and Electronic Cigarettes.

BACKGROUND: Dual 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. METHODS: Adult 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. RESULTS: Of 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. CONCLUSIONS: In 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. IMPLICATIONS: This 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.

Anxiety Sensitivity and Distress Tolerance in Smokers: Relations With Tobacco Dependence, Withdrawal, and Quitting Success†.

INTRODUCTION: This study examined relations of two affective vulnerabilities, high anxiety sensitivity (AS) and low distress tolerance (DT), with tobacco dependence, withdrawal, smoking cessation, and pharmacotherapy response. METHODS: Smokers interested in quitting (N = 1067; 52.2% female, 28.1% African American) were randomized to 12 weeks of nicotine patch, nicotine patch plus nicotine lozenge, or varenicline. Baseline questionnaires assessed AS, DT, negative affect, anxiety, and dependence. Withdrawal was assessed the first-week post-quit via ecological momentary assessment. RESULTS: DT, but not AS, predicted biochemically confirmed point-prevalence abstinence at multiple endpoints: weeks 4, 12, 26, and 52 post-quit (ps < .05); relations remained after controlling for pharmacotherapy treatment, AS, baseline negative affect, anxiety, and anxiety disorder history (ps < .05). Additional exploratory analyses examining week 4 abstinence showed DT predicted abstinence (p = .004) even after controlling for baseline dependence, post-quit withdrawal (craving and negative affect), and treatment. DT moderated treatment effects on abstinence in exploratory analyses (interaction p = .025); those with high DT were especially likely to be abstinent at week 4 with patch plus lozenge versus patch alone. CONCLUSIONS: DT, but not AS, predicted abstinence over 1 year post-quit (higher DT was associated with higher quit rates), with little overlap with other affective measures. DT also predicted early abstinence independent of dependence and withdrawal symptoms. Results suggest low DT may play a meaningful role in motivation to use tobacco and constitute an additional affective risk factor for tobacco cessation failure beyond negative affect or clinical affective disorders. IMPLICATIONS: People in a stop-smoking study who reported a greater ability to tolerate distress were more likely to quit smoking and remain smoke-free 1 year later. Smokers with high DT were more likely to be smoke-free 4 weeks after their target quit day if they received nicotine patch plus nicotine lozenge rather than nicotine patch alone. TRIAL REGISTRATION: NCT01553084.

E-cigarette Dependence Measures in Dual Users: Reliability and Relations With Dependence Criteria and E-cigarette Cessation.

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 (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. 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.

Defining and Measuring Abstinence in Clinical Trials of Smoking Cessation Interventions: An Updated Review.

BACKGROUND: Changes in tobacco products, use patterns, and assessment technology in the last 15 years led the Society for Research on Nicotine and Tobacco (SRNT) Treatment Research Network to call for an update to the 2003 SRNT recommendations for assessing abstinence in clinical trials of smoking cessation interventions. METHODS: The SRNT Treatment Research Network convened a group of investigators with decades of experience in conducting tobacco treatment clinical trials. To arrive at the updated recommendations, the authors reviewed the recommendations of the prior SRNT Workgroup as well as current literature. Ten additional experts in the field provided feedback on this paper and these recommendations. RESULTS: With respect to defining abstinence, the authors recommend: (1) continuing to use the definition of no use of combustible tobacco products (regardless of use of noncombustible tobacco products [e.g., snus] and alternative products [e.g., e-cigarettes]) and collecting additional data to permit alternate abstinence definitions; (2) no use of combustible or smokeless tobacco products; and (3) no use of combustible or smokeless tobacco products or alternative products, as appropriate for the research question being addressed. The authors also recommend reporting point prevalence and prolonged abstinence at multiple timepoints (end of treatment, ≥3 months after the end of treatment, and ≥6 months postquit or posttreatment initiation). CONCLUSIONS: Defining abstinence requires specification of which products a user must abstain from using, the type of abstinence (i.e., point prevalence or continuous), and the duration of abstinence. These recommendations are intended to serve as guidelines for investigators as they collect the necessary data to accurately describe participants' abstinence during smoking cessation clinical trials. IMPLICATIONS: This paper provides updated recommendations for defining abstinence in the context of smoking cessation treatment clinical trials.