Pleasure and Satisfaction as Predictors of Future Cigarette and E-Cigarette Use: A Novel Two-Stage Modeling Approach.

INTRODUCTION: Subjective experience of e-cigarettes may be an important factor in helping people who use combustible cigarettes switch completely to e-cigarettes to reduce harms from smoking. This paper describes a novel two-stage analysis using pleasure and satisfaction responses from Ecological Momentary Assessments (EMA) of both cigarette and e-cigarette use to predict future cigarette and e-cigarette tobacco use. METHODS: This observational study included adult users of cigarettes and e-cigarettes who provided 7-days of EMA, capturing cigarette and e-cigarette use, followed by biweekly reports of cigarette and e-cigarette use over one year. Participants were 279 adults who provided both cigarette and e-cigarette responses during the EMA. We employed a two-stage analytic approach in which EMA data were used to predict subsequent levels of cigarette and e-cigarette use. In the first stage, EMA responses of cigarette and e-cigarette events were modeled via a mixed-effects location scale (MELS) model to yield summaries of participants' means and variability on event-related ratings of pleasure and satisfaction. These EMA summaries served as predictors in the second stage analysis of the biweekly post-EMA longitudinal cigarette and e-cigarette use data. RESULTS: EMA pleasure and satisfaction ratings were similar for both products and predicted both longitudinal cigarette and e-cigarette use, even after controlling for baseline cigarette and e-cigarette dependence. Relatively higher levels of satisfaction with e-cigarettes were associated with greater decreases in cigarette use over time. CONCLUSIONS: Pleasure and satisfaction are important predictors of subsequent cigarette and e-cigarette use. IMPLICATIONS: Experienced subjective pleasure and satisfaction from e-cigarettes relative to cigarettes may be an important factor in helping individuals who smoke to switch completely to e-cigarettes as a harm reduction approach. In order to help sustain complete product switching and reduce dual use or relapse to smoking, e-cigarettes may need to deliver more satisfaction to the user compared to that experienced from cigarettes.

An Introduction and Practical Guide to Strategies for Analyzing Longitudinal Data in Clinical Trials of Smoking Cessation Treatment: Beyond Dichotomous Point-Prevalence Outcomes.

Conceptualizing tobacco dependence as a chronic relapsing condition suggests the need to use analytic strategies that reflect that premise. However, clinical trials for smoking cessation typically define the primary endpoint as a measure of abstinence at a single timepoint distal to the intervention, typically 3-12 months. This reinforces the concept of tobacco outcomes as a dichotomous state-one is, or is not, abstinent. Fortunately, there are several approaches available to handle longitudinal data that reflect the relapsing and remitting nature of tobacco use during treatment studies. In this paper, sponsored by the Society for Research on Nicotine and Tobacco's Treatment Research Network, we present an introductory overview of these techniques and their application in smoking cessation clinical trials. Topics discussed include models to examine abstinence outcomes (eg, trajectory models of abstinence, models for transitions in smoking behavior, models for time to event), models that examine reductions in tobacco use, and models to examine joint outcomes (eg, examining changes in the use of more than one tobacco product). Finally, we discuss three additional relevant topics (ie, heterogeneity of effects, handling missing data, and power and sample size) and provide summary information about the type of model that can be used based on the type of data collected and the focus of the study. We encourage investigators to familiarize themselves with these techniques and use them in the analysis of data from clinical trials of smoking cessation treatment. Implications Clinical trials of tobacco dependence treatment typically measure abstinence 3-12 months after participant enrollment. However, because smoking is a chronic relapsing condition, these measures of intervention success may not accurately reflect the common trajectories of tobacco abstinence and relapse. Several analytical techniques facilitate this type of outcome modeling. This paper is meant to be an introduction to these concepts and techniques to the global nicotine and tobacco research community including which techniques can be used for different research questions with visual summaries of which types of models can be used for different types of data and research questions.

Introducing a Real-Time Method for Identifying the Predictors of Noncompliance with Event-Based Reporting of Tobacco Use in Ecological Momentary Assessment.

BACKGROUND: Little is known about the factors that bias event-based (i.e., self-initiated) reporting of health behaviors in ecological momentary assessment (EMA) due to the difficulty inherent to tracking failures to self-initiate reports. PURPOSE: To introduce a real-time method for identifying the predictors of noncompliance with event-based reporting. METHODS: N = 410 adults who used both cigarettes and e-cigarettes completed a 1-week EMA protocol that combined random reporting of current contexts with event-based reporting of tobacco use. Each random assessment first asked if participants were currently using tobacco and, if so, the assessment converted into a "randomly captured" event report-indicating failure to self-initiate that report. Multilevel modeling tested predictors of failing to complete random reports and failing to self-initiate event reports. RESULTS: On the person level, male sex, higher average cigarette rate, and higher average cigarette urge each predicted missing random reports. The person-level predictors of failing to self-initiate event reports were older age, higher average cigarette and e-cigarette rates, higher average cigarette urge, and being alone more on average; the moment-level predictors were lower cigarette urge, lower positive affect, alcohol use, and cannabis use. Strikingly, the randomly captured events comprised more of the total EMA reports (28%) than did the self-initiated event reports (24%). These report types were similar across most variables, with some exceptions, such as momentary cannabis use predicting the random capture of tobacco events. CONCLUSIONS: This study demonstrated a method of identifying predictors of noncompliance with event-based reporting of tobacco use and enhancing the real-time capture of events.

This study introduced a real-time method for identifying person- and moment-level predictors of failing to self-initiate tobacco event reports during ecological momentary assessment (EMA), and for capturing a large number of events that would have likely otherwise been missed. The method has implications for behavioral health research more broadly.

Smoking Status, Nicotine Medication, Vaccination, and COVID-19 Hospital Outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) Study.

INTRODUCTION: Available evidence is mixed concerning associations between smoking status and COVID-19 clinical outcomes. Effects of nicotine replacement therapy (NRT) and vaccination status on COVID-19 outcomes in smokers are unknown. METHODS: Electronic health record data from 104 590 COVID-19 patients hospitalized February 1, 2020 to September 30, 2021 in 21 U.S. health systems were analyzed to assess associations of smoking status, in-hospital NRT prescription, and vaccination status with in-hospital death and ICU admission. RESULTS: Current (n = 7764) and never smokers (n = 57 454) did not differ on outcomes after adjustment for age, sex, race, ethnicity, insurance, body mass index, and comorbidities. Former (vs never) smokers (n = 33 101) had higher adjusted odds of death (aOR, 1.11; 95% CI, 1.06-1.17) and ICU admission (aOR, 1.07; 95% CI, 1.04-1.11). Among current smokers, NRT prescription was associated with reduced mortality (aOR, 0.64; 95% CI, 0.50-0.82). Vaccination effects were significantly moderated by smoking status; vaccination was more strongly associated with reduced mortality among current (aOR, 0.29; 95% CI, 0.16-0.66) and former smokers (aOR, 0.47; 95% CI, 0.39-0.57) than for never smokers (aOR, 0.67; 95% CI, 0.57, 0.79). Vaccination was associated with reduced ICU admission more strongly among former (aOR, 0.74; 95% CI, 0.66-0.83) than never smokers (aOR, 0.87; 95% CI, 0.79-0.97). CONCLUSIONS: Former but not current smokers hospitalized with COVID-19 are at higher risk for severe outcomes. SARS-CoV-2 vaccination is associated with better hospital outcomes in COVID-19 patients, especially current and former smokers. NRT during COVID-19 hospitalization may reduce mortality for current smokers. IMPLICATIONS: Prior findings regarding associations between smoking and severe COVID-19 disease outcomes have been inconsistent. This large cohort study suggests potential beneficial effects of nicotine replacement therapy on COVID-19 outcomes in current smokers and outsized benefits of SARS-CoV-2 vaccination in current and former smokers. Such findings may influence clinical practice and prevention efforts and motivate additional research that explores mechanisms for these effects.

A shared-parameter location-scale mixed model to link the responsivity in self-initiated event reports and the event-contingent Ecological Momentary Assessments.

We address the issue of (non-) responsivity of self-initiated assessments in Ecological Momentary Assessment (EMA) or other mobile health (mHealth) studies, where subjects are instructed to self-initiate reports when experiencing defined events, for example, smoking. Since such reports are self-initiated, the frequency and determinants of nonresponse to these event reports is usually unknown, however it may be suspected that nonresponse of such self-initiated reports is not random. In this case, existing methods for missing data may be insufficient in the modeling of these observed self-initiated reports. In certain EMA studies, random prompts, distinct from the self-initiated reports, may be converted to event reports. For example, such a conversion can occur if during a random prompt a subject is assessed about the event (eg, smoking) and it is determined that the subject is engaging in the event at the time of the prompt. Such converted prompts can provide some information about the subject's non-responsivity of event reporting. Furthermore, such non-responsivity can be associated with the primary longitudinal EMA outcome (eg, mood) in which case a joint modeling of the non-responsivity and the mood outcome is possible. Here, we propose a shared-parameter location-scale model to link the primary outcome model for mood and a model for subjects' non-responsivity by shared random effects which characterize a subject's mood level, mood change pattern, and mood variability. Via simulations and real data analysis, our proposed model is shown to be more informative, have better coverage of parameters, and provide better fit to the data than more conventional models.

Tailored text message and web intervention for smoking cessation in U.S. socioeconomically-disadvantaged young adults: A randomized controlled trial.

The prevalence of cigarette smoking in young adults is higher among those with socioeconomic disadvantage than those without. Low treatment-seeking among young adult smokers is compounded by few efficacious smoking cessation interventions for this group, particularly socioeconomically-disadvantaged young adults (SDYA) who smoke cigarettes. The goal of this study was to test a tailored smoking-cessation intervention for SDYA. 343 SDYA aged 18-30 living in the U.S. (85% female) who smoke cigarettes with access to a smartphone and interest in quitting smoking in the next six months were recruited online in Spring 2020 and randomized to referral to online quit resources (usual care control; n = 171) or a 12-week tailored text message smoking-cessation program with a companion web-based intervention (n = 172). Intent to treat analyses examined associations between study condition, self-reported 30-day point prevalence abstinence (PPA), and confidence to quit smoking at 12 weeks, controlling for potential confounders. Intervention group participants had greater self-reported 30-day PPA at 12-weeks than controls (adjusted relative risk 3.93, 95% CI 2.14-7.24). Among those who continued smoking, the intervention increased confidence to quit (0.81 points, 95% confidence interval 0.08-1.53). Weekly engagement in the intervention predicted greater cessation. A tailored text message intervention for SDYA increased smoking abstinence and confidence to quit at the end-of-treatment. Findings may have been influenced by recruitment at the start of the COVID pandemic but suggest that text messaging is an acceptable and efficacious cessation strategy for SDYA smokers. Future studies should examine the impact on longer-term smoking-cessation and importance of intervention tailoring for SDYA.

The Mood Boost from Tobacco Cigarettes is More Erratic with the Additions of Cannabis and Alcohol.

INTRODUCTION: A large body of literature indicates that nicotine results in an acute mood "boost," including increased positive affect and decreased negative affect. Young adults frequently engage in polysubstance use of cigarettes with cannabis and alcohol-a trend that is likely to accelerate with the expanding legalization of cannabis. However, little is known about whether polysubstance use, defined here as combustible tobacco cigarette use within the same hour as cannabis and alcohol, is associated with changes in the nicotine mood boost. The present study aimed to address this gap. METHODS: Young adults (N = 202, 52% female, mean age = 21 years at time 1) provided ecological moments assessment (EMA) reports of cigarette use over two 7-day bursts spaced 1 year apart. In each report, participants rated mood levels before and after smoking, and indicated cannabis and alcohol use. Mixed-effects location-scale modeling simultaneously tested changes in mood levels and variability related to smoking events with cannabis and/or alcohol compared with smoking-only events. RESULTS: From before to after smoking, positive affect increased and negative affect decreased, on average. Overall, the additions of cannabis and/or alcohol had nonsignificant associations with these mean changes. However, polysubstance use, as well as cigarette-cannabis co-use, were each associated with significantly greater within-person variability in the positive and negative affect changes related to smoking. CONCLUSIONS: The mood benefits associated with smoking were more erratic in the contexts of polysubstance use and cigarette-cannabis co-use. Potential implications for young adults' long-term nicotine use trajectories are discussed. IMPLICATIONS: Among young adults who smoke cigarettes, the mood "boost" from smoking may be more erratic-which is to say, more likely to be either amplified or attenuated-with the additions of cannabis and alcohol together, or cannabis alone. On occasions when young adults seek out cannabis and alcohol to enhance their smoking mood boost, but instead experience a dampening effect, they might consume more nicotine, contributing over time to greater dependence. Future investigation is warranted, with particular attention to nicotine-cannabis co-use.

Negative Affect and Cigarette Cessation in Dual Users of Cigarettes and Electronic Nicotine Delivery Systems.

Objectives: Dual use of cigarettes and electronic nicotine delivery systems (ENDS) is increasingly common in adult smokers, who often report using ENDS to quit smoking. Elevated negative affect is an established predictor of increased difficulty quitting smoking combustible cigarettes but has not yet been examined in the context of cigarette cessation for dual users. Method: This study examined whether mood-related factors predict cigarette smoking cessation among dual users (N = 364) over 12-months. Self-reported cigarette smoking at 12 months, with abstinence defined as no smoking for the past 7 days, was the primary outcome variable. Logistic regression included baseline levels of depression symptoms (CES-D), anxiety symptoms (MASQ), and negative affect expectancies for smoking, with baseline nicotine dependence for cigarettes (NDSS), motivation to quit, age, race/ethnicity, rate of cigarette smoking at baseline, and ENDS usage at baseline and 12 months as covariates. Interactions between CES-D, MASQ, and negative affect expectancies were examined. We predicted that negative affect, especially for smokers who had high negative affect expectancies for smoking, would be negatively associated with quitting. Results: Contrary to expectations, negative affect constructs did not predict quitting. Baseline nicotine dependence for cigarettes, gender, and race/ethnicity significantly predicted the likelihood of cigarette cessation. Higher rates of ENDS use, higher motivation, and lower negative affect smoking expectancies were significantly correlated with quitting cigarettes. Conclusion: In this non-treatment seeking sample of dual users, negative affect did not predict cigarette cessation over and above nicotine dependence for cigarettes, gender, and race/ethnicity.

Balancing Consideration of the Risks and Benefits of E-Cigarettes.

The topic of e-cigarettes is controversial. Opponents focus on e-cigarettes' risks for young people, while supporters emphasize the potential for e-cigarettes to assist smokers in quitting smoking. Most US health organizations, media coverage, and policymakers have focused primarily on risks to youths. Because of their messaging, much of the public-including most smokers-now consider e-cigarette use as dangerous as or more dangerous than smoking. By contrast, the National Academies of Science, Engineering, and Medicine concluded that e-cigarette use is likely far less hazardous than smoking. Policies intended to reduce adolescent vaping may also reduce adult smokers' use of e-cigarettes in quit attempts. Because evidence indicates that e-cigarette use can increase the odds of quitting smoking, many scientists, including this essay's authors, encourage the health community, media, and policymakers to more carefully weigh vaping's potential to reduce adult smoking-attributable mortality. We review the health risks of e-cigarette use, the likelihood that vaping increases smoking cessation, concerns about youth vaping, and the need to balance valid concerns about risks to youths with the potential benefits of increasing adult smoking cessation.

Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model.

BACKGROUND: Longitudinal assessments of usage are often conducted for multiple substances (e.g., cigarettes, alcohol and marijuana) and research interests are often focused on the inter-substance association. We propose a multivariate longitudinal modeling approach for jointly analyzing the ordinal multivariate substance use data. METHODS: We describe how the binary random slope logistic regression model can be extended to the multi-category ordinal outcomes. We also describe how the proportional odds assumption can be relaxed by allowing differential covariate effects on different cumulative logits for multiple outcomes. Data are analyzed from a P01 study that evaluates the usage levels of cigarettes, alcohol and marijuana repeatedly across 8 measurement waves during 7 consecutive years. RESULTS: 1263 subjects participated in the study with informed consent, among whom 56.6% are females. Males and females show significant differences in terms of the time trend for substance use. Specifically, males showed steeper trends on cigarette and marijuana use over time compared to females, while less so for alcohol. For all three substances, age effects appear to be different for different cumulative logits, indicating the violation of proportional odds assumption. CONCLUSIONS: The multivariate mixed cumulative logit model offers the most flexibility and allows one to examine the inter-substance association when proportional odds assumption is violated.

Nicotine Dependence in Dual Users of Cigarettes and E-Cigarettes: Common and Distinct Elements.

INTRODUCTION: In a sample of dual users of cigarettes and electronic nicotine delivery systems (ENDS; e-cigarettes), we evaluated psychometric properties of ENDS versions of the Nicotine Dependence Syndrome Scale (NDSS), the brief Wisconsin Inventory of Smoking Dependence Motives (WISDM), and the Fagerström Test for Nicotine Dependence (FTND). Using the NDSS, we tested the hypothesis that there would be one common underlying factor of dependence across the cigarette and ENDS scales and other product-specific factors. AIMS AND METHODS: Adult dual users (N = 404) completed baseline cigarette and ENDS versions of the NDSS, WISDM, and FTND, and biweekly surveys of their smoking and vaping. Analyses included bifactor modeling, which helps to identify both a general and product-specific factor for each item, and exploratory factor analyses of the combined cigarette and ENDS NDSS items and examinations of concurrent and predictive validity. RESULTS: The bifactor model was not a good fit, suggesting the lack of one common underlying dependence factor. Factor analyses revealed separate, similar factors for both products, with only one factor (priority) showing overlap of cigarette and ENDS items. ENDS scales significantly predicted ENDS use over time, but not cigarette use. Cigarette scales did not predict ENDS use over time. CONCLUSIONS: Although the cigarette and ENDS NDSS versions showed similar factor structure, there was not a primary common underlying factor reflecting drive or tolerance, but rather product-specific factors. The cigarette scales were not valid for predicting ENDS use. These results highlight the importance of separately assessing dependence for cigarettes and ENDS in dual users. IMPLICATIONS: Although underlying dimensions of nicotine dependence may be similar for ENDS and cigarettes, separate, product-specific measures may be needed to understand differences in product-specific dependency and predict changes in use of each product over time.

Harmonizing Cigar Survey Data Across Tobacco Centers of Regulatory Science, Center for Tobacco Products, and Population Assessment of Tobacco and Health Studies: The Cigar Collaborative Research Group.

INTRODUCTION: Cigars are a popular tobacco product of choice for youth and young adults. Despite growing interest in cigar research, there are gaps in the available literature limiting an ability to set evidence-based policies. Too small research samples, the heterogeneity of types of cigars when asking a single question about use, makes analyzing data difficult. Given the Food and Drug Administration's (FDA's) authority granted in 2016 to regulate cigars, and its popularity, data to better understand use and preference for cigars will help FDA set appropriate regulatory policies. METHODS: We harmonized cigar survey data previously collected by five independent tobacco regulatory science survey research projects. Data supplying participants included three Tobacco Centers of Regulatory Science, one Center for Tobacco Products grantee, and data from Population Assessment of Tobacco and Health (PATH) study's public use dataset. RESULTS: Analyzing 92 data variables from across five studies, and applying a rigorous data harmonization protocol, we report findings on 24 key cigar use variables. The step by step protocol for harmonizing is presented. Selected findings showing strict reproducibility across all five studies reveal youth 17-19 years at highest risk for cigar initiation; relative reproducibility shows males more likely to try cigars than females but with significant differences in magnitude across studies; and areas of inconsistent reproducibility are revealed when evaluating brand preferences. CONCLUSION: Harmonizing data from multiple sources fosters a broader view of the robustness and generalizability of survey data than that from a single source. These observations raise awareness to look for the highest degree of reproducibility among and across data sources to inform policy. IMPLICATIONS: Harmonizing data from discrete datasets provides insights into cigar initiation and use and is presented showing opportunities, challenges, and solutions. Comparing observational data from PATH and four independent research studies provides a best-practices approach and example of data synthesis for the tobacco research community. The dataset of five studies offers a look at the degree of confidence in analyzing harmonized survey results. Variable conclusions raise the need to strive for the highest degree of reproducibility, to best understand the behaviors of cigar users, and allow for the future development of the most effective interventions to alter tobacco use patterns.

The Effect of Maternal Smoking on Offspring Smoking Is Unrelated to Heritable Personality Traits or Initial Subjective Experiences.

INTRODUCTION: Maternal smoking is a risk factor for offspring smoking. Lifetime maternal smoking vs. prenatal tobacco exposure (PTE) appears to act through different mechanisms. This study tested the hypothesis that maternal smoking measures' effects on offspring smoking could be attributable to hereditary mechanisms: personality traits (novelty-seeking, impulsivity, neuroticism, and self-esteem) and initial subjective smoking experiences (pleasurable, unpleasurable, and dizziness). METHODS: Data were drawn from the Social and Emotional Contexts of Adolescent Smoking Patterns study, an 8-year longitudinal study of 9th or 10th graders at baseline (≈age 15) who experiment with smoking (<100 lifetime cigarettes; N = 594) at baseline. The young adult smoking frequency at the 8-year follow-up (≈age 23) was examined as a function of baseline characteristics (heritable trait, maternal smoking, PTE, and sex) and baseline smoking frequency and nicotine dependence. Structural equation models determined whether the inclusion of each heritable trait among offspring confounded the effects of maternal smoking (PTE or maternal smoking) on offspring smoking and nicotine dependence. RESULTS: Impulsiveness was associated with intermediate adolescent smoking frequency (B = 0.135, SD = 0.043, p = .002) and nicotine dependence (B = 0.012, SD = 0.003, p < .001). Unpleasurable first experience (B = 0.886, SD = 0.374, p = .018) and dizziness (B = 0.629, SD = 0.293, p = .032) showed a trend with intermediate smoking frequency that was nonsignificant after correcting for multiple comparisons. These traits did not confound maternal smoking's effects. CONCLUSIONS: None of the heritable traits examined in this model explained the effect of maternal smoking measures on adolescence or young adulthood offspring smoking. Further research is needed to elucidate the mechanism by which PTE and maternal smoking are linked to offspring smoking. IMPLICATIONS: Prenatal tobacco exposure (PTE) and mother's lifetime smoking present separate and independent risks for offspring smoking; however, their mechanisms seem unrelated to heritable personality traits and initial subjective smoking experiences. These findings have implications for separate screening strategies tailored to different age groups, especially related to PTE's risk of smoking in young adulthood. Additionally, these findings add to the known risks of maternal smoking. Further research is needed to understand the mechanism underlying the risk posed by maternal lifetime smoking and PTE on offspring smoking behavior.

A tractable method to account for high-dimensional nonignorable missing data in intensive longitudinal data.

Despite the need for sensitivity analysis to nonignorable missingness in intensive longitudinal data (ILD), such analysis is greatly hindered by novel ILD features, such as large data volume and complex nonmonotonic missing-data patterns. Likelihood of alternative models permitting nonignorable missingness often involves very high-dimensional integrals, causing curse of dimensionality and rendering solutions computationally prohibitive to obtain. We aim to overcome this challenge by developing a computationally feasible method, nonlinear indexes of local sensitivity to nonignorability (NISNI). We use linear mixed effects models for the incomplete outcome and covariates. We use Markov multinomial models to describe complex missing-data patterns and mechanisms in ILD, thereby permitting missingness probabilities to depend directly on missing data. Using a second-order Taylor series to approximate likelihood under nonignorability, we develop formulas and closed-form expressions for NISNI. Our approach permits the outcome and covariates to be missing simultaneously, as is often the case in ILD, and can capture U-shaped impact of nonignorability in the neighborhood of the missing at random model without fitting alternative models or evaluating integrals. We evaluate performance of this method using simulated data and real ILD collected by the ecological momentary assessment method.

Predictors of Smoking Cessation Attempts and Success Following Motivation-Phase Interventions Among People Initially Unwilling to Quit Smoking.

INTRODUCTION: Most people who smoke cigarettes are not willing (ie, not ready) to make a quit attempt (QA) at any given time. Unfortunately, interventions intended to increase QAs and the success of QAs are only modestly effective. Identifying processes leading to QAs and quitting success could guide intervention development. AIMS AND METHODS: This is a secondary analysis of a randomized factorial trial of 6 weeks of motivation-phase interventions among primary care patients (N = 517) who were initially unwilling to quit but were willing to reduce their smoking. Using logistic regression, we controlled for treatment condition and tested whether baseline or change in smoking-related constructs after 6 weeks of treatment predicted (1) making an at least 24 h QA between weeks 6 and 26 and (2) quitting success at week 26 (7-day point-prevalence abstinence among those who made a QA). Predictors included cigarettes/day, time to first cigarette, motivation to quit, quitting self-efficacy, anticipated urges to smoke if quit, positive affect, negative affect, and time spent around others who smoke. RESULTS: In multivariable models that included all smoking-related constructs, changes in the following variables predicted initiating a QA above and beyond other variables: greater baseline time to first cigarette (odds ratio [OR] = 1.60), increases in time to first cigarette (OR = 1.27), and increases in quitting self-efficacy (OR = 1.14). Increased motivation to quit predicted conversion of a QA into quitting success at 26 weeks (OR = 1.36). CONCLUSION: Predictors of making a QA differed from predictors of quitting success. Predictors of QAs and success could each serve as important treatment targets of motivation-phase interventions. IMPLICATIONS: Motivation-phase interventions for people initially unwilling to quit smoking cigarettes may be improved by striving to increase their (1) time to first cigarette and quitting self-efficacy to promote QAs and (2) motivation to quit to promote quit success. Future experimental tests of such interventions are needed to identify causal determinants of QAs and quitting success.

Tobacco Use and Cessation Behaviors in Young Adults: 2016 National Health Interview Survey.

OBJECTIVES: To examine differences in tobacco use and cessation between young adults (aged 18-24 years) and adults aged 25 years or older. METHODS: We used data from the 2016 US National Health Interview Survey (n = 33 028) to identify 13 494 current and former cigarette smokers (562 aged 18-24 years; 12 932 aged 25 years or older). We analyzed correlations between age group, cigarette smoking, cessation behaviors, and other tobacco and nicotine use. RESULTS: Among current and former smokers, those aged 18 to 24 years had lower odds of having quit for 1 year or longer and higher odds of having tried e-cigarettes, cigars, pipes, and smokeless tobacco compared with adults aged 25 years or older, as well as higher odds of daily e-cigarette and smokeless tobacco use. Young adult smokers used fewer cigarettes per day than did those aged 25 years or older (mean = 8.8 vs 12.3), had higher odds of making a past-year quit attempt (adjusted odds ratio [AOR] = 1.42), and had lower odds of having a health professional talk to them about smoking (AOR = 0.44). CONCLUSIONS: Greater experimentation with noncigarette products, lower cigarette consumption, and greater interest in quitting smoking invite novel intervention approaches to tobacco reduction, cessation, and relapse prevention in young adults.

Latent trait shared-parameter mixed models for missing ecological momentary assessment data.

Latent trait shared-parameter mixed models for ecological momentary assessment (EMA) data containing missing values are developed in which data are collected in an intermittent manner. In such studies, data are often missing due to unanswered prompts. Using item response theory models, a latent trait is used to represent the missing prompts and modeled jointly with a mixed model for bivariate longitudinal outcomes. Both one- and two-parameter latent trait shared-parameter mixed models are presented. These new models offer a unique way to analyze missing EMA data with many response patterns. Here, the proposed models represent missingness via a latent trait that corresponds to the students' "ability" to respond to the prompting device. Data containing more than 10 300 observations from an EMA study involving high school students' positive and negative affects are presented. The latent trait representing missingness was a significant predictor of both positive affect and negative affect outcomes. The models are compared to a missing at random mixed model. A simulation study indicates that the proposed models can provide lower bias and increased efficiency compared to the standard missing at random approach commonly used with intermittent missing longitudinal data.

Preventing Smoking Progression in Young Adults: the Concept of Prevescalation.

As adolescents cross the threshold to young adulthood, they encounter more opportunities to engage in or accelerate previously discouraged or prohibited behaviors. Young adults, therefore, are more apt to initiate cigarette smoking and, more importantly, to accelerate their use if they tried and experimented as an adolescent. Preventing the escalation and entrenchment of smoking in the young adult years is critically important to reducing tobacco's long-term health toll. However, traditional interventions for youth have focused on preventing smoking initiation, and interventions for adults have focused on smoking cessation; both have failed to address the needs of young adults. We introduce the concept of "prevescalation" to capture the need and opportunity to prevent the escalation of risk behaviors that typically occur during young adulthood, with a focus on the example of cigarette smoking. Prevescalation negates the notion that prevention has failed if tobacco experimentation occurs during adolescence and focuses on understanding and interrupting transitions between experimentation with tobacco products and established tobacco use that largely occur during young adulthood. However, since risk behaviors often co-occur in young people, the core concept of prevescalation may apply to other behaviors that co-occur and become harder to change in later adulthood. We present a new framework for conceptualizing, developing, and evaluating interventions that better fit the unique behavioral, psychosocial, and socio-environmental characteristics of the young adult years. We discuss the need to target this transitional phase, what we know about behavioral pathways and predictors of cigarette smoking, potential intervention considerations, and research challenges.

A 3-level Bayesian mixed effects location scale model with an application to ecological momentary assessment data.

Ecological momentary assessment studies usually produce intensively measured longitudinal data with large numbers of observations per unit, and research interest is often centered around understanding the changes in variation of people's thoughts, emotions and behaviors. Hedeker et al developed a 2-level mixed effects location scale model that allows observed covariates as well as unobserved variables to influence both the mean and the within-subjects variance, for a 2-level data structure where observations are nested within subjects. In some ecological momentary assessment studies, subjects are measured at multiple waves, and within each wave, subjects are measured over time. Li and Hedeker extended the original 2-level model to a 3-level data structure where observations are nested within days and days are then nested within subjects, by including a random location and scale intercept at the intermediate wave level. However, the 3-level random intercept model assumes constant response change rate for both the mean and variance. To account for changes in variance across waves, as well as clustering attributable to waves, we propose a more comprehensive location scale model that allows subject heterogeneity at baseline as well as across different waves, for a 3-level data structure where observations are nested within waves and waves are then further nested within subjects. The model parameters are estimated using Markov chain Monte Carlo methods. We provide details on the Bayesian estimation approach and demonstrate how the Stan statistical software can be used to sample from the desired distributions and achieve consistent estimates. The proposed model is validated via a series of simulation studies. Data from an adolescent smoking study are analyzed to demonstrate this approach. The analyses clearly favor the proposed model and show significant subject heterogeneity at baseline as well as change over time, for both mood mean and variance. The proposed 3-level location scale model can be widely applied to areas of research where the interest lies in the consistency in addition to the mean level of the responses.