Examining Links Between Distinct Affective States and Tobacco Lapse During a Cessation Attempt Among African Americans: A Cohort Study.

BACKGROUND: Affect states are posited to play a pivotal role in addiction-related processes, including tobacco lapse (i.e., smoking during a quit attempt), and distinct affective states (e.g., joy vs. happiness) may differentially influence lapse likelihood. However, few studies have examined the influence of distinct affective states on tobacco lapse. PURPOSE: This study examines the influence of 23 distinct affect states on tobacco lapse among a sample of tobacco users attempting to quit. METHODS: Participants were 220 adults who identified as African American (50% female, ages 18-74). Ecological momentary assessment was used to assess affect and lapse in real-time. Between and within-person associations testing links between distinct affect states and lapse were examined with multilevel modeling for binary outcomes. RESULTS: After adjusting for previous time's lapse and for all other positive or negative affect items, results suggested that at the between-person level, joy was associated with lower odds of lapse, and at the within-person level, attentiveness was associated with lower odds of lapse. Results also suggested that at the between-person level, guilt and nervous were associated with higher odds of lapse, and at the within-person level, shame was associated with higher odds of lapse. CONCLUSIONS: The present study uses real-time, real-world data to demonstrate the role of distinct positive and negative affects on momentary tobacco lapse. This work helps elucidate specific affective experiences that facilitate or hinder the ability to abstain from tobacco use during a quit attempt.

Co-action and changes in alcohol use during a smoking cessation attempt.

AIMS: Three smoking cessation studies (CARE, Break Free, Por Nuestra Salud [PNS]) were used to measure changes in average alcohol consumption, binge drinking and alcohol-related problems during a smoking cessation attempt and to explore co-action with smoking abstinence. DESIGN: CARE and PNS were longitudinal cohort cessation studies; Break Free was a two-arm randomized clinical trial. SETTING: Texas, USA. PARTICIPANTS: Participants were current smokers who were recruited from the community and received smoking cessation interventions. All participants received nicotine replacement therapy and smoking cessation counseling. CARE included 424 smokers (1/3 White, 1/3 African American and 1/3 Latino); Break Free included 399 African American smokers; PNS included 199 Spanish-speaking Mexican-American smokers. MEASUREMENTS: Weekly alcohol consumption was collected multiple times pre and post-quit, and binge drinking and alcohol-related problems were collected at baseline and 26 weeks post-quit. Analyses included only those who indicated current alcohol use. FINDINGS: Average alcohol consumption decreased from baseline to 26 weeks post-quit in CARE (F = 17.09, P < 0.001), Break Free (F = 12.08, P < 0.001) and PNS (F = 10.21, P < 0.001). Binge drinking decreased from baseline to 26 weeks post-quit in CARE (F = 3.94, P = 0.04) and Break Free (F = 10.41, P < 0.001) but not PNS. Alcohol-related problems decreased from baseline to 26 weeks post-quit in CARE (Chi-sq = 6.41, P = 0.010) and Break Free (Chi sq = 14.44, P = 0.001), but not PNS. CONCLUSIONS: Among current drinkers, alcohol use/problems appear to decrease during a smoking cessation attempt and remain low through 26 weeks after the quit attempt. Little evidence was found for co-action, with smoking abstainers and relapsers showing similar change in alcohol use/problems.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data.

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

The Effects of Scheduled Smoking Reduction and Precessation Nicotine Replacement Therapy on Smoking Cessation: Randomized Controlled Trial With Compliance.

BACKGROUND: Smoking remains a major public health problem, and it is important to provide a variety of efficacious and appealing options to encourage smokers to quit smoking. Scheduled smoking is a method of gradual reduction, preparing smokers to quit by systematically reducing cigarette consumption according to a predetermined schedule that increases the time between cigarette consumption. Gradual reduction may be preferred to abrupt quitting, but the efficacy of this cessation approach is unclear. OBJECTIVE: This study aims, first, to evaluate the overall effectiveness of scheduled smoking alone, or in combination with precessation nicotine replacement therapy (NRT), versus standard NRT starting on the quit date with no prior smoking reduction and, second, to evaluate the impact of schedule compliance on the effectiveness of the intervention. METHODS: A total of 916 participants recruited from the Houston metropolitan area were randomly assigned to 1 of the following 3 groups: scheduled smoking plus a precessation nicotine patch (n=306, 33.4%), scheduled smoking only with no precessation patch (n=309, 33.7%), and enhanced usual care (n=301, 32.9%) control. The primary abstinence outcomes were carbon monoxide-verified, self-reported, 7-day point prevalence abstinence at 2 and 4 weeks after the quit date. Unadjusted and adjusted logistic regression analyses were performed to evaluate the intervention effect. Scheduled smoking was implemented using a handheld device for 3 weeks before quitting. This trial was not registered because data collection began before July 1, 2005. RESULTS: Results for the first aim showed no overall differences in abstinence among the 3 groups in both the unadjusted and adjusted models. However, the results for the second aim showed a clear effect on abstinence by schedule compliance at 2 and 4 weeks and 6 months after quitting (odds ratio [OR] 2.01, 95% CI 1.31-3.07), 4 weeks (OR 1.58, 95% CI 1.05-2.38), and 6 months (OR 1.68, 95% CI 1.04-2.64), with the differences at 2 and 4 weeks after quitting being the most robust. We also found that scheduled smoking was related to a reduction in nicotine withdrawal, negative affect, and craving when compared with the controls. CONCLUSIONS: Scheduled smoking, when combined with precessation use of NRT, can result in significantly higher abstinence rates than usual care (abrupt quitting with NRT), particularly in the early postquit phase (2 and 4 weeks after cessation) when smokers are compliant with the procedure. Scheduled smoking also produced a better overall quitting experience by reducing symptoms of nicotine withdrawal and craving, in comparison with usual care, which could encourage future quit attempts. Studies in this area should focus on the use of counseling or other methods to improve adherence.

Data envelopment analysis to evaluate the efficiency of tobacco treatment programs in the NCI Moonshot Cancer Center Cessation Initiative.

BACKGROUND: The Cancer Center Cessation Initiative (C3I) is a National Cancer Institute (NCI) Cancer Moonshot Program that supports NCI-designated cancer centers developing tobacco treatment programs for oncology patients who smoke. C3I-funded centers implement evidence-based programs that offer various smoking cessation treatment components (e.g., counseling, Quitline referrals, access to medications). While evaluation of implementation outcomes in C3I is guided by evaluation of reach and effectiveness (via RE-AIM), little is known about technical efficiency-i.e., how inputs (e.g., program costs, staff time) influence implementation outcomes (e.g., reach, effectiveness). This study demonstrates the application of data envelopment analysis (DEA) as an implementation science tool to evaluate technical efficiency of C3I programs and advance prioritization of implementation resources. METHODS: DEA is a linear programming technique widely used in economics and engineering for assessing relative performance of production units. Using data from 16 C3I-funded centers reported in 2020, we applied input-oriented DEA to model technical efficiency (i.e., proportion of observed outcomes to benchmarked outcomes for given input levels). The primary models used the constant returns-to-scale specification and featured cost-per-participant, total full-time equivalent (FTE) effort, and tobacco treatment specialist effort as model inputs and reach and effectiveness (quit rates) as outcomes. RESULTS: In the DEA model featuring cost-per-participant (input) and reach/effectiveness (outcomes), average constant returns-to-scale technical efficiency was 25.66 (SD = 24.56). When stratified by program characteristics, technical efficiency was higher among programs in cohort 1 (M = 29.15, SD = 28.65, n = 11) vs. cohort 2 (M = 17.99, SD = 10.16, n = 5), with point-of-care (M = 33.90, SD = 28.63, n = 9) vs. no point-of-care services (M = 15.59, SD = 14.31, n = 7), larger (M = 33.63, SD = 30.38, n = 8) vs. smaller center size (M = 17.70, SD = 15.00, n = 8), and higher (M = 29.65, SD = 30.99, n = 8) vs. lower smoking prevalence (M = 21.67, SD = 17.21, n = 8). CONCLUSION: Most C3I programs assessed were technically inefficient relative to the most efficient center benchmark and may be improved by optimizing the use of inputs (e.g., cost-per-participant) relative to program outcomes (e.g., reach, effectiveness). This study demonstrates the appropriateness and feasibility of using DEA to evaluate the relative performance of evidence-based programs.

Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studies.

Objective: Insufficient engagement is a critical barrier impacting the utility of digital interventions and mobile health assessments. As a result, engagement itself is increasingly becoming a target of studies and interventions. The purpose of this study is to investigate the dynamics of engagement in mobile health data collection by exploring whether, how, and why response to digital self-report prompts change over time in smoking cessation studies. Method: Data from two ecological momentary assessment (EMA) studies of smoking cessation among diverse smokers attempting to quit (N = 573) with a total of 65,974 digital self-report prompts. We operationalize engagement with self-reporting in term of prompts delivered and prompt response to capture both broad and more granular engagement in self-reporting, respectively. The data were analyzed to describe trends in prompt delivered and prompt response over time. Time-varying effect modeling (TVEM) was employed to investigate the time-varying effects of response to previous prompt and the average response rate on the likelihood of current prompt response. Results: Although prompt response rates were relatively stable over days in both studies, the proportion of participants with prompts delivered declined steadily over time in one of the studies, indicating that over time, fewer participants charged the device and kept it turned on (necessary to receive at least one prompt per day). Among those who did receive prompts, response rates were relatively stable. In both studies, there is a significant, positive and stable relationship between response to previous prompt and the likelihood of response to current prompt throughout all days of the study. The relationship between the average response rate prior to current prompt and the likelihood of responding to the current prompt was also positive, and increasing with time. Conclusion: Our study highlights the importance of integrating various indicators to measure engagement in digital self-reporting. Both average response rate and response to previous prompt were highly predictive of response to the next prompt across days in the study. Dynamic patterns of engagement in digital self-reporting can inform the design of new strategies to promote and optimize engagement in digital interventions and mobile health studies.

Socio-economic status moderates within-person associations of risk factors and smoking lapse in daily life.

BACKGROUND AND AIMS: Individuals of lower socio-economic status (SES) display a higher prevalence of smoking and have more diffxiculty quitting than higher SES groups. The current study investigates whether the within-person associations of key risk (e.g. stress) and protective (self-efficacy) factors with smoking lapse varies by facets of SES. DESIGN AND SETTING: Observational study using ecological momentary assessment to collect data for a 28-day period following a smoking quit attempt. Multi-level mixed models (i.e. generalized linear mixed models) examined cross-level interactions between lapse risk and protective factors and indicators of SES on smoking lapse. PARTICIPANTS: A diverse sample of 330 adult US smokers who completed a larger study examining the effects of race/ethnicity and social/environmental influences on smoking cessation. MEASUREMENTS: Risk factors included momentary urge, negative affect, stress; protective factors included positive affect, motivation, abstinence self-efficacy; SES measures: baseline measures of income and financial strain; the primary outcome was self-reported lapse. FINDINGS: Participants provided 43 297 post-quit observations. Mixed models suggested that income and financial strain moderated the effect of some risk factors on smoking lapse. The within-person association of negative [odds ratio (OR) = 0.967, 95% CI= 0.945, 0.990, P < 0.01] and positive affect (OR = 1.023, 95% CI = 1.003, 1.044, P < 0.05) and abstinence self-efficacy (OR = 1.020, 95% CI = 1.003, 1.038, P < 0.05) on lapse varied with financial strain. The within-person association of negative affect (OR = 1.005, 95% CI = 1.002, 1.008, P < 0.01), motivation (OR = 0.995, 95% CI = 0.991, 0.999, P < 0.05) and abstinence self-efficacy (OR = 0.996, 95% CI = 0.993, 0.999, P < 0.01) on lapse varied by income. The positive association of negative affect with lapse was stronger among individuals with higher income and lower financial strain. The negative association between positive affect and abstinence self-efficacy with lapse was stronger among individuals with lower financial strain, and the negative association between motivation and abstinence self-efficacy with lapse was stronger among those with higher income. The data were insensitive to detect statistically significant moderating effects of income and financial strain on the association of urge or stress with lapse. CONCLUSION: Some risk factors (e.g. momentary negative affect) exert a weaker influence on smoking lapse among lower compared to higher socio-economic status groups.

An ecological momentary assessment study of outcome expectancies and smoking lapse in daily life.

OBJECTIVE: Outcome expectancies have been identified as key components of behavior change. Expectancies related to affect control are hypothesized to play an important role in smoking cessation, such that smokers may be more likely to lapse if they believe they can control their affect by smoking and less likely if they believe they can control their affect by means other than smoking. However, little is known about whether real-time, real-world changes in affect control expectancies influence smoking lapse during a quit attempt. METHODS: A diverse sample (N = 369) of adult smokers completed ecological momentary assessment of smoking expectancies and lapse for 28 days following a quit attempt. Multilevel logistic regression was used to examine whether the difference score of positive smoking outcome expectancies (the belief that smoking would improve mood) minus positive coping outcome expectancies (the belief that something other than smoking would improve mood) was related to smoking lapse in daily life. RESULTS: There was a significant within-person association between the expectancies difference score and lapse likelihood. When the difference score was 1 unit above a person's typical level, odds of lapse increased by 18.65 % (ß = 0.174, SE = 0.024, p < .0001, OR = 1.189, 95 % CI [1.135, 1.247]). CONCLUSION: Smokers undergoing a quit attempt were more likely to lapse in moments when the difference in the belief that smoking would improve their mood minus the belief that something other than smoking would improve their mood was larger. This work has relevance for tailoring interventions to both cultivate positive coping outcome expectancies and reduce smoking outcome expectancies, and informs theoretical models about the dynamic nature of outcome expectancies.

Trajectories of motivation and self-efficacy during a smoking quit attempt: An ecological momentary assessment study.

OBJECTIVE: Conceptual models of addiction highlight self-efficacy and motivation as key variables important during a smoking quit attempt; however, recent research has primarily focused on self-efficacy. Given the importance of motivation in these models, a clearer understanding of the effects of motivation during a smoking quit attempt is needed. METHOD: This study utilized ecological momentary assessment (EMA) to investigate the dynamic effects of motivation and self-efficacy early in a quit attempt. Participants were 356 smokers (45% male; 34% African American, 33% non-Hispanic White, 33% Latino). Participants completed EMAs of motivation, self-efficacy, and smoking for 4 days prequit through 1 week postquit, and returned for a follow-up assessment at 4 weeks postquit. Trajectory parameters of motivation and self-efficacy (mean, slope, and volatility) were analyzed in separate and combined regression models to predict smoking outcomes. RESULTS: Prequit results showed that parameters of motivation and self-efficacy were not associated with smoking on quit day. Postquit analyses revealed that participants with lower mean levels of motivation and self-efficacy were more likely to smoke at the end of Week 1. Moreover, individuals with decreasing levels of motivation over the first week of their quit attempt were more likely to be smoking at the end of Week 4. CONCLUSIONS: These findings highlight the need to incorporate dynamic measures of motivation in smoking research. Furthermore, the results underscore the value of utilizing EMA methods and trajectory parameters to gain a more nuanced understanding of the dynamic effects that key mechanisms have on smoking during a quit attempt. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol.

Smoking is the leading preventable cause of death and disability in the U.S. Empirical evidence suggests that engaging in evidence-based self-regulatory strategies (e.g., behavioral substitution, mindful attention) can improve smokers' ability to resist craving and build self-regulatory skills. However, poor engagement represents a major barrier to maximizing the impact of self-regulatory strategies. This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) - a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies. The study will employ a 10-day Micro-Randomized Trial (MRT) enrolling 112 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either: (a) no intervention prompt; (b) a prompt recommending brief (low effort) cognitive and/or behavioral self-regulatory strategies; or (c) a prompt recommending more effortful cognitive or mindfulness-based strategies. Prompts will be delivered via push notifications from the MARS mobile app. The goal is to investigate whether, what type of, and under what conditions prompting the individual to engage in self-regulatory strategies increases engagement. The results will build the empirical foundation necessary to develop a mHealth intervention that effectively utilizes intensive longitudinal self-report and sensor-based assessments of emotions, context and other factors to engage an individual in the type of self-regulatory activity that would be most beneficial given their real-time, real-world circumstances. This type of mHealth intervention holds enormous potential to expand the reach and impact of smoking cessation treatments.

Mixed-methods economic evaluation of the implementation of tobacco treatment programs in National Cancer Institute-designated cancer centers.

BACKGROUND: The Cancer Center Cessation Initiative (C3I) was launched in 2017 as a part of the NCI Cancer Moonshot program to assist NCI-designated cancer centers in developing tobacco treatment programs for oncology patients. Participating centers have implemented varied evidence-based programs that fit their institutional resources and needs, offering a wide range of services including in-person and telephone-based counseling, point of care, interactive voice response systems, referral to the quitline, text- and web-based services, and medications. METHODS: We used a mixed methods comparative case study design to evaluate system-level implementation costs across 15 C3I-funded cancer centers that reported for at least one 6-month period between July 2018 and June 2020. We analyzed operating costs by resource category (e.g., personnel, medications) concurrently with transcripts from semi-structured key-informant interviews conducted during site visits. Personnel salary costs were estimated using Bureau of Labor Statistics wage data adjusted for area and occupation, and non-wage benefits. Qualitative findings provided additional information on intangible resources and contextual factors related to implementation costs. RESULTS: Median total monthly operating costs across funded centers were $11,045 (range: $5129-$20,751). The largest median operating cost category was personnel ($10,307; range: $4122-$19,794), with the highest personnel costs attributable to the provision of in-person program services. Monthly (non-zero) cost ranges for other categories were medications ($17-$573), materials ($6-$435), training ($96-$516), technology ($171-$2759), and equipment ($10-$620). Median cost-per-participant was $466 (range: $70-$2093) and cost-per-quit was $2688 (range: $330-$9628), with sites offering different combinations of program components, ranging from individually-delivered in-person counseling only to one program that offered all components. Site interviews provided context for understanding variations in program components and their cost implications. CONCLUSIONS: Among most centers that have progressed in tobacco treatment program implementation, cost-per-quit was modest relative to other prevention interventions. Although select centers have achieved similar average costs by offering program components of various levels of intensity, they have varied widely in program reach and effectiveness. Evaluating implementation costs of such programs alongside reach and effectiveness is necessary to provide decision makers in oncology settings with the important additional information needed to optimize resource allocation when establishing tobacco treatment programs.

Evaluation of Revised US Preventive Services Task Force Lung Cancer Screening Guideline Among Women and Racial/Ethnic Minority Populations.

Importance: Lung cancer incidence and mortality disproportionately affect women and racial/ethnic minority populations, yet screening guidelines for the past several years were derived from clinical trials of predominantly White men. To reflect current evidence, the US Preventive Services Task Force (USPSTF) has revised the eligibility criteria, which may help to ameliorate sex- and race/ethnicity-related disparities in lung cancer screening. Objective: To determine the changes associated with the revised USPSTF guideline for lung cancer screening eligibility among female, Black, and Hispanic populations using a large nationwide survey. Design, Setting, and Participants: This cross-sectional study included respondents to the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System who were 50 to 80 years of age with a smoking history in 19 states that used the optional lung cancer screening module. The change in eligibility among female, male, Black, Hispanic, and White participants was examined. Eligibility by sex and race/ethnicity was compared with a reference population. Data were collected from January 1, 2017, to December 31, 2018, and analyzed from May 8 to June 11, 2020. Exposures: Self-reported sex, race/ethnicity, age, and smoking history. Main Outcomes and Measures: Lung cancer screening eligibility using the revised USPSTF criteria. The previous criteria included current or past smokers (within 15 years) who were 55 to 80 years of age and had a smoking history of more than 30 pack-years. In the revised criteria, age was modified to 50 to 80 years; smoking history, to 20 pack-years. Results: Among 40 869 respondents aged 50 to 80 years with a smoking history, 21 265 (52.0%) were women, 3430 (8.4%) were Black, and 1226 (30.0%) were Hispanic (mean [SD] age, 65.6 [7.9] years). The revised criteria increased eligibility for the following populations: men (29.4% to 38.3% [8.9% difference]; P < .001), women (25.9% to 36.4% [10.5% difference]; P < .001), White individuals (31.1% to 40.9% [9.8% difference]; P < .001), Black individuals (16.3% to 28.8% [12.5% difference]; P < .001), and Hispanic individuals (10.5% to 18.7% [8.2% difference]; P < .001). The odds of eligibility were lower for women compared with men (adjusted odds ratio [AOR], 0.88; 95% CI, 0.79-0.99; P = .04) and for Black (AOR, 0.43; 95% CI, 0.33-0.56; P < .001) and Hispanic populations (AOR, 0.70; 95% CI, 0.62-0.80; P < .001) compared with the White population. Conclusions and Relevance: The revised USPSTF guideline may likely increase lung cancer screening rates for female, Black, and Hispanic populations. However, despite these potential improvements, lung cancer screening inequities may persist without tailored eligibility criteria.

Application of community - engaged dissemination and implementation science to improve health equity.

Community engagement is critical to accelerate and improve implementation of evidence-based interventions to reduce health inequities. Community-engaged dissemination and implementation research (CEDI) emphasizes engaging stakeholders (e.g., community members, practitioners, community organizations, etc.) with diverse perspectives, experience, and expertise to provide tacit community knowledge regarding the local context, priorities, needs, and assets. Importantly, CEDI can help improve health inequities through incorporating unique perspectives from communities experiencing health inequities that have historically been left out of the research process. The community-engagement process that exists in practice can be highly variable, and characteristics of the process are often underreported, making it difficult to discern how engagement of community partners was used to improve implementation. This paper describes the community-engagement process for a multilevel, pragmatic randomized trial to increase the reach and impact of evidence-based tobacco cessation treatment among Community Health Center patients; describes how engagement activities and the resulting partnership informed the development of implementation strategies and improved the research process; and presents lessons learned to inform future CEDI research.

Intersectionality and Smoking Cessation: Exploring Various Approaches for Understanding Health Inequities.

OBJECTIVE: Many marginalized groups smoke at higher rates and have greater difficulty quitting than less marginalized groups. Most research on smoking cessation inequities has focused on a single sociodemographic attribute (eg, race or socioeconomic status), yet individuals possess multiple attributes that may increase risk. The current study used an intersectionality framework to examine how the interplay between multiple marginalized attributes may impact smoking cessation outcomes. METHODS: A diverse sample of 344 adults enrolled in a smoking cessation program and reported on sociodemographic attributes (eg, race/ethnicity, gender, income) and continuous smoking abstinence on their quit date and at 1, 2, and 4 weeks postquit date. A Cox proportional hazard regression model was used to estimate whether intersectional links among race/ethnicity, gender, and income were related to smoking cessation outcomes. RESULTS: Lower household income may be related to higher risk of smoking cessation failure. There were no significant interactions among race/ethnicity, gender, and income in predicting relapse. Pairwise intersectional group differences suggested some groups may be at higher risk of relapse. Number of marginalized sociodemographic attributes did not predict relapse. CONCLUSIONS: Intersectionality may be a promising framework for addressing health inequities, and may help elucidate how to best design and target intervention efforts for individuals characterized by sociodemographic intersections that concur particularly high risk for poor tobacco cessation outcomes. IMPLICATIONS: Despite an overall decline in smoking rates, socioeconomic inequities in smoking prevalence and cancer mortality are widening. Efforts targeting tobacco cessation should incorporate new theory to capture the complex set of factors that may account for tobacco cessation inequities (eg, multiple aspects of identity that may influence access to tobacco cessation treatment and exposure to certain stressors that impede cessation efforts). Intersectionality may be a promising framework for addressing health inequities in tobacco use and cessation and may help elucidate how to best design and target intervention efforts for individuals that concur particularly high risk for poor tobacco cessation outcomes.

A time-varying model of the dynamics of smoking lapse.

OBJECTIVE: The majority of smokers who make a quit attempt experience their first lapse within the first week of quitting, yet limited research to date has examined how the strength and direction of the relationship between smoking risk factors and lapse may change over longer periods of time. Time-varying effect modeling (TVEM) was used to address this gap. METHOD: A diverse sample (N = 325) of adult smokers completed ecological momentary assessments of risk factors for lapse for 28 days after quitting. TVEM was used to examine the relationship between risk factors (abstinence self-efficacy, positive affect, positive coping expectancies, smoking expectancies, motivation, negative affect, stress, and urge) and lapse for 28 days postquit. RESULTS: Some associations were stable (e.g., negative affect, motivation), whereas others varied over time. Abstinence self-efficacy, positive affect, and positive coping expectancies were most strongly associated with lapse between Days 3 and 8 postquit. The association of urge with lapse was strongest between Days 4 and 10, as well as near the end of the quit attempt. Stress was also most strongly associated with lapse near the beginning and end of the postquit period and was the only predictor associated with lapse on quit date. The strength of the association between smoking expectancies and lapse increased over time. CONCLUSION: There may be periods during a quit attempt when certain risk factors are more strongly related to lapse. This work has relevance for tailoring interventions designed to deliver intervention components in particular contexts or times of need. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

QuitSMART Utah: an implementation study protocol for a cluster-randomized, multi-level Sequential Multiple Assignment Randomized Trial to increase Reach and Impact of tobacco cessation treatment in Community Health Centers.

BACKGROUND: Tobacco use remains the leading cause of death and disability in the USA and is disproportionately concentrated among low socioeconomic status (SES) populations. Community Health Centers (CHCs) are a key venue for reaching low SES populations with evidence-based tobacco cessation treatment such as Quitlines. Electronic health record (EHR)-based interventions at the point-of-care, text messaging (TM), and phone counseling have the potential to increase Quitline reach and are feasible to implement within CHCs. However, there is a lack of data to inform how, when, and in what combination these strategies should be implemented. The aims of this cluster-randomized trial are to evaluate multi-level implementation strategies to increase the Reach (i.e., proportion of tobacco-using patients who enroll in the Quitline) and Impact (i.e., Reach × Efficacy [efficacy is defined as the proportion of tobacco-using patients who enroll in Quitline treatment that successfully quit]) and to evaluate characteristics of healthcare system, providers, and patients that may influence tobacco-use outcomes. METHODS: This study is a multilevel, three-phase, Sequential Multiple Assignment Randomized Trial (SMART), conducted in CHCs (N = 33 clinics; N = 6000 patients). In the first phase, clinics will be randomized to two different EHR conditions. The second and third phases are patient-level randomizations based on prior treatment response. Patients who enroll in the Quitline receive no further interventions. In phase two, patients who are non-responders (i.e., patients who do not enroll in Quitline) will be randomized to receive either TM or continued-EHR. In phase three, patients in the TM condition who are non-responders will be randomized to receive either continued-TM or TM + phone coaching. DISCUSSION: This project will evaluate scalable, multi-level interventions to directly address strategic national priorities for reducing tobacco use and related disparities by increasing the Reach and Impact of evidence-based tobacco cessation interventions in low SES populations. TRIAL REGISTRATION: This trial was registered at ClinicalTrials.gov (NCT03900767) on April 4th, 2019.

Socioeconomic Status, Social Context, and Smoking Lapse During a Quit Attempt: An Ecological Momentary Assessment Study.

BACKGROUND: Low socioeconomic status (SES) is linked to failure to quit smoking. Health inequity models suggest that low SES smokers experience barriers to quitting in part due to greater exposure to pro-smoking social contexts. PURPOSE: The current study examined longitudinal associations among socioeconomic status, pro-smoking social context factors (i.e., exposure to other smokers, places where smoking was allowed), cigarette availability, and smoking lapse during a quit attempt. METHODS: Ecological momentary assessments (EMA) were gathered from a multiethnic sample of 365 smokers engaged in a quit attempt. A multilevel structural equation model estimated a latent variable for SES indicated by income, education, health insurance, and employment, associations among EMAs for pro-smoking social contexts and cigarette availability, and indirect effects of SES through, pro-smoking social contexts and cigarette availability to subsequent smoking lapse. RESULTS: Lower SES scores were associated with a higher likelihood of smoking lapse. Decomposition of the path from SES to smoking lapse into indirect effects showed significant paths through exposure to places where smoking is allowed and cigarette availability. Additionally, significant serial indirect paths from SES through both exposure to other smokers and places where smoking was allowed, in turn, through cigarette availability, and, ultimately, to smoking lapse were noted. CONCLUSIONS: Consistent with models positing that SES influences health behaviors via contextual factors, the current study demonstrated that low SES smokers attempting to quit experienced greater pro-smoking social contexts that affected subsequent risk for lapse.

Tobacco-related knowledge following a comprehensive tobacco-free workplace program within behavioral health facilities: Identifying organizational moderators.

OBJECTIVE: Although smoking prevalence rates among behavioral health consumers is nearly five times that of the general population, evidence-based policies and practices to address tobacco use are uncommon within behavioral health settings. This study assessed changes in non-clinical, general staff and clinician tobacco-related knowledge following brief education provided as part of a comprehensive tobacco-free workplace program implementation and explored organizational moderators of pre- to post-education knowledge change. METHODS: Fifteen behavioral health facilities, comprising hundreds of individual clinics in Texas, participated in a one (for general staff) or two (for clinicians) hour educational session. RESULTS: There were large effect sizes in general staff knowledge gain within each consortium, and large effect sizes in clinician knowledge gain in all but one consortium. Knowledge of the requirements for change, perceived availability of resources, and total number of client contacts moderated general staff knowledge gain. Value in the change and total number of client contacts moderated training effectiveness among clinicians. CONCLUSIONS: We conclude that a brief tobacco-related education for behavioral health employees was effective in increase attendee knowledge. PRACTICE IMPLICATIONS: Attention to organization-level factors moderating knowledge gain has the potential to guide and improve program implementation.

Brain Responses to Cigarette-Related and Emotional Images in Smokers During Smoking Cessation: No Effect of Varenicline or Bupropion on the Late Positive Potential.

Introduction: Varenicline and bupropion are two effective smoking cessation pharmacotherapies. Researchers have hypothesized that they might be effective, in part, because they reduce cue reactivity and cue-induced cravings. Here, we used event-related potentials (ERPs) to directly measure brain responses to cigarette-related and other motivationally relevant images during a pharmacologically aided quit attempt. Methods: Smokers involved in a 12-week placebo-controlled double-blind clinical trial of smoking cessation medications (varenicline, bupropion, placebo) took part in the study. We assessed participants at two time points: 24 h (n = 140) and 4 weeks (n = 176) after the quit date. At both sessions, we measured the amplitude of the late positive potential (LPP), an ERP component reliably associated with motivational relevance, and self-reported tonic craving using the brief version of the Questionnaire of Smoking Urges (QSU-Brief). Results: At both sessions, emotional and cigarette-related images evoked significantly larger LPPs than neutral images. Neither drug type nor smoking abstinence altered this effect at either session. At both sessions, varenicline and bupropion significantly reduced self-reported tonic craving relative to the placebo condition. Conclusions: While both varenicline and bupropion reduced self-reported tonic craving, neither medication altered the amplitude of the LPP to cigarette-related or emotional pictures in smokers attempting to quit. These medications may influence abstinence by means other than by reducing neuroaffective responses to cigarette-related cues. Smokers should be prepared for the likelihood that even after several weeks of successful abstinence, once treatment ends, cigarette-related cues may remain motivationally relevant and trigger cravings that might lead to relapse. Implications: Bupropion and varenicline do not alter electrophysiological responses, as measured by the LPP, to cigarette-related and emotional images. These findings help explain why cigarette-related cues can trigger relapse when smoking cessation medication treatments end.

Socioeconomic indicators as predictors of smoking cessation among Spanish-Speaking Mexican Americans.

Objective: Smoking-related illnesses are the leading cause of death among Latinos, and within this ethnic group, Mexican Americans are the largest subgroup in the U.S. Understanding the factors associated with successful smoking cessation could inform interventions for this population. Although socioeconomic status (SES) is a powerful predictor of cessation outcomes in the general U.S. population, it has generally been a poor predictor of quitting smoking among Latinos. Within a sample of Spanish-speaking Mexican Americans (n = 199), this study examined a broad array of objective and subjective indicators of SES (i.e. income, education, employment, subjective social status, financial strain, insurance status) as predictors of smoking cessation. Design: Data for the current study came from a longitudinal cohort study examining the pathways linking the social determinants of health with smoking cessation. Generalized estimating equation modeling examined the association of each predictor variable with smoking abstinence across quit day, and the 3 and 26-weeks post-quit time points. Results: Results indicated that both low financial strain and insurance status predicted an increased likelihood of abstinence when controlling for covariates in the intention-to-treat analyses (p = .02 and p = .01, respectively). However, these models only approached significance in the multiple imputation analyses (all ps > .05). Other indicators of SES (i.e. income, education, employment) that have been predictive of cessation in other populations were not predictive of abstinence in this sample. Conclusions: These findings suggest that SES may indeed influence smoking cessation among Spanish-speaking Mexican Americans similarly to its influence in other populations, but that capturing the construct of SES may require assessing a broader range of SES indicators. Specifically, low financial strain and having insurance predicted a greater likelihood of achieving smoking abstinence, whereas other indicators of SES (i.e. income, education) were not predictive.