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Abstract Introduction Preliminary trial data suggest group-delivered acceptance and commitment therapy (ACT) might be effective for smoking cessation. If so, this could offer a viable alternative to mainstream behavioral therapies, such as those grounded in cognitive behavioral therapy (CBT). The goal of the current study was to compare the effectiveness of group-delivered ACT versus group-delivered CBT in a rigorous randomized trial design with long-term follow-up. Methods Participants (n = 450) were recruited from the Kaiser Permanente Washington health care system and randomized to either ACT-based group counseling or an attention-matched CBT-based group program. All were prescribed an 8-week course of nicotine patches. The primary outcome was self-reported 30-day point prevalence abstinence at 12 months post-randomization assessed with missing values imputed as smoking. Sensitivity analyses using multiple imputation and complete cases were examined, as were biochemically confirmed and 6-month outcomes. Results Thirty-day point prevalence abstinence rates at the 12-month follow-up did not differ between study arms in the primary analysis (13.8% ACT vs. 18.1% CBT, adjusted odds ratio = 0.68 [95% CI = 0.35 to 1.27], p = .23) or the sensitivity analyses. Conclusions Group-based ACT and CBT had similar long-term quit rates in this methodologically rigorous randomized trial. Group-based ACT is a reasonable alternative to group-based CBT for smoking cessation. Implications This study compared the effectiveness of group-based ACT with group-based CBT for smoking cessation using a rigorous, large-scale, attention-matched, randomized trial with 1-year follow-up. One-year cessation rates did not differ between group-based ACT and CBT, suggesting ACT-based intervention is a reasonable alternative to CBT-based counseling for smoking cessation. The results add to the nascent but growing literature assessing ACT and other mindfulness-based treatments for smoking cessation.

Despite having many advantages, online eHealth trials are not without challenges-notably, participant recruitment, and outcome data retention. Moreover, publications from these trials rarely provide detailed information on the methods used for recruitment and retention or discuss implications of the methods for future studies. To address this need for empirical guidance regarding recruitment and outcome data retention planning, we aim to describe the methods and lessons learned from the recruitment and retention procedures used in a large randomized trial of 2 Web-based smoking cessation interventions. To ensure a demographically and geographically diverse participant sample, we used the recruitment strategies (1) traditional, (2) Web-based, and (3) online survey panel methods and adaptively modified each in response to recruitment success. At baseline, participants indicated how they heard about the study and answered demographic questions. To maximize trial retention at each of the 3-, 6-, and 12-month assessment points, 4 survey modalities (first Web, followed by phone, mail, and postcard) were sequentially timed over a 30-day period. Participants received US $25 for submitting their responses, regardless of modality, and received an additional US $10 bonus for completing the Web survey within 24h of electronic notification. We randomized 2637 smokers in 16 months and achieved 88% retention at 12-months. Participants (79.26% female, 72.60% Caucasian) were recruited from all 50 states. The majority of participants were recruited through Facebook (49.43%), followed by the survey panel (20.85%), free internet sources (14.54%), traditional media (11.34%), and Google ads (3.84%). Descriptively, participant demographics varied by recruitment source. Of the completed follow-up surveys, most were completed by Web (92%). Retention rates did not vary by recruitment source. Continuous monitoring and refinement of multiple recruitment methods, particularly of online advertising campaigns, allowed us to maximize the effectiveness of recruitment strategies in recruiting a large, diverse sample of smokers. Likewise, offering multiple follow-up survey modalities in sequential order along with time-dependent bonus incentives enabled us to obtain outcome data from a very high level of enrolled participants for the duration of the trial protocol. These strategies may be similarly useful in other trials. ClinicalTrials.gov NCT01812278; https://clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/71gy5GLvO).

Machine learning algorithms such as elastic net regression and backward selection provide a unique and powerful approach to model building given a set of psychosocial predictors of smoking lapse measured repeatedly via ecological momentary assessment (EMA). Understanding these predictors may aid in developing interventions for smoking lapse prevention. In a randomized-controlled smoking cessation trial, smartphone-based EMAs were collected from 92 participants following a scheduled quit date. This secondary analysis utilized elastic net-penalized cox proportional hazards regression and model approximation via backward elimination to (1) optimize a predictive model of time to first lapse and (2) simplify that model to its core constituent predictors to maximize parsimony and generalizability. Elastic net proportional hazards regression selected 17 of 26 possible predictors from 2065 EMAs to model time to first lapse. The predictors with the highest magnitude regression coefficients were having consumed alcohol in the past hour, being around and interacting with a smoker, and having cigarettes easily available. This model was reduced using backward elimination, retaining five predictors and approximating to 93.9% of model fit. The retained predictors included those mentioned above as well as feeling irritable and being in areas where smoking is either discouraged or allowed (as opposed to not permitted). The strongest predictors of smoking lapse were environmental in nature (e.g., being in smoking-permitted areas) as opposed to internal factors such as psychological affect. Interventions may be improved by a renewed focus of interventions on these predictors. The present study demonstrated the utility of machine learning algorithms to optimize the prediction of time to smoking lapse using EMA data. The two models generated by the present analysis found that environmental factors were most strongly related to smoking lapse. The results support the use of machine learning algorithms to investigate intensive longitudinal data, and provide a foundation for the development of highly tailored, just-in-time interventions that can target on multiple antecedents of smoking lapse.

Lower rates of smoking cessation are a major reason for the higher prevalence of smoking among socioeconomically disadvantaged adults. Because barriers to quitting are both more numerous and severe, socioeconomically disadvantaged smokers may benefit from more intensive intervention. We sought to determine whether a smoking cessation intervention delivered by public housing residents trained as Tobacco Treatment Advocates (TTAs) could increase utilization of cessation resources and increase abstinence. We conducted a group-randomized trial among Boston public housing residents who were interested in quitting smoking. Participants at control sites received standard cessation materials and a one-time visit from a TTA who provided basic counseling and information about cessation resources. Participants at intervention sites were eligible for multiple visits by a TTA who employed motivational interviewing, cessation counseling, and navigation to encourage smokers to utilize cessation treatment (Smokers' Quitline and clinic-based programs). Utilization and 7-day and 30-day point prevalence abstinence were assessed at 12 months. Self-reported abstinence was biochemically verified. Intervention participants (n = 121) were more likely than control participants (n = 129) to both utilize treatment programs (adjusted odds ratio [aOR]: 2.15; 95% confidence interval [CI]: 0.93-4.91) and 7-day and 30-day point prevalence abstinence (aOR: 2.60 (1.72-3.94); 2.98 (1.56-5.68), respectively). Mediation analysis indicated that the higher level of utilization did not explain the intervention effect. An intervention delivered by peer health advocates was able to increase utilization of treatment programs and smoking abstinence among public housing residents. Future studies of similar types of interventions should identify the key mechanisms responsible for success. In order to narrow the large and growing socioeconomic disparity in smoking rates, more effective cessation interventions are needed for low-income smokers. Individual culturally-relevant coaching provided in smokers' residences may help overcome the heightened barriers to cessation experienced by this group of smokers. In this study among smokers residing in public housing, an intervention delivered by peer health advocates trained in motivational interviewing, basic smoking cessation skills, and client navigation significantly increased abstinence at 12 months. Future research should address whether these findings are replicable in other settings both within and outside of public housing.

Abstract Introduction Adolescence is a critical time when the majority of tobacco users initiate smoking. Contingency management for adolescent smoking cessation has shown promise in previous studies, but efficacy following removal of contingencies is not well understood. This study examined a remote form of contingency management among non-treatment-seeking adolescent smokers. Methods Participants (N = 127) submitted breath carbon monoxide (CO) three times daily throughout a 42-day program. For this randomized trial, participants in the active condition (n = 63) were reinforced for providing CO measurements on schedule and below a set criterion, whereas those in the control condition (n = 64) were reinforced for providing CO measurements on schedule. Self-reported smoking and urinary cotinine levels were collected at several timepoints. Results Active condition showed greater within-group reductions in CO levels relative to control condition, but not at 3- or 6-month follow-up. Active condition reported significantly less smoking during treatment compared to control condition, but not at follow-up. There were no significant differences for urinary cotinine. Overall treatment adherence was low, with only 37% and 51% of possible CO samples being submitted among active and control, respectively. Poor treatment adherence may explain the disparity between CO and cotinine results, and poor follow-up treatment efficacy. Conclusions This study replicates feasibility of a remote form of contingency management for adolescent smoking. CO results suggest active condition reduced smoking within group, but treatment adherence and posttreatment efficacy was poor. Future research should focus on increasing adherence for this type of program among adolescent smokers. Implications This study demonstrates feasibility of a remote form of contingency management therapy for smoking cessation among adolescents, while providing posttreatment efficacy data. Within-group efficacy of this form of treatment is suggested, but treatment adherence and follow-up efficacy were poor. This study underscores the need for further development of contingency management therapy for adolescent smoking cessation, which emphasizes better treatment adherence and posttreatment efficacy.

Rationale Relative reinforcing efficacy (RRE) is a core feature of behavioral economic theories of addiction. A measure of RRE in smokers is the cigarette purchase task (CPT). Only one previous study has assessed whether CPT indices predict treatment outcomes among smokers with substance use disorders (SUDs) who were not motivated to quit smoking, limiting the generalizability of the results. Objectives This study was aimed at exploring demand indices as predictors of smoking abstinence among participants receiving a cognitive behavioral treatment (CBT) combined with contingency management (CM) versus CBT alone. It also sought to validate the CPT as a measure of RRE. Methods Participants were 159 (62.3 % women) smokers aged 44.8 (SD 12.6) years randomized to CBT alone or combined with CM. They smoked an average of 21.1 (SD 8.5) cigarettes per day, and their mean score on the Fagerström Test for Nicotine Dependence (FTND) was 5.5 (SD 1.9). Demand indices were generated from a hypothetical CPT assessed at baseline. Results Smoking levels were high (≥20 cigarettes) at prices of up to €0.10 per cigarette. Elasticity was the only index that predicted smoking abstinence at the end of treatment, but only in the CBT + CM condition. The variance explained by elasticity was 6.8 % in the independent model and 4.6 % in the covariate model. Cigarette demand indices and smoking-related measures were significantly associated. Conclusion This is the first research showing that CPT is a valid measure of CM outcome among treatment-seeking smokers. This study also provides evidence of convergent validity for the CPT.

The Internet offers an interesting alternative to face-to-face and telephone-based support for smoking cessation. This study was designed to assess the effectiveness of a personalized and automated Internet-based program. French current adult smokers willing to quit within 2 weeks were recruited for a randomized controlled trial. The intervention consisted of an automated program of 45 e-mails (\"e-coaching\") sent over a 3-month period. The control group received a PDF version of a booklet on smoking cessation. Self-reported 7-day point prevalence smoking abstinence was measured at 6 months (primary outcome), at 3 and 12 months of follow-up (secondary outcomes). 2478 smokers were randomized (1242 for e-coaching, 1236 for the booklet). Cessation rate in the intention-to-treat population was not significantly different between the two groups at 6 and 12 months, but was higher in the e-coaching group at 3 months than in the control group (27.5% vs. 23.5%, p = .02, odds ratio [OR] = 1.24, confidence interval [CI] = [1.03-1.49]). After adjustment for baseline conditions, the effect of the intervention in the per-protocol (PP) sample was significant at 3 months (adjusted odds ratio [aOR] = 1.72 [1.31-2.28], p < .001, N = 1042) and at 6 months (aOR = 1.27 [1.00-1.60], p = .05, N = 1082). GLM repeated measure analyses showed significant group by time interaction in the intent-to-treat and a significant group effect in the PP population. Analyzed intention-to-treat, e-coaching was superior to a booklet at 3 months (end of intervention) but no more superior at 6 and 12 months follow-up. Among those who actually followed the program, the effectiveness is also observed 3 months after the intervention is stopped. Analyzed intention-to-treat, our French tailored and personalized Internet-based cessation program was superior to a smoking cessation booklet at 3 months (end of intervention) but no more superior at 6 months follow-up. Among those who actually followed the program (PP population), the effectiveness is observed in the short-term but also 3 months after the intervention is stopped.

IntroductionIn 2021, among French women who smoked when they knew they were pregnant, 59% still smoked at the end of pregnancy. Support for pregnant women to stop smoking must include a structured organisational perspective. The main objective of the study is to evaluate the effectiveness of the 5A-QUIT-N organisational intervention on smoking cessation at delivery among pregnant women who smoke during pregnancy.Methods and analysisThe overarching goal of the 5A-QUIT-N intervention, which aims to organise the healthcare professionals monitoring pregnancy, specialists in addiction and tobacco use, and clinical and training tools, using the 5As method. The 5A-QUIT-N intervention will be evaluated in a pragmatic stepped-wedge cluster randomised trial. Within each cluster, during the 6 months before (control) and after (intervention) the intervention, women who smoke tobacco during pregnancy will be enrolled during their maternity stay after delivery. A transition period is planned between the control and intervention periods to deploy the intervention. All participating women will be interviewed using a heteroquestionnaire to assess smoking cessation, tobacco use monitoring by healthcare professionals and individual factors associated with tobacco use during pregnancy. The primary outcome was the point prevalence of abstinence at delivery, which is the proportion of women reporting abstinence from smoking for at least 7 days at delivery. 4200 women who smoked tobacco during pregnancy will be recruited over the entire study period (33 months) to evaluate the effectiveness. An estimated 4585 participants will be included for all aims.Ethics and disseminationThe study will be implemented in accordance with French regulations. The study including the consent process has been independently reviewed and approved by the French ethical board ‘CPP Ile de France I’ on 10 February 2022 (No CPPIDF1-2022-DI08-cat.2). The results will be disseminated on various academic and non-academic platforms. The results will be reported in international peer-reviewed journals and presented at international and national conferences.

Objective This study aimed to examine the associations of individual trajectories of three types of negative affect (NA: anxiety, depression, and anger) and craving during a 44-day period of incentivized smoking abstinence period with cessation outcome at 3 months and at 1 year. Methods Adult smokers ( N  = 140) completed questionnaire assessments of NA and craving during pre-quit baseline sessions and 15 postquit sessions over the 45 days of biochemically verified abstinence while on nicotine or placebo patch treatment. Growth curve and logistic regression analyses were used to examine the associations of trajectory parameters of the individual NA states and craving with the abstinence outcomes at 3 months and 1 year postquit. Results Greater declines in anxiety, depression, and anger symptoms over the first 44 days of smoking cessation were predictive of higher odds of abstinence at both 3 months and 1 year. Moreover, the greater declines in anxiety and anger remained as significant predictors of abstinence at both time points, independent of the predictive ability of the trajectory profiles of craving. Conclusions The findings suggest that slower dissipation of NA, especially anxiety and anger, represents a greater risk for relapse to smoking beyond that predicted by craving during early abstinence. Thus, temporal profiles of the affective symptoms convey unique motivational significance in relapse. Reduction in NA during early abstinence may be a valid target for interventions to increase long-term cessation success rates particularly among individuals with refractory affective symptoms.

This study investigated whether tobacco smoking affected outcomes of brief alcohol interventions (BAIs) in at-risk alcohol-drinking general hospital patients. Between 2011 and 2012 among patients aged 18–64 years, 961 patients were allocated to in-person counseling (PE), computer-based BAI containing computer-generated individual feedback letters (CO), and assessment only. PE and CO included contacts at baseline, 1, and 3 months. After 6, 12, 18, and 24 months, self-reported reduction of alcohol use per day was assessed as an outcome. By using latent growth curve models, self-reported smoking status, and number of cigarettes per day were tested as moderators. In PE and CO, alcohol use was reduced independently of smoking status (IRRs ≤ 0.61, ps < 0.005). At month 24, neither smoking status nor number of cigarettes per day moderated the efficacy of PE (IRR = 0.69, ps > 0.05) and CO (IRR = 0.85, ps > 0.05). Up to month 12, among persons smoking ≤ 19 cigarettes per day, the efficacy of CO increased with an increasing number of cigarettes (ps < 0.05). After 24 months, the efficacy of PE and CO that have been shown to reduce drinking did not differ by smoking status or number of cigarettes per day. Findings indicate that efficacy may differ by the number of cigarettes in the short term.