Using eye tracking to evaluate the impact of smartphone-delivered attentional bias modification training for smokers.

Attentional bias modification (ABM) has been proposed to treat tobacco use disorder by reducing attentional bias (AB) to smoking-related cues. We sought to determine the extent to which AB to smoking cues, as measured by eye-tracking technology, was sensitive to multisession ABM among treatment-seeking adult smokers. The participants (N = 203; 74 women) completed 13 days of daily ABM or sham training using a smartphone, followed by 8 weeks of nicotine replacement therapy and cessation counseling. ABM and sham training were administered using the modified dot-probe task (i.e., neutral cues probed 100% of the time) and the unmodified dot-probe task (i.e., cue types probed equally), respectively. Eye gaze dwell time proportions to paired presentations of smoking and neutral cues were measured at baseline, 1 day post-ABM training, and 8 weeks post-ABM training. At baseline, younger, more dependent smokers and those with higher smoking satisfaction scores looked longer at smoking cues than neutral ones. ABM training resulted in greater gaze preference for the smoking cues than sham training at 1 day posttraining. Gaze preference for smoking cues was positively associated with AB to smoking cues as measured by reaction time during the laboratory dot-probe assessment. At 8 weeks posttraining, gaze preference was not associated with any of the smoking outcome measures. These findings suggest that multisession ABM training resulted in changes in AB by increasing time spent looking at neutral compared with smoking cues in the short term. However, this effect was not sustained and was not associated with smoking behavior outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Smoking Cessation After Initial Treatment Failure With Varenicline or Nicotine Replacement: A Randomized Clinical Trial.

Importance: Most people who smoke do not quit on their initial attempt. Objective: To determine the best subsequent strategy for nonabstinence following initial treatment with varenicline or combined nicotine replacement therapy (CNRT). Design, Setting, and Participants: Using a double-blind, placebo-controlled, sequential multiple assignment randomized trial, 490 volunteers were randomized to receive 6 weeks of varenicline or CNRT. After 6 weeks, nonabstainers were rerandomized to continue, switch, or increase medication dosage for 6 additional weeks. The study was conducted from June 2015 through October 2019 in a Texas tobacco treatment clinic. Interventions: The initial treatment was 2 mg/d of varenicline or the combined replacement therapy of a 21-mg patch plus 2-mg lozenge. The rerandomized participants either continued with their initial therapies, switched between varenicline and CNRT, or increased dosages either to 3-mg or more of varenicline or to a 42-mg patch and lozenges. All received weekly brief counseling. Main Outcomes and Measures: Biochemically verified 7-day point prevalence abstinence at the end of treatment at 12 weeks. Results: The 490 randomized participants (210 female [43%], 287 non-Hispanic White [58%], mean age, 48.1 years) smoked an average of 20 cigarettes per day. After the first phase, 54 participants in the CNRT group were abstinent and continued their therapy; of the 191 who were not abstinent, 151 were rerandomized, and the 40 who did not return for rerandomization were assigned to continue their initial CNRT condition in phase 2. The end-of-treatment abstinence rate for the 191 phase 1 nonabstainers was 8% (95% credible interval [CrI], 6% to 10%) for the 90 (47%) who continued at the dosage condition, 14% (CrI, 10% to 18%) for the 50 (33%) who increased their dosage, and 14% (95% CrI, 10% to 18%) for the 51 (34%) who switched to varenicline (absolute risk difference [RD], 6%; 95% CrI, 6% to 11%) with more than 99% posterior probability that either strategy conferred benefit over continuing the initial dosage. After the first phase, 88 participants in the varenicline group were abstinent and continued their therapy; of the 157 who were not abstinent, 122 were rerandomized and 35 who did not return for rerandomization were assigned to continue with the varenicline condition. The end-of-treatment abstinence rate for the 157 phase 1 nonabstainers was 20% (95% CrI, 16% to 26%) for the 39 (32%) who increased their varenicline dosage, 0 (95% CrI, 0 to 0) for the 41 (34%) who switched CNRT, and 3% (95% CrI, 1% to 4%) for the 77 (49%) who were assigned to the continued varenicline condition (absolute RD, -3%; 95% CrI, -4% to -1%) with more than 99% posterior probability that continuing varenicline at the initial dosage was worse than switching to a higher dosage. Furthermore, increasing the varenicline dosage had an absolute RD of 18% (95% CrI, 13% to 24%) and a more than 99% posterior probability of conferring benefit. The secondary outcome of continuous abstinence at 6 months indicated that only increased dosages of the CNRT and varenicline provided benefit over continuation of the initial treatment dosages. Conclusions and Relevance: For individuals who smoked but did not achieve abstinence after treatment with varenicline, increasing the dosage enhanced abstinence vs continuing, whereas for nonabstainers initially treated with CNRT, a dosage increase or switch to varenicline enhanced abstinence and may be viable rescue strategies. Trial Registration: ClinicalTrials.gov Identifier: NCT02271919.

Comparing the Fagerström Test and Heaviness of Smoking Index in Predicting Smoking Abstinence in Cancer Patients.

INTRODUCTION: People with cancer who smoke exhibit greater cigarette dependence than people without cancer who smoke, a crucial factor in smoking cessation. Research is limited on the predictive potential of the Fagerström Test for Cigarette Dependence (FTCD) and the Heaviness of Smoking Index (HSI) on smoking abstinence in cancer patients undergoing smoking cessation treatment. METHODS: We analyzed data from 5,934 cancer patients seeking smoking cessation treatment at The University of Texas MD Anderson Cancer Center (female 52.08%; Mean age = 55.52, SD = 11.17). We evaluated the predictive accuracy of FTCD and HSI on abstinence at 3-, 6-, and 9-months from first consultation, and assessed the concordance between these tools in measuring cigarette dependence using Cohen's kappa test and different correlation and regression models. We also analyzed variations across sex at birth and race/ethnicity. RESULTS: Both the FTCD and the HSI demonstrated comparable predictive accuracy for smoking cessation at all follow-ups, with neither showing high accuracy (Areas Under the Curve scores around 0.6). Concordance analysis revealed substantial agreement between FTCD and HSI scores (Cohen's kappa ~ 0.7), particularly at lower levels of dependence. However, this agreement varied by race, with reduced concordance observed in Non-Hispanic Blacks. CONCLUSIONS: Our results indicate that both the FTCD and HSI are effective tools for predicting smoking cessation in cancer patients, with the HSI offering a less burdensome assessment option. Nevertheless, the findings suggest the need for tailored approaches in assessing cigarette dependence that could predict smoking cessation more accurately, considering racial differences. IMPLICATIONS: The burden of assessing cigarette dependence in cancer care settings can be reduced by using the HSI instead of the FTCD. In addition, both instruments could be substantially interchanged and used for meta-analytic studies examining dependence and abstinence, but race/ethnicity should be considered.

Deep sequencing of candidate genes identified 14 variants associated with smoking abstinence in an ethnically diverse sample.

Despite the large public health toll of smoking, genetic studies of smoking cessation have been limited with few discoveries of risk or protective loci. We investigated common and rare variant associations with success in quitting smoking using a cohort from 8 randomized controlled trials involving 2231 participants and a total of 10,020 common and 24,147 rare variants. We identified 14 novel markers including 6 mapping to genes previously related to psychiatric and substance use disorders, 4 of which were protective (CYP2B6 (rs1175607105), HTR3B (rs1413172952; rs1204720503), rs80210037 on chr15), and 2 of which were associated with reduced cessation (PARP15 (rs2173763), SCL18A2 (rs363222)). The others mapped to areas associated with cancer including FOXP1 (rs1288980) and ZEB1 (rs7349). Network analysis identified significant canonical pathways for the serotonin receptor signaling pathway, nicotine and bupropion metabolism, and several related to tumor suppression. Two novel markers (rs6749438; rs6718083) on chr2 are flanked by genes associated with regulation of bodyweight. The identification of novel loci in this study can provide new targets of pharmacotherapy and inform efforts to develop personalized treatments based on genetic profiles.

Brief report: Characterization of electronic cigarette use among patients of a comprehensive cancer center.

BACKGROUND AND OBJECTIVES: We provide an initial characterization of e-cigarette use among adult cancer patients. METHODS: Data were collected between November 2020 and August 2022 at a comprehensive cancer center. RESULTS: Relatively few (4.59%) of the assessed patients (n = 47,117) reported ever using e-cigarettes. Over one-third of current e-cigarette users reported being current combustible cigarette users. DISCUSSION AND CONCLUSIONS: These data suggest that e-cigarette use is uncommon but associated with other tobacco use among adult cancer patients. SCIENTIFIC SIGNIFICANCE: This is among the first comprehensive surveys of adult cancer patient e-cigarette use that details the types of e-cigarette and other tobacco products used by this population.

Examining the Association between Abstinence from Smoking and Healthcare Costs Among Patients with Cancer.

Continuous tobacco use in cancer patients is linked to substantial healthcare costs due to increased risks and complications, whereas quitting smoking leads to improved treatment outcomes and cost reductions. Addressing the need for empirical evidence on the economic impact of smoking cessation, this study examined the association between smoking cessation and healthcare cost utilization among a sample of 930 cancer patients treated at The University of Texas MD Anderson Cancer Center's Tobacco Research and Treatment Program (TRTP). Applying conditional quantile regression and propensity scores to address confounding, our findings revealed that abstinence achieved through the TRTP significantly reduced the median cost during a 3-month period post-quitting by $1,095 (ß=-$1,095, p=0.007, 95%CI=[-$1,886, -$304]). Sensitivity analysis corroborated these conclusions, showing a pronounced cost reduction when outlier data were excluded. The long-term accrued cost savings from smoking cessation could potentially offset the cost of participation in the TRTP program, underscoring its cost-effectiveness. An important implication of this study is that by reducing smoking rates, healthcare systems can more efficiently allocate resources, enhance patient health outcomes, and lessen the overall cancer burden.

Comparable cigarette consumption data collected using timeline follow-back and digital diary among treatment-seeking smokers.

OBJECTIVE: The timeline follow-back interview is a common method of collecting daily cigarette consumption (cigarettes per day [CPD]) in smoking research. However, it may be subject to recall bias due to its reliance on retrospective reports. The increasing ownership of smartphones allows researchers to administer app-based digital diaries (DD) to collect CPD, which is expected to have less recall bias. Several studies have compared these two methods and found a noticeable discrepancy between them. However, these studies have mainly focused on the time window when smokers were smoking ad libitum. In this study, we wanted to determine the comparability of these two methods when treatment-seeking smokers are attempting to quit smoking. METHOD: In a cessation trial, treatment-seeking smokers (n = 251) reported their CPD using the timeline follow-back and DD methods over a 12-week treatment period. To evaluate the comparability, we used the Bland-Altman comparison approach for agreement, correlational analysis between CPD and biochemical measures, digit bias, and logistic regression for predicting abstinence. RESULTS: We found that the two methods exhibited good agreement, and the agreement did not vary as a function of consumption levels. Consistent with this agreement, CPD data from both methods showed similar correlations with biochemical measures of smoking and predicted 6-month abstinence in a comparable fashion. Despite the agreement, the DD method appeared to be more precise by having a lower digit bias than the timeline follow-back method. CONCLUSIONS: Capturing smoking behavior using either TLFB or DD approaches yields similar data while smokers are attempting to quit smoking. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.

Towards neuromarkers for tailored smoking cessation treatments.

Vulnerability to compulsive drug use stems from dysregulated activity within the neural networks that underlie reward and executive functions. Empirical evidence suggests that a) attributing high motivational salience to drug-related stimuli leads to compulsive drug seeking and b) cognitive control deficits lead to compulsive drug taking. Noninvasive neuroimaging techniques enable brain activity monitoring during affective and cognitive processing and are paving the way to precision medicine for substance use disorders. Identifying robust neuromarkers of affective and cognitive dysregulation would allow clinicians to personalize treatments by targeting individual psychophysiological vulnerabilities. However, methodological choices have biased the field toward experimental paradigms that cannot optimally assess individual differences in the motivational salience of drug-related cues and in the ability to control drug-related decisions, choices which have hindered the identification of clinically relevant neuromarkers. Here, we show that once these shortcomings are amended, replicable neuromarkers of the tendency to attribute motivational salience to drug-related cues and the ability to control drug-related decisions emerge. While we use tobacco use disorder as a model, we also show that the methodological issues highlighted here are relevant to other disorders characterized by maladaptive appetitive behaviors.

Smoking abstinence and cessation-related outcomes one month after an immediate versus gradual reduction in nicotine content of cigarettes.

The United States Food and Drug Administration has the authority to reduce the nicotine content in cigarettes to minimal or non-addictive levels and could do so immediately or gradually over time. A large clinical trial compared the two approaches. This secondary analysis assesses abstinence and cessation-related outcomes one month after the trial concluded, when participants no longer had access to very low nicotine content (VLNC) research cigarettes. Smokers not interested in quitting (N = 1250) were recruited for the parent trial from 2014 to 2016 across 10 sites throughout the US and randomized to a 20-week study period during which they immediately switched to VLNC cigarettes, gradually transitioned to VLNC cigarettes with five monthly dose reductions, or smoked normal nicotine research cigarettes (control). At the one-month follow-up, both immediate and gradual reduction resulted in greater mean cigarette-free days (4.7 and 4.6 respectively) than the control group (3.2, both p < .05). Immediate reduction resulted in fewer mean cigarettes per day (CPD = 10.3) and lower Fagerström Test for Cigarette Dependence (FTCD = 3.7) than the gradual (CPD = 11.7, p = .001; FTCD = 3.8, p = .039) and control (CPD = 13.5, p < .001; FTCD = 4.0, p < .001) groups. Compared to controls, gradual reduction resulted in reduced CPD (p = .012) but not FTCD (p = .13). Differences in CO-verified 7-day point-prevalence abstinence were not significant. Findings demonstrate that switching to VLNC cigarettes resulted in reduced smoking and nicotine dependence severity that was sustained for at least a month after the VLNC trial period in smokers who were not interested in cessation. The greatest harm reduction endpoints were observed in those who immediately transitioned to VLNC cigarettes.

Short- and Long-Term Cigarette and Tobacco Abstinence Among Daily and Nondaily Older Smokers.

INTRODUCTION: There is mixed evidence regarding whether older (vs. younger) smokers are more or less likely to quit smoking. We examined how age is associated with cigarette and all tobacco product abstinence and the potential moderating effects of smoking frequency. AIMS AND METHODS: Data from a 4-year cohort of the Population Assessment of Tobacco and Health (PATH) study were used, including 7512 smokers at Wave 1 who had smoking status data at Wave 4. Logistic regression models were used to examine the effects of age (18-24, 25-34, 35-44, 45-54, and ≥55 years) on Wave 4, 30-day and 12-month cigarette and all tobacco product abstinence, adjusting for covariates and the interaction between age and cigarette use frequency (nondaily, light daily, and heavy daily). RESULTS: Older smokers (≥55 years) were more likely to be heavy daily smokers than younger smokers 18-24 and 25-34 years, but were less likely to have a past-year cigarette quit attempt. Younger smokers 45-54 years were less likely to report 12-month cigarette abstinence than older smokers (odds ratio = 0.72 [0.54-0.95]). Younger smokers 18-24 and 45-54 years were less likely to report 12-month tobacco product abstinence than older smokers (odds ratio = 0.65 [0.45-0.93]; odds ratio = 0.73 [0.55-0.96], respectively). Thirty-day cigarette abstinence significantly decreased as age increased for nondaily smokers, significantly increased for heavier daily smokers, but remained similar across age for light daily smokers. CONCLUSIONS: Older smokers were more likely to report 12-month cigarette and tobacco abstinence than younger smokers 45-54 years old, and the effect of age on abstinence differed by smoking frequency/intensity. Smoking cessation interventions need to be age specific and consider smoking frequency. IMPLICATIONS: This study shows that although older smokers are more likely to be heavy smokers and less likely to have a quit attempt at baseline, they are more likely to have 12-month cigarette and tobacco abstinence than younger smokers. Furthermore, 30-day cigarette abstinence significantly decreases as age increases for nondaily smokers and significantly increases for heavy daily smokers, suggesting that the effect of cigarette smoking frequency and intensity changes with age. Smoking cessation interventions need to be age specific as well as consider the smoking frequency/intensity of each age group. Younger smokers may need more targeted cessation interventions to successfully quit.

The Impact of Treatment for Smoking on Breast Cancer Patients' Survival.

BACKGROUND: Smoking negatively affects overall survival after successful breast cancer (BC) treatment. We hypothesized that smoking cessation would improve survival outcomes of BC patients who were smokers at the time of diagnosis. METHODS: This was a retrospective analysis of self-identified smokers with BC treated at The University of Texas MD Anderson Cancer Center. Patient demographics, date of diagnosis, tumor stage, tobacco treatment program (TP) participation, and time to death were extracted from our departmental databases and institutional electronic health records. We examined associations between tobacco abstinence status and survival using survival models, with and without interactions, adjusted for personal characteristics and biomarkers of disease. RESULTS: Among all 31,069 BC patients treated at MD Anderson between 2006 and 2017, we identified 2126 smokers (6.8%). From those 2126 self-identified smokers, 665 participated in the TP, reporting a conservative estimate of 31% abstinence (intent-to-treat) 9 months into the program. Patients without reported follow-up abstinence status (including TP and non-TP participants) were handled in the analyses as smokers. Survival analysis controlled for multiple factors, including disease characteristics and participation in the TP, indicated that abstainers were more likely to be alive with no evidence of disease compared to non-abstainers (HR, 0.593; 95% CI, 0.386-0.911; p = 0.017). CONCLUSION: Our results suggest that quitting smoking is associated with improved survival among BC patients who were smokers at time of diagnosis across all tumor stages. Comprehensive approaches for smoking cessation in patients diagnosed with BC may prolong survival when started as early as the time of diagnosis.

Bupropion XL and SR have similar effectiveness and adverse event profiles when used to treat smoking among patients at a comprehensive cancer center.

BACKGROUND AND OBJECTIVES: Bupropion extended-release (XL; once-daily dosing) has equal efficacy with the sustained-release (SR) formulation (twice-daily dosing) for treating depression, but no studies have compared the two formulations for the treatment of smoking. In a naturalistic open-label study, we compared the effectiveness and the adverse event profiles of XL and SR in treating cancer patients for smoking. METHODS: Cancer patients (N = 648) were prescribed bupropion XL (n = 454) or SR (n = 194) alone or in combination with nicotine replacement therapy (NRT) for treating smoking from September 2006 to December 2017. We analyzed 7-day point prevalence abstinence at end-of-treatment (EOT; 3 months postmedication initiation) and evaluated for noninferiority. We also analyzed the adverse event profile differences between the medications. RESULTS: There were no significant differences in abstinent rates at EOT between bupropion XL and SR when using intent-to-treat models, regardless of concomitant NRT. XL demonstrated noninferiority in treatment efficacy compared to SR when excluding those on combined treatment with NRT. Further, there were no significant differences in spontaneously reported adverse events between XL and SR. CONCLUSIONS: Our data did not reveal a difference between bupropion XL and SR formulations in terms of effectiveness or adverse event profiles among cancer patients prescribed bupropion alone or in combination with NRTs to quit smoking. SCIENTIFIC SIGNIFICANCE: In this first published direct comparison of their effectiveness and adverse event profiles, we found that bupropion XL is likely therapeutically equivalent to bupropion SR when treating smoking among cancer patients, and produces similar side effects.

Topiramate decreases the salience of motivationally relevant visual cues among smokers with alcohol use disorder.

BACKGROUND: There is preliminary evidence that the anticonvulsant topiramate increases the likelihood of both smoking and alcohol abstinence among smokers with alcohol use disorder (AUD), but its therapeutic mechanism has not been determined. We used event-related potentials (ERPs) to evaluate topiramate's effect on the salience of drug-related, emotional, and neutral pictorial cues to identify whether one of its potential therapeutic mechanisms involves reduction of the salience of motivationally relevant cues. METHODS: Participants enrolled in a multisite clinical trial treating smokers with AUD were randomly assigned to receive placebo, low-dose topiramate (up to 125 mg/day), or high-dose topiramate (up to 250 mg/day), along with brief behavioral compliance enhancement treatment. A subsample (n = 101) completed ERP assessments at baseline (1 week pre-medication) and week 5 (5 weeks on medication; 1 week pre-quit). We assessed the salience of pleasant, unpleasant, cigarette-related, alcohol-related, and neutral pictorial cues using the late positive potential (LPP) ERP component and measured self-reported substance use, reinforcement, craving, and withdrawal. RESULTS: Five weeks of high-dose topiramate treatment decreased LPP amplitudes in response to both emotional (pleasant and unpleasant) and drug-related cues (alcohol and cigarette), but not to neutral cues. However, results showed that the LPPs were not significant mediators of the relationship between topiramate dose and post-quit measures of substance use, reinforcement, craving, or withdrawal. CONCLUSIONS: These findings suggest that high-dose topiramate (up to 250 mg/day) decreases the motivational salience of both drug-related and emotional cues among smokers with AUD. However, the nonsignificant mediation analyses preclude any firm conclusions about whether this effect represents one of topiramate's therapeutic mechanisms of action.

Sustained reduction of attentional bias to smoking cues by smartphone-delivered attentional bias modification training for smokers.

OBJECTIVE: Cigarette smoking is thought to be at least partially maintained by the attentional bias (AB) toward smoking cues that develops as a consequence of drug dependence. This study evaluated the impact of smartphone-delivered, in-home attentional bias modification (ABM) to reduce AB to smoking cues and to reduce smoking behavior and withdrawal-related symptoms when used as an adjunct to conventional smoking cessation treatment. METHOD: Participants (N = 246) were treatment-seeking smokers who completed up to 13 days of either ABM designed to train attention away from smoking cues, using a modified dot-probe task, or sham training, followed by 8 weeks of nicotine replacement therapy and counseling. Outcomes measured at baseline, 1-day post-ABM training, and 8 weeks post-ABM training included AB to smoking images and words using the dot-probe and smoking Stroop tasks, respectively, along with cigarettes per day, craving, and smoking abstinence. RESULTS: We found that ABM training reduced AB to smoking stimuli on both the dot-probe task, ηp² = 0.056, 90% CI [0.024, 0.097], and the smoking Stroop task, ηp² = 0.017, 90% CI [0.002, 0.044], up to 8 weeks after ABM training when covarying for baseline response, but did not concurrently decrease smoking behavior or craving. CONCLUSIONS: Thirteen days of smartphone-delivered ABM training, as an adjunct to smoking cessation treatment, reduced AB to both modality-specific and cross-modality smoking cues but did not impact smoking-related behavior. While ABM can reduce AB to smoking cues across modalities, it is unclear whether it has therapeutic potential as an adjunct to conventional smoking cessation therapy. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Python Package abstcal: An Open-Source Tool for Calculating Abstinence From Timeline Followback Data.

INTRODUCTION: In smoking cessation clinical trials, timeline followback (TLFB) interviews are widely used to track daily cigarette consumption. However, there are no standard tools for calculating abstinence based on TLFB data. Individual research groups have to develop their own calculation tools, which is not only time- and resource-consuming but might also lead to variability in the data processing and calculation procedures. AIMS AND METHODS: To address these issues, we developed a novel open-source Python package named abstcal to calculate abstinence using TLFB data. This package provides data verification, duplicate and outlier detection, missing-data imputation, integration of biochemical verification data, and calculation of a variety of definitions of abstinence, including continuous, point-prevalence, and prolonged abstinence. RESULTS: We verified the accuracy of the calculator using data derived from a clinical smoking cessation study. To improve the package's accessibility, we have made it available as a free web app. CONCLUSIONS: The abstcal package is a reliable abstinence calculator with open-source access, providing a shared validated online tool to the addiction research field. We expect that this open-source abstinence calculation tool will improve the rigor and reproducibility of smoking and addiction research by standardizing TLFB-based abstinence calculation. IMPLICATIONS: Abstinence calculation is an essential task in any smoking intervention study. However, there have not been standard open-source tools available to the researchers. This commentary describes a Python-based package called abstcal that can calculate abstinence from TLFB data, a common methodology to collect smoking consumption data in research settings. The package supports the calculation of point-prevalence, prolonged, and continuous abstinence. Importantly, the package has a web app interface that allows researchers to use the tool without any coding experience. This tool will facilitate smoking research by providing a standardized and easy-to-use abstinence calculation tool.

Behavioral Economic Assessment of Alcohol and Cigarette Demand in Smokers With Alcohol Use Disorder.

Background and Objectives: Behavioral economic purchase tasks are widely used to assess drug demand in substance use disorder research. Comorbid alcohol use is common among cigarette smokers and associated with greater difficulty in quitting smoking. However, demand for alcohol and cigarettes in this population has not been fully characterized. The present study addressed this gap by examining alcohol and cigarette demand among treatment-seeking smokers with alcohol use disorder (AUD). Methods: Alcohol and cigarette demand was assessed among 99 smokers with AUD. We conducted Principal Component Analysis (PCA) and correlational analyses on the demand indices. Results: Participants showed higher demand for alcohol than for cigarettes, as evidenced lower elasticity (resistance to increasing price) and higher Omax (maximum response output for drug). PCA revealed a two-factor structure (Persistence and Amplitude) for both alcohol and cigarette demand indices. Cigarette-related demand indices were positively correlated with nicotine dependence, but alcohol-related demand indices were not associated with alcohol dependence, suggesting dissociation between alcohol demand and use behaviors. Discussion and Conclusions: Our results suggest that smokers with AUD were more resistant to price elevations in relation to reducing alcohol consumption as compared to cigarette consumption, suggesting preferential demand for alcohol over cigarettes. However, it is unclear how acute substance exposure/withdrawal impacts the demand indices. Scientific Significance: Potentially differential alcohol and cigarette demands among smokers with AUD should be considered in the concurrent treatment of smoking and alcohol.

You Don't Need an App-Conducting Mobile Smoking Research Using a Qualtrics-Based Approach.

With the increasing availability of smartphones, many tobacco researchers are exploring smartphone-delivered mobile smoking interventions as a disseminable means of treatment. Most effort has been focused on the development of smartphone applications (apps) to conduct mobile smoking research to implement and validate these interventions. However, developing project-specific smartphone apps that work across multiple mobile platforms (e.g., iOS and Android) can be costly and time-consuming. Here, using a hypothetical study, we present an alternate approach to demonstrate how mobile smoking cessation and outcome evaluation can be conducted without the need of a dedicated app. Our approach uses the Qualtrics platform, a popular online survey host that is used under license by many academic institutions. This platform allows researchers to conduct device-agnostic screening, consenting, and administration of questionnaires through Qualtrics's native survey engine. Researchers can also collect ecological momentary assessment data using text messaging prompts with the incorporation of Amazon Web Services' Pinpoint. Besides these assessment capabilities, Qualtrics has the potential for delivering personalized behavioral interventions through the use of JavaScript code. By customizing the question's web elements in Qualtrics (e.g., using texts, images, videos, and buttons), researchers can integrate interactive web-based interventions and complicated behavioral and cognitive tasks into the survey. In conclusion, this Qualtrics-based methodology represents a novel and cost-effective approach for conducting mobile smoking cessation and assessment research.

Very Low Nicotine Content Cigarettes Disrupt the Feedback Loop of Affective States and Smoking Behavior.

INTRODUCTION: Smoking to reduce negative affect has been identified as a key motivational feature of tobacco use. Our recent work suggests that smoking very low nicotine content (VLNC) cigarettes reduces the relationship between negative affect and smoking behavior over a 6-week period. Here, we sought to extend our findings by evaluating whether a gradual or immediate approach to switching to VLNC cigarettes led to a differential reduction in the relationship between affect and smoking behavior over a longer (20-week) period. AIMS AND METHODS: Participants (n = 1250) were adult smokers from 10 US sites randomized to one of three groups: gradual nicotine reduction (15.5, 11.7, 5.2, 2.4, and 0.4 mg of nicotine per gram of tobacco [mg/g]), immediate nicotine reduction (0.4 mg/g), or standard nicotine content cigarettes (15.5 mg/g; control), for 20 weeks. We examined whether the relationship between affect-both negative and positive-and cigarettes per day differed as a function of reduction group. RESULTS: We found that both negative and positive affect were associated with cigarette consumption in the control group, but not in the gradual or immediate reduction groups across the 20 weeks of exposure. CONCLUSIONS: Our results extend previous findings that switching to VLNC cigarettes disrupts the relationship between affect and cigarette consumption by showing that either gradually or immediately reducing cigarette nicotine content achieves this disruption. These findings provide further evidence that switching to VLNC cigarettes reduces nicotine-related reinforcement of cigarette smoking. IMPLICATIONS: These findings support the notion that switching to very low nicotine content cigarettes reduces the association between affect and smoking behavior, and that either a gradual or immediate nicotine reduction approach achieves this reduction. This provides further evidence that switching to very low nicotine content cigarettes weakens reinforcement mechanisms associated with nicotine dependence.

Estimating statistical power for event-related potential studies using the late positive potential.

The late positive potential (LPP) is a common measurement used to study emotional processes of subjects in ERP paradigms. Despite its extensive use in affective neuroscience, there is presently no gold standard for how to appropriately power ERP studies using the LPP. The present study investigates how the number of trials, number of subjects, and magnitude of the effect size affect statistical power in analyses of the LPP. Using Monte Carlo simulations of ERP experiments with varying numbers of trials, subjects, and synthetic effects of known magnitude, we measured the probability of obtaining a statistically significant effect in 1,489 experiments repeated 1,000 times each. Predictably, our results showed that statistical power increases with increasing numbers of trials and subjects and at larger effect sizes. We also found that higher levels of statistical power can be achieved with lower numbers of subjects and trials and at lower effect sizes in within-subject than in between-subjects designs. Furthermore, we found that, as subjects are added to an experiment, the slope of the relationship between effect size and statistical power increased and shifted to the left until the power asymptoted to nearly 100% at higher effect sizes. This suggests that adding more subjects greatly increases statistical power at lower effect sizes (<1 µV) compared with more robust (>1.5 µV) effect sizes. We confirmed the results from the simulations based on the synthetic effects by running a new series of simulated experiments based on real data collected while participants looked at emotional images.