Dataset Information

Temperament and impulsivity predictors of smoking cessation outcomes.

ABSTRACT:

Aims

Temperament and impulsivity are powerful predictors of addiction treatment outcomes. However, a comprehensive assessment of these features has not been examined in relation to smoking cessation outcomes.

Methods

Naturalistic prospective study. Treatment-seeking smokers (n = 140) were recruited as they engaged in an occupational health clinic providing smoking cessation treatment between 2009 and 2013. Participants were assessed at baseline with measures of temperament (Temperament and Character Inventory), trait impulsivity (Barratt Impulsivity Scale), and cognitive impulsivity (Go/No Go, Delay Discounting and Iowa Gambling Task). The outcome measure was treatment status, coded as "dropout" versus "relapse" versus "abstinence" at 3, 6, and 12 months endpoints. Participants were telephonically contacted and reminded of follow-up face to face assessments at each endpoint. The participants that failed to answer the phone calls or self-reported discontinuation of treatment and failed to attend the upcoming follow-up session were coded as dropouts. The participants that self-reported continuing treatment, and successfully attended the upcoming follow-up session were coded as either "relapse" or "abstinence", based on the results of smoking behavior self-reports cross-validated with co-oximetry hemoglobin levels. Multinomial regression models were conducted to test whether temperament and impulsivity measures predicted dropout and relapse relative to abstinence outcomes.

Results

Higher scores on temperament dimensions of novelty seeking and reward dependence predicted poorer retention across endpoints, whereas only higher scores on persistence predicted greater relapse. Higher scores on the trait dimension of non-planning impulsivity but not performance on cognitive impulsivity predicted poorer retention. Higher non-planning impulsivity and poorer performance in the Iowa Gambling Task predicted greater relapse at 3 and 6 months and 6 months respectively.

Conclusion

Temperament measures, and specifically novelty seeking and reward dependence, predict smoking cessation treatment retention, whereas persistence, non-planning impulsivity and poor decision-making predict smoking relapse.

SUBMITTER: Lopez-Torrecillas F

PROVIDER: S-EPMC4256301 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Temperament and impulsivity predictors of smoking cessation outcomes.

López-Torrecillas Francisca F Perales José C JC Nieto-Ruiz Ana A Verdejo-García Antonio A

PloS one 20141204 12

<h4>Aims</h4>Temperament and impulsivity are powerful predictors of addiction treatment outcomes. However, a comprehensive assessment of these features has not been examined in relation to smoking cessation outcomes.<h4>Methods</h4>Naturalistic prospective study. Treatment-seeking smokers (n = 140) were recruited as they engaged in an occupational health clinic providing smoking cessation treatment between 2009 and 2013. Participants were assessed at baseline with measures of temperament (Temper ...[more]

PMID: 25474540

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Project description:BackgroundMigrant populations usually report higher smoking rates. Among those migrant populations, Turkish- and Kurdish-speaking migrants are often overrepresented. Providing equal access to health services is one of the major challenges of our time. The need for adapted smoking-cessation treatments for Turkish-speaking populations to achieve equity in health led, in 2006, to the development and implementation of the Tiryaki-Kukla smoking-cessation program. The aims of the current study were to evaluate one-year quit rates for smoking-cessation courses held from 2006-2018 and investigate whether certain characteristics predict long-term smoking cessation or reduction.MethodsProgram evaluation included a pre/post questionnaire (session 1/ 3 months after the quit day) and a follow-up telephone call twelve months after the quit day. To elucidate factors associated with long-term smoking cessation and reduction, Cox regression analysis and Weighted Generalized Equation Models were used.ResultsOf the 478 who participated in smoking-cessation courses, 45.4% declared themselves non-smokers at one-year follow-up. This quit rate is higher than that achieved during the preliminary evaluation of the program involving 61 participants (37.7%). Predictors of long-term smoking cessation were course length (eight vs. six sessions) (95% CI = 1.04-1.36, p = .01), adherence to the course (95% CI = 0.98-0.99, p<0.01), use of pharmacotherapy or nicotine replacement therapy products (95% CI = 0.74-0.98, p = .02), and time passed in the morning until the first cigarette is smoked (95% CI5min = 1.17-1.77, p<0.001; 95% CI30min = 1.09-1.65, p<0.01). Predictors of change in cigarettes smoked per day among smokers were-the time passed until the first cigarette in the morning (5min p < .001; 30min p < .001; 60min p < .01)-, gender (p < .001), and level of motivation to quit at baseline (p = .04).ConclusionsOur findings are consistent with existing evidence supporting adapted smoking cessation interventions to reduce health inequity in migrant populations. However, achieving harm reduction in smokers with higher dependence scores remains challenging.

Project description:Many lifelong smokers establish smoking habits during young adulthood. A university can be an effective setting for early smoking cessation. We evaluated long-term predictors of smoking cessation among smokers in a university setting.We longitudinally followed a cohort of smokers enrolled in a university smoking cessation program in Seoul, South Korea. Sociodemographic factors, smoking-related variables, and changes in smoking habits were assessed during 6-week visit sessions and follow-up telephone interviews conducted 1 year or more later.A total of 205 participants were followed up (mean follow-up duration: 27.1 months). Cessation rates were 47.3% at the end of the visit sessions and 28.8% at follow-up. The long-term persistent smoking rate was significantly higher among individuals with peers who smoked (odds ratio [OR] = 8.64; 95% confidence interval [CI] = 1.75, 42.80), with family members who smoked (OR = 3.28; 95% CI = 1.20, 9.00), and who smoked 10 to 19 cigarettes/day (OR?=?4.83; 95% CI?=?1.49, 15.69). Conversely, persistent smoking was less likely among those who attended the program regularly (OR?=?0.84 per visit; 95% CI?=?0.72, 0.99) and attempted quitting more frequently (OR?=?0.93 per attempt; 95% CI?=?0.87, 0.99). Use of smoking cessation medications (varenicline or bupropion) was not significantly associated with long-term quitting (OR?=?0.71; 95% CI?=?0.26, 1.93).Peer influences were the strongest predictors of failure in long-term cessation among smokers who attempted to quit. Similarly, the existence of smokers in the family was negatively associated with successful quitting. Regular attendance at a smoking cessation program and a high number of attempts to quit were positively associated with successful quitting. Targeting peer and family smoking groups together rather than targeting individual smokers alone, implementing active cessation programs encouraging regular attendance, and providing comprehensive antismoking environments might be effective strategies in a university setting.

Project description:BACKGROUND:Blended face-to-face and Web-based treatment is a promising way to deliver cognitive behavioral therapy. Since adherence has been shown to be a measure for treatment's acceptability and a determinant for treatment's effectiveness, in this study, we explored adherence to a new blended smoking cessation treatment (BSCT). OBJECTIVE:The objective of our study was to (1) develop an adequate method to measure adherence to BSCT; (2) define an adequate degree of adherence to be used as a threshold for being adherent; (3) estimate adherence to BSCT; and (4) explore the possible predictors of adherence to BSCT. METHODS:The data of patients (N=75) were analyzed to trace adherence to BSCT delivered at an outpatient smoking cessation clinic. In total, 18 patient activities (eg, using a Web-based smoking diary tool or responding to counselors' messages) were selected to measure adherence; the degree of adherence per patient was compared with quitting success. The minimum degree of adherence of patients who reported abstinence was examined to define a threshold for the detection of adherent patients. The number of adherent patients was calculated for each of the 18 selected activities; the degree of adherence over the course of the treatment was displayed; and the number of patients who were adherent was analyzed. The relationship between adherence and 33 person-, smoking-, and health-related characteristics was examined. RESULTS:The method for measuring adherence was found to be adequate as adherence to BSCT correlated with self-reported abstinence (P=.03). Patients reporting abstinence adhered to at least 61% of BSCT. Adherence declined over the course of the treatment; the percentage of adherent patients per treatment activity ranged from 82% at the start of the treatment to 11%-19% at the final-third of BSCT; applying a 61% threshold, 18% of the patients were classified as adherent. Marital status and social modeling were the best independent predictors of adherence. Patients having a partner had 11-times higher odds of being adherent (OR [odds ratio]=11.3; CI: 1.33-98.99; P=.03). For social modeling, graded from 0 (=partner and friends are not smoking) to 8 (=both partner and nearly all friends are smoking), each unit increase was associated with 28% lower odds of being adherent (OR=0.72; CI: 0.55-0.94; P=.02). CONCLUSIONS:The current study is the first to explore adherence to a blended face-to-face and Web-based treatment (BSCT) based on a substantial group of patients. It revealed a rather low adherence rate to BSCT. The method for measuring adherence to BSCT could be considered adequate because the expected dose-response relationship between adherence and quitting could be verified. Furthermore, this study revealed that marital status and social modeling were independent predictors of adherence. TRIAL REGISTRATION:Netherlands Trial Registry NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113 (Archived by WebCite at http://www.webcitation.org/71BAPwER8).

Project description:BACKGROUND:Active smoking increases the risk of tuberculosis (TB) infection 2 to 2.5 times and is significantly associated with recurrent TB and TB mortality. Observational studies have shown associations between smoking and poor TB treatment outcomes such as increased loss to follow-up rate, severity of disease, drug resistance and slow smear conversion. Since most smoking-related immunologic abnormalities are reversible within six weeks of stopping smoking, smoking cessation may have substantial positive effects on TB treatment outcomes, TB relapse and future lung disease. OBJECTIVES:To analyse the effect of tobacco smoking cessation interventions (SCIs) on the treatment outcomes of people with adult pulmonary TB. SEARCH METHODS:We searched the Cochrane Tobacco Addiction Group Specialised Register using free-text and MeSH terms for TB and antitubercular treatment. We also searched MEDLINE and EMBASE using the same topic-related terms, combined with the search terms used to identify trials of tobacco cessation interventions from the Specialised Register. We also searched reference list of articles and reviews, the Conference Paper Index, clinicaltrials.gov and grey literature. The searches are current to 29th July 2015. SELECTION CRITERIA:Individual and cluster-randomised controlled trials (RCTs), regardless of date, language and publication status, studies of adults with pulmonary TB on first-line anti-tubercular drugs, with interventions at either an individual or a population level, delivered separately or as part of a larger tobacco control package. This included any type of behavioural or pharmaceutical intervention or both for smoking cessation. DATA COLLECTION AND ANALYSIS:Using the eligibility criteria, two authors independently checked the abstracts of retrieved studies for relevance, and acquired full trial reports of candidates for inclusion. The authors resolved any disagreements on eligibility by mutual consent, or by recourse to a third author. Two authors intended to independently extract study data from eligible studies into a data extraction form and compare the findings, synthesise data using risk ratios, and assess risk of bias using standard Cochrane methodologies. However, we found no eligible trials. MAIN RESULTS:There were no randomised controlled trials that met the eligibility criteria. A number of potentially eligible studies are underway, and we will assess them for inclusion in the next update of this review. AUTHORS' CONCLUSIONS:There is a lack of high-quality evidence, i.e. RCTs, that tests the effectiveness of cessation interventions in improving TB treatment outcomes. There is a need for good-quality randomised controlled trials that assess the effect of SCIs on TB treatment outcomes in both the short and long term. Establishing such an evidence base would be an essential step towards the implementation of SCIs in TB control programmes worldwide.

Project description:AIMS:To assess (1) how far the efficacies of front-line smoking cessation pharmacotherapies vary as a function of smoker characteristics and (2) associations between these characteristics and success of smoking cessation attempts. DESIGN:Prospective correlational study in the context of a double-blind randomized trial. The outcome was regressed individually onto each covariate after adjusting for treatment, and then a forward stepwise model constructed. Treatment moderator effects of covariates were tested by treatment × covariate interactions. SETTING:Health service facilities in multiple countries. PARTICIPANTS:Data came from 8120 smokers willing to make a quit attempt, randomized to varenicline, bupropion, nicotine replacement therapy (NRT) or placebo in Evaluating Adverse Events in a Global Smoking Cessation Study (EAGLES) between 30 November 2011 and 13 January 2015. MEASUREMENTS:Smoker characteristics measured at baseline were country, psychiatric history, sex, age, body mass index (BMI), ethnic group, life-time suicidal ideation/behaviour, anxiety, depression, aggression, psychotropic medication, history of alcohol/substance use disorder, age of starting smoking, cigarette dependence [Fagerström Test for Cigarette Dependence (FTCD)] and prior use of study medicines. Outcome was biochemically confirmed continuous abstinence at weeks 9-24 from start of treatment. FINDINGS:No statistically significant treatment × covariate interactions were found. Odds of success were associated independently positively with age [odds ratio (OR) = 1.01; 95% confidence interval (CI) = 1.00, 1.01], BMI (1.01; 95% CI = 1.00, 1.02) and age of starting smoking (1.03; 95% CI = 1.02, 1.04). Odds were associated independently negatively with US (versus non-US) study site (0.53; 95% CI = 0.46, 0.61), black (versus white) ethnic group (0.57; 95% CI = 0.45, 0.72), mood disorder (0.85; 95% CI = 0.73, 0.99), anxiety disorder (0.71; 95% CI = 0.55, 0.90) and psychotic disorder (0.73; 95% CI = 0.50, 1.07), taking psychotropic medication (0.81; 95% CI = 0.68, 0.95), FTCD (0.89; 95% CI = 0.87, 0.92) and previous use of NRT (0.78; 95% CI = 0.67, 0.91). CONCLUSIONS:While a range of smoker characteristics-including psychiatric history, cigarette dependence and prior use of nicotine replacement therapy (NRT)-are associated with lower cessation rates, they do not substantially influence the efficacy of varenicline, bupropion or NRT.

Project description:AIMS:Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches. METHODS:Two cohorts (training: N = 90, validation: N = 71) of cigarette smokers enrolled in group cognitive-behavioral therapy (CBT). Generalized estimating equations were used to identify baseline predictors of outcome, as defined by breath carbon monoxide and urine cotinine. Significant measures were entered as candidate variables to predict quit status. The resulting decision trees were used to predict cessation outcomes in a validation cohort. RESULTS:In the training cohort, the decision trees significantly improved on chance classification of smoking status following treatment and at 6-month follow-up. The first split of all decision trees, which was delay discounting, significantly improved on chance classification rates in both the training and validation cohort. Delay discounting emerged as the single best predictor of group CBT treatment response with an average baseline discount rate of ln(k) = -7.1, correctly predicting smoking status of 80% of participants at posttreatment and 81% of participants at follow-up. CONCLUSIONS:This study provides a first step toward personalized care for smoking cessation though future work is needed to identify individuals that are likely to be successful in treatments beyond group CBT. IMPLICATIONS:This study provides a first step toward personalized care for smoking cessation. Using a novel machine-learning approach, baseline measures of clinical and executive functioning are used to predict smoking cessation outcomes following group CBT. A decision point is recommended for the single best predictor of treatment outcomes, delay discounting, to inform future research or clinical practice in an effort to better allocate patients to treatments that are likely to work.

Dataset Information

Temperament and impulsivity predictors of smoking cessation outcomes.

Aims

Methods

Results

Conclusion

Publications

Temperament and impulsivity predictors of smoking cessation outcomes.

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