Distinct coping strategies differentially predict urge levels and lapses in a smoking cessation attempt

This study analysed mechanisms through which stress-coping and temptation-coping strategies were associated with lapses. Furthermore, we explored whether distinct coping strategies differentially predicted reduced lapse risk, lower urge levels, or a weaker association between urge levels and lapses during the first week of an unassisted smoking cessation attempt. Participants were recruited via the internet and mass media in Switzerland. Ecological momentary assessment (EMA) with mobile devices was used to assess urge levels and lapses. Online questionnaires were used to measure smoking behaviours and coping variables at baseline, as well as smoking behaviour at the three-month follow-up. The sample consisted of 243 individuals, aged 20 to 40, who reported 4199 observations. Findings of multilevel regression analyses show that coping was mainly associated with a reduced lapse risk and not with lower urge levels or a weaker association between urge levels and lapses. 'Calming down' and 'commitment to change' predicted a lower lapse risk and also a weaker relation between urge levels and lapses. 'Stimulus control' predicted a lower lapse risk and lower urge levels. Conversely, 'task-orientation' and 'risk assessment' were related to higher lapse risk and 'risk assessment' also to higher urge levels. Disengagement coping i.e. 'eating or shopping', 'distraction', and 'mobilising social support' did not affect lapse risk. Promising coping strategies during the initial stage of smoking cessation attempt are targeted directly at reducing the lapse risk and are characterised by engagement with the stressor or one's reactions towards the stressor and a focus on positive consequences instead of health risks.

Smoking behaviour, former quit attempts and intention to quit in urban adolescents and young adults: A five-year longitudinal study

OBJECTIVES: To examine smoking behaviour, former quit attempts and intention to quit among Swiss adolescents and young adults over five year's time. STUDY DESIGN: five-year longitudinal study (2003, 2005 and 2008) based on a random urban community sample (N = 1345 complete cases). METHODS: Data were collected by computer-assisted telephone interviews with adolescents (16-17) and young adults (18-24). Main outcome measures included self-reported smoking behaviour, former quit attempts, smoking cessation methods and current intentions to quit smoking. RESULTS: Adolescents were more often non-smokers and less often daily smokers when compared to young adults at baseline (χ(2)(4) = 28.68, P < .001). Their smoking behaviour increased significantly from baseline to follow-up (T = 1445.50, r = .20, P < .001) in contrast to the stable smoking behaviour in young adults (χ(2)(2) = .12, n.s.). In longitudinal analyses young adults were also more stable in their smoking status at the later measurement points. In comparison adolescents changed their smoking status more often being non-smokers at baseline and smokers later on. Independently of the age group, the majority of smokers already had previously attempted to quit (65%) or intended to give up smoking at some point (72%). However only 17% were motivated to make the quit attempt within the next 6 months. Self-quitting was the preferred method, and 25% of the self-quitters had been successful. CONCLUSION: This study illustrates that different developments in smoking behaviour exist in adolescents and young adults. Our study reveals that a majority of smokers are willing to quit but often fail. Furthermore, the data indicates that for adolescents the focus should lie on primary prevention.

Identifying Distinct Quitting Trajectories after an Unassisted Smoking Cessation Attempt: An Ecological Momentary Assessment Study

Objectives: This study aimed at identifying distinct quitting trajectories over 29 days after an unassisted smoking ces- sation attempt by ecological momentary assessment (EMA). In order to validate these trajectories we tested if they predict smoking frequency up to six months later. Methods: EMA via mobile phones was used to collect real time data on smoking (yes/no) after an unassisted quit attempt over 29 days. Smoking frequency one, three and six months after the quit attempt was assessed with online questionnaires. Latent class growth modeling was used to analyze the data of 230 self-quitters. Results: Four different quitting trajectories emerged: quitter (43.9%), late quitter (11.3%), returner (17%) and persistent smoker (27.8%). The quitting trajectories predicted smoking frequency one, three and six months after the quit attempt (all p < 0.001). Conclusions: Outcome after a smoking cessation attempt is better described by four distinct trajectories instead of a binary variable for abstinence or relapse. In line with the relapse model by Marlatt and Gordon, late quitter may have learned how to cope with lapses during one month after the quitting attempt. This group would have been allocated to the relapse group in traditional outcome studies.

Association of alcohol craving and proximal outcomes of a residential treatment program for patients with alcohol use disorders

Background: Alcohol craving is an essential construct in research and treatment of alcohol use disorders (AUD). Craving is mostly investigated in association with concurrent variables or distal treatment outcomes at follow-up. Objectives: The aim of this study is to examine craving at admission and its relevance for essential proximal outcomes at discharge from AUD treatment such as positive alcohol expectancy, abstinent-related self-efficacy, and substance-related coping, as well as patients’ demographic and AUD characteristics. Methods: In total, 36 patients were recruited within an inpatient treatment AUD program. Results: An association between craving and positive alcohol expectancies at discharge was found in the regression model even when the respective expectancies, age, gender, and severity of alcohol dependence at admission were controlled for (F(2,29)1⁄432.71, p50.001). Craving explained 2.3% of the variance of change in positive alcohol expectancy. Conclusion: The results suggest a low predictive value of craving for positive alcohol expectancy. In addition, we found significant associations between the craving and the severity of AUD and alcohol consumption before admission. Future studies should include proximal outcomes related to treatment efficacy as well as distal outcomes.

Smoking, apolipoprotein E genotypes, and mortality (the Ludwigshafen RIsk and Cardiovascular Health study).

AIMS The genetic polymorphism of apolipoprotein E (APOE) has been suggested to modify the effect of smoking on the development of coronary artery disease (CAD) in apparently healthy persons. The interaction of these factors in persons undergoing coronary angiography is not known. METHODS AND RESULTS We analysed the association between the APOE-genotype, smoking, angiographic CAD, and mortality in 3263 participants of the LUdwigshafen RIsk and Cardiovascular Health study. APOE-genotypes were associated with CAD [ε22 or ε23: odds ratio (OR) 0.56, 95% confidence interval (CI) 0.43-0.71; ε24 or ε34 or ε44: OR 1.10, 95% CI 0.89-1.37 compared with ε33] and moderately with cardiovascular mortality [ε22 or ε23: hazard ratio (HR) 0.71, 95% CI 0.51-0.99; ε33: HR 0.92, 95% CI 0.75-1.14 compared with ε24 or ε34 or ε44]. HRs for total mortality were 1.39 (95% CI 0.39-0.1.67), 2.29 (95% CI 1.85-2.83), 2.07 (95% CI 1.64-2.62), and 2.95 (95% CI 2.10-4.17) in ex-smokers, current smokers, current smokers without, or current smokers with one ε4 allele, respectively, compared with never-smokers. Carrying ε4 increased mortality in current, but not in ex-smokers (HR 1.66, 95% CI 1.04-2.64 for interaction). These findings applied to cardiovascular mortality, were robust against adjustment for cardiovascular risk factors, and consistent across subgroups. No interaction of smoking and ε4 was seen regarding non-cardiovascular mortality. Smokers with ε4 had reduced average low-density lipoprotein (LDL) diameters, elevated oxidized LDL, and lipoprotein-associated phospholipase A2. CONCLUSION In persons undergoing coronary angiography, there is a significant interaction between APOE-genotype and smoking. The presence of the ε4 allele in current smokers increases cardiovascular and all-cause mortality.

Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study

OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.

Assessment of smoking behaviour in a dental setting: a 1-year follow-up study using self-reported questionnaire data and exhaled carbon monoxide levels.

OBJECTIVES This study analyses the changes in smoking habits over the course of 1 year in a group of patients referred to an oral medicine unit. MATERIALS AND METHODS Smoking history and behaviour were analysed at baseline and after 1 year based on a self-reported questionnaire and on exhaled carbon monoxide levels [in parts per million (ppm)]. During the initial examination, all smokers underwent tobacco use prevention and cessation counselling. RESULTS Of the initial group of 121 patients, 98 were examined at the follow-up visit. At the baseline examination, 33 patients (33.67 %) indicated that they were current smokers. One year later, 14 patients (42.24 % out of the 33 smokers of the initial examination) indicated that they had attempted to stop smoking at least once over the follow-up period and 15.15 % (5 patients) had quit smoking. The mean number of cigarettes smoked per day by current smokers decreased from 13.10 to 12.18 (p = 0.04). The exhaled CO level measurements showed very good correlation with a Spearman's coefficient 0.9880 for the initial visit, and 0.9909 for the follow-up examination. For current smokers, the consumption of one additional cigarette per day elevated the CO measurements by 0.77 ppm (p < 0.0001) at the baseline examination and by 0.84 ppm (p < 0.0001) at the 1-year follow-up. CONCLUSIONS In oral health care, where smoking cessation is an important aspect of the treatment strategy, the measurement of exhaled carbon monoxide shows a very good correlation with a self-reported smoking habit. CLINICAL RELEVANCE Measurement of exhaled carbon monoxide is a non-invasive, simple and objective measurement technique for documenting and monitoring smoking cessation and reduction.

Volitional processes and daily smoking: Examining inter- and intraindividual associations around a quit attempt

The Health Action Process Approach (HAPA) assumes that volitional processes are important for effective behavioral change. However, intraindividual associations have not yet been tested in the context of smoking cessation. This study examined the inter- and intraindividual associations between volitional HAPA variables and daily smoking before and after a quit attempt. Overall, 100 smokers completed daily surveys on mobile phones from 10 days before until 21 days after a self-set quit date, including self-efficacy, action planning, action control, and numbers of cigarettes smoked. Negative associations between volitional variables and daily numbers of cigarettes smoked emerged at the inter- and intraindividual level. Except for interindividual action planning, associations were stronger after the quit date than before the quit date. Self-efficacy, planning and action control were identified as critical inter- and intraindividual processes in smoking cessation, particularly after a self-set quit attempt when actual behavior change is performed.

Invisible social control as predictor of daily negative affect and smoking after a self-set quit date

The dual-effects model of social control proposes that social control leads to increased psychological distress but also to better health practices. However, findings are inconsistent, and recent research suggests that the most effective control is unnoticed by the receiver (i. e., invisible). Yet, investigations of the influence of invisible control on daily negative affect and smoking have been limited. Using daily diaries, we investigated how invisible social control was associated with negative affect and smoking. Overall, 100 smokers (72.0 % men, age M = 40.48, SD = 9.82) and their nonsmoking partners completed electronic diaries from a self-set quit date for 22 consecutive days, reporting received and provided social control, negative affect, and daily smoking. We found in multilevel analyses of the within-person process that on days with higher-than-average invisible control, smokers reported more negative affect and fewer cigarettes smoked. Findings are in line with the assumptions of the dual-effects model of social control: Invisible social control increased daily negative affect and simultaneously reduced smoking at the within-person level.

Change in physical activity after smoking cessation: the Coronary Artery Risk Development in Young Adults (CARDIA) study

AIMS To estimate physical activity trajectories for people who quit smoking, and compare them to what would have been expected had smoking continued. DESIGN, SETTING AND PARTICIPANTS A total of 5115 participants in the Coronary Artery Risk Development in Young Adults Study (CARDIA) study, a population-based study of African American and European American people recruited at age 18-30 years in 1985/6 and followed over 25 years. MEASUREMENTS Physical activity was self-reported during clinical examinations at baseline (1985/6) and at years 2, 5, 7, 10, 15, 20 and 25 (2010/11); smoking status was reported each year (at examinations or by telephone, and imputed where missing). We used mixed linear models to estimate trajectories of physical activity under varying smoking conditions, with adjustment for participant characteristics and secular trends. FINDINGS We found significant interactions by race/sex (P = 0.02 for the interaction with cumulative years of smoking), hence we investigated the subgroups separately. Increasing years of smoking were associated with a decline in physical activity in black and white women and black men [e.g. coefficient for 10 years of smoking: -0.14; 95% confidence interval (CI) = -0.20 to -0.07, P < 0.001 for white women]. An increase in physical activity was associated with years since smoking cessation in white men (coefficient 0.06; 95% CI = 0 to 0.13, P = 0.05). The physical activity trajectory for people who quit diverged progressively towards higher physical activity from the expected trajectory had smoking continued. For example, physical activity was 34% higher (95% CI = 18 to 52%; P < 0.001) for white women 10 years after stopping compared with continuing smoking for those 10 years (P = 0.21 for race/sex differences). CONCLUSIONS Smokers who quit have progressively higher levels of physical activity in the years after quitting compared with continuing smokers.