Predictors Of Smoking Status Research Articles

Support vector machine based classification of smokers and nonsmokers using diffusion tensor imaging.

Despite significant progress in treatments for smoking cessation, smoking continues to be a significant public health concern, especially in young adulthood. Thus, developing a predictive model that can classify and characterize the brain-based biomarkers predicting smoking status would be imperative to improving treatment development. In this study, we applied a support vector machine-based classification method to discriminate 70 young male smokers and 70 matched nonsmokers using their diffusion tensor imaging (DTI) data. The classification procedure achieved an average accuracy of 88.6% and an average area under the curve of 0.95. The most discriminative features that contributed to the classification were primarily located in the sagittal stratum (SS), external capsule (EC), superior longitudinal fasciculus (SLF), anterior corona radiata (ACR) and inferior front-occipital fasciculus (IFOF). The following regression analysis showed a significant negatively correlation between the average RD values of the left ACR (r = -0.247, p = 0.039) and FTND. The average MD values in the right EC (r = -0.254, p = 0.034) and RD values in the right IFOF (r = -0.240, p = 0.046) were inversely associated with pack-years. Our findings indicate that the discriminative white matter (WM) features as brain biomarkers provide great predictive power for smoking status and suggest that machine learning techniques can reveal underlying smoking-related neurobiology.

Saliva DNA Methylation Detects Nascent Smoking in Adolescents.

Objectives: Early identification of smoking, essential for the successful implementation of interventions, arrests the escalation of smoking and smoking-associated risk behaviors in adolescents. However, because nascent smoking is typically episodic and infrequent, enzyme-linked immunoassay reagent-based approaches that detect cotinine, a key nicotine metabolite, are not effective in identifying adolescents in the earliest stages of smoking. Epigenetic methods may offer an alternative approach for detecting early-stage smokers. In prior work, we and others have shown that the methylation status of cg05575921 of whole-blood DNA accurately predicts smoking status in regularly smoking adults and is sensitive to nascent smoking. Yet, the blood draws necessary to obtain DNA for this method may be poorly accepted by adolescents. Saliva could be an alternative source of DNA. However, the ability of saliva DNA methylation status to predict smoking status among adolescents is unknown. Methods: To explore the possibility of using salivary DNA for screening purposes, we examined the DNA methylation status at cg05575921 in saliva DNA samples from 162 high school aged subjects for whom we also had paired serum cotinine values. Results: Overall, the reliability of self-report of nicotine/tobacco use in these adolescents was poor with 67% of all subjects whose serum levels of cotinine was ≥2 ng/mL (n = 75) denying any use of nicotine-containing products in the past 6 months. However, the correspondence of the two biological measures of smoking was high, with serum cotinine positivity being strongly correlated with cg05575921 methylation (p < 0.0001). Receiver operating characteristic (ROC) analyses showed that cg05575921 methylation status could be used to classify those with positive serum cotinine values (≥2 ng/mL) from those denying smoking and have undetectable levels of cotinine. Conclusions: We conclude that saliva DNA methylation assessments hold promise as a means of detecting nascent smoking.

Factors associated with different smoking statuses among Malaysian adolescent smokers: a cross-sectional study

BackgroundThis study focused on the associations between socioeconomic status (SES) and adolescent smoking among secondary school students (13 to 17 years) in the Federal Territory of Kuala Lumpur, Malaysia. Our objective was to evaluate the relationships between adolescent demographics, socioeconomic status and smoking status.MethodsThe survey data were based on baseline findings from a cross-sectional study (N = 422 adolescents). Chi-square test was used to assess the relationship between demographic characteristics, socioeconomic status (household monthly income and daily allowance) and adolescent smoking status. Exhaled carbon monoxide (CO) reading and the Hooked on Nicotine Checklist (HONC) were used to evaluate adolescent smoking status. A Multivariate Multinomial Logistic Regression (MMLR) was employed to test selected demographic and socioeconomic predictors of smoking status.ResultsOf the 422 adolescents (M age = 15.58, SD = 1.24), more than half of the participants initiated smoking between 13 to 17 years old (59.0%). A total of 308 (73.0%) were electronic cigarette users, with more than 50% comprising of single users. The mean CO reading was 2.14 ppm with 78.0% of adolescents scoring more than 0 on the Hooked on Nicotine Checklist (HONC). Males and participants aged 15 and 16 years were at increased risks of sole CC smoking. Meanwhile, males, those who are not hooked on smoking and with a non-smoker CO reading were at increased risks of sole EC smoking. Finally, Bumiputeras were at less risk of EC smoking.ConclusionsDemographic variables such as age, gender and ethnicity predicted smoking status predicted smoking risk, but not socioeconomic factors. The findings allow policy makers to target specific high-risk demographic groups when designing smoking cessation programs for adolescents.

Is the National Institute on Alcohol Abuse and Alcoholism Screening Guide Useful for Identifying Adolescents at Risk for Later Cigarette Smoking? A Prospective Study in Primary Care Clinics.

This paper evaluated whether a 2-item assessment of alcohol use risk, developed by the National Institute on Alcohol Abuse and Alcoholism (NIAAA SG), prospectively predicted smoking status among a sample of adolescents visiting their primary care physician. We analyzed a sample of 651 adolescents (57.80% female; 56.53% Hispanic, 22.73% African American, 14.44% White, 6.30% other) who completed the NIAAA SG at a baseline appointment and were assessed for future smoking risk. We obtained prospective data on smoking status using data from the 6-month post-baseline follow-up assessment. Logistic regression analyses revealed that adolescents who were identified as at risk by the NIAAA SG were more likely to report smoking within 6 months. However, the association between the NIAAA SG and future smoking status became non-significant after controlling for future smoking risk measured at baseline. Questions that ask about past smoking and future smoking intentions are best used to screen adolescents in primary care for smoking risk.

Longitudinal Analysis of Associations Between Reasons for Electronic Cigarette Use and Change in Smoking Status Among Adults in the Population Assessment of Tobacco and Health Study.

Electronic cigarette (ECIG) use and changes in cigarette smoking status may be influenced by self-reported reasons for using ECIGs. We analyzed adult current and former cigarette smokers who were also current or former ECIG users at wave 1 (n = 3044) using wave 1 and wave 2 Population Assessment of Tobacco and Health Study data (2013-2015). Prevalence of reporting 13 reasons for ECIG use at wave 1 was examined and weighted logistic regressions were conducted predicting smoking status changes from wave 1 to wave 2. Reasons for ECIG use ranged from 18.1% (people in the media or public figures use them) to 82.5% (they might be less harmful to people around me than cigarettes). From wave 1 to wave 2, 27.2% of former smokers (n = 249) became current smokers and 11.6% of current smokers (n = 246) became former smokers. Among wave 1 former smokers, using ECIGs because of the availability of flavors (AOR = 0.57, 95% CI = 0.39-0.85) or because they don't smell (AOR = 0.64, 95% CI = 0.42-0.97) was associated with lower odds of relapse to smoking, but using ECIGs because using them helps people quit smoking (AOR = 1.55, 95% CI = 1.01-2.38) was associated with greater odds of relapse. Among wave 1 current smokers, using ECIGs because they can be used where smoking is not allowed (AOR = 0.56, 95% CI = 0.38-0.85) was associated with reduced odds of quitting cigarettes. Some reasons for ECIG use are associated with changes in self-reported smoking status. Researchers should examine ECIG user characteristics when assessing associations between ECIG use and smoking status transitions. Given that certain reasons for ECIG use, such as using ECIGs in locations are where smoking is not allowed, may inhibit smoking reduction, policies may be developed to prevent ECIG use in locations where smoking is banned. In addition, because certain reasons for ECIG use may aid in relapse prevention, such as availability of desired flavors, efforts should be made to identify ECIG device characteristics that are appealing to smokers but not youth or nontobacco users. These results provide support for future research on reasons for ECIG use to inform regulatory policies.

A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes.

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. 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. 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. 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. 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.

Discrimination of smoking status by MRI based on deep learning method.

This study aimed to assess the feasibility of deep learning-based magnetic resonance imaging (MRI) in the prediction of smoking status. The head MRI 3D-T1WI images of 127 subjects (61 smokers and 66 non-smokers) were collected, and 176 image slices obtained for each subject. These subjects were 23-45 years old, and the smokers had at least 5 years of smoking experience. Approximate 25% of the subjects were randomly selected as the test set (15 smokers and 16 non-smokers), and the remaining subjects as the training set. Two deep learning models were developed: deep 3D convolutional neural network (Conv3D) and convolution neural network plus a recurrent neural network (RNN) with long short-term memory architecture (ConvLSTM). In the prediction of smoking status, Conv3D model achieved an accuracy of 80.6% (25/31), a sensitivity of 80.0% and a specificity of 81.3%, and ConvLSTM model achieved an accuracy of 93.5% (29/31), a sensitivity of 93.33% and a specificity of 93.75%. The accuracy obtained by these methods was significantly higher than that (<70%) obtained with support vector machine (SVM) methods. The deep learning-based MRI can accurately predict smoking status. Studies with large sample size are needed to improve the accuracy and to predict the level of nicotine dependence.

Weight concerns as a predictor of smoking cessation according to nicotine dependence: A population-based study.

Background:Nicotine-dependent smokers find it difficult to quit smoking. Additionally, smoking-specific weight concerns may affect smoking cessation although the evidence is controversial. We investigated whether smoking-specific weight concerns predict the probability of cessation and, if so, whether the effect varies according to the level of nicotine dependence.Methods:The study was conducted with a population-based sample of 355 adult daily smokers who participated in the baseline examination in 2007 and in the 2014 follow-up. Baseline nicotine dependence was classified as low or high (Fagerström Test for Nicotine Dependence; 0–3 vs. 4–10 points). Within these groups, we examined whether baseline weight concerns predict smoking status (daily, occasional, ex-smoker) at follow-up by using multinomial logistic regression with adjustment for multiple covariates.Results:Among low-dependent participants at baseline, 28.5% had quit smoking, while among highly dependent participants 26.1% had quit smoking. The interaction between weight concerns and nicotine dependence on follow-up smoking status was significant. Among participants with low nicotine dependence per the fully adjusted model, greater weight concerns predicted a lower likelihood of both smoking cessation (relative risk ratio 0.93 [95% CI 0.87–1.00]) and smoking reduction to occasional occurrence (0.89 [95% CI 0.81–0.98]). Weight concerns were not associated with follow-up smoking status among participants with high nicotine dependence.Conclusions:Weight concerns are associated with a smaller likelihood of quitting among smokers with low nicotine dependence. Weight concerns should be addressed in smoking cessation interventions, especially with smokers who have low nicotine dependence.

Individual differences in learning from probabilistic reward and punishment predicts smoking status

IntroductionThe ability to update reward and punishment contingencies is a fundamental aspect of effective decision-making, requiring the ability to successfully adapt to the changing demands of one's environment. In the case of nicotine addiction, research has predominantly focused on reward- and punishment-based learning processes among current smokers relative to non-smokers, whereas less is known about these processes in former smokers. MethodsIn a total sample of 105 students, we used the Probabilistic Selection Task to examine differences in reinforcement learning among 41 current smokers, 29 ex-smokers, and 35 non-smokers. The PST was comprised of a training and test phase that allowed for the comparison of learning from positive versus negative feedback. ResultsThe test phase of the Probabilistic Selection Task significantly predicted smoking status. Current and non-smokers were classified with moderate accuracy, whereas ex-smokers were typically misclassified as smokers. Lower rates of learning from rewards were associated with an increased likelihood of being a smoker or an ex-smoker compared with being a non-smoker. Higher rates of learning from punishment were associated with an increased likelihood of being a smoker relative to non-smoker. However, learning from punishment did not predict ex-smoker status. ConclusionsCurrent smokers and ex-smokers were less likely to learn from rewards, supporting the hypothesis that deficient reward processing is a feature of chronic addiction. In addition, current smokers were more sensitive to punishment than ex-smokers, contradicting some recent findings.

Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis

Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis

Conscientiousness and occupational prestige as independent predictors of the change of tobacco use in adulthood

This study investigated the association between childhood intelligence, personality traits, parental social class, maternal smoking, educational qualifications and occupation prestige, and smoking status assessed at different ages. The study was based on a British birth cohort with nine follow-ups. At age 54 years, cohort members provided information on current tobacco use (Yes/No) and the frequency of daily cigarettes smoking. Response of the same questions was also collected four years earlier, at age 50 years. Results showed that among the 5316 participants with complete data, there was a significant (p < 0.001) decrease in the rates of current tobacco use from age 50 to age 54 years (17.9% and 15.0%). Logistic regression analyses showed that, whilst educational qualifications, occupational prestige, and three of the Big-Five personality traits (Extraversion, Conscientiousness, and Openness) were all significant predictors of current smoking status at age 54 years. After controlling for the initial smoking status, measured four years earlier, only trait Conscientiousness and occupational prestige were significant and independent predictors of the outcome variable. Low intelligence predicted smoking status >39 years later, mediated by educational qualifications. Implications and limitations are noted.

Predicting smoking status using machine learning algorithms and statistical analysis

Smoking has been proven to negatively affect your health in a multitude of ways. As of 2009, smoking is the leading cause of preventable morbidity and mortality in the United States, continuing to ...

Combat, Posttraumatic Stress Disorder, and Smoking Trajectory in a Cohort of Male Australian Army Vietnam Veterans.

Whether trauma exposure itself or consequent posttraumatic stress disorder (PTSD) is primarily responsible for smoking and failure to quit remains unclear. A cohort of male Australian Vietnam veterans (N = 388) was interviewed twice, 22 and 36 years after their return to Australia using standardized psychiatric diagnostic and health interviews and assessment of combat exposure. The smoking trajectory over time revealed a spectrum of outcomes (never smoked, early quitters, late quitters, and continuing smokers). Analysis used multivariate statistics to assess the relative contributions of combat trauma exposure and PTSD while controlling for potential confounders. The trajectory of smoking over time revealed that 21.9% of veterans had never smoked, 45.1% had quit smoking by the time of wave 1, 16.2% were current smokers at wave 1 who had quit by the time of wave 2, 2.8% were late adopters who were current smokers, and 13.9% were continuing smokers. Smoking was associated in single-predictor models with demographics, intelligence, combat exposure, PTSD symptom clusters and diagnosis, and alcohol disorders. Multivariate analysis revealed that PTSD, combat, and intelligence were related to the smoking spectrum but, after adding demographics and other Axis I psychiatric diagnoses, only combat remained significant. No PTSD symptom cluster uniquely predicted smoking status. The results suggest that trauma exposure in the form of military combat may be a more robust predictor of smoking status over time than PTSD. It may be stress itself, rather than poststress disorder, that is more germane to smoking and failure to quit. Exposure to traumatic stress and development of PTSD have been implicated separately in the maintenance of smoking. This longitudinal cohort study of smoking in war veterans up to three decades postwar enabled evaluation of traumatic stress exposure in combat and the course of PTSD in smoking and quitting while controlling for intelligence, background disadvantage, and other psychiatric conditions. Combat rather than PTSD emerged as more significant to smoking status, suggesting that it may be the traumatic stress itself rather than the development of a poststress disorder that is more germane to smoking in war veterans.

Impulsivity and approach tendencies towards cigarette stimuli: Implications for cigarette smoking and cessation behaviors among youth.

Impulsivity is associated with smoking, difficulties quitting smoking, and approach tendencies toward cigarette stimuli among adolescents. We examined the effects of impulsivity on (a) the association between approach tendencies and adolescents' smoking status and (b) the effectiveness of Cognitive Bias Modification (CBM), a smoking cessation intervention focused on changing approach tendencies, among adolescent smokers. We conducted a secondary analysis of evidence from 2 previous published studies: Study 1: a cross-sectional study comparing impulsivity and approach tendencies between adolescent smokers (n = 67) and nonsmokers (n = 58); Study 2: a treatment study that randomized 60 adolescent smokers to receive either CBM or sham training. Impulsivity was measured using the Barratt Impulsiveness Scale (BIS) and the Experiential Discounting Task (EDT). We found higher impulsivity, as determined by the BIS but not the EDT, increased adolescents' odds of being smokers. We observed that the interaction between EDT and approach tendencies also significantly predicted smoking status, however post hoc comparisons were not significant. Adolescents with higher BIS scores receiving CBM had increased odds of being abstinent at the end of treatment, but we found no association between EDT and treatment outcome. Our findings suggest that approach-bias modification (a form of CBM) may be more effective in impulsive adolescent smokers. Differences in findings for BIS and EDT highlight the complexity of the construct of impulsivity. Future studies with larger samples are needed to further disentangle the effects of different aspects of impulsivity on smoking behaviors and cessation outcomes among youth. (PsycINFO Database Record

Dual tobacco smoking is the new trend among adolescents: Update from the Irbid-TRY

ABSTRACTThe current study evaluated the prevalence and contributing factors to smoking styles, particularly cigarette (Cg) and waterpipe (Wp), and dual (combined Cg and Wp) smoking, in adolescents. Tobacco consumption pattern and style, and demographic and socioeconomic characteristics were obtained from all participants. A total of 2,407 adolescents participated, of which 57.6% consumed tobacco. The prevalence of dual was 30%, which was greater than Wp only (21.1%) and Cg only (6.7%). Additionally, male adolescents are more likely to smoke Cg only (OR = 2.15; 95%CI: 1.3/3.4; p = 0.001), Wp only (OR = 1.3; 95%CI: 1/1.8; p = 0.05), and dual (OR = 2.95; 95%CI: 2.2/3.9; p = 0.001). Adolescents in grade 10 were more likely to be dual smokers as compared to grades 7 (OR = 0.5; 95%CI: 0.3/0.7; p = 0.001) and 8 (OR = 0.5; 95%CI: 0.3/0.7; p = 0.001). The study indicates that gender and grade can predict smoking status with dual is most prevalent in older boys. The data also show that dual smoking exceeds combined Cg-only and Wp-only smoking. Compared with previous studies, we are witnessing an increase in dual consumption, while Wp only and Cg only are decreasing, suggesting a shift in adolescent smoking-style selections. Therefore, studies and cessation programs for adolescents, especially for dual smoking among older boys, are warranted. Additionally, studies are needed to examine the health effects of dual smoking among adolescents.

Accuracy and utility of an epigenetic biomarker for smoking in populations with varying rates of false self-report.

Better biomarkers to detect smoking are needed given the tremendous public health burden caused by smoking. Current biomarkers to detect smoking have significant limitations, notably a short half-life for detection and lack of sensitivity for light smokers. These limitations may be particularly problematic in populations with less accurate self-reporting. Prior epigenome-wide association studies indicate that methylation status at cg05575921, a CpG residue located in the aryl hydrocarbon receptor repressor (AHRR) gene, may be a robust indicator of smoking status in individuals with as little as half of a pack-year of smoking. In this study, we show that a novel droplet digital PCR assay for measuring methylation at cg05575921 can reliably detect smoking status, as confirmed by serum cotinine, in populations with different demographic characteristics, smoking histories, and rates of false-negative self-report of smoking behavior. Using logistic regression models, we show that obtaining maximum accuracy in predicting smoking status depends on appropriately weighting self-report and cg05575921 methylation according to the characteristics of the sample being tested. Furthermore, models using only cg05575921 methylation to predict smoking perform nearly as well as those also including self-report across populations. In conclusion, cg05575921 has significant potential as a clinical biomarker to detect smoking in populations with varying rates of accuracy in self-report of smoking behavior.

Response Biases on the IRAP for Adults and Adolescents with Respect to Smokers and Nonsmokers: The Impact of Parental Smoking Status

The current research aimed to examine the implicit biases of smokers and nonsmokers to others who did or did not smoke. Study 1 presented adult smokers and nonsmokers with an Implicit Relational Assessment Procedure (IRAP) that assessed bias toward or against smokers and nonsmokers. Study 2 replicated this with adolescent smokers and nonsmokers. Both studies also presented self-report measures. Both adult and adolescent smokers produced IRAP effects that indicated prosmoker biases; nonsmokers’ biases were relatively neutral. Trends in the data from Studies 1 and 2 led to a post hoc analysis of the nonsmoker data to investigate the potential impact of parental smoking status on nonsmokers’ biases. Both the IRAP and self-report measures data suggested that parental smoking status increased positivity in attitudes toward smokers among nonsmokers. Hierarchical logistic regression analyses indicated that the IRAP data in Study 1, but not Study 2, predicted smoking status above and beyond the self-report measures. The post hoc analyses showed a similar trend. The consistency of the findings with the only existing IRAP study of attitudes toward smokers, as well as with the broader literature, supports the view that response biases toward smokers may not change fundamentally from adolescence to adulthood, and that parental smoking status may having a moderating influence on these biases.

Cigarette dependence and depressive symptoms as predictors of smoking status at five-year follow-up after a workplace smoking cessation program

Workplace smoking cessation interventions increase quit rates compared to no treatment or minimal interventions. However, most studies report data up to one year. This study aims to evaluate long-term effects of a worksite smoking cessation intervention based on cognitive behavioral cessation groups combined with first-line medications, and determine to what extent cigarette dependence (FTCD) and depressive symptoms may influence results at five-year follow-up. Participants were invited to answer a short survey five years after starting the program. A total of 90.4% (n=227) of those who had attended at least one treatment session and were alive, completed the survey. At the five-year follow-up, 29.5% participants reported continuous abstinence. Low scores in the FTCD and low depressive symptoms at baseline predicted continuous abstinence. Three out of four continuous abstainers at twelve months remained abstinent at the five-year follow-up. The study shows that workplace smoking cessation interventions have long-term effects and supports the traditional one-year follow-up period to assess smoking cessation.

Association of Vitamin D and Smoking in Acute Kidney Injury

The eighth leading cause of death in the United States, kidney disease affects 20 million adults with the majority unaware of their condition. Acute kidney injury (AKI) is defined as an abrupt or rapid decline in renal filtration function. Examining modifiable risk measures could add important insights to management in patients with AKI. Vitamin D dysfunction may influence common acute complications such as organ failure, leading to worse outcomes. Vitamin D deficiency is associated with inflammatory disease and smoking is a well‐known risk factor for inflammation. The study examined the associations between vitamin D and cigarette smoking in patients with AKI. A single‐center prospective cohort study was conducted among 60 AKI patients, aged 18 years and older, hospitalized at Staten Island University Hospital. Calcifediol [25(OH)D] and calcitriol [1,25(OH)D] serum levels were determined using LC‐MS/MS method. Smoking status was self‐reported. Linear regressions to evaluate the associations yield the following: 25(OH)D has a significant association with smoking (p = 0.008). Results revealed that smokers have approximately 50% less 25(OH)D levels as compared to non‐smokers. Prospective studies to determine cotinine levels to predict smoking status in patients with AKI are recommended to further verify these findings.Support or Funding InformationThis research project is funded by The Professional Staff Congress‐City University of New York (PSC‐CUNY) Research Award # 67543‐00 45.

The Impact of Weight and Weight-Related Perceptions on Smoking Status Among Young Adults in a Text-Messaging Cessation Program.

Weight gain and concerns about weight can influence a smoker's ability to successfully quit, and young adults are a subgroup of smokers who are particularly concerned about the impact of quitting on their body weight. This study explored the associations between body mass index, weight perceptions, and smoking status among young adults. The sample consisted of 4027 young adults between the ages of 18 and 29 who participated in a randomized control trial of the National Cancer Institute's SmokefreeTXT program. Multivariable logistic regression models were used to examine the associations between weight related variables and smoking status. Obese participants had a 0.72 lower odds (95% CI: 0.62, 0.85) of reporting smoking at the end of the program than participants of normal weight, and this difference persisted over time. Weight perceptions were also associated with smoking status. Those who perceived themselves to be slightly underweight/underweight were more likely to report smoking than those who reported being just about the right weight (OR: 1.53, 95% CI: 1.20, 1.95), and those who strongly disagreed that smoking cigarettes helps people keep their weight down were less likely to report smoking at the end of treatment than those who neither agreed nor disagreed with this statement (OR: 0.69, 95% CI: 0.54, 0.87). Weight related factors assessed at baseline predicted smoking status at the end of treatment and through long term follow-up. Smoking cessation programs that tailor content to addresses the specific needs of weight concerned smokers may enhance effectiveness. This study explores the association between weight related factors and smoking status among young adults, a priority population for smoking cessation efforts. This study demonstrates that both actual weight and weight perceptions (eg, perception of body weight, perception of associations between smoking and weight) are associated with smoking outcomes, and thus need to be a considered in the development of smoking cessation programs.