Contrasting Smoking Behaviors of Non-daily Smokers – A Study based on Clustering of Repeated Daily Measures of Nicotine Exposure

Abstract Background: Over one-quarter of all smokers in the U.S. identify as non-daily smokers and this number is projected to rise. Despite the negative health effects of non-daily smoking and high rates of unsuccessful quit attempts, we know little about the variability and correlates of nicotine exposure among non-daily smokers. Methods: A community sample of non-daily smokers (n=60) ages 21 to 60 completed baseline measures of nicotine dependence, a consecutive 7-day at-home protocol to log each smoking session, ecological momentary assessments of mood and activity during smoking, and daily saliva samples for cotinine and nicotine metabolite ratio analysis. Hierarchical cluster analysis categorized participants based on their mean cotinine levels across days. Clusters were compared on biological and behavioral smoking characteristics, nicotine dependence, and activities and emotions during smoking using linear mixed effect regression models.Results: Cluster analysis of cotinine revealed four distinct clusters of nicotine exposure, ranging from high levels (above 200 ng/ml) to very low levels (near 0 ng/ml). Compared to the other clusters, the cluster with the highest mean levels of cotinine also had the largest day-to-day variation in cotinine and was associated with significantly higher levels of nicotine dependence on the Fagerström Test of Nicotine Dependence and the Penn State Cigarette Dependence Index, lower rates of social smoking, and the highest frequency of negative emotions during smoking. Conclusions: Our findings highlight the variability in nicotine exposure across days among non-daily smokers and show that some are exposed to levels near those of daily smokers. The results point to individual differences and existence of several sub-groups of nondaily smokers based on their nicotine exposure, which varied considerably across days for some, but not all groups. The results highlight the need to better characterize non-daily smokers and the factors that maintain smoking behavior to inform personalized cessation treatments for this growing population of smokers.