Cigarette smoking is the leading cause of morbidity and mortality in the United States, including accounting for 30% of all cancers and approximately 90% of lung cancer. Additionally, recent estimates place the annual economic burden of smoking at $96 billion in medical costs with an additional $97 billion in lost worker productivity. The majority of individuals who smoke indicate they want to quit, and although half of all smokers make a quit attempt every year, only 6% successfully do so. Poverty, lower education, unemployment, uninsurance, and other factors related to low socioeconomic status (SES) are potent predictors of greater difficulty quitting smoking and SES-related tobacco disparities have widened over the last several decades. Neighborhood-level factors such as poverty, social trust, and density of tobacco outlets, while clearly linked to individual-level SES, present unique factors that are disadvantageous for smoking cessation. Furthermore, health behavior models posit that low SES and disadvantageous neighborhood factors can impact intrapersonal experiences (e.g. negative affect, low motivation to quit, low self-efficacy, high stress) and social experiences (e.g. greater cigarette availability, more time spent with smokers or in places that allow smoking) that, in turn, increase risk for smoking lapse during a quit attempt. Advanced research approaches using mobile health technology (?mHealth?) offer an exciting path to gathering real-time, real-world measures on the dynamics of intrapersonal and social experiences during a quit attempt. These data can help elucidate how SES and neighborhoods connect to smoking cessation through intrapersonal and social experiences. This project hypothesizes that 1) SES and neighborhood environments are connected to smoking lapse/abstinence during a quit attempt; 2) the dynamic features of intrapersonal and social experiences (e.g. mean levels, trajectories over time, stability/volatility) are connected to smoking lapse/abstinence during a quit attempt; and 3) the associations of SES and neighborhoods with smoking lapse/abstinence are in part mediated by the dynamic features of intrapersonal and social experiences during a quit attempt. Increased knowledge regarding the dynamic features of intrapersonal and social experiences that inhibit individuals from successfully staying quit is key to eliminating smoking-related disease and disability. A deeper understanding of the extent to which the dynamics of intrapersonal and social experiences operate as mechanisms connecting SES and neighborhood environments to smoking cessation will provide a wealth of new information that can be used to help low SES populations successfully stay quit and drive programs and policies to encourage smoking cessation. Findings from this project will be critical in continuing to propel the scientific study of health disparities, smoking cessation, and the prevention of cancer and other chronic diseases; and, ultimately, can help improve the health and well-being of underserved populations.
This project will provide novel information on associations between socioeconomic status (SES), neighborhood environments, intrapersonal lived and social experiences, and smoking cessation. Using mHealth technology to examine of how SES and neighborhoods operate through lived experiences (e.g. affect, stress, craving, motivation, cigarette availability, and contact with other smokers) during a quit attempt will provide a wealth of new information for smoking cessation programs. Moreover, the results of this project will be key in propelling efforts to address tobacco-related health disparities.
