SP 2: Socioeconomic Status, Stress, and Smoking Cessation

SP2 is examining the influence of demographics and social history, biobehavioral and psychosocial predispositions, contextual and environmental factors, and acute individual and contextual precipitants on stress, smoking lapse, and abstinence among 300 smokers from low socioeconomic status (SES) who are attempting to quit (evenly split between African Americans, Latinos, and Whites). Participants are being monitored using mobile physiological sensors to passively and objectively measure stress and smoking lapse. GPS tracking paired with self-report and sensor data can be associated with spatially- and temporally-relevant characteristics of the built environment (e.g., tobacco outlets) as well as area-level characteristics (e.g., poverty, racial composition) using geographic information system (GIS) data. Principal outcomes of interest are stress and lapse ascertained in real time through sensors, and early and long-term abstinence from smoking. Thus, key pathways can be generated that link distal predictors including demographic and social history factors, biobehavioral and psychological predispositions, neighborhood and built environment characteristics, acute momentary precipitants (e.g., discrimination, craving, self-efficacy), stress, and lapse. Similar to CP5, SP2 is recruiting daily smokers who are interested in quitting but recruiting from three major ethnicities. Data collection is ongoing; over 200 participants have already completed.

Since SP2 uses a similar setup as CP5, it can be used to evaluate the ease of deployment of TRD1 technologies in projects involving daily smokers without the need for close collaboration. SP3 is recruiting and collecting data in Houston (in Texas); CP5 is recruiting participants in Salt Lake City (in Utah). SP2 is ideally suited to disseminate the novel analytics methods of TR&D1 that can be used to analyze its dense multimodal sensor and self-report data to understand the dynamics of SES, daily behaviors, and physical environment and their impact on lapse likelihood. This will be an ideal service project to test and deploy the models of lapse likelihood and multiscale predictive phenotypes in the context of smoking cessation developed in collaboration with CP5 and test its applicability in low SES population across three ethnicities.