Using Mobile Technology to Examine Mechanisms Linking Sleep and Smoking Cessation

PROJECT ABSTRACT Tobacco use disorder is the most common substance use disorder. Cigarette smoking is highly concentrated among individuals with low socioeconomic status (SES); however, they often lack access to smoking cessation services. Thus, smoking cessation in low SES adults remains a critical public health concern that warrants further study and

attention. Smokers attempting to quit are at the highest risk for lapse within the first weeks of their quit attempt, and an

initial lapse is highly likely to lead to full relapse. It is essential to identify and understand behavioral factors that may increase or decrease the likelihood of successful smoking cessation among low SES adults during a quit attempt (pre- and post-quit). Recently, sleep dysregulation, such as insufficient sleep duration, has been considered as a potential

intervention target to improve smoking behavior (e.g., number of cigarettes smoked per day) and smoking cessation outcomes (e.g., abstinence). On the other hand, studies have found the lower SES is associated with higher rates of poor sleep. Thus, SES must be accounted for when assessing sleep dysregulation and smoking behavior and cessation

outcomes. Although previous studies have examined the relationship between sleep dysregulation and smoking behavior and/or cessation outcomes, they have several methodological limitations, including the use of retrospective

survey methods, not using the integrated sleep health dimensions (usually only sleep duration or quality), use of cross- sectional surveys, laboratory-based data collection, omitting low SES smokers, and focusing on a single pathway rather than dynamic associations. Therefore, this proposal will use a Real-Time Data Capture approach among low SES

smokers who are attempting to quit. This approach will involve a granular examination of the bidirectional and temporal association of daily sleep dysregulation and smoking cession processes (pre- and post-quit) using smartphone-based ecological momentary assessment (EMA) and wearable sensors among low SES smokers. Specifically, we aim: 1)

During the pre-quit period, to examine the within-person and between-person effects of sleep dysregulation on smoking behavior and cessation and 2) During the post-quit period, to identify bidirectional and temporal associations of daily smoking abstinence and sleep dysregulation via EMA and wrist-worn sensors during the first four weeks of a smoking

cessation attempt. The proposed research and training plan will take place at Rutgers Cancer Institute of New Jersey. Training will focus on increasing knowledge of the understanding and measuring of sleep dimensions, health disparities in smoking cessation studies, and application of advanced mHealth technologies (including

EMA and actigraphy data analyses techniques). It is expected that completion of these aims will yield the necessary preliminary data for developing and implementing Just-in-Time-Adaptive Interventions (JITAI) that aim to improve sleep health during smoking cessation.