Grant Number:	5R01CA224537-04 Interpret this number
Primary Investigator:	Lam, Cho
Organization:	University Of Utah
Project Title:	Affective Science and Smoking Cessation: Real Time Real World Assessment
Fiscal Year:	2021

Tobacco use plays a causal role in almost 20 different types of cancer, and although smoking cessation is a cornerstone of cancer risk reduction, the vast majority of smoking quit attempts fail. Numerous conceptual models, as well as a large body of empirical evidence, underscore that affect is a potent determinant of smoking lapse. Unfortunately, very little is known about how the constellation and temporal dynamics of distinct emotions and other factors play out in real time in the real world to influence lapse risk. This lack of knowledge severely hampers both our conceptual models and our ability to optimally intervene. Thus, the overarching objectives of this research are to create a more detailed and comprehensive conceptual model of the role of distinct emotions in self-regulation, as well as the technical, empirical, and analytic foundation necessary to develop effective interventions for smoking cessation and other cancer risk behaviors that can target real time, real world mechanisms. The proposed research directly addresses several objectives from the PAR including the influence of distinct emotions and their time course on cancer risk behaviors, whether the role of distinct emotions is altered by the presence of other emotions (e.g., “blended” emotional states), and how the influence of affective experience is modified by context. The proposed longitudinal cohort study among 300 smokers attempting to quit is guided by a conceptual framework grounded in affective science and conceptual models of self-regulation and addiction. Participants will be followed from 1 week prior to their quit date through 6 months post-quit date. They will be assessed from 1 week pre-quit date through 2 weeks post-quit date using AutoSense, geographic positioning system (GPS), and ecological momentary assessment (EMA). AutoSense, GPS, and EMA collect real time data in natural environments, communicate wirelessly with each other, and data are processed in real time on a smartphone. AutoSense detects specific behavioral and physiologic “signatures” of smoking (the primary outcome) and self regulatory capacity (an intermediate outcome; assessed using high frequency heart rate variability) in real time. GPS real time spatial tracking will be linked with spatially and temporally relevant characteristics of the environment using geographic information system (GIS) data. EMAs assess self-reported emotions, cognition, and context. Analyses utilize advanced dynamic risk prediction models and machine learning approaches to model the dynamics of real time, real world associations among distinct emotions, SRC, and lapse.





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