||5R01CA224537-03 Interpret this number
||University Of Utah
||Affective Science and Smoking Cessation: Real Time Real World Assessment
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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