Abstract: Smoking cessation decreases morbidity and mortality and is a cornerstone of cancer prevention.
The ability to impact current and future vulnerability (e.g., high risk for a lapse) in real-time via engagement in
self-regulatory activities (e.g., behavioral substitution, mindful attention) is considered an important pathway
to quitting success. However, poor engagement represents a major barrier to maximizing the impact of self-
regulatory activities. Hence, enhancing real-time, real-world engagement in evidence-based self-regulatory
activities has the potential to improve the effectiveness of smoking cessation interventions. Just-In-Time
Adaptive Interventions (JITAIs) delivered via mobile devices have been developed for preventing and treating
addictions. JITAIs adapt over time to an individual’s changing status and are optimized to provide appropriate
intervention strategies based on real time, real world context. Organizing frameworks on JITAIs emphasize
minimizing disruptions to the daily lives and routines of the individual, by tailoring strategies not only to
vulnerability, but also to receptivity (i.e., an individual’s ability and willingness to utilize a particular
intervention). Although both vulnerability and receptivity are considered latent states that are dynamically and
constantly changing based on the constellation and temporal dynamics of emotions, context, and other factors,
no attempt has been made to systematically investigate the nature of these states, as well as how knowledge of
these states can be used to optimize real-time engagement in self-regulatory activities. To close this gap, the
proposed project will apply innovative computational approaches to one of the most extensive and
racially/ethnically diverse collection of real time, real world data on health behavior change (smoking
cessation). Intensive longitudinal self-reported and sensor data from 5 studies (3 completed and 2 ongoing) of
~1,500 smokers attempting to quit will be analyzed with advanced probabilistic latent variable models and
machine learning to investigate how the temporal dynamics and interactions of emotions, self-regulatory
capacity (SRC), context, and other factors can be used to detect (Aim 1) states of vulnerability to a lapse and
(Aim 2) states of receptivity to engaging in self-regulatory activities. We will also investigate (Aim 3) how
knowledge of these states can be used to optimize real-time engagement in self-regulatory activities by
conducting a Micro-Randomized Trial (MRT) enrolling 150 smokers attempting to quit. Utilizing a mobile
smoking cessation app, the MRT will randomize each individual multiple times per day to either (a) no
intervention prompt; (b) a prompt recommending engagement in brief (low effort) strategies; or (c) a prompt
recommending a more effortful practice of self-regulation strategies. The proposed research will be the first to
yield a comprehensive conceptual, technical, and empirical foundation necessary to develop effective JITAIs
based on dynamic models of vulnerability and receptivity.
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