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Data Science for Dynamic Intervention Decision-making Lab

National Institutes of Health (NIH)/National Cancer Institute (NCI) – U01 CA229437
Key collaborators: Inbal Nahum-Shani, David Wetter
 

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., relaxation techniques, 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) are suites of intervention strategies that adapt over time to an individual’s changing status and are optimized to provide appropriate intervention strategies based on real time, real world context. Extant literature on JITAIs emphasize the importance of minimizing disruptions to the daily lives and routines of the individual, by offering an intervention based on both vulnerability (e.g., at high risk for lapse) and 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 decide when and how to intervene. We propose to investigate (a) how the temporal dynamics and interactions of emotion, context, and self-regulatory control (SRC) can be used to detect states of vulnerability to a lapse and states of receptivity to self-regulatory activities; and (b) how knowledge of these states can be used to optimize engagement in self-regulatory activities. This will be done by analyzing intensive longitudinal self-reported and sensor-based measures of affect, SRC and context from 5 studies (3 completed and 2 ongoing) of smokers attempting to quit. The data will be used to identify (Aim 1) states of vulnerability, and (Aim 2) states of receptivity. Additionally, we propose to conduct a study with 150 smokers attempting to quit who will be offered a collection of brief, evidence-based self-regulatory activities on their mobile device. A Micro-Randomized Trial (MRT) design will be utilized to randomize each individual multiple times per day to either (a) no prompt; (b) a prompt encouraging engagement in brief (low effort) strategies; or (c) a prompt encouraging a more effortful practice of self-regulation strategies via a mobile app. The data will be used to (Aim 3) investigate whether, what type, and under what conditions (e.g., current state of vulnerability and/or receptivity) a recommendation to engage the individual in self-regulatory activities increases engagement and reduces vulnerability. The proposed research will be the first to yield a comprehensive conceptual, technical, and empirical foundation necessary to develop effective JITAIs for smoking cessation based on dynamic, personalized models of vulnerability and receptivity (NIH Award Number: U01CA229437)

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