||5U01CA229445-04 Interpret this number
||University Of Southern California
||Operationalizing Behavioral Theory for Mhealth: Dynamics, Context, and Personalization
Unhealthy behaviors contribute to the majority of chronic diseases, which account for 86% of all healthcare
spending in the US. Despite a great deal of research, the development of behavior change interventions that
are effective, scalable, and sustainable remains challenging. Recent advances in mobile sensing and
smartphone-based technologies have led to a novel and promising form of intervention, called a “Just-in-time,
adaptive intervention” (JITAI), which has the potential to continuously adapt to changing contexts and
personalize to individual needs and opportunities for behavior change. Although interventions have been
shown to be more effective when based on sound theory, current behavioral theories lack the temporal
granularity and multiscale dynamic structure needed for developing effective JITAIs based on measurements
of complex dynamic behaviors and contexts. Simultaneously, there is a lack of modeling frameworks that can
express dynamic, temporally multiscale theories and represent dynamic, temporally multiscale data. This
project will address the theory-development, measurement, and modeling challenges and opportunities
presented by intensively collected longitudinal data, with a focus on physical activity and sedentary behavior,
and broad implications for other behaviors. For efficiency, we build on the NIH-funded year-long micro-
randomized trial (MRT) of HeartSteps (n=60), an adaptive mHealth intervention based on Social-Cognitive
Theory (SCT) developed to increase walking and decrease sedentary behavior in patients with cardiovascular
disease. The aims of this new proposal are: 1) Refine and develop dynamic measures of theoretical constructs
that influence our target behaviors, 2) Enhance HeartSteps with the measures developed in Aim 1 and collect
data from two additional year-long HeartSteps cohorts (sedentary overweight/obese adults (n=60) and type 2
diabetes patients (n=60), total n=180), 3) Develop a modeling framework to operationalize dynamic and
contextualized theories of behavior in an intervention setting, and 4) Improve prediction of SCT outcomes
using increasingly complex models. The work proposed here will provide new digital, data driven measures of
key behavioral theory constructs at the momentary, daily, and weekly time scales, provide new tools tailored
for the specification of complex models of behavioral dynamics, as well as new model estimation tools tailored
specifically to the complex, longitudinal, multi-time scale behavioral and contextual data that are now
accessible using mHealth technologies. Finally, we will leverage the collected data and the proposed modeling
tools to develop and test enhanced, dynamic extensions of social cognitive theory operationalized as fully
quantified, predictive dynamical models. Collectively, this work will provide the theoretical foundations and
tools needed to significantly increase the effectiveness of physical activity-based mobile health interventions
over multiple time scales, including their ability to effectively support behavior change over longer time scales.
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