Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04043650 |
| Other study ID # |
UP-18-00791 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
June 10, 2020 |
| Est. completion date |
August 31, 2022 |
Study information
| Verified date |
October 2022 |
| Source |
University of Southern California |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
The aim of this research is to evaluate the efficacy of contextually tailored activity
suggestions and activity planning for increasing physical activity among sedentary adults.
Description:
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, the study builds 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 the study's 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, the investigators 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.
