Clinical trial NCT04043650: Developing Dynamic Theories for Behavior Change
Official title: Operationalizing Behavioral Theory for mHealth: Dynamics, Context, and Personalization
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Physical Activity
Keywords: Physical Activity
Mobile Health
Self Monitoring
Wearable Sensors
Tailored Health Communication
Implementation Intentions
Mobile Apps
Anti-Sedentary Behavior
Opportunistic Physical Activity
Health Belief Model
Lead sponsor: University of Southern California
Sponsor class: OTHER
Collaborators: name: University of California, San Diego
name: Arizona State University
name: Kaiser Permanente
name: Northeastern University
name: University of Massachusetts, Amherst
First posted: 2019-08-02
Enrollment: 97
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: At each "decision time"-a time point when an intervention component can be delivered-each day of the study each participant is randomized between intervention or no intervention (delivery of a contextually tailored activity suggestion or no suggestion; morning motivational message or no motivational message)
Primary purpose: PREVENTION

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* Individuals are able to participate in mild or moderate physical activity
* They are competent to give informed consent
* Individuals are regular (daily) users of a smartphone (iPhone or Android)
* Individuals are willing to participate in the study protocols, including regularly carrying a mobile phone, using the HeartSteps application, answering phone-based questionnaires, and tracking their physical activity using the Fitbit Versa activity tracker
* Body Mass Index (BMI, weight in kilograms (kg) divided by height in meters squared) between 25--45
* Able to walk one mile without significant discomfort.

Exclusion Criteria:

* Being mentally incapable of giving informed consent
* Current enrollment in a formal exercise program
* Psychiatric disorder which limits patients' ability to follow the study protocol, including psychosis or dementia
* Orthopedic problems that prevent participation in a walking program
* Significant peripheral neuropathy
* Severe cognitive impairment
* Pregnancy
* Non-English speaking.

Primary outcomes:
measure: 30 minute step count
timeFrame: 30 minutes
measure: Daily step count
timeFrame: 24 hours
Secondary outcomes:
measure: Moderate or Vigorous Physical Activity (MVPA)
timeFrame: 24 hours
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: University of Southern California
state: California
Study officials:
affiliation: University of Southern California
name: Donna Spruijt-Metz, MFA, PhD
role: PRINCIPAL_INVESTIGATOR
References: