Developing Dynamic Theories for Behavior Change
Condition: Physical Activity
Study type: Interventional

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.
    

Intervention name(s): HeartSteps
Intervention type(s): Behavioral
Intervention description(s): HeartSteps is a smartphone based mHealth intervention that contains the following intervention components: (1) contextually-tailored suggestions for activity; (2) motivational messages aimed at keeping individuals motivated to be active; (3) planning of the next week's activity; and (4) adaptive weekly activity goals. Activity suggestions provide individuals with suggestions for how they can be active, and are tailored based on time of day, user's location, day of the week (weekend/weekday), and weather. Motivational messages are delivered to individuals via a push notification. Activity planning asks users to create a plan of how they will be active in the coming week. Participants are prompted to plan once a week. Each week, as part of the weekly planning, HeartSteps suggests an activity goal for the coming week based on their activity levels the previous week. Participants can edit the suggested goal, and the system-suggested goals top out at 150 minutes of activity per week.
Other intervention name(s): A just-in-time intervention for increasing physical activity among sedentary adults
Intervention model: Single Group Assignment
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)
Masking: None (Open Label)
Primary purpose: Prevention
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