Using Mobile Technology to Better Understand and Measure Self-Regulation
Condition: Self-regulation; Binge Eating; Smoking
MeSH terms: Bulimia; Binge-Eating Disorder
Study type: Interventional

Summary: 
      This study will evaluate the extent to which we can engage and manipulate putative targets
      within the self-regulation domain outside of laboratory settings in samples of smokers and
      overweight/obese individuals with binge eating disorder. Fifty smokers and 50
      overweight/obese individuals with binge eating disorder will be recruited to participate in a
      non-lab experimental paradigm in which we will leverage our novel mobile behavioral
      assessment/intervention technology platform. We will measure and modulate engagement of
      potential self-regulation targets and collect data in real time and in real-world conditions.
      Mobile sensing will be added to up to 50 additional participants.
    

Detailed description: 
      Health risk behavior, including poor diet, physical inactivity, tobacco and other substance
      use, causes as much as 40% of the illness, suffering, and early death related to chronic
      diseases. Non-adherence to medical regimens is an important exemplar of the challenges in
      changing health behavior and its associated impact on health outcomes. Although an array of
      interventions has been shown to be effective in promoting initiation and maintenance of
      health behavior change, the mechanisms by which they actually work are infrequently
      systematically examined. One promising domain of mechanisms to be examined across many
      populations and types of health behavior is of self-regulation. Self-regulation involves
      identifying one's goals, and maintaining goal-directed behavior. A large scientific
      literature has identified the role of self-regulation as a potential causal mechanism in
      promoting health behavior.

      Advances in digital technologies have created unprecedented opportunities to assess and
      modify self-regulation and health behavior. In this project, we plan to use a systematic,
      empirical process to integrate concepts across the divergent self-regulation literatures to
      identify putative mechanisms of behavior change to develop an overarching "ontology" of
      self-regulatory processes.

      This multi-year, multi-institution project aims to identify an array of putative
      psychological and behavioral targets within the self-regulation domain implicated in medical
      regimen adherence and health behavior. This is in service of developing an "ontology" of
      self- regulation that will provide structure and integrate concepts across diverse
      literatures. We aim to examine the relationship between various constructs within the
      self-regulation domain, the relationship among measures and constructs across multiple levels
      of analysis, and the extent to which these patterns transcend population and context. The
      project consists of four primary aims:

      Aim 1. Identify an array of putative targets within the self-regulation domain implicated in
      medical regimen adherence and health behavior across these 3 levels of analysis. We will
      build on Multiple PI Russ Poldrack's pioneering "Cognitive Atlas" ontology to integrate
      concepts across divergent literatures to develop an "ontology" of self-regulatory processes.
      Our expert team will catalog tasks in the self-regulation literature, implement tasks via
      online testing (Mechanical Turk) to rapidly obtain large datasets of self-regulatory
      function, assess the initial ontology via confirmatory factor analysis and structural
      equation modeling, and assess and revise the resulting ontology according to neural
      similarity patterns across tasks (to identify tasks for Aim 2).

      Aim 2. Evaluate the extent to which we can engage and manipulate putative targets within the
      self-regulation domain both within and outside of laboratory settings. Fifty smokers and 50
      overweight/obese persons with binge eating disorder will participate in a lab study (led by
      Poldrack) to complete the tasks identified under Aim 1. We will experimentally modulate
      engagement of targets (e.g., stimulus set of highly palatable foods images or tobacco-related
      images as well as self-regulation interventions). A comparable sampling of 100 persons will
      participate in a non-lab study (led by Multiple PI Lisa Marsch) in which we will leverage our
      novel mobile-based behavioral assessment/intervention platform to modulate target engagement
      and collect data in real-world conditions.

      Aim 3. Identify or develop measures and methods to permit verification of target engagement
      within the self-regulation domain. Led by Co-I Dave MacKinnon, we will examine cross-assay
      validity and cross-context and cross-sample reliability of assays. We will employ
      discriminant and divergent validation methods and Bayesian modeling to refine an
      empirically-based ontology of self-regulatory targets (to be used in Aim 4).

      Aim 4. We will evaluate the degree to which engaging targets produces a desired change in
      medical regimen adherence (across 4 week interventions) and health behavior among smokers
      (n=100) and overweight/obese persons with binge eating disorder (n=100) (objectively measured
      smoking in the former sample and binge eating in the latter sample). We will employ our novel
      mobile behavioral assessment/intervention platform to engage targets in these samples, given
      that (1) it offers self-regulation assessment and behavior change tools via an integrated
      platform to a wide array of populations, and (2) content within the platform can be quickly
      modified as needed to better impact targets. The proposed project is designed to identify
      valid and replicable assays of mechanisms of self-regulation across populations to inform an
      ontology of self-regulation that can ultimately inform development of health behavior
      interventions of maximal efficacy and potency.

      This protocol details the Aim 2 non-lab study led by Multiple PI Marsch.

      This phase of the study takes what we learned about self-regulation in the first phase and
      tests it in two samples that are exemplary for "lapses" in self-regulation: individuals who
      smoke and overweight/obese individuals with binge eating disorder. We expect that many
      real-world conditions (e.g., temptation, negative affect) may decrease self-regulation,
      whereas training through the mobile intervention described below may increase
      self-regulation. The primary purpose of this study is to determine whether we can shift
      self-regulation for the ultimate goal (in Aim 4) of targeting self-regulation to impact
      health behaviors.
    

Intervention name(s): Laddr
Intervention type(s): Behavioral
Intervention description(s): Laddr is an integrated, personalized, web-based self-regulation assessment and behavior change system. It integrates tools that have been shown to be effective for a wide array of behavioral phenomena ranging from substance use and abuse, mental health, risk-taking, chronic pain management, medication adherence, diet, exercise, diabetes and other chronic disease management, and smoking. The organizational structure, functionality and content within Laddr's system centrally embrace these fundamental aspects of behavior change; thus, the Laddr platform is not "diagnosis-specific" but rather enables integrated care for any combination of individuals' goals, needs, and preferences.
Intervention model: Single Group Assignment
Masking: None (Open Label)
Primary purpose: Basic Science
Keywords: self-regulation; binge eating; smoking; ecological momentary assessment; mobile sensing; mHealth