Topics

Using Mobile Technology to Better Understand and Measure Self-regulation

2017-12-02 07:17:10 | BioPortfolio

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 obese individuals with binge eating disorder. Fifty smokers and 50 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 of these 100 participants.

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 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 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 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.

Study Design

Conditions

Self-regulation

Intervention

Laddr

Status

Not yet recruiting

Source

Dartmouth-Hitchcock Medical Center

Results (where available)

View Results

Links

Published on BioPortfolio: 2017-12-02T07:17:10-0500

Clinical Trials [260 Associated Clinical Trials listed on BioPortfolio]

Applying Novel Technologies and Methods to Self-Regulation: Behavior Change Tools for Smoking and Binge Eating

This study will evaluate the extent to which we can engage and manipulate putative targets within the self-regulation domain within and outside of laboratory settings in samples of smokers...

Using Mobile Technology to Improve Self-Regulation

This study 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=50) ...

Behavioral Self-Regulation

The purpose of this pilot study is to determine whether incorporating self-regulation training using daily weighing is efficacious within a behavioral weight loss program specifically targ...

Brain Games - Teen Self Regulation Intervention

This goal of this project is to test whether self-regulation assays and interventions can be delivered and change self-regulation in a sample of adolescents, specifically to test in a smal...

Scaling Up Science-based Mental Health Interventions in Latin America

Conduct systematic, multi-site mental health implementation research in both rural and urban primary care settings with a broad group of stakeholders in the US and Latin America.

PubMed Articles [4498 Associated PubMed Articles listed on BioPortfolio]

Structural and functional neural correlates of vigilant and avoidant regulation style.

Regulation of emotional arousal is a relevant factor for mental health. The investigation of neural underpinnings of regulation styles in healthy individuals may provide important insights regarding p...

Legal regulation of professional obligations of physicians in Ukraine.

Introduction: It was identified that one of the priorities of medical reform in Ukraine is the establishment of an effective system of legal regulation of professional physician's obligations that mee...

How do I want to feel? The link between emotion goals and difficulties in emotion regulation in borderline personality disorder.

Appropriate contextualized emotion goals (i.e., desired emotional endpoints that facilitate goal attainment) are fundamental to emotion regulation, as they may determine the direction of regulation ef...

Comparison of Ca2+ Handling for the Regulation of Vasoconstriction between Rat Coronary and Renal Arteries.

Ca2+ plays an important role in the regulation of vasoconstriction. Ca2+ signaling is regulated by a number of Ca2+-handling proteins. However, whether differences in Ca2+ handling affect the regulati...

Relations Between Tic Severity, Emotion Regulation, and Social Outcomes in Youth with Tourette Syndrome.

This study examined associations between tic severity, emotion regulation, social functioning, and social impairment in youth with Tourette Syndrome (TS). Emotion regulation was examined as a mediator...

Medical and Biotech [MESH] Definitions

A plasma membrane exchange glycoprotein transporter that functions in intracellular pH regulation, cell volume regulation, and cellular response to many different hormones and mitogens.

A family of plasma membrane exchange glycoprotein antiporters that transport sodium ions and protons across lipid bilayers. They have critical functions in intracellular pH regulation, cell volume regulation, and cellular response to many different hormones and mitogens.

A family of structurally related proteins that were originally discovered for their role in cell-cycle regulation in CAENORHABDITIS ELEGANS. They play important roles in regulation of the CELL CYCLE and as components of UBIQUITIN-PROTEIN LIGASES.

A negative regulatory effect on physiological processes at the molecular, cellular, or systemic level. At the molecular level, the major regulatory sites include membrane receptors, genes (GENE EXPRESSION REGULATION), mRNAs (RNA, MESSENGER), and proteins.

A positive regulatory effect on physiological processes at the molecular, cellular, or systemic level. At the molecular level, the major regulatory sites include membrane receptors, genes (GENE EXPRESSION REGULATION), mRNAs (RNA, MESSENGER), and proteins.

More From BioPortfolio on "Using Mobile Technology to Better Understand and Measure Self-regulation"

Quick Search

Relevant Topic

Obesity
Obesity is the condition in which excess fat has accumulated in the body (mostly in subcutaneous tissues). clinical obesity is considered to be present when a person has a BMI of over 30 (Oxford Dictionary of Medicine). It is becoming increasing common i...


Searches Linking to this Trial