Using Mobile Technology to Improve Self-Regulation

NCT ID: NCT03774433

Last Updated: 2021-08-24

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 114 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-02-27
• Study Completion Date: 2020-06-29

Brief Summary

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) and overweight/obese persons with binge eating disorder (n=50) (smoking in the former sample and binge eating in the latter sample). The investigators will employ a 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. This is the fourth and final phase of a study that aims to identify putative mechanisms of behavior change to develop an overarching "ontology" of self-regulatory processes.

This trial builds on NCT03352713.

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 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, the investigators 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. The investigators 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 across two phases of funding (UH2 and UH3 phases). Note that Aims 1-3 were conducted under our prior UH2 phase, and the investigators herein include the protocol for Aim 4 to be conducted in the UH3 phase:

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. The investigators will build on Multiple PI Poldrack's pioneering "Cognitive Atlas" ontology to integrate concepts across divergent literatures to develop an "ontology" of self-regulatory processes. The 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 putative targets can be engaged and manipulated 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. The investigators 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 Marsch) in which the investigators 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 MacKinnon, the investigators will examine cross-assay validity and cross-context and cross-sample reliability of assays. The investigators 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. The investigators 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 each at Dartmouth and Stanford) and overweight/obese persons with binge eating disorder (n=50 each at Dartmouth and Stanford) (smoking in the former sample and binge eating in the latter sample). The investigators will employ a 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 4 study at Dartmouth led by Multiple PI Marsch.

This phase of the study takes what the investigators learned about self-regulation in the first three phases and applies it in two samples that are exemplary for "lapses" in self-regulation: individuals who smoke and overweight/obese individuals with binge eating disorder. The investigators learned in Aim 2 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 Aim 4 study is to target self-regulation to impact health behaviors.

Conditions

Self-regulation; Binge Eating; Smoking

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: BASIC_SCIENCE
• Blinding Strategy: NONE

Study Groups

Laddr

All participants in the study will be invited to use Laddr, described in the intervention section.

Group Type: EXPERIMENTAL

Laddr

Intervention Type: BEHAVIORAL

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.

Interventions

Laddr

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 Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• Age 18-50 years
• Understand English sufficiently to provide informed consent
• Access to a computer in a setting in which the participant is comfortable providing sensitive information
• Use a smartphone operating system compatible with Laddr

• 27 ≤ BMI ≤ 45 kg/m2
• Have binge eating disorder according to DSM-5 criteria
• Non-smoking (defined as no cigarettes in past 12 months-this includes former and never smokers)
• Confirmed interest in an eating intervention
• Use a smartphone compatible with Fitbit

• Smoke 5 or more tobacco cigarettes/day for past year
• 17 ≤ BMI < 27 kg/m2
• Confirmed interest in a smoking quit attempt
• Use a smartphone compatible with the iCO Smokerlyzer

Exclusion Criteria

• Enrolled in Aim 2 study
• Any current substance use disorder

o Will not exclude based on use of substances

• Currently pregnant or plans to become pregnant in next 3 months
• Lifetime history of mental disorder due to a medical condition
• Lifetime history of major psychotic disorders (including schizophrenia and bipolar disorder)
• Current use of prescription pain medications (e.g., Vicodin, oxycodone)
• Current use of any medication for smoking (e.g., Wellbutrin, varenicline)

o Exceptions: will not screen out for nicotine replacement therapy (e.g., patch, gum, lozenge, nasal spray, inhaler)

• Current use of any medication for weight loss
• Have undergone weight-loss surgery (e.g., gastric bypass, lap band)
• Current nighttime shift work or obstructive sleep apnea
• Note: We will not exclude based on e-cigarette use.

• Compensatory behavior (e.g., purging, excessive exercise, fasting)

o Already excluded as part of the DSM-5 binge eating disorder criteria

• Lost weight in recent past (>10 pounds in past 6 months)
• Currently in a weight-loss program (e.g., Weight Watchers, Jenny Craig)

o Will ask about, but won't exclude on, online/mobile app weight-loss programs as part of the screener

• Currently on a special diet for a serious health condition
• Currently in therapy with a clinician for binge eating
• Nickel allergy (because Fitbit band contains nickel)

• Currently in therapy with a clinician for smoking
• Binge eating behavior

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 50 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institutes of Health (NIH)

NIH

Sponsor Role: collaborator

National Institute on Drug Abuse (NIDA)

NIH

Sponsor Role: collaborator

Dartmouth-Hitchcock Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Lisa A. Marsch

Andrew G. Wallace Professor; Director, Center for Technology and Behavioral Health

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Lisa A Marsch, PhD

Role: PRINCIPAL_INVESTIGATOR

Dartmouth College

Locations

Center for Technology and Behavioral Health, Dartmouth College

Lebanon, New Hampshire, United States

Countries

United States

References

Plaitano EG, McNeish D, Bartels SM, Bell K, Dallery J, Grabinski M, Kiernan M, Lavoie HA, Lemley SM, Lowe MR, MacKinnon DP, Metcalf SA, Onken L, Prochaska JJ, Sand CL, Scherer EA, Stoeckel LE, Xie H, Marsch LA. Adherence to a digital therapeutic mediates the relationship between momentary self-regulation and health risk behaviors. Front Digit Health. 2025 Feb 4;7:1467772. doi: 10.3389/fdgth.2025.1467772. eCollection 2025.

Reference Type: DERIVED

PMID: 39981105 (View on PubMed)

Provided Documents

Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

UH3DA041713

Identifier Type: NIH

Identifier Source: secondary_id

View Link

4UH3DA041713-04 D19035

Identifier Type: -

Identifier Source: org_study_id