Using Mobile Technology to Improve Self-Regulation

Dartmouth Health

Status

Completed

Conditions

Binge Eating

Smoking

Self-regulation

Treatments

Behavioral: Laddr

Study type

Interventional

Funder types

Other

NIH

Identifiers

NCT03774433

UH3DA041713 (U.S. NIH Grant/Contract)

4UH3DA041713-04 D19035

Details and patient eligibility

About

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.

Full 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.

Enrollment

114 patients

Sex

All

Ages

18 to 50 years old

Volunteers

No Healthy Volunteers

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

Additional inclusion criteria for binge eating sample:

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

Additional inclusion criteria for smoking sample:

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.

Additional exclusion criteria for binge eating sample:

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)