Applying Novel Technologies and Methods to Inform the Ontology of Self-Regulation - Aim 4 Dartmouth Study: Using Mobile Technology to Improve Self-Regulation

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.