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 risk behavior — and is common, costly (due to increased utilization of healthcare services), and associated with poor patient outcomes. Although an array of interventions have been shown to be effective in promoting health behavior change, much of this work has been siloed (focused on one disorder at a time). Additionally, interventions are typically intended to engage multiple mechanisms of behavior change, but the mechanisms by which they actually work are infrequently systematically examined.
One promising domain of putative behavior change targets is that of self-regulation — a person’s ability to manage cognitive, motivational, and emotional resources to act in accordance with his/her long-term goals. The Center for Technology and Behavioral Health, a national NIH-funded P30 “Center of Excellence” uses science to inform the development, evaluation, and implementation of technology (web, mobile)-based self-regulation tools for behavior change targeting a wide array of populations and health behaviors. This work examines behavioral phenomena (and the mechanisms by which they work) ranging from substance abuse, mental health, chronic pain management, medication adherence, diet, exercise, diabetes, and smoking. Self-regulation tools offered on mobile platforms enable widespread reach and scalability of effective interventions.
In this project, we will examine putative targets (processes) of behavior change within the self-regulation mechanism domain across contexts, populations, and assays – in 3 primary levels of analysis: (1) psychological (e.g., constructs such as self-efficacy; emotion regulation; response inhibition), (2) behavioral (e.g., tasks of reward responsiveness; temporal horizon), and (3) biological (structural and functional MRI of key neural circuitry). We will evaluate the extent to which we can engage and manipulate these putative targets both within and outside of laboratory settings (using a novel mobile self-regulation monitoring and intervention platform). We will conduct this work with two populations for which behavior plays a critical role in the course of medical regimen adherence, health, and health outcomes: (1) smokers and (2) obese/overweight persons. We will then examine cross-assay validity and cross-context and cross-sample reliability of assays to identify relations among self-regulatory targets. We will finally evaluate the degree to which engaging targets produces a desired change in medical regimen adherence (across 4 week self-regulation interventions) and health behavior among smokers and obese/overweight persons.
This project will 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.