Targeting Neural Self-regulation with Technology for Overweight Teens

Dartmouth SYNERGY Translational Program (Methodology and Technology Innovations for Translational Research Awards (MITRA))

7/1/16 – 6/30/17

Catherine Stanger, PhD; Todd Heatherton, PhD, Psychology and Brain Sciences

Co-Investigators: James Sargent, MD, Professor, Pediatrics; William Kelley, PhD, Professor, Psychology and Brain Sciences; Emily Scherer, PhD, Assistant Professor, Biomedical Data Sciences.

This project proposes a novel integration of basic neural and clinical intervention science designed to test the impact of a self-regulation mechanism and intervention model on health outcomes for overweight teens. A growing literature supports the important role of self-regulation, i.e., the process of managing emotional, motivational and cognitive resources to align mental states and behavior with personal goals, in predicting health behavior and health outcomes among overweight teens and adults. In our model, self-regulatory success results from the interaction of two underlying neurobehavioral systems—cognitive control and reward. Strengths and weaknesses in these systems combine to facilitate or hinder the initiation of key health behaviors. A large literature supports the relations between specific neural networks involved in cognitive control and reward, their malleability, especially in adolescence, and their relevance to weight loss, exercise and other health behaviors including medical adherence. This pilot study will test two distinct mobile health intervention approaches that target these self-regulation systems (cognitive control vs. reward).

A multimethod approach to the measurement of self-regulation will be used. Self-regulation measures of cognitive control and reward will include: self-reports (trait self-control, food craving), event-related neural activity during laboratory tasks (delay discounting, go/no go, cue-reactivity), and fMRI resting state network measurement (default, frontoparietal, cingular opercular, and reward subnetworks). We will identify (sets of) behavioral and neural measures that best differentiate cognitive control and reward system phenotypes among overweight teens. This project takes a highly innovative approach, linking functional neuroimaging findings to real-world behavior, using cutting-edge resting state functional connectivity methodology to examine individual differences in malleable brain networks that represent desire strength and self-control capacity, and using advanced computational modeling to study these interacting systems.

We propose to randomly assign 30 overweight teens (BMI%ile >95) to receive one of two interventions: behavioral economic incentives targeting exercise and self-regulation training targeting inhibitory control (Go/No-Go Training; GNGT). BEI will involve delivery of incentives for improving the frequency of daily exercise (verified by fitbit uploads via the teen’s smartphone). GNGT will be delivered via a smartphone app that targets the cognitive capacity to respond to healthy foods while inhibiting responses to unhealthy foods. The effects of these distinct interventions on reward and cognitive control and their relation to health behavior (BMI and exercise outcomes) will be tested. Results from these studies will contribute important knowledge about how self-regulation impacts health behaviors, and how interventions can be targeted to specific self-regulation systems. They will also identify evidence based approaches to improve outcomes for overweight teens.

This project involves an innovative, interdisciplinary pilot study with clear potential for translation into patient-‐oriented care and for improving population health by targeting obesity, a major public health crisis. In particular, it represents an innovative blending of basic research on self-regulation, and intervention research for a clinical population in critical need.