Healthy Eating Study

The Healthy Eating Study is an innovative translational neuroscience experiment designed to help individuals who are overweight or obese help manage food cravings.


Interested in participating? Visit healthyeatingstudy.com 

Questions? Email us! sanlab@uoregon.edu 

Need us urgently? Text us at (541) 204-2520

Participants in the Healthy Eating Study study receive two MRI brain scans, 5 Bod Pod body composition assessments, and a set of neural, behavioral, self-report, and physiological assessments over the course of 13 months. 150 individuals have enrolled in the study currently, and at the end of data collection we will have scanned 249 brains for 747 hours.

More Information

Obesity and intake of certain foods increase cancer risk, but the most common treatment (behavioral weight loss programs) rarely produces lasting weight loss and eating behavior change, apparently because caloric restriction increases the reward value of food and prompts energy-sparing adaptations. Interventions that reduce the implicit valuation of cancer-risk foods (e.g., red meats, refined sugar) may be more effective.

Emerging data suggest that behavioral response training and cognitive reappraisal training reduce valuation of such foods, which leads to decrease intake of these foods and weight loss. Internalized incentive value is reflected in a ventromedial prefrontal/orbitofrontal cortex valuation system (“vmPFC”), which encodes the implicit reward value of food and is central to a reinforcement cycle that perpetuates unhealthy eating. Thus, the vmPFC valuation system is a promising target for intervention because changes to the system might disrupt the unhealthy reinforcement cycle. Interestingly, various interventions influence the vmPFC through distinct pathways. Behavioral training alters motor input to valuation regions, whereas cognitive training relies on lateral prefrontal “top-down” regions.

The Healthy Eating Study compares the efficacy with which two novel treatments cause lasting change in food valuation, and whether a composite of theory-based baseline individual differences in relevant processes (such as response tendencies and cognitive styles) moderate treatment effects.

The study is in the process of randomizing 300 overweight/obese adults who are at risk for eating- and obesity-related cancers to behavioral response training toward healthy foods and away from cancer-risk foods, a cognitive reappraisal intervention focused on cancer-risk foods, or non–food inhibitory control training.

Aim 1

compares the efficacy and mechanisms of action of these two interventions to reduce valuation of cancer-risk foods relative to the active control condition, using neural, behavioral, self-report, and physiological measures of the process and outcomes.

Aim 2

is to establish the temporal pattern and durability of the effects across time; food intake and habits, body fat, BMI, and waist-to-hip ratio will be measured pre, post, and at 3-, 6-, and 12-month follow-up.

Aim 3

uses machine learning to build and validate a low-cost, easy-to-administer composite that predicts whether and for how long an individual is likely to respond to intervention, and to which treatment. We hypothesize that self-report measures specifically related to valuation (e.g., willingness-to-pay) and to intervention-specific pathways to valuation (e.g., behavioral response tendencies, cognitive style) will predict differential response. Discovering these individual differences will provide a practical, low-cost tool to help interventionists “match” a given person to an effective treatment for that person . This project is very innovative because no study has directly compared the distinct and common effects of these treatments on valuation, used brain imaging to study the mechanism of effects, tested whether these interventions produce a lasting change in food valuation and body fat, or built and validated a composite that moderates response .

Project Team: 
Primary Investigator
Project Coordinator

Brendan Cullen

Brendan Cullen profile picture
Data Management
Data Management

Publications

,
Zeithamova, D
,
Stice, E
, (In press)
. Multivariate neural signatures for health neuroscience: Assessing spontaneous regulation during food choice. Social Cognitive and Affective Neuroscience. OSF

, , (2019). Comparing two neurocognitive models of self-control during dietary decisions. Soc Cogn Affect Neurosci. 10.1093/scan/nsz068