Devaluing Foods to Change Eating Behavior

NCT03557710 | Completed DMC

• 1 other identifier: EPCS24327
• Study Type: interventional
• Target: 253
• Locations: 1 country
• Sites: 1

Brief Summary

Excessive eating of energy-dense foods and obesity are risk factors for a range of cancers. There are programs to reduce intake of these foods and weight loss, but the effects of the programs rarely last. This project tests whether altering the value of cancer-risk foods can create lasting change, and uses neuroimaging to compare the efficacy of two programs to engage the valuation system on a neural level. Results will establish the pathways through which the programs work and suggest specific treatments for individuals based on a personalized profile.

Conditions

Overweight and Obesity; Cancer

Keywords

eating; overweight; obesity; cancer risk; translational neuroscience; cognitive reappraisal; functional magnetic resonance imaging (fMRI); vmPFC; inhibitory control; valuation

Outcome Measures

Primary Outcomes (2)

• Change from Baseline Food Intake at 1 month using dietary assessment tool

  Assessed with the Automated Self-Administered 24-Hour (ASA24) Dietary Assessment Tool The National Cancer Institutes's standard self-assessment instrument to comprehensively measure food intake.

  baseline, 1 month

• Change from Baseline Food Intake at 1 month, Self-Report Questionnaire

  Food-Frequency Questionnaire modified to include cancer risk foods

  baseline, 1 month

Secondary Outcomes (19)

• Change from Baseline Body Fat Percent at 1 month

  baseline, 1 month

• Change from Baseline Body Mass Index at 1 month

  baseline, 1 month

• Change from Baseline Waist-to-Hip Ratio at 1 month

  baseline, 1 month

• Change from Baseline Food Approach and Avoidance Behavior at 1 month, Self-Report Questionnaire 2

  baseline, 1 month

• Change from Baseline Food Approach and Avoidance Behavior at 1 month, Self-Report Questionnaire 3

  baseline, 1 month

Study Arms (3)

Behavioral Response Training

EXPERIMENTAL

In Arm 1 of Devaluing energy-dense foods for cancer-control, participants will complete computer delivered versions of the stop-signal, go/no-go, and dot-probe training tasks in 8 30-min biweekly visits to the lab, with breaks between training blocks in which participants sit with their eyes closed to allow consolidation of learning. Participants will also complete a weekly 15-min training task online from home. Total training time = 345 min. Training will involve 100 images of cancer risk foods that participants regularly eat, including red and processed meats; high-sugar foods; heavily salted, smoked, and pickled foods; fries, chips, and snacks with trans-fats, and 100 images of healthy foods that participants rate as palatable, including vegetables, fruits, nuts, and whole grains.

Behavioral: Devaluing energy-dense foods for cancer-control

Cognitive Reappraisal Training

EXPERIMENTAL

Arm 2 of the Devaluing energy-dense foods for cancer-control intervention will be delivered via computer-assisted in-person training. Between baseline and endpoint sessions, participants will practice reappraisal on a computer, under close supervision of a facilitator, in 8 30-min twice-weekly individual sessions. During sessions, participants will practice cognitive reappraisal to reduce the value of cancer risk foods. Participants will also practice reappraisal of cancer risk foods on a computer at home, twice weekly for 15 minutes, for a total intervention time of contact of 345 minutes. The facilitator will review homework completed by participants and offer corrective feedback. The home practice is intended to promote generalization of use of this skill in the natural environment.

Behavioral: Devaluing energy-dense foods for cancer-control

Generic Response Training

ACTIVE COMPARATOR

In Arm 3 (active control) of the Devaluing energy-dense foods for cancer-control intervention will be identical in duration and contact time to the behavioral response training described above (345 min total), but will involve nonfood images (birds and flowers), as described in the pilot trial. Participants will be informed that this intervention is designed to improve response inhibition, which should lead to eating change and weight loss given that impulsivity increases the risk for overeating, ensuring the credibility of the control arm.

Behavioral: Devaluing energy-dense foods for cancer-control

Interventions

Devaluing energy-dense foods for cancer-control BEHAVIORAL

A 3-arm randomized controlled trial experiment study over 12 months. At baseline, participants will complete behavioral, neural, and self-report measures related to food, specifically measures of food valuation and of the proximal neural systems hypothesized to be linked to each of the 2 experimental arms. We will also measure food intake and body composition at baseline. Then participants will be randomized to one of 3 arms (2 experimental + 1 active control) for 8 30-min sessions to occur twice weekly at the University of Oregon for 30 days. At endpoint (\~1 month following baseline), all behavioral, neural, and self-report measures will be reassessed, as will eating, habit, and body composition measures. Follow-ups at 3, 6, and 12 months will assess all measures except neuroimaging.

Also known as: Devaluing foods to change eating behavior

Behavioral Response Training; Cognitive Reappraisal Training; Generic Response Training

Eligibility Criteria

• Age: 18 Years - 60 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• \- overweight to obese range (BMI 25-35)

You may not qualify if:

• metal implants (e.g., braces, permanent retainers, pins)
• metal fragments, pacemakers or other electronic medical implants
• claustrophobia
• weight ˃ 550 lbs.
• Women who are pregnant or believe they might be pregnant
• people who have been diagnosed with past or current medical, psychiatric, neurological, eating disorders, or are taking psychotropic medications
• urine screen to exclude participants who are acutely intoxicated
• screen for handedness

Sponsors & Collaborators

• University of Oregon: lead

Study Sites (1)

University of Oregon, Lewis Integrative Sciences Building

Eugene, Oregon, 97403, United States

Location

Related Publications (17)

• Berkman ET, Burklund L, Lieberman MD. Inhibitory spillover: intentional motor inhibition produces incidental limbic inhibition via right inferior frontal cortex. Neuroimage. 2009 Aug 15;47(2):705-12. doi: 10.1016/j.neuroimage.2009.04.084. Epub 2009 May 6.

  PMID: 19426813 BACKGROUND

• Berkman ET, Falk EB. Beyond Brain Mapping: Using Neural Measures to Predict Real-World Outcomes. Curr Dir Psychol Sci. 2013 Feb;22(1):45-50. doi: 10.1177/0963721412469394.

  PMID: 24478540 BACKGROUND

• Berkman ET, Falk EB, Lieberman MD. In the trenches of real-world self-control: neural correlates of breaking the link between craving and smoking. Psychol Sci. 2011 Apr;22(4):498-506. doi: 10.1177/0956797611400918. Epub 2011 Mar 4.

  PMID: 21378368 BACKGROUND

• Berkman ET, Kahn LE, Merchant JS. Training-induced changes in inhibitory control network activity. J Neurosci. 2014 Jan 1;34(1):149-57. doi: 10.1523/JNEUROSCI.3564-13.2014.

  PMID: 24381276 BACKGROUND

• Giuliani NR, Calcott RD, Berkman ET. Piece of cake. Cognitive reappraisal of food craving. Appetite. 2013 May;64:56-61. doi: 10.1016/j.appet.2012.12.020. Epub 2013 Jan 9.

  PMID: 23313699 BACKGROUND

• Giuliani NR, Mann T, Tomiyama AJ, Berkman ET. Neural systems underlying the reappraisal of personally craved foods. J Cogn Neurosci. 2014 Jul;26(7):1390-402. doi: 10.1162/jocn_a_00563. Epub 2014 Jan 6.

  PMID: 24392892 BACKGROUND

• Giuliani NR, Tomiyama AJ, Mann T, Berkman ET. Prediction of daily food intake as a function of measurement modality and restriction status. Psychosom Med. 2015 Jun;77(5):583-90. doi: 10.1097/PSY.0000000000000187.

  PMID: 25984820 BACKGROUND

• Stice E, Burger K, Yokum S. Caloric deprivation increases responsivity of attention and reward brain regions to intake, anticipated intake, and images of palatable foods. Neuroimage. 2013 Feb 15;67:322-30. doi: 10.1016/j.neuroimage.2012.11.028. Epub 2012 Nov 28.

  PMID: 23201365 BACKGROUND

• Stice E, Lawrence NS, Kemps E, Veling H. Training motor responses to food: A novel treatment for obesity targeting implicit processes. Clin Psychol Rev. 2016 Nov;49:16-27. doi: 10.1016/j.cpr.2016.06.005. Epub 2016 Jul 21.

  PMID: 27498406 BACKGROUND

• Stice E, Marti CN, Spoor S, Presnell K, Shaw H. Dissonance and healthy weight eating disorder prevention programs: long-term effects from a randomized efficacy trial. J Consult Clin Psychol. 2008 Apr;76(2):329-40. doi: 10.1037/0022-006X.76.2.329.

  PMID: 18377128 BACKGROUND

• Stice E, Presnell K, Gau J, Shaw H. Testing mediators of intervention effects in randomized controlled trials: An evaluation of two eating disorder prevention programs. J Consult Clin Psychol. 2007 Feb;75(1):20-32. doi: 10.1037/0022-006X.75.1.20.

  PMID: 17295560 BACKGROUND

• Stice E, Rohde P, Durant S, Shaw H. A preliminary trial of a prototype Internet dissonance-based eating disorder prevention program for young women with body image concerns. J Consult Clin Psychol. 2012 Oct;80(5):907-16. doi: 10.1037/a0028016. Epub 2012 Apr 16.

  PMID: 22506791 BACKGROUND

• Stice E, Rohde P, Gau J, Shaw H. An effectiveness trial of a dissonance-based eating disorder prevention program for high-risk adolescent girls. J Consult Clin Psychol. 2009 Oct;77(5):825-34. doi: 10.1037/a0016132.

  PMID: 19803563 BACKGROUND

• Stice E, Rohde P, Shaw H, Gau J. An effectiveness trial of a selected dissonance-based eating disorder prevention program for female high school students: Long-term effects. J Consult Clin Psychol. 2011 Aug;79(4):500-8. doi: 10.1037/a0024351.

  PMID: 21707136 BACKGROUND

• Stice E, Yokum S, Burger K, Rohde P, Shaw H, Gau JM. A pilot randomized trial of a cognitive reappraisal obesity prevention program. Physiol Behav. 2015 Jan;138:124-32. doi: 10.1016/j.physbeh.2014.10.022. Epub 2014 Oct 30.

  PMID: 25447334 BACKGROUND

• Stice E, Yokum S, Veling H, Kemps E, Lawrence NS. Pilot test of a novel food response and attention training treatment for obesity: Brain imaging data suggest actions shape valuation. Behav Res Ther. 2017 Jul;94:60-70. doi: 10.1016/j.brat.2017.04.007. Epub 2017 Apr 19.

  PMID: 28505470 BACKGROUND

• Fisher PA, Berkman ET. Designing Interventions Informed by Scientific Knowledge About Effects of Early Adversity: A Translational Neuroscience Agenda for Next Generation Addictions Research. Curr Addict Rep. 2015 Dec 1;2(4):347-353. doi: 10.1007/s40429-015-0071-x. Epub 2015 Sep 28.

  PMID: 26985399 BACKGROUND

Related Links

• Social and Affective Neuroscience (SAN) Laboratory website
• Description: Study recruitment website

MeSH Terms

Conditions

Overweight; Obesity; Neoplasms

Condition Hierarchy (Ancestors)

Overnutrition; Nutrition Disorders; Nutritional and Metabolic Diseases; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Elliot Berkman, Ph.D.

  University of Oregon

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: FACTORIAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Quantify the degree to which cognitive and behavioral interventions alter the valuation of cancer-risk foods relative to an active control. We will recruit 300 overweight/obese adults who are at risk for eating- and obesity-related cancers and randomize them to a (a) behavioral response training toward low cancer-risk foods and away from high cancer-risk foods, (b) cognitive reappraisal intervention focused on cancer-risk foods (experimental arms), or (c) non-food inhibitory control training (active control arm). Valuation, our primary mediating process as implicated in the incentive sensitization model, will be measured using behavioral economics tasks and functional magnetic resonance imaging (fMRI) of the vmPFC at pre- and posttraining. Proximal, intervention-specific mediators will also be indexed with fMRI. A final analysis will compare the potency of the intervention-specific neural systems to alter valuation via connectivity to vmPFC.

Study Record Dates

• First Submitted: September 12, 2017
• First Posted: June 15, 2018
• Study Start: May 1, 2018
• Primary Completion: May 1, 2023
• Study Completion: June 30, 2023
• Last Updated: August 14, 2023

Record last verified: 2022-11

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)