Study of Brain, Reward, and Kids' Eating

NCT05456516 | Completed Results

• 2 other identifiers: STUDY00020463KL2TR002015
• Study Type: interventional
• Target: 76
• Locations: 1 country
• Sites: 1

Brief Summary

Children from rural communities are at greater risk for obesity than children from more urban communities. However, some children are resilient to obesity despite greater exposure to obesogenic influences in rural communities (e.g., fewer community-level physical activity or healthy eating resources). Identifying factors that promote this resiliency could inform obesity prevention. Eating habits are learned through reinforcement (e.g., hedonic, familial environment), the process through which environmental food cues become valued and influence behavior. Therefore, understanding individual differences in reinforcement learning is essential to uncovering the causes of obesity. Preclinical models have identified two reinforcement learning phenotypes that may have translational importance for understanding excess consumption in humans: 1) goal-tracking-environmental cues have predictive value; and 2) sign-tracking-environmental cues have predictive and hedonic value (i.e., incentive salience). Sign-tracking is associated with poorer attentional control, greater impulsivity, and lower prefrontal cortex (PFC) engagement in response to reward cues. This parallels neurocognitive deficits observed in pediatric obesity (i.e., worse impulsivity, lower PFC food cue reactivity). The proposed research aims to determine if reinforcement learning phenotype (i.e., sign- and goal-tracking) is 1) associated with adiposity due to its influence on neural food cue reactivity, 2) associated with reward-driven overconsumption and meal intake due to its influence on eating behaviors; and 3) associated with changes in adiposity over 1 year. The investigators hypothesize that goal-tracking will promote resiliency to obesity due to: 1) reduced attribution of incentive salience and greater PFC engagement to food cues; and 2) reduced reward-driven overconsumption. Finally, the investigators hypothesize reinforcement learning phenotype will be associated due to its influence on eating behaviors associated with overconsumption (e.g., larger bites, faster bite rat and eating sped). To test this hypothesis, the investigators will enroll 76, 8-10-year-old children, half with healthy weight and half with obesity based on Centers for Disease Control definitions. Methods will include computer tasks to assess reinforcement learning, dual x-ray absorptiometry to assess adiposity, and neural food cue reactivity from functional near-infrared spectroscopy (fNIRS).

Conditions

Obesity, Childhood; Eating Behavior

Keywords

reinforcement learning; fNIRS; food-cue reactivity

Outcome Measures

Primary Outcomes (6)

• Child Body Mass Index

  child height and weight will be measured

  baseline and 1 year follow-up

• Body Composition

  The BodPod uses air displacement plethysmography to assess body composition including fat mass and fat-free mass in children

  baseline and 1-year follow-up

• Food Intake in Grams During a Standard Meal

  Intake in grams from standard meal

  baseline and 1-year follow-up

• Food Intake in kcal During a Standard Meal

  Intake in kcal during a standard meal

  baseline and 1-year follow-up

• Food Intake in Grams During a Snack Buffet When Not Hungry

  Intake in grams during a snack buffet using a standard eating in the absence of hunger paradigm (i.e., non-homeostatic intake)

  baseline

• Food Intake in kcal During a Snack Buffet When Not Hungry

  Intake in kcal during a snack buffet using a standard eating in the absence of hunger paradigm (i.e., non-homeostatic intake)

  baseline

Other Outcomes (47)

• Oxy- and Deoxyhemoglobin in Response to Rating Food Health, Taste, and Wanting

  baseline

• Oxy- and Deoxyhemoglobin in Response to Food Choice

  baseline

• Oxy- and Deoxyhemoglobin in Response to Consumption of Foods

  1-year follow-up

Study Arms (1)

All participants

OTHER

There is only 1 arm in this study

Behavioral: Food Rating

Interventions

Food Rating BEHAVIORAL

Children will rate foods on taste, health, and desire to eat. The order in which they rate the food characteristics is randomly assigned and counter-balanced across participants

All participants

Eligibility Criteria

• Age: 8 Years - 10 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17)

You may qualify if:

• In order to be enrolled, children must be of good health based on parental self-report.
• Have no neurodevelopmental disorder (e.g., attention deficit hyperactivity disorder - ADHD) or learning disabilities (e.g., dyslexia).
• Have no allergies to the foods or ingredients used in the study.
• Not be taking any medications known to influence body weight, taste, food intake, behavior, or blood flow.
• Be 8-10 years-old at enrollment.
• speaks English.
• The parent who has the most knowledge of the child's eating behavior, sleep and behavior must be available to attend the visits with their child. This would be decided among the parents.

You may not qualify if:

• They are not within the age requirements (\< than 8 years old or \> than 10 years-old at baseline).
• If they are taking cold or allergy medication, or other medications known to influence cognitive function, taste, appetite, or blood flow.
• don't speak English.
• are colorblind.
• has a learning disability, ADHD, language delays, autism or other neurological or psychological conditions.
• has a pre-existing medical condition such as type I or type II diabetes, rheumatoid arthritis, Cushing's syndrome, Down's syndrome, severe lactose intolerance, Prader-Willi syndrome, HIV, cancer, renal failure, or cerebral palsy.
• is allergic to foods or ingredients used in the study.
• the parent is unable to attend the study visits

Sponsors & Collaborators

• Penn State University: lead
• National Center for Advancing Translational Sciences (NCATS): collaborator

Study Sites (1)

Chandlee Laboratory

University Park, Pennsylvania, 16802, United States

Location

Related Publications (6)

• Pearce AL, Adise S, Roberts NJ, White C, Geier CF, Keller KL. Individual differences in the influence of taste and health impact successful dietary self-control: A mouse tracking food choice study in children. Physiol Behav. 2020 Sep 1;223:112990. doi: 10.1016/j.physbeh.2020.112990. Epub 2020 Jun 4.

  PMID: 32505786 BACKGROUND

• Pearce AL, Cevallos MC, Romano O, Daoud E, Keller KL. Child meal microstructure and eating behaviors: A systematic review. Appetite. 2022 Jan 1;168:105752. doi: 10.1016/j.appet.2021.105752. Epub 2021 Oct 16.

  PMID: 34662600 BACKGROUND

• Fuchs BA, Roberts NJ, Adise S, Pearce AL, Geier CF, White C, Oravecz Z, Keller KL. Decision-Making Processes Related to Perseveration Are Indirectly Associated With Weight Status in Children Through Laboratory-Assessed Energy Intake. Front Psychol. 2021 Aug 18;12:652595. doi: 10.3389/fpsyg.2021.652595. eCollection 2021.

  PMID: 34489782 BACKGROUND

• Rangel A. Regulation of dietary choice by the decision-making circuitry. Nat Neurosci. 2013 Dec;16(12):1717-24. doi: 10.1038/nn.3561. Epub 2013 Nov 22.

  PMID: 24270272 BACKGROUND

• van Meer F, Charbonnier L, Smeets PA. Food Decision-Making: Effects of Weight Status and Age. Curr Diab Rep. 2016 Sep;16(9):84. doi: 10.1007/s11892-016-0773-z.

  PMID: 27473844 BACKGROUND

• Colaizzi JM, Flagel SB, Joyner MA, Gearhardt AN, Stewart JL, Paulus MP. Mapping sign-tracking and goal-tracking onto human behaviors. Neurosci Biobehav Rev. 2020 Apr;111:84-94. doi: 10.1016/j.neubiorev.2020.01.018. Epub 2020 Jan 20.

  PMID: 31972203 BACKGROUND

MeSH Terms

Conditions

Pediatric Obesity; Feeding Behavior

Condition Hierarchy (Ancestors)

Obesity; Overweight; Overnutrition; Nutrition Disorders; Nutritional and Metabolic Diseases; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Behavior, Animal; Behavior

Results Point of Contact

• Title: Alaina Pearce
• Organization: Pennsylvania State University

azp271@psu.edu

3206305713

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: No

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Research Professor

Study Record Dates

• First Submitted: July 6, 2022
• First Posted: July 13, 2022
• Study Start: January 10, 2023
• Primary Completion: December 30, 2024
• Study Completion: December 30, 2024
• Last Updated: April 13, 2026
• Results First Posted: April 13, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ANALYTIC CODE
• Time Frame: The data will be available within 6 mo of completion of data collection
• Access Criteria: No access criteria

Locations

US (1)