Study of Brain, Reward, and Kids' Eating

NCT ID: NCT05456516

Last Updated: 2025-01-16

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

76 participants

Study Classification

INTERVENTIONAL

Study Start Date

2023-01-10

Study Completion Date

2024-12-30

Brief Summary

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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).

Detailed Description

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Conditions

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Obesity, Childhood Eating Behavior

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

Primary Study Purpose

PREVENTION

Blinding Strategy

NONE

Study Groups

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Health

Children will rate foods on health

Group Type EXPERIMENTAL

Food Rating

Intervention Type 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

Taste

Children will rate foods on taste

Group Type EXPERIMENTAL

Food Rating

Intervention Type 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

Wanting

Children will rate foods on desire to eat

Group Type EXPERIMENTAL

Food Rating

Intervention Type 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

Interventions

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Food Rating

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

Intervention Type BEHAVIORAL

Eligibility Criteria

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Inclusion Criteria

* 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.

Exclusion Criteria

* 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
Minimum Eligible Age

8 Years

Maximum Eligible Age

10 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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National Center for Advancing Translational Sciences (NCATS)

NIH

Sponsor Role collaborator

Penn State University

OTHER

Sponsor Role lead

Responsible Party

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Alaina Pearce

Assistant Research Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Chandlee Laboratory

University Park, Pennsylvania, United States

Site Status

Countries

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United States

References

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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.

Reference Type BACKGROUND
PMID: 32505786 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 34662600 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 34489782 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 24270272 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 27473844 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 31972203 (View on PubMed)

Other Identifiers

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KL2TR002015

Identifier Type: NIH

Identifier Source: secondary_id

View Link

STUDY00020463

Identifier Type: -

Identifier Source: org_study_id

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