Neurocognitive and Behavioral Factors That Promote Resiliency to Pediatric Obesity
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Penn State University
- Enrollment
- 76
- Locations
- 2
- Primary Endpoint
- Child Body Mass Index
Study Overview
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).
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Intervention Model
- Single Group
- Primary Purpose
- Prevention
- Masking
- None
Eligibility Criteria
- Ages
- 8 Years to 10 Years (Child)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •Child 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.
- •Parent Inclusion Criteria:
- •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.
- •Parent Exclusion Criteria:
- •the parent is unable to attend the study visits
Arms & Interventions
All participants
There is only 1 arm in this study
Intervention: Food Rating (Behavioral)
Outcomes
Primary Outcomes
Child Body Mass Index
Time Frame: baseline and 1 year follow-up
child height and weight will be measured
Food Intake in Grams During a Standard Meal
Time Frame: baseline and 1-year follow-up
Intake in grams from standard meal
Food Intake in kcal During a Standard Meal
Time Frame: baseline and 1-year follow-up
Intake in kcal during a standard meal
Food Intake in Grams During a Snack Buffet When Not Hungry
Time Frame: baseline
Intake in grams during a snack buffet using a standard eating in the absence of hunger paradigm (i.e., non-homeostatic intake)
Food Intake in kcal During a Snack Buffet When Not Hungry
Time Frame: baseline
Intake in kcal during a snack buffet using a standard eating in the absence of hunger paradigm (i.e., non-homeostatic intake)
Child body mass index
Time Frame: baseline and 1 year follow-up
child height and weight will be measured
Oxy- and deoxyhemoglobin in response to food cues
Time Frame: baseline
Functional near infrared spectroscopy (fNIRS) will measure brain activity through oxy- and deoxyhemoglobin in response to images of high and low energy dense foods.
Food intake in grams during a standard meal
Time Frame: baseline and 1-year follow-up
Intake in grams from standard meal
Food intake in kcal during a standard meal
Time Frame: baseline and 1-year follow-up
Intake in kcal during a standard meal
Video coding of standard meal
Time Frame: baseline and 1-year follow-up
A digital recording of the child eating a standard meal will be saved. We have developed a behavior coding protocol to measure child meal microstructure (e.g., bites, bite size, meal duration). We have also validated a computational model to assess cumulative intake curves from video coded bite data.
Food intake in grams during a snack buffet when not hungry
Time Frame: baseline
Intake in grams during a snack buffet using a standard eating in the absence of hunger paradigm (i.e., non-homeostatic intake)
Food intake in kcal during a snack buffet when not hungry
Time Frame: baseline
Intake in kcal during a snack buffet using a standard eating in the absence of hunger paradigm (i.e., non-homeostatic intake)
Reward-related decision making during 2-stage reinforcement learning task
Time Frame: baseline
The 2-stage reinforcement learning task has a first stage two arm bandit with deterministic stage progression and a second stage one arm bandit. Reward distributions between the two second-stage states gradually drift throughout the task. Half the trials will be 'bonus' trials. Performance will be assessed using a computational model in addition to looking at trial-to-trial decisions.
Value modulated attentional capture
Time Frame: baseline
The value-modulated attentional capture task uses two phases - a training phase during which high and low reward conditions are learned and a test phase during which participants complete a task that no longer depends upon the previously learned reward contingencies. During the test phase, stimuli from the training phase are used as distractors. Attentional capture is measured by comparing performance on trials that have distractors previously associated with high reward to those with distractors previously associated with low reward.
Body Composition
Time Frame: baseline and 1-year follow-up
The BodPod uses air displacement plethysmography to assess body composition including fat mass and fat-free mass in children
Secondary Outcomes
- Oxy- and deoxyhemoglobin in response to rating food health, taste, and wanting(baseline)
- Oxy- and deoxyhemoglobin in response to food choice(baseline)
- Eye-tracking during the value-modulated attentional capture task(baseline)
- Eye-tracking during the food choice task (during functional near infrared spectroscopy)(baseline)
- Video coding of snack buffet(baseline)
- Population density(baseline)
- Child Pavlovian Instrumental Transfer Task(1-year follow-up)
- Oxy- and deoxyhemoglobin in response to consumption of foods(1-year follow-up)
- Oxy- and deoxyhemoglobin in response to rating food taste and wanting after consumption(1-year follow-up.)
Investigators
Alaina Pearce
Assistant Research Professor
Penn State University