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 Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
NONE
Enrollment
76
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
Chandlee Laboratory
University Park, Pennsylvania, United States
Child body mass index
child height and weight will be measured
Time frame: baseline and 1 year follow-up
Oxy- and deoxyhemoglobin in response to food cues
Functional near infrared spectroscopy (fNIRS) will measure brain activity through oxy- and deoxyhemoglobin in response to images of high and low energy dense foods.
Time frame: baseline
Food intake in grams during a standard meal
Intake in grams from standard meal
Time frame: baseline and 1-year follow-up
Food intake in kcal during a standard meal
Intake in kcal during a standard meal
Time frame: baseline and 1-year follow-up
Video coding of standard meal
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.
Time frame: 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)
Time frame: 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)
Time frame: baseline
Reward-related decision making during 2-stage reinforcement learning task
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.
Time frame: baseline
Value modulated attentional capture
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.
Time frame: baseline
Body Composition
The BodPod uses air displacement plethysmography to assess body composition including fat mass and fat-free mass in children
Time frame: baseline and 1-year follow-up
Oxy- and deoxyhemoglobin in response to rating food health, taste, and wanting
Functional near infrared spectroscopy will measure brain activity through oxy- and deoxyhemoglobin while children rate food images on health, taste and wanting
Time frame: baseline
Oxy- and deoxyhemoglobin in response to food choice
Functional near infrared spectroscopy will measure brain activity through oxy- and deoxyhemoglobin while children choose which of two foods they would like to eat
Time frame: baseline
Eye-tracking during the value-modulated attentional capture task
The extent to which previously reward distractors capture attention will be assessed with eye tracking
Time frame: baseline
Eye-tracking during the food choice task (during functional near infrared spectroscopy)
Eye-tracking will be measured to determine if attention is drawn to the tastier food item prior to making a food choice
Time frame: baseline
Video coding of snack buffet
A digital recording of the child during the eating in the absence of hunger protocol will be saved. We will use the video to characterize the amount of attention children give toward the food items when they are not hungry and code behaviors associated with self-control
Time frame: baseline
Population density
The population density of the child's primary residence will be used as a measure of rurality
Time frame: baseline
Child Pavlovian Instrumental Transfer Task
An experimental task assessing pavlovian instrumental transfer for food cues.
Time frame: 1-year follow-up
Oxy- and deoxyhemoglobin in response to consumption of foods
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Functional near infrared spectroscopy (fNIRS) will measure brain activity through oxy- and deoxyhemoglobin in response to consumption of taste-test samples of foods both before and after a standard laboratory meal
Time frame: 1-year follow-up
Oxy- and deoxyhemoglobin in response to rating food taste and wanting after consumption
Functional near infrared spectroscopy will measure brain activity through oxy- and deoxyhemoglobin while children rate how much they want foods samples and how each sample tasted after consumption. This is completed before and after a standard test meal
Time frame: 1-year follow-up.