Strong empirical evidence shows food marketing promotes excess energy intake and obesity. Yet, not all children are susceptible to its effects and this variability is poorly understood. Identifying sources of this variability is a public health priority not only because it may elucidate characteristics of children who are most susceptible, but also because it may highlight novel sources of resiliency to overconsumption. The proposed research will use state-of-the art, data-driven approaches to identify neural, cognitive and behavioral phenotypes associated with resiliency to food-cue (i.e. food advertisement) induced overeating and determine whether these phenotypes protect children from weight gain during the critical pre-adolescent period.
The investigator's central hypothesis is that children who are resistant to food-cue induced overeating will exhibit a distinct cluster of neural, behavioral, and cognitive traits that protect them from weight gain, even in the context of high-familial obesity risk. Identifying these traits is critical to the development of successful, individually tailored obesity prevention programs. This hypothesis is informed by compelling preliminary research showing that children who are less susceptible to food-cue induced eating in the laboratory show decreased neural activation in somatosensory (i.e., post-central gyrus) and reward (i.e., striatum) regions and increased activation in cognitive control regions (i.e., dorsolateral prefrontal cortex-dlPFC) following food commercial exposure. These results provide a strong foundation for characterizing neural responses that are associated with resiliency to food-cue induced eating, but highlight major gaps in the literature that must be addressed to advance the field. This proposal will make three novel contributions to the understanding of the etiology of obesity. Using a prospective, family-risk design, the investigators will follow 100, 7-9 year-old children who have healthy weight but vary by risk for obesity (based on maternal weight status) for 1 year to characterize neural and behavioral responses to food commercials and identify common neural networks associated with resiliency to food-cue induced overconsumption. Second, the investigators will use sophisticated behavioral coding to characterize children's eating following food commercial exposure at both homeostatic (i.e., meal consumed when hungry) and non-homeostatic (i.e., eating in the absence of hunger - EAH snack buffet) events and relate individual differences in eating behavior to neural phenotypes. Finally, the investigators will follow children over 1 year to determine whether the neural and behavioral responses at baseline are protective against adiposity gains, during a critical period where children are cognizant of the purpose of advertising, but cannot fully defend against its effects. Baseline data will be collected over 4 weekly initial visits, followed by a 5th visit one year later.
Study Type
OBSERVATIONAL
Enrollment
200
The Pennsylvania State University
University Park, Pennsylvania, United States
RECRUITINGfMRI blood oxygen level dependent (BOLD) response to food commercials
Whole-brain response to food commercials, followed by images of high and low energy density foods in a fMRI scan
Time frame: baseline
fMRI blood oxygen level dependent (BOLD) response to toy commercials
Whole-brain response to toy commercials, followed by images of high and low energy density foods in a fMRI scan
Time frame: baseline
Food intake in grams after no commercial viewing
Intake in grams from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake)
Time frame: baseline
Food intake in kcals after no commercial viewing
Intake in kcals from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake)
Time frame: baseline
Food intake in grams after viewing food commercials
Intake in grams from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake)
Time frame: baseline
Food intake in kcals after viewing food commercials
Intake in kcals from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake)
Time frame: baseline
Food intake in grams after food commercial viewing
Intake in grams from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake)
Time frame: 1 year
Food intake in kcals after food commercial viewing
Intake in kcals from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake)
Time frame: 1 year
Food intake in grams after viewing toy commercials
Intake in grams from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake)
Time frame: baseline
Food intake in kcals after viewing toy commercials
Intake in kcals from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake)
Time frame: baseline
Video recording of meal and EAH snack buffet
A digital recording of the Child eating the Test Meal and the EAH snack buffet will be saved. We have developed a behavior coding technique to count specific behaviors while the child eats. We will also be transcribing the audio.
Time frame: baseline
Video recording of meal and EAH snack buffet
A digital recording of the Child eating the Test Meal and the EAH snack buffet will be saved. We have developed a behavior coding technique to count specific behaviors while the child eats. We will also be transcribing the audio.
Time frame: 1 year
Change from baseline DXA analysis for total body fat mass of child at 1 year
Examine change in children's fat mass index = total fat mass(kg) / height (m2)
Time frame: baseline
fMRI Region of Interest (ROI) response to toy commercials and subsequent views of high and low energy density food pictures.
We will examine the strength of the neural connections between reward/somatosensory and cognitive control regions.
Time frame: baseline
fMRI Region of Interest (ROI) response to food commercials and subsequent views of high and low energy density food pictures.
We will examine the strength of the neural connections between reward/somatosensory and cognitive control regions.
Time frame: baseline
Change in Android fat mass as measured by DXA analysis
Android fat mass (%) = (Android fat mass (kg) / total fat mass (kg))\*100
Time frame: Baseline and 1 year
Change in Gynoid fat mass as measured by DXA analysis
Gynoid fat mass (%) = (Gynoid fat mass (kg) / total fat mass (kg))\*100
Time frame: Baseline and 1 year
Child screen time
Child questionnaire quantifying the number of hours per day a child is exposed to different types of media.
Time frame: Baseline and 1 year
Child's brand awareness
Child's brand awareness assessed by a child computerized task. Children are asked to match pictures of products and brand logos. No scaling is used.
Time frame: Baseline and 1 year
Change in scores from the NIH Toolbox: Child Cognitive battery-Flanker test
The Flanker is a measure of executive function, specifically tapping inhibitory control and attention. Scoring is based on a combination of accuracy and reaction time. A 2-vector scoring method is employed that uses accuracy and reaction time, where each of these "vectors" ranges in value between 0 and 5, and the computed score, combining each vector score, ranges in value from 0-10. For any given individual, accuracy is considered first. If accuracy levels for the participant are less than or equal to 80%, the final "total" computed score is equal to the accuracy score. If accuracy levels for the participant reach more than 80%, the reaction time score and accuracy score are combined. Higher scores indicate higher levels of ability to attend to relevant stimuli and inhibit attention from irrelevant stimuli.
Time frame: Baseline and 1 year
Change in scores from the NIH Toolbox: Child Cognitive battery-List Sorting Working memory test
The List Sorting Working memory test assesses working memory. The List Sorting test requires immediate recall and sequencing of different visually and orally presented stimuli (i.e., "working memory"). Pictures of different foods and animals are displayed with accompanying audio recording and written text (e.g., "elephant"), and the participant is asked to say the items back in size order from smallest to largest, first within a single dimension (either animals or foods, called 1-List) and then on two dimensions (foods, then animals, called 2-List). The test takes approximately seven minutes to administer. List Sorting is scored by summing the total number of items correctly recalled and sequenced on 1-List and 2-List, which can range from 0-26.Higher scores on each of these indicate higher levels of working memory within the normative standard being applied.
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Time frame: Baseline and 1 year
Change in scores from the NIH Toolbox: Child Cognitive battery-Dimensional Change Card Sort Test (DCCS)
The Dimensional Change Card Sort Test is used to measure cognitive flexibility. Two target pictures are presented that vary along two dimensions (e.g., shape and color). Scoring is based on a combination of accuracy and reaction time. A 2-vector scoring method is employed that uses accuracy and reaction time, scores ranging from 0-10. For any given individual, accuracy is considered first. If accuracy levels for the participant are less than or equal to 80%, the final "total" computed score is equal to the accuracy score. If accuracy levels for the participant reach more than 80%, the reaction time score and accuracy score are combined. Higher scores indicate higher levels of cognitive flexibility.
Time frame: Baseline and 1 year