Pediatric Obesity
Conditions
Keywords
eating behavior, meal microstructure, child, obesity
Brief summary
Increased availability of high-energy dense foods has contributed to a pediatric obesity epidemic, with 23% of United States children currently presenting with the disease. How children eat contributes to both overconsumption and greater adiposity. However, it is unclear if laboratory measures of children's eating style generalize to the home environment, where children consume two thirds of their total energy. The study will 1) test if child eating styles observed in the lab generalize to more ecologically valid home environments and 2) identify aspects of home food environment that amplify obesogenic eating behaviors. We will assess laboratory and home eating styles (e.g., bite rate) in 100 prepubertal 6-9-year-old children to constrain variability in energy requirements. Children will be video-recorded while consuming identical study-provided meals at home and in the laboratory (counter-balanced order) in addition to a 'typical' meal at home. To study how adiposity relates to "obesogenic" styles of eating, gold standard dual x-ray absorptiometry will be used.
Interventions
BEHAVIORALMeal Location
The location at which the child will eat the experimental meal - home or lab
Sponsors
Penn State University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
BASIC_SCIENCE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
6 Years to 9 Years
Healthy volunteers
Yes
Inclusion criteria
* children must be between the ages of 6-9 years-old * children are of good health with no learning disabilities (e.g., ADHD, determined by parent report) * children are not on any medications known to impact body weight, taste, food intake, behavior, or blood flow * parents report that children like and are willing to eat study foods
Exclusion criteria
* Children are not within the age requirements (\<6 years old or \> 9 years old) * If children are taking cold or allergy medication, or other medications known to influence cognitive function, taste, appetite, or blood flow. * If children don't speak English. * If children are colorblind. * If children have a learning disability, ADD/ADHD, language delays, autism or other neurological or psychological conditions. * If children have 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. * If children are allergic to foods or ingredients used in the study. * child received an X-ray in the previous year (to avoid excess radiation exposure due to the DXA scans performed in the research)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Child body mass index | Day 1 | child height and weight will be measured |
| Food intake in grams during a standard meal | Day 1 and Day 2 or 3 depending on randomization | Intake in grams from standard meal |
| Food intake in kcal during a standard meal | Day 1 and Day 2 or 3 depending on randomization | Intake in kcal during a standard meal |
| Video coding of standard meal | Day 1 and Day 2 or 3 depending on randomization | A digital recording of the child eating a standard meal will be saved. The study team have developed a behavior coding protocol to measure child meal microstructure (e.g., bites, bite size, meal duration) and 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 | Day 1 | 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 | Day 1 | Intake in kcal during a snack buffet using a standard eating in the absence of hunger paradigm (i.e., non-homeostatic intake) |
| Body Composition | Day 1 | Dual-energy X-ray absorptiometry to assess body composition including fat mass and fat-free mass in children |
| Food intake in grams during a Study Meal | Day 2 or 3 depending on randomization and home meal administration | Intake in grams from Study Meal |
| Food intake in kcal during the Study Meal | Day 2 or 3 depending on randomization and home meal administration | Intake in kcal during the Study Meal |
| Video coding of the study meal | Day 2 or 3 depending on randomization and home meal administration | A digital recording of the child eating a Study Meal will be saved. The study team have developed a behavior coding protocol to measure child meal microstructure (e.g., bites, bite size, meal duration) and have also validated a computational model to assess cumulative intake curves from video coded bite data. |
| Video coding of home meals | Week 1 and Week 2 | Digital recordings of the child eating a typical meals at home. The study team have developed a behavior coding protocol to measure child meal microstructure (e.g., bites, bite size, meal duration) and have also validated a computational model to assess cumulative intake curves from video coded bite data. |
Countries
United States
Outcome results
None listed