Snacking Among US Children: Patterns Differ by Time of Day
Bibliographic record
Abstract
OBJECTIVE: Evaluate foods and beverages consumed, and energy and nutrient densities that constitute morning, afternoon, and evening snacking periods among children in the US. DESIGN: Evaluate snacking behavior using the National Health and Nutrition Examination Survey, 2009-2012. PARTICIPANTS: Children aged 4-8 and 9-13 years (n = 3,647). VARIABLES MEASURED: Self-reported foods and beverages reported as snacks were aggregated into morning (0:00-12:00 hours), afternoon (12:00-18:00 hours), and evening (18:00-24:00 hour) snacking periods. ANALYSIS: Intakes and amounts consumed during different snacking periods were analyzed from per capita weighted averages using SAS 9.2 and SUDAAN software. Statistical differences were detected using Student t test for means and chi-square test for prevalence data. RESULTS: Snacking contributed 25% of daily energy in both age groups, with the morning snacking period contributing the least energy. The most frequently consumed food groups across all snacking periods were water, snacks and sweets, nonalcoholic beverages, milk and dairy, and fruit. Nutrient densities varied among different snacking periods; the morning snack period for 4- to 8-year-olds was the most nutrient dense. CONCLUSIONS AND IMPLICATIONS: Snacking is an important contributor to energy and nutrients in children's diets, but foods and beverages selected vary by time of day and age. Modifying snacking choices, especially in the afternoon, could improve the nutritional quality of children's diets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".