Mealtime exposure to food advertisements while watching television increases food intake in overweight and obese girls but has a paradoxical effect in boys
Bibliographic record
Abstract
Food advertisements (ads) in TV programs influence food choice and have been associated with higher energy intake from snacks in children; however, their effects at mealtime have not been reported. Therefore, we measured energy intake at a pizza meal consumed by normal weight (NW) and overweight/obese (OW/OB) children (aged 9-14 years) while they watched a TV program with or without food ads and following pre-meal consumption of a sweetened beverage with or without calories. NW and OW/OB boys (experiment 1, n = 27) and girls (experiment 2, n = 23) were randomly assigned to consume equally sweetened drinks containing glucose (1.0 g/kg body weight) or sucralose (control). Food intake was measured 30 min later while children watched a program containing food or nonfood ads. Appetite was measured before (0-30 min) and after (60 min) the meal. Both boys and girls reduced energy intake at the meal in compensation for energy in the glucose beverage (p < 0.05). Food ads resulted in further compensation (51%) in boys but not in girls. Food ads increased energy intake at the meal (9%; p = 0.03) in OW/OB girls only. In conclusion, the effects of TV programs with food ads on mealtime energy intake and response to pre-meal energy consumption in children differ by sex and body mass index.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".