A pre‐meal glucose drink, but not video game playing, suppresses food intake in overweight and obese boys (1040.8)
Bibliographic record
Abstract
We previously reported decreased food intake (FI) after a glucose drink and 30 min of video game playing (VGP) in normal weight boys, but the effect on FI in boys with increased adiposity is unknown. Therefore, we examined the effect of 30 min of pre‐meal VGP on subjective appetite, emotions and FI in overweight/obese (>85th BMI percentile) boys after a glucose drink. On four mornings, in random order and one‐week apart, boys (n = 22; age = 11.9 ± 0.3 y) consumed equally sweetened drinks (250 mL) of sucralose (0 kcal) or 50 g (200 kcal) glucose, with or without 30 min of subsequent VGP, 2 h after a standardized breakfast. Immediately after all test conditions FI (mean ± SEM kcal) from an ad libitum pizza meal was measured. Subjective appetite was measured at baseline (0 min), 20, 35, and 65 min (post‐meal). While glucose (p < 0.01) decreased FI (∆ = ‐103 ± 48 kcal) compared with the sucralose control, cumulative FI (drink kcal + meal kcal) was higher (p < 0.01). Fullness (p < 0.05) was lower, and subjective aggressiveness (p < 0.01), excitement (p < 0.01), upset (p = 0.05), and frustration (p < 0.05) higher after VGP; however, subjective emotions did not correlate with FI. In conclusion, the stronger effect of glucose compared with VGP on FI is indicative of the primary role of physiologic factors in the short‐term regulation of FI in overweight/obese boys. Grant Funding Source : Supported by The Danone Institute of Canada, Grant‐in‐aid program
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".