Effect of video game playing and a glucose preload on subjective appetite, subjective emotions, and food intake in overweight and obese boys
Bibliographic record
Abstract
Video game playing (VGP) is associated with overweight/obesity (OW/OB). VGP and caloric preloads in the pre-meal environment influence short-term food intake (FI) in healthy-weight children. Therefore, the purpose of the present study was to examine the effect of pre-meal VGP and a glucose preload on subjective emotions, subjective appetite, and FI in boys with OW/OB. On 4 separate mornings, boys with OW/OB (n = 22; mean ± SD: age = 11.9 ± 1.6 years; body mass index percentile = 94.3 ± 3.9) participated in 4 test conditions. Two hours after a standardized breakfast, boys consumed equally sweetened preloads (250 mL) of sucralose (0 kcal) or glucose (200 kcal), with or without 30 min of subsequent VGP. Immediately after each test condition, FI was evaluated during an ad libitum pizza meal. Subjective appetite was measured at 0 (baseline), 15, and 30 min. Subjective emotions (aggression, anger, excitement, disappointment, happiness, upset, and frustration) were measured at 0 and 30 min. VGP did not affect FI, but the glucose preload decreased FI compared with the sucralose control (Δ = -103 ± 48 kcal, p < 0.01). However, cumulative FI (preload kcal + meal kcal) was 9% higher after the glucose preload (p < 0.01). Subjective appetite increased with time (p < 0.05) but was not influenced by preload or VGP. Frustration was the only subjective emotion that increased following VGP (p < 0.01). A glucose preload, but not VGP, suppressed FI in boys with OW/OB, suggesting a primary role of physiological factors in short-term FI regulation.
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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) | 0.001 | 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) | 0.002 | 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".