Pre‐Meal Television Viewing and Exercise on Subjective Appetite and Food Intake in Children
Bibliographic record
Abstract
Purpose The effects of pre‐meal television viewing (TVV) and exercise (EXR) on subjective appetite and food intake (FI) regulation are not well understood in the paediatric population. The objective of the present study was to investigate separate and concurrent effects of 30‐min TVV and moderate‐intensity EXR immediately before mealtime on subjective appetite and FI in 9 to 14 year old boys and girls. Methods During four test mornings one week apart and 2 h after a standardized breakfast, 13 children (n=7 boys, n=6 girls; age 11.1 ± 0.5 years) were randomly assigned to one of four 30‐min treatment conditions: TVV only, EXR only, TVV and EXR, and sitting control. Immediately following the treatment, FI was measured via an ad libitum pizza meal. Subjective appetite was assessed at baseline, 15 and 30‐min during the treatment condition, and upon trial completion at 60‐min. Subjective emotions were assessed at baseline and at 30 min. Results FI and water intake were not affected by treatment condition, but after subtracting exercise energy expenditure (EE), there was a main effect of the exercise on FI (p=0.01), which lead to an average ~20% reduction in FI compared to the sedentary conditions (TVV and control). Thirst scores were higher in the exercise vs. sedentary groups at 15‐min (p<0.01) and 30‐min (p<0.001). Subjective average appetite and emotion were not affected by treatment, but increased with time in all conditions (p<0.0001 and p<0.0001, respectively). Conclusions Exposure to 30‐min TVV and EXR before eating did not affect subjective emotions, subjective appetite, or FI. TVV alone or concurrent with EXR did not influence FI regulation, suggesting that physiologic signals of satiation and satiety are not overridden by environmental stimuli of pre‐meal screen‐time exposure among young children. Increased EE did not result in compensatory increases in short‐term FI, resulting in a decreased net energy balance during the measurement period. Therefore, moderate‐intensity exercise leading to increased EE, independent of TVV, influences energy balance and may support achieving or maintaining healthy weights in children. Support or Funding Information Faculty of Community Services Seed Grant (Nick Bellissimo), and the Undergraduate Research Award (Melissa Da Silva).
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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 | 0.000 |
| 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".