Effect of pre‐meal screen‐time activities on subjective appetite and food intake in normal weight girls
Bibliographic record
Abstract
Evidence for an effect of pre‐meal television viewing (TVV) or other screen‐time activities on food intake (FI) is limited in children. Therefore, the purpose of this study was to determine the effect of pre‐meal screen‐time activities on subjective appetite and FI in 9–14 year old normal weight (NW) girls. NW girls (n=19) completed 45 min of TVV, computer use, video‐game playing (VGP), or sitting quietly (control). FI (mean kcal ± SEM) from an ad libitum pizza meal was measured immediately after each condition. Subjective appetite was measured at baseline, 15, 30, and 45 min. FI following TVV (575 ± 50), computer use (601 ± 42), and VGP (600 ± 54) did not differ from control (629 ± 56) (P = 0.48). However, FI was greater in girls with a higher BMI percentile (range: 65.1 – 82; n = 9) compared to the lower BMI percentile group (range: 10 – 65; n=10) (P < 0.001), but the response to screen‐time activities on FI was similar (P = 0.53). Average appetite positively correlated with FI, but only after VGP (r = 0.5; P < 0.01). Fat‐mass (kg) positively correlated with FI following TVV (r = 0.43; P = 0.06), computer use (r = 0.60; P < 0.01), and control (r = 0.59; P < 0.01). In conclusion, weight status and body fat, are stronger determinants of FI than pre‐meal screen‐time exposure in NW girls. Supported by Danone Institute of Canada‐Grant‐in‐Aid Program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".