Television viewing and using screens while eating: Associations with dietary intake in children and adolescents
Bibliographic record
Abstract
Screen time has been associated with overweight and obesity, as well as with poorer dietary quality. However, the reasons explaining these associations are not well understood. The objectives of this cross-sectional study were [1] to determine the extent of overall TV viewing as well as using screens while eating (e.g., watching TV or using a tablet), [2] to compare food and nutrient consumption of on-versus off-screen eating occasions, and [3] to determine whether TV viewing and using screens while eating is associated with overall dietary intake. Participants were from the Food Environment Chilean Cohort (n = 938, 4-6 y) and the Growth and Obesity Cohort Study (n = 752, 12-14 y). Dietary data was collected via one 24-h food recall. For each eating occasion, activity performed during consumption (e.g., watching TV, playing sports) was reported. Weekly TV viewing time was collected via an additional survey instrument. Analyses included multivariable linear and logistic regression. Post-hoc pairwise comparisons examined differences in outcomes by tertiles. Our sample reported a median of 9-13.5 weekly hours of TV viewing and 87.5% reported consuming at least one meal or snack per day while using screens. The median kilocalories contributed by eating during screen use was 387 kcal/d in children and 848 kcal/day in adolescents, which represents 34.7% and 42.3% of daily energy intake, respectively. There were no consistent differences when comparing eating occasions consumed on-screen versus off-screen. Higher weekly TV viewing was associated with elements of a less healthy diet including more sweets and desserts in children, and more sugar sweetened beverages in adolescents. A large percentage of Chilean children and adolescents' daily energy is consumed while using screens. In depth, longitudinal work is needed to understand how screen time eating affects diet quality and nutritional status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".