Screen time is associated with dietary intake in overweight Canadian children
Bibliographic record
Abstract
OBJECTIVES: To describe the relationship between screen time and dietary intake among children, and to examine this association in relation to body weight. METHODS: A cross-sectional analysis of 630 Canadian children aged 8-10 years with at least one obese biological parent. Measurements included body mass index (BMI), screen time (television, video game, computer), physical activity (accelerometer over 7 days), and diet (three 24-hour recalls for the calculation of the Canadian Healthy Eating Index (HEI-C)). Multivariate linear regression models were used to describe the relationship between screen time (≥ 2 h/d vs. < 2 h/d) and intake of nutrients and foods among healthy weight and overweight/obese children. RESULTS: The overall median [interquartile range] daily screen time was 2.2 [2.4] hours and 43% of children had a BMI of ≥ 85th percentile. Longer screen time above the recommendation (≥ 2 h/d) was associated with higher intake of energy (74 kcal, SE = 35), lower intake of fiber (- 0.6 g/1000 kcal, SE = 0.2) and vegetables & fruit (- 0.3 serving/1000 kcal, SE = 0.1) among all participants and with higher estimates in the overweight subgroup. An overall lower HEI-C (- 1.6, SE = 0.8) was also observed among children with screen time of ≥ 2 h/d. Among children of < 85th BMI percentile, longer screen time was associated with lower intake of vegetables & fruit (- 0.3 serving/1000 kcal, SE = 0.1) only. CONCLUSION: Screen time is associated with less desirable food choices, particularly in overweight children.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".