Screen Exposure and Childhood Adiposity in Socio-Vulnerable School Settings: Evidence from a Portuguese Cross-Sectional Study (Preprint)
Bibliographic record
Abstract
BACKGROUND: Screen-based media use among children has been increasing, particularly in lower socioeconomic groups. As this behavior is linked to obesogenic habits, it is crucial to examine the associations between screen-based media use and adiposity in primary schoolchildren, particularly those from socially vulnerable contexts, such as children from the Educational Territories of Priority Intervention program. OBJECTIVE: This study aimed to examine the associations between screen-based media use and adiposity in primary schoolchildren from socially vulnerable contexts. METHODS: This study, part of the BeE-school Project, included 735 children (mean age 7.7, SD 1.2 years; n=380, 51.7% boys and n=355, 48.3% girls) from 10 primary schools located in vulnerable contexts in northern Portugal. Researchers recorded weight, height, and waist circumference and calculated BMI z scores and the waist-to-height ratio (WHtR). Screen-based media use was reported by parents using the ScreenQ tool, which includes 4 domains (screen access, frequency of use, media content, and caregiver-child coviewing). Sociodemographic and anthropometric data of parents were obtained via a questionnaire. Generalized linear models were applied. RESULTS: A higher screen-based media use score was associated with higher BMI z scores and WHtR (b=0.064, 95% CI 0.034-0.094 and b=0.002, 95% CI 0.001-0.003, respectively) even after adjusting for children's sex and age and parents' education and BMI. Significant associations (P<.05) were also observed for the domains of screen access, frequency of use, and media content. CONCLUSIONS: Screen-based media use is linked to higher BMI and WHtR in vulnerable children. Reducing screen access, limiting use frequency, and curating media content could improve health outcomes. Interventions for obesity prevention should consider these factors. TRIAL REGISTRATION: ClinicalTrials.gov NCT05395364; https://clinicaltrials.gov/study/NCT05395364.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".