Longitudinal Associations Between Sleep Habits, Screen Time and Overweight, Obesity in Preschool Children
Bibliographic record
Abstract
Introduction: Decreased sleep duration and increased screen time as early as preschool age may contribute to overweight and obesity. The effects of bedtime together with nocturnal sleep duration remain unclear with a paucity of data evaluating these associations longitudinally. We aim to evaluate the independent and joint effects of sleep duration, sleep bedtime, and screen time at 3 years of age on BMI status, particularly overweight and obesity by age 5 years. Methods: Data from 2185 participants of the CHILD Cohort Study were analyzed longitudinally using generalized estimating equations (GEE). Models included changes in overweight/obesity status from 3 to 5 years of age as outcome, and nocturnal sleep duration, bedtime, and daily screen time at 3 years of age as explanatory variables. The joint effects of nocturnal sleep time and excess screen time, late bedtime on overweight/obesity were subsequently analyzed. Results: The median nocturnal sleep time at 3 and 5 years of age was 11.0 hours/night [IQR 10.5, 11.5]. A total of 14.5% children went to bed after 9PM at 3 years and 7.2% at 5 years. Median screen time was 1.0 hr/day [IQR 1.0, 2.0] at both ages. Longitudinal analyses showed that sleeping less than 10.5 hours at age 3 years was associated with 46% greater odds of overweight/obesity by age 5 years (OR 1.46, 95% CI 1.07, 2.00). The risk was higher when coupled with late bedtime after 9pm (OR 1.60, 95% CI 1.12, 2.31). Children with both short nocturnal sleep duration and excess screen time (>1hr/day) had twice the associated risk of overweight/obesity by age 5 years (OR 1.96, 95% CI 1.34, 2.88). Conclusion: Nocturnal sleep duration and screen time are modifiable risk factors in young children, which may have important implications for obesity prevention as early as infancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".