Physical Activity, Screen Time, and Sleep Trajectories From Childhood to Adolescence: The Influence of Sex and Body Weight Status
Bibliographic record
Abstract
BACKGROUND: Behavioral trajectories from childhood to adolescence may differ and are poorly understood. The authors estimated the trajectories of moderate to vigorous physical activity (MVPA), screen time, and sleep duration during this period, by sex and initial weight status. METHODS: Data from Quebec Adiposity and Lifestyle Investigation in Youth, an ongoing cohort study in Canada on the natural history of obesity, were used. Participants predisposed to obesity attended baseline (8-10 y old, n = 630) and follow-up visits 2 years (n = 564) and 7 years (n = 359) after baseline. Participants with completed self-reported and accelerometer-based data were included in the analyses (n = 191, 353, and 240 for MVPA, screen time, and sleep, respectively). The authors performed group-based trajectory analyses and multinomial logistic regression models. RESULTS: Two MVPA, 3 screen time, and 2 sleep trajectories were identified. Girls were more likely than boys to belong to trajectory with lower MVPA means (odds ratio [OR] = 6.45; 95% confidence interval [CI], 3.08 to 13.49), yet less likely to belong to the trajectory with higher screen time (OR = 0.47; 95% CI, 0.23 to 0.97) and lower sleep duration (OR = 0.46; 95% CI, 0.27 to 0.78). Overweight or obesity at baseline was associated with a greater likelihood of belonging to the trajectory with lower MVPA (OR = 10.99; 95% CI, 1.31 to 91.14) and higher screen time (OR = 2.01; 95% CI, 1.04 to 4.06), respectively. CONCLUSIONS: It appears to be gender- and weight-based determinants of behavioral trajectories in this sample. These results may provide guidance for interventions in similar populations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".