Behavior Tracking and 3-Year Longitudinal Associations Between Physical Activity, Screen Time, and Fitness Among Young Children
Bibliographic record
Abstract
Purpose: Understanding the correlates of children’s fitness as they develop is needed. The objectives of this study were to 1) examine the longitudinal associations between physical activity (PA), screen time (ST), and fitness; 2) determine if sex moderates associations; and 3) track PA and ST over 3 years. Methods: Findings are based on 649 children [baseline = 4.5 (0.5) y; follow-up = 7.8 (0.6) y] from Edmonton, Canada. Parental-reported hour per week of PA and ST were measured at baseline and 3 years later. Fitness (vertical jump, sit and reach, waist circumference, grip strength, predicted VO2max, push-ups, and partial curl-ups) was measured using established protocols at follow-up. Sex-specific z scores or low/high fitness groups were calculated. Linear or logistic multiple regression models and Spearman correlations were conducted. Results: Baseline ST was negatively associated with follow-up grip strength [β = −0.010; 95% confidence interval (CI), −0.019 to −0.001]. Associations between baseline PA and follow-up overall fitness (β = 0.009; 95% CI, 0.002 to 0.016) were significant, whereas baseline PA and follow-up VO2max (β = 0.014; 95% CI, 0.000 to 0.027) approached significance (P < .06). No sex interactions were observed. Moderate and large tracking were observed for PA (rs = .30) and ST (rs = .53), respectively. Conclusions: PA and ST may be important modifiable correlates of overall fitness in young 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".