Does Controlling for Biological Maturity Improve Physical Activity Tracking?
Bibliographic record
Abstract
UNLABELLED: Tracking of physical activity through childhood and adolescence tends to be low. Variation in the timing of biological maturation within youth of the same chronological age (CA) might affect participation in physical activity and may partially explain the low tracking. PURPOSE: To examine the stability of physical activity over time from childhood to late adolescence when aligned on CA and biological age (BA). METHODS: A total of 91 males and 96 females aged 8-15 yr from the Saskatchewan Pediatric Bone Mineral Accrual Study (PBMAS) were assessed annually for 8 yr. BA was calculated as years from age at peak height velocity. Physical activity was assessed using the Physical Activity Questionnaire for Children/Adolescents. Tracking was analyzed using intraclass correlations for both CA and BA (2-yr groupings). To be included in the analysis, an individual required a measure at both time points within an interval; however, not all individuals were present at all tracking intervals. RESULTS: Physical activity tracking by CA 2-yr intervals were, in general, moderate in males (r=0.42-0.59) and females (r=0.43-0.44). However, the 9- to 11-yr CA interval was low and nonsignificant (r=0.23-0.30). Likewise, tracking of physical activity by BA 2-yr intervals was moderate to high in males (r=0.44-0.60) and females (r=0.39-0.62). CONCLUSIONS: Accounting for differences in the timing of biological maturity had little effect on tracking physical activity. However, point estimates for tracking are higher in early adolescence in males and to a greater extent in females when aligned by BA versus CA. This suggests that maturity may be more important in physical activity participation in females than males.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".