Preschool to School-Age Physical Activity Trajectories and School-Age Physical Literacy: A Longitudinal Analysis
Bibliographic record
Abstract
PURPOSE: The associations between longitudinal physical activity (PA) patterns across childhood and physical literacy have not been studied. The purpose of this study was to identify PA trajectories from preschool to school-age, and to determine if trajectory group membership was associated with school-age physical literacy. METHODS: Participants (n = 279, 4.5 [0.9] y old, 48% girls) enrolled in this study and completed annual assessments of PA with accelerometry over 6 timepoints. Physical literacy was assessed at timepoint 6 (10.8 [1.0] y old). Group-based trajectory analysis was applied to identify trajectories of total volume of PA and of moderate to vigorous PA and to estimate group differences in physical literacy. RESULTS: Three trajectories of total volume of PA and of moderate to vigorous PA were identified. Groups 1 (lowest PA) included 40% to 53% of the sample, groups 2 included 39% to 44% of the sample, and groups 3 (highest PA) included 8% to 16% of the sample. All trajectories declined from timepoint 1 to timepoint 6. School-age physical literacy was lowest in trajectory groups with the lowest total volume of PA or moderate to vigorous PA over time (P < .05). CONCLUSIONS: PA should be promoted across early and middle childhood, as it may play a formative role in the development of school-age physical literacy.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".