Physical Activity Trajectories in Early Childhood: Investigating Personal, Environmental, and Participation Factors
Bibliographic record
Abstract
INTRODUCTION/PURPOSE: To determine personal, environmental, and participation factors that predict children's physical activity (PA) trajectories from preschool through to school years. METHODS: Two hundred seventy-nine children (4.5 ± 0.9 yr, 52% boys) were included in this study. Physical activity was collected via accelerometry at six different timepoints over 6.3 ± 0.6 yr. Time-stable variables were collected at baseline and included child's sex and ethnicity. Time-dependent variables were collected at six timepoints (age, years) and included household income (CAD), parental total PA, parental influence on PA, and parent-reported child's quality of life, child's sleep, and child's amount of weekend outdoor PA. Group-based trajectory modeling was applied to identify trajectories of moderate-to-vigorous PA (MVPA) and total PA (TPA). Multivariable regression analysis identified personal, environmental, and participation factors associated with trajectory membership. RESULTS: Three trajectories were identified for each of MVPA and TPA. Group 3 in MVPA and TPA expressed the most PA over time, with increased activity from timepoints 1 to 3, and then declining from timepoints 4 to 6. For the group 3 MVPA trajectory, male sex (β estimate, 3.437; P = 0.001) and quality of life (β estimate, 0.513; P < 0.001) were the only significant correlates for group membership. For the group 3 TPA trajectory, male sex (β estimate, 1.970; P = 0.035), greater household income (β estimate, 94.615; P < 0.001), and greater parental total PA (β estimate, 0.574; P = 0.023) increased the probability of belonging to this trajectory group. CONCLUSIONS: These findings suggest a need for interventions and public health campaigns to increase opportunities for PA engagement in girls starting in the early years. Policies and programs to address financial inequities, positive parental modeling, and improving quality of life are also warranted.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".