The Role of Fundamental Movement Skills and Health-Related Fitness on Physical Activity During Guided Active Play for 8- to 10-Year-Old Children
Bibliographic record
Abstract
Background: Active play has been proposed to complement school-based physical activity (PA) and promote increased movement-related activities relevant for the development of motor competence. Guided active play (GAP) paired with cooperative games provides sufficient moderate-to-vigorous physical activity (MVPA) to improve motor competence for younger children. Whether guided active play exhibits physical activity outputs that are related to motor competence is uncertain. This study assessed the strength of relationships between play-based physical activity and movement skills by comparing linear regression and chi-square analyses. Methods: Forty-two children (Mage = 8.8 ± 0.8 years) participated in a community center program. PA was measured via accelerometry for GAP, alongside assessments of anthropometrics, fitness (leg power, strength, VO2max), and FMS (Test of Gross Motor Development-2). Multiple linear regression analysis examined reciprocal relationships. Chi-square and cross-tabulations analyzed categorical variables based on lab percentiles (low < 33%, high > 66%) for PA energy expenditure (PAEE), intensity (MVPA), FMS, and fitness. Results: GAP MVPA and object control skills (OC) showed positive reciprocal pathways (β = 0.308, β = 0.394; p ≤ 0.05). VO2max predicted MVPA (β = 0.408; p < 0.01), with leg power related to PAEE (β = 0.456; p ≤ 0.01). Chi-square analysis revealed significant associations between high OC skills and high PAEE (X2 = 15.12, p ≤ 0.05), and high individual average scores of OC with high MVPA (X2 = 11.90, p < 0.05. The high performance of AP and LP was associated with MVPA and PAEE, respectively. Conclusions: Findings support a positive feedback loop between MVPA and OC skills for GAP. GAP is an effective strategy for program interventions for children 8 to 10-year old.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".