Feasibility and Effects of a Neuromuscular Warm-Up Based on the Physical Literacy Model for 8-12-Year-Old-Children
Bibliographic record
Abstract
Background: Physical literacy and injury prevention strategies use similar movement-related constructs and can be connected to develop comprehensive interventions. We aimed to test the feasibility and effects of a neuromuscular warm-up based on physical literacy and injury prevention strategies for 8–12-year-old children. Methods: We conducted a cluster non-randomized controlled trial. We defined a priori feasibility criteria and studied the effects of the intervention on physical literacy constructs, movement competence, and neuromuscular performance. We used generalized linear mixed models controlling for covariates and clustering with a significance level of 0.001. Results: We recruited 18 groups (n = 363) and randomly allocated nine to intervention (n = 179; female = 63.7%, age = 9.8 ± 1 years) and nine to control (n = 184, female = 53.3%, age = 9.9 ± 0.9 years). We met four of seven feasibility criteria (i.e. recruitment, adherence, enjoyment, perceived exertion). The three feasibility criteria that were not met (i.e. compliance, fidelity, follow-up) were slightly below the predefined threshold (90%). Model-adjusted mean differences for physical literacy constructs, movement competence, vertical jump height, horizontal jump distance, 20-m sprint time, and dynamic balance favored the intervention (p < .001). Conclusion: The feasibility evidence indicates that the intervention should be slightly modified before implementing it in a larger study. The observed mean differences are promising and can be used in planning future interventions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".