Integrated Strength and Fundamental Movement Skill Training in Children: A Pilot Study
Bibliographic record
Abstract
Competence in fundamental movement skills is essential to enable children to be physically active. We investigated the effect of an integrated fundamental movement skill with a strength training intervention on children’s fundamental movement skills. Seventy-two (53% female) 10- to 11-year-old children from three primary schools assented to take part in this study (87% compliance). Schools were randomly allocated to a control (no intervention; n = 21), fundamental movement skill (FMS) (n = 18) or FMS and strength (FMS+; n = 20) group. Interventions were delivered twice weekly for four weeks, in addition to normal physical education. FMS competence was measured through the Canadian agility and movement skills assessment (CAMSA) (product-process) and through countermovement jump (CMJ) and 40-m sprint tests (product). Improvements were observed in the CAMSA in both FMS (4.6, 95% confidence intervals 2.9 to 6.4 Arbitrary Units (AUs), second-generation p-value (pδ) = 0.03) and FMS+ (3.9, 2.1 to 5.3 AU, pδ = 0.28) with no difference beyond our minimum threshold of 3 AU observed between these intervention groups (pδ = 1). Clear improvements in CMJ were observed in FMS+ relative to the control (25, 18 to 32%, pδ = 0) and FMS groups (15, 6.1 to 24%, pδ = 0). These preliminary data suggest combined FMS and strength training warrants further investigation as a tool to develop fundamental movement skills in children.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".