Assessing the Validity of Standing Long Jump to Predict Muscle Power in Children With and Without Motor Delays
Bibliographic record
Abstract
PURPOSE: To determine the validity of standing long jump (SLJ) for predicting muscle power in children with and without developmental coordination disorder (DCD). METHODS: A total of 589 children were recruited as part of the Coordination and Activity Tracking in CHildren study (251 girls and 338 boys; mean age 59.2 mo). Children were classified as typically developing (>16th percentile), at risk for DCD (sixth to 16th percentile), or probable DCD (<sixth percentile) based on Movement Assessment Battery for Children-Second Edition scores. SLJ was measured from the back of the heel. Peak power and mean power over 10 seconds and 30 seconds were measured using the Wingate test. RESULTS: SLJ was moderately correlated with peak and mean powers in all groups (R = .51-.55). Regression analysis showed that when combined with weight and age, SLJ performance could predict peak power and mean power over 10 seconds and 30 seconds in typically developing children (adjusted R2 = .68, .61, and .58, P < .001, respectively) and in children with risk for DCD (adjusted R2 = .74, .65, and .60, respectively) and probable DCD (adjusted R2 = .68, .61, and .59, respectively). CONCLUSIONS: SLJ, in combination with weight and age, may be used to measure muscle power in typically developing children, and in children with risk for DCD and probable DCD. This measure can be used as an inexpensive estimate of musculoskeletal fitness in children regardless of motor abilities.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".