Estimation of Peak Muscle Power From a Countermovement Vertical Jump in Children and Adolescents
Bibliographic record
Abstract
Gomez-Bruton, A, Gabel, L, Nettlefold, L, Macdonald, H, Race, D, and McKay, H. Estimation of peak muscle power from a countermovement vertical jump in children and adolescents. J Strength Cond Res 33(2): 390-398, 2019-Several equations to predict muscle power (MP) from vertical jump height (VJH) have been developed in adults. However, few have been derived in children. We therefore aimed to: (a) evaluate the validity of existing MP estimation equations from a vertical countermovement jump (CMJ) in children and adolescents and (b) develop and validate a new MP estimation equation for use in children and adolescents. We measured peak MP (in watts) and VJH (in centimeters) during a CMJ using a force platform in 249 children and adolescents (9-17 years; 119 boys and 130 girls). We compared actual (force platform) with predicted (12 existing prediction equations) MP using repeated-measures analysis of variance and estimated bias using modified Bland-Altman plots. We developed a new prediction equation using stepwise linear regression, assessed predictive error using leave-one-out and 10-fold cross-validation, and externally validated the equation in an independent sample (n = 100). All existing prediction equations demonstrated some degree of bias, either systematic bias (mean differences ranging 178-1,377 W; 8-64%) or bias at the extremes or interactions with sex. Our new prediction equation estimates MP from VJH and body mass: Power (W) = 54.2 × VJH (cm) + 34.4 × body mass (kg) - 1,520.4. With this new equation, there was no difference between actual and predicted MP (0%) and negligible differences (0.2-0.9%) in R and root mean square error between our observed and cross-validated sets. Actual and predicted MP were not different in our external validation (p = 0.12). The new equation demonstrates excellent validity and can be used to predict MP from a CMJ in children and adolescents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".