Age-Related Variation in Male Youth Athletes' Countermovement Jump After Plyometric Training: A Meta-Analysis of Controlled Trials
Bibliographic record
Abstract
Moran, J, Sandercock, GRH, Ramírez-Campillo, R, Meylan, CMP, Collison, J, and Parry, DA. Age-related variation in male youth athletes' countermovement jump after plyometric training: A meta-analysis of controlled trials. J Strength Cond Res 31(2): 552-565, 2017-Recent debate on the trainability of youths has focused on the existence of periods of accelerated adaptation to training. Accordingly, the purpose of this meta-analysis was to identify the age- and maturation-related pattern of adaptive responses to plyometric training in youth athletes. Thirty effect sizes were calculated from the data of 21 sources with studies qualifying based on the following criteria: (a) healthy male athletes who were engaged in organized sport; (b) groups of participants with a mean age between 10 and 18 years; and (c) plyometric-training intervention duration between 4 and 16 weeks. Standardized mean differences showed plyometric training to be moderately effective in increasing countermovement jump (CMJ) height (Effect size = 0.73 95% confidence interval: 0.47-0.99) across PRE-, MID-, and POST-peak height velocity groups. Adaptive responses were of greater magnitude between the mean ages of 10 and 12.99 years (PRE) (ES = 0.91 95% confidence interval: 0.47-1.36) and 16 and 18 years (POST) (ES = 1.02 [0.52-1.53]). The magnitude of adaptation to plyometric training between the mean ages of 13 and 15.99 years (MID) was lower (ES = 0.47 [0.16-0.77]), despite greater training exposure. Power performance as measured by CMJ may be mediated by biological maturation. Coaches could manipulate training volume and modality during periods of lowered response to maximize performance.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".