Countermovement Jump Performance with Increased Training Loads in Elite Female Rugby Athletes
Bibliographic record
Abstract
Countermovement jump (CMJ) performance is typically analyzed through single-point concentric-based variables (e. g., peak power or force and height). However, methodological approaches examining movement strategies may be more sensitive to neuromuscular fatigue. 12 elite female rugby sevens athletes undertook weekly CMJ testing throughout a 6-week training block involving progressively increased training loads. Athletes self-reported training load (TRIMP) and wellness daily. 22 CMJ variables were assessed, incorporating analyses of force, velocity, power and time measured during eccentric and concentric jump phases. Differences over time were examined using the magnitude of change (effect sizes; ES) compared to baseline. Pearson correlations examined relationships between CMJ variables, wellness and TRIMP. TRIMP displayed large increases (mean ES; weeks 2-6: 2.47). Wellness decreased in week 3 (-0.41), with small reductions following (weeks 4-6: -0.34). Flight time (weeks 3-6: -1.84), peak displacement (weeks 2-6: -2.24), time to peak force (weeks 3-6: 2.58), force at zero velocity (F@0V) (weeks 5-6: -1.28) displayed multiple changes indicative of diminished neuromuscular function. Wellness scores and max rate of force development (mean; r=0.32), F@0V (r=0.28) and flight time (r=0.34) displayed positive correlations. Intensified training decreased CMJ output and altered CMJ mechanics. Longitudinal neuromuscular fatigue monitoring of team-sport athletes appears improved through CMJ mechanics analysis.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".