Countermovement Jump Performance with Increased Training Loads in Elite Female Rugby Athletes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 it