Changes in Joint Power and Energetics during a Sport-Specific Jumping Fatigue Protocol
Bibliographic record
Abstract
Kinematic and kinetic changes in the lower extremities occur as an athlete becomes fatigued during vertical jumping; however, the specifics of these changes are not well-understood. Therefore, the purpose of this study was to quantify the influence of a sport-specific, vertical jumping fatigue protocol on the biomechanics of the ankle, knee, and hip joint. Twenty male varsity athletes performed repetitive standing countermovement squat jumps every 20 s until fatigued (vertical jump and reach height decreased to 88% of their maximum height for three consecutive jumps). The kinematics and kinetics of their lower extremities (ankle, knee, and hip) were quantified, and the ankle, knee, and hip joint’s moments, angular velocity, and joint power were compared. The participants performed an average of 175 jumps before they were classified as being fatigued. When they became fatigued, the peak power of the ankle and hip joints were substantially reduced due to a decrease in the angular velocity at both joints. Ankle and hip joint moments were unchanged. Peak power at the knee joint was also unchanged over the course of the jumping protocol. To maintain vertical jumping performance over the course of a game or to delay the influence of fatigue, training should be targeted at maintaining the angular velocity of the ankle and hip joints.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".