Influence of Compression and Stiffness Apparel on Vertical Jump Performance
Bibliographic record
Abstract
Compression apparel alters both compression of the soft tissues and the hip joint stiffness of athletes. It is not known whether it is the compression elements, the stiffness elements, or some combination that increases performance. Therefore, the purpose of this study was to determine how systematically increasing upper leg compression and hip joint stiffness independently from one another affects vertical jumping performance. Ten male athletes performed countermovement vertical jumps in 8 concept apparel conditions and 1 control condition (loose fitting shorts). The 8 apparel conditions, 4 that specifically altered the amount of compression exerted on the thigh and 4 that altered the hip joint stiffness by means of elastic thermoplastic polyurethane bands, were tested on 2 separate testing sessions (one testing the compression apparel and the other testing the stiffness apparel). Maximum jump height was measured, while kinematic data of the hip, knee, and ankle joint were recorded with a high-speed camera (480 Hz). Both compression and stiffness apparel can have a positive influence on vertical jumping performance. The increase in jump height for the optimal compression was due to increased hip joint range of motion and a trend of increasing the jump time. Optimal stiffness also increased jump height and had the trend of decreasing the hip joint range of motion and hip joint angular velocity. The exact mechanisms by which apparel interventions alter performance is not clear, but it may be due to alterations to the force-length and force-velocity relationships of muscle.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".