ECCENTRIC AND CONCENTRIC JUMPING PERFORMANCE DURING AUGMENTED JUMPS WITH ELASTIC RESISTANCE: A META-ANALYSIS.
Bibliographic record
Abstract
INTRODUCTION: The initial rapid eccentric contraction of a stretch-shortening cycle (SSC) activity is typically reported to accentuate the subsequent concentric jump performance. Some researchers have rationalized that adding elastic resistance (ER) to explosive type activities (e.g. countermovement jumps and drop jumps) would increase excitatory stretch reflex activity and mechanical recoil characteristics of the musculotendinous tissues. The purpose of this meta-analysis was to examine the available literature on jumping movements augmented with ER and to provide a quantitative summary on the effectiveness of this technique for enhancing acute eccentric and concentric jumping performance. METHODS: In a random-effects model, the Hedges`s g effect size (ES) was used to calculate the biased corrected standardized mean difference between the augmented and similar non-augmented jumps. RESULTS: The results demonstrated that augmented jumps provided a greater eccentric loading compared to free jumps (Hedges`s g ES = 0.237, p = 0.028). However the concentric performance was significantly impaired, particularly if the downward elastic force was used during concentric phase as well (ES = -2.440, p < 0.001). Interestingly, no performance decrement was observed in those studies, which released the bands at the beginning of the concentric phase (ES = 0.397, p = 0.429). DISCUSSION: The authors postulated that the excessive eccentric loading might trigger reflex inhibition, alter the muscle stiffness, increase downward hip displacement and dissipate mechanical recoil properties. These results suggest that the release of elastic force at the beginning of the concentric phase seems to be a critical point to avoid impairment of acute concentric performance in augmented jumps. LEVEL OF EVIDENCE: 2a.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.023 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".