Comparison of the Capacity of Different Jump and Sprint Field Tests to Detect Neuromuscular Fatigue
Bibliographic record
Abstract
Different jump and sprint tests have been used to assess neuromuscular fatigue, but the test with optimal validity remains to be established. The current investigation examined the suitability of vertical jump (countermovement jump [CMJ], squat jump [SJ], drop jump [DJ]) and 20-m sprint (SPRINT) testing for neuromuscular fatigue detection. On 6 separate occasions, 11 male team-sport athletes performed 6 CMJ, SJ, DJ, and 3 SPRINT trials. Repeatability was determined on the first 3 visits, with subsequent 3 visits (0-, 24-, and 72-hour postexercise) following a fatiguing Yo-Yo running protocol. SPRINT performance was most repeatable (mean coefficient of variation ≤2%), whereas DJ testing (4.8%) was significantly less repeatable than CMJ (3.0%) and SJ (3.5%). Each test displayed large decreases at 0-hour (33 of 49 total variables; mean effect size = 1.82), with fewer and smaller decreases at 24-hour postexercise (13 variables; 0.75), and 72-hour postexercise (19 variables; 0.78). SPRINT displayed the largest decreases at 0-hour (3.65) but was subsequently unchanged, whereas SJ performance recovered by 72-hour postexercise. In contrast, CMJ and DJ performance displayed moderate (12 variables; 1.18) and small (6 variables; 0.53) reductions at 72-hour postexercise, respectively. Consequently, the high repeatability and immediate and prolonged fatigue-induced changes indicated CMJ testing as most suitable for neuromuscular fatigue monitoring.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".