Alternative Countermovement-Jump Analysis to Quantify Acute Neuromuscular Fatigue
Bibliographic record
Abstract
PURPOSE: To examine the reliability and magnitude of change after fatiguing exercise in the countermovement-jump (CMJ) test and determine its suitability for the assessment of fatigue-induced changes in neuromuscular (NM) function. A secondary aim was to examine the usefulness of a set of alternative CMJ variables (CMJ-ALT) related to CMJ mechanics. METHODS: Eleven male college-level team-sport athletes performed 6 CMJ trials on 6 occasions. A total of 22 variables, 16 typical (CMJ-TYP) and 6 CMJ-ALT, were examined. CMJ reproducibility (coefficient of variation; CV) was examined on participants' first 3 visits. The next 3 visits (at 0, 24, and 72 h postexercise) followed a fatiguing high-intensity intermittent-exercise running protocol. Meaningful differences in CMJ performance were examined through effect sizes (ES) and comparisons to interday CV. RESULTS: Most CMJ variables exhibited intraday (n = 20) and interday (n = 21) CVs of <10%. ESs ranging from trivial to moderate were observed in 18 variables at 0 h (immediately postfatigue). Mean power, peak velocity, flight time, force at zero velocity, and area under the force-velocity trace showed changes greater than the CV in most individuals. At 24 h, most variables displayed trends toward a return to baseline. At 72 h, small increases were observed in time-related CMJ variables, with mean changes also greater than the CV. CONCLUSIONS: The CMJ test appears a suitable athlete-monitoring method for NM-fatigue detection. However, the current approach (ie, CMJ-TYP) may overlook a number of key fatigue-related changes, and so practitioners are advised to also adopt variables that reflect the NM strategy used.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".