Does The Countermovement Jump Accurately Assess Lower-limb Neuromuscular Fatigue?
Bibliographic record
Abstract
Neuromuscular fatigue is monitored in athletes following intense exercise to inform future training prescription and to improve performance. Temporal analysis of the countermovement jump (CMJ) is frequently used to assess fatigue in athletes but is infrequently compared directly to low-frequency fatigue (LFF), a long-lasting fatigue typically assessed in the laboratory. LFF may be present in athletes. PURPOSE: To determine the time-course of changes in CMJ and lower limb LFF following an exhaustive cycling test and the relationship between LFF and temporal CMJ variables. METHODS: LFF and CMJ were assessed in seven active adults (24±2 years, 78±14 kg) prior to and following (0, 8, 15, 30, 60 mins and 24 and 48 hours) an incremental cycling test to exhaustion. Cycling began at 100 W and increased 25 W every 2 min until exhaustion. Temporal analysis of CMJ for peak power (PP), jump height (JH), and concentric contraction duration (CCD) were calculated from bilateral force plates. Low-frequency (10 Hz) force (F10) was elicited from transcutaneous femoral nerve stimulation prior to a maximal voluntary contraction (MVC). LFF was quantified as the F10:MVC ratio. The order of CMJ or LFF was randomized across time points. Changes in LFF and CMJ variables were assessed from two-way repeated measures analysis of variance (measure x time). RESULTS: Maximal aerobic power was 234±67 W (96±4% HRmax). A significant measure x time interaction (p<0.0001) and significant main effects of time (p=0.05) and method (p=0.02) were found. Compared to baseline, significant reductions in LFF (i.e., greater fatigue) were seen at 0-, 8-, 15- and 30-mins post-exercise (all p<0.02). No significant changes in any of the CMJ variables were observed post-exercise (p = 1.0). Furthermore, changes in LFF during exercise recovery was not correlated to changes in any of CMJ variables (r2<0.04, p>0.20), whereas all CMJ variables were significantly correlated with each other (0.22<r2<0.42, p<0.001). CONCLUSION: These results show that following exhaustive exercise, LFF persists for at least 30 mins whereas no change in CMJ performance variables were seen. These results call into question the practical utility of interpreting neuromuscular fatigue in athletes using the CMJ, even within 30 minutes of exhaustive exercise.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".