Motor Unit Discharge Rates During Maximal Fatiguing Eccentric Contractions and Recovery in the Human Elbow Extensors
Bibliographic record
Abstract
INTRODUCTION: Eccentric (ECC) contractions are uniquely characterized by higher maximal torque output compared with concentric or isometric actions. However, this advantage is accompanied by greater and more prolonged torque loss following fatiguing ECC exercise compared with other modalities. Although muscle contractile responses to dynamic fatiguing contractions have been documented, the neural control of ECC contractions-particularly at the level of individual motor units-remains poorly understood. The purpose was to evaluate motor unit discharge rate (MUDR) modulation during and in recovery after a maximal effort ECC fatiguing task. METHODS: Single motor unit activity was recorded with fine-wire electrodes inserted into the triceps brachii muscle. Participants completed an elbow extension protocol with subsequent recovery for 30 min, involving repetitive sets of 10 maximal repetitions until 50% torque loss relative to baseline maximal ECC torque. RESULTS: MUDR declined substantially (>35%) from baseline to task failure (37.1 vs 23.1 Hz, P ≤ 0.001) and did not return to baseline values until 10 min of recovery ( P = 0.597). However, both electrically stimulated twitch torque and maximal voluntary ECC torque remained depressed at 30 min ( P = 0.001), relative to baseline. CONCLUSIONS: This study provides a novel characterization of motor unit behavior during fatigue induced by maximal ECC contractions, revealing a significant reduction consistent with prior evidence of impaired MUDR under other fatiguing tasks. In addition, results indicate that prolonged torque depression (30 min post-task failure) following the ECC fatiguing task is primarily attributable to peripheral muscle impairments, as MUDR recovered to baseline within ~10 min.
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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) | 0.001 | 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".