Contraction velocity influence the magnitude and etiology of neuromuscular fatigue during repeated maximal contractions
Bibliographic record
Abstract
This study aimed to compare the magnitude and etiology of neuromuscular fatigue during maximal repeated contractions performed in two contraction modes (concentric vs isometric) and at two contraction velocities (30/s vs 240°/s). Eleven lower limb-trained males performed 20 sets of maximal contractions at three different angular velocities: 0°/s (KE0), 30/s (KE30), and 240°/s (KE240). Cumulated work, number of contraction, duty cycle, and contraction time were controlled. Torque, superimposed and resting twitches, as well as gas exchange, were analyzed. Increasing contraction velocity was associated with greater maximal voluntary torque loss (KE0: -9.8 ± 3.9%; KE30: -16.4 ± 8.5%; KE240: -32.6 ± 6.3%; P < 0.05). Interestingly, the torque decrease was similar for a given cumulated work. Compared with KE0, KE240 generated a greater evoked torque loss (Db100: -24.3 ± 5.3% vs -5.9 ± 6.9%; P < 0.001), a higher O2 consumption (23.7 ± 6.4 mL/min/kg vs 15.7 ± 3.8 mL/min/kg; P < 0.001), but a lower voluntary activation (VA) loss (-4.3 ± 1.6% vs -11.2 ± 4.9%; P < 0.001). The neuromuscular perturbations were intermediate for KE30 (Db100: -10.0 ± 6.8%; VA: -7.2 ± 2.8%). Although the amount of mechanical work cumulated strongly determined the magnitude of torque decrease, the contraction velocity and mode influenced the origin of the neuromuscular fatigue. The metabolic stress and peripheral fatigue increased but reduction of VA is attenuated when the contraction velocity increased from 0°/s to 240°/s.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".