Arm-cycling sprints induce neuromuscular fatigue of the elbow flexors and alter corticospinal excitability of the biceps brachii
Bibliographic record
Abstract
We examined the effects of arm-cycling sprints on maximal voluntary elbow flexion and corticospinal excitability of the biceps brachii. Recreationally trained athletes performed ten 10-s arm-cycling sprints interspersed with 150 s of rest in 2 separate experiments. In experiment A (n = 12), maximal voluntary contraction (MVC) force of the elbow flexors was measured at pre-sprint 1, post-sprint 5, and post-sprint 10. Participants received electrical motor point stimulation during and following the elbow flexor MVCs to estimate voluntary activation (VA). In experiment B (n = 7 participants from experiment A), supraspinal and spinal excitability of the biceps brachii were measured via transcranial magnetic and transmastoid electrical stimulation that produced motor evoked potentials (MEPs) and cervicomedullary motor evoked potentials (CMEPs), respectively, during a 5% isometric MVC at pre-sprint 1, post-sprint 1, post-sprint 5, and post-sprint 10. In experiment A, mean power output, MVC force, potentiated twitch force, and VA decreased 13.1% (p < 0.001), 8.7% (p = 0.036), 27.6% (p = 0.003), and 5.6% (p = 0.037), respectively, from pre-sprint 1 to post-sprint 10. In experiment B, (i) MEPs decreased 42.1% (p = 0.002) from pre-sprint 1 to post-sprint 5 and increased 40.1% (p = 0.038) from post-sprint 5 to post-sprint 10 and (ii) CMEPs increased 28.5% (p = 0.045) from post-sprint 1 to post-sprint 10. Overall, arm-cycling sprints caused neuromuscular fatigue of the elbow flexors, which corresponded with decreased supraspinal and increased spinal excitability of the biceps brachii. The different post-sprint effects on supraspinal and spinal excitability may illustrate an inhibitory effect on supraspinal drive that reduces motor output and, therefore, decreases arm-cycling sprint performance.
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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 | 0.000 |
| 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".