Neuromuscular fatigue of the elbow flexors during repeated maximal arm cycling sprints: the effects of forearm position
Bibliographic record
Abstract
Repeated sprint exercise (RSE) is often used to induce neuromuscular fatigue (NMF). It is currently not known whether NMF is influenced by different forearm positions during arm cycling RSE. The purpose of this study was to investigate the effects of a pronated versus supinated forearm position on elbow flexor NMF during arm cycling RSE. Participants (n = 12) completed ten 10-s maximal arm cycling sprints interspersed by 60 s of rest on 2 separate days using either a pronated or supinated forearm position. All sprints were performed on an arm cycle ergometer in a reverse direction. Prior to and following RSE, NMF measurements (i.e., maximal voluntary contraction (MVC), potentiated twitch (PT), electromyography median frequencies) were recorded. Sprint performance measures, ratings of perceived exertion (RPE) and pain were also recorded. Irrespective of forearm position, sprint performance decreased as sprint number increased. These decreases were accompanied by significant increases in RPE (p < 0.001, ηp2 = 0.869) and pain (p < 0.001, ηp2 = 0.745). Participants produced greater power output during pronated compared with supinated sprinting (p < 0.001, ηp2 = 0.728). At post-sprinting, the percentage decrease in elbow flexor MVC and PT force from pre-sprinting was significantly greater following supinated than pronated sprinting (p < 0.001), suggesting greater peripheral fatigue occurred in this position. The data suggest that supinated arm cycling RSE results in inferior performance and greater NMF compared with pronated arm cycling RSE. Novelty: NMF of the elbow flexors is influenced by forearm position during arm cycling RSE. Supinated arm cycling sprints resulted in worse repeated sprint performance and also greater NMF than pronated RSE.
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 | 0.001 | 0.002 |
| 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".