Acute adaptation of central and peripheral motor unit features to exercise‐induced fatigue differs with concentric and eccentric loading
Bibliographic record
Abstract
NEW FINDINGS: What is the central question of this study? Conflicting evidence exists on motor unit (MU) firing rate in response to exercise-induced fatigue, possibly due to the contraction modality used: Do MU properties adapt similarly following concentric and eccentric loading? What is the main finding and its importance? MU firing rate increased following eccentric loading only despite a decline in absolute force. Force steadiness deteriorated following both loading methods. Central and peripheral MU features are altered in a contraction type-dependant manner, which is an important consideration for training interventions. ABSTRACT: Force output of muscle is partly mediated by the adjustment of motor unit (MU) firing rate (FR). Disparities in MU features in response to fatigue may be influenced by contraction type, as concentric (CON) and eccentric (ECC) contractions demand variable amounts of neural input, which alters the response to fatigue. This study aimed to determine the effects of fatigue following CON and ECC loading on MU features of the vastus lateralis (VL). High-density surface (HD-sEMG) and intramuscular (iEMG) electromyography were used to record MU potentials (MUPs) from bilateral VLs of 12 young volunteers (six females) during sustained isometric contractions at 25% and 40% of the maximum voluntary contraction (MVC), before and after completing CON and ECC weighted stepping exercise. Multi-level mixed effects linear regression models were performed with significance assumed as P < 0.05. MVC decreased in both CON and ECC legs post-exercise (P < 0.0001), as did force steadiness at both 25% and 40% MVC (P < 0.004). MU FR increased in ECC at both contraction levels (P < 0.001) but did not change in CON. FR variability increased in both legs at 25% and 40% MVC following fatigue (P < 0.01). From iEMG measures at 25% MVC, MUP shape did not change (P > 0.1) but neuromuscular junction transmission instability increased in both legs (P < 0.04), and markers of fibre membrane excitability increased following CON only (P = 0.018). These data demonstrate that central and peripheral MU features are altered following exercise-induced fatigue and differ according to exercise modality. This is important when considering interventional strategies targeting MU function.
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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 | 0.002 |
| 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) | 0.002 | 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".