Neural adaptations to fatigue: implications for muscle strength and training
Bibliographic record
Abstract
PURPOSE: This paper investigates the neural mechanisms responsible for the increase in strength that occurs during serial isometric contractions. METHODS: A three-session design was used. Thirteen subjects (N = 13) were asked to perform five maximal isometric elbow extension strength trials to serve as baseline. After a 5-min rest, the subjects were administered a 30-trial fatigue protocol. This process was repeated two more times at 2-wk intervals. Elbow extension torque and surface electromyography (EMG) of the triceps and biceps brachii were monitored concurrently. The criterion measures were elbow extension torque, root-mean-square EMG amplitude, and mean power frequency (MPF). RESULTS: Intraclass reliability ranged from good to excellent. Within each experimental session, the fatigue protocol resulted in a decrease in maximal isometric elbow extension torque as well as biceps and triceps EMG amplitude and MPF (P < 0.05). However, the mean of the 30 trials and the magnitude of the linear decrease in elbow extension torque increased across the three sessions (P < 0.05). Biceps and triceps EMG amplitude increased and MPF decreased as the number of sessions increased (P < 0.05). CONCLUSIONS: These findings suggest that the fatigue protocol served as a training stimulus to down regulate motor-unit firing frequency.
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) | 0.002 | 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".