Age-related neuromuscular fatigue and recovery after cycling: Measurements in isometric and dynamic modes
Bibliographic record
Abstract
Studies have suggested that older individuals are more fatigable than young adults when power loss, measured during single-joint contractions, is considered the fatigue index; however, age-related differences in fatigue considering power measurements during multi-joint movements (e.g., cycling) have not been fully elucidated yet. This study examined age-related differences in dynamic and isometric measures of fatigue in response to three cycling exercises. Ten young (27 ± 4 years) and ten old (74 ± 4 years) men performed exercises on different days, 30-s Wingate, 10-min at severe-intensity, and 90-min at moderate-intensity. Dynamic measures-maximal power, torque, and velocity-were assessed after cycling and during recovery (1-8 min post-exercise) through 7-s cycling sprints and isometric force and fatigue etiology (central and peripheral components) through isometric contractions. There were no age-related differences in the relative reduction of dynamic and isometric measures following the Wingate and moderate-intensity tasks. Maximal power, isometric force, and indices of peripheral function (e.g., high-frequency doublet) decreased more in young compared with older individuals after the severe-intensity exercise (all p < .05). The only observed age-related difference in the recovery of NM fatigue was a slower recovery of power and torque from 1 to 8 min (p < .05) and at 4 min (p = .015), respectively, in younger males after the Wingate. Age-related fatigue and recovery depend on the fatiguing exercise intensity and duration and on the fatigue assessment mode. This study provides novel information on age-related neuromuscular fatigue responses to multi-joint dynamic exercises performed at different intensities and durations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".