Impact of age on the development of fatigue during large and small muscle mass exercise
Bibliographic record
Abstract
To examine the impact of aging on neuromuscular fatigue following cycling (CYC; large active muscle mass) and single-leg knee-extension (KE; small active muscle mass) exercise, 8 young (25 ± 4 years) and older (72 ± 6 years) participants performed CYC and KE to task failure at a given relative intensity (80% of peak power output). The young also matched CYC and KE workload and duration of the old (iso-work comparison). Peripheral and central fatigue were quantified via pre-/postexercise decreases in quadriceps twitch torque (∆Qtw, electrical femoral nerve stimulation) and voluntary activation (∆VA). Although young performed 77% and 33% more work during CYC and KE, respectively, time to task failure in both modalities was similar to the old (~9.5 min; P > 0.2). The resulting ΔQtw was also similar between groups (CYC ~40%, KE ~55%; P > 0.3); however, ∆VA was, in both modalities, approximately double in the young (CYC ~6%, KE ~9%; P < 0.05). While causing substantial peripheral and central fatigue in both exercise modalities in the old, ∆Qtw in the iso-work comparison was not significant (CYC; P = 0.2), or ~50% lower (KE; P < 0.05) in the young, with no central fatigue in either modality ( P > 0.4). Based on iso-work comparisons, healthy aging impairs fatigue resistance during aerobic exercise. Furthermore, comparisons of fatigue following exercise at a given relative intensity mask the age-related difference observed following exercise performed at the same workload. Finally, although active muscle mass has little influence on the age-related difference in the rate of fatigue at a given relative intensity, it substantially impacts the comparison during exercise at a given absolute intensity.
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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 | 0.001 |
| 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".