Fatigue and recovery measured with dynamic properties vs isometric force: effects of exercise intensity
Bibliographic record
Abstract
While fatigue can be defined as an exercise-related decrease in the maximal power or isometric force, most studies have assessed only isometric force. The main purpose of this experiment was to compare dynamic measures of fatigue [maximal torque (Tmax), maximal velocity (Vmax) and maximal power (Pmax)] with measures associated with maximal isometric force [isometric maximal voluntary contraction (IMVC) and maximal rate of force development (MRFD)] 10 s after different fatiguing exercises and during the recovery period (1-8 min after). Ten young men completed 6 experimental sessions (3 fatiguing exercises×2 types of fatigue measurements). The fatiguing exercises were: a 30-s all out (WING), 10-min at severe-intensity (SEV) and 90-min at moderate-intensity (MOD). Relative Pmax decreased more than IMVC after WING (p=0.005) while the opposite was found after SEV (p=0.005) and MOD tasks (p<0.001). There was no difference between the decrease in IMVC and Tmax after the WING, but IMVC decreased more than Tmax immediately following and during the recovery from the SEV (p=0.042) and MOD exercises (p<0.001). Depression of MRFD was greater than Vmax after all the fatiguing exercises and during recovery (all p<0.05). Despite the general definition of fatigue, isometric assessment of fatigue is not interchangeable with dynamic assessment following dynamic exercises with large muscle mass of different intensities, i.e. the results from isometric function cannot be used to estimate dynamic function and vice-versa. This implies different physiological mechanisms for the various measures of fatigue.
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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".