Effects of Consecutive Days of Exercise and Recovery on Muscle Mechanical Function
Bibliographic record
Abstract
PURPOSE: To investigate the effects of three consecutive days of prolonged exercise on muscle mechanical function, 12 volunteers (.VO(2peak) = 44.8 +/- 2.0 mL.kg(-1).min(-1), mean +/- SE) cycled at approximately 60% .VO(2peak) until fatigue. METHODS: Quadriceps muscle function was assessed before and after exercise on day 1 (E1) and day 3 (E3) and during three consecutive days of recovery (R1, R2, R3), using both voluntary and electrically induced contractions at various stimulation frequencies. RESULTS: Exercise on E1 and E3 resulted in a 40% (120 +/- 12 vs 72 +/- 10 N) and 35% (117 +/- 14 vs 78 +/- 8 N) deficit (P < 0.05) in force at 10 Hz, respectively, which remained depressed (P < 0.05) by 32-34% during R1-R3. At 100 Hz, force, although not altered by exercise at E1 or E3, was decreased (P < 0.05) by 12-16% during recovery. The maximal rate of relaxation (-dF/dtmax) at 10 Hz was reduced (P < 0.05) by 38% on E1, by 32% on E3, and remained depressed by 38% through R3. At 100 Hz, -dF/dtmax was only depressed (P < 0.05) during recovery. Maximal rate of force development (+dF/dtmax) at 10 Hz was reduced (P < 0.05) by exercise, but not in recovery. Maximal voluntary contraction force was depressed (P < 0.05) with exercise at both E1 and E3 and remained depressed (P < 0.05) throughout recovery. The reduction (P < 0.05) in motor unit activation assessed with the interpolated twitch technique, observed during recovery, suggests that part of the incomplete recovery (weakness) is central in origin. CONCLUSIONS: These results demonstrate that three consecutive days of prolonged exercise result in a weakness that persists for at least 3 d, compromising force during both voluntary and induced contractions.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".