Velocity-Specific Strength Recovery After a Second Bout of Eccentric Exercise
Bibliographic record
Abstract
A bout of eccentric exercise (ECC) has the protective effect of reducing muscle damage during a subsequent bout of ECC known as the "repeated bout effect" (RBE). The purpose of this study was to determine if the RBE is greater when both bouts of ECC are performed using the same vs. different velocity of contraction. Thirty-one right-handed participants were randomly assigned to perform an initial bout of either fast (3.14 rad·s [180°·s]) or slow (0.52 rad·s [30°·s]) maximal isokinetic ECCs of the elbow flexors. Three weeks later, the participants completed another bout of ECC at the same velocity (n = 16), or at a different velocity (n = 15). Indirect muscle damage markers were measured before, immediately after, and at 24, 48, and 72 hours postexercise. Measures included maximal voluntary isometric contraction (MVC) strength (dynamometer), muscle thickness (MT; ultrasound), delayed onset muscle soreness (DOMS; visual analog scale), biceps and triceps muscle activation amplitude (electromyography), voluntary activation (interpolated twitch), and twitch torque. After the repeated bout, MVC strength recovered faster compared with the same time points after the initial bout for only the same velocity group (p = 0.017), with no differences for all the other variables. Irrespective of velocity, MT and DOMS were reduced after the repeated bout compared with that of the initial bout at 24, 48, and 72 hours with a corresponding increase in TT at 72 hours (p < 0.05). Faster recovery of isometric strength associated with a repeated bout of ECC was evident when the velocity was matched between bouts, suggesting that specificity effects contribute to the RBE. The current findings support the idea of multiple mechanisms contributing to the RBE.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".