THE EFFECT OF SET STRUCTURE MANIPULATION ON NEUROMUSCULAR FATIGUE INDUCED BY MAXIMAL ECCENTRIC CONTRACTIONS.
Bibliographic record
Abstract
Few studies have combined neural and contractile measures to assess muscular fatigue induced by eccentric contractions in humans. PURPOSE To examine neuromuscular fatigue and recovery following maximal eccentric contractions in which the number of sets was varied but the total number of repetitions was held constant. This manipulation of set structure altered the total rest time during the fatigue protocol. METHODS Using a Biodex System3 isokinetic dynamometer, the tibialis anterior of both legs of 8 men (26±1 y) were fatigued with 150 maximal eccentric dorsiflexion contractions (−60°/s, 30° ROM, 1-s on, 1-s off). Set structure was manipulated such that one leg performed 3 sets of 50 repetitions, and the other leg performed 15 sets of 10 repetitions. A 1-minute rest separated each set. Isometric MVC and tetanic torque (10, 20 and 50Hz), and surface EMG were recorded prefatigue, 3 times during each fatigue protocol, 7 times during an acute 30-minute recovery period, and 48 (Day 2) and 96 hours later (Day 4). RESULTS Irrespective of set structure, MVC and 50Hz torque were reduced 30% after the 150 repetitions, and remained reduced after 30 minutes of recovery (16% and 23%, respectively). This impairment in MVC torque persisted on Day 2 (12%) and Day 4 (10%) following both protocols. Low-frequency fatigue developed equally from both protocols and was greatest at the end of the 30-minute recovery period (38% reduction in 20-to-50Hz torque ratio). CONCLUSION These findings suggest that fatigue induced by maximal eccentric contractions is likely the result of peripheral mechanisms (e.g., E-C coupling). Furthermore, the delayed recovery of isometric torque production is not dependent on the number of sets and rest periods, if the total number of repetitions is the same. Supported by NSERC and CIHR.
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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) | 0.001 | 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".