Neuromuscular, biomechanical, and energetic adjustments following repeated bouts of downhill running
Bibliographic record
Abstract
PURPOSE: This study used downhill running as a model to investigate the repeated bout effect (RBE) on neuromuscular performance, running biomechanics, and metabolic cost of running. METHODS: Ten healthy recreational male runners performed two 30-min bouts of downhill running (DR1 and DR2) at a -20% slope and 2.8 m/s 3 weeks apart. Neuromuscular fatigue, level running biomechanics during slow and fast running, and running economy parameters were recorded immediately before and after the downhill bouts, and at 24 h, 48 h, 72 h, 96 h, and 168 h thereafter (i.e., follow-up days). RESULTS: An RBE was confirmed by attenuated muscle soreness and serum creatine kinase rise after DR2 compared to DR1. An RBE was also observed in maximum voluntary contraction (MVC) force loss and voluntary activation where DR2 resulted in attenuated MVC force loss and voluntary activation immediately after the run and during follow-up days. The downhill running protocol significantly influenced level running biomechanics; an RBE was observed in which center of mass excursion and, therefore, lower-extremity compliance were greater during follow-up days after DR1 compared to DR2. The observed changes in level running biomechanics did not influence the energy cost of running. CONCLUSION: This study demonstrated evidence of adaptation in neural drive as well as biomechanical changes with the RBE after DR. The higher neural drive resulted in attenuated MVC force loss after the second bout. It can be concluded that the RBE after downhill running manifests as changes to global and central fatigue parameters and running biomechanics without substantially altering the energy cost of running.
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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) | 0.000 | 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".