Beneficial effects of reduced soft tissue vibrations with compression garments on delayed neuromuscular impairments induced by an exhaustive downhill run
Bibliographic record
Abstract
Introduction Soft tissue vibrations (STV) have been extensively researched for their effects on muscle fatigue and damage, but their influence during running remains unclear. As compression garments are known to lower STV, they have shown benefits on acute neuromuscular responses to downhill running. However, an in-depth analysis of changes in STV has never been proposed, and previous protocols did not overcome the repeated bout effect. This study aims to investigate whether compression shorts could reduce STV parameters and related neuromuscular impairments using a unilateral compression protocol. Materials and methods Twenty healthy men performed a downhill run until exhaustion while wearing shorts that compressed one thigh with the contralateral leg serving as a control. Foot–ground impacts (FGI), STV, and muscle activation of the Vastus Lateralis were measured on both legs while running using accelerometers to obtain FGI and STV, and surface electromyographic sensors (EMG) for muscle activation. Time–frequency analyses were applied to acceleration and EMG signals with statistical non-parametric mapping applied to the continuous data to assess time and compression effects. Neuromuscular parameters such as maximal voluntary contraction torque, voluntary activation, and torque evoked by 10 and 100 Hz doublet stimulation were assessed before, after, and 48 h post exercise, alongside perceived fatigue and muscle soreness. Mixed linear models and paired Student's t-tests were used to analyze neuromuscular outcomes. Results While results showed that both FGI and STV magnitude increased during the run by 19.7% (P = 0.002) and 17.8% (P = 0.003), respectively, compression reduced the magnitude and frequency of STV by 15.1% (P = 0.013) and 11.7% (P = 0.001), respectively, without influencing FGI or muscle activation. Although neuromuscular parameters were altered in both legs, losses of torque evoked by 10 and 100 Hz doublets were lower in the compressed leg 48 h post exercise (P < 0.001 and P = 0.001, respectively). Conclusion This study revealed the potential of compression garments to act as a mechanical support that attenuates high-frequency STV during downhill running and mitigates subsequent delayed neuromuscular alterations.
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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) | 0.001 | 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) | 0.003 | 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".