Fatiguing Exercise Intensity Influences the Relationship between Parameters Reflecting Neuromuscular Function and Postural Control Variables
Bibliographic record
Abstract
The purpose of this study was to investigate the influence of fatiguing exercise intensity on the nature and extent of fatigue-induced changes in neuromuscular function and postural stability in quiet standing. We also explored the contribution of selected neuromuscular mechanisms involved in force production to postural stability impairment observed following fatigue using an approach based on multivariate regressions. Eighteen young subjects performed 30-s postural trials on one leg with their eyes closed. Postural trials were performed before and after fatiguing exercises of different intensities: 25, 50 and 75% of maximal isometric plantarflexor torque. Fatiguing exercises consisted of sustaining a plantarflexor isometric contraction at the target intensity until task failure. Maximal isometric plantarflexor torque, electromyographic activity of plantarflexor and dorsiflexor muscles, activation level (twitch interpolation technique) and twitch contractile properties of plantarflexors were used to characterize neuromuscular function. The 25% exercise was associated with greater central fatigue whereas the 50 and 75% exercises involved mostly peripheral fatigue. However, all fatiguing exercises induced similar alterations in postural stability, which was unexpected considering previous literature. Stepwise multiple regression analyses showed that fatigue-related changes in selected parameters related to neuromuscular function could explain more than half (0.51≤R(2)≤0.82) of the changes in postural variables for the 25% exercise. On the other hand, regression models were less predictive (0.17≤R(2)≤0.73) for the 50 and 75% exercises. This study suggests that fatiguing exercise intensity does not influence the extent of postural stability impairment, but does influence the type of fatigue induced and the neuromuscular function predictors explaining changes in postural variables.
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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 | 0.001 | 0.004 |
| 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".