Effects of ankle and hip muscle fatigue on postural sway and attentional demands during unipedal stance
Bibliographic record
Abstract
The effect of muscle fatigue on quiet standing is equivocal, including its duration/recovery and whether it leads to an increase in attentional demands. The purpose of this study was to assess the effects of ankle and hip muscle fatigue on postural sway and simple reaction time during a unipedal task. Two groups of 14 young adults (mean age=22.50±3.23) had to stand on their dominant leg for 30-s trials before and after fatigue of hip or ankle flexors and extensors. Half of the unipedal trials were performed in a dual-task condition where subjects, in addition to standing, had to respond verbally to an auditory stimulus. Sway area, and sway variability and velocity in the AP and ML planes were calculated using center of pressure data obtained from a force platform. Voice reaction time was recorded seated and during the dual-task condition to assess attentional demands. A main effect of fatigue was found for AP sway variability (p=0.027), AP sway velocity (p=0.017) and ML sway velocity (p=0.004). Both groups showed increased sway velocity in both directions and in reaction time during the dual-task condition (p<0.001), but reaction time did not increase with fatigue. A group by fatigue interaction was found significant for ML sway velocity (p=0.043). Results suggest that hip and ankle fatigue affected postural control in the fatigued plane (AP) but only hip fatigue affected postural control in the non-fatigued plane (ML sway velocity). However, fatigue did not lead to an increase in attentional demands and increased AP and ML sway velocity had recovered within 30min.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".