Trunk Extensor Muscle Fatigue Does Not Affect Postural Control during Upright Static Stance in Young-adults and Middle-aged Adults
Bibliographic record
Abstract
Background. Falls can be extremely detrimental to someone’s daily living, as well as life threatening This is especially true for individuals who have back problems, are sedentary, and have other health disorders. The purpose of this study was to investigate the effect of trunk extensor muscles fatigue on static postural control in young healthy adults compared to middle aged subjects. Methods. University students (n=10), and Middle-aged subjects (n=6; 40+ years of age) stood as immobile as possible on a force plate, with their feet together and eyes closed under two conditions; Fatigue and No fatigue. Fatigue was achieved through repetitive extensions of the trunk until a subjective fatigue level was reached. We measured center of pressure (CoP) displacements in the A/P and M/L planes using a force plate to assess standing balance. Findings. We did not observe any significant interaction between age and fatigue indicating that fatigue affected both age groups similarly. The CoP variability was higher for the middle aged compared to the young adults in both the fatigue and no-fatigue conditions. Interpretation. The results of this study indicate that trunk extensor fatigue does not affect balance differently for young compared to middle-aged subjects. This study also suggests that postural control decreases significantly with increasing age. Our finding that trunk extensor fatigue did not significantly affect the postural control of upright stance contradicts previous studies; this difference may be due to the subject sample in our study.
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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) | 0.002 | 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".