The Effects of Upper-Body and Lower-Body Fatigue on Standing Balance
Bibliographic record
Abstract
The purpose of our study was to analyze the effects of upper-body fatigue and lower-body fatigue on post-exercise standing balance. Force plates were used to assess static balance by measuring the displacement of the Center of Pressure (COP) while subjects stood on the plate. Ten healthy male undergraduate students (mean age 20.7 years, SD = 1.3) each participated in one session. All subjects were considered to be physically fit, as determined by self-reported vigorous physical activity in hours per week (mean activity 7.1 hours, SD = 3.7). During sessions, participants performed two control trials and two fatigue trials, with each trial consisting of subjects standing motionless on a force plate for 20 seconds. Participants completed one upper-body fatiguing protocol and one lower-body fatiguing protocol in random order. Standing balance trials were completed before and immediately following each fatigue protocol. Lower-body fatigue was achieved by participants performing a maximum number of body-weight squats, while upper-body fatigue was attained by participants performing a maximum number of push-ups. Subjects reported their Rate of Perceived Exertion (RPE), which was used to determine individuals’ subjective level of exhaustion on a scale of 6-20 (mean squat RPE = 14.9, SD = 2.2; mean push-up RPE = 15.4, SD = 1.3). Each subject was required to rest during a 15-minute recovery period between each fatigue protocol. The upper-body fatigue protocol produced significant (p < 0.05) mediolateral variability of the COP when compared to the control condition, and additionally, the lower-body fatigue protocol resulted in a significantly greater path length in comparison to the control trials. There was no significant difference in standing balance between upper and lower-body fatigue trials. The most paramount findings of the present study indicate that the selective fatigue of upper-body musculature greatly impairs standing balance, particularly by causing significant mediolateral deviations.
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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 | 0.002 |
| 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) | 0.003 | 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".