An Inverted Seated Posture Decreases Elbow Flexion Force and Muscle Activation
Bibliographic record
Abstract
PURPOSE: While much is known about neuromuscular responses performed with an upright posture, muscle force and activation during an inverted posture have not been previously studied. METHODS: To determine if postural-induced discrepancies exist, maximal voluntary contraction (MVC) elbow flexor force, MVC force produced in the first 100 ms (F100), MVC rate of force development, electromyographic (EMG) activity of the biceps and triceps and trunk musculature were investigated during isometric elbow flexion in both an upright and inverted seated posture. Heart rate and blood pressure were monitored with upright and inverted positions at rest. RESULTS: Results showed significantly (p=0.01) higher MVC force (543.6 N ± 29.6 vs. 486.5 N ± 23.0), F100 (328.3 N ± 94.5 vs. 274.6 N ± 101.8) and rate of force development (p=0.003)(1851.9 N.s-1 ± 742.2 vs. 1591.0 N.s-1 ± 719.6). in the upright versus inverted condition. Biceps (48%; p=0.01) EMG activity was greater in the upright position. There was relatively greater co-contractions (86%, p=0.006) with the inverted position. Heart rate (16.8%), systolic (11.6%), and diastolic (12.1%) blood pressures were also significantly (p<0.0001) decreased with inversion. CONCLUSION: These results illustrate decrements in neuromuscular performance with an inverted seated posture which may be related to an altered sympathetic response.
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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) | 0.000 | 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) | 0.004 | 0.001 |
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".