Effect of An Inverted Seated Position with Upper Arm Blood Flow Restriction on Measures of Elbow Flexors Neuromuscular Performance
Bibliographic record
Abstract
ABSTRACT Purpose The objective of the investigation was to determine the concomitant effects of upper arm blood flow restriction (BFR) and inversion on elbow flexors neuromuscular responses. Methods Randomly allocated, 13 volunteers performed four conditions in a within-subject design: rest (control, 1-min upright position without BFR), control (1-min upright with BFR), 1-min inverted (without BFR), and 1-min inverted with BFR. Evoked and voluntary contractile properties, before, during and after a 30-s maximum voluntary contraction (MVC) exercise intervention were examined as well as pain scale. Results Inversion induced significant pre-exercise intervention decreases in elbow flexors MVC (21.1%, , p =0.02) and resting evoked twitch (29.4%, , p =0.03) forces. The 30-s MVC induced significantly greater pre- to post-test decreases in potentiated twitch force ( , p <0.0009) during inversion (↓75%) than upright (↓65.3%) conditions. Overall, BFR decreased MVC force 4.8% ( , p =0.05). For upright position, BFR induced 21.0% reductions in M-wave amplitude ( , p =0.04). There were no significant differences for electromyographic activity or voluntary activation as measured with the interpolated twitch technique. For all conditions, there was a significant increase in pain scale between the 40-60 s intervals and post-30-s MVC (upright Conclusion The concomitant application of inversion with elbow flexors BFR only amplified neuromuscular performance impairments to a small degree. Individuals who execute forceful contractions when inverted or with BFR should be cognizant that force output may be impaired.
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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) | 0.001 | 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".