The Interaction of Fatigue and Potentiation Following an Acute Bout of Unilateral Squats.
Bibliographic record
Abstract
A prior conditioning resistance exercise can augment subsequent performance of the affected muscles due to the effects of post-activation potentiation (PAP). The non-local muscle fatigue literature has illustrated the global neural effects of unilateral fatigue. However, no studies have examined the possibility of acute non-local performance enhancements. The objective of the study was to provide a conditioning stimulus in an attempt to potentiate the subsequent jump performance of the affected limb and determine if there were performance changes in the contralateral limb. Using a randomized allocation, 14 subjects (6 females, 8 males) completed three conditions on separate days: 1) unilateral, dominant leg, Bulgarian split squat protocol with testing of the exercised leg, 2) unilateral, dominant leg, Bulgarian split squat protocol with testing of the contralateral, non-exercised leg and 3) control session with testing of the non-dominant leg. Pre- and post-testing consisted of countermovement (CMJ) and drop jumps (DJ). The exercised leg exhibited CMJ height increases of 3.5% (p = 0.008; d = 0.28), 4.0% (p = 0.011; d = 0.33) and 3.2% (p = 0.013; d = 0.26) at 1, 5, and 10 min post-intervention respectively. The contralateral CMJ height had 2.0% (p = 0.034; d = 0.18), 1.2% (p = 0.2; d = 0.12), and 2.1% (p = 0.05; d = 0.17) deficits at 1, 5, and 10 min post-intervention respectively. Similar relative results were found for CMJ power. There were no significant interactions for DJ measures or control CMJ measures. The findings suggest that PAP effects were likely predominant for the exercised leg whereas the conditioning exercise provided trivial magnitude although statistically significant neural impairments for the contralateral limb.
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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) | 0.001 | 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".