Validation of jump squats as a practical measure of post-activation potentiation
Bibliographic record
Abstract
To determine if post-activation potentiation (PAP) can augment sports performance, it is pertinent that researchers be confident that any enhancement in performance is attributable to the PAP phenomenon. However, obtaining mechanistic measures of PAP in the daily training environment of highly trained athletes is impractical. We sought to validate jump squats as a practical measure with ecological validity to sports performance against a mechanistic measure of PAP. We assessed the evoked muscle twitch properties of the knee extensors and jump squat kinetics of 8 physically trained males in response to a 5-repetition-maximum back squat conditioning stimulus (CS). Evoked muscle twitch, followed by 3 jump squats, was assessed before and at 4, 8, and 12 min post CS. Time intervals were assessed on separate occasions using a Latin square design. Linear regression was used to determine the relationship between post-pre changes in kinetic variables and muscle twitch peak force (Ft) and twitch rate of force development (RFDt). Large correlations were observed for both concentric relative and absolute mean power and Ft (r = 0.50 ± 0.30) and RFDt (r = 0.56 ± 0.27 and r = 0.58 ± 0.26). Concentric rate of force development (RFD) showed moderate correlations with Ft (r = 0.45 ± 0.33) and RFDt (r = 0.49 ± 0.32). Small-to-moderate correlations were observed for a number of kinetic variables (r = -0.42-0.43 ± 0.32-0.38). Jump squat concentric mean power and RFD are valid ecological measures of muscle potentiation, capable of detecting changes in athletic performance in response to the PAP phenomenon.
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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 | 0.004 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".