Power-to-Strength Ratio Influences Performance Enhancement with Contrast Training
Bibliographic record
Abstract
PURPOSE: The effectiveness of contrast training (CST) for improving explosive exercise performance is modulated by various individual characteristics; however, further work is required to define these factors. METHODS: Subelite male Australian Football players (n = 22; age, 19 ± 2 yr; body mass, 80.4 ± 9.4 kg; one-repetition maximum [1-RM] half squat, 172 ± 18 kg; mean ± SD) completed two experimental trials involving two sets of squat jumps (six repetitions at 30% 1-RM) performed either alone (CTL condition) or after half squats (six repetitions at 85% 1-RM; CST condition). RESULTS: Squat jump peak power was similar between CTL and CST during set 1 (mean change: ±90% confidence interval, 2.8% ± 2.0%; effect size [ES]: ±90% confidence interval, 0.13 ± 0.09; P = 0.079) and set 2 (0.3% ± 1.7%; ES, 0.01 ± 0.08; P = 0.781). Peak power enhancement with CST was not related to maximal (1-RM half squat) strength (r = 0.001, P = 0.884), but was negatively correlated with both baseline peak power (r = 0.44, P < 0.001) and power-to-strength ratio (PSR); that is, the ratio between baseline peak power and 1-RM half squat strength (r = 0.65, P < 0.001). Using a median split, analyses were performed in participants with a low PSR (LPSR group; PSR = 15.4-19.1 W·kg; n = 11) or high PSR (HPSR group, PSR = 19.4-24.7 W·kg; n = 11). Peak power was enhanced with CST for the LPSR (8.1% ± 3.9%; ES, 0.44 ± 0.21; P = 0.004) but not HPSR (-2.1% ± 1.3%; ES, -0.14 ± 0.09; P = 0.010) groups. CONCLUSION: The PSR appears to influence the effectiveness of CST, with performance enhancement more likely in those with a lower PSR.
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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 | 0.002 |
| 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) | 0.002 | 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".