Contrast Training Generates Post-Activation Potentiation and Improves Repeated Sprint Ability in Elite Ice Hockey Players
Bibliographic record
Abstract
International Journal of Exercise Science 13(6): 183-196, 2020. The purpose of this study was to measure the generating effects of Contrast Training (CT) on 6-hour post-activation potentiation (PAP) and its influence on jumping and on on-ice repeated sprint performance in ice hockey players. Forty-one participants were divided in two groups: experimental (EG) and control group (CG). The EG followed the CT PAP protocol which consisted of 5 sets of 5 half inertia back squat superset with 6 squat jumps. The effects of PAP were measured with the vertical countermovement jump (CMJ), stationary broad jump (BJ) and 9 repeated on ice 40-meter maximal sprints with hockey equipment. Results showed that the PAP generated by the CT protocol had no significant impact (p≥ 0.05) on CMJ, BJ, blood lactate concentration, heart rate peak and rated perceived exertion as EG and CG group presented no significant differences in improvement. However, results show that there was a significant improvement (p< 0.05) for the EG in the total sprint time (-5.5 ± 2.6%; 56.2 ± 4.7 to 53.1 ± 3.9sec) mean sprint speed (+5.9 ± 3.0%; 6.4 ± 0.5 to 6.8 ± 0.5m/s) and in 1stsprint speed (+7.4% ± 5.9; 7.3 ± 0.7 to 7.8 ± 0.6m/s), but not for the CG (-1.4 ± 5.1%; 58.0 ± 5.4 to 57.2 ± 6.4sec), (+1.7 ± 5.1 %; 6.3 ± 0.6 to 6.4 ± 0.6m/s) and (+1.9 ± 7.7%; 6.9 ± 0.7 to 7.0 ± 0.7m/s) respectively. Thus, results show that the CT protocol utilized in this study generated PAP which had an acute effect on the on-ice hockey repeated sprint test performance. Therefore, CT could be utilized punctually to improve repeated sprint performance of elite hockey players as it could potentially help create odd man rushes during games.
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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".