The Optimal Time Window for Complex Training in Order to Increase Repeated Sprint Ability in Professional Ice Hockey Players
Bibliographic record
Abstract
ABSTRACT The goal of this study was to investigate the post-activation performance enhancement (PAPE) and optimal time window following complex training (CT) to increase ice-hockey player skating speed and endurance. Ten professional ice-hockey players (age= 19.8±1.23 years, height= 1.8± 0.06 m, weight= 83.37±4.79 Kg,) from the American Hockey League (AHL, n=7) and the National Hockey League (NHL, n=3) were assigned randomly into two groups. Both groups completedthe same CT training protocol, designed to induce post-activation potentiation (PAP). One group completed the training 8 hours prior to testing (8HPT), and the other 4 hours (4HPT) before testing. The CT PAP training protocol consisted of 3 sets of 5 repetitions of trap high bar deadlifts superset with 6 box jumps on a 20-inch plyometric box. The effect of CT PAP training on performance was assessed using the following testing: the countermovement jump (CMJ), the static squat jump (SSJ), the stationary broad jump (BJ), reactive strength index (RSI), eccentric utilization ratio (EUR), the double leg incremental drop jump tests (DJ), and on ice 40 meter sprint time during the Peterson On-Ice repeated shift Test (POIT). Results showed significant improvements for both groups for the after CT PAP training for the SJ (4HPT: p= 0.04), BJ (8HPT: p= 0.02; 4HPT: p=0.03), POIT (8HPT: Sprint 4, 5, 6, 8: p<0.05; 4HPT: Sprint 2, 3: p<0.05), POIT total sprint time (8HPT: p= 0. 01), mean 40-meter sprint time (8HPT: Sprint 1, 2, 3, 4, 5, 6 and 8: p<0.05; 4HPT: Sprint 2, 3, 4, 5, 6 and 7: p<0.05), and total 40 meter sprint time (8HPT and 4HPT: Sprint 2 p= 0.03). However, no significant difference (p≥0.05) was found following CT PAP training for the CMJ, DJ, and RPE. Thus, the present study suggests that PAP has a greater effect on jumping and on ice-hockey repeated sprint performance when completed 8 hours before. Nonetheless, CT PAP training appears to be beneficial to improve the rate of force development and performance when performed more than 4 hours before the competition in professional North-American ice-hockey players. The present protocol and timing window can be utilized by strength and conditioning specialists to improve on-ice repeated sprint performance of professional ice-hockey players.
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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) | 0.002 | 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".