Superior On-Ice Performance After Short-Interval vs. Long-Interval Training in Well-Trained Adolescent Ice Hockey Players
Bibliographic record
Abstract
ABSTRACT: Rønnestad, BR, Haugen, OC, and Dæhlin, TE. Superior on-ice performance after short-interval vs long-interval training in well-trained adolescent ice hockey players. J Strength Cond Res 35(12S): S76-S80, 2021-The purpose of this study was to compare the effects of 9 weeks with 3 weekly sessions of short intervals (SIs) against long intervals (LIs) on endurance performance in well-trained adolescent ice hockey players. Eighteen male adolescent ice hockey players volunteered to participate and were randomly allocated to perform SIs (n = 9; 3 series with 13 × 30 seconds work intervals) or LIs (n = 7; 4 series of 5 minutes work intervals). Subjects completed a skating multistage aerobic test (SMAT), maximal oxygen consumption, maximal power output, and maximal isokinetic knee-extensor strength tests before and after the intervention, and changes in performance were assessed using analysis of variance (p ≤ 0.05). Short intervals improved SMAT performance more from pretest to post-test than LIs (13.9 ± 8.1% vs. 3.7 ± 5.2%, respectively; p = 0.030, effect size [ES] = 1.48). No significant differences were observed between SIs and LIs in change of maximal oxygen uptake (SI: 3.8 ± 6.1% vs. LI: -0.4 ± 10.2%; p = 0.30) or 60 seconds maximal power output (SI: 1.0 ± 4.9% vs. LI: -3.7 ± 4.1%; p = 0.053). However, ESs were moderate (ES = 0.55) and large (ES = 1.07), respectively, in favor of SI for these dependent variables. There were no changes in isokinetic knee-extension strength (p > 0.05). The present SI protocol induced superior improvements in on-ice endurance performance compared with the LI protocol. Practitioners seeking to improve ice hockey players' on-ice endurance performance should consider including SI in their conditioning protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".