Changes In Elite Canadian Collegiate Hockey Player’S Body Compositions And Physiologic Tests Across Playing Careers
Bibliographic record
Abstract
The combined athletic and academic demands place a significant burden on collegiate hockey players. Numerous cross-sectional studies have been conducted with professional hockey players assessing body composition and skeletal fitness; yet, no research has investigated the longitudinal physiologic changes among elite collegiate athletes. PURPOSE: To examine changes in body composition and physiologic tests across a player’s collegiate hockey career. METHODS: Over three seasons, six elite male Canadian university hockey players (age = 21.35 ± .28 years, weight = 84.53 ± 7.26 kg , height = 179.48 ± 7.60 cm, body fat percentage = 15.55 ± 1.68% at baseline) participated in the study at the beginning of their hockey seasons. All participants underwent physical testing (as outlined in the 2016 NHL combine) and a day after testing, one total body dual energy x-ray absorptiometry (DXA) scan to measure body composition. RESULTS: A repeated measures ANOVA was used to track body composition and physiologic performance variables over a three-year period. Players gained body weight (1.66 ± 1.96 kg), total body fat percentage (2.83 ± 1.91%), visceral adiposity (.16 ± .15 kg), upper fat mass (1.57 ± 1.20 kg), and lower fat mass (.52 ± .36 kg) (p < .05 for all comparisons). Total and regional lean tissue mass stayed relatively constant throughout their careers. There were no significant changes in agility scores, left grip strength, long jump distance or impulse generated in the vertical jump as all of these assessments stayed relatively consistent throughout the seasons. As players progressed through their careers, they achieved significantly more bench press repetitions, pull-ups, and had higher Wingate peak power scores (p < .05 for all comparisons). CONCLUSIONS: Pilot findings suggest that as players progress through their collegiate hockey careers, they gain weight, total and regional body fat, and are typically stronger in respects to some fitness tests. With this knowledge, strength and conditioning coaches can work in tandem with food scientists and nutritionists to optimize meal plans in an effort to prevent weight and adipose tissue gain which may enhance on-ice play and player health across their three-year university careers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".