The Association Among Body Composition, Explosive Leg Power and Aerobic Capacity in Male Varsity Hockey Players
Bibliographic record
Abstract
Fitness testing and body composition assessments in sports are ubiquitous and rapidly becoming an indispensible resource for strength and conditioning coaches. Previous research has shown that higher amounts of lean tissue mass have been associated with increased power outputs and lower levels of body fat percentage have been associated with improved aerobic fitness. PURPOSE: To examine the relationship between body composition and lower body power and aerobic fitness in elite collegiate hockey players. METHODS: Sixteen elite male Canadian university hockey players (age = 22.194 ± 0.99 years, weight = 85.74 ± 5.80 kg, height = 182.25 ± 6.67 cm), participated in the study at the beginning of their hockey season. All participants completed the long jump and the beep-test and a day after testing, one total body dual energy x-ray absorptiometry scan to measure body composition. Simple linear regression was used to explore the relationship between body fat percentage, visceral adipose tissue, and abdominal adipose tissue with aerobic fitness evaluations and leg lean with lower body power. RESULTS: On average body fat percentage was 16.6 ± 3.0%, fat mass 13.7 ± 2.8 kg, abdominal adipose tissue 0.9 ± 0.3 kg, leg lean mass 23.8 ± 1.97 kg, long jump 2.67 ± 0.16 m, beep test 12.8 ± 1.32 min. Visceral adipose tissue explained 24.5% of the variance in the test of aerobic fitness (p .05), while other adiposity measures were non-significant. Body fat percentage and lower lean mass did not significantly contribute to aerobic fitness and lower body power respectively (all p> .05). CONCLUSIONS: Despite a lack of inter-relationships among field tests and body composition, these variables should remain part a test battery to allow strength and conditioning coaches to better tailor training programs for elite hockey players. Keywords: body composition, hockey, Fitness testing
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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 | 0.000 |
| 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) | 0.003 | 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".