Seasonal changes in whole body, and regional body composition profiles of elite collegiate hockey players
Bibliographic record
Abstract
The monitoring of a collegiate hockey playerâs body composition can reflect fitness characteristics, and may help players, coaches or strength & conditioning professionals optimize physiologic gains during an off-season, while simultaneously preventing performance decrements in-season. Two separate studies took place in this investigation. The first studyâs purpose was to examine changes in the whole-body, and regional-body composition profiles of elite collegiate hockey players in regards to fat and lean tissue mass during an off-season and the first half of a competitive season. The purpose of the second study was to evaluate if collegiate players could accurately perceive their fluctuations in body composition. In the first study, the body composition profiles of nineteen elite Canadian collegiate hockey players were assessed using dual energy x-ray absorptiometry at three different time-points (i.e. end of season, pre-season and mid-season). A repeated measures anova was used to compare the playerâs changes in body composition at the different time-points. Statistically significant changes in body composition profiles were observed as players showed various tissue gains/losses depending on the region assessed. Overall, players gained (1.38kg, p < .01) and lost (.79kg, p < .01) fat tissue during the off-season and in-season, respectively. Players also showed a significant gain of leg lean tissue (.29 kg, p = .02) and loss of arm tissue mass (-.25 kg, p = .02) during the first-half of the competitive season. Several correlations emerged that may provide insight into potential trends that could be more pronounced during longer and more demanding schedules. In the second study, a total of 24 players completed pre-season and mid-season assessments. Immediately before each scan, players answered questionnaires regarding their off-season and in-season training, and perceived change in their body composition and strength of particular regions during the 3-month time period. Two thirds of players and one-half of players accurately detected changes in arm-lean and arm-fat tissue respectively. Approximately two-thirds of players did not accurately perceive gains or losses of lean or fat tissue within their leg and overall body region. The findings from each study can have important implications for the performance and development of collegiate athletes. The accuracy of a playerâs perceived change in body composition may affect their acceptance and adherence to a dietary or training intervention. Overall, the understanding of body composition profiles, body composition fluctuations, and potential variables that may influence the composition of collegiate hockey players can help coaches and athletic programs tailor their teamâs training, nutrition, lifestyle and informative resources to further support their athletes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".