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Record W6987090028

Seasonal changes in whole body, and regional body composition profiles of elite collegiate hockey players

2015· dissertation· en· W6987090028 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsLean body massComposition (language)EliteLean tissueField hockeyBody weightMuscle massElite athletesAthletes
DOInot available

Abstract

fetched live from OpenAlex

The monitoring of a collegiate hockey player’s 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 study’s 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 player’s 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 player’s 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 team’s training, nutrition, lifestyle and informative resources to further support their athletes.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.280
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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