Maintenance Of Body Composition And Associations With On-ice Performance In Female Hockey Players
Bibliographic record
Abstract
Monitoring body composition longitudinally offers valuable insights into the effects of diet, training, and other yearly variations that may be associated with high-level sport participation. The demanding nature of ice hockey constantly places athletes under strain, leaving limited time for recovery and potentially causing reductions in whole-body and lower-body lean mass (LM) across a season. PURPOSE: To measure female ice hockey players’ body mass (BM) and body composition measures at three time points to examine any changes throughout the season and to investigate the relationship between player body composition and selected measures of on-ice performance. METHODS: Dual energy X-ray absorptiometry (DXA) was used to measure twenty-three female varsity ice hockey players’ (19.9 ± 1.4 y, 68.2 ± 7.3 kg, 167.7 ± 5.6 cm) BM and composition at three time points (PRE, MID, POST) during a season. Select on-ice performance metrics (mean accelerations/min, change of direction, and maximum acceleration) were captured using a local positioning system. One-way repeated measures ANOVA tests were used to evaluate changes in body composition. Pearson’s correlations and linear regressions were used to test the relationship between body composition and on-ice performance. RESULTS: PRE BM trended towards an increase at MID (1.5 ± 2.9 kg, p = 0.056) but was unchanged at POST (-0.1 ± 2.1 kg). PRE body fat-mass (FM;17.8 ± 4.5 kg) and percentage body fat (28.2 ± 4.8%) were unchanged (p > 0.05) across MID (17.9 ± 4.4 kg; 28.1 ± 4.5%) and POST (18.4 ± 4.5 kg; 28.6 ± 4.4%). There were significant increases (p < 0.01) in whole-body (2.0 ± 2.9%) and lower-body (2.4 ± 3.5%) LM when comparing PRE to MID. Only lower-body LM decreased (1.8 ± 2.6%, p = 0.01) from MID to POST. Interestingly, whole-body LM demonstrated no significant correlations with any performance metric, whereas whole-body FM moderately and negatively predicted all on-ice performance markers (r = 0.58-0.68, p < 0.05). CONCLUSION: Female varsity ice-hockey players maintained their BM and increased whole-body LM throughout the season, attributed in part to effective on and off-ice training, sound dietary habits, and sufficient recovery. Whole-body FM negatively predicted select measures of on-ice performance, intervention studies are needed to further examine this relationship. PepsiCo and Mitacs
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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) | 0.002 | 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".