Assessing External And Internal Loads Impacting Injury Odds In Male Collegiate Ice Hockey Players
Bibliographic record
Abstract
While considerable research has outlined high injury risks associated with playing ice hockey across many demographics, work examining player load as a risk for injury is less clear. Currently, no research has considered the association between wearable technology-derived player load and risk of injury measured through player self-reported complaints across a season. PURPOSE: To record self-reported complaints of injury/pain and investigate the associations between wearable technology-derived internal and external loads and odds of player complaints of injury and pain in male ice hockey players. METHODS: Self-reported complaints of injuries and pain were collected for 27 university male players (22.0 ± 1.2 y, 86.7 ± 5.5 kg, 181.4 ± 5.0 cm). Additionally, weekly cumulated external (exposure, skating distance, time spent skating in speed zones, and number of skating events measured via local positioning system) and internal (training impulse (TRIMP) and sessional rate of perceived exertion (sRPE)) loads were measured in practices and home games throughout the season. Binomial logistic regressions (crude odds ratios (OR)) were used to assess the associations between weekly load measures and the odds of players reporting a complaint, in both the week of and the week following load measurements. Load measures were grouped into low (control), low-medium, medium-high, and high quartiles. RESULTS: Players reported an incidence of 77 and a prevalence of 87 complaints during the season. When assessing complaints within the same week as the cumulative loads, there was an increase in the odds of a complaint within the medium-high load group for weekly cumulative sRPE compared to the low load group (OR = 1.86, p = 0.04), but no changes in the odds of a complaint in any external load measures when comparing high and low load groups. For complaints following a weekly load, there was an increase in the odds of a complaint in the high load group for the previous week’s cumulative exposure (OR = 2.75, p < 0.01), sRPE (OR = 3.03, p < 0.01), time spent in low-speed (OR = 2.09, p = 0.03), and medium speed zones (OR = 2.05, p = 0.04) compared to the low load group. CONCLUSION: Several load measures used in this novel study indicate greater odds of male ice hockey players reporting complaints when experiencing high levels of load. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".