Effect of On-ice General Aerobic Fitness on Head Impact Biomechanics in Youth Hockey Players
Bibliographic record
Abstract
A current focus of youth concussion prevention is centered on intrinsic factors that may mitigate the severity of head impacts sustained by young athletes. Athletes with decreased general aerobic fitness (GAF) may be more likely to fatigue and less likely to overcome the impact forces associated with body collisions during participation. PURPOSE: To examine the effect of GAF on head impact biomechanics in youth ice hockey players. METHODS: A quasi-experimental field study included 37 hockey players (age=15.0±1.0 yrs, ht=173.5±6.2 cm, mass=66.6±9.0 kg) equipped with accelerometer-instrumented helmets capable of recording linear and rotational acceleration during participation. Players completed the Faught Aerobic Skate Test (FAST) during three ice sessions (training, sessions 1 and 2) while wearing protective equipment. The FAST is an on-ice analogy to a traditional Leger-Boucher shuttle run "beep" test. The mean laps attained during the FAST (across both test sessions) and estimates of volume of maximal oxygen consumption (VO2max) were recorded. Participants were then separated into three GAF tertiles for each outcome (i.e. most fit, moderately fit, least fit) for the purposes of comparing our head impact biomechanics (linear and rotational acceleration) across GAF levels. RESULTS: Athletes attaining the highest number of laps during the FAST experienced greater head impact biomechanics (linear: 18.0 g, 95% CI: 17.4-18.6; rotational: 1678.6 rad/s2, 95% CI: 1573.7-1790.4) than athletes with the lowest GAF (linear: 17.1 g, 95% CI: 16.7-17.5; rotation: 1497.8 rad/s2, 95% CI: 1406.9-1594.5) (linear: F2,29 = 3.93, P = 0.031; rotation: F2,29 = 3.46, P = 0.045). A significant difference in linear acceleration was observed (F2,29 = 8.51; P < 0.001) suggesting athletes with the highest GAF levels based on estimated VO2max experienced higher linear accelerations (18.0 g; 95% CI: 17.7-18.2) than those who represented the lowest GAF tertile in our sample (17.0 g; 95% CI: 16.6-17.4). CONCLUSION: Increasing GAF does not appear to reduce head impact severity. We speculate those with greater GAF were more likely to have participated in late stages of competition where increased aggression may have resulted in more severe head impacts. Supported by Ontario Neurotrauma Foundation, NOCSAE, and USA Hockey.
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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 | 0.000 | 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".