Neurological Health In Former Amateur Ice Hockey Players: Considerations For Lifestyle Factors And Career Duration
Bibliographic record
Abstract
Repetitive head impacts (RHI) are common in contact sports like ice hockey and may increase the risk of later-life neurodegenerative disorders, cognitive impairment, and mental health decline. The Lifestyle for Brain Health (LIBRA) score quantifies modifiable lifestyle risk factors potentially linked to cognitive and functional decline. While LIBRA has predicted cognitive function and mild cognitive impairment in former professional football players, its use in predicting these outcomes among former amateur ice hockey players remains unexplored. PURPOSE: Evaluate the impact of RHI exposure and lifestyle factors on later-life neurological health in former ice hockey players. METHODS: 52 former ice hockey players (age: 54.1 ± 14.4 y.o., career duration: 26.3 ± 15.7 yrs.) completed an online questionnaire, including demographics, sports participation history, and health history. Mental health was evaluated using the Apathy Evaluation Scale (AES) and Center for Epidemiological Studies Depression Scale (CES-D); cognitive function was measured via the PROMIS Cognitive Function Short Form 4a (PROMIS CF). LIBRA scores were calculated as in prior studies, with higher scores indicating greater risk (range = -5.9 - 14.6). Career duration was based on self-reported start and end ages. Linear regressions assessed LIBRA scores and career duration associations with mental health and cognitive outcomes. RESULTS: LIBRA scores (-0.8 ± 2.4) were significantly associated with AES (28.4 ± 6.6, R2 = 0.14, ß = 0.38, p = 0.006) and CES-D (8.8 ± 6.4, R2 = 0.20, ß = 0.45, p = 0.002) scores, indicating worse mental health with higher LIBRA scores. Career duration showed no significant associations with AES (R2 = 0.01, ß = 0.10, p = 0.503) or CES-D (R2 = 0.02, ß = -0.15, p = 0.307), but longer career duration was significantly linked to higher (worse) PROMIS CF T scores (52.1 ± 7.9, R2 = 0.12, ß = 0.34, p = 0.014). No association was observed between LIBRA scores and cognitive function (R2 = 0.04, ß = -0.19, p = 0.181). CONCLUSION: Consistent with recent retired football player studies, LIBRA scores were associated with poorer mental health, but not with cognitive function or career duration. These findings underscore the importance of targeting modifiable lifestyle factors to support long-term health in individuals with prior RHI exposure.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".