Back

Season-long head acceleration exposure, prior contact-sport history, and cognitive performance in British amateur American football players

Weightman Potter, P. G.; Rose, E.; Tolliday, N.; Bond, B.

2026-10-07 neuroscience
10.64898/2026.09.30.755702 bioRxiv
Show abstract

Background: Head acceleration events (HAEs) are common in contact sports, but season-long exposure and its relationship with cognitive performance in amateur British American football remain poorly characterised. Methods: Thirty male players were recruited from three British American football teams. Cognitive performance was assessed pre-season, mid-season, and post-season using the Sport Concussion Assessment Tool 6 and computerised Stroop, 2-back, Go/No-go, and visual-search tasks. Seasonal cognitive analyses included 23 participants with available data. HAEs were quantified using helmet-mounted accelerometers; exposure-linked analyses were restricted to 19 participants who provided sufficiently complete sensor recordings across contact training sessions and competitive matches. Seasonal cognitive changes were assessed using mixed-effects models. Associations of in-season HAE metrics and previous contact-sport seasons with global cognitive change were examined using simple linear regression and elastic-net regression. Results: In the 19-participant exposure-linked cohort, prior contact-sport exposure showed the strongest nominal univariate association with worse global cognitive change (r squared = 0.337, p = 0.009), although this did not remain significant after correction for multiple comparisons (Benjamini-Hochberg-adjusted p = 0.055). Corrected nested cross-validation indicated little out-of-sample performance for the multivariable elastic-net model (r = 0.144, predictive R squared = -0.008). Although previous contact-sport seasons had the largest standardised coefficient in the final full-cohort fit, predictor retention varied substantially across resamples. Conclusion: Prior contact-sport exposure showed the strongest exploratory association with relatively worse global cognitive change, whereas in-season HAE metrics showed weaker associations. However, the multivariable model did not demonstrate useful out-of-sample predictive performance, and the small exposure-linked cohort precludes firm conclusions regarding the relative importance of individual predictors.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.