History of multiple sport-related concussions alters variability of heart rate response to activity
Bibliographic record
Abstract
Objective To examine the effect of multiple previous concussions on heart rate variability (HRV) in contact-sport athletes. Design Retrospective Cohort. Setting Laboratory. Participants 136 male elite contact-sport (hockey and football) athletes were recruited for this study. Forty-one had no previous concussion history (Hx-), while nineteen had a history of three or more concussions (Hx+). All testing was preformed prior to the start of the competitive season Interventions HRV was assessed during 5-minutes of quiet stance, and while actively squatting at 0.10 Hz via a 3-lead electrocardiogram, using Kubios software. Independent variables included condition (rest vs active) and group (Hx+ vs Hx-) Outcome measures Time-Domain: square root of mean squared differences of successive R-R intervals (RMSSD), percentage of successive R-R intervals that differ by more than 50 milliseconds (pNN50). Frequency-Domain: total power. Main results Mixed ANOVA revealed repeated squat-stand manoeuvres challenged the autonomic system, increasing total power by 2477 milliseconds2 (95% CI: 1153–3801, p<0.001). A significant interaction effect was observed for RMSSD (p=0.031) and pNN50 (p=0.012), characterised by a greater increase in HRV in the Hx+ group than in Hx- from rest to squatting (95%CIs for group differences, RMSSD: 13.2–26.3 ms, p=0.006; pNN50: 3.6–15.1%, p=0.002) Conclusions A history of multiple sport-related concussions impairs the ability of the autonomic system to respond to an everyday stressor applied to the cardiovascular system (squatting and standing). This is an important finding, as it reveals that cumulative concussions may impart long-term impairments in the ability to accurately regulate the autonomic system. Competing interests None.
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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 | 0.000 |
| 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) | 0.003 | 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".