Acute sport-related concussion suppresses heart rate variability beyond clinical recovery
Bibliographic record
Abstract
Objective To examine the effect of acute sport-related concussion on heart rate variability (HRV) in contact-sport athletes over 1 month post-injury. Design Prospective Cohort. Setting Laboratory. Participants 136 male contact-sport athletes (19.1±1.4 years) were recruited and a subset of 14 athletes sustained concussions (19±1.4 years) and were included in the current analyses. Intervention Participants completed baseline (T0) and post-injury testing at 72-hours (T1), 2-weeks (T2), and 1-month (T3). A three-lead electrocardiogram was recorded to assess HRV during 5-minutes of quiet stance and while actively squatting at 0.10 Hz (6 squats per minute). Recordings were analysed using Kubios software (Kuopio, Finland). Independent variables included condition (resting versus active) and time (4). 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). Non-linear: approximate entropy (ApEn). Main results RM-ANOVA revealed a significant main effect of time for ApEn only (p=0.016). ApEn was reduced at T2 (−0.126, 95% CI: 0.029–0.222, p=0.015) and T3 (−0.068, 95% CI: 0.009–0.128, p=0.027) compared to T0. Median return-to-play duration was 14.5 days. Conclusions Acute sport-related concussions induce a delayed reduction in heart rate variability that does not manifest until near-clinical recovery, and persists until at least 1-month post-injury. This is an important finding, indicating that concussions impair regulation of the autonomic nervous system for a duration persisting well beyond clinical recovery. 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 | 0.000 | 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".