Heart rate variability reductions following a season of sub-concussive head hits are related to the magnitude of impacts experienced
Bibliographic record
Abstract
Objective To examine the effect of repetitive contact sport-related head trauma on indices ofheart rate variability (HRV). Design Prospective Cohort. Setting Laboratory. Participants Forty-ninemale contact-sport (hockey or football) and four non-contact control athletes (age range: 17–22). Intervention Prior to (T1) and upon completion of (T2) the competitive athletic season, a three-lead electrocardiogram and Kubios software (Kuopio, Finland) were used to assess heart rate variability during 5-minutes of quiet stance and while actively squatting at 0.10 Hz. Independent variables included condition (rest-vs-active) and time (T1-vs-T2). 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: approximateentropy (ApEn). In a subset of participants (n=29) biomechanical head-impact exposure data was estimated usingthe xPatch (X2 Biosystems), affixed to the right mastoid. Main results RM-ANOVA indicateda significant main effect of timein contact sport but not control athletes (all p>0.133). HRV metrics were decreased post-season relative to baseline (95% CI:s for differences: MeanRR4–48 ms, p=0.045; RMSSD 1–9 ms, p=0.018; pNN50 0.689–5.98, p=0.010; ApEn 0.022–0.163, p=0.044). Significant correlations were observed between the average peak linear acceleration per hit experienced and the change in MeanRR(R²=0.1382), and RMSSD (R²=0.1525). Conclusions Exposure to repetitive subconcussive impacts overone season of contact-sport leads to decreases in heart rate variability. The magnitude of HRV reduction is related to the average magnitude of the linear component of hits to the head experienced during play 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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".