Heart rate variability: exploring age, sex & concussion symptoms in youth athletes
Bibliographic record
Abstract
Objective (1) Explore the influence of age, sex on heart rate variability (HRV) in youth athletes; (2) Examine the relationship between baseline/pre-injury concussion symptom domains (physical, cognitive, emotional, fatigue) and HRV Design Cross-sectional. Setting Pre-injury/baseline data was obtained from youth athletes across various sports in the Greater Toronto Area. Participants Youth athletes between 13–18 years of age (N=294), in which females and males were equally represented across age groups. Inclusion criteria: between 13–18 years old, English speaking. Exclusion criteria: developmental and neurological diagnoses. Intervention Independent variables of interest included demographic factors such as age and sex as well as concussion symptoms. Concussion symptoms were measured using the Post Concussion Symptom Inventory and were stratified by physical, cognitive, fatigue and emotional domains. Outcome measures Heart rate variability, collected over 24 hours was the main outcome of interest and included time (e.g. SDNN, RMSSD) and frequency domain measures (e.g. HF, Total Power). Variables were logarithmically transformed to increase robustness of linear regression models. Main results Statistical threshold set at p ≤ 0.05. Significant age effects revealed that older participants displayed higher HRV compared to younger athletes. Significant interaction effect between concussion symptoms and sex on HRV. Cognitive and fatigue symptoms in healthy youth athletes had significant effect on HRV. Conclusions This study highlights the potential value of a novel neurophysiological indicator used in conjunction with the self-report of symptoms for clinical management. Prospective longitudinal research is needed to further explore the multi-faceted contextual influences of a youth athlete’s environment. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".