5.9 Normative values of resting heart rate variability in young male contact sport athletes: reference values for the assessment and treatment of concussion
Bibliographic record
Abstract
Objective Identify key determinants of heart rate variability (HRV) in male athletes aged 14–21 years participating in competitive contact sports and integrate these determinants to define normative short-term HRV values. Design Transverse. Setting Secondary care. Participants 369 male contact sports athletes aged 14 to 21. Interventions (or Assessment of Risk Factors) HRV was measured for 5 minutes at rest. Outcome Measures Standard HRV parameters in the time and frequency domains were calculated. Normative limits for the corrected HRV parameters were established. Main Results Heart rate (HR), age, body mass index, number of sports weekly practices, and concussion history were considered potential determinants that could influence HRV. Multiple regression analysis revealed that HR was the primary determinant of standard HRV parameters. The models accounted for 13–55% of the total variance in HRV. The contribution of HR to this model was significant (β ranged from -0.34 to -0.75). Therefore, the HRV parameters were normalized and their normative limits were developed relative to the mean heart rate. After corrections, these parameters were no longer dependent on any potential determinant, and normal value ranges were calculated for the HRV time- and frequency-domain indices. Conclusions In this study, corrected normative values of short-term and resting state HRV parameters are provided. These values were developed independently of the major determinants of HRV. The baseline values for HRV parameters provided by the present study could be used in clinical practice during the assessment and follow-up of concussions and may assist in decision-making for a safe return to play.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".