Heart rate variability analysis and mental health outcomes in university female hockey players
Bibliographic record
Abstract
Exercise improves anxiety and depression, both of which are associated with impaired autonomic regulation of heart rate. Measuring heart rate variability (HRV) provides a means to measure physiologic consequences of external stressors and has a primary application in clinical settings. The influence of mental health on HRV remains understudied in university student athletes who experience stresses to perform both academically and athletically. The current study evaluated HRV as an indicator of psychological resilience in female varsity hockey players. Seventeen healthy female hockey players at Western University aged 17-23 (M = 21, SD = 1.5, BMI: 26.3 ± 1.9kg/m2) participated in this study at three time points throughout the 7-month season. Five minutes of steady-state R-R recordings (Bodyguard-2 device; Firstbeat Technologies Ltd.) were collected 30 minutes following the participants' recorded onset of sleep. Surveys of general anxiety (GAD-7) Brief Resilience Scale (BRS), and Mental Health Inventory (MHI) were completed prior to games. Measures of HRV, specifically the root mean square of successive differences (RMSSD) were calculated from continuous R-R data. Throughout the season, BRS scores significantly increased from time one to time three (3.64 ± 0.28 vs. 4.18 ± 0.36, p < 0.05, respectively), and RMSSD correspondingly increased from time one to time three (59 ± 18 ms vs. 72 ± 28 ms, p < 0.01, respectively). Despite the demanding 7-month hockey season both RMSSD and BRS improved. However, they showed no correlation signifying the proposed relationship between RMSSD and resilience is not supported by the current data.
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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) | 0.001 | 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".