Heart Rate Dynamics and Quantifying Physical Fatigue in Canadian Football
Bibliographic record
Abstract
The cardiac response to physical exertion is linked to factors such as age, work intensity, and fitness levels. The primary objective of this study was to characterize within-session changes in cardiac response to running in Canadian football athletes, which may indicate physical fatigue. Performance profiles were collected from GPS and heart rate (HR) sensors worn by 30 male Canadian varsity football players (20–26 years old) over 28 games and practices. Running efforts with 60–180 s of rest were detected, and the maximum HR (HRmax) and peak HR recovery (HRRpk) during rest were extracted. Additionally, a new metric of cardiovascular cost (CVC) was developed to reflect the efficiency of the HR response to physical workload. HRmax was higher in games (p < 0.001) and in linemen (p < 0.001), and it increased over time (p < 0.001). HRRpk was higher in skilled players (p < 0.001) and changed over time (p < 0.001) depending on the rest period. CVC was higher in linemen (p < 0.001) and increased over time (p < 0.001). This study demonstrated the utility of HR response metrics to quantify ongoing fatigue experienced by Canadian football athletes and proposed a novel fatigue metric capable of monitoring an athlete’s fatigue state in real time.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".