A comparison of sessional ratings of perceived exertion to cardiovascular indices of exercise intensity during competition in elite field hockey players
Bibliographic record
Abstract
Abstract Study aim: Recent evidence has revealed a reduction in the strength of correlation between ratings of perceived exertion and a heart rate (HR) derived training load in elite field hockey players during competition. These competitive periods involve sustained levels of cardiovascular performance coupled with considerable time performing above the anaerobic threshold. As such, the purpose of this investigation was to examine the magnitude of correlation between ratings of perceived exertion and time spent above threshold and two HR derived training loads. Material and methods: Seventeen (n = 17) international caliber female field hockey players competing as a national team were monitored over four matches during a seven-day competition period within the 2016 Olympic Cycle. Cardiovascular indices of exercise intensity were derived from HR dynamics and were quantified through estimating time spent above anaerobic threshold (LT2), the Edwards training load model (TLED) and the Polar Training Load (TLPOL). Sessional ratings of perceived exertion (sRPE) were recorded after each match. Results: 64 samples were recorded for analysis. HR derived (TLED& TL POL) and sRPE training loads remained comparable between matches. A large correlation (p = 0.01) was observed between sRPE and each heart rate derived training load (TLED& TLPOL). An unremarkable relationship (p = 0.06) was revealed between time spent above LT2 and sRPE. Conclusions: Our results demonstrate HR derived training loads (TLPOL& TLED) exhibit a stronger correlation with sRPE than time spent above LT2 in elite field hockey players during competition.
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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 | 0.002 |
| 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) | 0.002 | 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".