Session Rating of Perceived Exertion Is a Superior Method to Monitor Internal Training Loads of Functional Fitness Training Sessions Performed at Different Intensities When Compared to Training Impulse
Bibliographic record
Abstract
Despite its increase in popularity, little is known about how to best quantify internal training loads from functional fitness training (FFT) sessions. The purpose of this study was to assess which method (training impulse – TRIMP or session-rating of perceived exertion – sRPE) is more accurate to monitor training loads in FFT. Eight trained males (age 28.1±6.0 years) performed an ALL-OUT FFT session and an intensity-controlled session (RPE of 6 out of 10). Internal load was determined via Edward’s TRIMP (eTRIMP), Bannister’s TRIMP (bTRIMP) and sRPE. Heart rate was measured continuously during the session, while blood lactate and rate of perceived exertion were measured at baseline, and immediately and 30 minutes after the sessions. ALL-OUT blood lactate and RPE were significantly higher immediately and 30 minutes after the session compared to the RPE6 condition. ALL-OUT training load was significantly different between conditions using bTRIMP (61.1 ± 10.6 vs 55.7 ± 12.4 au), and sRPE (91.7 ± 30.4 vs 42.6 ± 14.9 au), with sRPE being more sensitive to such differences (p = 0.045, ES = 0.76, and p = 0.002, ES = 1.82, respectively). No differences in the training loads of the different sessions were found using eTRIMP (93.1 ± 9.5 vs 84.9 ± 13.7 au, p = 0.085). Only sRPE showed a significant correlation with lactate 30-min post session (p = 0.015; ρ = 0.596, large). Conclusion: sRPE was more accurate than both TRIMP methods to represent the overall training load of the FFT sessions. While the use of sRPE is advised, further research is necessary to establish its ability to reflect changes in fitness, fatigue, and performance during a period of training.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".