Precision, Accuracy, and Performance Outcomes of Perceived Exertion vs. Heart Rate Guided Run-training
Bibliographic record
Abstract
Johnson, EC, Pryor, RR, Casa, DJ, Ellis, LA, Maresh, CM, Pescatello, LS, Ganio, MS, Lee, EC, and Armstrong, LE. Precision, accuracy, and performance outcomes of perceived exertion vs. heart rate guided run-training. J Strength Cond Res 31(3): 630-637, 2017-The purpose of this investigation was to compare run-prescription by heart rate (HR) vs. rating of perceived exertion (RPE) during 6 weeks to determine which is superior for consistent achievement of target intensities and improved performance. Forty untrained men participated in this laboratory-controlled and field-controlled trial. Participants were divided into heart rate (HRTG) and rating of perceived exertion training groups (RPETG). All underwent maximal-graded exercise testing and a 12-minute run test before and after training. Intensity was prescribed as either a target HR or RPE that corresponded to 4 relative intensity levels: 45, 60, 75, and 90% V[Combining Dot Above]O2 reserve (V[Combining Dot Above]O2R). Mean exercise intensity over the 6 weeks did not differ between HRTG (65.6 ± 7.2%HRR) and RPETG (61.9 ± 9.0%HRR). V[Combining Dot Above]O2max (+4.1 ± 2.5 ml·kg·min) and 12 minutes run distance (+240.1 ± 150.1 m) improved similarly in HRTG and RPETG (p > 0.05). HRTG displayed lower coefficients of variation (CV) (5.9 ± 4.1%, 3.3 ± 3.8%, and 3.0 ± 2.2%) and %error (4.1 ± 4.7%, 2.3 ± 4.1% and 2.6 ± 3.2%) at 45, 60, and 75% V[Combining Dot Above]O2R compared with RPETG (CV 11.1 ± 5.0%, 7.7 ± 4.1% and 5.6 ± 3.2%; all p < 0.005) %error (15.7 ± 9.2%, 10.6 ± 9.2% and 6.7 ± 3.2%; all p < 0.001), respectively. Overall, HR-prescribed and RPE-prescribed run-training resulted in similar exercise intensity and performance outcomes over 6 weeks. Differences in the CV and %error suggest use of HR monitoring for individuals that are new to running as it improves precision and accuracy but does not increase performance improvements across 6 weeks.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".