Test–retest reliability of a 30-min fixed perceived effort cycling exercise
Bibliographic record
Abstract
Abstract Purpose Using exercise protocols at a fixed rating of perceived effort (RPE) is a useful method for exploring the psychophysical influences on exercise performance. However, studies that have employed this protocol have arbitrarily selected RPE values without considering how these values correspond to exercise intensity thresholds and domains. Therefore, aligning RPE intensities with established physiological thresholds seems more appropriate, although the reliability of this method has not been assessed. Methods Eight recreationally active cyclists completed two identical ramped incremental trials on a cycle ergometer to identify gas exchange threshold (GET). A linear regression model plotted RPE responses during this test alongside gas parameters to establish an RPE corresponding to GET (RPE GET ) and 15% above GET (RPE +15%GET ). Participants then completed three trials at each intensity, in which performance, physiological, and psychological measures were averaged into 5-min time zone (TZ) intervals and 30-min ‘overall’ averages. Data were assessed for reliability using intraclass correlation coefficients (ICC) and accompanying standard error measurements (SEM), 95% confidence intervals, and coefficient of variations (CoV). Results All performance and gas parameters showed excellent levels of test–retest reliability (ICCs = > .900) across both intensities. Performance, gas-related measures, and heart rate averaged over the entire 30-min exercise demonstrated good intra-individual reliability (CoV = < 5%). Conclusion Recreationally active cyclists can reliably replicate fixed perceived effort exercise across multiple visits when RPE is aligned to physiological thresholds. Some evidence suggests that exercise at RPE +15%GET is more reliable than RPE GET .
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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 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".