Reproducibility of Rate of Perceived Exertion–Based Self-Selected Running Speeds on Indoor Track and Treadmill Conditions in Recreational Runners
Bibliographic record
Abstract
ABSTRACT: Nguyen, AP, Kisita, V, Van Cant, J, Monnet, T, and Bosquet, L. Reproducibility of rate of perceived exertion-based self-selected running speeds on indoor track and treadmill conditions in recreational runners. J Strength Cond Res 40(2): e125-e130, 2026-This study evaluates the reproducibility of self-selected speeds at a rate of perceived exertion (RPE) of 3/10 in both track and treadmill conditions. It also investigates the differences between 2 conditions: i.e., track versus treadmill and 2 RPE instructions, i.e., 3/10 and 8/10. In addition, it compares spatiotemporal parameters across conditions and RPE levels. Fifty-five recreational runners completed six 1-km runs under randomized conditions: 4 at 3/10 and 2 at 8/10 RPE on both track and treadmill. Spatiotemporal parameters, heart rate, and self-selected speeds were recorded. Reliability was assessed using intraclass correlation coefficients (ICC), standard error of measurement ( SEM ), and minimal detectable change (MDC). Statistical significance for all tests was set at α = 0.05. Self-selected speeds showed excellent reliability on both surfaces (ICC = 0.93-0.97). The track showed lower SEM (0.3 km·h -1 ) and MDC (0.7 km·h -1 ) values compared with the treadmill ( SEM : 0.6 km·h -1 ; MDC: 1.6 km·h -1 ). Speeds were 20% slower at 3/10 RPE and 10% slower at 8/10 RPE on the treadmill. Treadmill running exhibited longer contact times (+13%), shorter flight times (-35%), and shorter step lengths (-6%) at 3/10 RPE, with similar trends observed at 8/10 RPE. Rate of perceived exertion appears to be a reliable and practical tool for monitoring and prescribing running intensity levels. The slower treadmill speeds at comparable RPE levels may help reduce biomechanical loading on the lower limb joints, offering advantages for rehabilitation. However, differences in biomechanics and perceived effort between treadmill and track running highlight the need for context-specific applications in training and rehabilitation.
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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 | 0.007 |
| 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) | 0.001 | 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".