New Approach in Assessing Core Muscle Endurance Using Ratings of Perceived Exertion
Bibliographic record
Abstract
George, JD, Tolley, JR, Vehrs, PR, Reece, JD, Akay, MF, and Cambridge, EDJ. New approach in assessing core muscle endurance using ratings of perceived exertion. J Strength Cond Res 32(4): 1081-1088, 2018-This study sought to develop regression models to estimate maximal endurance time using data from 4 core muscle endurance tests. Eighty healthy university students (age: 22.7 ± 1.9 years) performed the plank, right side-bridge, left side-bridge, and back extension tests in a random order. Participants were instructed to hold each static position for a maximal endurance time, while maintaining proper form, and then rest for 5 minutes between tests. A test administrator recorded participants' ratings of perceived exertion (RPE; a modified 10-point scale) every 5 seconds. Based on regression analysis, the elapsed time to reach an RPE of 8 (RPE8) exhibited statistical significance (p < 0.0001) and the highest accuracy as compared with lower RPE values. The following univariate regression models were generated to estimate maximal endurance time across the 4 tests: plank (r = 0.94; standard error of estimate [SEE] = 17.6 seconds; n = 77) = 23.9 + (1.110 × RPE8); right side-bridge (r = 0.92; SEE = 11.4 seconds; n = 80) = 18.5 + (1.022 × RPE8); left side-bridge (r = 0.93; SEE = 10.8 seconds; n = 80) = 16.8 + (1.062 × RPE8); and back extension (r = 0.93; SEE = 14.2 seconds; n = 79) = 21.5 + (1.027 × RPE8). These results suggest that submaximal protocols based on elapsed time to reach RPE8 provide strength and conditioning professionals relatively accurate univariate regression equation estimates of maximal core muscle endurance time and offer a viable submaximal alternative to maximal capacity testing when time efficiency, participant safety, or certain educational objectives may be a priority.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".