Impact of dehydration on perceived exertion during endurance exercise: A systematic review with meta-analysis
Bibliographic record
Abstract
Background: Understanding the impact of stressors on the rating of perceived exertion (RPE) is relevant from a performance and exercise adherence/participation standpoint. Athletes and recreationally active individuals dehydrate during exercise. No attempt has been made to systematically determine the impact of exercise-induced dehydration (EID) on RPE. Objective: The present meta-analysis aimed to determine the effect of EID on RPE during endurance exercise and examine the moderating effect of potential confounders. Data analyses: Performed on raw RPE values using random-effects models weighted mean effect summaries and meta-regressions with robust standard errors, and with a practical meaningful effect set at 1 point difference between euhydration (EUH) and EID. Only controlled crossover studies measuring RPE with a Borg scale in healthy adults performing ≥30 min of continuous endurance exercise while dehydrating or drinking to maintain EUH were included. Results: Sixteen studies were included, representing 147 individuals. Mean body mass loss with EUH was 0.5 ± 0.4%, compared to 2.3 ± 0.5% with EID (range 1.7-3.1%). Within an EID of 0.5-3% body mass, a maximum difference in RPE of 0.81 points (95% CI: 0.36-1.27) was observed between conditions. A meta-regression revealed that RPE increases by 0.21 points for each 1% increase in EID (95% CI: 0.12-0.31). Humidity, ambient temperature and aerobic capacity did not alter the relationship between EID and RPE. Conclusion: Therefore, the effect of EID on RPE is unlikely to be practically meaningful until a body mass loss of at least 3%.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".