Effect of exercise-induced dehydration on endurance performance: evaluating the impact of exercise protocols on outcomes using a meta-analytic procedure
Bibliographic record
Abstract
OBJECTIVE: It is purported that exercise-induced dehydration (EID), especially if ≥ 2% bodyweight, impairs endurance performance (EP). Field research shows that athletes can achieve outstanding EP while dehydrated > 2% bodyweight. Using the meta-analytic procedure, this study compared the findings of laboratory-based studies that examined the impact of EID upon EP using either ecologically valid (EV) (time-trial exercise) or non-ecologically valid (NEV) (clamped-intensity exercise) exercise protocols. METHODS: EP outcomes were put on the same scale and represent % changes in power output between euhydrated and dehydrated exercise tests. Random-effects model meta-regressions and weighted mean effect summaries, mixed-effects model analogue to the ANOVAs and magnitude-based effect statistics were used to delineate treatment effects. MAIN RESULTS: Fifteen research articles were included, producing 28 effect estimates, representing 122 subjects. Compared with euhydration, EID increased (0.09±2.60%, (p=0.9)) EP under time-trial exercise conditions, whereas it reduced it (1.91±1.53%, (p<0.05)) with NEV exercise protocols. Only with NEV exercise protocols did EID ≥ 2% body weight impair EP (p=0.03). CONCLUSIONS: Evidence indicates that (1) EID ≤ 4% bodyweight is very unlikely to impair EP under real-world exercise conditions (time-trial type exercise) and; (2) under situations of fixed-exercise intensity, which may have some relevance for military and occupational settings, EID ≥ 2% bodyweight is associated with a reduction in endurance capacity. The 2% bodyweight loss rule has been established from findings of studies using NEV exercise protocols and does not apply to out-of-doors exercise conditions. Athletes are therefore encouraged to drink according to thirst during exercise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".