The effect of pre-exercise oral hyperhydration on endurance exercise performance, heart rate, and thermoregulation: a meta-analytical review
Bibliographic record
Abstract
This study aimed to determine the effect of pre-exercise hyperhydration on endurance performance (primary outcome), heart rate, thermoregulation, and perceptual responses (secondary outcomes). Six academic databases were searched to February 2023. Only studies reporting differences in hydration between intervention and placebo/control were included. Meta-analysis determined overall effect size (Hedges’ g), and meta-regression the influence of independent moderators (ambient temperature, hyperhydration agent, exercise mode, extent of hyperhydration). Overall, 10 publications generating 19 effect estimates for primary outcomes, and 11 publications reporting 48 effect estimates for secondary outcomes, were included. A small-to-moderate improvement in time-to-exhaustion (TTE) (Hedges’ g = 0.31, 95% CI: 0.13–0.50, p = 0.001) and time trial (TT) ( g = 0.25, 95% CI: 0.002–0.51, p = 0.049) but not total work (TW) tasks ( p = 0.120) was found following hyperhydration. No moderating effects were observed. No effect of hyperhydration was found for heart rate following steady state (SS) exercise ( p = 0.069) or the performance task ( p = 0.072), nor for body temperature post-SS ( p = 0.132) or post-performance task ( p = 0.349), but meta-regression of sodium versus glycerol showed lower body temperature post-performance task with sodium ( g = 0.80, t (5) = 2.65, p = 0.046). No effects were found for perceived exertion or thermal comfort. Study heterogeneity was low, lacking representation of elite and female athletes, and weight-bearing (i.e., running) exercise modalities. These results suggest pre-exercise hyperhydration provides a small-to-moderate benefit to endurance performance in TTE and TT, but not TW performance tasks. While no moderating effects were observed, lack of heterogeneity makes it difficult to generalise these findings.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".