Meta-Analysis Of The Effects Of Pre-Exercise Hypohydration On Endurance Performance, Lactate Threshold And Vo2max
Bibliographic record
Abstract
Exercise-induced dehydration (EID) ≥ 2% bodyweight (BW) impairs endurance performance (EP). It is unclear how pre-exercise hypohydration (PEH), which may impose physiological challenges differing from those of EID, affects EP, VO2 at lactate threshold (VO2LT) and maximal oxygen consumption (VO2max). PURPOSE: To determine the magnitude of the effects of PEH on EP, VO2LT and VO2max using a meta-analytic approach. METHODS: Studies were located via database searches and cross-referencing. Inclusion criteria: BW loss incurred ≥ 1h prior to the exercise tests; hydration level in the control group < 0.5% ≥ -1% BW; hypohydration level in the experimental group > 1% BW and > 0.5% than control. Exclusion criteria: diuretics- and caloric restriction-induced BW loss. STATISTICS: Random-effect model for mean weighted summary effects, random-effect (method-of-moments) meta-regression for relationships between variables and random-effect analog to the ANOVA for subgroup analyses. Mean changes in EP times during incremental or fixed-power output tests to exhaustion were converted to mean changes in power output. RESULTS: Twenty-nine research articles were retrieved, among which 10, 7 and 7 met the inclusion criteria for EP, VO2LT and VO2max, respectively, providing 14, 10 and 11 effect estimates. Mean BW decreases in all experimental groups were 3.9 ± 0.9% (SD) (range 2.6 to 5.6%). EP and VO2LT: Mean exercise test times among studies was 21 ± 15 min. Mean power output fell by 3.2% (95% CI: 2.4 to 4.0%) and VO2LT by 5.63% (95% CI: 2.8 to 8.4%) in hypohydrated group relative to control. Percent change in power output and VO2LT did not differ between studies with BW losses ≤ 4% or > 4%. Meta-regression analyses did not establish significant relationship patterns between BW losses and percent changes in power output or VO2LTs. VO2max: With PEH VO2max decreased by 2.4% (95% CI: 1.1 to 3.8%) compared with control group. Percent change in VO2max in studies with BW losses ≤ 4% was significantly lower than in those with BW losses > 4%. VO2max was found to decrease by 2.9% for each percent loss in BW above a threshold loss of 3.1% (P = 0.027). CONCLUSIONS: Results indicate that PEH ≥ 3% BW impairs EP, VO2LT and VO2max in a practically important manner. Studies are required to establish how PEH in the 1-5% BW range affects EP during prolonged exercise.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".