Effect of exercise-induced dehydration on time-trial exercise performance: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To use the meta-analytical procedures to determine the magnitude of the effect of exercise-induced dehydration (EID) upon time-trial (TT) exercise performance. METHODS: Studies were located via database searches and cross-referencing. TT performance outcomes were converted to mean percentage changes in power output. Random-effects model meta-regressions, analogue to the ANOVA and weighted mean effect summaries were used to delineate the effect of the EID-associated body weight (BW) loss on TT performance. RESULTS: Five research articles, all using cycling TTs, were included, producing 13 effect estimates and representing 39 subjects. The mean ambient temperature, relative humidity, exercise intensity and duration of the exercise trials were 26.0 ± 6.7°C, 61 ± 9%, 68 ± 14% of VO(2max) and 86 ± 34 min, respectively. The effect of EID (mean BW loss of 2.20 ± 1.0%) during self-paced exercise conditions was to produce a non-significant increase in endurance performance of 0.06 ± 2.72% (p=0.94), compared with the maintenance of euhydration (mean BW loss of 0.44 ± 0.48%). Meta-regression analyses revealed a statistically significant relationship between the percentage changes in power output and exercise intensity and duration, but not with the EID-associated percentage changes in BW loss. Drinking according to the dictate of thirst was associated with an increase in TT performance compared with a rate of drinking below (+5.2 ± 4.6%, p=0.01) or above (+2.4 ± 5.0%, p=0.40) thirst. The probability that drinking to thirst confers a real and meaningful advantage on TT performances conducted under field conditions compared with a rate of drinking below and above thirst sensation is of the order of 98% and 62%, respectively. CONCLUSIONS: (1) Compared with euhydration, EID (up to 4% BW loss) does not alter cycling performances during out-of-door exercise conditions; (2) exercise intensity and duration have a much greater impact on cycling TT performances than EID and; (3) relying on thirst sensation to gauge the need for fluid replacement maximises cycling TT performances.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.051 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".