Postexercise rehydration with beer impairs fluid retention, reaction time, and balance
Bibliographic record
Abstract
Beer is promoted by popular media as a good choice for rehydration, but there is limited support for the claim. To assess the effect of beer alcohol on rehydration and motor control, 11 young (24.4 ± 3.7 years old) males of legal drinking age were dehydrated to 2.12% ± 0.20% body mass (mean ± SD) by exercising in a climatic chamber (31.7 ± 1.6 °C, 55.0% ± 8.3% relative humidity) on 3 different days, 1 week apart, and rehydrated with 100% of their sweat loss using water (WATER), 4.6% alcohol beer (BEER), or low-alcohol beer (LAB), in random order. Urine output, blood alcohol content (BAC), reaction time (RT), and balance (as measured by center of pressure velocity (VCoP)) were measured every 30 min over 3 h and compared via 2-way, repeated-measures analyses of variance (ANOVAs). After consuming ≈1.6 L in 1 h, urine output was greater for BEER (1218 ± 279 mL) than for LAB (745 ± 313 mL, p = 0.007) and WATER (774 ± 304 mL, p = 0.043). BAC remained at 0 with WATER and LAB; with BEER, BAC reached 0.857 g/L (95% confidence intervals [0.752, 0.963]) immediately postrehydration. RT was longer for BEER (0.314 ± 0.039 s) than for LAB (0.294 ± 0.034 s, p = 0.009), but was no different from WATER (0.293 ± 0.049 s, p = 0.077). VCoPx was significantly higher for BEER (0.0284 ± 0.0020 m/s) compared with LAB (0.0233 ± 0.0010 m/s) or WATER (0.0238 ± 0.0010 m/s) (p = 0.022), but VCoPy was not different among beverages. In conclusion, rehydration with BEER resulted in higher diuresis, slower RT, and impaired VCoP than rehydration with LAB or WATER.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".