Drinking hydrogen water enhances endurance and relieves psychometric fatigue: a randomized, double-blind, placebo-controlled study
Bibliographic record
Abstract
Acute physical exercise increases reactive oxygen species in skeletal muscle, leading to tissue damage and fatigue. Molecular hydrogen (H2) acts as a therapeutic antioxidant directly or indirectly by inducing antioxidative enzymes. Here, we examined the effects of drinking H2 water (H2-infused water) on psychometric fatigue and endurance capacity in a randomized, double-blind, placebo-controlled fashion. In Experiment 1, all participants drank only placebo water in the first cycle ergometer exercise session, and for comparison they drank either H2 water or placebo water 30 min before exercise in the second examination. In these healthy non-trained participants (n = 99), psychometric fatigue judged by visual analogue scales was significantly decreased in the H2 group after mild exercise. When each group was divided into 2 subgroups, the subgroup with higher visual analogue scale values was more sensitive to the effect of H2. In Experiment 2, trained participants (n = 60) were subjected to moderate exercise by cycle ergometer in a similar way as in Experiment 1, but exercise was performed 10 min after drinking H2 water. Endurance and fatigue were significantly improved in the H2 group as judged by maximal oxygen consumption and Borg’s scale, respectively. Taken together, drinking H2 water just before exercise exhibited anti-fatigue and endurance effects.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".