The effects of drinking hydrogen-rich water for six weeks on exercise-related biomarkers in exercise-naïve men and women over 50 years following resistance training program: a randomized controlled pilot trial
Bibliographic record
Abstract
The primary objective of this pilot study was to assess the impact of consuming hydrogen-rich water (HRW) for a duration of six weeks on exercise-related biomarkers in previously untrained men and women aged over 50 years, subsequent to a resistance training program. Twenty-seven apparently healthy middle-aged adults (age 57.6 ± 6.7 years; 18 females) voluntarily provided written consent to participate in this randomized, placebo-controlled experimental trial. All participants were allocated in a double-blind parallel-group design to receive either HRW (12 mg of dihydrogen per serving) or control water (<0.1 ppm of dihydrogen) administered two times per day during a 6-week intervention interval. Muscle performance indices showed a significant improvement following both HRW and control water interventions compared to the baseline values (p ≤ 0.05). HRW led to a significant increase in serum free testosterone and cortisol levels, along with reductions in total cholesterol and LDL-cholesterol levels at the follow-up (p ≤ 0.05). Moreover, HRW significantly outperformed the control water in reducing biomarkers of acute muscular damage caused by resistance exercise (p ≤ 0.05) and tended to outcompete placebo in improving sleep quality (p = 0.119). HRW could be advanced as a risk-free and effective beverage for promoting training-specific adaptations in exercise-naïve men and women over 50 years of age.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".