The Effects of Acute Deep Seawater Supplementation on Muscle Function after Triathlon
Bibliographic record
Abstract
(1) Background: Trainers and athletes have always sought to reduce the failure of muscle function during long endurance events. However, nowadays, it is a topic that is generating much debate in the scientific field. Currently, deep-sea water (DSW) intake seems to be a suitable hydration alternative for this type of endurance event. Therefore, the aim of this study was to determine whether DSW consumption during a triathlon event could preserve muscle function after exercise. (2) Methods: Nineteen trained male triathletes (age = 39.0 ± 4.25 years; BMI = 23.67 ± 1.81 kg/m2) randomly performed three triathlons, one of them consuming DSW (Totum SPORT 30 AB, Laboratories Quinton International, S.L., Spain), the other consuming isotonic placebo and the last with tap water-hydration. A vertical jump test with countermovement and an isometric muscle strength test were conducted before and after the triathlon test. (3) Results: There was a significant difference between treatment × time during the isometric muscle strength test. Based on the Tukey post hoc analysis, the peak net force decreased statistically in the placebo (p = 0.045) and control conditions (p = 0.026), but not in the experimental condition (p = 0.121). In addition, all of the conditions studied obtained similar results in the countermovement vertical jump after exercise. (4) Conclusions: As a result, consumption of DSW seems to delay the failure of muscle function specifically in isometric exercises but does not improve performance in sports. Thus, DSW does not alter muscle capacity in a negative way; therefore, its consumption may be recommended.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".