<33™ has conducted a hydration study on human performance, which was performed by one of Canada’s top Hydration and Thermophysiology Experts, Prof. Dr. Matthew White, (Vancouver, B.C.) at one of Canada’s most advanced environmental chambers. The study protocol was developed over a period of two years and peer reviewed by two hydration experts in the US.
In one of our Clinical University Studies, we showed that <33 treated water results in increased hydration and blood plasma volume in human subjects.
The double-blind, randomized cross-over study showed that drinking <33 treated-water decreased the extent of dehydration in athletes and in 87% of them prevented dehydration. Close to 90% of subjects drinking <33 treated water showed an improvement in hydration compared to those drinking untreated water.
The results are statistically significant (p<0.05) showing <33 treated water provides superior hydration over untreated water. ‘For main outcome variables power calculations for sample size justification were completed with at an alpha level of 5% to achieve a power of 80% for the analysis.’
University athletes were subjected to strenuous exercise in one of Canada’s most advanced environmental chambers, which consisted of prolonged exercise on a stationary bicycle in a controlled hot temperature and humidity environment.
The average change in plasma volume showed a significant increase for subjects drinking treated water and this was significantly greater (p<0.05) than the decrease in plasma volume in the subjects drinking untreated water.
In the case for subjects drinking <33™ treated water the plasma sodium concentration and blood osmolality also showed significant time dependent changes (p<0.05) such that these values became greater than for subjects drinking non-treated water. As well, the cumulative water intake in the treated water condition showed a trend to increase more slowly over time with respect to the untreated water condition.
The results conclude (p<0.05) that <33™ water will not only give better hydration; it also has a significant effect on preventing dehydration.
The difference in blood plasma / blood volume is also directly related to a noticeable increase in performance as demonstrated in a number of clinical trials on correlations coefficients (r) for linear regressions between total blood volume and maximal oxygen uptake (Figure 1).
STATISTICALLY SIGNIFICANT: In statistical hypothesis testing, a result has statistical significance when it is very unlikely to have occurred given the null hypothesis (simply by chance alone). More precisely, a study’s defined significance level, denoted by α, is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; and the p-value of a result is the probability of obtaining a result at least as extreme, given that the null hypothesis is true.
The result is statistically significant, by the standards of the study, when the significance level for a study is chosen before data collection, and is typically set to 5% or lower—depending on the field of study. (p<0.05)
In any experiment or observation that involves drawing a sample from a population, there is always the possibility that an observed effect would have occurred due to sampling error alone. But if the p-value of an observed effect is less than (or equal to) the significance level, an investigator may conclude that the effect reflects the characteristics of the whole population, thereby rejecting the null hypothesis.
Statistical significance plays a pivotal role in statistical hypothesis testing. It is used to determine whether the null hypothesis should be rejected or retained. The null hypothesis is the default assumption that nothing happened or changed. For the null hypothesis to be rejected, an observed result has to be statistically significant, i.e. the observed p-value is less than the pre-specified significance level α.
To determine whether a result is statistically significant, a researcher calculates a p-value, which is the probability of observing an effect of the same magnitude or more extreme given that the null hypothesis is true. The null hypothesis is rejected if the p-value is less than (or equal to) a predetermined level α. α is also called the significance level, and is the probability of rejecting the null hypothesis given that it is true (a type I error). It is usually set at or below 5%.