Salivary Markers for Quantitative Dehydration Estimation During Physical Exercise

Predicting hydration status using machine learning models from physiological and sweat biomarkers during endurance exercise: a single case study

Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured four noninvasive physiological and sweat biomarkers including heart rate, core temperature, sweat sodium concentration, and whole-body sweat rate. Sweat sodium concentration was measured from six body regions using absorbent patches. We used three machine learning models to determine the percentage of body weight loss as an indicator of dehydration with these biomarkers and compared the prediction accuracy. The results on this single subject show that these models gave similar mean absolute errors, while in general the nonlinear models slightly outperformed the linear model in most of the experiments. The prediction accuracy of using the whole-body sweat rate or heart rate was higher than using core temperature or sweat sodium concentration. In addition, the model trained on the sweat sodium concentration collected from the arms gave slightly better accuracy than from the other five body regions. This exploratory work paves the way for the use of these machine learning models to develop personalized health monitoring together with emerging, noninvasive wearable sensor devices.

The Effect of Acute Hypohydration on Indicators of Glycemic Regulation, Appetite, Metabolism and Stress: A Systematic Review and Meta-Analysis

Evidence synthesizing the effects of acute body water losses on various markers of glycemic regulation, appetite, metabolism, and stress is lacking. Thus, the purpose of this review was to summarize the response of various hormonal changes involved in these physiologic functions to dehydration. A comprehensive literature search for peer-reviewed research in the databases PubMed, Scopus, CINAHL, and SportDiscus was conducted. Studies were included if they contained samples of adults (>18 years) and experimentally induced dehydration as measured by acute body mass loss. Twenty-one articles were eligible for inclusion. Findings suggested cortisol is significantly elevated with hypohydration (standard mean difference [SMD] = 1.12, 95% CI [0.583, 1.67], p < 0.0001). Testosterone was significantly lower in studies where hypohydration was accompanied by caloric restriction (SMD= -1.04, 95% CI [-1.93, -0.14], p = 0.02), however, there were no changes in testosterone in studies examining hypohydration alone (SMD = -0.17, 95% CI [-0.51 0.16], p = 0.30). Insulin and ghrelin were unaffected by acute total body water losses. Acute hypohydration increases markers of catabolism but has a negligible effect on markers of glycemic regulation, appetite, anabolism and stress. Given the brevity of existing research, further research is needed to determine the impact of hydration on glucagon, leptin, peptide YY and the subsequent outcomes relevant to both health and performance.

Two Distinct Types of Sweat Profile in Healthy Subjects While Exercising at Constant Power Output Measured by a Wearable Sweat Sensor

Wearable sweat sensors have enabled real-time monitoring of sweat profiles (sweat concentration versus time) and could enable monitoring of electrolyte loss during exercise or for individuals working in extreme environments. To assess the feasibility of using a wearable sweat chloride sensor for real-time monitoring of individuals during exercise, we recorded and analyzed the sweat profiles of 50 healthy subjects while spinning at 75 Watts for 1 hour. The measured sweat chloride concentrations were in the range from 2.9-34 mM. The sweat profiles showed two distinct sweat responses: Type 1 (single plateau) and Type 2 (multiple plateaus). Subjects with Type 2 profiles had higher sweat chloride concentration and weight loss, higher maximum heart rate, and larger changes in heart rate and rating of perceived exertion during the trial compared to subjects with Type 1 profiles. To assess the influence of level of effort, we recorded sweat profiles for five subjects at 75 W, 100 W, and 125 W. While all five subjects showed Type 1 sweat profiles at 75 W, four of the subjects had Type 2 profiles at 125 W, showing an increase in sweat chloride with exercise intensity. Finally, we show that sweat profiles along with other physiological parameters can be used to predict fluid loss.