Case Study:
Relationships Between Whole-Body Sweat and Electrolyte Loss

with Rate of Perceived Exertion by Sport

written by Meghan E. Garvey, PhD, Pratik Patel, Victor Adewusi, Sasang Balachadran, and Bradley Wright


Athletes often train and compete in varying conditions which impact internal and external factors of thermoregulation, thus eliciting varying sweat losses and rates. It is important to consider that within the variances of sweat losses, electrolyte losses are also greatly important. It has been found that dehydration, defined as a percent of body mass lost through sweat, of just 2% can have impacts on sports performance (2). Research has indicated that sweat rates and the performance impacts at different levels of dehydration may vary between sports, even those with the classification of endurance sports (1).


Exercise intensity level has an influence on elements of thermoregulation; however, less research has been conducted on varying associations by sport.


Secondary data analysis was conducted on deidentified data from 190 individual athletes with 644 total observations, compiled through observational testing (Oct 2021 – Mar 2023) (62.3% running, 86.7% female).

Sweat Measures: Whole-body sweat rate (WBSR) and whole-body electrolyte loss rate (WBER) were measured during device validation data collection procedures for Nix Biosensors (Medford, MA). Ground-truth data of nude pre- and post-workout weigh-ins were set as the validation standard for the device. WBEL is extrapolated from the osmolality of regional sweat in the chamber.

Statistical Analysis: Data was tested for normality and summarized as mean (standard deviation). One-way analysis of variance (ANOVA) was then used to detect differences in study outcomes (WBSL, WBSR, and WBEL) amongst RPE levels of low, moderate, and high intensity and then stratified by sport. Pearson product-moment correlation was used to assess the relationships between sweat rates and electrolyte losses. Significance was set at p<0.05 and all analyses were conducted using STATA 17.0.


ANOVA analyses found significant differences in mean RPE and WBSL by sport but not in WBEL. In the full sample a positive correlation between WBSL and WBEL (R = 0.25, p<0.001). Significant increases were observed between low and high RPE in all variables except WBSR in running and WBER in cycling data.


Results indicate that intensity level, defined by RPE, has a varying influence on WBSL, WBSR, WBER, and WBEL by sport. However, due to the subjective manner of RPE, more research is needed to explore the mechanisms of action behind these relationships, especially in real-time versus end-of-exercise duration cumulative relationships. More objective measurements, such as percent VO max, heart rate max, or heart rate reserve. It is also of value to continue exploring if these relationships not only vary by sport, but also by race and gender.