Evaluation Of The Fluidlogic Sweat Prediction Algorithm.

Dehydration can impair cognitive function, physical performance, and be fatal if severe. To mitigate the negative effects of dehydration individuals are encouraged to consume appropriate levels of fluid to replenish sweat loss. However, most individuals cannot determine when they are dehydrated nor the correct amount of fluid to consume. PURPOSE: FluidLogic has developed an algorithm to predict sweat loss during exercise in an effort to prevent dehydration. Thus, the purpose of the present investigation was to validate the algorithm in a variety of temperature and exercise scenarios. METHODS: A convenient sample of 165 individuals (22.4 ± 5.4 years old, 170.2 ± 9.3 cm tall, and 72.2 ± 13.2 kg body mass, with 54% of the sample being assigned female at birth) exercised in an environmental chamber in six distinct conditions (Cold/Dry, Cold/Humid, Mild/Dry, Mild/Humid, Hot/Dry and Hot/Humid) at an exercise intensity determined on a 1-10 RPE scale. Sweat loss was measured via a pre and post exercise nude body mass. An individual’s predicted sweat loss was calculated by using the FluidLogic Algorithm. A Pearson Correlation was utilized to determine the relationship between actual and predicted sweat loss. RESULTS: For all conditions it was determined that the FluidLogic algorithm overestimated sweat loss by 37.6 ml and was highly correlated (r = 0.73) to actual sweat loss. The mild conditions were more accurate at predicting sweat loss as compared to the Hot/Dry condition. CONCLUSIONS: The FluidLogic sweat prediction algorithm is correlated with actual sweat loss but refinement to the algorithm could improve validity in extreme temperature and exercise conditions. Supported by: Rainmaker Solutions LLC