The accurate prediction of sweat rate from energy expenditure and air temperature: a proof-of-concept study

Abstract

This proof-of-concept study used a web application to predict runner sweat losses using only energy expenditure and air temperature. A field study (FS) of n = 37 runners was completed with n = 40 sweat loss observations measured over 1 h (sweat rate, SR). Predictions were also compared with 10 open literature (OL) studies in which individual runner SR was reported (n = 82; 109 observations). Three prediction accuracy metrics were used: for FS, the mean absolute error (MAE) and concordance correlation coefficient (CCC) were calculated to include a 95% confidence interval [CI]; for OL, the percentage concordance (PC) was examined against calculation of accumulated under- and over-drinking potential. The MAE for FS runners was 0.141 kg [0.105, 0.177], which was less than estimated scale weighing error on 85% of occasions. The CCC was 0.88 [0.82, 0.93]. The PC for OL was 96% for avoidance of both under- and over-drinking and 93% overall. All accuracy metrics and their CIs were below acceptable error tolerance. Input errors of ±10% and ±1 °C for energy expenditure and air temperature dropped the PC to between 84% and 90%. This study demonstrates the feasibility of accurately predicting SR from energy expenditure and air temperature alone. Novelty Results demonstrate that accurate runner SR prediction is possible with knowledge of only energy expenditure and air temperature. SR prediction error was smaller than scale weighing error in 85% of observations. Accurate runner SR prediction could help mitigate the common risks of over- and under-drinking.