Evaluation of a Wearable Fabric-based Sensor for Accurate Sodium Determination in Sweat During Exercise

Document Type


Publication Date


Publication Title

European Journal of Applied Physiology


Introduction Newly developed wearable fabric sensors (WFS) can increase the ease and accuracy of sweat sodium measurements by performing simultaneous sampling and analysis on the body during exercise.

Purpose Determine the accuracy of a WFS for measurement of sodium concentration in sweat.Methods Subjects wore a WFS prototype and sweat collectors on their forearm during cycle ergometry. Subjects exercised at a moderate intensity (similar to 65% heart rate reserve) for 30-60 min. Sweat samples were collected and analyzed using a commercial sweat sodium analyzer (SSA) every 10-15 min. WFS were adhered with an armband and connected to custom built electronics. Accuracy was determined by comparing predicted WFS concentration to the actual concentration from the commercial SSA and analyzed statistically using ANOVA and Bland-Altman plots.

Results A total of 19 subjects completed the study. The average sweat sodium concentration was 59 mM +/- 22 mM from a SSA compared with 54 mM +/- 22 mM from the WFS. Overall, the average accuracy of the WFS was 88% in comparison to the SSA with p = 0.45. A line of best fit comparing predicted versus actual sweat sodium concentration had a slope of 0.99, intercept of - 4.46, and an r(2) of 0.90. Bland-Altman analysis showed the average concentration difference between the WFS and the SSA was 5.35 mM, with 99% of data points between +/- 1.96 times the standard deviation.

Conclusion The WFS accurately predicted sweat sodium concentration during moderate intensity cycle ergometry. With the need for precise assessment of sodium loss, especially during long duration exercise, this novel analysis method can benefit athletes and coaches. Further research involving longer duration and more intense exercise is warranted.


This work was supported by the National Science Foundation [grant number 1843539] and the Ohio Third Frontier Technology Validation and Startup Fund [grant number 20-0004].