A wireless sweat sensing with a pH-based correlation model for continuous glucose monitoring and diabetes management during exercise

Abstract

In situ monitoring of sweat glucose during exercise can provide a real-time and continuous assessment of blood glucose dynamics. However, the relatively poor correlation between sweat and blood glucose concentrations during exercise makes it challenging for blood glucose management (BGM) during exercise therapy for diabetes, along with training for athletes and fitness enthusiasts. This work presents a flexible wireless sweat glucose and pH sensing platform integrated with a pH-based correlation model to accurately predict the continuous changes in blood glucose. The pH-based correlation model calibrates enzyme activity changes in glucose oxidase and accounts for the effects of sweat dilution and filtering during paracellular transport of glucose from interstitial fluid and plasma to sweat during exercise. The correlation model has been validated in both healthy individuals and diabetic patients, revealing distinct blood glucose dynamic patterns between the two cohorts. The observed different glucose fluctuations after the intake of various nutritive foods further facilitate the management of diabetes and allow for the identification of hypo-/hyperglycemic risks during training or fitness exercise. The exercise-based device platform combines continuous blood glucose monitoring with diabetes management through effective treatment evaluation and can also provide early prevention for the at-risk population and reduce or even reverse diabetes.