A new paradigm in tracking the dynamics of glucose and cortisol: An observational study from human sweat enabled by a skin sensor

A sedentary lifestyle is one of the leading risk factors to develop obesity. Abdominal obesity is one of the metabolic disorders that lead to an increased risk of developing cardiovascular disease. In this work, we developed a novel approach of tracking glucose and cortisol. With the aim of observing the dynamic relationship between the target biomarkers as a result of physical activity, nutrition, and the circadian cycle, with the help of the machine learning-assisted wearable sweat-based sensor designed in our previous work. The machine learning approach helps to provide real-time biomarker value and a basis for actionable insight. Through this observational study, we illustrate the real-world performance of our sweat-based sensor by examining the glucose and cortisol levels of 4 healthy participants over multiple days. This novel glucose and cortisol tracking paradigm establishes the foundation for future research on the effect of lifestyle choices on target biomarkers for metabolic disorders.