Assessing the performance of a robust multiparametric wearable patch integrating silicon-based sensors for real-time continuous monitoring of sweat biomarkers.

The development of wearable devices for sweat analysis has experienced significant growth in the last two decades, being the main focus the monitoring of athletes health during workouts. One of the main challenges of these approaches has been to attain the continuous monitoring of sweat for time periods over 1 h. This is the main challenge addressed in this work by designing an analytical platform that combines the high performance of potentiometric sensors and a fluidic structure made of a plastic fabric into a multiplexed wearable device.

Multisensing Wearables for Real-Time Monitoring of Sweat Electrolyte Biomarkers During Exercise and Analysis on Their Correlation With Core Body Temperature.

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. This work describes a wearable sweat biomonitoring patch incorporating printed electrochemical sensors into a plastic microfluidic sweat collector and data analysis that shows the real-time recorded sweat biomarkers can be used to predict a physiological biomarker.

Antibody-Coated Wearable Organic Electrochemical Transistors for Cortisol Detection in Human Sweat.

The dysregulation of the hormone cortisol is related to several pathological states, and its monitoring could help prevent severe stress, fatigue, and mental diseases. While wearable antibody-based biosensors could allow real-time and simple monitoring of antigens, an accurate and low-cost antibody-based cortisol detection through electrochemical methods is considerably challenging due to its low concentration and the high ionic strength of real biofluids. Here, a label-free and fast sensor for cortisol detection is proposed based on antibody-coated organic electrochemical transistors.

Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study.

Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects.