Tracking metabolic responses based on macronutrient consumption: A comprehensive study to continuously monitor and quantify dual markers (cortisol and glucose) in human sweat using WATCH sensor

Passive sweat wearable: A new paradigm in the wearable landscape toward enabling "detect to treat" opportunities

Growing interest over recent years in personalized health monitoring coupled with the skyrocketing popularity of wearable smart devices has led to the increased relevance of wearable sweat-based sensors for biomarker detection. From optimizing workouts to risk management of cardiovascular diseases and monitoring prediabetes, the ability of sweat sensors to continuously and noninvasively measure biomarkers in real-time has a wide range of applications. Conventional sweat sensors utilize external stimulation of sweat glands to obtain samples, however; this stimulation influences the expression profile of the biomarkers and reduces the accuracy of the detection method. To address this limitation, our laboratory pioneered the development of the passive sweat sensor subfield, which allowed for our progress in developing a sweat chemistry panel. Passive sweat sensors utilize nanoporous structures to confine and detect biomarkers in ultra-low sweat volumes. The ability of passive sweat sensors to use smaller samples than conventional sensors enable users with sedentary lifestyles who perspire less to benefit from sweat sensor technology not previously afforded to them. Herein, the mechanisms and strategies of current sweat sensors are summarized with an emphasis on the emerging subfield of passive sweat-based diagnostics. Prospects for this technology include discovering new biomarkers expressed in sweat and expanding the list of relevant detectable biomarkers. Moreover, the accuracy of biomarker detection can be enhanced with machine learning using prediction algorithms trained on clinical data. Applying this machine learning in conjunction with multiplex biomarker detection will allow for a more holistic approach to trend predictions. This article is categorized under: Diagnostic Tools > Diagnostic Nanodevices Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Diagnostic Tools > Biosensing.

Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review

Researchers are interested in measuring mental stress because it is linked to a variety of diseases. Real-time stress monitoring via wearable sensor systems can aid in the prevention of stress-related diseases by allowing stressors to be controlled immediately. Physical tests, such as heart rate or skin conductance, have recently been used to assess stress; however, these methods are easily influenced by daily life activities. As a result, for more accurate stress monitoring, validations requiring two or more stress-related biomarkers are demanded. In this review, the combinations of various types of sensors (hereafter referred to as multiplexed sensor systems) that can be applied to monitor stress are discussed, referring to physical and chemical biomarkers. Multiplexed sensor systems are classified as multiplexed physical sensors, multiplexed physical-chemical sensors, and multiplexed chemical sensors, with the effect of measuring multiple biomarkers and the ability to measure stress being the most important. The working principles of multiplexed sensor systems are subdivided, with advantages in measuring multiple biomarkers. Furthermore, stress-related chemical biomarkers are still limited to cortisol; however, we believe that by developing multiplexed sensor systems, it will be possible to explore new stress-related chemical biomarkers by confirming their correlations to cortisol. As a result, the potential for further development of multiplexed sensor systems, such as the development of wearable electronics for mental health management, is highlighted in this review.

Flexible Sweat Sensors: From Films to Textiles

Flexible sweat sensors have found widespread potential applications for long-term wear and tracking and real-time monitoring of human health. However, the main substrate currently used in common flexible sweat sensors is thin film, which has disadvantages such as poor air permeability and the need for additional wearables. In this Review, the recent progress of sweat sensors has been systematically summarized by the types of monitoring methods of sweat sensors. In addition, this Review introduces and compares the performance of sweat sensors based on thin film and textile substrates such as fiber/yarn. Finally, opportunities and suggestions for the development of flexible sweat sensors are presented by summarizing the integration methods of sensors and human body monitoring sites.

A machine learning-based on-demand sweat glucose reporting platform

Diabetes is a chronic endocrine disease that occurs due to an imbalance in glucose levels and altering carbohydrate metabolism. It is a leading cause of morbidity, resulting in a reduced quality of life even in developed societies, primarily affected by a sedentary lifestyle and often leading to mortality. Keeping track of blood glucose levels noninvasively has been made possible due to diverse breakthroughs in wearable sensor technology coupled with holistic digital healthcare. Efficient glucose management has been revolutionized by the development of continuous glucose monitoring sensors and wearable, non/minimally invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1-5 min. This paper presents a highly novel and completely non-invasive sweat sensor platform technology that can measure and report glucose concentrations from passively expressed human eccrine sweat using electrochemical impedance spectroscopy and affinity capture probe functionalized sensor surfaces. The sensor samples 1-5 µL of sweat from the wearer every 1-5 min and reports sweat glucose from a machine learning algorithm that samples the analytical reference values from the electrochemical sweat sensor. These values are then converted to continuous time-varying signals using the interpolation methodology. Supervised machine learning, the decision tree regression algorithm, shows the goodness of fit R² of 0.94 was achieved with an RMSE value of 0.1 mg/dL. The output of the model was tested on three human subject datasets. The results were able to capture the glucose progression trend correctly. Sweet sensor platform technology demonstrates a dynamic response over the physiological sweat glucose range of 1-4 mg/dL measured from 3 human subjects. The technology described in the manuscript shows promise for real-time biomarkers such as glucose reporting from passively expressed human eccrine sweat.