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Wearable Sensor Patch with Hydrogel Microneedles for In Situ Analysis of Interstitial Fluid
Yumin Dai1, James Nolan2, Emilee Madsen2
1School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States.
ACS Applied Materials & Interfaces
|December 2, 2023
Summary
A new wearable sensor patch uses hydrogel microneedles for minimally invasive, real-time monitoring of glucose and lactate in interstitial fluid. This technology improves chronic disease management through continuous health tracking.
Area of Science:
- Biomedical Engineering
- Wearable Technology
- Biosensors
Background:
- Continuous monitoring of interstitial fluid biomarkers is crucial for managing chronic diseases like diabetes.
- Challenges exist in developing minimally invasive sensors and addressing signal delays for in situ analysis.
Purpose of the Study:
- To introduce a wearable sensor patch with hydrogel microneedles for real-time, in situ biomarker monitoring in interstitial fluid.
- To address challenges in minimally invasive sensing and signal delay compensation.
Main Methods:
- Development of a stretchable wearable sensor patch with hydrogel microneedles for interstitial fluid extraction.
- In situ measurement of glucose and lactate concentrations in both in vitro and in vivo (mouse model) settings.
- Integration of a predictive model to analyze and compensate for signal delays.
Main Results:
- The sensor patch demonstrated high sensitivity and linear ranges for glucose and lactate detection in vitro.
- In vivo glucose sensing in a mouse model showed significant sensitivity and a practical detection range.
- The integrated predictive model improved calibration reliability by compensating for signal delays.
Conclusions:
- The developed sensor patch offers a promising minimally invasive platform for continuous, in situ analysis of multiple biomarkers in interstitial fluid.
- This technology has the potential to significantly advance continuous health monitoring and chronic disease management.
- Further optimization guided by the predictive model can enhance overall sensing performance.

