Researchers at the University of California San Diego have developed a sweating sensor that measures glucose levels on the skin and converts these measurements into accurate blood sugar estimates. Since glucose levels in sweat can vary from person to person, the sensor includes algorithms that adjust the measurement for each user, requiring a finger prick calibration once or twice each month.
The need for regular fingerpricks is a barrier for many patients with diabetes by regularly testing their glucose levels, as the procedure is painful, inconvenient, and for many patients, it must be done many times each day. Poor control of glucose levels leads to a wide range of serious long-term health problems, so it is crucial for the health of this patient population to ensure that patients can frequently test and adjust their glucose levels.
This question has inspired new forms of testing technology that are minimally invasive and avoid or reduce the number of fingerprints required. Such a promising approach involves sweat testing. As sweat is released in small amounts almost continuously under normal conditions and contains glucose concentrations that reflect blood sugar levels, it represents a promising test method.
Although glucose levels in sweat correlate loosely with blood sugar levels, there are significant levels of variation from person to person. The levels of glucose in sweat tend to be much lower than the level in the blood and sweating can also affect the measurements.
Therefore, a ‘one size fits all’ approach to sweat glucose testing is clearly not as accurate as it could be. To address this, these researchers have developed a device that can provide a personal measurement to each patient. A user simply puts his finger on the sensor for a period of 1 minute to collect enough sweat to test.
The sensor consists of a polyvinyl alcohol hydrogel that absorbs sweat. The gel is located above an electrochemical sensor that detects and measures the amount of glucose present through an enzymatic reaction that creates an electric charge. Collected data is interpreted using an algorithm that corrects the reading for each user based on a monthly fingerprick calibration.
So far, the device has been tested by a small number of volunteers and was able to accurately predict blood sugar levels before and after a meal with over 95% accuracy.
Studying in ACS sensors: Touch-based fingertip bloodless reliable glucose monitoring: Personal data processing to predict blood glucose concentrations