Researchers at the University of California, San Diego, have developed a sweat sensor that measures glucose levels in the skin and converts these readings into accurate estimates of blood sugar. Because sweat glucose levels can vary from person to person, the sensor incorporates algorithms that customize the measurement for each user, requiring finger calibration once or twice each month.
The need to prick your fingers regularly is a barrier for many patients with diabetes when it comes to regularly testing their glucose levels, as the procedure is painful, uncomfortable, and for many patients, many times a day. Poor control of glucose levels leads to a number of serious long-term health problems, so ensuring that patients can test and adjust their glucose levels is often crucial to the health of this patient population.
This number has inspired new forms of minimally invasive testing technology that prevent or reduce the number of finger tips needed. One of these promising approaches is to test for sweat. Because sweat is released in small amounts almost continuously under normal conditions and contains glucose concentrations that reflect blood glucose levels, it represents a promising test method.
Although sweat glucose levels correlate with blood glucose levels, there are significant levels of person-to-person variability. Glucose levels in sweat tend to be much lower than in blood, and sweating rates can also affect measurements.
Consequently, a “one-size-fits-all” approach to testing sweat glucose is clearly not as accurate as it could be. To address this, these researchers have developed a device that can provide a personalized measurement for each patient. A user simply places their finger on the sensor for a period of 1 minute to collect enough sweat to test it.
The sensor consists of a polyvinyl alcohol hydrogel that absorbs sweat. The gel is placed on an electrochemical sensor, which detects and measures the amount of glucose present through an enzymatic reaction that creates an electrical charge. The data collected is interpreted using an algorithm that corrects each user’s reading based on a monthly fingerprint calibration.
To date, the device has been tested on a small number of volunteers and could accurately predict blood glucose levels before and after a meal with more than 95% accuracy.