BUCHS, Switzerland — Could fertility-tracking bracelets used by women to help achieve pregnancy be useful for health-centric men too? According to a new study, one such wearable device can also signal COVID-19 infection as early as 48 hours before symptoms develop!

The fertility tracker by Ava, which claims to the first approved by the FDA, may reduce spread of COVID-19 by acting as an early warning system. Worn like a watch, it helps women get pregnant by identifying the most promising days in their menstrual cycle. Information is based on skin temperature, pulse, blood flow, sleep patterns, breathing and heart rate, before being sent to a smartphone app.

The same measurements also detect initial signs of coronavirus, such as fever, say scientists. Corresponding author Dr. Lorenz Risch and colleagues predicted almost 70 percent of cases up to two days in advance.

“To our knowledge, this is the first prospective study to measure physiological changes in respiratory rate, heart rate, skin temperature and perfusion to develop an algorithm to detect pre-symptomatic COVID-19 infection,” the authors write in their paper.

These patients are likely to ignore safety precautions, leading to increased virus transmission. Early isolation would limit contact with susceptible individuals.

The findings are based on 1,163 adults under 50 in Liechtenstein, tracked between March 2020 and April 2021. Participants wore the bracelet at night. It saves data every 10 seconds and requires at least four hours of relatively uninterrupted sleep. Devices were synchronized with a complementary smartphone app that recorded consumption of alcohol, prescription or recreational drugs and possible COVID symptoms.

Regular rapid antibody tests for the virus were also undertaken. Those with indicative symptoms took a PCR swab test as well. Everyone provided personal information on age, sex, smoking status, blood group, number of children, exposure to household contacts or work colleagues who tested positive for COVID, and vaccination status.

Some 127 people (11 percent) developed the infection, of whom 66 had worn their bracelet for at least 29 days before the start of symptoms. They were confirmed as positive by PCR swab test, so were included in the final analysis. The monitoring data revealed significant changes in all five physiological indicators. COVID symptoms lasted an average of 8.5 days.

The algorithm was “trained” using 70 percent of the data from day 10 to day two before the start of symptoms within a 40 day period of continuous monitoring of the 66 people who tested positive. It was then tested on the remaining 30 percent. Some 73 percent of laboratory confirmed positive cases were picked up in the training set and 68 percent in the test set up to two days before the start of symptoms.

“Wearable sensor technology can enable Covid-19 detection during the pre-symptomatic period. Our proposed algorithm identified 68% of COVID- 19 positive participants two days prior to symptom onset,” the authors conclude.

It is now being tested in a much larger group of 20,000 people in The Netherlands. Results expected later this year.

“Recent studies have highlighted the need to identify potential cases prior to symptom onset to prevent virus transmission. Our findings suggest that a wearable-informed machine learning algorithm may serve as a promising tool for presymptomatic or asymptomatic detection of COVID-19,” the paper notes.

The Ava Fertility Tracker is a device that pinpoints the fertile days in your cycle, taking some of the guesswork out of the process.

“Wearable sensor technology is an easy-to-use, low-cost method for enabling individuals to track their health and wellbeing during a pandemic,” the authors say. “Our research shows how these devices, partnered with artificial intelligence, can push the boundaries of personalized medicine and detect illnesses prior to symptom occurrence, potentially reducing virus transmission in communities.”

The research is published in the journal BMJ Open.

South West News Service writer Mark Waghorn contributed to this report.

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