According to researchers, health trackers worn on the wrist can be used to detect Covid-19 days before any symptoms appear.
More and more people around the world are using devices to monitor changes in skin temperature, heart rate and respiration. A new study now shows that these data can be combined with artificial intelligence (AI) to diagnose Covid-19 even before the first signs of the disease appear.
“Carrying sensor technology may allow Covid-19 to be detected during the pre-symptomatic period,” the researchers concluded. The findings were published in the journal BMJ Open.
The discovery could lead to the adaptation of health trackers with AI for early detection of Covid-19, simply by noticing major physiological changes. This can help provide an early warning system for consumers that they may be infected, which in turn can help prevent the disease from spreading more widely.
Researchers from Dr. Rich’s Medical Laboratory in Liechtenstein, the University of Basel in Switzerland, McMaster University in Canada and Imperial College London tested the Ava bracelet, a fertility tracking tool that people can buy online to track the best time. for conception. It monitors respiration rate, heart rate, heart rate variability, wrist skin temperature and blood flow.
The study traced 1,163 people under the age of 51 in Liechtenstein from the start of the pandemic to April 2021. They were asked to wear the Ava bracelet at night, with the device, which costs £ 249, recording data every 10 seconds. People need to sleep at least four hours to work.
The bracelets were synchronized with a smartphone app, and people recorded all sorts of activities that could affect the results, such as alcohol, prescription drugs and entertainment drugs. They also recorded possible symptoms of Covid-19 such as fever.
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All study participants underwent regular rapid tests for antibodies to Covid-19, while those with symptoms also underwent PCR testing.
A total of 1.5 million hours of physiological data were recorded and Covid-19 was confirmed in 127 people, of whom 66 (52%) wore their device for at least 29 consecutive days and were included in the analysis.
The study found that there were significant changes in the body during the incubation period of the infection, the period before the onset of symptoms, when the symptoms appeared and during recovery, compared to non-infection.
Overall, the dual combination of health tracking and a computer algorithm correctly identified 68% of Covid-19-positive people two days before their symptoms appeared. The team said there were limitations to the study, including that not all Covid cases had been filmed.
While the PCR test remains the gold standard for confirming Covid-19, the findings “suggest that a wear-informed machine learning algorithm may serve as a promising tool for presymptomatic or asymptomatic detection of Covid-19,” the researchers said.
They added: “Wearable sensor technology is an easy-to-use and inexpensive method that allows people to monitor their health and well-being during a pandemic.
“Our research shows how these devices, in partnership with artificial intelligence, can push the boundaries of personalized medicine and detect disease before (symptoms appear), potentially reducing the transmission of viruses in communities.
Typical symptoms of Covid-19 may take several days after infection before they appear, during which time the infected person may inadvertently spread the virus.
The algorithm is now being tested in a much larger group of 20,000 people in the Netherlands, with results expected later this year.
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