More and more people worldwide are using the devices to monitor changes in skin temperature, heart rate and respiration rate. Now a new study shows that these data could be combined with artificial intelligence (AI) to diagnose Covid-19 even before the first signs of the disease appear. “Portable sensor technology can detect Covid-19 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 artificial intelligence for early detection of Covid-19, simply by detecting key physiological changes. This could help provide an early warning system to users that they may be infected, which in turn can help prevent the disease from spreading more widely. Researchers from Dr Risch Medical Laboratory in Liechtenstein, the University of Basel in Switzerland, McMaster University in Canada and Imperial College London tested the Ava bracelet, a fertility detector that people can buy online to better watch to capture. Monitors breathing rate, heart rate, heart rate variability, wrist skin temperature and blood flow. In the study, 1,163 people under the age of 51 in Liechtenstein were monitored from the beginning of the pandemic until April 2021. They were asked to wear the Ava bracelet at night, with the device, which costs £ 249, saving data every 10 seconds. . People need at least four hours of sleep to function. The bracelets were synchronized with a smartphone app, with individuals recording any activities that might affect the results, such as alcohol, prescription drugs and recreational drugs. They also recorded possible Covid-19 symptoms such as fever. Subscribe to the First Edition, our free daily newsletter – every morning at 7 p.m. BST Everyone in the study had regular rapid antibody tests for Covid-19, and those with symptoms also had PCR tests. In total, 1.5 million hours of normal data were recorded and Covid-19 was confirmed in 127 individuals, of whom 66 (52%) had worn their device for at least 29 consecutive days and were included in the assay. The study found that there were significant changes in the body during the incubation period for the infection, the period before the onset of symptoms, when the symptoms occurred and during recovery, compared to non-infection. Overall, the dual combination of the health monitoring algorithm and the computer correctly identified 68% of Covid-19 positive individuals two days before their symptoms appeared. The team noted that there were limits to the investigation, including that not all Covid cases were recorded. While the PCR test remains the gold standard for confirmation of Covid-19, the findings “suggest that a mobile learning algorithm with information on mobile devices could serve as a promising tool for pre-symptomatic or asymptomatic detection,” Covid said. Investigators. They added: “Portable sensor technology is an easy-to-use low-cost method that allows people to monitor their health and well-being during a pandemic. “Our research shows how these devices, in conjunction with artificial intelligence, can push the boundaries of personalized medicine and detect disease before (symptoms), possibly reducing virus transmission to communities.” The typical symptoms of Covid-19 may take several days after infection before they appear, during which time an infected person can inadvertently transmit the virus. The algorithm is now being tested on a much larger group of 20,000 people across the Netherlands, with results expected later this year.
