Wrist health trackers help detect Covid before signs appear

According to a study published in the journal BMJ open, health trackers strapped to the wrist to monitor changes in skin temperature, heart and respiratory rate could help detect Covid-19 days before the first signs of the viral disease appear.

The study shows that this data can be combined with artificial intelligence (AI) to diagnose Covid-19 before symptoms appear.

“While a PCR smear remains the gold standard for confirming Covid-19, our findings suggest that a wearable informed machine learning algorithm could serve as a promising tool for presymptomatic or asymptomatic detection of Covid-19,” the researchers said.

The researchers, including those from Risch Medical Laboratory, Liechtenstein, base their findings on wearers of the AVA bracelet. The regulated and commercially available fertility tracker monitors respiratory rate, heart rate, heart rate variability, skin temperature and blood flow in the wrist, as well as the amount and quality of sleep.

Typical Covid-19 symptoms can take several days to appear after infection, during which time an infected person can inadvertently spread the virus.

The researchers wanted to see if physiological changes, followed by an activity tracker, could be used to develop a machine learning algorithm to detect Covid-19 infection before symptoms start.

As many as 1,163 participants under the age of 51 were pulled from the GAPP study between March 2020 and April 2021. Participants wore the AVA bracelet at night. The device stores data every 10 seconds and requires a minimum of four hours of relatively uninterrupted sleep. The bracelets were synchronized with an additional smartphone app upon awakening.

They regularly did rapid antibody tests for SARS-CoV-2, the virus responsible for Covid-19 infection. Those with indicative symptoms also took a PCR smear.

About 127 people (11 percent) developed a Covid-19 infection during the study period.

Of these, 66 (52 percent) had worn their bracelet at least 29 days before the onset of symptoms and were found positive on a PCR smear, so they were included in the final analysis.

The monitoring data revealed significant changes in all five physiological indicators during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared to baseline measurements.

About 73 percent of lab-confirmed positives were picked up in the training set up to two days before symptom onset and 68 percent in the test set.

“Our research shows how these devices, when combined with artificial intelligence, can push the boundaries of personalized medicine and detect disease prior to[the appearance of symptoms]potentially reducing the transmission of viruses in communities,” they said.

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