Fertility tracker and AI used to identify Covid days before symptoms appeared

In study conducted since start of pandemic, 68 per cent of Covid cases were successfully diagnosed two days before onset of symptoms

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A wrist-worn health tracker typically used for monitoring fertility could be used to diagnosis a Covid-19 infection days before symptoms appear, new research suggests.

Combined with artificial intelligence (AI), the tracker picks up on changes in skin temperature, heart and breathing rates, blood flow, as well as sleep quantity and quality to determine whether an individual has been infected with coronavirus.

According to the study, which followed 1,163 people under the age of 51 from the start of the pandemic, 68 per cent of Covid cases were successfully diagnosed two days before the onset of symptoms.

It’s hoped that a quicker diagnosis of Covid-19 could facilitate early isolation and help to limit the spread of the virus.

The researchers tested the AVA bracelet, a fertility tracker that people can buy online to track the best time to conceive, and wanted wanted to see if physiological changes, monitored by the tracker, could be used to develop a machine learning algorithm to identify an infection.

Participants in the study were asked to wear the AVA bracelet at night, with the device saving data every 10 seconds. People have to sleep for at least four hours for it to work.

The bracelets were synchronised with a smartphone app, with people recording any activities that could affect the results, such as alcohol, prescription medications and recreational drugs. They also recorded possible Covid-19 symptoms such as fever.

All those in the study took regular rapid antibody tests for Covid while those with symptoms also took a PCR swab test.

In total, 1.5 million hours of physiological data were recorded and Covid was confirmed in 127 people, of which 66 (52 per cent) had worn their device for at least 29 consecutive days and were included in the analysis.

The study, published in the journal BMJ Open, found there were significant changes in the body during the incubation period for the infection, the period before symptoms appeared, when symptoms appeared and during recovery, compared to non-infection.

Overall, the tracker and computer algorithm identified 68 per cent of Covid-19 positive people two days before their symptoms appeared.

The team, including from the Cardiovascular Research Institute of Basel, concluded there were limits to the research, including that not all Covid cases were captured.

But they added: “Wearable sensor technology can enable Covid-19 detection during the pre-symptomatic period.

“Wearable sensor technology is an easy-to-use, low-cost method for enabling individuals to track their health and wellbeing during a pandemic.

“Our research shows how these devices, partnered with artificial intelligence, can push the boundaries of personalised medicine and detect illnesses prior to (symptom occurrence), potentially reducing virus transmission in communities.”

The algorithm is now being tested in a much larger group (20,000) of people in the Netherlands, with results expected later this year.

While a PCR test remains the gold standard for confirming a Covid infection, “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 authors wrote.

“These devices, partnered with artificial intelligence, can push the boundaries of personalised medicine and detect illnesses prior to [symptom occurrence], potentially reducing virus transmission in communities.”