Artificial intelligence (AI) and wearable activity trackers that measure changes in skin temperature, heart rate, and breathing rate may be used to detect Covid-19 infection days before symptoms appear.

After infection, typical Covid-19 symptoms may take a few days to manifest, during which time an infected person may unintentionally spread the virus.

However, the researchers discovered that overall, the health tracker and computer algorithm properly identified 68% of Covid positive participants two days before their symptoms manifested in the study, which was published in the open access journal BMJ Open.

The international team including from University of Basel (Switzerland) and Imperial College London, pointed out that while a PCR swab test remains the gold standard for confirming Covid-19 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”.

All participants took regular rapid antibody tests for Covid infection. Those with indicative symptoms took a PCR swab test as well.

The team conducted a trial, on a AVA bracelet, and included 1,163 participants all under the age of 51 who wore the tracker at night. The device saves data every 10 seconds and requires at least 4 hours of relatively uninterrupted sleep. The bracelets were synchronised with a complementary smartphone app on waking.

Some 127 people (11 per cent) developed Covid-19 infection during the study period, of which 66 (52 per cent) had worn their bracelet for at least 29 days before the start of symptoms and were confirmed as positive by PCR swab test, so were included in the final analysis.

The algorithm was `trained` using 70 per cent of the data from day 10 to day 2 before the start of symptoms within a 40 day period of continuous monitoring of the 66 people who tested positive for SARS-CoV-2. It was then tested on the remaining 30 per cent of the data.

News Summary:

  • Wearable AI trackers can identify Covid-19 days before symptoms appear
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