New Zealand border officials have begun testing an app designed to detect Covid-19 before the first symptoms of the disease, known as Larm, appear. The platform was developed by artificial intelligence company Datamine (AI) and is linked to smart watches and other wearable devices to measure metrics such as heart rate, temperature, or oxygen saturation.
Datamin claims the app can detect the warning signs of Covid-19 with up to 90% accuracy, up to three days before symptoms appear. The app creates a custom baseline for each user from the wearable device’s data record and then uses AI to detect physiological changes that might indicate the user is sick before they feel bad. The platform is not device specific and can run on many different parts of the hardware.
Up to 500 border workers can volunteer to take part in the trial, which will run through early May to see how the platform works in real-life settings. Given that new cases of Covid-19 in New Zealand are actually not noticed until after the arrival of international travelers, border workers are likely to face the greatest risks of exposure to the virus and thus could benefit more from the warning.
“If ëlarm achieves its potential, it can provide early notification to our critical workforce at our borders if they improve. This means they can take appropriate measures, such as self-isolation and testing for Covid-19,” the New Zealand MP said. Director of Health, Shane Hunter.
Can a platform like ëlarm really work?
Data from several studies suggests that wearable devices can actually help predict disease onset before it occurs. Researchers at the Rockerfeller Institute for Neuroscience report that the data from Aura RingIt is a wearable device to track sleep and activity that can be combined with an app that measures vital signs to predict the emergence of Covid-19 symptoms in advance. They found that the device successfully predicted symptoms such as coughing, fever and shortness of breath for up to three days before they appeared.
Early results from the Scripps Research Translations Institute’s DETECT study found that wearable fitness equipment could boost public health efforts to control Covid-19. DETECT researchers report that evaluating changes in metrics such as heart rate, sleep, and activity levels, along with self-reported symptom data, can help identify more successful cases than simply observing symptoms alone.
“Early identification of those who are asymptomatic or even asymptomatic will be particularly helpful, as people are likely to be more contagious during this period. This is the ultimate goal,” said Giorgio Coyer, director of the Scripps Institute for Translational Research in Artificial Intelligence. .
Individual cases can have the impact of data from mobile devices
A study published in January 2020 a Lancet Digital Health Evaluation of the use of resting heart rate and sleep data obtained from wearable devices to improve state surveillance of influenza-like illness (ILI) in the United States. Using unspecified sensor data from 200,000 Fitbit users, the researchers found a strong correlation between measures of the abnormal data and weekly rates of suspected flu cases.
They argued that this information could be vital in implementing timely outbreak response measures to prevent further transmission of influenza cases during seasonal outbreaks. If government agencies had access to these metrics, they could detect outbreaks of infectious respiratory diseases, including Covid-19, before they spread dramatically.
Of course, implementing something like this as a public health policy brings with it the usual questions about patient data privacy. It would be necessary, if such a scheme was implemented, that the data be completely anonymous and used only with the consent of the user of the mobile device. But if the right conditions here are met and technologies like ëlarm continue to be tested to yield positive results, then wearable devices could have an important role to play in the future of infection control.
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