Re:Show HN: An attempt to spot Covid-19 outbreaks using wear...
by helloworld on Wednesday April 15, 2020 @03:44PM
(#22882155)
How can we spot the "silent spreaders" of SARS-CoV-2 -- the people who are infected and contagious, but have no symptoms? [1] Our digital medicine group at Scripps Research Translational Institute has just launched a health study [2] to explore one possible approach: crowdsourcing data from wearable devices like the Apple Watch and Fitbit, and then attempting to use machine learning (or less grandly, linear regression) to identify COVID-19 infections.
In January, my colleagues at Scripps published a study in the Lancet that used heart rate and sleep data from about 47,000 Fitbit users in the U.S. to predict which areas would be hardest hit by flu. [3] The correlation with actual reports from the Centers for Disease Control was very high: 0.9.
We’re hoping that we can do something similar for COVID-19 infection. In particular, elevated resting heart rate appears to be a reliable indicator of viral infection. [4] We’re wondering whether it might also be a leading indicator, i.e, spiking to a detectable degree before any measurable fever. If it were, we might be able to identify COVID-19 infections in individuals -- and areas with outbreaks -- before people even have any symptoms.
To figure this out, we’re going to need a lot of participants. And that’s really the greatest challenge for us now. A similar project in Germany [5], which was inspired by our Lancet flu study, already has more than 160,000 volunteers. But they have the advantage of being sponsored by a national research institute. We don’t.
If you have suggestions about how we might reach people with wearables who want to help out -- or if you have questions about the DETECT study -- please add them to the comments.
In January, my colleagues at Scripps published a study in the Lancet that used heart rate and sleep data from about 47,000 Fitbit users in the U.S. to predict which areas would be hardest hit by flu. [3] The correlation with actual reports from the Centers for Disease Control was very high: 0.9.
We’re hoping that we can do something similar for COVID-19 infection. In particular, elevated resting heart rate appears to be a reliable indicator of viral infection. [4] We’re wondering whether it might also be a leading indicator, i.e, spiking to a detectable degree before any measurable fever. If it were, we might be able to identify COVID-19 infections in individuals -- and areas with outbreaks -- before people even have any symptoms.
To figure this out, we’re going to need a lot of participants. And that’s really the greatest challenge for us now. A similar project in Germany [5], which was inspired by our Lancet flu study, already has more than 160,000 volunteers. But they have the advantage of being sponsored by a national research institute. We don’t.
If you have suggestions about how we might reach people with wearables who want to help out -- or if you have questions about the DETECT study -- please add them to the comments.
Thanks, Danny
[1] Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus. https://science.sciencemag.org/content/early/2020/03/24/scie...
[2] Pivoting health research to take on COVID-19. https://www.weforum.org/agenda/2020/03/pivoting-health-resea...
[3] Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA. https://www.thelancet.com/journals/landig/article/PIIS2589-7...
[4] Fever and cardiac rhythm. https://jamanetwork.com/journals/jamainternalmedicine/articl...
[5] Germany launches smartwatch app to monitor coronavirus spread. https://www.nytimes.com/reuters/2020/04/07/technology/07reut...