Wearables may be able to detect flu even before a patient begins to show symptoms, according to a new study in JAMA.

The study, conducted by researchers at Duke University, showed that a wristband with biometric sensors could detect an influenza infection (H1N1). For the study, the team employed the Empatica E4 wristband, a medical-grade wearable device that measures heart rate, skin temperature, electrodermal activity, and movement in real-time.

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"Approximately 9% of the world is infected with influenza annually, resulting in 3 million to 5 million severe cases and 300 000 to 500 000 deaths per year. Adults are infected with approximately 4 to 6 common colds per year, and children are infected with approximately 6 to 8 common colds per year, with more than half of infections caused by human rhinoviruses (RVs),” the researchers wrote. “Given the highly infectious nature of respiratory viruses and their variable incubation periods, infections are often transmitted unwittingly in a manner that results in community spread, especially as no presymptomatic screening methods currently exist to identify respiratory viral diseases. With the increasing emergence of novel viruses, such as SARS-CoV-2, it is critical to quickly identify and isolate contagious carriers of a virus, including presymptomatic and asymptomatic individuals, at the population level to minimize the viral spread and associated severe health outcomes.”

In this study, the researchers recorded biometric data from young people before and after they were inoculated with H1N1 influenza and human rhinovirus.

In the first study, which involved 31 participants inoculated with influenza, Empatica's E4 wristband detected the difference between infection and non-infection with up to 92% accuracy. The second phase involved 18 participants inoculated with human rhinovirus, and here the E4 wristband detected the difference between infection and non-infection with 88% accuracy, reported principal investigator Jessilyn Dunn, Ph.D., of Duke University in Durham, North Carolina, and colleagues.

“This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual’s response to viral exposure prior to symptoms is feasible,” the authors of the study wrote. “Harnessing this technology would support early interventions to limit the presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.”

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“We hope to learn what sickness with COVID-19 looks like at the physiological level, and how parameters around heart rate, sleep, and movement change when a person gets infected,” explains Dunn. “We are also interested in comparing data from unvaccinated individuals, as well as people who develop breakthrough infections.”