Oct. 11, 2021 -- A simple wristband containing special sensors is able to pick up early infection from both the flu and the common cold before symptoms develop. It can also predict how severe illness will be once it becomes symptomatic, new research shows.

“Prior to the development of symptoms, people are still infectious and can potentially infect others,” Jessilyn Dunn, PhD, assistant professor of biomedical engineering at Duke University inDurham, NC, says.

“That’s why it’s so important to be able to detect infection even when a person doesn’t feel symptomatic — as this would help prevent the spread of pathogens that occurs before somebody knows they are sick and which is why it is important from a public health perspective,” she added.

The study was published online Sept. 29, in JAMA Network Open.

Two Challenge Studies

The study involved participants who were injected with the H1N1 flu virus and 18 others who were injected with rhinovirus. The rhinovirus challenge study was done in 2015, and the H1N1 challenge study was done in 2018.

Participants in both challenge studies wore the E4 wristband from the company Empatico. Those in the influenza study wore the wristband 1 day before and 11 days after being infected and those in the rhinovirus study wore the wristband for 4 days before and 5 days after infection. The E4 wristband measures heart rate, skin temperature, and movement.

The biosensors contained in the wristband were able to detect the H1N1 infection with an accuracy of 79% within 12 hours after infection and 92% within 24 hours, the authors report.

The median time for symptom onset following the rhinovirus challenge was 36 hours after inoculation.

Prediction of Severity

Twelve hours after participants had been infected, the technology was also able to predict the severity of H1N1 infection with 83% accuracy. For rhinovirus, the accuracy was slightly higher at 92% while for both viruses combined.

As the authors point out, the ability to identify infection could have wide-ranging effects. “In the midst of the global [coronavirus] pandemic, the need for novel approaches like this has never been more apparent,” they suggest.

Steven Steinhubl, MD, a research scientist and former director of digital medicine at Scripps Research's Translational Institute, says he has a lot of faith in this type of technology.

“Unfortunately, COVID-19 has changed our perspective about respiratory infections but if you think of the bad flu seasons we’ve had in the past, people do die from influenza, so I think there is a lot of value [in this technology], although the degree of value depends on the severity of the infection,” he said.

Steinhubl was not involved in the study.

If people actually ever go back into work together, he says, early recognition that an employee might have the flu or another highly contagious infection could alert them to the need to stay home.

“We have a bit to go before we get there,” Steinhubl admitted, “but you could have a really big impact on the spread of any infectious disease that would be better for everybody.”

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