Can a wearable device diagnose virus infections?

Oct. 1, 2021

In a cohort study by Jessilyn Dunn, PhD, Biomedical Engineering Department, and other researchers at Duke University worked with 31 participants inoculated with H1N1 and 18 participants with rhinovirus. The goal was to determine whether a wearable device could be used for infection detection and severity prediction. They ascertained that wearable devices were able to distinguish between infection and noninfection with 92% accuracy for H1N1 and 88% accuracy for rhinovirus and were able to distinguish between mild and moderate infection 24 hours prior to symptom onset with 90% accuracy for H1N1 and 89% accuracy for rhinovirus.

This study suggests that the use of wearable devices to identify individuals with presymptomatic acute viral respiratory infection is feasible; because wearable devices are common in the general population, using them for infection screening may help limit the spread of contagion. The researchers used noninvasive, wrist-worn devices to predict an individual’s response to viral exposure is feasible. The study was published in JAMA Network Open.

Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus.

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).

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 viral spread and associated severe health outcomes.

Symptoms used were either observable events (fever, stuffy nose, runny nose, sneezing, coughing, shortness of breath, hoarseness, diarrhea, and wheezy chest) or unobservable events (muscle soreness, fatigue, headache, ear pain, throat discomfort, chest pain, chills, malaise, and itchy eyes). Viral shedding was quantified by nasal lavage polymerase chain reaction each morning, and symptoms were self-reported twice daily.

Participants in the RV challenge study wore an E4 wristband for 4 days before and 5 days after inoculation, which occurred in the afternoon (1-5 pm) via intranasal drops of diluted human RV strain type 16 with a count of 100 using the TCID50 assay in 1 mL of lactated Ringer solution. Participants underwent daily nasal lavage, and the symptoms were reported as previously described. Participants lived on a college campus and were not isolated.

Presymptomatic detection of respiratory viral infection and infection severity prediction may enable better medical resource allocation, early quarantine, and more effective prophylactic measures. Our results show that an accuracy plateau occurred in the 12- to 24-hour period after inoculation for 24 of 25 infection detection models (96.0%) and for 64 of 66 infection severity models (97.0%). This finding indicates that the most critical of the physiologic changes occurred within 12 to 24 hours after exposure.

Two factors associated with model accuracy are (1) knowledge of the exact time and dosage of inoculation and (2) the high-fidelity measurements of the research-grade wearable that enable intricate feature engineering, neither of which are possible in existing observational studies using consumer-grade devices.

JAMA Network release