Smartwatches Offer Early Detection of Viral Infections

A recent study by researchers from Aalto, Stanford, and Texas A&M demonstrates how smartwatches could significantly reduce the unintentional spread of disease by pre-symptomatic or asymptomatic individuals. The study, published in PNAS Nexus, introduces a powerful new tool for pandemic management.

Early detection of illness is crucial for preventing its spread, whether it's COVID-19, influenza, or the common cold. However, many illnesses are most contagious before individuals realize they are sick. Research shows that 44 % of COVID-19 infections were transmitted several days before symptoms appeared.

Unlike during the pandemic, we now have concrete data on how pandemics develop, and how effective different measures are at curbing the spread. Add to this that wearable technology is now extremely effective when it comes to detecting the very early physiological signs of infection, and we are much better prepared.

Märt Vesinurm, Aalto University

How an Early “Ping” Could Flatten the Curve

Wearable devices have consistently shown the ability to detect physiological indicators of illnesses days before individuals notice symptoms. For example, smartwatches can predict COVID-19 with 88 % accuracy and influenza with 90 % accuracy, using metrics like respiration, heart rate, and skin temperature. Studies also indicate that people naturally reduce social contact by 66-90 % once they realize they are ill, even outside of pandemic contexts.

Even at the lower end of compliance, if people receive and act on an earlier warning by self-isolating, the impact is significant. Even just a 66-75 % reduction in social contacts soon after detection by smartwatches — keeping in mind that that’s on a par with what you’d normally do if you had cold symptoms — can lead to a 40-65 % decrease in disease transmission compared to someone isolating from the onset of symptoms.

Märt Vesinurm, Aalto University

The study suggests that increased adherence to health measures, similar to those observed during pandemics, could effectively halt the spread of disease. This raises the question: could smartwatches play a valuable role in managing a potential H5N1 avian flu epidemic?

I see no reason why not. As we gather more specific data about how different illnesses affect these measurements, there’s no reason we couldn’t distinguish between diseases, from bird flu and HIV to the common cold, especially when used in conjunction with advanced machine learning methods and other data from the user.

Märt Vesinurm, Aalto University

Towards Smarter Pandemic Policies

The research team is taking a new approach by combining real-world data from various epidemiological, biological, and behavioral studies to create a mathematical model for population-level infection spread. Through this integration of data and technology, Vesinurm sees smartwatches playing a transformative role in pandemic management, both on a personal and policy level.

“People are used to wearable devices, and they’re likely to trust them. While they’re not yet diagnostic, they could help make unpopular approaches like masks, lockdowns, and invasive testing more targeted and less of a blunt instrument. An early warning from your smartwatch would be the cue to take other actions, like getting a PCR test, wearing a mask, or ‘not visiting your grandma,’” said Vesinurm.

The response would depend on the social context at the time and an individual’s risk assessment.

This information empowers individuals to protect their loved ones and communities while also providing decision-makers with a crucial tool.

“It could be that governments find it most cost effective in a pandemic situation to provide every person who wants one with a smartwatch — although of course, this comes with its own ethical considerations. Either way, with early detection literally at our fingertips, I see a lot of reason to hope,” said Vesinurm.

Journal Reference:

Vesinurm, M., et al. (2025) Terminating pandemics with smartwatches. PNAS Nexus. doi.org/10.1093/pnasnexus/pgaf044