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Biometric Changes on Fitness Trackers Smartwatches Detect COVID-19

Biometric Changes on Fitness Trackers Smartwatches Detect COVID-19

Biometric Changes on Fitness Trackers Smartwatches Detect COVID-19
Biometric Changes on Fitness Trackers Smartwatches Detect COVID-19

A smartphone app that combines passively collected physiologic data from wearable devices, such as fitness

trackers, and self-reported symptoms can discriminate between COVID-19–positive and –negative individuals

among those who report symptoms, new data suggest.

After analyzing data from more than 30,000 participants, researchers from the Digital Engagement and Tracking

for Early Control and Treatment (DETECT) study concluded that adding individual changes in sensor data

improves models based on symptoms alone for differentiating symptomatic persons who are COVID-19 positive

and symptomatic persons who are COVID-19 negative.

The combination can potentially identify infection clusters before wider community spread occurs, Giorgio Quer,

PhD, and colleagues report in an article published online October 29 in Nature Medicine. DETECT investigators

note that marrying participant-reported symptoms with personal sensor data, such as deviation from normal

sleep duration and resting heart rate, resulted in an area under the curve (AUC) of 0.80 (interquartile range

[IQR], 0.73 – 0.86) for differentiating between symptomatic individuals who were positive and those who were

negative for COVID-19.

Biometric Changes on Fitness Trackers Smartwatches Detect COVID-19


“By better characterizing each individual’s unique baseline, you can then identify changes that may indicate that

someone has a viral illness,” said Quer, director of artificial intelligence at Scripps Research Translational

Institute in La Jolla, California. “In previous research, we found that the proportion of individuals with elevated

resting heart rate and sleep duration compared with their normal could significantly improve real-time detection of

influenza-like illness rates at the state level,” he told Medscape Medical News.

Thus, continuous passively captured data may be a useful adjunct to bricks-and-mortar site testing, which is

generally a one-off or infrequent sampling assay and is not always easily accessible, he added. Furthermore,

traditional screening with temperature and symptom reporting is inadequate. An elevation in temperature is not

as common as frequently believed for people who test positive for COVID-19, Quer continued. “Early

identification via sensor variables of those who are presymptomatic or even asymptomatic would be especially

valuable, as people may potentially be infectious during this period, and early detection is the ultimate goal,”

Quer said.

Biometric Changes on Fitness Trackers Smartwatches Detect COVID-19


According to his group, adding these physiologic changes from baseline values significantly outperformed

detection (P < .01) using a British model described in an earlier study by by Cristina Menni, PhD, and associates.

That method, in which symptoms were considered alone, yielded an AUC of 0.71 (IQR, 0.63 – 0.79).

According to Quer, 1 in 5 Americans currently wear an electronic device. “If we could enroll even a small

percentage of these individuals, we’d be able to potentially identify clusters before they have the opportunity to

spread,” he said.

DETECT Study Details

During the period March 15 to June 7, 2020, the study enrolled 30,529 participants from all 50 US states. They

ranged in age from younger than 35 years (23.1%) to older than 65 years (12.8%); the majority (63.5%) were

aged 35 to 65 years, and 62% were women. Sensor devices in use by the cohort included Fitbit activity trackers

(78.4%) and Apple HealthKit (31.2%).

Biometric Changes on Fitness Trackers Smartwatches Detect COVID-19

Participants downloaded an app called MyDataHelps, which collects smartwatch and activity tracker information,

including self-reported symptoms and diagnostic testing results. The app also monitors changes from baseline in

resting heart rate, sleep duration, and physical activity, as measured by steps.

Overall, 3811 participants reported having at least one symptom of some kind (eg, fatigue, cough, dyspnea, loss

of taste or smell). Of these, 54 reported testing positive for COVID-19, and 279 reported testing negative.

Sleep and activity were significantly different for the positive and negative groups, with an AUC of 0.68 (IQR,

0.57 – 0.79) for the sleep metric and 0.69 (IQR, 0.61 – 0.77) for the activity metric, suggesting that these

parameters were more affected in COVID-positive participants.

When the investigators combined resting heart rate, sleep, and activity into a single metric, predictive

performance improved to an AUC of 0.72 (IQR, 0.64 – 0.80).

The next step, Quer said, is to include an alert to notify users of possible infection.

Alerting Users to Possible COVID-19 Infection

In a similar study, an alert feature was already incorporated. The study, which was led by Michael P. Snyder,

PhD, director of the Center for Genomics and Personalized Medicine at Stanford University in Stanford,

California, will soon be published online in Nature Biomedical Engineering. In that study, presymptomatic

detection of COVID-19 was achieved in more than 80% of participants using resting heart rate.

Biometric Changes on Fitness Trackers Smartwatches Detect COVID-19

“The median is 4 days prior to symptom formation,” Snyder told Medscape Medical News. “We have an alarm

system to notify people when their heart rate is elevated. So a positive signal from a smartwatch can be used to

follow up by polymerase chain reaction [testing].”

Snyder believes these approaches offer a roadmap to containing widespread infections. “Public health

authorities need to be open to these technologies and begin incorporating them into their tracking,” he said.

“Right now, people do temperature checks, which are of limited value. Resting heart rate is much better


Although the DETECT researchers have not yet received feedback on their results, they believe public health

authorities could recommend the use of such apps. “These are devices that people routinely wear for tracking

their fitness and sleep, so it would be relatively easy to use the data for viral illness tracking,” said co–lead author

Jennifer Radin, PhD, an epidemiologist at Scripps. “Testing resources are still limited and don’t allow for routine

serial testing of individuals who may be asymptomatic or presymptomatic. Wearables can offer a different way to

routinely monitor and screen people for changes in their data that may indicate COVID-19.”

The marshaling of data through consumer digital platforms to fight the coronavirus is gaining ground. New York

State and New Jersey are already embracing smartphone apps to alert individuals to possible exposure to the


Biometric Changes on Fitness Trackers Smartwatches Detect COVID-19


More than 710,000 New Yorkers have downloaded the COVID NY Alert app, launched in October to help protect

individuals and communities from COVID-19 by sending alerts without compromising privacy or personal

information. “Upon receiving a notification about a potential exposure, users are then able to self-quarantine, get

tested, and reduce the potential exposure risk to family, friends, coworkers, and others,” Jonah Bruno, a

spokesperson for the New York State Department of Health, told Medscape Medical News.

And recently the Mayo Clinic and Safe Health Systems launched a platform to store COVID testing and

vaccination data.

Both the Scripps and Stanford platforms are part of a global technologic response to the COVID-19 pandemic.

Prospective studies, led by device manufacturers and academic institutions, allow individuals to voluntarily share

sensor and clinical data to address the crisis. Similar approaches have been used to track COVID-19 in large

populations in Germany via the Corona Data Donation app.

The study by Quer and colleagues was funded by a grant from the National Center for Advancing Translational

Sciences at the National Institutes of Health. Coauthor Steinhubl has reported grants from Janssen and personal

fees from Otsuka and Livongo outside of the submitted work. The other authors have disclosed no relevant

financial relationships. Snyder has ties to Personalis, Qbio, January, SensOmics, Protos, Mirvie, and Oralome.

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