Fitness app biometrics help detect COVID-19 in NCATS-funded study

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, according to new research funded by the national Center for Advancing Translational Sciences (NCATS).

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 reported in an article published online October 29 in Nature Medicine. The study was funded by a grant from NCATS, part of the National Institutes of Health.

DETECT investigators noted 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 for differentiating between symptomatic individuals who were positive and those who were negative for COVID-19. A higher AUC indicates more accurate discrimination.

“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.”

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.

According to his group, adding these physiologic changes from baseline values significantly outperformed the detection rate 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.

According to Quer, one in five 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%).

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 for the sleep metric and 0.69 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.

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

Read more: https://www.squidnews.net/biometric-changes-on-fitness-trackers-detect-c...

Read the study: https://www.nature.com/articles/s41591-020-1123-x