Fitness Trackers and COVID-19 Prediction

The Importance of Early Detection

Early research indicates that fitness trackers may play a critical role in predicting COVID-19 by monitoring changes in a person’s activity levels. Rapid identification, tracing, and isolation of cases are essential to prevent the spread of SARS-CoV-2, the virus responsible for COVID-19. However, this has proven difficult, partly due to the absence of quick and reliable testing methods.

Current Screening Challenges

In the United States, the existing screening protocols for COVID-19 include survey questions regarding travel history and symptoms, along with temperature checks. Unfortunately, these methods may lack reliability. A significant percentage of individuals with COVID-19 are either asymptomatic or pre-symptomatic, accounting for approximately 40% to 45% of confirmed cases. Moreover, elevated temperature readings (above 37.8°C or 100°F) are not as prevalent among COVID-19 patients as commonly assumed. Research shows that only 12% of individuals who test positive for COVID-19 exhibit a high temperature, and merely 31% of hospitalized patients have a fever at the time of admission.

Utilizing Wearable Sensor Data

In light of these challenges, U.S. researchers are exploring the potential of wearable sensor data from devices such as smartwatches and activity trackers to enhance COVID-19 detection. They have developed an app-based research platform and database known as DETECT (Digital Engagement and Tracking for Early Control and Treatment), allowing users to share wearable sensor data, self-reported symptoms, diagnoses, and electronic health information.

DETECT Study Insights

The DETECT study aims to determine whether wearable sensor data can effectively complement self-reported symptoms in identifying COVID-19 positive cases. The findings were published in Nature Medicine. As of June 7, 2020, over 30,000 participants had joined the study, representing diverse demographics across the United States and utilizing various fitness tracker devices, including Fitbit, Apple HealthKit, and Google Fit. Among the 3,811 participants who reported symptoms, 54 tested positive for COVID-19, while 279 tested negative.

Key Findings and Future Directions

Analysis of the collected sensor and health data revealed that decreased activity levels and increased sleep, relative to individual baseline measurements, were significant predictors of a positive COVID-19 case. The research team achieved 80% accuracy in predicting whether individuals reporting symptoms were likely infected with the SARS-CoV-2 virus.

The preliminary results suggest that physiological changes detected by fitness trackers could lead to more efficient and cost-effective testing strategies, aiding public health officials in controlling the disease’s spread. Researchers are currently seeking additional participants to enhance their study, with the aim of refining their model for predicting COVID-19 and other viral illnesses.

Conclusion

This ongoing research may revolutionize how we monitor and respond to viral infections, highlighting the potential benefits of integrating technology into public health strategies.

References

Quer, G., Radin, J.M., Gadaleta, M. et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat Med (2020). https://doi.org/10.1038/s41591-020-1123-x

Early results from DETECT study suggest fitness trackers can predict COVID-19 infections. (2020, October 29). Retrieved from https://www.eurekalert.org/pub_releases/2020-10/sri-erf102820.php

Image by Steve Buissinne from Pixabay