Could fitness trackers predict COVID-19?
Early research suggests that fitness trackers can predict COVID-19 by tracking changes in a person's activity.
One of the keys to preventing the spread of SARS-CoV-2 – the virus responsible for COVID-19 disease – is to quickly identify, track, and isolate cases before they can be passed on to others. This was particularly difficult in part due to the lack of quick and reliable tests. In the United States, current screening measures for COVID-19 consist of temperature measurements as well as survey questions about travel history and symptoms. Current screening methods may not be reliable enough, however, as many people with COVID-19 may be asymptomatic or pre-symptomatic (which makes up approximately 40 to 45% of those infected with COVID-19) but are still infectious. Additionally, a high temperature reading (above 37.8 ° C or 100 ° F) is not seen as often in COVID-19 illnesses as many believe. Research shows that only 12% of those COVID-19 who tested positive have a high temperature and only 31% of hospitalized patients with COVID-19 have a high temperature upon admission.
Therefore, researchers in the United States have begun studying the role of wearable sensor data (e.g., from smartwatches or activity trackers) to understand how fitness trackers can predict COVID-19. Researchers developed an app-based research platform and database (DETECT) where wearable sensor data, self-reported symptoms, diagnoses and electronic health information can be shared by users. The aim is to use the shared health data to better identify and track viral diseases such as COVID-19 in individuals and in the population. In the DETECT (Digital Engagement and Tracking for Early Control and Treatment) study, researchers examined whether wearable sensor data could be used in addition to self-reported symptom data to help identify COVID-19 positive cases versus COVID-19 negative ones Cases to improve participating users. The results of the study were published in Nature Medicine.
By June 7, 2020, more than 30,000 participants had registered for the study. Individuals were represented throughout the USA and connected to various fitness tracker devices such as Fitbit, Apple HealthKit or Google Fit. Of the 3,811 participants who self-reported symptoms, 54 had tested positive for COVID-19 and 279 had tested negative. Analysis of sensor and health data from people who reported symptoms and had tested for COVID-19 found that decreased activity and increased sleep (compared to the patient's normal baseline) were significant factors in predicting positive COVID-19 19 falls were. Using the data from the fitness trackers, the research team was able to predict with an accuracy of 80% whether a single self-reporting symptom is likely to be infected with the SARS-CoV-2 coronavirus.
The early results of this research indicate that changes in physiological activity detected by fitness trackers could potentially be used to enable a more efficient and cost-effective testing strategy to help health authorities control the spread of the disease. The research team is now recruiting more participants to advance their research in hopes of improving the model of using fitness trackers to predict COVID-19 and other viral diseases.
Written by Maggie Leung, PharmD.
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
Initial results from the DETECT study suggest that 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
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