A preliminary study conducted by the University of California (UC) San Francisco and UC San Diego suggests that a health tracker could detect early signs of COVID-19. An analysis of health data recorded by the Oura Ring showed that the device accurately identified higher temperatures in the early stages of the disease, even when participants had subtle or unnoticed symptoms.
Baseline body temperatures vary between individuals; they also shift continuously in response to activity levels, food intake, and other factors. A typical oral thermometer offers only a snapshot of body temperature at the moment of use. Furthermore, any temp above 98.6F is considered elevated and anything about 100.4 is labeled as a fever. However, “normal” adult body temperatures can range from 97.6–99.6°F, meaning that some individuals may have a fever at 99.4F, while others could fall within a normal range at 101.2F.
Smart wearables like the Oura Ring, on the other hand, continuously measure body temperature, along with other metrics such as heart and respiratory rates, sleep, and physical activity. Continuous monitoring helps establish a user’s unique baseline temperature while tracking temps that stay elevated for a significant amount of time.
The preliminary study is part of a much larger, worldwide effort to study the Oura Ring’s potential for COVID-19 detection. It involved a granular review of health data from 50 Oura Ring users who have recovered from a confirmed case of the illness. The study showed an accurate recording of fever in all 50 individuals around the onset of symptoms. In 38 of the participants, the Oura Ring detected fever before the user was aware of symptoms.
While some COVID-19 cases are truly asymptomatic, many cases present with subtle symptoms that the patient might not even recognize. Identifying COVID-19 in such instances would allow earlier isolation, treatment, and tracking of those patients before they unwittingly spread the disease. Researchers intend to use data from the more extensive study to develop an algorithm that screens health tracker data in real-time and alerts the user if a COVID-19 infection is suspected.
The smaller study found that respiratory rate changes and other metrics did not show a strong correlation with the onset of COVID-19. The UC team recently published the results of the preliminary study in the journal Nature: Scientific Reports.
