Preliminary findings from a study aimed at building an algorithm that detects COVID-19 before symptoms start reveal that wearable devices can detect physiological signs of the disease simultaneously with the participants’ reporting the onset of symptoms, and in some cases even before they appear.

The COVID-19 study was conducted by Fitbit, Inc. (San Francisco, CA, USA) which has joined the fight against the pandemic by accelerating work on early disease detection through its team of data scientists with deep expertise in machine learning and predictive modeling. In just over two months, more than 100,000 Fitbit users across the US and Canada have enrolled for the Fitbit COVID-19 study, with more than 1,000 positive cases of the virus reported.

Based on the findings of the study, Fitbit devices can detect nearly 50% of COVID-19 cases one day before participants reported the onset of symptoms with 70% specificity. The finding is significant as it lets people know they should get tested a day before symptoms begin so that they can isolate and seek care sooner, thus helping to reduce the spread of COVID-19. The study also reinforces that breathing rate, resting heart rate and heart rate variability (HRV) are all useful metrics for indicating onset of illness and are best tracked at night, when the body is at rest. Fitbit’s research has shown that HRV, which is the beat-to-beat variation of the heart, often decreases in people who are exhibiting symptoms of illness, while resting heart rate and breathing rate are often elevated. In some cases, those metrics begin to signal changes nearly a week before participants reported symptoms.

Among other findings, the study revealed that on average, heart rate variability hits its lowest point the day after symptoms are reported and the increases in resting heart rate normalize, on average, at least 5–7 days after the start of symptoms. Breathing rate peaks typically on day 2 of symptoms, but there is a slight elevation, on average, for up to three weeks after symptoms start.

In addition to detecting early signals of COVID-19, the Fitbit COVID-19 study also provided some insights into the common symptoms, as well as severity, duration of illness and the symptoms most likely linked to hospitalization. For example, being older, male, or having a high BMI increased the likelihood of severe outcomes. Moreover, the study found that shortness of breath and vomiting are the symptoms most likely to predict that someone with COVID-19 will need to be hospitalized, while sore throat and stomachache were the symptoms least likely to predict the need for hospitalization.

Among the most common symptom reported by individuals with COVID-19 was fatigue, which was present in 72% of participants reporting having COVID-19. This was followed by headache (65%), body ache (63%), decrease in taste and smell (60%), and cough (59%). Of note, fever was present in just 55% of people reporting COVID-19, an indicator that temperature screening alone may not be enough to understand who might be infected. Mild cases (those who recovered at home on their own) show a median duration of eight days, while moderate cases (those who recovered at home with help from others) last about a week longer, with a median duration of 15 days. For severe cases (patients who end up requiring hospitalization), the median duration of illness was approximately 24 days. But this duration had a large spread, with several cases lasting longer than two months. Based on its study’s findings, Fitbit plans to continue developing an algorithm to detect diseases like COVID-19 and focus on expanded research in a real-world environment.

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