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Early Detection Of COVID-19 Using A Smartwatch

Our take —

Physiological data derived from wearable technology in 24 US COVID-19 patients revealed latent physiological disturbance patterns—including heightened heart rate, decreased physical activity, and increased sleep duration—prior to symptom onset. A wearable technology detection algorithm correctly identified physiological abnormalities associated with pre-symptomatic COVID-19 infection in two-thirds of COVID-19 patients prior to symptom onset.

Study design

Retrospective Cohort

Study population and setting

Investigators collected survey data and physiological markers, generated from wearable technologies (e.g., Fitbits, Smart Watches), through a smartphone application from a cohort of 5,262 US-based individuals. Investigators integrated participants’ physiological and activity data from wearable technologies with other self-reported metadata (demographics, medical history, daily COVID-19 symptoms, and COVID-19 testing/diagnoses) to: 1) identify physiological changes associated with COVID-19 infection and 2) determine precision with which wearable technologies could detect these physiological changes by, or prior to, symptom onset.

Summary of Main Findings

Using Fitbit data from enrolled participants (n = 24) with self-reported COVID-19 diagnoses and complete physiological markers (from 14 days prior to symptom onset to at least 7 days after), COVID-19 diagnosis was associated with increased heart rate (median: 7 beats/minute increase) 3-7 days before symptom onset. Decreases in daily steps and increased sleep duration were observed primarily in pre-symptomatic periods but following onset of resting heart rate signals associated with COVID-19 illness. There was high variability between individuals’ physiological markers and the progression/severity of COVID-19 illness. Based on these findings, investigators developed an algorithm detecting abnormal resting heart rates associated with pre-symptomatic COVID-19 infection. The algorithm detected 67% of COVID-19 cases prior to symptom onset in 24 participants supplying 28 days of physiological data ahead of symptom onset.

Study Strengths

The authors collated large quantities of physiological data, collected in pre- and post-symptomatic periods among participants with COVID-19 infections, to characterize latent physiological markers of early COVID-19 disease. Additionally, investigators drew from various measurement approaches and statistical modeling techniques to appraise the robustness of their findings.

Limitations

Despite recruiting a large participant cohort, inferences from this study are drawn only from 24 participants with complete Fitbit records and self-reported COVID-19 diagnoses. As information about participants’ activities or behaviors was limited, some observed physiological changes could be attributed to unmeasured events (i.e., stress, other illness), rather than pre-symptomatic COVID-19 infection. In some cases, the time interval between the detection of physiological aberrations and symptoms onset stretched credulity (e.g, 15 days) given the 5 day median incubation period of COVID-19.The high volume of incomplete physiological records indicates participant data may not be missing at random, given individuals with more severe illness symptoms may have temporarily discontinued use of wearable technology. Lastly, Fitbit technology is not a gold standard for measurement of specific physiological markers and could bias the magnitudes of association reported.

Value added

This is the first study to characterize pre-symptomatic physiological changes in individuals with COVID-19 using wearable technology data, and to determine the precision with which wearable technologies, like Fitbits, can detect these physiological changes before symptom onset.

This review was posted on: 22 July 2020