Ever since the earliest pedometers, portable activity trackers have only grown in popularity. Smartwatches and other comparable devices are now able to monitor breathing, heart rate, temperature, sleep quality and blood flow.

While these impressive features are useful for health-conscious people who want to track their workouts, new research published in BMJ open suggests that portable activity monitors may be able to identify COVID-19 infections days before a person becomes symptomatic.

Commonly reported COVID-19 infections include fever, cough, chest tightness, difficulty breathing, fatigue, dyspnoea, myalgia, salivation, headache, and gastrointestinal symptoms. But now that asymptomatic COVID-19 is becoming more common among healthy and vaccinated individuals, there is a proven need to identify COVID-19 cases without or before the typical symptoms.

This study examined whether the body’s physiological changes associated with COVID-19 infection can be identified by portable devices. Investigators specifically examined the Ava bracelet, a regulated, portable medical device that is commercially available as a fertility meter.

The Ava bracelet was chosen because its data was previously used for a machine learning algorithm that detected women’s ovulation in real time and tracks its wearers’ most fertile days with 90% accuracy.

The investigators analyzed COVID-19-related body changes across 4 periods of infection: incubation, presymptomatic, symptomatic, and recovery. This prospective cohort study was the first to measure physiological changes in respiratory rate, heart rate, heart rate variability, wrist skin temperature, and skin perfusion to develop a machine learning algorithm to detect presymptomatic COVID-19 infection.

Participants were recruited from the ongoing observational, population-based, prospective cohort study, genetic and phenotypic determinants of blood pressure and other cardiovascular risk factors (GAPP). A total of 1163 female participants, all under the age of 51, were included in the study from March 2020-April 2021.

Study participants wore the Ava bracelet at night, and the data collected during sleep was synced to a smartphone app every 10 seconds. Participants used the app to record activities that could potentially alter the central nervous system, such as alcohol, prescription drugs and recreational drugs. They also detected any COVID-19 symptoms.

Participants regularly took rapid antibody tests for SARS-CoV-2, and those with possible symptoms also took a PCR nasal swab test. A total of 127 subjects (11%) received COVID-19 during the study. There were no significant differences in reported background factors between the subjects who tested positive, although they were more likely to have been in contact with household members or work colleagues who had COVID-19.

Of the participants receiving COVID-19, 66 (52%) had regularly worn their Ava bracelet for at least 29 days before infection, and were thus included in the final analysis. Among the positive cohort, there were significant changes in all 5 physiological indicators of COVID-19 during incubation, presymptomatic, symptomatic, and recovery periods for infection. COVID-19 symptoms lasted an average of 8.5 days.

The machine learning algorithm was trained using 70% of the data for 2-10 days before the onset of COVID-19 symptoms, during a 40-day period of continuous tracking of the 66 participants who tested positive. This algorithm was then tested on the remaining 30% of data.

Approximately 73% of laboratory-confirmed COVID-19 infections were identified in the former “training set” and 68% in the latter “test set”.

The investigators note that these results may not be widely applicable as they were based on a small and relatively young sample population that were less likely to have severe COVID-19 infection.

However, the algorithm developed during this study is now being tested in a sample group of 20,000 people in the Netherlands, with results expected later this year. In addition, this algorithm could be applied to other portable health sensors.

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