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    Home > Active Ingredient News > Infection > NAT BME: Predict COVID-19 from smartwatch data

    NAT BME: Predict COVID-19 from smartwatch data

    • Last Update: 2021-01-01
    • Source: Internet
    • Author: User
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    Early detection of infectious diseases is important to reduce the spread of the disease by strengthening self-isolation and early treatment.
    currently, most diagnostic methods include sampling from nasal fluid, saliva or blood, and then detecting active infections with nucleic acids, or serologically detecting infections.
    nucleic acid-based diagnosis may require samples to be collected a few days after exposure for testing.
    these tests cannot be routinely implemented at low cost.
    Consumer wearables are an accurate and widely used technology that has previously been proven to be available for early detection of Lyme disease, and in retrospective studies, heart rate and skin temperature can be used to detect viral respiratory infections, including asymptomatic infections.
    wearable sensors are also used to detect atrial fibrillation.
    recent studies have shown that elevated heart rates measured with smartwatches can be used in epidemiological studies to track the spread of respiratory viruses.
    use of wearables has great potential to alleviate the 2019 coronavirus (COVID-19) pandemic.
    , the epidemic has infected tens of millions of people and killed more than 1 million people worldwide.
    , active infections are determined by PCR testing, which may require reliable positive results within 3 days of infection.
    PCR detection is not widely used in daily life.
    Moremore, since most infections become apparent only when symptoms appear, it is unlikely that current testing methods will identify pre-symptom carriers, so a quick and inexpensive approach to early detection of COVID-19 is urgently needed.
    smartwatches and other wearable devices are already used by tens of millions of people around the world and can measure many physiological parameters, such as heart rate, skin temperature and sleep.
    retrospectively studies the application of wearable devices in early detection of COVID-19, and proposes a method for real-time health monitoring and monitoring using physiological parameters detected by wearable devices.
    used heart rate and gait data from 5,262 people to develop an online detection algorithm that identifies the early stages of an infection through real-time heart rate monitoring and studies the relationship between symptom type and severity, heart rate signals, and the effects of the infection on activity and sleep.
    recruited a group of patients with COVID-19 or other infections, 4,642 wore smartwatches: 3,325 wore Fitbits, 984 wore Apple Watches and 428 wore Garmin devices.
    114 people reported symptoms and diagnosis dates for COVID-19 disease, and 47 others reported symptoms of a different respiratory infection and pathogens with diagnostic dates.
    developed two methods to detect abnormal physiology.
    (1) uses the RHR-Diff method (2) and the Heart Rate Step Abnormal Detection (HROS-AD) method.
    Define the disease cycle using the date the symptom appears and is diagnosed, and then we define a disease detection window for each individual based on the date the symptom appears (14 d before and after 7 d) and the date of diagnosis (when the symptom date is not available).
    the 14 d time range because 14 days in most cases covers the duration of the COVID-19 incubation period.
    , COVID-19 disease changes our pace and sleep patterns, which can be tracked by wearables.
    is currently unable to distinguish SARS-COV-2 infections from other viral infections (except for pre-symptom duration), as RHR increases are common in many respiratory infections.
    , information about the ons of any disease is valuable, especially during a pandemic, and can be followed up with appropriate testing.
    It is also possible to obtain other types of physiological measurements from wearable devices (e.g., heart rate variability, breathing rate, skin temperature, oxygen saturation and electrostatic readings) that will help distinguish diseases caused by different sources of infection and can be used to increase diagnostic sensitivity and may even predict the severity and symptoms of the disease.
    COVID-19 risk screening using wearables can provide a solution to help overcome current testing barriers and provide information for early diagnosis and treatment to reduce the spread of the disease.
    information will inform patients of self-isolation, diagnostic confirmation and early treatment.
    Mishra, T., Wang, M., Metwally, A.A. et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng 4, 1208–1220 (2020). MedSci Original Source: MedSci Original Copyright Notice: All text, images and audio and video materials on this website that indicate "Source: Mets Medicine" or "Source: MedSci Originals" are owned by Mets Medicine and are not authorized to be reproduced by any media, website or individual, and are authorized to be reproduced with the words "Source: Mets Medicine".
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