Research shows that smartwatches can help track and detect COVID-19

London: Wearable technologies such as smartwatches and activity trackers have attracted a lot of interest over the past few years due to their potential to monitor our health. During the pandemic, attention has been drawn to whether these wearables can detect physiological changes that could indicate a COVID infection. This, in turn, can help with early isolation and testing, reducing the spread of the virus. OnePlus Nord Watch launched in India; Price, features and specifications.

So what does the evidence say? Can these technologies become an effective tool in the fight against the pandemic? Let’s see. Elevated respiratory rate has been shown to be a useful biomarker for early detection of COVID. Breathing rate can be assessed using a technique called photoplethysmography, which requires only one point of contact (such as your finger or wrist).

Photoplethysmography is often affected by external factors such as ambient lighting, pressure or movement. Therefore, most of the studies that have focused on using this method to detect COVID have focused on observing people while they sleep. Electronics company Fitbit analyzed the nightly breathing rates of thousands of its device users to see if the measure could help detect COVID.

They found that during a seven-day period (one day before symptoms appeared or one day before a positive test for participants without symptoms), a proportion of people with COVID showed at least one measure of elevated respiratory rate. Although it was only detected in about one-third of patients with COVID symptoms and one-quarter of asymptomatic patients, this study shows that commercial wearable devices could potentially be a non-invasive way to detect and screen for possible COVID infections. India surpasses China to become the second largest smartwatch market in the world for the first time.

Another study looked at the potential of the American brand WHOOP’s fitness tracker to predict the risk of COVID. Data on respiratory rate and other cardiac performance from a group of people with COVID were used to train an infection prediction algorithm. The model was then tested on a separate group of people, some with COVID and others without COVID but with similar symptoms.

Based on breathing rates during sleep, the technology was able to identify 20 percent of COVID-positive cases two days before symptoms appeared and 80 percent of cases by the third day of symptoms.

A recent study found that a fertility tracker called Ava, also worn on the wrist, can identify physiological changes up to two days before the onset of COVID symptoms. The device measures signals including breathing rate, heart rate, skin temperature and blood flow, as well as the amount and quality of sleep. Data from COVID-positive patients was similarly used to inform a machine learning algorithm. Testing showed it was able to detect 68 percent of positive cases two days before symptoms became apparent.

In addition to wearables, digital technologies can also be used in other ways to detect COVID. High-quality microphones are already built into smartphones and other gadgets, paving the way for audio analytics. COVID usually affects the upper respiratory tract and vocal cords, causing a person’s voice to change. A mobile phone app trained on hundreds of audio samples from people with and without COVID has been shown to accurately determine whether a person has the virus 89 percent of the time.

My colleagues and I have developed a program that aims to tell if you have COVID by the sound of your cough. The technology is currently being studied. Research has also explored the potential of smart technology and wearables to monitor people during COVID.

For example, one team used ear devices to measure oxygen saturation, respiratory rate, heart rate and temperature every 15 minutes in high-risk patients being treated for COVID at home.

The data was monitored by a trained team and used to determine which patients might need additional medical care. At the start of the pandemic, smartphones were proposed as a potential solution to detect hypoxia through the user’s fingertip.

Hypoxia refers to low levels of oxygen in the body’s tissues and occurs silently in some COVID patients with more serious illnesses.

Wearable technology has also been used to map the effects of COVID on a larger scale. For example, data from many thousands of Fitbits highlighted changes in sleep during the pandemic (for example, at the beginning of the pandemic, people generally slept longer). Most wearable and other technologies being tested for their potential to detect COVID rely on artificial intelligence (AI) techniques, particularly machine and deep learning.

Artificial intelligence can efficiently scan large amounts of data in detail to identify relevant patterns in body signals to recognize interesting health conditions. However, biological signaling patterns can be highly variable within and between patients, so these AI models may have limitations in the real world. It should also be noted that standard wearables were not specifically designed for continuous monitoring of infectious disease symptoms.

Therefore, technology and algorithm improvements may be necessary.

We will need continued research to address these issues, as well as careful consideration of any potential privacy issues associated with collecting biological data for this purpose. But wearables and other digital technologies can provide an extra line of defense to help us contain COVID and other infectious diseases.

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