Here's what the research shows: Smartwatches Could AidDetect and Track COVID

Here's what the research shows: Smartwatches Could AidDetect and Track COVID ...

Wearable technologies, such as smartwatches and activity trackers, have attracted lots of interest over their potential to monitor our health during the epidemic. During the epidemic, attention has focused on whether these wearable devices might detect physiological changes that might indicate a COVID infection. This in turn could aid in early isolation and testing, reducing the spread of the virus.

What does the evidence say? Could these technologies be an effective tool to assist navigate the pandemic?

Let''s take a look at the past.

A higher respiratory rate, or breathing rate, has been shown to be a useful biomarker for the early detection of COVID. Respiratory rates can be estimated using a method called photoplethysmography, which requires only a single contact point (for example, your finger or wrist).

Photoplethysmography is often sensitive to external influences such as ambient light, pressure, or motion. So, most research on how to detect COVID have focused on monitoring people during sleep.

Fitbit, an electronic company, examined the nocturnal respiratory rates of tens of thousands of users of their devices to establish if this measure would help with COVID detection.

A portion of people with COVID showed at least one improvement in their health within a seven-day period (from one day before symptom onset or one day before a positive study).

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Although this was found in only one-third of symptomatic COVID sufferers and one-quarter of asymptomatic patients, this study suggests that commercial wearables may be a non-invasive technique to detect potentially COVID infections and obtain them tested.

Another study examined the potential for Whoop, a US-based fitness tracker, to predict COVID risk.

An algorithm to predict infection was used to generate data on respiratory rate and other indicators of cardiac function from a group of people with COVID.

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The model was then tested on a group of people, some with COVID, and others without COVID, but with similar symptoms.

The study found that based on the respiratory rate during sleep, the technology was able to identify 20 percent of COVID-positive cases in the two days before the diagnosis, and 80 percent of cases by the third day of symptoms.

A recent study found that a fertility tracker called Ava, which is also worn around the wrist, may detect physiological changes until two days before symptoms of COVID.

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The device is able to measure breathing rate, heart rate, skin temperature, and blood flow, as well as sleep quantity and quality. A machine learning algorithm was similar to that of COVID-positive patients.

Tests revealed that it was able to pick up 68% of positive cases up to two days before symptoms became evident.

Other forms of digital detection Outside wearables, digital technologies may be used in other ways to detect COVID. High-quality microphones are already embedded in smartphones and other devices, paving the way for audio analytics.

COVID occurs in the upper respiratory tract and vocal cords, causing modifications in a person''s voice. A mobile phone app that was developed using hundreds of audio samples from people with and without COVID has been shown to accurately detect whether a person has the virus 89 percent of the time.

My colleagues and I have developed a software that aims to detect if you have COVID due to the sound of your cough.

The technology is currently under review.

Tracking illness Research has also explored the potential for smart technologies and wearable devices to monitor individuals during a COVID infection.

In high-risk individuals who manage COVID at home, one team used an in-ear measuring cup of oxygen saturation, respiratory rate, heart rate, and temperature every 15 minutes.

The data was monitored by a trained team and used to identify which patients might need additional medical treatment. Smartphones were proposed as a viable method to detect hypoxia via the user''s fingertip.

Hypoxia is referred to low oxygen levels in the body tissues and is common among COVID patients with more serious problems, only to occur silently.

Wearable technology has been used to elucidate COVID''s effects on a broad scale. For example, data from tens of thousands of Fitbits highlighted changes in sleep during the pandemic (early in the pandemic people were generally sleeping for longer).

A new line of defence Most wearable and other technology being investigated for their potential to detect COVID are dependent on artificial intelligence (AI) methods, particularly machine learning, and deep learning.

In order to identify appropriate body signals, the AI can effectively scan a large amount of data in great detail.

In the real world, biological signals may be highly variable involving patients, so these AI models may be limitless. It''s also worth noting that outside-the-shelf wearable devices haven''t specifically been designed to continuously monitor infectious disease symptoms.

So, there may be improvements to the technology and algorithms.

For this purpose, we will be required to conduct ongoing research to address these challenges, as well as close scrutiny of possible privacy concerns.

Wearables and other digital techniques might help us maintain COVID and other infectious diseases.