Smartwatches are currently the most common wearables, along with smart wristbands, finger rings, and earbuds. Some of the most popular wearables in the market are Fitbits, Apple Watches, and Garmin watches. These wearable devices collect information about physical activity, body temperature, heart rate, and sleep quality, which they use to help people track their general well-being.
During this COVID-19 pandemic, the launch of vaccines in the market is yet to emerge. It brings us a necessity for robust disease detection and monitoring of individual health through wearable sensors. With the help of these predictive platforms, wearable device users can be alerted when changes in their metrics match the symptoms associated with COVID-19.
Measuring Physiological Metrics Through Wearable Sensors for COVID-19
Like other viral illnesses, COVID-19 is associated with various physiological changes that can be monitored via wearable sensors. Many metrics derived from heartbeat such as heart rate (HR), resting heart rate (RHR), heart rate variability (HRV), and respiration rate (RR) can be the potential markers of COVID-19. Wearable devices like Apple Watch, Fitbit, WHOOP Strap, Zephyr BioHarness, and VivaLNK Vital Scout can monitor them. Many wearables can report complex metrics such as stress, activity, recovery, and sleep, calculated using cardiac and accelerometer-derived parameters.
Detecting Influenza-Like Illnesses
Wearables serve as an excellent tool for monitoring general health conditions at home. Even before the COVID-19 pandemic, researchers started studying how these wearable devices can help detect underlying illnesses. For instance, researchers had used Fitbit data to identify people who could have flu-like diseases by recording their resting heart rate and daily activity patterns.
Most Fitbit models can record and measure heart rate, which can be used for observing the user’s periods of increased resting heart rate. These models also record and measure activity, through which it indicates if there is any reduction in daily levels of activity. These two symptoms help to detect if there is any possibility of influenza-like illness. Though it is not possible to determine if the user is suffering from severe diseases like COVID-19, a sudden change in the recordings can prompt them to get diagnostic tests and remain isolated to prevent further spread.
Fever and cough are the two most common symptoms of COVID-19, which has resulted in increased use of thermometers. Body temperature usually depends on stress levels and environmental conditions. Extreme sweat evaporation results in lower skin temperature, whereas excessive physical activity may increase body temperature. There are a few wearables with temperature patches that record temperature continuously by communicating with smart devices. Though body temperature changes do not indicate a confirmed illness case, a fever alert can lead the user to take precautionary measures and get it diagnosed.
Cardiovascular Sleep, Strain and Activity Levels
Many currently available devices provide users with advanced metrics information like strain, sleep, activity, and recovery. These metrics are based on a combination of measurements that are calculated daily. The combined analysis of these metrics yields a high Signal-To-Noise Ratio (SNR), making it more likely to predict COVID-19 infection chances.
Elevated sleep duration usually predicts Influenza-like-illnesses, and in COVID-19, cases of an increase in sleep duration and a decrease in sleep quality are observed. Activity metrics report the amount of physical exertion of the wearer throughout the day or a given timeframe. Wearable sensors for monitoring activity levels can help stay physically active and healthy during the COVID-19 pandemic.
Respiration Rate (RR) monitoring is critical in COVID-19 cases as the virus severely affects the lungs. It causes a lower respiratory tract infection resulting in inflammation of lung tissues, excessive coughing, and shortness of breath. The respiratory damage caused by COVID-19 weakens the lungs’ overall efficiency, which results in an increased Respiratory Rate (RR).
About 70% of frontline staff who take care of COVID-19 patients contract the virus from them. So, the remote use of a wearable-sensor monitoring mechanism will help them objectively monitor any pre-clinical signs of infection to prevent other colleagues or patients from spreading.
Early Detection Algorithm Technology For Remote COVID-19 Monitoring
Most of the physiological changes measured by wearable devices discussed above can help detect whether a user is potentially experiencing any significant clinical symptoms of illness. By notifying the users through wearable devices, the possibilities of early-stage infections, it could allow them to get diagnosed, seek medical care, or self-isolate. It will reduce the risk of transmitting the virus during the pandemic.
Additionally, the wearables could be used for remote patient monitoring in mild cases where patients can report their vitals from home. It will save critical hospital resources and reduce the risk of transmission to others by avoiding in-person assessments. Remote patient monitoring with wearable sensor technology will provide an opportunity for developing more effective patient interventions, which will balance nurse-patient care ratios and decrease costs associated with readmission rates and futile medical care.
Adopting Wearable Sensor Technology in the Future
The development of wearable technology makes it possible to remotely measure many physiological parameters clinically useful in monitoring disease in viral illnesses. The scope of the utilization of this technology is extensive and can be used to identify individuals under home quarantine in case they need a higher level of care.
The current trials’ implementation has demonstrated the unity of self-reported symptoms, wearable data, molecular testing, and geospatial data towards developing platforms for managing COVID-19 and similar outbreaks, which may arise in the future. Hence, it is an excellent opportunity to design more devices that can accurately monitor many metrics through machine learning and detect population health status changes.
Oishee Mukherjee is a blogger, published author of 2 books, and a full-time content writer. She has worked for various industries like entertainment, technology, finance, sports, social, and digital media to name a few. Her educational background includes graduation in English Literature and an MBA in Media & Communications, which has taught her the power of storytelling that she implements to bring out the voice of the brands she works for.