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Reviews of "Assessment of physiological signs associated with COVID-19 measured using wearable devices"

Reviewers: Chi Hwan Lee (Purdue University) | 📘📘📘📘📘 • Yehuda Weizman (Swinburne University of Technology) | 📗📗📗📗 ◻️

Published onNov 07, 2020
Reviews of "Assessment of physiological signs associated with COVID-19 measured using wearable devices"
key-enterThis Pub is a Review of
Assessment of physiological signs associated with COVID-19 measured using wearable devices

Respiration rate, heart rate, and heart rate variability are some health metrics that are easily measured by consumer devices and which can potentially provide early signs of illness. Furthermore, mobile applications which accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. We considered two approaches to assessing COVID-19 - a symptom-based approach, and a physiological signs based technique. Firstly, we trained a Logistic Regression classifier to predict the need for hospitalization of COVID-19 patients given the symptoms experienced, age, sex, and BMI. Secondly, we trained a neural network classifier to predict whether a person is sick on any specific day given respiration rate, heart rate, and heart rate variability data for that day and and for the four preceding days. Data on 1,181 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 - July 14, 2020. 11.0% of COVID-19 subjects were asymptomatic, 47.2% of subjects recovered at home by themselves, 33.2% recovered at home with the help of someone else, 8.16% of subjects required hospitalization without ventilation support, and 0.448% required ventilation. Fever was present in 54.8% of subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.77 +/- 0.05 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 +/- 0.03 for the prediction of illness on a specific day with 4 previous days of history. Respiration rate and heart rate are typically elevated by illness, while heart rate variability is decreased. Measuring these metrics can help in early diagnosis, and in monitoring the progress of the disease.

To read the original manuscript, click the link above.

Summary of Reviews: This study leverages wearable device technology to track biometrics in COVID19-afflicted individuals and develop models that predict both illness and risk of hospitalization. These results should be considered reliable.

Reviewer 1 (Chi Hwan Lee) | 📘📘📘📘📘

Reviewer 2 (Yehuda Weizman) | 📗📗📗📗◻️

RR:C19 Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

To read the reviews, click the links below.

Marge Buker:

The assessment of physiological signs using wearable devices has revolutionized the healthcare industry. These devices, such as smartwatches and fitness trackers, continuously monitor vital signs like heart rate, blood pressure, and oxygen saturation, providing real-time data that can be crucial for early detection of health issues. Wearable technology allows for constant health monitoring, which is particularly beneficial for individuals with chronic conditions, as it enables proactive management and timely interventions. Additionally, advancements in wearable technology have made these devices more accessible and user-friendly, encouraging widespread adoption and better health outcomes.

In addition to their health benefits, understanding the SIM registration process is essential for ensuring the seamless connectivity of wearable devices. A properly registered SIM card guarantees that the device remains connected to the network, enabling uninterrupted data transmission and enhancing the overall user experience.