Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India)
Abstract The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01287-2 |
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| author | Olivia K. Botonis Jonathan Mendley Shreya Aalla Nicole C. Veit Michael Fanton JongYoon Lee Vikrant Tripathi Venkatesh Pandi Akash Khobragade Sunil Chaudhary Amitav Chaudhuri Vaidyanathan Narayanan Shuai Xu Hyoyoung Jeong John A. Rogers Arun Jayaraman |
| author_facet | Olivia K. Botonis Jonathan Mendley Shreya Aalla Nicole C. Veit Michael Fanton JongYoon Lee Vikrant Tripathi Venkatesh Pandi Akash Khobragade Sunil Chaudhary Amitav Chaudhuri Vaidyanathan Narayanan Shuai Xu Hyoyoung Jeong John A. Rogers Arun Jayaraman |
| author_sort | Olivia K. Botonis |
| collection | DOAJ |
| description | Abstract The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses. |
| format | Article |
| id | doaj-art-83256a96ab9f4be2ae6c541245e4a2a7 |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-83256a96ab9f4be2ae6c541245e4a2a72025-08-20T02:17:52ZengNature Portfolionpj Digital Medicine2398-63522024-10-017111210.1038/s41746-024-01287-2Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India)Olivia K. Botonis0Jonathan Mendley1Shreya Aalla2Nicole C. Veit3Michael Fanton4JongYoon Lee5Vikrant Tripathi6Venkatesh Pandi7Akash Khobragade8Sunil Chaudhary9Amitav Chaudhuri10Vaidyanathan Narayanan11Shuai Xu12Hyoyoung Jeong13John A. Rogers14Arun Jayaraman15Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLabMax Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLabMax Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLabMax Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLabMax Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLabSibel HealthClinfinite SolutionsInduss HospitalGrant Medical College and Sir Jamshedjee Jeejeebhoy Group of HospitalsLifepoint Multispecialty HospitalTimetooth Technologies Pvt LtdBionic YantraSibel HealthCenter for Bio-Integrated Electronics, Northwestern UniversityDepartment of Biomedical Engineering, Northwestern UniversityMax Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLabAbstract The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses.https://doi.org/10.1038/s41746-024-01287-2 |
| spellingShingle | Olivia K. Botonis Jonathan Mendley Shreya Aalla Nicole C. Veit Michael Fanton JongYoon Lee Vikrant Tripathi Venkatesh Pandi Akash Khobragade Sunil Chaudhary Amitav Chaudhuri Vaidyanathan Narayanan Shuai Xu Hyoyoung Jeong John A. Rogers Arun Jayaraman Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India) npj Digital Medicine |
| title | Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India) |
| title_full | Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India) |
| title_fullStr | Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India) |
| title_full_unstemmed | Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India) |
| title_short | Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India) |
| title_sort | feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness covid infection in india |
| url | https://doi.org/10.1038/s41746-024-01287-2 |
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