Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial
Introduction There is an outbreak of COVID-19 worldwide. As there is no effective therapy or vaccine yet, rigorous implementation of traditional public health measures such as isolation and quarantine remains the most effective tool to control the outbreak. When an asymptomatic individual with COVID...
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| Format: | Article |
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BMJ Publishing Group
2020-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/10/7/e038555.full |
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| author | Chung Wah Siu Chun Ka Wong Deborah Tip Yin Ho Anthony Raymond Tam Mi Zhou Yuk Ming LAU Milky Oi Yan Tang Raymond Cheuk Fung Tong Kuldeep Singh Rajput Gengbo Chen Soon Chee Chan Ivan Fan Ngai Hung |
| author_facet | Chung Wah Siu Chun Ka Wong Deborah Tip Yin Ho Anthony Raymond Tam Mi Zhou Yuk Ming LAU Milky Oi Yan Tang Raymond Cheuk Fung Tong Kuldeep Singh Rajput Gengbo Chen Soon Chee Chan Ivan Fan Ngai Hung |
| author_sort | Chung Wah Siu |
| collection | DOAJ |
| description | Introduction There is an outbreak of COVID-19 worldwide. As there is no effective therapy or vaccine yet, rigorous implementation of traditional public health measures such as isolation and quarantine remains the most effective tool to control the outbreak. When an asymptomatic individual with COVID-19 exposure is being quarantined, it is necessary to perform temperature and symptom surveillance. As such surveillance is intermittent in nature and highly dependent on self-discipline, it has limited effectiveness. Advances in biosensor technologies made it possible to continuously monitor physiological parameters using wearable biosensors with a variety of form factors.Objective To explore the potential of using wearable biosensors to continuously monitor multidimensional physiological parameters for early detection of COVID-19 clinical progression.Method This randomised controlled open-labelled trial will involve 200–1000 asymptomatic subjects with close COVID-19 contact under mandatory quarantine at designated facilities in Hong Kong. Subjects will be randomised to receive a remote monitoring strategy (intervention group) or standard strategy (control group) in a 1:1 ratio during the 14 day-quarantine period. In addition to fever and symptom surveillance in the control group, subjects in the intervention group will wear wearable biosensors on their arms to continuously monitor skin temperature, respiratory rate, blood pressure, pulse rate, blood oxygen saturation and daily activities. These physiological parameters will be transferred in real time to a smartphone application called Biovitals Sentinel. These data will then be processed using a cloud-based multivariate physiology analytics engine called Biovitals to detect subtle physiological changes. The results will be displayed on a web-based dashboard for clinicians’ review. The primary outcome is the time to diagnosis of COVID-19.Ethics and dissemination Ethical approval has been obtained from institutional review boards at the study sites. Results will be published in peer-reviewed journals. |
| format | Article |
| id | doaj-art-eadbb6da55e2450ba8c4b3147bdd7d98 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2020-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-eadbb6da55e2450ba8c4b3147bdd7d982024-12-05T04:50:13ZengBMJ Publishing GroupBMJ Open2044-60552020-07-0110710.1136/bmjopen-2020-038555Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trialChung Wah Siu0Chun Ka Wong1Deborah Tip Yin Ho2Anthony Raymond Tam3Mi Zhou4Yuk Ming LAU5Milky Oi Yan Tang6Raymond Cheuk Fung Tong7Kuldeep Singh Rajput8Gengbo Chen9Soon Chee Chan10Ivan Fan Ngai Hung11Division of Cardiology, Department of Medicine, University of Hong Kong, Hong Kong, Hong KongCardiology Division, Department of Medicine, University of Hong Kong, Hong Kong, Hong KongDivision of Infectious Diseases, Department of Medicine, University of Hong Kong, Hong Kong, Hong KongDivision of Infectious Diseases, Department of Medicine, University of Hong Kong, Hong Kong, Hong KongCardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, ChinaCardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, ChinaDivision of Infectious Diseases, Department of Medicine, University of Hong Kong, Hong Kong, Hong KongHarmony Medical Inc, Hong Kong, Hong KongBiofourmis, SingaporeResearch and Development, Biofourmis, SingaporeResearch and Development, Biofourmis, SingaporeDivision of Infectious Diseases, Department of Medicine, University of Hong Kong, Hong Kong, Hong KongIntroduction There is an outbreak of COVID-19 worldwide. As there is no effective therapy or vaccine yet, rigorous implementation of traditional public health measures such as isolation and quarantine remains the most effective tool to control the outbreak. When an asymptomatic individual with COVID-19 exposure is being quarantined, it is necessary to perform temperature and symptom surveillance. As such surveillance is intermittent in nature and highly dependent on self-discipline, it has limited effectiveness. Advances in biosensor technologies made it possible to continuously monitor physiological parameters using wearable biosensors with a variety of form factors.Objective To explore the potential of using wearable biosensors to continuously monitor multidimensional physiological parameters for early detection of COVID-19 clinical progression.Method This randomised controlled open-labelled trial will involve 200–1000 asymptomatic subjects with close COVID-19 contact under mandatory quarantine at designated facilities in Hong Kong. Subjects will be randomised to receive a remote monitoring strategy (intervention group) or standard strategy (control group) in a 1:1 ratio during the 14 day-quarantine period. In addition to fever and symptom surveillance in the control group, subjects in the intervention group will wear wearable biosensors on their arms to continuously monitor skin temperature, respiratory rate, blood pressure, pulse rate, blood oxygen saturation and daily activities. These physiological parameters will be transferred in real time to a smartphone application called Biovitals Sentinel. These data will then be processed using a cloud-based multivariate physiology analytics engine called Biovitals to detect subtle physiological changes. The results will be displayed on a web-based dashboard for clinicians’ review. The primary outcome is the time to diagnosis of COVID-19.Ethics and dissemination Ethical approval has been obtained from institutional review boards at the study sites. Results will be published in peer-reviewed journals.https://bmjopen.bmj.com/content/10/7/e038555.full |
| spellingShingle | Chung Wah Siu Chun Ka Wong Deborah Tip Yin Ho Anthony Raymond Tam Mi Zhou Yuk Ming LAU Milky Oi Yan Tang Raymond Cheuk Fung Tong Kuldeep Singh Rajput Gengbo Chen Soon Chee Chan Ivan Fan Ngai Hung Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial BMJ Open |
| title | Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial |
| title_full | Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial |
| title_fullStr | Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial |
| title_full_unstemmed | Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial |
| title_short | Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial |
| title_sort | artificial intelligence mobile health platform for early detection of covid 19 in quarantine subjects using a wearable biosensor protocol for a randomised controlled trial |
| url | https://bmjopen.bmj.com/content/10/7/e038555.full |
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