Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP)
Objectives We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device.Design Interim analysis of a prospective cohort study.Setting, participants and interventions Participants from a national cohort...
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2022-06-01
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| author | Stefanie Aeschbacher David Conen Diederick E Grobbee Raphael Twerenbold Thomas Lung Theo Rispens Jakob Kjellberg Lorenz Risch Martin Risch Marianna Mitratza Harald Renz Spiros Denaxas Billy Franks Diederick Grobbee Martina Rothenbühler Janneke Wijgert Santiago Montes Richard Dobson Hans Reitsma Christian Simon Titia Leurink Charisma Hehakaya Patricia Bruijning Kirsten Grossmann Ornella C Weideli Marc Kovac Fiona Pereira Nadia Wohlwend Corina Risch Dorothea Hillmann Daniel Leibovitz Vladimir Kovacevic Andjela Markovic Paul Klaver Timo B Brakenhoff George S Downward Ariel Dowling Maureen Cronin Brianna M Goodale Brianna Goodale Ornella Weideli Regien Stokman Hans Van Dijk Eric Houtman Jon Bouwman Kay Hage Lotte Smets Marcel van Willigen Maui Chodura Niki de Vink Tessa Heikamp Timo Brakenhoff Wendy van Scherpenzeel Wout Aarts Alison Kuchta Antonella Chiucchiuini Steve Emby Annemarijn Douwes George Downward Nathalie Vigot Pieter Stolk Duco Veen Daniel Oberski Amos Folarin Pablo Fernandez Medina Eskild Fredslund |
| author_facet | Stefanie Aeschbacher David Conen Diederick E Grobbee Raphael Twerenbold Thomas Lung Theo Rispens Jakob Kjellberg Lorenz Risch Martin Risch Marianna Mitratza Harald Renz Spiros Denaxas Billy Franks Diederick Grobbee Martina Rothenbühler Janneke Wijgert Santiago Montes Richard Dobson Hans Reitsma Christian Simon Titia Leurink Charisma Hehakaya Patricia Bruijning Kirsten Grossmann Ornella C Weideli Marc Kovac Fiona Pereira Nadia Wohlwend Corina Risch Dorothea Hillmann Daniel Leibovitz Vladimir Kovacevic Andjela Markovic Paul Klaver Timo B Brakenhoff George S Downward Ariel Dowling Maureen Cronin Brianna M Goodale Brianna Goodale Ornella Weideli Regien Stokman Hans Van Dijk Eric Houtman Jon Bouwman Kay Hage Lotte Smets Marcel van Willigen Maui Chodura Niki de Vink Tessa Heikamp Timo Brakenhoff Wendy van Scherpenzeel Wout Aarts Alison Kuchta Antonella Chiucchiuini Steve Emby Annemarijn Douwes George Downward Nathalie Vigot Pieter Stolk Duco Veen Daniel Oberski Amos Folarin Pablo Fernandez Medina Eskild Fredslund |
| collection | DOAJ |
| description | Objectives We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device.Design Interim analysis of a prospective cohort study.Setting, participants and interventions Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays.Results A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.Conclusion Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial.Trial registration numberISRCTN51255782; Pre-results. |
| format | Article |
| id | doaj-art-b1badc9ebb2148ef845a940ceb6d2222 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-b1badc9ebb2148ef845a940ceb6d22222025-01-27T23:20:09ZengBMJ Publishing GroupBMJ Open2044-60552022-06-0112610.1136/bmjopen-2021-058274Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP) Stefanie Aeschbacher0David Conen1Diederick E Grobbee2Raphael Twerenbold3Thomas Lung4Theo Rispens5Jakob Kjellberg6Lorenz Risch7Martin Risch8Marianna Mitratza9Harald Renz10Spiros Denaxas11Billy Franks12Diederick GrobbeeMartina Rothenbühler13Janneke WijgertSantiago Montes14Richard Dobson15Hans ReitsmaChristian SimonTitia LeurinkCharisma HehakayaPatricia Bruijning16Kirsten Grossmann17Ornella C Weideli18Marc Kovac19Fiona Pereira20Nadia Wohlwend21Corina Risch22Dorothea Hillmann23Daniel Leibovitz24Vladimir Kovacevic25Andjela Markovic26Paul Klaver27Timo B Brakenhoff28George S Downward29Ariel Dowling30Maureen Cronin31Brianna M Goodale32Brianna GoodaleOrnella WeideliRegien StokmanHans Van DijkEric HoutmanJon BouwmanKay HageLotte SmetsMarcel van WilligenMaui ChoduraNiki de VinkTessa HeikampTimo BrakenhoffWendy van ScherpenzeelWout AartsAlison KuchtaAntonella ChiucchiuiniSteve EmbyAnnemarijn DouwesGeorge Downward33Nathalie VigotPieter StolkDuco VeenDaniel OberskiAmos FolarinPablo Fernandez MedinaEskild FredslundCardiology Division, Department of Medicine, University Hospital Basel, Basel, SwitzerlandDepartment of Medicine, McMaster University, Hamilton, Ontario, CanadaJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands2 Department of Cardiology, University Hospital Basel, Basel, Switzerland3 Dr Risch Medical Laboratory, Buchs, SwitzerlandSanquin Research, Amsterdam, The NetherlandsVIVE - The Danish Center for Social Science Research, Copenhagen, Denmark1 Dr Risch Medical Laboratory, Vaduz, Liechtenstein1 Dr Risch Medical Laboratory, Vaduz, LiechtensteinDepartment of Public Health, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands24 Institute of Laboratory Medicine and Pathobiochemistry, Phillips University of Marburg, Marburg, Germany1 Institute of Health Informatics, University College London, London, UK12 Julius Clinical, Zeist, The NetherlandsDepartment of Cardiology, University of Bern, Bern, Switzerland16 Roche Diagnostics Nederland B.V, Almere, The Netherlands4Institute of Psychiatry, Psychology and Neuroscience, King’s College London12 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands1 Dr Risch Medical Laboratory, Vaduz, Liechtenstein1 Dr Risch Medical Laboratory, Vaduz, Liechtenstein3 Dr Risch Medical Laboratory, Buchs, Switzerland6 Department of Metabolism, Digestive Diseases and Reproduction, Imperial College London, London, UK3 Dr Risch Medical Laboratory, Buchs, Switzerland3 Dr Risch Medical Laboratory, Buchs, Switzerland3 Dr Risch Medical Laboratory, Buchs, Switzerland9 Ava AG, Zurich, Switzerland9 Ava AG, Zurich, Switzerland9 Ava AG, Zurich, Switzerland12 Julius Clinical, Zeist, The Netherlands12 Julius Clinical, Zeist, The Netherlands13 UMC Utrecht, Utrecht, The Netherlands15 Takeda Pharmaceuticals, Digital Clinical Devices, Cambridge, UKindustry representative9 Ava AG, Zurich, SwitzerlandInstitute for Risk Assessment Sciences, Utrecht University, Utrecht, NetherlandsObjectives We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device.Design Interim analysis of a prospective cohort study.Setting, participants and interventions Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays.Results A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.Conclusion Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial.Trial registration numberISRCTN51255782; Pre-results.https://bmjopen.bmj.com/content/12/6/e058274.full |
| spellingShingle | Stefanie Aeschbacher David Conen Diederick E Grobbee Raphael Twerenbold Thomas Lung Theo Rispens Jakob Kjellberg Lorenz Risch Martin Risch Marianna Mitratza Harald Renz Spiros Denaxas Billy Franks Diederick Grobbee Martina Rothenbühler Janneke Wijgert Santiago Montes Richard Dobson Hans Reitsma Christian Simon Titia Leurink Charisma Hehakaya Patricia Bruijning Kirsten Grossmann Ornella C Weideli Marc Kovac Fiona Pereira Nadia Wohlwend Corina Risch Dorothea Hillmann Daniel Leibovitz Vladimir Kovacevic Andjela Markovic Paul Klaver Timo B Brakenhoff George S Downward Ariel Dowling Maureen Cronin Brianna M Goodale Brianna Goodale Ornella Weideli Regien Stokman Hans Van Dijk Eric Houtman Jon Bouwman Kay Hage Lotte Smets Marcel van Willigen Maui Chodura Niki de Vink Tessa Heikamp Timo Brakenhoff Wendy van Scherpenzeel Wout Aarts Alison Kuchta Antonella Chiucchiuini Steve Emby Annemarijn Douwes George Downward Nathalie Vigot Pieter Stolk Duco Veen Daniel Oberski Amos Folarin Pablo Fernandez Medina Eskild Fredslund Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP) BMJ Open |
| title | Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP) |
| title_full | Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP) |
| title_fullStr | Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP) |
| title_full_unstemmed | Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP) |
| title_short | Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP) |
| title_sort | investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to covid 19 an interim analysis of a prospective cohort study covi gapp |
| url | https://bmjopen.bmj.com/content/12/6/e058274.full |
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