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Switzerland"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Vladimir Kovacevic"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Ava AG, Zurich, Switzerland"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Duco Veen"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands, 7Optentia Research Program, North-West University, Potchefstroom, South Africa"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Marianna Mitratza"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Janneke van de Wijgert"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Billy Franks"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Clinical, Zeist, the Netherlands"},"authorIdentifierScheme":{"typeName":"authorIdentifierScheme","multiple":false,"typeClass":"controlledVocabulary","value":"ORCID"},"authorIdentifier":{"typeName":"authorIdentifier","multiple":false,"typeClass":"primitive","value":"0000-0002-6450-123X"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Santiago 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Risch"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Labormedizinisches zentrum Dr. Risch, Vaduz, Liechtenstein / Faculty of Medical Sciences, Private Universität im Fürstentum Liechtenstein, Triesen, Liechtenstein / Center of Laboratory Medicine, University Institute of Clinical Chemistry, University of Bern, Bern, Switzerland"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Ariel V Dowling"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Data Sciences Institute, Takeda Pharmaceuticals U.S.A. Inc., Cambridge, MA, USA"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Amos A Folarin"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"(Institute of Health Informatics, University College London, London, UK / National Institute for Health and Care Research Biomedical Research Centre, University College London Hospitals National Health Service Foundation Trust, London, UK / Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK / NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Patricia Bruijning"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Richard Dobson"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Institute of Health Informatics, University College London, London, UK / National Institute for Health and Care Research Biomedical Research Centre, University College London Hospitals National Health Service Foundation Trust, London, UK / Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK / NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Tessa Heikamp"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Clinical, Zeist, the Netherlands"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Paul Klaver"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Clinical, Zeist, the Netherlands"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Xi Bai"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Institute of Health Informatics, University College London, London, UK / National Institute for Health and Care Research Biomedical Research Centre, University College London Hospitals National Health Service Foundation Trust, London, UK"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Kirsten Grossman"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Labormedizinisches zentrum Dr. Risch, Vaduz, Liechtenstein"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Weideli Ornella"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Labormedizinisches zentrum Dr. Risch, Vaduz, Liechtenstein"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Paul Klaver"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Clinical, Zeist, the Netherlands"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Maureen Cronin"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Ava AG, Zurich, Switzerland"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Diederick E Grobbee"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"On behalf of COVID-RED Consortium"}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"data management"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"UMC Utrecht"}},{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Marcel van Willigen"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Clinical, Zeist, the Netherlands"}},{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Timo B Brakenhoff"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"Julius Clinical, Zeist, the Netherlands"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"Rationale:  The World Health Organization (WHO) has declared the current coronavirus disease (COVID-19) outbreak, caused by the SARS-CoV-2 virus, to be a pandemic and, therefore, a Public Health Emergency of International Concern. The COVID-19 outbreak has a huge impact on health care, but also on economic and social costs. Track-and-trace programs are important measures to control the virus, but they have their limitations such as delays in the test results (e.g., it takes time to develop symptoms after infection, to access a test, receive the test result, and for close contacts to be traced). Early traceability of the virus may help in the track-and-trace programs to control the virus. \nIt is currently thought that most – but not all – infected individuals develop symptoms, but that the infectious period starts on average two days before the first overt symptoms appear. It is estimated that pre- and asymptomatic individuals are responsible for up to half of all transmissions. By detecting infected individuals before they have overt symptoms, the proportion of transmissions by pre-symptomatic individuals could potentially be significantly reduced. \nPrimary Objective: Using laboratory-confirmed SARS-CoV-2 infections (detected via serology, PCR and/or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each of the following two algorithms to detect first time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava bracelet data when coupled with self-reported Daily Symptom Diary data, and the algorithm using self-reported Daily Symptom Diary data alone. In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. \n\nStudy design: Randomized, single-blinded, two-period, two-sequence crossover trial. The study will start with an initial Learning Phase (maximum 3 months), followed by a 3-month Period 1 and a 3-month Period 2. Each subject will undergo the experimental condition (=algorithm uses data from app and bracelet) in one of these periods and the control condition (=algorithm uses data from the app only) in the other period, but the order will be randomly assigned, resulting in Sequence 1 (experimental condition first) and Sequence 2 (control condition first). \nStudy population: A target of 20,000 subjects will be enrolled in this study. Subjects will be recruited from previously studied cohorts as well as via public campaigns. They will be invited to visit the COVID-RED web portal. When they have successfully completed the survey questions in the COVID-RED web portal, are considered eligible and have indicated interest in joining the study, then they will receive the subject information sheet and consent form. Subjects can be enrolled when they comply with the following inclusion and exclusion criteria: \nKey Inclusion criteria:\n•\tResident of the Netherlands\n•\tAt least 18 years old \n•\tMust have a smartphone that runs at least Android 8.0 or iOS 13.0 operating systems and is active for the duration of the study (in the case of a change of mobile number, study team should be notified)\n•\tBe able to read, understand and write Dutch\nKey Exclusion criteria\n•\tPrevious positive SARS-CoV-2 test result (confirmed either through PCR/antigen or antibody tests) (self-reported)\n•\tCurrent suspected (e.g., waiting for test result) coronavirus infection or symptoms of a coronavirus infection (self-reported)\n•\tElectronic implanted device (such as a pacemaker)\n•\tSuffering from cholinergic urticaria\nIntervention: All subjects will be instructed to complete the Daily Symptom Diary in the Ava COVID-RED app, wear their Ava bracelet each night and synchronise it with the app each day, during the entire period of study participation. The experimental condition (=algorithm uses app and bracelet data) will be compared to the control condition (=algorithm uses app data only).\nMain study parameters/endpoints: The primary endpoint for this study for each subject is the daily indication of potential SARS-CoV-2 infection as provided by the algorithm of the Ava COVID-RED app with or without using data from the Ava bracelet. This daily endpoint will be compared with actual SARS-CoV-2 test results (PCR/antigen and/or serology) collected before, during and at the end of study participation. For the primary comparison, this daily endpoint will be summarized over each trial period per subject to determine (1) whether a subject was ever judged to have had a high risk for a potential SARS-Cov-2 infection, and (2) whether a subject was ever confirmed to have had a SARS-CoV-2 infection by PCR/antigen and/or serology testing. For this comparison, parameters such as sensitivity, specificity, positive predictive value, and negative predictive value will be calculated.\nNature and extent of the burden and risks associated with participation, benefit and group relatedness: Subjects wearing the Ava bracelet may experience skin irritation or sensitization due to rubbing and friction. Subjects are instructed to only wear the device at night to allow the skin to dry and breath during the day. They will be instructed to discontinue wearing the Ava bracelet and contact the study team in case they experience any signs of allergic reaction, feel soreness, tingling, numbness, burning or stiffness in their hands or wrists while or after wearing the Ava bracelet. Subjects may feel uncomfortable answering health questions in the Ava COVID-RED app, but they have the choice of not responding to the questions in the app. Subjects will be asked to donate fingerprick blood for SARS-CoV-2 antibody testing at up to 4 different timepoints, which may cause minor discomfort. This study will use the existing testing infrastructure in the Netherlands provided by the Municipal Health services (GGD) for SARS-CoV-2 infection, and, only when this is not possible, PCR testing in the central study laboratory will be arranged. Recruitment and follow-up will be completely remote and take place via post, email, phone and electronic web portals. In this way, risk of SARS-CoV-2 infection is minimized as much as possible for those wanting to participate in the trial and for the staff conducting the trial.\nAnother risk for the subject is the potential breach of data security. The study team will implement security measures to prevent loss of data or unauthorised access to the data and we will follow the General Data Protection Regulation (GDPR). Data will be pseudo-anonymized within the platforms where data analysis will be performed. Data transfers will use a trial-specific identifier which is not linked to any external participant identifiers.\nOverall, the burden for the subjects is assessed as small and is justified given the importance of assessing a potential method in early detection of COVID-19. The expected benefit is large as the algorithms trained on the obtained data recordings from the Ava bracelet are expected to recognize COVID-19 earlier than the presentation of clinical symptoms. The latter would allow for earlier isolation and stratification as well as monitoring of SARS-CoV-2  infected persons preventing further spread and, if applicable, allowing for appropriate healthcare.\n\nAlgorithm; COVID-19; Early detection; Machine learning; Mobile application; Physiological parameters; Prospective; Protocol; Randomized controlled trial; SARS-CoV-2; Symptom diary; Wearable device"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Medicine, Health and Life Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Algorithm"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"COVID-19"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Early detection"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Machine learning"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Mobile application"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Physiological parameters"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Prospective"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Protocol"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Randomized controlled trial"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"SARS-CoV-2"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Symptom diary"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Wearable device"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Risch, M., Grossman, K., Aeschbacher, S., Weideli, O. C., Kovac, M., Pereira, F., Wohlwend, N., Risch, C., Hillmann, D., Lung, T., Renz, H., Twerenbold, R., Rothenbühler, M, Leibovitz, D., Kovacevic, V., Klaver, P., Brakenhoff, T. B., Franks, B., Mitratza, M., Downward, G. S., Dowling, A., Montes, S., Grobbee, D.*, Cronin, M.*, Conen, D.*, Goodale, B. M.*, & Risch, L.* (2022). 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, 12, e058274"}},{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Mitratza, M.,* Goodale, B.M.,* Shagadatova, A., Kovacevic, V., van de Wijgert, J., Brakenhoff, T.B., Dobson, R., Franks, B., Veen, D., Folarin, A.A., Grobbee, D.E., Cronin, M., & Downward, G.S. (2022). The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review. The Lancet. Digital health, 4(5), e370-e383. https://doi.org/10.1016/S2589-7500(22)00019-X"},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.1016/S2589-7500(22)00019-X"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://doi.org/10.1016/S2589-7500(22)00019-X"}},{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Brakenhoff, T. B.*, Franks, B.*, Goodale, B. M., van de Wijgert, J., Montes, S., Veen, D., Fredslund, E., Rispens, T., Risch, L., Dowling, A., Folarin, A., Bruijning, P., Dobson, R., Heikamp, T., Klaver, P., Cronin, M.*, & Grobbee, D.* (2021). A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the remote early detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial. BioMed Central: Trials, 22: 412. https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-021-05241-5"},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.1186/s13063-021-05241-5"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-021-05241-5"}},{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Kovacevic, V., Markovic, A., Veen, D., Brakenhoff, T. B., Mitratza, M., & Goodale, B. M. (2022, May). Menstrual cycle influence on COVID-19 detection using machine learning and a wearable medical device. Poster presented at the Annual Clinical and Scientific Meeting of the American College of Obstetricians and Gynecologists, San Diego, CA. https://acog.multilearning.com/acog/2022/eposters/351108/brianna.goodale.menstrual.cycle.influence.on.covid-19.detection.using.machine.html"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://acog.multilearning.com/acog/2022/eposters/351108/brianna.goodale.menstrual.cycle.influence.on.covid-19.detection.using.machine.html"}}]},{"typeName":"notesText","multiple":false,"typeClass":"primitive","value":"Any free text fields were removed from the dataset as they were: i) in Dutch and ii) occasionally contained personal identifiable information. Additionally, raw wearable data collected by the Ava data every 10 seconds while warn was pre-processed according to Ava AG’s internal, proprietary algorithms; resultingly, the dataset contains only the aggregated, smoothed data in the form of 1 data point per biophysical parameter per participant per night. \n\nThe COVID-RED project has received funding from the Innovative Medicines Initiative (https://www.imi.europa.eu) 2 Joint Undertaking under grant agreement No 101005177. This Joint Undertaking receives support from the European Union's Horizon 2020 (https://ec.europa.eu/programmes/horizon2020/) research and innovation programme and EFPIA (https://www.efpia.eu/)."},{"typeName":"language","multiple":true,"typeClass":"controlledVocabulary","value":["English"]},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"Willigen van, Marcel"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2022-12-23"}]},"dansDataVaultMetadata":{"displayName":"Data Vault 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