Journal article
Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study
Scientific reports, v 12(1), pp 3463-3463
02 Mar 2022
PMID: 35236896
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.
Metrics
Details
- Title
- Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study
- Creators
- Ashley E Mason - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA. ashley.mason@ucsf.eduFrederick M Hecht - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USAShakti K Davis - MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USAJoseph L Natale - Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USAWendy Hartogensis - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USANatalie Damaso - MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USAKajal T Claypool - Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USAStephan Dilchert - Department of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY, USASubhasis Dasgupta - San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USAShweta Purawat - San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USAVarun K Viswanath - Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USAAmit Klein - Department of Bioengineering: Bioinformatics, University of California San Diego, San Diego, CA, USAAnoushka Chowdhary - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USASarah M Fisher - Drexel University, Psychological and Brain Sciences (Psychology)Claudine Anglo - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USAKarena Y Puldon - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USADanou Veasna - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USAJenifer G Prather - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USALeena S Pandya - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USALindsey M Fox - Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USAMichael Busch - Vitalant Research Institute, University of California San Francisco, San Francisco, CA, USACasey Giordano - Department of Psychology, University of Minnesota - Twin Cities, Minneapolis, MN, USABrittany K Mercado - Love School of Business, Elon University, Elon, NC, USAJining Song - San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USARafael Jaimes - MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USABrian S Baum - MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USABrian A Telfer - MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USACasandra W Philipson - MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USAPaula P Collins - MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USAAdam A Rao - School of Medicine, University of California San Francisco, San Francisco, CA, USAEdward J Wang - Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USARachel H Bandi - Department of Anesthesiology, Northwestern McGaw Medical Center, Feinberg School of Medicine, Chicago, IL, USABianca J Choe - Department of Emergency Medicine, University of California Los Angeles Health, Los Angeles, CA, USAElissa S Epel - Center for Health and Community, University of California San Francisco, San Francisco, CA, USAStephen K Epstein - Beth Israel Deaconess Medical CenterJoanne B Krasnoff - Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USAMarco B Lee - Department of Neurosurgery, Santa Clara Valley Medical Center, Stanford University, San Jose, CA, USAShi-Wen Lee - Department of Emergency Medicine, Jamaica Hospital Medical Center, Jamaica, NY, USAGina M Lopez - Department of Emergency Medicine, Boston Medical Center, Boston, MA, USAArpan Mehta - Department of Anesthesiology: Pain Management and Perioperative Medicine, University of Miami, Miami, FL, USALaura D Melville - Department of Emergency Medicine, New York Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, USATiffany S Moon - Department of Anesthesiology and Pain Management, University of Texas Southwestern, Dallas, TX, USALilianne R Mujica-Parodi - Department of Biomedical Engineering, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USAKimberly M Noel - Stony Brook Medicine, Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USAMichael A Orosco - Department of Anesthesia: Perioperative and Pain Medicine, Kaiser Permanente San Diego, San Diego, CA, USAJesse M Rideout - Department of Emergency Medicine, Tufts Medical Center, Boston, MA, USAJanet D Robishaw - Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USARobert M Rodriguez - University of California, San FranciscoKaushal H Shah - Weill Cornell Medical Center, Weill Cornell Medical School, New York, NY, USAJonathan H Siegal - New York Presbyterian Queens, Weill-Cornell Medical College, Queens, NY, USAAmarnath Gupta - San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USAIlkay Altintas - San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USABenjamin L Smarr - Department of Bioengineering: Bioinformatics, University of California San Diego, San Diego, CA, USA
- Publication Details
- Scientific reports, v 12(1), pp 3463-3463
- Publisher
- Springer Nature
- Grant note
- MTEC-20-12-COVID19-Diagnostics-023 / Medical Technology Enterprise Consortium MIT CA-0156247 3144-41 / Department of Defense, Air Force Office of Scientific Research
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000763603800044
- Scopus ID
- 2-s2.0-85125600349
- Other Identifier
- 991020876805604721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Multidisciplinary Sciences