Your browser doesn't support javascript.
Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.
Mason, Ashley E; Hecht, Frederick M; Davis, Shakti K; Natale, Joseph L; Hartogensis, Wendy; Damaso, Natalie; Claypool, Kajal T; Dilchert, Stephan; Dasgupta, Subhasis; Purawat, Shweta; Viswanath, Varun K; Klein, Amit; Chowdhary, Anoushka; Fisher, Sarah M; Anglo, Claudine; Puldon, Karena Y; Veasna, Danou; Prather, Jenifer G; Pandya, Leena S; Fox, Lindsey M; Busch, Michael; Giordano, Casey; Mercado, Brittany K; Song, Jining; Jaimes, Rafael; Baum, Brian S; Telfer, Brian A; Philipson, Casandra W; Collins, Paula P; Rao, Adam A; Wang, Edward J; Bandi, Rachel H; Choe, Bianca J; Epel, Elissa S; Epstein, Stephen K; Krasnoff, Joanne B; Lee, Marco B; Lee, Shi-Wen; Lopez, Gina M; Mehta, Arpan; Melville, Laura D; Moon, Tiffany S; Mujica-Parodi, Lilianne R; Noel, Kimberly M; Orosco, Michael A; Rideout, Jesse M; Robishaw, Janet D; Rodriguez, Robert M; Shah, Kaushal H; Siegal, Jonathan H.
  • Mason AE; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA. ashley.mason@ucsf.edu.
  • Hecht FM; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA.
  • Davis SK; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.
  • Natale JL; Halicioglu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
  • Hartogensis W; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA.
  • Damaso N; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.
  • Claypool KT; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.
  • Dilchert S; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Dasgupta S; Department of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY, USA.
  • Purawat S; San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USA.
  • Viswanath VK; San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USA.
  • Klein A; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
  • Chowdhary A; Department of Bioengineering: Bioinformatics, University of California San Diego, San Diego, CA, USA.
  • Fisher SM; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA.
  • Anglo C; Department of Psychology, Drexel University, Pennsylvania, PA, USA.
  • Puldon KY; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA.
  • Veasna D; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA.
  • Prather JG; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA.
  • Pandya LS; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA.
  • Fox LM; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA.
  • Busch M; Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, USA.
  • Giordano C; Vitalant Research Institute, University of California San Francisco, San Francisco, CA, USA.
  • Mercado BK; Department of Psychology, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
  • Song J; Love School of Business, Elon University, Elon, NC, USA.
  • Jaimes R; San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USA.
  • Baum BS; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.
  • Telfer BA; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.
  • Philipson CW; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.
  • Collins PP; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.
  • Rao AA; MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.
  • Wang EJ; School of Medicine, University of California San Francisco, San Francisco, CA, USA.
  • Bandi RH; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
  • Choe BJ; Department of Anesthesiology, Northwestern McGaw Medical Center, Feinberg School of Medicine, Chicago, IL, USA.
  • Epel ES; Department of Emergency Medicine, University of California Los Angeles Health, Los Angeles, CA, USA.
  • Epstein SK; Center for Health and Community, University of California San Francisco, San Francisco, CA, USA.
  • Krasnoff JB; Department of Emergency Medicine, Beth Israel Deaconess Medical Center Boston, Boston, MA, USA.
  • Lee MB; Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USA.
  • Lee SW; Department of Neurosurgery, Santa Clara Valley Medical Center, Stanford University, San Jose, CA, USA.
  • Lopez GM; Department of Emergency Medicine, Jamaica Hospital Medical Center, Jamaica, NY, USA.
  • Mehta A; Department of Emergency Medicine, Boston Medical Center, Boston, MA, USA.
  • Melville LD; Department of Anesthesiology: Pain Management and Perioperative Medicine, University of Miami, Miami, FL, USA.
  • Moon TS; Department of Emergency Medicine, New York Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, USA.
  • Mujica-Parodi LR; Department of Anesthesiology and Pain Management, University of Texas Southwestern, Dallas, TX, USA.
  • Noel KM; Department of Biomedical Engineering, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA.
  • Orosco MA; Stony Brook Medicine, Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA.
  • Rideout JM; Department of Anesthesia: Perioperative and Pain Medicine, Kaiser Permanente San Diego, San Diego, CA, USA.
  • Robishaw JD; Department of Emergency Medicine, Tufts Medical Center, Boston, MA, USA.
  • Rodriguez RM; Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USA.
  • Shah KH; Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA.
  • Siegal JH; Weill Cornell Medical Center, Weill Cornell Medical School, New York, NY, USA.
Sci Rep ; 12(1): 3463, 2022 03 02.
Article in English | MEDLINE | ID: covidwho-1721583
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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Body Temperature / Wearable Electronic Devices / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-07314-0

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Body Temperature / Wearable Electronic Devices / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-07314-0