Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.

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 da

SUBMITTER: Mason AE 

PROVIDER: S-EPMC8891385 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature