15/03/2022

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

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.

Lire l'article complet sur : www.nature.com

15/03/2022

Successfully Implementing Digital Health to Ensure Future Global Health Security During Pandemics: A Consensus Statement | Emergency Medicine | JAMA Network Open | JAMA Network

This consensus statement discusses how stakeholders, including governments, can implement digital health policy to prepare for and address current and future pandemics.

Lire l'article complet sur : jamanetwork.com

15/03/2022

COVID-19 mapper reveals symptom variation between countries and health status | Imperial News | Imperial College London

Imperial research has unveiled how COVID-19 symptoms varied between country and health condition in 2020 in partnership with symptom mapper Healthily. Looking at 190 countries, researchers from Imperial College London found that symptoms of COVID-19, including the initial trifecta of cough, fever,...

Lire l'article complet sur : www.imperial.ac.uk

06/03/2022

Adoption of eHealth in Selected European Countries

This mini-report contains topline information on the demographics of selected European countries, their eHealth initiatives, as well as the potential impact of COVID-19 on their development. These eHealth initiatives ar

Lire l'article complet sur : echalliance.com

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