Here is another Oura COVID study, aimed at detecting COVID-19 onset.
https://www.nature.com/articles/s41598-022-07314-0.pdf
Abstract:
Blockquote
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 confrmation of
COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset
of COVID-19 using machine learning classifcation. The algorithm identifed COVID-19 an average
of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specifcity
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
identifed 10 additional participants who self-reported COVID-19 disease with antibody confrmation.
The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90%
and specifcity 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.