Publications by authors named "Karena Y Puldon"
Article Synopsis
- Early detection of COVID-19 is crucial for controlling transmission, and consumer wearables like the Oura Ring can help by tracking physiological metrics and gathering user-reported data.
- In a study with over 63,000 participants, a machine learning algorithm successfully predicted COVID-19 onset an average of 2.75 days before testing, achieving a sensitivity of 82% and specificity of 63%.
- The algorithm's accuracy improved when including continuous temperature data, and results showed variations based on age and sex, emphasizing the need for diverse representation in detection technology development.
There is significant variability in neutralizing antibody responses (which correlate with immune protection) after COVID-19 vaccination, but only limited information is available about predictors of these responses. We investigated whether device-generated summaries of physiological metrics collected by a wearable device correlated with post-vaccination levels of antibodies to the SARS-CoV-2 receptor-binding domain (RBD), the target of neutralizing antibodies generated by existing COVID-19 vaccines. One thousand, one hundred and seventy-nine participants wore an off-the-shelf wearable device (Oura Ring), reported dates of COVID-19 vaccinations, and completed testing for antibodies to the SARS-CoV-2 RBD during the U.
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