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.
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.S. COVID-19 vaccination rollout. We found that on the night immediately following the second mRNA injection (Moderna-NIAID and Pfizer-BioNTech) increases in dermal temperature deviation and resting heart rate, and decreases in heart rate variability (a measure of sympathetic nervous system activation) and deep sleep were each statistically significantly correlated with greater RBD antibody responses. These associations were stronger in models using metrics adjusted for the pre-vaccination baseline period. Greater temperature deviation emerged as the strongest independent predictor of greater RBD antibody responses in multivariable models. In contrast to data on certain other vaccines, we did not find clear associations between increased sleep surrounding vaccination and antibody responses.
Purpose
– This paper provides a new Digital Library architecture that supports polyhierarchic ontology structure where a child concept representing an interdisciplinary subject area can have multiple parent concepts. The paper further proposes an access control mechanism for controlled access to different concepts by different users depending on the authorizations available to each such user. The proposed model thus provides a better knowledge representation and faster searching possibility of documents for modern Digital Libraries with controlled access to the system.
Design/methodology/approach
– Since the proposed Digital Library Architecture considers polyhierarchy, the underlying hierarchical structure becomes a Directed Acyclic Graph instead of a tree. A new access control model has been developed for such a polyhierarchic ontology structure. It has been shown that such model may give rise to undecidability problem. A client specific view generation mechanism has been developed to solve the problem.
Findings
– The paper has three major contributions. First, it provides better knowledge representation for present-day digital libraries, as new interdisciplinary subject areas are getting introduced. Concepts representing interdisciplinary subject areas will have multiple parents, and consequently, the library ontology introduces a new set of nodes representing document classes. This concept also provides faster search mechanism. Secondly, a new access control model has been introduced for the ontology structure where a user gets authorizations to access a concept node only if its credential supports it. Lastly, a client-based view generation algorithm has been developed so that a client’s access remains limited to its view and avoids any possibility of undecidability in authorization specification.
Research limitations/implications
– The proposed model, in its present form, supports only read and browse facilities. It would later be extended for addition and update of documents. Moreover, the paper explains the model in a single user environment. It will be augmented later to consider simultaneous access from multiple users.
Practical implications
– The paper emphasizes the need for changing the present digital library ontology to a polyhierarchic structure to provide proper representation of knowledge related to the concepts covering interdisciplinary subject areas. Possible implementation strategies have also been mentioned. This design method can also be extended for other semantic web applications.
Originality/value
– This paper offers a new knowledge management strategy to cover the gradual proliferation of interdisciplinary subject areas along with a suitable access control model for a digital library ontology. This methodology can also be extended for other semantic web applications.
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