dc.creator | Fuster Casanovas, Aina | es |
dc.creator | Fernández Luque, Luis | es |
dc.creator | Nuñez Benjumea, Francisco J. | es |
dc.creator | Moreno Conde, Alberto | es |
dc.creator | Luque Romero, Luis Gabriel | es |
dc.creator | Bilionis, Ioannis | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Chicchi Giglioli, Irene Alice | es |
dc.creator | Vidal Alaball, Josep | es |
dc.date.accessioned | 2023-07-25T08:51:42Z | |
dc.date.available | 2023-07-25T08:51:42Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Fuster Casanovas, A., Fernandez Luque, L., Nuñez Benjumea, F.J., Moreno Conde, A., Luque Romero, L.G., Bilionis, I.,...,Vidal Alaball, J. (2022). An Artificial Intelligence–Driven Digital Health Solution to Support Clinical Management of Patients With Long COVID-19: Protocol for a Prospective Multicenter Observational Study. JMIR RESEARCH PROTOCOLS, 11 (10), e37704. https://doi.org/10.2196/37704. | |
dc.identifier.issn | 1929-0748 | es |
dc.identifier.uri | https://hdl.handle.net/11441/148195 | |
dc.description.abstract | Background: COVID-19 pandemic has revealed the weaknesses of most health systems around the world, collapsing them and
depleting their available health care resources. Fortunately, the development and enforcement of specific public health policies,
such as vaccination, mask wearing, and social distancing, among others, has reduced the prevalence and complications associated
with COVID-19 in its acute phase. However, the aftermath of the global pandemic has called for an efficient approach to manage
patients with long COVID-19. This is a great opportunity to leverage on innovative digital health solutions to provide exhausted
health care systems with the most cost-effective and efficient tools available to support the clinical management of this population.
In this context, the SENSING-AI project is focused on the research toward the implementation of an artificial intelligence–driven
digital health solution that supports both the adaptive self-management of people living with long COVID-19 and the health care
staff in charge of the management and follow-up of this population.
Objective: The objective of this protocol is the prospective collection of psychometric and biometric data from 10 patients for
training algorithms and prediction models to complement the SENSING-AI cohort.
Methods: Publicly available health and lifestyle data registries will be consulted and complemented with a retrospective cohort
of anonymized data collected from clinical information of patients diagnosed with long COVID-19. Furthermore, a prospective
patient-generated data set will be captured using wearable devices and validated patient-reported outcomes questionnaires to
complement the retrospective cohort. Finally, the ‘Findability, Accessibility, Interoperability, and Reuse’ guiding principles for
scientific data management and stewardship will be applied to the resulting data set to encourage the continuous process of
discovery, evaluation, and reuse of information for the research community at large.
Results: The SENSING-AI cohort is expected to be completed during 2022. It is expected that sufficient data will be obtained
to generate artificial intelligence models based on behavior change and mental well-being techniques to improve patients’
self-management, while providing useful and timely clinical decision support services to health care professionals based on risk
stratification models and early detection of exacerbations.
Conclusions: SENSING-AI focuses on obtaining high-quality data of patients with long COVID-19 during their daily life.
Supporting these patients is of paramount importance in the current pandemic situation, including supporting their health care
professionals in a cost-effective and efficient management of long COVID-19. | es |
dc.format | application/pdf | es |
dc.format.extent | 7 p. | es |
dc.language.iso | eng | es |
dc.publisher | JMIR PUBLICATIONS, INC | es |
dc.relation.ispartof | JMIR RESEARCH PROTOCOLS, 11 (10), e37704. | |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | COVID-19 syndrome | es |
dc.subject | Artificial intelligence | es |
dc.subject | AI | es |
dc.subject | Primary health care | es |
dc.subject | Postacute COVID-19 syndrome | es |
dc.subject | COVID-19 | es |
dc.subject | Health system | es |
dc.subject | Health care | es |
dc.subject | Health care resource | es |
dc.subject | Public health policy | es |
dc.subject | Long COVID-19 | es |
dc.subject | Mhealth | es |
dc.subject | Digital health solution | es |
dc.subject | Patient | es |
dc.subject | Clinical information | es |
dc.subject | Clinical decision support system | es |
dc.title | An Artificial Intelligence–Driven Digital Health Solution to Support Clinical Management of Patients With Long COVID-19: Protocol for a Prospective Multicenter Observational Study | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Medicina Preventiva y Salud Pública | es |
dc.relation.publisherversion | https://www.researchprotocols.org/2022/10/e37704 | es |
dc.identifier.doi | 10.2196/37704 | es |
dc.journaltitle | JMIR RESEARCH PROTOCOLS | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 10 | es |
dc.publication.initialPage | e37704 | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |