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dc.contributor.authorCamacho Magriñán, Patricia 
dc.contributor.authorSales Lérida, Diego 
dc.contributor.authorLeón Jiménez, Antonio
dc.contributor.authorSánchez Morillo, Daniel 
dc.contributor.otherIngeniería en Automática, Electrónica, Arquitectura y Redes de Computadoreses_ES
dc.contributor.otherIngeniería Mecánica y Diseño Industriales_ES
dc.date.accessioned2025-03-25T10:14:08Z
dc.date.available2025-03-25T10:14:08Z
dc.date.issued2025-03-18
dc.identifier.issn2227-7080
dc.identifier.urihttp://hdl.handle.net/10498/35967
dc.description.abstractChronic respiratory diseases (CRD), which include Chronic Obstructive Pulmonary Disease (COPD) and asthma, are significant global health issues, with air quality playing a vital role in exacerbating these conditions. This systematic review explores how monitoring indoor air quality (IAQ) can help manage and reduce respiratory exacerbations in CRD patients. A search of the Web of Science database, yielding 301 articles, was conducted following PRISMA guidelines. Of these, 60 met the inclusion criteria, and after screening, 21 articles were analyzed. The review identified substantial gaps in current research: the lack of standardization in IAQ monitoring; the need for considering geographic variability and for long-term longitudinal studies; and the importance of linking monitored air quality data with respiratory health indicators. It also stressed the importance of considering the heterogeneity of patients in the methodological study design, as well as the convenience of introducing recommendation systems to assess the true impact of corrective measures on indoor air quality in the homes of chronic respiratory patients. The integration of home-based IAQ monitoring with machine learning techniques to enhance our understanding of the relationship between IAQ and respiratory health is emerging as a key area for future research. Addressing all these challenges has the potential to mitigate the impact of CRD and improve the quality of life for patients.es_ES
dc.description.sponsorshipThis contribution has been supported by grant PID2021-126810OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceTechnologies, 13(3), 122es_ES
dc.subjectindoor air qualityes_ES
dc.subjectCOPDes_ES
dc.subjectasthmaes_ES
dc.subjectmachine learninges_ES
dc.subjectrespiratory diseaseses_ES
dc.subjectexacerbationses_ES
dc.titleIndoor Environmental Monitoring and Chronic Respiratory Diseases: A Systematic Reviewes_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.3390/technologies13030122
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126810OB-I00/ES/INTELIGENCIA ARTIFICIAL, SENSORES INTELIGENTES Y NUEVOS PREDICTORES FISIOLOGICOS Y MEDIOAMBIENTALES PARA UNA MEJOR GESTION DE LA EPOC/es_ES
dc.type.hasVersionVoRes_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This work is under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 Internacional