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Validation of Self-Quantification Xiaomi Band in a Clinical Sleep Unit
dc.contributor.author | Martínez-Martínez, Francisco José | |
dc.contributor.author | Concheiro-Moscoso, Patricia | |
dc.contributor.author | Miranda-Duro, María del Carmen | |
dc.contributor.author | Docampo Boedo, Francisco | |
dc.contributor.author | Mejuto Muiño, Francisco Javier | |
dc.contributor.author | Groba, Betania | |
dc.date.accessioned | 2020-10-19T08:24:45Z | |
dc.date.available | 2020-10-19T08:24:45Z | |
dc.date.issued | 2020-08-21 | |
dc.identifier.citation | Martínez-Martínez FJ, Concheiro-Moscoso P, Miranda-Duro MC, Docampo Boedo F, Mejuto Muiño FJ, Groba B. Validation of Self-Quantification Xiaomi Band in a Clinical Sleep Unit. Proceedings. 2020; 54(1):29 | es_ES |
dc.identifier.issn | 2504-3900 | |
dc.identifier.uri | http://hdl.handle.net/2183/26457 | |
dc.description.abstract | [Abstract] Polysomnography (PSG) is currently the accepted gold standard for sleep studies, as it measures multiple variables that lead to a clear diagnosis of any sleep disorder. However, it has some clear drawbacks, since it can only be performed by qualified technicians, has a high cost and complexity and is very invasive. In the last years, actigraphy has been used along PSG for sleep studies. In this study, we intend to assess the capability of the new Xiaomi Mi Smart Band 5 to be used as an actigraphy tool. Sleep measures from PSG and Xiaomi Mi Smart Band 5 recorded in the same night will be obtained and further analysed to assess their concordance. For this analysis, we perform a paired sample t-test to compare the different measures, Bland–Altman plots to evaluate the level of agreement between the Mi Band and PSG and Epoch by Epoch analysis to study the ability of the Mi Band to correctly identify PSG-defined sleep stages. This study belongs to the research field known as participatory health, which aims to offer an innovative healthcare model driven by the patients themselves, leading to civic empowerment and self-management of health. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.uri | https://doi.org/10.3390/proceedings2020054029 | es_ES |
dc.rights | Atribución 4.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/es/ | * |
dc.subject | Sleep | es_ES |
dc.subject | Polysomnography | es_ES |
dc.subject | Participatory health | es_ES |
dc.subject | Xiaomi Mi Smart Band 5 | es_ES |
dc.subject | Internet of Things | es_ES |
dc.title | Validation of Self-Quantification Xiaomi Band in a Clinical Sleep Unit | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Proceedings | es_ES |
UDC.volume | 54 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 29 | es_ES |
UDC.conferenceTitle | 3rd XoveTIC Conference; A Coruña, Spain; 8–9 October 2020 | es_ES |