Stress among portuguese medical students: the EuStress solution
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/68080 |
Resumo: | There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this paper, we introduce the EuStress Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit. |
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Stress among portuguese medical students: the EuStress solutionStressHeart rate variability metricsWearable devicesMedical studentsEngenharia e Tecnologia::Outras Engenharias e TecnologiasScience & TechnologyThere has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this paper, we introduce the EuStress Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit.This work was supported by the Portuguese Foundation for Science and Technology within NUP=NORTE-01-0247-FEDER-017832 (EUSTRESS Project) and by the QVida+ Project (FEDER-003446, supported by Norte Portugal Regional Operational Programme, under the PORTUGAL 2020 Partnership Agreement).SpringerUniversidade do MinhoSilva, ElianaAguiar, JoyceReis, L. P.Oliveira e Sá, JorgeGonçalves, JoaquimCarvalho, Victor20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/68080eng1573-689X10.1007/s10916-019-1520-131897774https://link.springer.com/article/10.1007%2Fs10916-019-1520-1info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-07T01:22:03Zoai:repositorium.sdum.uminho.pt:1822/68080Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:36:52.723337Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Stress among portuguese medical students: the EuStress solution |
title |
Stress among portuguese medical students: the EuStress solution |
spellingShingle |
Stress among portuguese medical students: the EuStress solution Silva, Eliana Stress Heart rate variability metrics Wearable devices Medical students Engenharia e Tecnologia::Outras Engenharias e Tecnologias Science & Technology |
title_short |
Stress among portuguese medical students: the EuStress solution |
title_full |
Stress among portuguese medical students: the EuStress solution |
title_fullStr |
Stress among portuguese medical students: the EuStress solution |
title_full_unstemmed |
Stress among portuguese medical students: the EuStress solution |
title_sort |
Stress among portuguese medical students: the EuStress solution |
author |
Silva, Eliana |
author_facet |
Silva, Eliana Aguiar, Joyce Reis, L. P. Oliveira e Sá, Jorge Gonçalves, Joaquim Carvalho, Victor |
author_role |
author |
author2 |
Aguiar, Joyce Reis, L. P. Oliveira e Sá, Jorge Gonçalves, Joaquim Carvalho, Victor |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Silva, Eliana Aguiar, Joyce Reis, L. P. Oliveira e Sá, Jorge Gonçalves, Joaquim Carvalho, Victor |
dc.subject.por.fl_str_mv |
Stress Heart rate variability metrics Wearable devices Medical students Engenharia e Tecnologia::Outras Engenharias e Tecnologias Science & Technology |
topic |
Stress Heart rate variability metrics Wearable devices Medical students Engenharia e Tecnologia::Outras Engenharias e Tecnologias Science & Technology |
description |
There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this paper, we introduce the EuStress Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2020-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/68080 |
url |
https://hdl.handle.net/1822/68080 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1573-689X 10.1007/s10916-019-1520-1 31897774 https://link.springer.com/article/10.1007%2Fs10916-019-1520-1 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132899914547200 |