Stress among portuguese medical students: the EuStress solution

Detalhes bibliográficos
Autor(a) principal: Silva, Eliana
Data de Publicação: 2020
Outros Autores: Aguiar, Joyce, Reis, L. P., Oliveira e Sá, Jorge, Gonçalves, Joaquim, Carvalho, Victor
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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spelling 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
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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
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instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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