Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/19230 |
Resumo: | Objective: This study aimed to identify health behaviours that determine adolescent's adherence to the Mediterranean diet (MD) through a decision tree statistical approach. Design: Cross-sectional study, with data collected through a self-fulfilment questionnaire with five sections: (1) eating habits; (2) adherence to the MD (KIDMED index); (3) physical activity; (4) health habits and (5) socio-demographic characteristics. Anthropometric and blood pressure data were collected by a trained research team. The Automatic Chi-square Interaction Detection (CHAID) method was used to identify health behaviours that contribute to a better adherence to the MD. Setting: Eight public secondary schools, in Algarve, Portugal. Participants: Adolescents with ages between 15 and 19 years (n 325). Results: According to the KIDMED index, we found a low adherence to MD in 9 center dot 0 % of the participants, an intermediate adherence in 45 center dot 5 % and a high adherence in 45 center dot 5 %. Participants that regularly have breakfast, eat vegetable soup, have a second piece of fruit/d, eat fresh or cooked vegetables 1 or more times a day, eat oleaginous fruits at least 2 to 3 times a week, and practice sports and leisure physical activities outside school show higher adherence to the MD (P < 0 center dot 001). Conclusions: The daily intake of two pieces of fruit and vegetables proved to be a determinant health behaviour for high adherence to MD. Strategies to promote the intake of these foods among adolescents must be developed and implemented. |
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Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approachMediterranean DietAdolescenceHealth behavioursDietary habitsDecision treesMachine learningObjective: This study aimed to identify health behaviours that determine adolescent's adherence to the Mediterranean diet (MD) through a decision tree statistical approach. Design: Cross-sectional study, with data collected through a self-fulfilment questionnaire with five sections: (1) eating habits; (2) adherence to the MD (KIDMED index); (3) physical activity; (4) health habits and (5) socio-demographic characteristics. Anthropometric and blood pressure data were collected by a trained research team. The Automatic Chi-square Interaction Detection (CHAID) method was used to identify health behaviours that contribute to a better adherence to the MD. Setting: Eight public secondary schools, in Algarve, Portugal. Participants: Adolescents with ages between 15 and 19 years (n 325). Results: According to the KIDMED index, we found a low adherence to MD in 9 center dot 0 % of the participants, an intermediate adherence in 45 center dot 5 % and a high adherence in 45 center dot 5 %. Participants that regularly have breakfast, eat vegetable soup, have a second piece of fruit/d, eat fresh or cooked vegetables 1 or more times a day, eat oleaginous fruits at least 2 to 3 times a week, and practice sports and leisure physical activities outside school show higher adherence to the MD (P < 0 center dot 001). Conclusions: The daily intake of two pieces of fruit and vegetables proved to be a determinant health behaviour for high adherence to MD. Strategies to promote the intake of these foods among adolescents must be developed and implemented.European RegionalDevelopment Fund (ERDF) through the Operational Program (POCTEP) 0290_MEDITA_5_PCambridge University PressSapientiaBôto, Joana MargaridaMarreiros, AnaDiogo, PatriciaPinto, EzequielMateus, Maria Palma2023-03-13T11:13:44Z2021-072021-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/19230eng10.1017/S13689800210032931475-2727info: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:RCAAP2023-07-24T10:31:39Zoai:sapientia.ualg.pt:10400.1/19230Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:08:51.689084Repositório 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 |
Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach |
title |
Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach |
spellingShingle |
Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach Bôto, Joana Margarida Mediterranean Diet Adolescence Health behaviours Dietary habits Decision trees Machine learning |
title_short |
Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach |
title_full |
Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach |
title_fullStr |
Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach |
title_full_unstemmed |
Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach |
title_sort |
Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach |
author |
Bôto, Joana Margarida |
author_facet |
Bôto, Joana Margarida Marreiros, Ana Diogo, Patricia Pinto, Ezequiel Mateus, Maria Palma |
author_role |
author |
author2 |
Marreiros, Ana Diogo, Patricia Pinto, Ezequiel Mateus, Maria Palma |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Bôto, Joana Margarida Marreiros, Ana Diogo, Patricia Pinto, Ezequiel Mateus, Maria Palma |
dc.subject.por.fl_str_mv |
Mediterranean Diet Adolescence Health behaviours Dietary habits Decision trees Machine learning |
topic |
Mediterranean Diet Adolescence Health behaviours Dietary habits Decision trees Machine learning |
description |
Objective: This study aimed to identify health behaviours that determine adolescent's adherence to the Mediterranean diet (MD) through a decision tree statistical approach. Design: Cross-sectional study, with data collected through a self-fulfilment questionnaire with five sections: (1) eating habits; (2) adherence to the MD (KIDMED index); (3) physical activity; (4) health habits and (5) socio-demographic characteristics. Anthropometric and blood pressure data were collected by a trained research team. The Automatic Chi-square Interaction Detection (CHAID) method was used to identify health behaviours that contribute to a better adherence to the MD. Setting: Eight public secondary schools, in Algarve, Portugal. Participants: Adolescents with ages between 15 and 19 years (n 325). Results: According to the KIDMED index, we found a low adherence to MD in 9 center dot 0 % of the participants, an intermediate adherence in 45 center dot 5 % and a high adherence in 45 center dot 5 %. Participants that regularly have breakfast, eat vegetable soup, have a second piece of fruit/d, eat fresh or cooked vegetables 1 or more times a day, eat oleaginous fruits at least 2 to 3 times a week, and practice sports and leisure physical activities outside school show higher adherence to the MD (P < 0 center dot 001). Conclusions: The daily intake of two pieces of fruit and vegetables proved to be a determinant health behaviour for high adherence to MD. Strategies to promote the intake of these foods among adolescents must be developed and implemented. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07 2021-07-01T00:00:00Z 2023-03-13T11:13:44Z |
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 |
http://hdl.handle.net/10400.1/19230 |
url |
http://hdl.handle.net/10400.1/19230 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1017/S1368980021003293 1475-2727 |
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 |
Cambridge University Press |
publisher.none.fl_str_mv |
Cambridge University Press |
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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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) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133335344119808 |