Quality of life and socio-demographic factors associated with nutritional risk in Brazilian community-dwelling individuals aged 80 and over: cluster analysis and ensemble methods

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art_BRECH_Quality_of_life_and_sociodemographic_factors_associated_with_2024.PDF (1.93 MB)
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0
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article
Data de publicação
2024
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
FRONTIERS MEDIA SA
Autores
MACHADO-LIMA, Adriana
SILVA, Daiane Fuga da
MICILLO, Glaucia Pegorari
BASTOS, Marta Ferreira
AQUINO, Rita de Cassia de
Citação
FRONTIERS IN NUTRITION, v.10, article ID 1183058, 13p, 2024
Projetos de Pesquisa
Unidades Organizacionais
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Resumo
IntroductionThe aim of the present study was to use cluster analysis and ensemble methods to evaluate the association between quality of life, socio-demographic factors to predict nutritional risk in community-dwelling Brazilians aged 80 and over.MethodsThis cross-sectional study included 104 individuals, both sexes, from different community locations. Firstly, the participants answered the sociodemographic questionnaire, and were sampled for anthropometric data. Subsequently, the Mini-Mental State Examination (MMSE) was applied, and Mini Nutritional Assessment Questionnaire (MAN) was used to evaluate their nutritional status. Finally, quality of life (QoL) was assessed by a brief version of World Health Organizations' Quality of Life (WHOQOL-BREF) questionnaire and its older adults' version (WHOQOL-OLD).ResultsThe K-means algorithm was used to identify clusters of individuals regarding quality-of-life characteristics. In addition, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms were used to predict nutritional risk. Four major clusters were derived. Although there was a higher proportion of individuals aged 80 and over with nutritional risk in cluster 2 and a lower proportion in cluster 3, there was no statistically significant association. Cluster 1 showed the highest scores for psychological, social, and environmental domains, while cluster 4 exhibited the worst scores for the social and environmental domains of WHOQOL-BREF and for autonomy, past, present, and future activities, and intimacy of WHOQOL-OLD.ConclusionHandgrip, household income, and MMSE were the most important predictors of nutritional. On the other hand, sex, self-reported health, and number of teeth showed the lowest levels of influence in the construction of models to evaluate nutritional risk. Taken together, there was no association between clusters based on quality-of-life domains and nutritional risk, however, predictive models can be used as a complementary tool to evaluate nutritional risk in individuals aged 80 and over.
Palavras-chave
aged, quality of life, nutritional risk, cross-sectional study, machine learning
URI
https://observatorio.fm.usp.br/handle/OPI/58268
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Coleções
Artigos e Materiais de Revistas Científicas - HC/IOT
Artigos e Materiais de Revistas Científicas - HU
Artigos e Materiais de Revistas Científicas - ODS/02