Use of structural equation models to evaluate the relationship between eating patterns and obesity in elderly people
Author(s) -
José Eduardo Corrente,
Rafaela Vitória Barbosa Trombacco,
Giovana Fumes-Ghantous,
Sílvia Justina Papini,
Patrícia Moraes Ferreira Nunes
Publication year - 2020
Publication title -
nutrición hospitalaria
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.31
H-Index - 53
eISSN - 1699-5198
pISSN - 0212-1611
DOI - 10.20960/nh.02730
Subject(s) - confirmatory factor analysis , obesity , structural equation modeling , waist , body mass index , medicine , population , environmental health , demography , exploratory factor analysis , food frequency questionnaire , gerontology , statistics , mathematics , sociology
Introduction: in the Brazilian population it is noted that obesity is increasing in all ages, particularly in the elderly, due to changes in habits and the consumption of foods with high energy density. The objective of this study was to reanalyze the data from a Food Frequency Questionnaire (FFQ) in order to obtain new food patterns from the elderly population. Methods: sociodemographic data, morbidities, and Food Frequency Questionnaire (FFQ) results were collected from a representative sample of 355 elderly in the city of Botucatu, São Paulo, Brazil, and stratified by Basic Health Unit in the municipality. The data from the FFQ were transformed into daily consumption, and only foods with an intake of at least 40 % were included in the analysis. Eating patterns were obtained by means of an exploratory and confirmatory factor analysis using the principal component method, associating them with obesity and demographic variables obtained via structural equation models (SEMs). Results: with the data from the FFQ, four eating patterns were obtained using the exploratory and confirmatory factor analysis: healthy, traditional, snacks and weekend meals, and mild diet. Using SEMs and considering general obesity as measured by the body mass index (BMI), being female, younger, hypertensive, diabetic, and having lower adherence to the traditional pattern increases BMI. Additionally, using SEMs and considering central obesity as measured by waist circumference (WC), being hypertensive or diabetic, and having a lower adherence to the traditional pattern increases WC. Conclusion: removing excess zeroes from FFQ data it was possible to obtain well-defined eating patterns using the exploratory and confirmatory analysis, and to associate them with obesity through SEMs.
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