A comparison of three statistical methods applied in the identification of eating patterns

Cunha, Diana Barbosa; Almeida, R. M. V. R.; Pereira, Rosângela Alves

doi:10.1590/s0102-311x2010001100015

Cited by 30 publications

(34 citation statements)

References 43 publications

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“…Regarding lunch and dinner, the foods that characterised the meal as 'traditional' were the combination of rice and beans, and in dinner, in addition to these foods, the DP was characterised by greens, beef, salad dressings and natural condiments. This food combination is shown in most Brazilian studies about overall DP (10,12,14,(39)(40)(41)(42)(43) . It is interesting to point out that the traditional DP of dinner comprises rice, beans, salads and beef, whereas for lunch the traditional pattern was just identified with rice and beans, a pattern for the salads and another one for the meats.…”

Section: Discussionmentioning

confidence: 64%

Dietary patterns for meals of Brazilian adults

et al. 2015

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The use of dietary patterns (DP) in nutritional research is well established; however, only a few studies of DP according to specific meals have been conducted. The purpose of this study was to identify the DP regarding breakfast, lunch and dinner meals of the population (aged 20 years and older of both sexes) that participated in the Health Care Survey of Sao Paulo. Food intake was estimated by using the Multiple Source Method - considering two 24- h dietary recalls. On the basis of the food groups for each meal, a factor analysis, with a principal component estimation, was applied (varimax rotation) in order to derive the DP. Prevalences of meal skipping were 5·6 % for breakfast, 3·6 % for lunch and 12·8 % for dinner. The findings revealed three breakfast DP: healthy, traditional and snack; five lunch DP: traditional, salad, sweetened juice, Western and meats; and four dinner DP: coffee with milk and bread, transitional, traditional, and soup and fruits. The results of this study indicate that the DP identified in accordance with the meal nicely discriminates food intake, emphasising peculiarities that are not found in global analyses and might support dietary advice.

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Section: Discussionmentioning

confidence: 64%

Dietary patterns for meals of Brazilian adults

et al. 2015

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“…The cut-off point for communality is arbitrary, and each author makes his or her own decision based on the desired explanation level. In the nutrition field, some authors used cut-off values equal to or greater than 0.10 23 and 0.25 24 , that is, they considered acceptable variables that explained at least 10 and 25% of variance; however, most articles do not mention it. In this study, we decided to present all communalities.…”

Section: Discussionmentioning

confidence: 99%

Principal Component Analysis and Factor Analysis: differences and similarities in Nutritional Epidemiology application

Santos

Gorgulho

Castro

et al. 2019

Rev. bras. epidemiol.

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Introduction: Statistical methods such as Principal Component Analysis (PCA) and Factor Analysis (FA) are increasingly popular in Nutritional Epidemiology studies. However, misunderstandings regarding the choice and application of these methods have been observed. Objectives: This study aims to compare and present the main differences and similarities between FA and PCA, focusing on their applicability to nutritional studies. Methods: PCA and FA were applied on a matrix of 34 variables expressing the mean food intake of 1,102 individuals from a population-based study. Results: Two factors were extracted and, together, they explained 57.66% of the common variance of food group variables, while five components were extracted, explaining 26.25% of the total variance of food group variables. Among the main differences of these two methods are: normality assumption, matrices of variance-covariance/correlation and its explained variance, factorial scores, and associated error. The similarities are: both analyses are used for data reduction, the sample size usually needs to be big, correlated data, and they are based on matrices of variance-covariance. Conclusion: PCA and FA should not be treated as equal statistical methods, given that the theoretical rationale and assumptions for using these methods as well as the interpretation of results are different.

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“…Because of the complexity of diet, in which foods and nutrients more likely act in synergy rather than a simple additive fashion [ 13 , 14 ], dietary patterns analyses play an important role in assessing the relations between diet and disease [ 15 , 16 ]. Two approaches of deriving dietary patterns have been frequently used: the cluster analysis and the exploratory factor analysis (EFA) [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. According to individual variations of dietary intakes, cluster analysis can assign participants to subgroups in which dietary intakes are relatively homogeneous.…”

Section: Introductionmentioning

confidence: 99%

Gender Differences in Dietary Patterns and Their Association with the Prevalence of Metabolic Syndrome among Chinese: A Cross-Sectional Study

Qiao

Huang

et al. 2016

Nutrients

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Few studies have investigated gender differences in dietary intake. The objective of this cross-sectional study was to examine gender differences in dietary patterns and their association with the prevalence of metabolic syndrome. The food intakes of 3794 subjects enrolled by a two-stage cluster stratified sampling method were collected using a valid semi-quantitative food frequency questionnaire (FFQ). Metabolic syndrome (MetS) was defined according to the International Diabetes Federation (IDF) and its prevalence was 35.70% in the sample (37.67% in men and 24.67% in women). Dietary patterns were identified using factor analysis combined with cluster analysis and multiple group confirmatory factor analysis was used to assess the factorial invariance between gender groups. The dominating dietary pattern for men was the “balanced” dietary pattern (32.65%) and that for women was the “high-salt and energy” dietary pattern (34.42%). For men, the “animal and fried food” dietary pattern was related to higher risk of MetS (odds ratio: 1.27; 95% CI: 1.01–1.60), after adjustment for age, marital status, socioeconomic status and lifestyle factors. For women, the “high-salt and energy” dietary pattern was related to higher risk of MetS (odds ratio: 2.27; 95% CI: 1.24–4.14). We observed gender differences in dietary patterns and their association with the prevalence of MetS. For men, the “animal and fried food” dietary pattern was associated with enhancive likelihood of MetS. For women, it was the “high-salt and energy” dietary pattern.

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A comparison of three statistical methods applied in the identification of eating patterns

Abstract: Abstract

Cited by 30 publications

References 43 publications

Dietary patterns for meals of Brazilian adults

Dietary patterns for meals of Brazilian adults

Principal Component Analysis and Factor Analysis: differences and similarities in Nutritional Epidemiology application

Gender Differences in Dietary Patterns and Their Association with the Prevalence of Metabolic Syndrome among Chinese: A Cross-Sectional Study

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