Comparison of habitual and meal-specific dietary patterns identified by latent class analysis and confirmatory factor analysis in adults: A cross-sectional study.
Nutrition
; 115: 112185, 2023 Nov.
Article
in En
| MEDLINE
| ID: mdl-37634394
ABSTRACT
OBJECTIVE:
The aim of this study was to compare characteristics of habitual and meal-specific dietary patterns identified by latent class analysis (LCA) and confirmatory factor analysis (CFA).METHODS:
Participants included 778 adults selected for the present cross-sectional study from local health care centers in Tehran, Iran. Three 24-h dietary recalls evaluated dietary intake. LCA was used to group study participants into exclusive subgroups of individuals with similar patterns of dietary intake. CFA was applied to identify patterns of habitual and meal-specific dietary intake. Analysis of variance was used to compare the average scores of habitual and meal-specific CFA-derived dietary patterns across classes identified by LCA.RESULTS:
Using habitual dietary intake, CFA grouped correlated food items into three major factors fruits and vegetables, mixed, and Western dietary patterns. LCA grouped study participants with similar patterns of habitual intake into four subgroups of individuals fruits and vegetables, mixed, Western, and low consumer classes. LCA-fruits and vegetables, LCA-Western, and LCA-mixed classes had, respectively, higher mean scores of CFA-fruits and vegetables, CFA-Western, and CFA-mixed dietary patterns compared with other classes (P < 0.001). Similar findings were observed for meal-specific dietary intake, where classes identified by LCA had the highest mean scores of their corresponding dietary pattern identified by CFA.CONCLUSION:
Habitual and meal-specific classes identified by LCA were well characterized by the dietary patterns derived by CFA, suggesting that LCA may be an appropriate statistical approach to classify study participants with similar patterns of intake into exclusive subgroups of individuals.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Observational_studies
/
Prevalence_studies
/
Prognostic_studies
Language:
En
Journal:
Nutrition
Journal subject:
CIENCIAS DA NUTRICAO
Year:
2023
Document type:
Article
Affiliation country:
Iran