The Significance of Cultural Context in the Classification of Preferred Mouth Behavior: Exploring Eating Behavior Using Causal Network Analysis

Abstract

Preferred mouth behavior studies have mostly focused on Western populations using the JBMB tool to determine how food is manipulated in the mouth. The objective of this study was to determine the relationship between preferred mouth behavior and eating behavior among the diverse group of 209 Malaysians participants. A series of Analysis of Variances and an unsupervised machine learning technique known as Latent Class Clustering were utilized to determine individual's oral behavior. The Adult Eating Behavior Questionnaire was later modeled using causal networks, specifically Partial Least Squares-Confirmatory Factor Analysis, to uncover relationship between appetitive qualities. The results from this study showed that cultural context and appropriateness play an essential role when classifying preferred mouth behavior using behavioral questionnaires and models. Furthermore, this study also determined the relationship between eating behavior and preferred mouth behavior, as well as unique differences in eating behavior observed among the mouth behavior groups.