Related Experiment Video
Updated: Jul 18, 2025

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
Published on: February 19, 2021
When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles
Johana Monthuy-Blanc1,2, Usef Faghihi1,3, Mahan Najafpour Ghazvini Fardshad1,3
1Unité de Recherche Loricorps, Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, (CR-IUSMM), 7331, Rue Hochelaga, Montreal, QC H1N 3V2, Canada.
Background:
The aim of the present study was to identify eaters profiles using the latest advantages of Machine Learning approach to cluster analysis.
Methods:
A total of 317 participants completed an online-based survey including self-reported measures of body image dissatisfaction, bulimia, restraint, and intuitive eating. Analyses were conducted in two steps: (a) identifying an optimal number of clusters, and (b) validating the clustering model of eaters profile using a procedure inspired by the Causal Reasoning approach.
Results:
This study reveals a 7-cluster model of eaters profiles. The characteristics, needs, and strengths of each eater profile are discussed along with the presentation of a continuum of eaters profiles.
Conclusions:
This conceptualization of eaters profiles could guide the direction of health education and treatment interventions targeting perceptual and eating dimensions.
Related Concept Videos
Regulation of Food Intake
Binge Eating Disorders
Self-Schemas
Assessment of the Gastrointestinal System II: Health Perception Pattern
Health Perception Patterns
Health perception patterns offer valuable insights into a patient's lifestyle habits and how they may impact their GI health. These patterns include:
Confirmation Biases
Taste Buds and Receptors

