When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles

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, restra...

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Autores principales: Monthuy-Blanc, Johana, Faghihi, Usef, Fardshad, Mahan Najafpour Ghazvini, Corno, Giulia, Iceta, Sylvain, St-Pierre, Marie-Josée, Bouchard, Stéphane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455794/
https://www.ncbi.nlm.nih.gov/pubmed/37629214
http://dx.doi.org/10.3390/jcm12165172
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author Monthuy-Blanc, Johana
Faghihi, Usef
Fardshad, Mahan Najafpour Ghazvini
Corno, Giulia
Iceta, Sylvain
St-Pierre, Marie-Josée
Bouchard, Stéphane
author_facet Monthuy-Blanc, Johana
Faghihi, Usef
Fardshad, Mahan Najafpour Ghazvini
Corno, Giulia
Iceta, Sylvain
St-Pierre, Marie-Josée
Bouchard, Stéphane
author_sort Monthuy-Blanc, Johana
collection PubMed
description 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.
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spelling pubmed-104557942023-08-26 When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles Monthuy-Blanc, Johana Faghihi, Usef Fardshad, Mahan Najafpour Ghazvini Corno, Giulia Iceta, Sylvain St-Pierre, Marie-Josée Bouchard, Stéphane J Clin Med Article 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. MDPI 2023-08-08 /pmc/articles/PMC10455794/ /pubmed/37629214 http://dx.doi.org/10.3390/jcm12165172 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Monthuy-Blanc, Johana
Faghihi, Usef
Fardshad, Mahan Najafpour Ghazvini
Corno, Giulia
Iceta, Sylvain
St-Pierre, Marie-Josée
Bouchard, Stéphane
When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles
title When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles
title_full When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles
title_fullStr When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles
title_full_unstemmed When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles
title_short When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles
title_sort when eating intuitively is not always a positive response: using machine learning to better unravel eaters profiles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455794/
https://www.ncbi.nlm.nih.gov/pubmed/37629214
http://dx.doi.org/10.3390/jcm12165172
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