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TL;DR
This cross-sectional study used machine learning clustering on survey data to identify seven distinct eaters profiles in the general population during the COVID-19 pandemic, revealing a continuum from functional to dysfunctional eaters.
Journal of Clinical Medicine · 7 authors, 5 centres
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This cross-sectional study used machine learning clustering on survey data to identify seven distinct eaters profiles in the general population during the COVID-19 pandemic, revealing a continuum from functional to dysfunctional eaters.
The analysis identified seven distinct eaters profiles, grouped into four categories along a continuum from functional to dysfunctional eaters. The central finding is the creation of a more nuanced continuum of eaters profiles, which refines previous models. Key limitations include the cross-sectional design, which prevents causal conclusions, a sample predominantly of self-identified women, and reliance on self-reported data. The authors suggest that the identified profiles could guide health interventions, particularly for the five profiles showing problematic relationships between body perceptions and eating attitudes.