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Updated: May 28, 2026

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
Published on: May 10, 2022
Sleep Quality Profiles in Youth with Eating Disorders: A Latent Profile Analysis
Elvira Anna Carbone1, Matteo Aloi2, Renato de Filippis1,3
1Department of Health Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy.
Background/Objectives:
Sleep disturbances are highly prevalent in young individuals with eating disorders (EDs) and are associated with increased psychopathology and poorer clinical outcomes. However, sleep alterations in ED populations are heterogeneous and may reflect distinct underlying clinical profiles. The study aimed to identify sleep quality profiles and examine their clinical correlates in youth with EDs.
Methods:
A total of 288 youth outpatients with EDs completed the Pittsburgh Sleep Quality Index (PSQI), along with measures of eating and general psychopathology. Latent Profile Analysis (LPA) was conducted using PSQI scores to identify distinct sleep profiles. Multinomial logistic regression models were performed to assess clinical variables of profile membership.
Results:
A four-profile solution was identified: (1) less impaired sleepers, (2) medication-using sleepers, (3) global poor sleepers, and (4) sleep-initiation-difficulty sleepers. Profiles differed significantly in ED severity, affective symptoms, emotion regulation difficulties, and sleep-related eating behaviors. Profiles characterized by greater sleep impairment exhibited higher levels of binge eating, night eating, and psychological distress. Multinomial logistic regression analyses indicated that night eating was the largest contributor to latent profile membership across all comparisons, significantly increasing the likelihood of belonging to more impaired sleep profiles.
Conclusions:
Sleep in individuals with EDs is characterized by distinct and clinically meaningful profiles rather than a uniform pattern of impairment. These findings support the clinical utility of person-centered approaches to better characterize sleep disturbances in ED populations.
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