Emotional regulation in eating disorders and gambling disorder: A transdiagnostic approach

Abstract

Background and aimsDifficulties in Emotion Regulation (ER) are related to the etiology and maintenance of several psychological disorders, including Eating Disorders (ED) and Gambling Disorder (GD). This study explored the existence of latent empirical groups between both disorders, based on ER difficulties and considering a set of indicators of personality traits, the severity of the disorder, and psychopathological distress.MethodsThe sample included 1,288 female and male participants, diagnosed with ED (n = 906) and GD (n = 382). Two-step clustering was used for the empirical classification, while analysis of variance and chi-square tests were used for the comparison between the latent groups.ResultsThree empirical groups were identified, from the most disturbed ER profile (Subgroup 1) to the most functional (Subgroup 3). The ER state showed a linear relationship with the severity of each disorder and the psychopathological state. Different personality traits were found to be related to the level of emotion dysregulation.Discussion and conclusionIn this study, three distinct empirical groups based on ER were identified across ED and GD, suggesting that ER is a transdiagnostic construct. These findings may lead to the development of common treatment strategies and more tailored approaches.

Emotion regulation as a transdiagnostic factor in eating disorders and gambling disorder: Treatment outcome implications

“…Results: The three subgroups found in the previous cross-sectional study were taken for the performance of the present research. ED and GD distribution in each subgroup replicates the one exposed by Munguía et al (2021), as well as their characterization, considering psychopathology, disorder severity and personality traits. The low ER subgroup reported a better response to treatment, whereas the severe group had the highest rates of non-remission and dropouts.…”

“…In this sense, ER difficulties have been suggested as an underlying transdiagnostic factor (Sloan et al, 2017). Munguía et al (2021) explored ER as a transdiagnostic construct between EDs and GD through a joint clinical clustering analysis. The ER severity profile followed a linear relationship with the general psychopathology and severity of the disorder: subgroup-1 (Sg1) corresponds to the "severe profile", subgroup-2 (Sg2) to the "moderate profile", and subgroup-3 (Sg3) to the "low profile".…”

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Background: A first approach addressed to ascertain whether emotion regulation (ER) could be a transdiagnostic construct between eating disorders (EDs) and gambling disorder (GD) (through a joint clinical clustering analysis of both disorders) was performed by Munguía et al (2021). Both conditions were represented by a severe, moderate, and low ER profile subgroups, according to the degree of ER difficulties.

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Metabolic, Affective and Neurocognitive Characterization of Metabolic Syndrome Patients with and without Food Addiction. Implications for Weight Progression

“…A possible common feature between both comorbidities could be the use of the eating behavior as a way to regulate negative emotional states that may lead to overeating [ 64 ], as well as to changes in the food intake regulation [ 2 ], establishing a continuous feedback between each other [ 10 ]. Depression has been associated with higher daily caloric intake [ 64 ], and high adipose mass has been associated with several metabolic disturbances that are implicated in the control of emotions and mood [ 65 , 66 ]. This may explain the increase of depression in our sample, from minimal depression in those without FA, to mild depression in those with FA, according to the standardized cut-offs [ 37 ].…”

Profiles of Stress and Coping Associated With Mental, Behavioral, and Internet Use Problems Among Adolescents During the COVID-19 Pandemic: A Stratified Random Sampling and Cluster Analysis

“…Silhouette values fall within the range of −1 to 1 and higher values denote better matching in one's own cluster (similarity within the cluster) and poor matching in other clusters (different compared to the other clusters). Values lower than 0.30 are interpreted as having a poor fit, between 0.30 and 0.50 as fair, and higher than 0.50 as good ( 37 ).…”