The psychometric network structure of mental health in eating disorder patients.
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ABSTRACT: Objective
Psychometric network analysis has led to new possibilities to assess the structure and dynamics of psychiatric disorders. The current study focuses on mental health networks in patients with anorexia nervosa, bulimia nervosa, binge eating disorder and other specified eating disorders (EDs).Method
Network analyses were applied with five mental health domains (emotional, psychological and social well-being, and general and specific psychopathology) among 905 ED patients. Also, networks of 36 underlying symptoms related to the domains were estimated. The network stability, structure and (bridge) centrality of the nodes were assessed for the total group and each ED type. Network differences between the ED types were also examined.Results
ED psychopathology was only weakly connected with the well-being domains. Psychological well-being was the most central node in the domain network. The most central nodes in the symptom network were feeling depressed, feeling worthless, purpose in life and self-acceptance. Bridge symptoms between well-being and psychopathology were self-acceptance, environmental mastery, interested in life and feeling depressed. There were no network differences between the ED types in both the domain and symptom networks.Conclusions
This study shows novel associations between well-being and psychopathology in ED patients. Central domains and their underlying symptoms may be especially important to consider in treatment for promoting mental health in ED patients.
SUBMITTER: de Vos JA
PROVIDER: S-EPMC8252750 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveEffective treatments exist for binge-eating disorder (BED), although roughly 50% of patients fail to attain binge-eating abstinence. Evidence on how to refine treatments is lacking. Conceptualizing BED as arising from a network of symptom-to-symptom interactions allows for the identification of the most strongly connected symptoms, which could inform intervention targets. This study assessed how BED symptom centrality changed with behaviorally based weight-loss treatments (BBWLTs).MethodsParticipants were 191 adult patients (71% female, 79% White) with BED with comorbid obesity participating in a randomized controlled trial testing 6-month BBWLTs for BED. Independent assessments of BED symptoms were performed at pretreatment, posttreatment, and 12 months after treatment. Strength centrality indicated how strongly and frequently symptoms were associated with each other in the network. Significant changes in centrality between timepoints were determined using permutation tests.ResultsAt pretreatment, overvaluation of shape/weight and preoccupation with shape/weight and food/eating had the highest strength centrality. At posttreatment and 12-month follow-up, dissatisfaction with shape/weight had the highest centrality, which significantly increased from pretreatment.ConclusionsThe relations among symptoms of BED are not static and change over time with treatment. BBWLTs do not appear to reduce connectivity of overvaluation of shape/weight (the most central BED symptom prior to treatment), but instead increase connectivity of dissatisfaction with shape/weight with other symptoms following treatment. The observed network structure of symptoms following BBWLTs resembles network analyses of people without eating disorders. Findings highlight the importance of understanding how treatments impact symptom relationships, not just symptom intensities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Project description:BackgroundDespite the worldwide growth in mobile health (mHealth) tools and the possible benefits for both patients and health care providers, the overall adoption levels of mHealth tools by health professionals remain relatively low.ObjectiveThis study aimed (1) to investigate attitudes of health care providers and mHealth experts toward mHealth tools in the health context in general, and this study aimed (2) to test the acceptability and feasibility of a specific mHealth tool for patients with an eating disorder (ED), called TCApp, among patients and ED specialists.MethodsTo this purpose, we conducted an explorative qualitative study with 4 in-depth group discussions with several groups of stakeholders: our first focus group was conducted with 11 experts on mHealth from the Catalan Association of Health Entities; the second focus group included 10 health care professionals from the Spanish College of Doctors of Barcelona; the third focus group involved 9 patients with an ED who had used the TCApp over a 12-week period, and the fourth and last focus group involved 8 ED specialists who had monitored such ED patients on the Web.ResultsThe focus groups showed that health care providers and mHealth experts reported barriers for mHealth adoption more often than facilitators, indicating that mHealth techniques are difficult to obtain and use. Most barriers were attributed to external factors relating to the human or organizational environment (ie, lack of time because of workload, lack of direct interest on a legislative or political level) rather than being attributed to internal factors relating to individual obstacles. The results of the mHealth intervention study indicate that the TCApp was considered as easy to use and useful, although patients and the ED specialists monitoring them on the Web reported different adoption problems, such as the inability to personalize the app, a lack of motivational and interactive components, or difficulties in adhering to the study protocol.ConclusionsIn general, this paper indicates that both health professionals and patients foresee difficulties that need to be addressed before comprehensive adoption and usage of mHealth techniques can be effectively implemented. Such findings are in line with previous studies, suggesting that although they acknowledge their possible benefits and cost-effectiveness, health care providers are quite resistant and conservative about integrating mHealth technologies in their daily practice.
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Project description:ObjectiveAs network models of eating disorder (ED) psychopathology become increasingly popular in modeling symptom interconnectedness and identifying potential treatment targets, it is necessary to contextualize their performance against other methods of modeling ED psychopathology and to evaluate potential ways to optimize and capitalize on their use. To accomplish these goals, we used generalized network psychometrics to estimate and compare latent variable models and network models, as well as hybrid models.MethodWe tested the structure of the Eating Pathology Symptoms Inventory (EPSI) and Eating Disorder Examination-Questionnaire (EDE-Q) in Recovery Record, Inc. mobile phone application users (N = 6856).ResultsAlthough all models fit well, results favored a hybrid latent variable and network framework, which showed that ED symptoms fit best when modeled as higher-order constructs, rather than direct symptom-to-symptom connections, and when the relationships between those constructs are described as a network. Hybrid models in which latent factors were modeled as nodes within a network showed that EPSI Purging, Binge Eating, Cognitive Restraint, Body Dissatisfaction, and Excessive Exercise had high importance in the network. EDE-Q Eating Concern and Shape Concern were also important nodes. Results showed that the EPSI network was highly stable and replicable, whereas the EDE-Q network was not.DiscussionIntegrating latent variable and network model frameworks enables tests of centrality to identify important latent variables, such as purging, that may promote the spread of ED psychopathology throughout a network, allowing for the identification of future treatment targets.
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2016-12-22 | GSE70872 | GEO
Project description:BackgroundNetwork science represents a powerful and increasingly promising method for studying complex real-world problems. In the last decade, it has been applied to psychometric data in the attempt to explain psychopathologies as complex systems of causally interconnected symptoms. One category of mental disorders, relevant for their severity, incidence and multifaceted structure, is that of eating disorders (EDs), serious disturbances that negatively affect a person's eating behavior.AimsWe aimed to review the corpus of psychometric network analysis methods by scrutinizing a large sample of network-based studies that exploit psychometric data related to EDs. A particular focus is given to the description of the methodologies for network estimation, network description and network stability analysis providing also a review of the statistical software packages currently used to carry out each phase of the network estimation and analysis workflow. Moreover, we try to highlight aspects with potential clinical impact such as core symptoms, influences of external factors, comorbidities, and related changes in network structure and connectivity across both time and subpopulations.MethodsA systematic search was conducted (February 2022) on three different literature databases to identify 57 relevant research articles. The exclusion criteria comprehended studies not based on psychometric data, studies not using network analysis, studies with different aims or not focused on ED, and review articles.ResultsAlmost all the selected 57 papers employed the same analytical procedures implemented in a collection of R packages specifically designed for psychometric network analysis and are mostly based on cross-sectional data retrieved from structured psychometric questionnaires, with just few exemptions of panel data. Most of them used the same techniques for all phases of their analysis. In particular, a pervasive use of the Gaussian Graphical Model with LASSO regularization was registered for in network estimation step. Among the clinically relevant results, we can include the fact that all papers found strong symptom interconnections between specific and nonspecific ED symptoms, suggesting that both types should therefore be addressed by clinical treatment.ConclusionsWe here presented the largest and most comprehensive review to date about psychometric network analysis methods. Although these methods still need solid validation in the clinical setting, they have already been able to show many strengths and important results, as well as great potentials and perspectives, which have been analyzed here to provide suggestions on their use and their possible improvement.
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