Preliminary identification of clinical cut-off of the vegetarian vegan eating disorder screener (V-EDS) in a community and self-reported clinical sample of vegetarians and vegans
Abstract
Background The vegetarian vegan eating disorder screener (V-EDS) is an 18-item self-report screening tool designed to assess the unique elements of eating disorder symptomology in vegetarians and vegans. Previous results have suggested strong initial psychometric properties in non-clinical community samples of vegetarians and vegans. The present study sought to identify a preliminary threshold cut-off score to discriminate eating disorder pathology in a self-reported clinical and community sample. Methods This study involved secondary analysis using data collected in McLean et al. (Development and preliminary validation of a novel eating disorder screening tool for vegetarians and vegans: the V-EDS, 2023), comprising 599 non-clinical participants and 51 self-reported clinical participants. Receiver operating characteristic (ROC) curve analysis was used to compute possible cut-off values for the V-EDS. Results: ROC analysis indicated good performance of the V-EDS (area under the curve = 0.87), with integration of the Youden index demonstrating a global score of ≥ 18 to be optimal in predicting clinical caseness with good sensitivity (0.804) and specificity (0.843). Conclusions The present study fills an important gap as the first to investigate an optimal V-EDS score to discriminate level of impairment from eating disorder pathology in a sample of vegetarian and vegan community and self-reported clinical participants. We extend the utility of the V-EDS in discovering good…
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.