ArticleThe International journal of eating disorders2026
Weight Trajectories During Inpatient Treatment for Anorexia Nervosa: A Dynamic Time Warp Analysis.
Article in The International journal of eating disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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- Weight Trajectories During Inpatient Treatment for Anorexia Nervosa: A Dynamic Time Warp Analysis.The International journal of eating disorders · 2026Article
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6 authors.
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Abstract
backgroundRestoring weight is a primary goal during anorexia nervosa (AN) treatment. Previous studies linked different weight gain profiles to treatment outcomes, but there is currently no consensus on profile shapes and numbers. We argue that heterogeneity stems from temporal distortions ("warping") in weight gain, and that similar weight improvements can stretch over different time periods. We thus favor a novel non-parametric solution that accounts for warping to identify weight trajectories.
methodTime series clustering with dynamic time warping (DTW) was used to identify weight change trajectories among N = 518 patients with AN during inpatient treatment. Within-person body-mass-index gain (∆ BMI) served as our primary dependent variable to identify clusters. We characterized clusters based on admission psychopathology scores, and analyzed associations of cluster affiliation with changes in clinical outcomes between admission and discharge using linear and logistic models.
resultsWe identified four distinct clusters, with n = 76 patients showing initial weight gain (Cluster 1), n = 329 showing continuous weight gain (Cluster 2), n = 70 showing initial weight loss and recovery (Cluster 3), and n = 43 showing weight loss (Cluster 4). The four clusters differed in terms of admission BMI, psychopathology scores, and days spent in treatment, and cluster assignment predicted treatment outcomes.
conclusionUsing one of the largest hitherto examined samples for weight gain profile analysis, the novel DTW-based approach provided an overall more elaborated set of outcome-predictive profiles compared to previous studies, which could help inform individualized treatment strategies and allocate therapeutic resources efficiently.
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