Using individual networks to identify treatment targets for eating disorder treatment: a proof-of-concept study and initial data

Abstract

Background Eating disorders (EDs) are severe mental illnesses, with high morbidity, mortality, and societal burden. EDs are extremely heterogenous, and only 50% of patients currently respond to first-line treatments. Personalized and effective treatments for EDs are drastically needed. Methods The current study (N = 34 participants with an ED diagnosis collected throughout the United States) aimed to investigate best methods informing how to select personalized treatment targets utilizing idiographic network analysis, which could then be used for evidence based personalized treatment development. We present initial data collected via experience sampling (i.e., ecological momentary assessment) over the course of 15 days, 5 times a day (75 total measurement points) that were used to select treatment targets for a personalized treatment for EDs. Results Overall, we found that treatment targets were highly variable, with less than 50% of individuals endorsing central symptoms related to weight and shape, consistent with current treatment response rates for treatments designed to target those symptoms. We also found that different aspects of selection methods (e.g., number of items, type of centrality measure) impacted treatment target selection. Conclusions We discuss implications of these data, how to use idiographic network analysis to personalize treatment, and identify areas that need future research. Trial registration: Clinicaltrials.gov, NCT04183894. Registered 3 December 2019—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04183894. NCT04183894 (ClinicalTrials.gov identifier).

Network-Based Methods for Psychometric Data of Eating Disorders: A Systematic Review

“…Later, they repeated an analogous study on a different dataset composed of longitudinal data of N = 1272 participants with the additional goal of comparing adolescents and adults network structures (Levinson et al, 2020). More recently, another study was conducted to exclusively estimate the individual networks of N = 34 participants with the aim of identifying and discussing a range of target symptoms for personalized ED treatment (Levinson et al, 2021).…”

“…As for the group-level temporal networks, the symptoms with highest in-strength centrality were desire to be thin, body checking (Levinson et al, 2018b), fasting, fear of weight gain and feeling fat (Levinson et al, 2021; Levinson et al, 2020). Those with highest out-strength centrality were exercise, binge eating (Levinson et al, 2018b), feeling fat and fear of weight gain (Levinson et al, 2020).…”

“…The strongest symptoms in the contemporaneous and between-subject group-level networks were desire to be thin (Levinson et al, 2018b), and feeling fat (Levinson et al, 2020). Among the individual networks, nodes with highest in-strength centrality in the temporal networks were overvaluation of weight and shape, fear of weight gain, and body dissatisfaction (Levinson et al, 2020); while those with highest out-strength were: exercise, fear of weight gain, overvaluation of weight and shape (Levinson et al, 2020) , and body dissatisfaction (Levinson et al, 2021). Finally, strongest symptoms in the individual contemporaneous networks were thinking about dieting, binge eating (Levinson et al, 2018b), fear of weight gain (Levinson et al, 2020) , body dissatisfaction and drive for thinness (Levinson et al, 2021)…”

“…The parallel estimation of symptom networks on different subsamples was achieved in few cases through the FGL technique (Forrest, Perkins, et al, 2019; Martini et al, 2021; Schlegl et al, 2021; Smith et al, 2020). Finally, different studies based on panel data were also found to employ mlVAR to estimate between-subject networks and graphicalVAR to estimate temporal and contemporaneous networks (Levinson et al, 2021; Levinson et al, 2018; Levinson et al, 2020).…”

The future of eating disorders research: an editorial

“…Just published in the Journal of Eating Disorders , Levinson et al [ 10 ] report on a proof of concept study and initial data in using networks to identify treatment targets for eating disorder treatment. They are adopting individualised network analyses utilising comprehensive longitudinal data via ecological momentary assessment to “model how dynamic systems of symptoms interrelate with each other to maintain pathology, within one person”.…”