TY - JOUR
T1 - Integrating clinician and patient case conceptualization with momentary assessment data to construct idiographic networks
T2 - Moving toward personalized treatment for eating disorders
AU - Burger, Julian
AU - Ralph-Nearman, Christina
AU - Levinson, Cheri A.
N1 - Funding Information:
JB is funded by the NWO research talent grant “Advancing Personalized Network Modelling in Clinical Practice” (project number: 406.18.542). CRN is funded by grant P20GM103436-20 (KY-INBRE) from the National Institute of General Medical Sciences , and by grant R15 MH121445 from the National Institute of Mental Health, National Institutes of Health . Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Publisher Copyright:
© 2022 The Authors
PY - 2022/12
Y1 - 2022/12
N2 - Eating disorders are serious psychiatric illnesses with treatments ineffective for about 50% of individuals due to high heterogeneity of symptom presentation even within the same diagnoses, a lack of personalized treatments to address this heterogeneity, and the fact that clinicians are left to rely upon their own judgment to decide how to personalize treatment. Idiographic (personalized) networks can be estimated from ecological momentary assessment data, and have been used to investigate central symptoms, which are theorized to be fruitful treatment targets. However, both efficacy of treatment target selection and implementation with ‘real world’ clinicians could be maximized if clinician input is integrated into such networks. An emerging line of research is therefore proposing to integrate case conceptualizations and statistical routines, tying together the benefits from clinical expertise as well as patient experience and idiographic networks. The current pilot compares personalized treatment implications from different approaches to constructing idiographic networks. For two patients with a diagnosis of anorexia nervosa, we compared idiographic networks 1) based on the case conceptualization from clinician and patient, 2) estimated from patient EMA data (the current default in the literature), and 3) based on a combination of case conceptualization and patient EMA data networks, drawing on informative priors in Bayesian inference. Centrality-based treatment recommendations differed to varying extent between these approaches for patients. We discuss implications from these findings, as well as how these models may inform clinical practice by pairing evidence-based treatments with identified treatment targets.
AB - Eating disorders are serious psychiatric illnesses with treatments ineffective for about 50% of individuals due to high heterogeneity of symptom presentation even within the same diagnoses, a lack of personalized treatments to address this heterogeneity, and the fact that clinicians are left to rely upon their own judgment to decide how to personalize treatment. Idiographic (personalized) networks can be estimated from ecological momentary assessment data, and have been used to investigate central symptoms, which are theorized to be fruitful treatment targets. However, both efficacy of treatment target selection and implementation with ‘real world’ clinicians could be maximized if clinician input is integrated into such networks. An emerging line of research is therefore proposing to integrate case conceptualizations and statistical routines, tying together the benefits from clinical expertise as well as patient experience and idiographic networks. The current pilot compares personalized treatment implications from different approaches to constructing idiographic networks. For two patients with a diagnosis of anorexia nervosa, we compared idiographic networks 1) based on the case conceptualization from clinician and patient, 2) estimated from patient EMA data (the current default in the literature), and 3) based on a combination of case conceptualization and patient EMA data networks, drawing on informative priors in Bayesian inference. Centrality-based treatment recommendations differed to varying extent between these approaches for patients. We discuss implications from these findings, as well as how these models may inform clinical practice by pairing evidence-based treatments with identified treatment targets.
KW - Case conceptualization
KW - Eating disorders
KW - Network analysis
KW - Treatment
U2 - 10.1016/j.brat.2022.104221
DO - 10.1016/j.brat.2022.104221
M3 - Article
AN - SCOPUS:85140873445
SN - 0005-7967
VL - 159
JO - Behaviour Research and Therapy
JF - Behaviour Research and Therapy
M1 - 104221
ER -