Lachlan A McWilliams1, Gordon Sarty, John Kowal, Keith G Wilson. 1. *Department of Psychology, University of Saskatchewan, Saskatoon, Saskatchewan†Department of Psychology, The Ottawa Hospital‡Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Abstract
OBJECTIVES: Major depression in the context of chronic pain has been conceptualized implicitly as a latent variable, in which symptoms are viewed as manifestations of an underlying disorder. A network approach provides an alternative model and posits that symptoms are causally connected, rather than merely correlated, and that disorders exist as systems, rather than as entities. The present study applied a network analysis to self-reported symptoms of major depression in patients with chronic pain. The goals of the study were to describe the network of depressive symptoms in individuals with chronic pain and to illustrate the potential of network analysis for generating new research questions and treatment strategies. MATERIALS AND METHODS: Patients (N=216) admitted to an interdisciplinary chronic pain rehabilitation program provided symptom self-reports using the Patient Health Questionnaire-9. Well-established network analyses methods were used to illustrate the network of depressive symptoms and determine the centrality of each symptom (ie, the degree of connection with other symptoms in the network). RESULTS: The most central symptoms were difficulty concentrating, loss of interest or pleasure, depressed mood, and fatigue, although the relative position of each symptom varied slightly, depending on the centrality measure considered. DISCUSSION: Consistent with past research with patients undergoing treatment for major depression, the current findings are supportive of a model in which depressive symptoms are causally connected within a network rather than being manifestations of a common underlying disorder. The research and clinical implications of the findings, such as developing treatments targeting the most central symptoms, are discussed.
OBJECTIVES: Major depression in the context of chronic pain has been conceptualized implicitly as a latent variable, in which symptoms are viewed as manifestations of an underlying disorder. A network approach provides an alternative model and posits that symptoms are causally connected, rather than merely correlated, and that disorders exist as systems, rather than as entities. The present study applied a network analysis to self-reported symptoms of major depression in patients with chronic pain. The goals of the study were to describe the network of depressive symptoms in individuals with chronic pain and to illustrate the potential of network analysis for generating new research questions and treatment strategies. MATERIALS AND METHODS:Patients (N=216) admitted to an interdisciplinary chronic pain rehabilitation program provided symptom self-reports using the Patient Health Questionnaire-9. Well-established network analyses methods were used to illustrate the network of depressive symptoms and determine the centrality of each symptom (ie, the degree of connection with other symptoms in the network). RESULTS: The most central symptoms were difficulty concentrating, loss of interest or pleasure, depressed mood, and fatigue, although the relative position of each symptom varied slightly, depending on the centrality measure considered. DISCUSSION: Consistent with past research with patients undergoing treatment for major depression, the current findings are supportive of a model in which depressive symptoms are causally connected within a network rather than being manifestations of a common underlying disorder. The research and clinical implications of the findings, such as developing treatments targeting the most central symptoms, are discussed.
Authors: Melanie P J Schellekens; Marije D J Wolvers; Maya J Schroevers; Tom I Bootsma; Angélique O J Cramer; Marije L van der Lee Journal: J Behav Med Date: 2019-08-22
Authors: Miguel Ángel Castellanos; Berta Ausín; Sara Bestea; Clara González-Sanguino; Manuel Muñoz Journal: Int J Environ Res Public Health Date: 2020-12-01 Impact factor: 3.390