Exploring relapse through a network analysis of residual depression and anxiety symptoms after cognitive behavioural therapy: A proof-of-concept study. (3rd July 2020)

• Record Type: Journal Article
• Authors: Lorimer, Ben; Delgadillo, Jaime; Kellett, Stephen; Brown, Gary
• Is Part Of: Psychotherapy research. Volume 30:Number 5(2020)
• Page Start: 650
• Page End: 661
• Publication Date: 2020-07-03
• Subjects: depression -- anxiety -- relapse -- network analysis -- cognitive behaviour therapy; depressione -- ansia -- ricaduta -- network analysis -- terapia cognitivo-comportamentale; depressão -- ansiedade -- recaída -- análise de rede -- terapia cognitivo comportamental; 憂鬱 -- 焦慮 -- 復發 -- 網絡分析 -- 認知行為治療; Psychotherapy -- Periodicals; Psychotherapy -- Research -- Periodicals; Psychotherapy -- Periodicals; Psychothérapie -- Périodiques; Psychothérapie -- Recherche -- Périodiques; 616.891405
• DOI: 10.1080/10503307.2019.1650980
• Languages: English
• ISSNs: 1050-3307

Abstract

Abstract: Objective: Many patients relapse within one year of completing effective cognitive behavioural therapy (CBT) for depression and anxiety. Residual symptoms at treatment completion have been demonstrated to predict relapse, and so this study used network analyses to improve specificity regarding which residual anxiety and depression symptoms predict relapse. Method: A cohort study identified relapse cases following low- and high-intensity CBT in a stepped care psychological therapy service. The sample included N = 867 "recovered" treatment completers that attended a six-month follow-up review. At follow-up, N = 93 patients had relapsed and N = 774 remained in-remission. Networks of final treatment session depression (PHQ-9) and anxiety (GAD-7) symptoms were estimated for both sub-groups. Results: Qualitatively similar symptom networks were found. Difficulty concentrating was a highly central symptom in the relapse network, whilst of only average centrality in the remission network. In contrast, trouble relaxing was highly central in the remission network, whilst of only average centrality in the relapse network. Discussion: Identification of central residual symptoms holds promise in improving the specificity of prognostic models and the design of evidence-based relapse prevention strategies. The small sample of relapse cases limits this study's ability to draw firm conclusions.