0522 Subtypes of Insomniacs Treated by Cognitive-Behavioral Therapy. (27th May 2020)

• Record Type: Journal Article
• Authors: Sforza, M; Castronovo, V; Galbiati, A; Zucconi, M; Oldani, A; Casoni, F; Ferini-Strambi, L
• Is Part Of: Sleep. Volume 43(2020)Supplement 1
• Page Start: A200
• Page End: A200
• Publication Date: 2020-05-27
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsaa056.519
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Insomnia disorder (ID) is characterized by high degree of heterogeneity. Aim of our study was to identify ID patients subtypes in terms of sleep and non-sleep clinical baseline (BL) features and CBT-I efficacy. Methods: 294 chronic insomnia patients (61.6% female, mean age 40.7 ± 12.3 yrs) underwent 7-sessions group CBT-I. By use of latent class analysis (LCA) we identified ID subtypes according to BL score of Glasgow Sleep Effort Scale (GSES); Epworth Sleepiness Scale (ESS); Dysfunctional Beliefs and Attitudes about Sleep (DBAS -16); Morningness-Eveningness Questionnaire Self-Assessment (MEQ-SA); Perceived Stress Scale (PSS); Profile of Mood States (POMS); Beck Depression Inventory (BDI-II); Stay-Trait Anxiety Inventory (STAI-Y); Treatment effectiveness (Delta score of Insomnia Severity Index ISI between BL and end-of-treatment). Results: We chose 3 latent classes as most parsimonious model. According to questionnaires' cut-off, we labeled three classes: Class 1 (insomnia+anxiety+depression+stress) (n=62), Class 2 (insomnia+anxiety+depression) (n=153), Class 3 (only insomnia) (n=79). The variables that best differentiate the 3 classes were POMS (.772), STAY (.660), PSS (.545), BDI (.406) and ISI (.228) at BL. In particular, for ISI, the best item predicting groups differentiation was item 3 on the impact of insomnia on daytime functioning (.224). Moreover, we found a significant interaction between CBT-I treatment effect and the 3 classes at the ISI score (p=.001), GSES score (p=.002), DBAS score (p<.05), PSS score (p<.001), POMS score (p<.001), BDI score (p<.001) and STAI-Y score (p<.001). Conclusion: Our data driven analysis results suggest that the heterogeneity of ID patients can be best represented by non-sleep scores, in particular those regarding depression, anxiety, stress and daytime functioning. These information can be useful in predicting the outcome of CBT-I. Support: No