FIGURE 3. FOREST PLOT AFTER CONTROLLING FOR NETWORK INCONSISTENCY The effectiveness of cognitive behavioral therapy for insomnia on fatigue: A network meta-analysis Ballesioa, M.R.J. Aquinob, B. Feigec, A. Johannc, S.D. Kyled, K. Spiegelhalderc, C. Lombardoa, D. Riemannc , G. Rückere & C. Baglionic aDepartment of Psychology, Sapienza University of Rome, Italy. bSchool of Health Sciences, City University London, UK. cDepartment of Clinical Psychology and Psychophysiology/Sleep Medicine, Center for Mental Disorders, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany. dSleep and Circadian Neuroscience Institute, University of Oxford, UK. eInstitute for Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany. FIGURE 1. SEARCH FLOW AIM To examine the effectiveness of behavioral therapy (BT) and cognitive behavioral therapy (BT+CT) for insomnia on daytime symptoms of fatigue using network meta-analysis, a statistical tool that : Extends standard meta-analysis to more than two treatments; Is based on direct evidence from RCTs; Combines direct and indirect evidence; Assumes consistency between both types of evidence, i.e. the assumption that direct and indirect evidence are similar in factors that could affect the relative treatment effects. Records identified through database searching: PubMed=(n=251); Scopus=(n=629); Web of Science=(n=188). Additional records identified through other sources (e.g. references): (n =333) Identification Screening Records excluded: (n = 104) -No trials (5) -Not including control group (20) -Not including SRT/SC (5) -Not including adult insomnia patients (9) -No measures of fatigue (65) Records screened: (n =125) Eligibility METHODS AND DATA EXTRACTION PubMed, Scopus and Web of Science were searched from 1986 to May 2015. The following eligibility criteria were applied: Randomized controlled trials; Inclusion of sleep restriction therapy within the treatment; Presence of an adult sample with diagnosis of insomnia; Presence of a measure of fatigue; English language. Eleven classes of treatments or control conditions were created: BT in individual, group or self-help setting; BT+CT in individual, group or self-help setting; Sleep hygiene/sleep education; Pharmachotherapy; Other psychological therapies; Placebo. Records included in the systematic review and network meta-analysis: (n=21)n =47) Included FIGURE 2. NETGRAPH SHOWING DIRECT COMPARISONS STATISTICAL ANALYSES Pre- and post- treatment means and standard deviations of self-reported questionnaires of fatigue were extracted to calculate effect sizes as standardized mean differences; All classes of intervention were compared against placebo considered as reference condition using a random-effects model; Cochran´s Q and Higgins´s I2were calculated to test heterogeneity and inconsistency in the network was explored; Sensitivity analyses were conducted to detect potential sources of heterogeneity considering sex, age, comorbidity and risk of bias. FIGURE 3. FOREST PLOT AFTER CONTROLLING FOR NETWORK INCONSISTENCY RESULTS Results show significant effects only for BT+CT Individual (d=.57, 95% CI: .14; 1.00). However, high heterogeneity was detected (Q= 97.31, df17, p<0.0001; I2= 82.5%). Q and I2 tests remained significant after sensitivity analyses (p<0.0001). After excluding studies which mostly contributed to network inconsistency, no significant effects were evidence, as shown in Figure 3. CONCLUSIONS: Findings suggest: The importance to include fatigue measures as primary outcome in CBT-I trials; A potential positive impact of cognitive behavioral therapy for insomnia on fatigue symptoms; The need to reduce variability between studies’ methodology. andrea.ballesio@uniroma1.it