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Major Depression (MDD) is an important challenge in mental healthcare, as MDD is a highly prevalent mental disorder (Gutiérrez-Rojas, Porras-Segovia, Dunne, Andrade-González, & Cervilla, 2020) with considerable individual (Ferrari et al., 2013; Lépine & Briley, 2011) as well as societal (Greenberg, Fournier, Sisitsky, Pike, & Kessler, 2015; Vos et al., 2004) burden. Estimates suggest that worldwide only 21% of individuals with MDD receive adequate treatment (Scott, de Jonge, Stein, & Kessler, 2018) and even in a hypothetical scenario with full coverage of and compliance to evidenced-based treatments models suggest that only a third of MDD-related disease burden could be avoided (Chisholm, Sanderson, Ayuso-Mateos, & Saxena, 2004). Barriers for help seeking most often include attitudinal barriers, such as the wish to handle one’s own problems or a perceived stigma of mental health problems and to a lesser degree structural barriers such as financing, time or transportation constraints (Andrade et al., 2014; Mojtabai et al., 2011). While some of the structural barriers can be countered with the use of internet-based interventions, which can be used independent of time and location (Ebert et al., 2018), some attitudinal barriers, like perceived stigma might be reduced by using an indirect approach (Cuijpers, 2021). In indirect interventions, instead of focusing on depression, common everyday problems such as low self-esteem, procrastination (Cuijpers et al., 2021), stress (Harrer et al., 2021; Weisel et al., 2018) or less stigmatized conditions such as insomnia (van der Zweerde, van Straten, Effting, Kyle, & Lancee, 2019) are addressed and by improving these also reduce depressive symptoms. Addressing sleep problems seems especially promising for targeting mental health problems, due to its association with multiple other mental health disorders (Hertenstein et al., 2019). Insomnia is especially linked to MDD in terms of predicting MDD onset (Baglioni et al., 2011; Li, Wu, Gan, Qu, & Lu, 2016), often being comorbid to MDD (Staner, 2010) and outlasting depression treatment (Vargas & Perlis, 2020). Several studies already showed the effects of (online) insomnia interventions on depressive symptom reduction (Cunningham & Shapiro, 2018) both in subthreshold (Batterham et al., 2017; Cheng et al., 2019; Christensen et al., 2016; van der Zweerde et al., 2019) and clinical relevant depression (Blom et al., 2015; Blom, Jernelöv, Rück, Lindefors, & Kaldo, 2017; Chan et al., 2021; Hertenstein et al., 2022). One study reporting on depression onset after an online-insomnia treatment found no group differences (Christensen et al., 2016) while another trial showed that in individuals with an insomnia subtype with a high risk for depression (characterized by different patterns in general distress, rumination and reduced positive effect), predicted symptom worsening could be avoided (Leerssen et al., 2021).To our knowledge, no studies directly compare subthreshold and clinically relevant levels of depressive symptoms in one study, so that effects of an indirect treatment or prevention approach concerning depressive symptom severity remain unclear. In terms of factors that possibly moderate the efficacy of an indirect approach and guide researchers and practitioners to individuals who would profit most from this approach, the literature is insufficient. (Work-related) ruminations and worries are suggested to mediate the effects of online insomnia interventions on depression (Behrendt, Ebert, Spiegelhalder, & Lehr, 2020; Cheng, Kalmbach, Castelan, Murugan, & Drake, 2020). Evidence of the influence of clinical (e.g. baseline severity) and demographic characteristic (e.g. sex, age, education) is mixed (Batterham et al., 2017; Cheng et al., 2019; Christensen et al., 2016). Therefore, the individual patient data from seven studies originally evaluating the efficacy of online sleep training will be pooled and analyzed to 1) evaluate their efficacy on depressive symptom reduction in both individuals with subclinical and clinical levels of depressive symptoms and 2) identify possible moderating and 3) mediating effects of clinical as well as demographic participants and intervention characteristics. References 1) Andrade, L. H., Alonso, J., Mneimneh, Z., Wells, J. E., Al-Hamzawi, A., Borges, G., … Kessler, R. C. (2014). 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Depression is one of the most common and consequential mental illnesses. According to Busch et al. (2013), the lifetime prevalence for depression is nearly 12%, while the 12-month prevalence is 6%. The disease is characterized by an increased mortality risk (Cuijpers & Smit, 2002; Walker et al., 2015), a considerable proportion of chronic trajectories, 15-20% according to the S-3 guideline for unipolar depression (DGPPN et al., 2015), a significant restriction of quality of life (IsHak et al., 2011), as well as high economic costs for society and individuals according to GBD Study 2018 and DAK-Healthreport 2020 (James et al., 2018; Storm, 2020). Depressive disorders show high comorbidity with sleep disorders; 90% of patients with major depressive disorder report having sleep problems, three quarters experience insomnia (American Psychiatric, 2013; Franzen & Buysse, 2009). Sleep problems appear to be not only a co-occurring condition but also a risk factor or warning signal for the onset of a depressive episode (Baglioni et al., 2011; Hertenstein et al., 2019; Riemann, 2007) or the relapse into one (Dombrovski et al., 2008). One of the most effective treatments for depressive disorders (Cuijpers et al., 2013; Lopez-Lopez et al., 2019) as well as for insomnia (Koffel et al., 2015; Okajima et al., 2011) is cognitive-behavioral therapy (CBT). CBT is generally the first-choice treatment for both disorders according to guidelines (DGPPN et al., 2015; Riemann et al., 2017). Studies have shown that the use of CBT interventions for sleep disorders (CBT-I) effectively contributes to the treatment of depressive symptoms and shows effects equivalent to classical interventions for the treatment of depression (CBT-D) (Ashworth et al., 2015; Blom et al., 2015; Carney et al., 2017; Cunningham & Shapiro, 2018). A recent meta-analysis by Furukawa and colleagues (2021) provided evidence that behavior therapy for insomnia is one of the most beneficial components of CBT for depression, in internet-delivered formats. However, in healthcare reality, the proportion of pharmacological monotherapy is higher than therapy with CBT or combination therapy (Lohse & Müller-Oerlinghausen, 2018; Melchior et al., 2014; Storm, 2017). This occurs although patient preference is different (Barlow, 2004; McHugh et al., 2013; van Schaik et al., 2004) and the effects of therapy with antidepressants are controversial (Cipriani et al., 2018; Kirsch et al., 2008; Munkholm et al., 2019). There are several possible explanations for the discrepancy between scientific recommendation, patient preference, and actual care reality. On the one hand, the limited availability and associated long time waiting periods (Rommel et al., 2017; Walendzik et al., 2014; Zepf et al., 2003), but on the other hand also the fear of stigmatization and the poor compatibility of therapy with work and private life, as well as limited mobility (Chekroud et al., 2018; Gulliver et al., 2010; Mohr et al., 2010; Siegel et al., 2017). Internet-based treatment (ICBT) offers an approach to reduce these problems, as it has high availability, low-threshold access, and cost-effectiveness (Andersson et al., 2019; Linardon et al., 2019). For ICBT, efficacies have been demonstrated for diverse mental disorders, particularly over the past 15 years (Andersson, 2018). These results have been shown in meta-analytic studies to be both stable over time (Andersson et al., 2019) and on the same level as traditional face-to-face therapy (Carlbring et al., 2018). In particular, the efficacy of ICBT interventions for sleep disorders is well researched. These interventions show only slightly lower effect sizes than classical face-to-face CBT (Spiegelhalder et al., 2020). However, the results on the efficacy of ICBT insomnia interventions for depressive symptomatology are rather heterogeneous to the present time, especially concerning the effect size (Christensen et al., 2016; Karyotaki et al., 2017; van der Zweerde et al., 2019). The aim of this study is to examine if: 1. an Internet-based cognitive behavioral therapy intervention for sleeping disorders is effective in reducing depressive symptoms within a sample of individuals with moderate depression; 2. long-term effects of ICBT for sleeping disorders on depressive symptoms are mediated by improvements in patients’ insomnia severity. Analyses will be conducted within an Individual Patient Data (IPD) meta-analysis framework, including the data of four randomized clinical trials (RCTs; Ebert et al. (2015); Behrendt et al. (2020); Thiart et al. (2015); Thiart (2014)). 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