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Description: 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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