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Mental disorders are a global health problem. In 2019, 1 in every 8 people, or 970 million people around the world were living with a mental disorder (The Institute for Health Metrics and Evaluation, 2023). Several treatments are available for mental disorders, with pharmacotherapy and psychotherapy as first line treatments. However, effect sizes of psychotherapies and pharmacotherapies for mental disorders are limited (Leichsenring et al. 2022). Preventing the onset of mental disorders and promoting mental health could be an additional method to reduce the disease burden. Over the past 20 years, mental health care has experienced a major technological revolution in that interventions are increasingly delivered via the Internet (Andersson et al. 2019). The digital delivery of mental health interventions has two remarkable advantages. First, digital interventions are highly scalable, as they can help many people at once. Second, digital interventions, compared to face-to-face contact, can be more resilient in the face of crises due to their independence from human contact. For example, participation at prevention courses offered by the public health care in Germany decreased by 44% between 2019 and 2021, which is attributable to the COVID-19 pandemic (Schempp & Römer, 2022). The body of primary intervention-studies on the effectiveness of digital mental health interventions is large, as well as the number of systematic reviews (SRs) and meta-analyses (MAs) that synthesize the evidence of the interventions' effects. Meta-analytic evidence shows the effectiveness of digital interventions for the prevention of depression (e.g., Reins et al. 2021, Sander et al. 2016, Deady et al. 2017), anxiety (e.g., Spijkerman et al., 2016), and suicide (e.g., Büscher et al., 2020), for the reduction of perceived stress levels (e.g, Heber et al. 2017), sleep problems (e.g., Soh et al., 2020), and alcohol consumption (e.g., Riper et al., 2018), as well as for the promotion of well-being (e.g., Ferrari et al., 2022) and resilience (e.g., Ang et al., 2022). Umbrella reviews, or, in other words, overviews of reviews, allow for a bird's eye view on a subtantial body of evidence and its quality (Ioannidis et al., 2017), typically for both beneficial and adverse outcomes (Aromataris et al., 2015). To our knowledge, no umbrella review has evaluated the effectiveness of different types of digital mental health interventions to prevent mental disorders and to promote mental health. The objective of this umbrella review and meta-analysis is to close this research gap by encompassing both an umbrella review (of systematic reviews and meta-analyses) and a meta-analysis (of individual studies) including various mental health conditions and intervention types (e.g., psychological, physical activity, web-based, app-based). Ang, W. H. D., Chew, H. S. J., Dong, J., Yi, H., Mahendren, R., & Lau, Y. (2022). Digital training for building resilience: Systematic review, meta-analysis, and meta-regression. Stress and Health, 38(5), 848–869. https://doi.org/10.1002/smi.3154 Andersson, G., Carlbring, P., Titov, N., & Lindefors, N. (2019). Internet Interventions for Adults with Anxiety and Mood Disorders: A Narrative Umbrella Review of Recent Meta-Analyses. The Canadian Journal of Psychiatry, 64(7), 465–470. https://doi.org/10.1177/0706743719839381 Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: Methodological development, conduct and reporting of an umbrella review approach. International Journal of Evidence-Based Healthcare, 13(3), 132–140. https://doi.org/10.1097/XEB.0000000000000055 Büscher, R., Torok, M., Terhorst, Y., & Sander, L. (2020). Internet-Based Cognitive Behavioral Therapy to Reduce Suicidal Ideation: A Systematic Review and Meta-analysis. JAMA Network Open, 3(4), e203933. https://doi.org/10.1001/jamanetworkopen.2020.3933 Deady, M., Choi, I., Calvo, R. A., Glozier, N., Christensen, H., & Harvey, S. B. (2017). eHealth interventions for the prevention of depression and anxiety in the general population: A systematic review and meta-analysis. BMC Psychiatry, 17(1), 310. https://doi.org/10.1186/s12888-017-1473-1 Ferrari, M., Allan, S., Arnold, C., Eleftheriadis, D., Alvarez-Jimenez, M., Gumley, A., & Gleeson, J. F. (2022). Digital Interventions for Psychological Well-being in University Students: Systematic Review and Meta-analysis. Journal of Medical Internet Research, 24(9), e39686. https://doi.org/10.2196/39686 Heber, E., Ebert, D. D., Lehr, D., Cuijpers, P., Berking, M., Nobis, S., & Riper, H. (2017). The Benefit of Web- and Computer-Based Interventions for Stress: A Systematic Review and Meta-Analysis. Journal of Medical Internet Research, 19(2), e5774. https://doi.org/10.2196/jmir.5774 The Institute for Health Metrics and Evaluation, 2023.https://www.healthdata.org/ Ioannidis, J. (2017). Next-generation systematic reviews: Prospective meta-analysis, individual-level data, networks and umbrella reviews. British Journal of Sports Medicine, 51(20), 1456–1458. https://doi.org/10.1136/bjsports-2017-097621 Leichsenring, F., Steinert, C., Rabung, S., & Ioannidis, J. P. A. (2022). The efficacy of psychotherapies and pharmacotherapies for mental disorders in adults: An umbrella review and meta-analytic evaluation of recent meta-analyses. World Psychiatry, 21(1), 133–145. https://doi.org/10.1002/wps.20941 Reins, J. A., Buntrock, C., Zimmermann, J., Grund, S., Harrer, M., Lehr, D., Baumeister, H., Weisel, K., Domhardt, M., Imamura, K., Kawakami, N., Spek, V., Nobis, S., Snoek, F., Cuijpers, P., Klein, J. P., Moritz, S., & Ebert, D. D. (2021). Efficacy and Moderators of Internet-Based Interventions in Adults with Subthreshold Depression: An Individual Participant Data Meta-Analysis of Randomized Controlled Trials. Psychotherapy and Psychosomatics, 90(2), 94–106. https://doi.org/10.1159/000507819 Riper, H., Hoogendoorn, A., Cuijpers, P., Karyotaki, E., Boumparis, N., Mira, A., Andersson, G., Berman, A. H., Bertholet, N., Bischof, G., Blankers, M., Boon, B., Boß, L., Brendryen, H., Cunningham, J., Ebert, D., Hansen, A., Hester, R., Khadjesari, Z., … Smit, J. H. (2018). Effectiveness and treatment moderators of internet interventions for adult problem drinking: An individual patient data meta-analysis of 19 randomised controlled trials. PLOS Medicine, 15(12), e1002714. https://doi.org/10.1371/journal.pmed.1002714 Sander, L., Rausch, L., & Baumeister, H. (2016). Effectiveness of Internet-Based Interventions for the Prevention of Mental Disorders: A Systematic Review and Meta-Analysis. JMIR Mental Health, 3(3), e6061. https://doi.org/10.2196/mental.6061 Schempp, N., & Kaun, L. (2022). Präventionsbericht 2022. Leistungen der gesetzlichen Krankenversicherung: Primärprävention und Gesundheitsförderung. Leistungen der sozialen Pflegeversicherung: Prävention in stationären Pflegeeinrichtungen. Berichtsjahr 2021. Medizinischer Dienst Bund. https://www.gkv-spitzenverband.de/media/dokumente/krankenversicherung_1/praevention __selbsthilfe__beratung/praevention/praeventionsbericht/2022_GKV_MDS _Praeventionsbericht_barrierefrei.pdf Soh, H. L., Ho, R. C., Ho, C. S., & Tam, W. W. (2020). Efficacy of digital cognitive behavioural therapy for insomnia: A meta-analysis of randomised controlled trials. Sleep Medicine, 75, 315–325. https://doi.org/10.1016/j.sleep.2020.08.020 Spijkerman, M. P. J., Pots, W. T. M., & Bohlmeijer, E. T. (2016). Effectiveness of online mindfulness-based interventions in improving mental health: A review and meta-analysis of randomised controlled trials. Clinical Psychology Review, 45, 102–114. https://doi.org/10.1016/j.cpr.2016.03.009

Subthreshold depression (sD) is defined as the presence of elevated symptoms of depression without fulfilling full criteria for Major Depressive Disorder (MDD) (Volz et al., 2022). Subthreshold depression is highly prevalent (Cuijpers et al., 2004), associated with poorer quality of life (Rucci et al., 2003), higher functional impairment (Backenstrass et al., 2006; Karsten et al., 2013) and a higher risk of developing MDD (Cuijpers and Smit, 2004; Lee et al., 2019), calling for intervention options (Volz et al., 2022). Meta-analytic evidence shows that psychological interventions can reduce the incidence of depression by 20% (Relative Risk = 0.81; 95% CI: 0.72–0.91) (Cuijpers et al., 2021) and reduce depressive symptom severity (Cuijpers et al., 2014). Similarly, low-threshold preventive internet interventions have shown potential to reduce the risk of depression onset in individuals with sD (Hazard Ratio = 0.72; 95% CI: 0.58–0.89) and reduce symptom severity (Reins et al., 2021). However, to further increase the effectiveness of preventive IMIs for depression, it is important to investigate factors that are associated with treatment outcome. Outcome expectancy - that is, paticipant’s belief of whether treatment will lead to an improvement in health status (Constantino et al., 2011; Thiruchselvam et al., 2019) – is discussed as a common factor that influences psychotherapy outcome (Cuijpers et al., 2019; Greenberg et al., 2006). This has been shown across different therapeutic approaches, including cognitive behavioral therapy (CBT) and different formats like individual (Constantino et al., 2011), group (Abouguendia et al., 2004; Safren et al., 1997), and couple therapy (Tambling, 2012). The effects of outcome expectations have been shown to be at least partly mediated by the therapeutic alliance (Abouguendia et al., 2004; Constantino et al., 2018; Vîslă et al., 2018) and influenced by patient’s age, degree of standardization in therapy and measurement instrument for outcome expectancy (Constantino et al., 2018). A recent meta-analysis summarized being female, of older age, therapy experienced, generally hopeful and psychologically minded as well as having less severe baseline symptoms were positively associated with initial outcome expectancies (Constantino et al., 2018). Visla and colleagues (2019) added having experienced previous depressive episodes and reporting lower well being as predictors of low initial outcome expectancies. Besides its positive influence on treatment outcome, low or pessimistic treatment expectancy could pose a warning mechanism for possible negative treatment effects (Locher et al., 2019; Petrie and Rief, 2019). In the field internet interventions, outcome expectancy has primarily been studied in terms of acceptability and uptake of diverse internet health services (Beatty and Binnion, 2016; Musiat et al., 2014; Philippi et al., 2021), but less in its persisting effects on the desired outcome. The few studies investigating the effect of participants’ expectancies on depression treatment outcomes remain inconclusive, with three studies supporting outcome expectancy as being predictive for treatment outcome (El Alaoui et al., 2016; de Graaf et al., 2009; Pearson et al., 2019) whereas three other studies did not find such an association (Cavanagh et al., 2009; Høifødt et al., 2015; Lüdtke et al., 2018) and one study reports an association fully mediated by therapeutic alliance (Zagorscak et al., 2020). For preventive intervention, evidence from studies investigating preventive IMIs for Generalized Anxiety Disorder and Obsessive Compulsive Disorders are also inconclusive, with outcome expectancy being correlated with reduction in anxiety symptoms (Kenardy et al., 2003) but not associated with post-treatment OCD symptoms (Boisseau et al., 2017). However, to our best knowledge, no study has investigated outcome expectancy for preventive interventions for depression. Therefore, the aim of this study is to explore the predictive role of outcome expectancy in an IMI for sD in terms of depressive symptom severity and depression onset. References Abouguendia, M., Joyce, A.S., Piper, W.E., Ogrodniczuk, J.S., 2004. Alliance as a Mediator of Expectancy Effects in Short-Term Group Psychotherapy. Gr. Dyn. 8, 3–12. https://doi.org/10.1037/1089-2699.8.1.3 Alaoui, S. El, Ljótsson, B., Hedman, E., Svanborg, C., Kaldo, V., Lindefors, N., 2016. 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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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Supplemental material, sj-docx-1-jtt-10.1177_1357633X221106027 for Janika Thielecke, Claudia Buntrock, Ingrid Titzler, Lina Braun, Johanna Freund, Matthias Berking, Harald Baumeister and David D. Ebert by Telephone coaching for the prevention of depression in depression in farmers: Results from a pragmatic from a pragmatic randomized controlled trial in Journal of Telemedicine and Telecare

Introduction: Chronic pain is highly prevalent, associated with substantial personal and economic burdens, and increased risk for mental disorders. Individuals in green professions (agriculturists, horticulturists, foresters) show increased prevalence of chronic pain and other risk factors for mental disorders. Available healthcare services in rural areas are limited. Acceptance towards face-to-face therapy is low. Internet and mobile-based interventions (IMIs) based on Acceptance and Commitment Therapy (ACT) might be a promising alternative for this population and may enable effective treatment of chronic pain. The present study aims to evaluate the clinical and cost-effectiveness of an ACT-based IMI for chronic pain in green professions in comparison with enhanced treatment as usual (TAU+). Methods and analysis: A two-armed pragmatic randomised controlled trial will be conducted. Two hundred eighty-six participants will be randomised and allocated to either an intervention or TAU+ group. Entrepreneurs in green professions, collaborating spouses, family members and pensioners with chronic pain are eligible for inclusion. The intervention group receives an internet-based intervention based on ACT (7 modules, over 7 weeks) guided by a trained e-coach to support adherence (eg, by positive reinforcement). Primary outcome is pain interference (Multidimensional Pain Interference scale; MPI) at 9 weeks post-randomisation. Secondary outcomes are depression severity (Quick Inventory Depressive Symptomology; QIDS-SR16), incidence of major depressive disorder, quality of life (Assessment of Quality of Life; AQoL-8D) and possible side effects associated with the treatment (Inventory for the Assessment of Negative Effects of Psychotherapy; INEP). Psychological flexibility (Chronic Pain Acceptance Questionnaire, Committed Action Questionnaire, Cognitive Fusion Questionnaire) will be evaluated as a potential mediator of the treatment effect. Furthermore, mediation, moderation and health-economic analyses from a societal perspective will be performed. Outcomes will be measured using online self-report questionnaires at baseline, 9-week, 6-month, 12-month, 24-month and 36-month follow-ups. Ethics and dissemination: This study was approved by the Ethics Committee of the University of Ulm, Germany (file no. 453/17-FSt/Sta; 22 February 2018). Results will be submitted for publication in peer-reviewed journals and presented at conferences. Trial registration number: German Clinical Trial Registration: DRKS00014619. Registered on 16 April 2018., publishedVersion

Introduction: Internet- and mobile-based interventions (IMIs) and their integration into routine psychotherapy (i.e., blended therapy) can offer a means of complementing psychotherapy in a flexible and resource optimized way. Objective: The present study will evaluate the non-inferiority, cost-effectiveness, and safety of two versions of integrated blended psychotherapy for depression and anxiety compared to standard cognitive behavioral therapy (CBT). Methods: A three-armed multicenter cluster-randomized controlled non-inferiority trial will be conducted comparing two implementations of blended psychotherapy (PSYCHOnlineTHERAPYfix/flex) compared to CBT. Seventy-five outpatient psychotherapists with a CBT-license will be randomized in a 1:1:1 ratio. Each of them is asked to include 12 patients on average with depressive or anxiety disorders resulting in a total sample size of N = 900. All patients receive up to a maximum of 16 psychotherapy sessions, either as routine CBT or alternating with Online self-help sessions (fix: 8/8; flex: 0–16). Assessments will be conducted at patient study inclusion (pre-treatment) and 6, 12, 18, and 24 weeks and 12 months post-inclusion. The primary outcome is depression and anxiety severity at 18 weeks post-inclusion (post-treatment) using the Patient Health Questionnaire Anxiety and Depression Scale. Secondary outcomes are depression and anxiety remission, treatment response, health-related quality of life, patient satisfaction, working alliance, psychotherapy adherence, and patient safety. Additionally, several potential moderators and mediators including patient characteristics and attitudes toward the interventions will be examined, complemented by ecological day-to-day digital behavior variables via passive smartphone sensing as part of an integrated smart-sensing sub-study. Data-analysis will be performed on an intention-to-treat basis with additional per-protocol analyses. In addition, cost-effectiveness and cost-utility analyses will be conducted from a societal and a public health care perspective. Additionally, qualitative interviews on acceptance, feasibility, and optimization potential will be conducted and analyzed. Discussion: PSYCHOnlineTHERAPY will provide evidence on blended psychotherapy in one of the largest ever conducted psychotherapy trials. If shown to be non-inferior and cost-effective, PSYCHOnlineTHERAPY has the potential to innovate psychotherapy in the near future by extending the ways of conducting psychotherapy. The rigorous health care services approach will facilitate a timely implementation of blended psychotherapy into standard care. Trial Registration: The trial is registered in the German Clinical Trials Register (DRKS00023973; date of registration: December 28th 2020)., publishedVersion