Search

IntroductionDepression has a significant impact on individuals and society, which is why preventive measures are important. Farmers represent an occupational group exposed to many risk factors for depression. The potential of guided, tailored internet-based interventions and a personalized telephone coaching is evaluated in a German project of the Social Insurance for Agriculture, Forestry and Horticulture (SVLFG). While user outcomes are promising, not much is known about actual routine care use and implementation of the two digital health interventions. This study evaluates the implementation from the perspective of social insurance employees to understand determinants influencing the uptake and implementation of digital interventions to prevent depression in farmers.MethodsThe data collection and analysis are based on the Consolidated Framework for Implementation Research (CFIR). Health care workers (n = 86) and implementers (n = 7) completed online surveys and/or participated in focus groups. The surveys consisted of validated questionnaires used in implementation research, adapted items from the CFIR guide or from other CFIR studies. In addition, we used reporting data to map implementation based on selected CFIR constructs.ResultsWithin the five CFIR dimensions, many facilitating factors emerged in relation to intervention characteristics (e.g., relative advantage compared to existing services, evidence and quality) and the inner setting of the SVLFG (e.g., tension for change, compatibility with values and existing working processes). In addition, barriers to implementation were identified in relation to the outer setting (patient needs and resources), inner setting (e.g., available resources, access to knowledge and information) and characteristics of individuals (e.g., self-efficacy). With regard to the implementation process, facilitating factors (formal implementation leaders) as well as hindering factors (reflecting and evaluating) were identified.DiscussionThe findings shed light on the implementation of two digital prevention services in an agricultural setting. While both offerings seem to be widely accepted by health care workers, the results also point to revealed barriers and contribute to recommendations for further service implementation. For instance, special attention should be given to “patient needs and resources” by raising awareness of mental health issues among the target population as well as barriers regarding the inner setting.Clinical Trial RegistrationGerman Clinical Trial Registration: [DRKS00017078]. Registered on 18.04.2019

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. Predicting outcome in internet-based cognitive behaviour therapy for major depression: A large cohort study of adult patients in routine psychiatric care. PLoS One 11, 1–16. https://doi.org/10.1371/journal.pone.0161191 Backenstrass, M., Frank, A., Joest, K., Hingmann, S., Mundt, C., Kronmüller, K.T., 2006. A comparative study of nonspecific depressive symptoms and minor depression regarding functional impairment and associated characteristics in primary care. Compr. Psychiatry 47, 35–41. https://doi.org/10.1016/j.comppsych.2005.04.007 Beatty, L., Binnion, C., 2016. A Systematic Review of Predictors of, and Reasons for, Adherence to Online Psychological Interventions. Int. J. Behav. Med. 23, 776–794. https://doi.org/10.1007/s12529-016-9556-9 Boisseau, C.L., Schwartzman, C.M., Lawton, J., Mancebo, M.C., 2017. App-guided exposure and response prevention for obsessive compulsive disorder: an open pilot trial. Cogn. Behav. Ther. 46, 447–458. https://doi.org/10.1080/16506073.2017.1321683 Buntrock, C., Ebert, D., Lehr, D., Riper, H., Smit, F., Cuijpers, P., Berking, M., 2015. Effectiveness of a Web-Based Cognitive Behavioural Intervention for Subthreshold Depression: Pragmatic Randomised Controlled Trial. Psychother. Psychosom. https://doi.org/10.1159/000438673 Cavanagh, K., Shapiro, D.A., van Den Berg, S., Swain, S., Barkham, M., Proudfoot, J., 2009. The acceptability of computer-aided cognitive behavioural therapy: A pragmatic study. Cogn. Behav. Ther. 38, 235–246. https://doi.org/10.1080/16506070802561256 Constantino, M.J., Arnkoff, D.B., Glass, C.R., Ametrano, R.M., Smith, J.A.Z., 2011. Expectations. J. Clin. Psychol. 67, 184–192. https://doi.org/10.1002/jclp.20754 Constantino, M.J., Coyne, A.E., Boswell, J.F., Iles, B.R., Visla, A., 2018. A Meta-Analysis of the Association between Patients’ Early Perception of Treatment Credibility and Their Posttreatment Outcomes. Psychotherapy 55, 486–495. https://doi.org/10.1037/pst0000168 Crippa, J.A.S., de Lima Osório, F., Del-Ben, C.M., Filho, A.S., da Silva Freitas, M.C., Loureiro, S.R., 2008. Comparability Between Telephone and Face-to-Face Structured Clinical Interview for DSM-IV in Assessing Social Anxiety Disorder. Perspect. Psychiatr. Care 44, 241–247. https://doi.org/10.1111/j.1744-6163.2008.00183.x Cuijpers, P., De Graaf, R., Van Dorsselaer, S., 2004. Minor depression: Risk profiles, functional disability, health care use and risk of developing major depression. J. Affect. Disord. 79, 71–79. https://doi.org/10.1016/S0165-0327(02)00348-8 Cuijpers, P., Pineda, B.S., Quero, S., Karyotaki, E., Struijs, S.Y., Figueroa, C.A., Llamas, J.A., Furukawa, T.A., Muñoz, R.F., 2021. Psychological interventions to prevent the onset of depressive disorders: A meta-analysis of randomized controlled trials. Clin. Psychol. Rev. 83. https://doi.org/10.1016/j.cpr.2020.101955 Cuijpers, P., Reijnders, M., Huibers, M.J.H., 2019. The Role of Common Factors in Psychotherapy Outcomes. Annu. Rev. Clin. Psychol. 15, 207–231. https://doi.org/10.1146/annurev-clinpsy-050718-095424 Cuijpers, P., Smit, F., 2004. Subthreshold depression as a risk indicator for major depressive disorder: A systematic review of prospective studies. Acta Psychiatr. Scand. 109, 325–331. https://doi.org/10.1111/j.1600-0447.2004.00301.x de Graaf, L.E., Huibers, M.J.H., Riper, H., Gerhards, S.A.H., Arntz, A., 2009. Use and acceptability of unsupported online computerized cognitive behavioral therapy for depression and associations with clinical outcome. J. Affect. Disord. 116, 227–231. https://doi.org/10.1016/j.jad.2008.12.009 Devilly, G.J., Borkovec, T.D., 2000. Psychometric properties of the credibility/expectancy questionnaire. J. Behav. Ther. Exp. Psychiatry. https://doi.org/10.1016/S0005-7916(00)00012-4 Donker, T., Batterham, P.J., Warmerdam, L., Bennett, K., Bennett, A., Cuijpers, P., Griffiths, K.M., Christensen, H., 2013. Predictors and moderators of response to internet-delivered Interpersonal Psychotherapy and Cognitive Behavior Therapy for depression. J. Affect. Disord. 151, 343–351. https://doi.org/10.1016/j.jad.2013.06.020 Furukawa, T.A., Noma, H., Caldwell, D.M., Honyashiki, M., Shinohara, K., Imai, H., Chen, P., Hunot, V., Churchill, R., 2014. Waiting list may be a nocebo condition in psychotherapy trials: A contribution from network meta-analysis. Acta Psychiatr. Scand. 130, 181–192. https://doi.org/10.1111/acps.12275 Greenberg, R.P., Constantino, M.J., Bruce, N., 2006. Are patient expectations still relevant for psychotherapy process and outcome? Clin. Psychol. Rev. 26, 657–678. https://doi.org/10.1016/j.cpr.2005.03.002 Hautzinger, M., Bailer, M., Hofmeister, D., Keller, F., 2012. Allgemeine Depressionsskala, 2nd editio. ed. Hogrefe, Göttingen. Høifødt, R.S., Mittner, M., Lillevoll, K., Katla, S.K., Kolstrup, N., Eisemann, M., Friborg, O., Waterloo, K., 2015. Predictors of response to web-based cognitive behavioral therapy with high-intensity face-to-face therapist guidance for depression: A Bayesian analysis. J. Med. Internet Res. 17, e197. https://doi.org/10.2196/jmir.4351 Karsten, J., Penninx, B.W.J.H., Verboom, C.E., Nolen, W.A., Hartman, C.A., 2013. Course and risk factors of functional impairment in subthreshold depression and anxiety. Depress. Anxiety 30, 386–394. https://doi.org/10.1002/da.22021 Kenardy, J., McCafferty, K., Rosa, V., 2003. Internet-delivered indicated prevention for anxiety disorders: A randomized controlled trial. Behav. Cogn. Psychother. 31, 279–289. https://doi.org/10.1017/S1352465803003047 Lee, Y.Y., Stockings, E.A., Harris, M.G., Doi, S.A.R., Page, I.S., Davidson, S.K., Barendregt, J.J., 2019. The risk of developing major depression among individuals with subthreshold depression: A systematic review and meta-analysis of longitudinal cohort studies. Psychol. Med. 49, 92–102. https://doi.org/10.1017/s0033291718000557 Little, R.J.A., Rubin, D.B., 2002. Statistical Analysis with Missing Data. John Wiley & Sons, Inc., Hoboken, NJ, USA. https://doi.org/10.1002/9781119013563 Lobbestael, J., Leurgans, M., Arntz, A., 2011. Inter-rater reliability of the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID I) and Axis II Disorders (SCID II). Clin. Psychol. Psychother. 18, 75–79. https://doi.org/10.1002/cpp.693 Locher, C., Koechlin, H., Gaab, J., Gerger, H., 2019. The Other Side of the Coin: Nocebo Effects and Psychotherapy. Front. Psychiatry 10, 1–6. https://doi.org/10.3389/fpsyt.2019.00555 Lüdtke, T., Westermann, S., Pult, L.K., Schneider, B.C., Pfuhl, G., Moritz, S., 2018. Evaluation of a brief unguided psychological online intervention for depression: A controlled trial including exploratory moderator analyses. Internet Interv. 13, 73–81. https://doi.org/10.1016/j.invent.2018.06.004 Lyketsos, C., Nestadt, G., Cwi, J., Heithoff K, Eaton, W. W., 1994. The Life Chart Interview: a standardized method to describe the course of psychopathology. Int J Methods Psychiatr Res 4, 143-155. Musiat, P., Goldstone, P., Tarrier, N., 2014. Understanding the acceptability of e-mental health - attitudes and expectations towards computerised self-help treatments for mental health problems. BMC Psychiatry 14, 1–8. https://doi.org/10.1186/1471-244X-14-109 Pearson, R., Pisner, D., Meyer, B., Shumake, J., Beevers, C.G., 2019. A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression. Psychol. Med. 49, 2330–2341. https://doi.org/10.1017/S003329171800315X Petrie, K.J., Rief, W., 2019. Psychobiological Mechanisms of Placebo and Nocebo Effects: Pathways to Improve Treatments and Reduce Side Effects. Annu. Rev. Psychol. 70, 599–625. https://doi.org/10.1146/annurev-psych-010418-102907 Philippi, P., Baumeister, H., Apolinário-Hagen, J., Ebert, D.D., Hennemann, S., Kott, L., Lin, J., Messner, E.M., Terhorst, Y., 2021. Acceptance towards digital health interventions – Model validation and further development of the Unified Theory of Acceptance and Use of Technology. Internet Interv. 26. https://doi.org/10.1016/j.invent.2021.100459 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. Psychother. Psychosom. 90, 94–106. https://doi.org/10.1159/000507819 Riley, R.D., Debray, T.P.A., Fisher, D., Hattle, M., Marlin, N., Hoogland, J., Gueyffier, F., Staessen, J.A., Wang, J., Moons, K.G.M., Reitsma, J.B., Ensor, J., 2020. Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning. Stat. Med. 39, 2115–2137. https://doi.org/10.1002/sim.8516 Rubin, D.B., 1987. Multiple Imputation for Nonresponse in Surveys, Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., Hoboken, NJ, USA. https://doi.org/10.1002/9780470316696 Rucci, P., Gherardi, S., Tansella, M., Piccinelli, M., Berardi, D., Bisoffi, G., Corsino, M.A., Pini, S., 2003. Subthreshold psychiatric disorders in primary care: Prevalence and associated characteristics. J. Affect. Disord. 76, 171–181. https://doi.org/10.1016/S0165-0327(02)00087-3 Safren, S.A., Heimberg, R.G., Juster, H.R., 1997. Clients’ expectancies and their relationship to pretreatment symptomatology and outcome of cognitive-behavioral group treatment for social phobia. J. Consult. Clin. Psychol. 65, 694–698. https://doi.org/10.1037//0022-006x.65.4.694 Tambling, R.B., 2012. A Literature Review of Therapeutic Expectancy Effects. Contemp. Fam. Ther. 34, 402–415. https://doi.org/10.1007/s10591-012-9201-y Thiruchselvam, T., Dozois, D.J.A., Bagby, R.M., Lobo, D.S.S., Ravindran, L.N., Quilty, L.C., 2019. The role of outcome expectancy in therapeutic change across psychotherapy versus pharmacotherapy for depression. J. Affect. Disord. 251, 121–129. https://doi.org/10.1016/j.jad.2019.01.046 Vîslă, A., Constantino, M.J., Newkirk, K., Ogrodniczuk, J.S., Söchting, I., 2018. The relation between outcome expectation, therapeutic alliance, and outcome among depressed patients in group cognitive-behavioral therapy. Psychother. Res. 28, 446–456. https://doi.org/10.1080/10503307.2016.1218089 Vîslă, A., Flückiger, C., Constantino, M.J., Krieger, T., Grosse Holtforth, M., 2019. Patient characteristics and the therapist as predictors of depressed patients’ outcome expectation over time: A multilevel analysis. Psychother. Res. 29, 709–722. https://doi.org/10.1080/10503307.2018.1428379 Volz, H.-P., Stirnweiß, J., Kasper, S., Möller, H.-J., Seifritz, E., 2022. Subthreshold depression – concept, operationalisation and epidemiological data. A scoping review. Int. J. Psychiatry Clin. Pract. 0, 1–15. https://doi.org/10.1080/13651501.2022.2087530 Zagorscak, P., Heinrich, M., Schulze, J., Böttcher, J., Knaevelsrud, C., 2020. Factors contributing to symptom change in standardized and individualized Internet-based interventions for depression: A randomized-controlled trial. Psychotherapy 57, 237–251. https://doi.org/10.1037/pst0000276

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). Barriers to mental health treatment: results from the WHO World Mental Health surveys. Psychological Medicine, 44(6), 1303–1317. https://doi.org/10.1017/S0033291713001943 2) Baglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., … Riemann, D. (2011). Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. Journal of Affective Disorders, 135(1–3), 10–19. https://doi.org/10.1016/j.jad.2011.01.011 3) Barnard, J., & Rubin, D. B. (1999). Small-sample degrees of freedom with multiple imputation. Biometrika, 86(4), 948–955. http://www.jstor.org/stable/2673599 4) Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01 5) Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Usinglme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01 6) Batterham, P. J., Christensen, H., Mackinnon, A. J., Gosling, J. A., Thorndike, F. P., Ritterband, L. M., … Griffiths, K. M. (2017). Trajectories of change and long-term outcomes in a randomised controlled trial of internet-based insomnia treatment to prevent depression. BJPsych Open, 3(5), 228–235. https://doi.org/10.1192/bjpo.bp.117.005231 7) Behrendt, D., Ebert, D. D., Spiegelhalder, K., & Lehr, D. (2020). Efficacy of a self-help web-based recovery training in improving sleep in workers: Randomized controlled trial in the general working population. Journal of Medical Internet Research, 22(1), 1–18. https://doi.org/10.2196/13346 8) Blom, K., Jernelöv, S., Kraepelien, M., Bergdahl, M. O., Jungmarker, K., Ankartjärn, L., … Kaldo, V. (2015). Internet Treatment Addressing either Insomnia or Depression, for Patients with both Diagnoses: A Randomized Trial. Sleep, 38(2), 267–277. https://doi.org/10.5665/sleep.4412 9) Blom, K., Jernelöv, S., Rück, C., Lindefors, N., & Kaldo, V. (2017). Three-year follow-up comparing cognitive behavioral therapy for depression to cognitive behavioral therapy for insomnia, for patients with both diagnoses. Sleep, 40(8). https://doi.org/10.1093/sleep/zsx108 10) Braun, L., Titzler, I., Ebert, D. D., Buntrock, C., Terhorst, Y., Freund, J., … Baumeister, H. (2019). Clinical and cost-effectiveness of guided internet-based interventions in the indicated prevention of depression in green professions (PROD-A): study protocol of a 36-month follow-up pragmatic randomized controlled trial. BMC Psychiatry, 19(1), 278. https://doi.org/10.1186/s12888-019-2244-y 11) Braun, L., Titzler, I., Terhorst, Y., Freund, J., Thielecke, J., Ebert, D. D., & Baumeister, H. (2021). Effectiveness of guided internet-based interventions in the indicated prevention of depression in green professions (PROD-A): Results of a pragmatic randomized controlled trial. Journal of Affective Disorders, 278, 658–671. https://doi.org/10.1016/j.jad.2020.09.066 12) Bürkner, P.-C. (2017). Brms: An r package for bayesian multilevel models using stan. Journal of Statistical Software, 80(1). https://doi.org/10.18637/jss.v080.i01 13) Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M.,Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1). https://doi.org/10.18637/jss.v076.i01 14) Chan, C. S., Wong, C. Y. F., Yu, B. Y. M., Hui, V. K. Y., Ho, F. Y. Y., & Cuijpers, P. (2021). Treating depression with a smartphone-delivered self-help cognitive behavioral therapy for insomnia: a parallel-group randomized controlled trial. Psychological Medicine, 1–15. https://doi.org/10.1017/S0033291721003421 15) Cheng, P., Kalmbach, D. A., Castelan, A. C., Murugan, N., & Drake, C. L. (2020). Depression prevention in digital cognitive behavioral therapy for insomnia: Is rumination a mediator? Journal of Affective Disorders, 273(August 2019), 434–441. https://doi.org/10.1016/j.jad.2020.03.184 16) Cheng, P., Luik, A. I., Fellman-Couture, C., Peterson, E., Joseph, C. L. M., Tallent, G., … Drake, C. L. (2019). Efficacy of digital CBT for insomnia to reduce depression across demographic groups: A randomized trial. Psychological Medicine, 49(3), 491–500. https://doi.org/10.1017/S0033291718001113 17) Chisholm, D., Sanderson, K., Ayuso-Mateos, J. L., & Saxena, S. (2004). Reducing the global burden of depression: Population-level analysis of intervention cost-effectiveness in 14 world regions. British Journal of Psychiatry. https://doi.org/10.1192/bjp.184.5.393 18) Christensen, H., Batterham, P. J., Gosling, J. A., Ritterband, L. M., Griffiths, K. M., Thorndike, F. P., … Mackinnon, A. J. (2016). Effectiveness of an online insomnia program (SHUTi) for prevention of depressive episodes (the GoodNight Study): A randomised controlled trial. The Lancet Psychiatry, 3(4), 333–341. https://doi.org/10.1016/S2215-0366(15)00536-2 19) Chung, Y., Rabe-Hesketh, S., Dorie, V., Gelman, A., & Liu, J. (2013). A nondegenerate penalized likelihood estimator for variance parameters in multilevel models. Psychometrika, 78(4), 685–709. https://doi.org/10.1007/S11336-013-9328-2 20) Cuijpers, P. (2021). Indirect prevention and treatment of depression: An emerging paradigm? Clinical Psychology in Europe, 3(4). https://doi.org/10.32872/cpe.6847 21) Cuijpers, P., Smit, F., Aalten, P., Batelaan, N., Klein, A., Salemink, E., … Karyotaki, E. (2021). The Associations of Common Psychological Problems With Mental Disorders Among College Students. Frontiers in Psychiatry, 12(September), 1–9. https://doi.org/10.3389/fpsyt.2021.573637 22) Cuijpers, P., Turner, E. H., Koole, S. L., Van Dijke, A., & Smit, F. (2014). What is the threshold for a clinically relevant effect? the case of major depressive disorders. Depression and Anxiety, 31(5), 374–378. https://doi.org/10.1002/da.22249 23) Cunningham, J. E. A., & Shapiro, C. M. (2018). Cognitive Behavioural Therapy for Insomnia (CBT-I) to treat depression: A systematic review. Journal of Psychosomatic Research, 106(December 2017), 1–12. https://doi.org/10.1016/j.jpsychores.2017.12.012 24) Ebert, D. D., Van Daele, T., Nordgreen, T., Karekla, M., Compare, A., Zarbo, C., … Taylor, J. (2018). Erratum: Internet and mobile-based psychological interventions: Applications, efficacy and potential for improving mental health. A report of the EFPA E-Health Taskforce (European Psychologist (2018) 23 (167-187) DOI: 10.1027/1016-9040/a000318). European Psychologist, 23(3), 269. https://doi.org/10.1027/1016-9040/a000346 25) Ferrari, A. J., Charlson, F. J., Norman, R. E., Patten, S. B., Freedman, G., Murray, C. J. L., … Whiteford, H. A. (2013). Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. PLoS Medicine. https://doi.org/10.1371/journal.pmed.1001547 26) Garge, N. R., Bobashev, G., & Eggleston, B. (2013). Random forest methodology for model-based recursive partitioning: The mobforest package for R. BMC bioinformatics, 14, 125. https://doi.org/10.1186/1471-2105-14-125 27) Gerber, M., Lang, C., Lemola, S., Colledge, F., Kalak, N., Holsboer-Trachsler, E., … Brand, S. (2016). Validation of the German version of the insomnia severity index in adolescents, young adults and adult workers: Results from three cross-sectional studies. BMC Psychiatry. https://doi.org/10.1186/s12888-016-0876-8 28) Greenberg, P. E., Fournier, A. A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015). The economic burden of adults with major depressive disorder in the United States (2005 and 2010). Journal of Clinical Psychiatry. https://doi.org/10.4088/JCP.14m09298 29) Grund, S., Lüdtke, O., & Robitzsch, A. (2016). Multiple Imputation of Multilevel Missing Data. SAGE Open, 6(4), 215824401666822. https://doi.org/10.1177/2158244016668220 30) Gutiérrez-Rojas, L., Porras-Segovia, A., Dunne, H., Andrade-González, N., & Cervilla, J. A. (2020). Prevalence and correlates of major depressive disorder: A systematic review. Brazilian Journal of Psychiatry, 42(6), 657–672. https://doi.org/10.1590/1516-4446-2020-0650 31) Harrer, M., Apolinário-Hagen, J., Fritsche, L., Salewski, C., Zarski, A. C., Lehr, D., … Ebert, D. D. (2021). Effect of an internet- and app-based stress intervention compared to online psychoeducation in university students with depressive symptoms: Results of a randomized controlled trial. Internet Interventions, 24.https://doi.org/10.1016/j.invent.2021.100374 32) Hedges, L. V., & Olkin, I. (1986). Statistical Methods for Meta-Analysis. Biometrics. https://doi.org/10.2307/2531069 33) Hertenstein, E., Feige, B., Gmeiner, T., Kienzler, C., Spiegelhalder, K., Johann, A., … Baglioni, C. (2019). Insomnia as a predictor of mental disorders: A systematic review and meta-analysis. Sleep Medicine Reviews, 43, 96–105. https://doi.org/10.1016/j.smrv.2018.10.006 34) Hertenstein, E., Trinca, E., Wunderlin, M., Schneider, C. L., Züst, M. A., Fehér, K. D., … Nissen, C. (2022). Cognitive behavioral therapy for insomnia in patients with mental disorders and comorbid insomnia: A systematic review and meta-analysis. Sleep Medicine Reviews, 101597. https://doi.org/10.1016/j.smrv.2022.101597 35) Jacobson, N. S., & Truax, P. (1991). Clinical Significance: A Statistical Approach to Defining Meaningful Change in Psychotherapy Research. Journal of Consulting and Clinical Psychology. https://doi.org/10.1037/0022-006X.59.1.12 36) Jahn, R., Baumgartner, J., van den Nest, M., Friedrich, F., Alexandrowicz, R., & Wancata, J. (2018). Kriteriumsvalidität der deutschsprachigen Version der CES-D in der Allgemeinbevölkerung. Psychiatrische Praxis, 45(08), 434–442. https://doi.org/10.1055/a-0584-9803 37) Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B. W., Berry, J. T., & Mokdad, A. H. (2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders, 114(1–3), 163–173. https://doi.org/10.1016/j.jad.2008.06.026 38) Leerssen, J., Lakbila-Kamal, O., Dekkers, L. M. S., Ikelaar, S. L. C., Albers, A. C. W., Blanken, T. F., … Van Someren, E. J. W. (2021). Treating Insomnia with High Risk of Depression Using Therapist-Guided Digital Cognitive, Behavioral, and Circadian Rhythm Support Interventions to Prevent Worsening of Depressive Symptoms: A Randomized Controlled Trial. Psychotherapy and Psychosomatics. https://doi.org/10.1159/000520282 39) Lépine, J. P., & Briley, M. (2011). The increasing burden of depression. Neuropsychiatric Disease and Treatment, 7(SUPPL.), 3–7. https://doi.org/10.2147/NDT.S19617 40) Li, L., Wu, C., Gan, Y., Qu, X., & Lu, Z. (2016). Insomnia and the risk of depression: A meta-analysis of prospective cohort studies. BMC Psychiatry, 16(1). https://doi.org/10.1186/s12888-016-1075-3 41) Mojtabai, R., Olfson, M., Sampson, N. a, Druss, B., Wang, P. S., Wells, K. B., … Kessler, R. C. (2011). Barriers to mental health treatment: results from the WHO World Mental Health surveys. Psychological Medicine, 41(8), 1751–1761. https://doi.org/10.1017/S0033291710002291.Barriers 42) Norell-Clarke, A., Tillfors, M., Jansson-Fröjmark, M., Holländare, F., & Engström, I. (2018). Does midtreatment insomnia severity mediate between cognitive behavioural therapy for insomnia and post-treatment depression? an investigation in a sample with comorbid insomnia and depressive symptomatology. Behavioural and Cognitive Psychotherapy, 46(6), 726–737. https://doi.org/10.1017/S1352465818000395 43) Quartagno, M., Grund, S., & Carpenter, J. (2019). jomo: A Flexible Package for Two-level Joint Modelling Multiple Imputation. The R Journal, 11(2), 205. https://doi.org/10.32614/RJ-2019-028 44) Reins, J. A., Buntrock, C., Zimmermann, J., Grund, S., Harrer, M., Lehr, D., … 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 45) Riley, R. D., & Fisher, D. J. (2021). Using IPD meta–analysis to examine interactions between treatment effect and participant–level covariates. In R. D. Riley, J. F. Tierney, & L. A. Stewart (Eds.), Individual participant data meta–analysis (pp. 163–198). Wiley. https://doi.org/10.1159/000507819 46) Robitzsch, A., & Grund, S. (2021). Miceadds: Some additional multiple imputation functions, especially for ’mice’ [R package version 3.11-6]. https://CRAN.R-project.org/package=miceadds 47) Rubin, D. B. (2004). Multiple imputation for nonresponse in surveys (Vol. 81). John Wiley & Sons.https://doi.org/10.1002/9780470316696 48) Schafer, J., & Yucel, R. (2012). Computational strategies for multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics, 11. https://doi.org/10.1198/106186002760180608 49) Scott, K. M., de Jonge, P., Stein, D. J., & Kessler, R. C. (2018). Mental disorders around the world: Facts and figures from the WHO World Mental Health surveys. Mental Disorders Around the World: Facts and Figures from the WHO World Mental Health Surveys. Cambridge University Press. https://doi.org/10.1017/9781316336168 50) Staner, L. (2010). Comorbidity of insomnia and depression. Sleep Medicine Reviews, 14(1), 35–46. https://doi.org/10.1016/j.smrv.2009.09.003 51) van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03 52) van der Zweerde, T., van Straten, A., Effting, M., Kyle, S. D., & Lancee, J. (2019). Does online insomnia treatment reduce depressive symptoms? A randomized controlled trial in individuals with both insomnia and depressive symptoms. Psychological Medicine, 49(3), 501–509. https://doi.org/10.1017/S0033291718001149 53) Vargas, I., & Perlis, M. L. (2020). Insomnia and depression: clinical associations and possible mechanistic links. Current Opinion in Psychology, 34, 95–99. https://doi.org/10.1016/j.copsyc.2019.11.004 54) Vos, T., Haby, M. M., Barendregt, J. J., Kruijshaar, M., Corry, J., & Andrews, G. (2004). The burden of major depression avoidable by longer-term treatment strategies. Archives of General Psychiatry. https://doi.org/10.1001/archpsyc.61.11.1097 55) Wahl, I., Löwe, B., Bjorner, J. B., Fischer, F., Langs, G., Voderholzer, U., … Rose, M. (2014). Standardization of depression measurement: a common metric was developed for 11 self-report depression measures. Journal of Clinical Epidemiology, 67(1), 73–86. https://doi.org/10.1016/j.jclinepi.2013.04.019 56) Weisel, K. K., Lehr, D., Heber, E., Zarski, A. C., Berking, M., Riper, H., & Ebert, D. D. (2018). Severely burdened individuals do not need to be excluded from internet-based and mobile-based stress management: Effect modifiers of treatment outcomes from three randomized controlled trials. Journal of Medical Internet Research, 20(6). https://doi.org/10.2196/jmir.9387 57) White, I. R., Daniel, R., & Royston, P. (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Comput Stat Data Anal, 54(10), 2267–2275. https://doi.org/10.1016/j.csda.2010.04.005 58) Zeileis, A., Hothorn, T., & Hornik, K. (2008). Model-based recursive partitioning. Journal of Computational and Graphical Statistics, 17(2), 492–514. https://doi.org/10.1198/106186008X319331

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