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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). 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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. 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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

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

Additional file 1. Standards for Reporting Implementation Studies: the StaRI checklist for completion

Additional file 3. Description of the personalised tele-based coaching (IVPNetworks)

Additional file 2. Description of the GET.ON online health trainings