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Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize depression in about 50% of cases mainly due to somatic comorbidities. Given the significant economic, social, and interpersonal impact of depression and its increasing prevalence, there is a need to improve its diagnosis and treatment in outpatient care. Various efforts have been made to isolate individual biological markers for depression to streamline diagnostic and therapeutic approaches. However, the intricate and dynamic interplay between neuroinflammation, metabolic abnormalities, and relevant neurobiological correlates of depression is not yet fully understood. To address this issue, we propose a naturalistic prospective study involving outpatients with unipolar depression, individuals without depression or comorbidities, and healthy controls. In addition to clinical assessments, cardiovascular parameters, metabolic factors, and inflammatory parameters are collected. For analysis we will use conventional statistics as well as machine learning algorithms. We aim to detect relevant participant subgroups by data-driven cluster algorithms and their impact on the subjects' long-term prognosis. The POKAL-PSY study is a subproject of the research network POKAL (Predictors and Clinical Outcomes in Depressive Disorders; GRK 2621). [ABSTRACT FROM AUTHOR]

Background: Depression is a common mental health disorder and the second leading cause of disability worldwide. In people with depression, low depression literacy, which could be characterized by a poor recognition of depressive symptoms and less knowledge about the availability of treatment options, can hinder adequate therapy for depression. Nevertheless, questionnaires measuring depression literacy in Germany are rare. Consequently, for the present study, the German Depression Literacy Scale (D-Lit) has been revised and evaluated. Methods: First, a team of clinical psychologists revised the D-Lit German scale. Next, cognitive interviews were conducted with patients with depression to improve the comprehensibility of the scale items. Our revision of the D-Lit-R German scale was then subjected to an anonymous online study. Finally, the data went through an exploratory factor analysis, and sociodemographic subgroup analyses were performed. Results: N = 524 individuals (age 18–80) completed the D-Lit-R German scale and a questionnaire on their sociodemographic data. Cronbach´s alpha was estimated as α =.72, and McDonald's Omega (categorical) was estimated as ω =.77. The mean Item difficulty was M =.75 (SD =.15). An EFA was performed for a unidimensional model, a 5-factor-model and at last a 3-factor-model. The 5-factorial model showed a good model fit (χ2emp,WLSMV(131) = 92.424, p >.05; CFI = 1, RMSEA = 0, SRMR =.07) but was rejected since the content of the potential 5 factors could not be determined. The 3-factor model showed an arguable model fit. The Chi2 test was significant (χ2emp,WLSMV(168) = 199.912, p <.05), but the CFI and the RMSEA met an acceptable model fit (CFI =.990, RMSEA of.019, 90% CI[.003,.029]). Substantively, the three factors were defined as (1) Distractors and other symptoms, (2) Depressive symptoms, and (3) Pharmacological and psychotherapeutic depression treatment. Furthermore, there were significant differences in sum scores regarding the subgroup's gender, treatment for mental health problems, depression treatment, experience with depression, and different career fields. Conclusions: The D-Lit-R German scale is a time-efficient scale to assess some aspects of the depression literacy construct that can be easily applied. Since there was no perfect model fit, it is recommended to continue to revise the scale. Further evaluation studies could ask for knowledge of the etiological factors of depression. Future studies could then use this instrument to convey depression literacy. This instrument could assess the growth of knowledge after psychoeducational interventions in different settings. Trial registration: This trial was preregistered at the platform osf.io (https://osf.io/49xdh). Registration number: https://doi.org/10.17605/OSF.IO/49XDH Date of registration: 28 April 2022. [ABSTRACT FROM AUTHOR]