Introduction: COVID-19 is causing an enormous psychological burden for most people. This study aims to assess individual changes in mental health and health status before and after the COVID-19 outbreak, and to explore potential predictors of change. Methods: A cross-sectional study in Germany (n = 15 037) were conducted. Demographics, depression and anxiety symptoms (PHQ-2, GAD-2), distress (DT), and health status (EQ-5D-3L) were assessed. Additionally, all instruments used were adapted to measure the participants’ mental health and health status before the COVID-19 outbreak. COVID-19-related fear, trust in governmental actions to face COVID-19, and the subjective level of information about COVID-19 were examined. Results: Overall, the participants showed a significant increase in depression and anxiety symptoms, and distress, while health status deteriorated since the COVID-19 outbreak. Impairment in mental health was predicted by COVID-19-related fear. Pre-existing mental illness predicted an increase in depression symptoms and a deterioration in health status. Trust in governmental actions and the subjective level of information predicted less increase in psychological burden. Conclusions: Our data revealed that there have been changes in mental health and health status at an individual level since the outbreak of COVID-19. In order to maintain mental health, the observed predictors should be addressed.

Background: Diabetes is a very common chronic disease that exerts massive physiological and psychological burdens on patients. The digitalization of mental health care has generated effective e-mental health approaches, which offer an indubitable practical value for patient treatment. However, before implementing and optimizing e-mental health tools, their acceptance and underlying barriers and resources should be first determined for developing and establishing effective patient-oriented interventions. Objective: This study aims to assess the acceptance of e-mental health interventions among patients with diabetes and explore its underlying barriers and resources. Methods: A cross-sectional study was conducted in Germany from April 9, 2020, to June 15, 2020, through a web-based survey for which patients were recruited via web-based diabetes channels. The eligibility requirements were adult age (18 years or older), a good command of the German language, internet access, and a diagnosis of diabetes. Acceptance was measured using a modified questionnaire, which was based on the well-established Unified Theory of Acceptance and Use of Technology (UTAUT) and assessed health-related internet use, acceptance of e-mental health interventions, and its barriers and resources. Mental health was measured using validated and established instruments, namely the Generalized Anxiety Disorder Scale-7, Patient Health Questionnaire-2, and Distress Thermometer. In addition, sociodemographic and medical data regarding diabetes were collected. Results: Of the 340 participants who started the survey, 261 (76.8%) completed it and the final sample comprised 258 participants with complete data sets. The acceptance of e-mental health interventions in patients with diabetes was overall moderate (mean 3.02, SD 1.14). Gender and having a mental disorder had a significant influence on acceptance (P<.001). In an extended UTAUT regression model (UTAUT predictors plus sociodemographics and mental health variables), distress (β=.11; P=.03) as well as the UTAUT predictors performance expectancy (β=.50; P<.001), effort expectancy (β=.15; P=.001), and social influence (β=.28; P<.001) significantly predicted acceptance. The comparison between an extended UTAUT regression model (13 predictors) and the UTAUT-only regression model (performance expectancy, effort expectancy, social influence) revealed no significant difference in explained variance (F10,244=1.567; P=.12). Conclusions: This study supports the viability of the UTAUT model and its predictors in assessing the acceptance of e-mental health interventions among patients with diabetes. Three UTAUT predictors reached a notable amount of explained variance of 75% in the acceptance, indicating that it is a very useful and efficient method for measuring e-mental health intervention acceptance in patients with diabetes. Owing to the close link between acceptance and use, acceptance-facilitating interventions focusing on these three UTAUT predictors should be fostered to bring forward the highly needed establishment of effective e-mental health interventions in psychodiabetology.

In the 11th version of the WHO´s International Classification of Diseases, complex post-traumatic stress disorder (CPTSD) is newly recognized as a variant of PTSD, characterized by additional and more severe symptoms and typically arising from prolonged or multiple traumatic experiences. Despite recent research supporting the differentiation between PTSD and CPTSD, studies also identify a third or even a fourth latent profile in trauma patients. This study employs latent profile analysis to identify the number of latent profiles within a clinical trauma population in Germany (N = 588) and aims to investigate their distinct symptomatology. The results of the present study reveal three latent profiles: two profiles similar to PTSD and CPTSD, and a third profile with a distinctively low quality of life that does not fit neatly into either category. This supports the updated classification in the ICD-11 but also aligns with previous research having identified more than two profiles. The present study provides a comprehensive examination of the distinct symptomatology of latent trauma profiles, enabling the development of tailored interventions.

Introduction: Cancer-affected patients experience high distress due to various burdens. One way to expand psycho-oncological support is through digital interventions. This protocol describes the development and structure of a web-based psycho-oncological intervention, the Make It Training optimized. This intervention is currently evaluated in the Reduct trial, a multicenter randomized controlled trial.Methods: The Make It Training optimized was developed in six steps: A patient need and demand assessment, development and acceptability analysis of a prototype, the formation of a patient advisory council, the revision of the training, implementation into a web app, and the development of a motivation and evaluation plan.Results: Through a process of establishing cancer-affected patients’ needs, prototype testing, and patient involvement, the Make It Training optimized was developed by a multidisciplinary team and implemented in a web app. It consists of 16 interactive self-guided modules which can be completed within 16 weeks.Discussion: Intervention protocols can increase transparency and increase the likelihood of developing effective web-based interventions. This protocol describes the process and results of developing a patient-oriented intervention. Future research should focus on the further personalization of web-based psycho-oncological interventions and the potential benefits of combining multiple psychotherapeutic approaches.

Climate change, considered as a pressing man-made disaster, poses a fundamental threat to global health, with significant psychological consequences. However, measurement instruments assessing these consequences of climate change remain limited. This study aimed to address this gap by developing and validating an adapted version of the Man-Made Disaster-Related Distress Scale (MMDS), specifically designed for the assessment of indirect climate change-related psychological consequences. The items of the MMDS were adapted to climate change. The resulting “Climate Change – Man-Made Disaster-Related Distress Scale” (CC-MMDS), comprising 16 items, was validated in an online-based survey in Germany. Initially, 1070 participants were engaged in the survey. The data of n = 715 participants was used for analysis. Exploratory and confirmatory factor analyses revealed a two-factor structure, encompassing “psychological distress” and “change of existing belief systems”, with acceptable to good model fit. The CC-MMDS demonstrated excellent reliability and good validity, with measurement invariance across gender, age, and educational level. This study validated the CC-MMDS, confirming a two-factor structure. The scale demonstrated strong measurement properties, making it a promising standardized instrument for assessing climate change-related distress. With its strong psychometric properties and efficient administration, the CC-MMDS offers valuable insights for research and can aid mental health providers in identifying and supporting individuals impacted by climate change.