An e-mental health intervention to reduce depression symptoms in individuals with obesity: study protocol for the randomized, controlled, two-armed, confirmatory LightMood trial.

BACKGROUND: Patients with obesity often experience psychological distress, specifically depression symptoms. Due to various barriers, such as limitations of healthcare offers, digital interventions, for example medical apps, can provide a suitable approach to support affected people. In the envisaged prospective randomized controlled trial, we aim to examine the efficacy of the LightMood intervention. The LightMood intervention is a manualized and user-centered, digital intervention for patients with obesity, with a duration of 4 months, which contains elements of cognitive behavioral therapy and mindfulness-based and skills-based exercises. We expect the LightMood intervention to be superior to treatment as usual (TAU) in terms of reducing depression symptoms. METHODS: The trial incorporates four distinct measurement time points: the baseline assessment, the post-treatment assessment, and 1- and 3-month follow-up assessments. Furthermore, we implemented in-treatment assessments for both groups. Participants will be randomized into two groups (LightMood intervention vs TAU). The aim is to include 128 participants (64 per group) in the study. Inclusion criteria are patients who are obese, at least 18 years old, with a private Internet access, and with adequate digital literacy and show depression symptoms (PHQ ≥ 10). Exclusion criteria are weekly outpatient individual psychotherapy, obesity surgery within the last year or planned within the next 7 months, no private Internet access, and the prescription of a new psychotropic drug within the last 2 weeks. The primary outcome is the post-assessment reduction in depression symptoms. Secondary outcomes will include the improvement in self-efficacy, quality of life, mindfulness, reduction in eating disorder symptoms, and body mass index (BMI). Furthermore, we expect a positive development of depression symptoms throughout the different time points (T1, T2, and T3) in patients with obesity. DISCUSSION: LightMood is an evidence-based, efficient, low-threshold online intervention that aims to reduce depression symptoms in people with obesity. Online interventions could offer a promising alternative to conventional face-to-face therapy. The primary objective of the current study is to add essential insight into the feasibility, efficacy, effectiveness, and acceptance of e-mental health interventions for people with obesity and depression symptoms. TRIAL REGISTRATION: German Clinical Trial Register (DRKS), DRKS00029219. Registered on May 19, 2023.

Development and psychometric evaluation of the Man-Made Disaster-Related Distress Scale (MMDS).

Man-made disasters (MMD2) are a widespread cause for psychological burden, both in directly and indirectly affected regions, as currently highlighted by the Russian invasion of Ukraine. Yet, measurement instruments that assess psychological distress associated with MMD are lacking. The study aimed to develop and validate such an instrument in an indirectly affected region. The resulting "man-made disaster-related distress scale" (MMDS) was tested within a German-speaking sample (N = 327). Exploratory and confirmatory factor analysis revealed a two-factorial structure ("Psychological distress" and "Change of existing belief systems") of the MMDS. The confirmatory model had an acceptable to good fit with CFI/TLI higher than 0.90. Internal consistency of the MMDS and its subscales was high with α and ω higher than 0.80. Correlational analyses suggested convergent, discriminant, and criterion validity. Measurement invariance was confirmed for gender and age with a change in CFI smaller than 0.01 but was rejected for educational level. The MMDS resulted in a 14-item assessment instrument based on a 7-point Likert-scale with good psychometric properties. Its administration is readily and economical, yielding the option to be used as a standardized measurement tool.

COVID-19 Vaccine for Children: Determinants and Beliefs Contributing to Vaccination Decision of Parents in Germany 2021/2022.

To reduce the number of COVID-19 cases, vaccines were rapidly made available worldwide. For a strategically targeted response to the COVID-19 pandemic, population vaccination coverage was to be maximized. The target groups also included healthy children. In this context, it is important to understand the determinants and beliefs that lead parents to favor or oppose COVID-19 immunization in children. This study aimed to investigate parents' COVID-19 vaccination willingness in Germany for children aged 5-11 years in 2021/2022. For this purpose, the determinants and beliefs behind parents' vaccination decisions were examined. Descriptive analysis and bivariate correlations were performed on COVID-19 vaccination willingness and parents' mental health status, general vaccination attitudes, and SARS-CoV-2 politics perceptions. In total, 2401 participants fully participated in this cross-sectional study. The COVID-19 vaccination uptake (71.4%) outweighed the vaccination refusal (19.4%). Correlations revealed higher vaccine acceptance in parents presenting full vaccination certificates (90.9%), COVID-19 immunizations (99.9%), or increased COVID-19 fear (93.6%). Vaccination-refusal was associated with higher perceived pressure by COVID-19 vaccination campaigns (87.7%), higher experienced restrictions due to COVID-19 protective measures in parents' social environment (83.6%), and engagement against COVID-19 protective measures (51.6%). Besides general anxiety, no significant correlations were observed between parents' mental health variables and vaccination willingness. Although several factors are ultimately associated with vaccination willingness, future vaccination campaigns should prioritize reducing pressure, increasing trust, and considering parents' differentiation between familiar and unfamiliar pathogens during their vaccination decision-making process.

Mindfulness and skills-based eHealth intervention to reduce distress in cancer-affected patients in the Reduct trial: Intervention protocol of the make it training optimized.

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.

Determining Acceptance of e-Mental Health Interventions in Digital Psychodiabetology Using a Quantitative Web-Based Survey: Cross-sectional Study.

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 (ß=.11; P=.03) as well as the UTAUT predictors performance expectancy (ß=.50; P<.001), effort expectancy (ß=.15; P=.001), and social influence (ß=.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.

Mental Health Burden of the COVID-19 Outbreak in Germany: Predictors of Mental Health Impairment.

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.