Childhood maltreatment (CM) is a risk factor for mental and physical health problems in adulthood, potentially mediated by long-term autonomic nervous system (ANS) dysregulation. To explore this link, the association between CM and vagal-sensitive heart rate variability (HRV) metrics in adults was examined, accounting for biopsychosocial factors.

Data from 4,420 participants in the Study of Health in Pomerania were analyzed, with CM assessed using the Childhood Trauma Questionnaire. HRV was derived from 10-second electrocardiograms and 5-minute pre-sleep polysomnographic recordings. Post hoc analyses examined abuse and neglect.

CM was associated with reduced HRV (logRMSSD: β = −0.20 [95%-CI: −0.28, −0.12], p = 1.2e−06), driven by neglect (β = −0.27 [−0.35, −0.18], p = 1.9e−09) rather than abuse (β = 0.01 [−0.12, 0.14], p = 1). Adjustments for age, sex, and medication attenuated these effects, which remained robust after additionally controlling for socioeconomic, lifestyle, body mass index, and depressive symptoms (fully adjusted model: CM β = −0.08 [−0.15, −0.001], p = .047; neglect β = −0.11 [−0.19, −0.03], p = .009; abuse β = −0.08 [−0.20, −0.04], p = .174). Age-related differences were found, with reduced HRV in both young and older participants but not in middle-aged participants (fully adjusted: F(2,743) = 6.75, p = .001).

This study highlights long-term ANS dysregulation following CM, particularly neglect, indicated by altered vagal-sensitive HRV metrics. Although small in magnitude, the effect on the ANS was independent of adult biopsychosocial factors. This long-term dysregulation may contribute to an increased risk of adverse health outcomes in adulthood.

Loneliness has become a major public health issue of the recent decades due to its severe impact on health and mortality. Little is known about the relation between loneliness and social anxiety. This study aimed (1) to explore levels of loneliness and social anxiety in the general population, and (2) to assess whether and how loneliness affects symptoms of social anxiety and vice versa over a period of five years.

The study combined data from the baseline assessment and the five-year follow-up of the population-based Gutenberg Health Study. Data of N = 15 010 participants at baseline (Mage = 55.01, s.d.age = 11.10) were analyzed. Multiple regression analyses with loneliness and symptoms of social anxiety at follow-up including sociodemographic, physical illnesses, and mental health indicators at baseline were used to test relevant covariates. Effects of loneliness on symptoms of social anxiety over five years and vice versa were analyzed by autoregressive cross-lagged structural equation models.

At baseline, 1076 participants (7.41%) showed symptoms of social anxiety and 1537 (10.48%) participants reported feelings of loneliness. Controlling for relevant covariates, symptoms of social anxiety had a small significant effect on loneliness five years later (standardized estimate of 0.164, p < 0.001). Vice versa, there was no significant effect of loneliness on symptoms of social anxiety taking relevant covariates into account.

Findings provided evidence that symptoms of social anxiety are predictive for loneliness. Thus, prevention and intervention efforts for loneliness need to address symptoms of social anxiety.

Obesity-related cardiometabolic comorbidity is common in major depressive disorder (MDD). However, sex differences and MDD recurrence may modify the MDD-obesity-link.

Sex-specific associations of MDD recurrence (single [MDDS] or recurrent episodes [MDDR]) and obesity-related traits were analyzed in 4.100 adults (51.6% women) from a cross-sectional population-based cohort in Germany (SHIP-Trend-0). DSM-IV-based lifetime MDD diagnoses and MDD recurrence status were obtained through diagnostic interviews. Obesity-related outcomes included anthropometrics (weight, body mass index, waist- and hip-circumference, waist-to-hip ratio, waist-to-height ratio), bioelectrical impedance analysis of body fat mass and fat-free mass, and subcutaneous (SAT) and visceral adipose tissue (VAT) from abdominal magnetic resonance imaging. Sex-stratified linear regression models predicting obesity-related traits from MDD recurrence status were adjusted for age, education, and current depressive symptoms.

790 participants (19.3%) fulfilled lifetime MDD criteria (23.8% women vs. 14.5% men, p<0.001). In women, MDDS was inversely associated with anthropometric indicators of general and central obesity, while MDDR was positively associated with all obesity-related traits, except waist-to-hip ratio and fat-free mass. In women, MDDR versus MDDS was associated with higher levels of obesity across all outcomes except fat-free mass. In men, MDD was positively associated with SAT regardless of MDD recurrence. Additionally, lifetime MDD was positively associated with VAT in men. Results remained significant in sensitivity analyses after exclusion of participants with current use of antidepressants.

The MDD-obesity association is modified by MDD recurrence and sex independent of current depressive symptoms. Accounting for sex and MDD recurrence may identify individuals with MDD at increased cardiometabolic risk.

Magnetic resonance imaging (MRI) studies on major depressive disorder (MDD) have predominantly found short-term electroconvulsive therapy (ECT)-related gray matter volume (GMV) increases, but research on the long-term stability of such changes is missing. Our aim was to investigate long-term GMV changes over a 2-year period after ECT administration and their associations with clinical outcome.

In this nonrandomized longitudinal study, patients with MDD undergoing ECT (n = 17) are assessed three times by structural MRI: Before ECT (t0), after ECT (t1) and 2 years later (t2). A healthy (n = 21) and MDD non-ECT (n = 33) control group are also measured three times within an equivalent time interval. A 3(group) × 3(time) ANOVA on whole-brain level and correlation analyses with clinical outcome variables is performed.

Analyses yield a significant group × time interaction (pFWE < 0.001) resulting from significant volume increases from t0 to t1 and decreases from t1 to t2 in the ECT group, e.g., in limbic areas. There are no effects of time in both control groups. Volume increases from t0 to t1 correlate with immediate and delayed symptom increase, while volume decreases from t1 to t2 correlate with long-term depressive outcome (all p ⩽ 0.049).

Volume increases induced by ECT appear to be a transient phenomenon as volume strongly decreased 2 years after ECT. Short-term volume increases are associated with less symptom improvement suggesting that the antidepressant effect of ECT is not due to volume changes. Larger volume decreases are associated with poorer long-term outcome highlighting the interplay between disease progression and structural changes.

One of the major challenges in clinical psychiatry remains the absence of well established objective measures of symptoms’ severity. Clinical insights are mainly provided through keen behavioral observation and subjective questionnaires and scales.

The aim of this paper is to predict depression severity through speech using the features extracted from the speech as provided by participants during a semi-structured dialogue with a virtual avatar.

We use data from a subset of the DAICWOZ dataset consisting in 142 dialogues between participants and a virtual avatar during which the avatar uses several prompts to maintain a conversation with the participant. The avatar uses prompts involving the topics of travel, dream jobs, and memorable experiences. From the speech generated from the dialogue, we extract participant utterances separated by prompt and extract features from the three sets of transcripts. We extract content features from the transcript and acoustic features from the excerpt corresponding to the speech from the participant for the prompt in question.We perform regression experiments on the PHQ8 items using the features extracted from each set of transcripts. Furthermore, we combine the features extracted from each set of transcripts and compute partial spearman correlations between them and the PHQ8 items using gender as a covariate.

With our best regression model we obtain an R2 of 0.1, explaining 10% of the variance in the PHQ total score. Additionally, we obtain a mean absolute error of 1.25, suggesting that the regressor can detect with more or less precision clinically meaningful differences in depression severity between participants. Partial correlations between the total score and the features show significant correlations between features dependent on the amount of speech generated by each participant, along with the complexity of syntactic structures used.

Automatic analysis of spontaneous speech could help with the detection and monitoring of signs of depression. By combining the use of this technology with timely intervention strategies for instance provided by a virtual agent it could contribute to timely prevention.

A. König: None Declared, M. Mina Employee of: ki:elements GmbH, S. Schäfer Employee of: ki:elements GmbH, N. Linz Shareolder of: ki:elements GmbH, Employee of: ki:elements GmbH, J. Tröger Shareolder of: ki:elements GmbH, Employee of: ki:elements GmbH

Health services research (HSR) is affected by a widespread problem related to service terminology including non-commensurability (using different units of analysis for comparisons) and terminological unclarity due to ambiguity and vagueness of terms. The aim of this study was to identify the magnitude of the terminological bias in health and social services research and health economics by applying an international classification system.

This study, that was part of the PECUNIA project, followed an ontoterminology approach (disambiguation of technical and scientific terms using a taxonomy and a glossary of terms). A listing of 56 types of health and social services relevant for mental health was compiled from a systematic review of the literature and feedback provided by 29 experts in six European countries. The disambiguation of terms was performed using an ontology-based classification of services (Description and Evaluation of Services and DirectoriEs – DESDE), and its glossary of terms. The analysis focused on the commensurability and the clarity of definitions according to the reference classification system. Interrater reliability was analysed using κ.

The disambiguation revealed that only 13 terms (23%) of the 56 services selected were accurate. Six terms (11%) were confusing as they did not correspond to services as defined in the reference classification system (non-commensurability bias), 27 (48%) did not include a clear definition of the target population for which the service was intended, and the definition of types of services was unclear in 59% of the terms: 15 were ambiguous and 11 vague. The κ analyses were significant for agreements in unit of analysis and assignment of DESDE codes and very high in definition of target population.

Service terminology is a source of systematic bias in health service research, and certainly in mental healthcare. The magnitude of the problem is substantial. This finding has major implications for the international comparability of resource use in health economics, quality and equality research. The approach presented in this paper contributes to minimise differentiation between services by taking into account key features such as target population, care setting, main activities and type and number of professionals among others. This approach also contributes to support financial incentives for effective health promotion and disease prevention. A detailed analysis of services in terms of cost measurement for economic evaluations reveals the necessity and usefulness of defining services using a coding system and taxonomical criteria rather than by ‘text-based descriptions’.

Depression, the most frequent and harmful mental disorder, has been associated with specific somatic diseases as the leading cause of death. The purposes of this prospective study were to predict incident chronic diseases based on baseline depressive symptoms and to test sex-dependent effects.

In a representative German community sample of over 12 000 participants, baseline depressive symptoms (assessed using the Patient Health Questionnaire-9) were tested as a predictor of new onset of cardiovascular disease (CVD), chronic obstructive lung disease, diabetes, cancer, and migraine at 5-year follow-up. To study disease incidence, we created subsamples for each chronic disease by excluding participants who already had the respective disease at baseline. Potential confounders were included in logistic regression models and sex-specific analyses were performed.

Controlling for demographic characteristics and loneliness, in men and women, baseline depressive symptoms were predictive of CVD, chronic obstructive lung disease, diabetes, and migraine, but not of cancer. When we additionally adjusted for metabolic and lifestyle risk factors, there was an 8% increase of chronic obstructive lung disease and migraine per point of depressive symptoms. There was a trend for CVD (4%; p = 0.053). Sex-sensitive analyses revealed trends for the relevance of depressive symptoms for CVD in men (p = 0.065), and for diabetes in women (p = 0.077).

These findings underscore the need to implement screening for depression in the treatment of major somatic illnesses. At the same time, depressed patients should be screened for metabolic and lifestyle risk factors and for somatic diseases and offered lifestyle interventions.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Accumulating evidence suggests beneficial effects of media stories featuring individuals mastering their suicidal crises, but effects have not been assessed for psychiatric patients.

We randomized n = 172 adult psychiatric patients (n = 172, 97.1% inpatients) to read an educative article featuring a person mastering a suicidal crisis (n = 92) or an unrelated article (n = 80) in a single-blind randomized controlled trial. Questionnaire data were collected before (T1) and after exposure (T2) as well as 1 week later (study end-point, T3). The primary outcome was suicidal ideation as assessed with the Reasons for Living Inventory; secondary outcomes were help-seeking intentions, mood, hopelessness, and stigmatization. Differences between patients with affective versus other diagnoses were explored based on interaction tests.

We found that patients with affective disorders (n = 99) experienced a small-sized reduction of suicidal ideation at 1-week follow up (mean difference to control group [MD] at T3 = −0.17 [95% CI −0.33, −0.03], d = −0.15), whereas patients with nonaffective diagnoses (n = 73) experienced a small-sized increase (T2: MD = 0.24 [95% CI 0.06, 0.42], d = 0.19). Intervention group participants further experienced a nonsustained increase of help-seeking intentions (T2: MD = 0.53 [95% CI 0.11, 0.95], d = 0.19) and a nonsustained deterioration of mood (T2: MD = −0.14 [95% CI −0.27, −0.02], d = −0.17).

This study suggests that patients with affective disorders appear to benefit from media materials featuring mastery of suicidal crises. More research is needed to better understand which patient groups are at possible risk of unintended effects.

Adolescent transitions to adulthood are a vulnerable phase for the development of mental illnesses. Additionally, there are often disruptions in psychiatric care delivery during the transition phase, potentially leading to a considerable treatment delay with a high risk of early chronification. Thus, the health care system and professionals in both child and adolescent psychiatry and adult psychiatry should be given greater consideration to the transition phase.

The aim of the project ProTransition is the development of an online course for health care professionals to give in-depth knowledge of “transition psychiatry”, practical guidance and to sensitize them for the special challenges and needs of young adults with mental illness.

The online-course is being developed at the Department of Child and Adolescent Psychiatry/ Psychotherapy, Ulm and is expected to start in May 2021. It comprises e.g. special psychopathology of emerging adulthood, clinical interventions for adolescents with mental illness or legal aspects. An innovative and multi-didactical approach with specialized texts, case-studies, online-chats and interviews with experts and young people is applied. Additionally, user satisfaction with the online course will be evaluated.

On the basis of the gained experiences, ideas for new transition-psychiatric treatment models will be derived. The accompanying research will point out the status quo and the course-related increasing knowledge of health care professionals regarding transition psychiatry. First results are expected in November 2021.

As transition psychiatry is facing great difficulties and challenges, professionals should be adequately educated. E-Learning offers a flexible and low-level approach to reach a broad target group.

German mental health care providers report to encounter many cases of NSSI in their professional context, but only around 50% know about the treatment guidelines for NSSI of children and adolescents. Many consider professional training about NSSI as necessary for themselves. In response to this need, within the project Star Train different strategies of dissemination of the content of the guidelines were developed.

This study aims at comparing the effectiveness of different strategies of dissemination: printed material, e-learning and blended-learning.

Participants were randomly assigned to one of three different learning formats: printed material, e-learning, and blended-learning. Via online-survey participants provide pre- and post-training self-assessments of their knowledge of NSSI, practical skills, self-efficacy in handling cases of NSSI and attitudes towards NSSI and those affected. Additionally a multiple-choice-test of NSSI is assessed. For data-analyses between-group and within-group comparisons were conducted using a mixed design ANOVA. To ensure quality of learning formats, user-satisfaction was surveyed.

Data of the pretest-posttest comparison group design show that knowledge, practical skills, and self-efficacy in handling cases of NSSI raise for all participants and that attitudes towards NSSI and those affected are improved. There is no difference between the learning formats, thus all participants profit equally from their education. User satisfaction is high.

Results of this study suggest that the developed different training strategies can contribute equally to a better understanding and enhance skills of professionals regarding NSSI. The choice of training method could be driven by considering which goals want to be achieved and cost-value ratio.

Seasonal patterns in hospitalizations have been observed in various psychiatric disorders, however, it is unclear whether they also exist in schizophrenia. Previous studies found mixed results and those reporting the presence of seasonality differ regarding the characteristics of these patterns. Further, they are inconclusive whether sex is an influencing factor. The aim of this study was therefore to examine if seasonal patterns in hospitalizations can be found in schizophrenia, with special regard to a possible influence of sex, by using a large national dataset.

Data on all hospital admissions within Austria due to schizophrenia (F20.0–F20.6) for the time period of 2003–2016 were included. Age standardized monthly variation of hospitalization for women and men was analyzed and the level of significance adjusted for multiple testing.

The database comprised of 110,735 admissions (59.6% men). Significant seasonal variations were found in the total sample with hospitalization peaks in January and June and a trough in December (p < 0.0001). No significant difference in these patterns was found between women and men with schizophrenia (p < 0.0001).

Our study shows that schizophrenia-related hospitalizations follow a seasonal pattern in both men and women. The distribution of peaks might be influenced by photoperiod changes which trigger worsening of symptoms and lead to exacerbations in schizophrenia. Further research is necessary to identify underlying factors influencing seasonal patterns and to assess whether a subgroup of patients with schizophrenia is especially vulnerable to the impact of seasonal variations.