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.

Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

In 410 male and female participants aged 17–35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.

Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.

Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.

Coercive measures such as involuntary psychiatric admission are considered a last resort in the treatment of people with psychiatric disorders. So far, numerous factors have been identified that influence their use. However, the link between a pandemic – in particular, restrictions such as lockdowns – and the use of involuntary psychiatric admission is unclear.

To examine the association between COVID-19 lockdowns and involuntary psychiatric admissions in Austria.

This retrospective exploratory study assessed all involuntary psychiatric admissions and use of mechanical restraint in Austria, except for the federal state of Vorarlberg, between 1 January 2018 and 31 December 2020. Descriptive statistics and regression models were used.

During the 3-year study period, 40 012 individuals (45.9% females, mean age 51.3 years) had 66 124 involuntary psychiatric admissions for an average of 10.9 days. Mechanical restraint was used during 33.9% of these admissions. In weeks of nationwide COVID-19 lockdowns (2020 v. 2018/2019), involuntary psychiatric admissions were significantly fewer (odds ratio = 0.93, P = 0.0001) but longer (11.6 (s.d.: 16) v. 10.9 (s.d.: 15.8) days). The likelihood of involuntary admission during lockdowns was associated with year (2020 v. 2018–2019; adjusted odds ratio = 0.92; P = 0.0002) but not with sex (P = 0.814), age (P = 0.310), use of mechanical restraint (P = 0.653) or type of ward (P = 0.843).

Restrictions such as lockdowns affect coercive measures and resulted in fewer but longer involuntary psychiatric admissions during weeks of lockdown in Austria. These results strengthen previous findings that showed the dependence of coercive measures on external factors, highlighting the need to further clarify causality and desired prevention effects when using coercive measures.

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them.

Literature and expert opinions were integrated to formulate relevant guiding principles.

Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it.

The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field.

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.

Exposure to infected healthcare workers (HCWs) is a source of hospital-acquired (HA) influenza. We estimated the risk of HA influenza for hospitalized patients by rate of influenza vaccine coverage (IVC) of HCWs.

A case–case negative control study nested in a prospective cohort was conducted in 2 French university hospitals during 2 influenza seasons. Each inpatient with influenza-like illness (ILI) provided a nasal swab sample that was systematically analyzed for influenza virus by polymerase chain reaction (PCR) testing. An HA influenza case was a patient with a virological confirmation of influenza with onset of symptoms ≥72 hours after admission to the ward. The IVC rate of HCWs in each participating ward was calculated from the data provided by the occupational health departments. A mixed-effect logistic regression was performed with adjustments on patient sex, age, the presence of a potential source of influenza on the ward in the 5 days prior to the start of the ILI, type of ward and influenza season.

The overall HA influenza attack rate was 1.9 per 1,000 hospitalized patients. In total, 24 confirmed HA influenza cases and 141 controls were included. The crude odds ratio (OR) of HA influenza decreased from 0.52 (95% confidence interval [CI], 0.21–1.29) to 0.14 (95% CI, 0.03–0.63) when the IVC of HCWs increased from 20% to 40%. After adjustment, IVC ≥40% was associated with a risk reduction of HA influenza (aOR, 0.07; 95% CI, 0.01–0.78).

Considering a limited sample size, influenza vaccination of HCWs is highly suggestive of HA flu prevention among hospitalized patients.

Trial Registration: clinicaltrials.gov identifier: NCT02198638.