Background Comorbidities in mental disorders are often understood by assuming a common cause. The network theory of mental disorders offers an alternative to this assumption by understanding comorbidities as mutually reinforced problems. In this study, we used network analysis to examine bridge symptoms between anxiety and depression in a large sample. Method Using data from a sample of patients diagnosed with both depression and an anxiety disorder before and after inpatient treatment (N = 5,614, mean age: 42.24, 63.59% female, average treatment duration: 48.12 days), network models of depression and anxiety symptoms are estimated. Topology, the centrality of nodes, stability, and changes in network structure are analyzed. Symptoms that drive comorbidity are determined by bridge node analysis. As an alternative to network communities based on categorical diagnosis, we performed a community analysis and propose empirically derived symptom subsets. Results The obtained network models are highly stable. Sad mood and the inability to control worry are the most central. Psychomotor agitation or retardation is the strongest bridge node between anxiety and depression, followed by concentration problems and restlessness. Changes in appetite and suicidality were unique to depression. Community analysis revealed four symptom groups. Conclusion The estimated network structure of depression and anxiety symptoms proves to be highly accurate. Results indicate that some symptoms are considerably more influential than others and that only a small number of predominantly physical symptoms are strong candidates for explaining comorbidity. Future studies should include physiological measures in network models to provide a more accurate understanding.

Sex differences in personality were found to be larger in more developed and more genderequal societies. However, the studies that report this effect either have methodological shortcomings or do not take into account possible underlying effects of ecological variables. Here, a large, multinational (N = 867,782) dataset of personality profiles was used to examine sex differences in Big Five facet scores for 50 countries. Gender differences were related to estimates of ecological stress as well as socio-cultural variables. Using a regularized partial-correlation approach, the unique associations of those correlates with sex differences were isolated. Sex differences were large (median Mahalanobis' D = 1.97) and varied substantially across countries (range 1.49 to 2.48). Global sex differences are larger in more developed countries with higher food availability, less pathogen prevalence, higher gender equality and an individualistic culture. However, after controlling for confounds, only historic pathogen prevalence, food availability and cultural individualism remained. Sex differences in personality are uniquely correlated to ecological stress. Previously reported correlations between greater sex differences and outcomes of gender equality could be due to confounding by influences of ecological stress.

Background Many people aim to eat healthily. Yet, affluent food environments encourage consumption of energy dense and nutrient-poor foods, making it difficult to accomplish individual goals such as maintaining a healthy diet and weight. Moreover, goal-congruent eating might be influenced by affects, stress and intense food cravings and might also impinge on these in turn. Directionality and interrelations of these variables are currently unclear, which impedes targeted intervention. Psychological network models offer an exploratory approach that might be helpful to identify unique associations between numerous variables as well as their directionality when based on longitudinal time-series data. Methods Across 14 days, 84 diet-interested participants (age range: 18–38 years, 85.7% female, mostly recruited via universities) reported their momentary states as well as retrospective eating episodes four times a day. We used multilevel vector autoregressive network models based on ecological momentary assessment data of momentary affects, perceived stress and stress coping, hunger, food craving as well as goal-congruent eating behaviour. Results Neither of the momentary measures of stress (experience of stress or stress coping), momentary affects or craving uniquely predicted goal-congruent eating. Yet, temporal effects indicated that higher anticipated stress coping predicted subsequent goal-congruent eating. Thus, the more confident participants were in their coping with upcoming challenges, the more they ate in line with their goals. Conclusion Most eating behaviour interventions focus on hunger and craving alongside negative and positive affect, thereby overlooking additional important variables like stress coping. Furthermore, self-regulation of eating behaviours seems to be represented by how much someone perceives a particular eating episode as matching their individual eating goal. To conclude, stress coping might be a potential novel intervention target for eating related Just-In-Time Adaptive Interventions in the context of intensive longitudinal assessment.

Practitioners and researchers alike assume that there is individual variability in the effects of treatments for mental disorders. However, for psychotherapy, up to now this assumption has never been empirically tested. Using a large database of randomized-controlled trials on psychotherapy of depression in adults (306 trials including a total of 51,853 patients), we performed a Bayesian variance ratio metaregression. For the entire sample, we found a 9% higher variance in the intervention groups compared with the control groups. Depending on the depression scale used, this corresponds to a standard deviation of the individual treatment effect of 3 to 4 points. Subgroup analyses revealed that the effect variability of some types of therapy is larger than others. Our results are the first to indicate that patients do benefit differently from psychotherapy. We conclude that there is a sound basis for the paradigm of personalized psychotherapy, which brings about implications for both research and clinical practice. Public Health Significance StatementIn recent years, studies with high methodological quality have pointed out that the efficacy of psychotherapy in the treatment of depression is less satisfactory than previous research suggested. To optimize psychotherapy for non-responders, the paradigm of personalized therapy is coming into the research focus. In this study, we show for the first time that the effects of psychological interventions vary more than those of control conditions. This shows that differential response to treatments is inherent to intervention effects. Thus, it could indeed be beneficial to better tailor psychotherapy to individual patients. In practice, session-by-session outcome monitoring should be used to detect non-responding cases in ongoing treatments. Statistical methods guiding the selection of treatment components capitalize on the heterogeneity of treatment effects and are thus likely to improve outcomes. These findings pave the way for broad research and implementation of approaches that support personalization (e.g., monitoring and feedback systems) as well as a new shaping of training beyond the traditional schools of thought in psychotherapy.

BackgroundIn recent years, the assessment of mental disorders has become more and more personalized. Modern advancements such as Internet-enabled mobile phones and increased computing capacity make it possible to tap sources of information that have long been unavailable to mental health practitioners.ObjectiveSoftware packages that combine algorithm-based treatment planning, process monitoring, and outcome monitoring are scarce. The objective of this study was to assess whether the DynAMo Web application can fill this gap by providing a software solution that can be used by both researchers to conduct state-of-the-art psychotherapy process research and clinicians to plan treatments and monitor psychotherapeutic processes.MethodsIn this paper, we report on the current state of a Web application that can be used for assessing the temporal structure of mental disorders using information on their temporal and synchronous associations. A treatment planning algorithm automatically interprets the data and delivers priority scores of symptoms to practitioners. The application is also capable of monitoring psychotherapeutic processes during therapy and of monitoring treatment outcomes. This application was developed using the R programming language (R Core Team, Vienna) and the Shiny Web application framework (RStudio, Inc, Boston). It is made entirely from open-source software packages and thus is easily extensible.ResultsThe capabilities of the proposed application are demonstrated. Case illustrations are provided to exemplify its usefulness in clinical practice.ConclusionsWith the broad availability of Internet-enabled mobile phones and similar devices, collecting data on psychopathology and psychotherapeutic processes has become easier than ever. The proposed application is a valuable tool for capturing, processing, and visualizing these data. The combination of dynamic assessment and process- and outcome monitoring has the potential to improve the efficacy and effectiveness of psychotherapy.