Psychological Determinants of Vaccination Readiness against COVID-19 and Seasonal Influenza of the Chronically Ill in Primary Care in Germany-A Cross-Sectional Survey.

Vaccines against COVID-19 and influenza are highly recommended for the chronically ill. They often suffer from co-morbid mental health issues. This cross-sectional observational study analyzes the associations between depression (PHQ-9) and anxiety (OASIS) with vaccination readiness (5C) against COVID-19 and influenza in chronically ill adults in primary care in Germany. Sociodemographic data, social activity (LSNS), patient activation measure (PAM), and the doctor/patient relationship (PRA) are examined as well. Descriptive statistics and linear mixed-effects regression models are calculated. We compare data from n = 795 study participants. The symptoms of depression are negatively associated with confidence in COVID-19 vaccines (p = 0.010) and positively associated with constraints to get vaccinated against COVID-19 (p = 0.041). There are no significant associations between symptoms of depression and vaccination readiness against influenza. Self-reported symptoms of a generalized anxiety disorder seem not to be associated with vaccination readiness. To address confidence in COVID-19 vaccines among the chronically ill, targeted educational interventions should be elaborated to consider mental health issues like depression. As general practitioners play a key role in the development of a good doctor/patient relationship, they should be trained in patient-centered communication. Furthermore, a standardized implementation of digital vaccination management systems might improve immunization rates in primary care.

The cost-utility of a return-to-work intervention in comparison to routine care for patients with mental disorders in Germany: Results from the RETURN project.

BACKGROUND: Only two-thirds of patients admitted to psychiatric wards return to their previous jobs. Return-to-work interventions in Germany are investigated for their effectiveness, but information regarding cost-effectiveness is lacking. This study investigates the cost-utility of a return-to-work intervention for patients with mental disorders compared to treatment as usual (TAU). METHODS: We used data from a cluster-randomised controlled trial including 166 patients from 28 inpatient psychiatric wards providing data at 6- and 12-month follow-ups. Health and social care service use was measured with the Client Sociodemographic and Service Receipt Inventory. Quality of life was measured with the EQ-5D-3L questionnaire. Cost-utility analysis was performed by calculating additional costs per one additional QALY (Quality-Adjusted Life Years) gained by receiving the support of return-to-work experts, in comparison to TAU. RESULTS: No significant cost or QALY difference between the intervention and control groups has been detected. The return-to-work intervention cannot be identified as cost-effective in comparison to TAU. CONCLUSIONS: The employment of return-to-work experts could not reach the threshold of providing good value for money. TAU, therefore, seems to be sufficient support for the target group.

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people's thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this opportunity, the present study demonstrates how smartphones can help establish associations between the psychological perception and physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective cues obtained via smartphone sensing. More precisely, we extracted a total of 1,356 granular cues from different sensing modalities to account for the complexity of real-world situations. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, Sociality (DIAMONDS), finding significant out-of-sample predictions for the five dimensions reflecting the situations' Duty, Intellect, Mating, pOsitivity, and Sociality. In a series of follow-up analyses, we further explored the data patterns captured by our models, revealing, for example, that those cues related to time and location were particularly informative of the respective situation characteristics. We conclude by interpreting the mapping between cues and characteristics in real-world situations and discussing how smartphone-based situational snapshots may push the boundaries of psychological research on situations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Supporting return to work after psychiatric hospitalization-A cluster randomized study (RETURN-study).

BACKGROUND: If people with episodic mental-health conditions lose their job due to an episode of their mental illness, they often experience personal negative consequences. Therefore, reintegration after sick leave is critical to avoid unfavorable courses of disease, longer inability to work, long payment of sickness benefits, and unemployment. Existing return-to-work (RTW) programs have mainly focused on "common mental disorders" and often used very elaborate and costly interventions without yielding convincing effects. It was the aim of the RETURN study to evaluate an easy-to-implement RTW intervention specifically addressing persons with mental illnesses being so severe that they require inpatient treatment. METHODS: The RETURN study was a multi-center, cluster-randomized controlled trial in acute psychiatric wards addressing inpatients suffering from a psychiatric disorder. In intervention wards, case managers (RTW experts) were introduced who supported patients in their RTW process, while in control wards treatment, as usual, was continued. RESULTS: A total of 268 patients were recruited for the trial. Patients in the intervention group had more often returned to their workplace at 6 and 12 months, which was also mirrored in more days at work. These group differences were statistically significant at 6 months. However, for the main outcome (days at work at 12 months), differences were no longer statistically significant (p = 0.14). Intervention patients returned to their workplace earlier than patients in the control group (p = 0.040). CONCLUSIONS: The RETURN intervention has shown the potential of case-management interventions when addressing RTW. Further analyses, especially the qualitative ones, may help to better understand limitations and potential areas for improvement.

Individual differences in everyday multitasking behavior and its relation to cognition and personality.

Our ability to multitask-focus on multiple tasks simultaneously-is one of the most critical functions of our cognitive system. This capability has shown to have relations to cognition and personality in empirical studies, which have received much attention recently. This review article integrates the available findings to examine how individual differences in multitasking behavior are linked with different cognitive constructs and personality traits to conceptualize what multitasking behavior represents. In this review, we highlight the methodological differences and theoretical conceptions. Cognitive constructs including executive functions (i.e., shifting, updating, and inhibition), working memory, relational integration, divided attention, reasoning, and prospective memory were investigated. Concerning personality, the traits of polychronicity, impulsivity, and the five-factor model were considered. A total of 43 studies met the inclusion criteria and entered the review. The research synthesis directs us to propose two new conceptual models to explain multitasking behavior as a psychometric construct. The first model demonstrates that individual differences in multitasking behavior can be explained by cognitive abilities. The second model proposes that personality traits constitute a moderating effect on the relation between multitasking behavior and cognition. Finally, we provide possible future directions for the line of research.

Limits of near transfer: Content- and operation-specific effects of working memory training.

Working memory (WM) training typically leads to large performance gains in the practiced tasks, but transfer of these gains to other contexts is elusive. One possible explanation for the inconsistent findings of past research is that transfer may only occur when cognitive strategies acquired during training can also be applied in the transfer tasks. Therefore, we systematically varied the content domains and WM operations assessed by training and transfer tasks and, thereby, the extent to which similar cognitive strategies could be applied. We randomly assigned 171 young adults to one of eight experimental groups who trained one of two WM operations (storage and processing or relational integration) with materials from one of four content domains (verbal, numerical, figural-icon, or figural-pattern) to an active or to a passive control group. Before and after 12 sessions of adaptive training within 2-3 weeks, performance was assessed in all eight WM tasks. Bayesian generalized linear mixed-effects models revealed improved performance in the trained tasks compared to the active control group. However, these improvements did not generalize to tasks measuring the same WM operation with different materials. Moreover, the comparison of the training groups with an active and a passive control group showed considerable differences, thus highlighting the importance of distinguishing between active and passive control groups. Overall, the findings revealed no evidence for transfer between tasks assumed to afford the same strategies. Therefore, the adoption of specific cognitive strategies alone is unlikely to be responsible for transfer of WM training gains between tasks. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Factor Retention Using Machine Learning With Ordinal Data.

Determining the number of factors in exploratory factor analysis is probably the most crucial decision when conducting the analysis as it clearly influences the meaningfulness of the results (i.e., factorial validity). A new method called the Factor Forest that combines data simulation and machine learning has been developed recently. This method based on simulated data reached very high accuracy for multivariate normal data, but it has not yet been tested with ordinal data. Hence, in this simulation study, we evaluated the Factor Forest with ordinal data based on different numbers of categories (2-6 categories) and compared it to common factor retention criteria. It showed higher overall accuracy for all types of ordinal data than all common factor retention criteria that were used for comparison (Parallel Analysis, Comparison Data, the Empirical Kaiser Criterion and the Kaiser Guttman Rule). The results indicate that the Factor Forest is applicable to ordinal data with at least five categories (typical scale in questionnaire research) in the majority of conditions and to binary or ordinal data based on items with less categories when the sample size is large.

Identifying potential mechanisms between childhood trauma and the psychological response to the COVID-19 pandemic in Germany: a longitudinal study.

Childhood maltreatment (CM) has been associated with adverse psychosocial outcomes during the pandemic, but the underlying mechanisms are unclear. In a prospective online study using baseline and 10-week follow-up data of 391 German participants, we applied multiple mediation analyses to test to what extent COVID-19 perceived stressors mediate the association between CM and later adverse psychosocial outcomes compared to established mediators of rumination and insecure attachment. We also explored the relative importance of different COVID-19 related stressors in predicting adverse psychological trajectories using elastic net regression. Results showed that CM was longitudinally associated with all adverse psychosocial outcome. COVID-19 perceived stressors, rumination, and insecure attachment mediated this relationship and full mediation was observed for the outcomes anxiety, stress and psychological well-being. COVID-19-related concerns about the future was most strongly and consistently associated with adverse psychosocial functioning. These findings provide preliminary evidence that COVID-19 perceived stressors, in particular concerns about the future, may be a key mechanism underlying the development of adverse psychosocial outcomes in individuals with a CM history. Thus, COVID-19 perceived stressors may require a higher priority for prevention and treatment efforts in vulnerable groups. Our results warrant replication in more representative cross-cultural samples.

Evidence-based Shared-Decision-Making Assistant (SDM-assistant) for choosing antipsychotics: protocol of a cluster-randomized trial in hospitalized patients with schizophrenia.

BACKGROUND: Choosing an antipsychotic medication is an important medical decision in the treatment of schizophrenia. This decision requires risk-benefit assessments of antipsychotics, and thus, shared-decision making between physician and patients is strongly encouraged. Although the efficacy and side-effect profiles of antipsychotics are well-established, there is no clear framework for the communication of the evidence between physicians and patients. For this reason, we developed an evidence-based shared-decision making assistant (SDM-assistant) that presents high-quality evidence from network meta-analysis on the efficacy and side-effect profile of antipsychotics and can be used as a basis for shared-decision making between physicians and patients when selecting antipsychotic medications. METHODS: The planned matched-pair cluster-randomised trial will be conducted in acute psychiatric wards (n = 14 wards planned) and will include adult inpatients with schizophrenia or schizophrenia-like disorders (N = 252 participants planned). On the intervention wards, patients and their treating physicians will use the SDM-assistant, whenever a decision on choosing an antipsychotic is warranted. On the control wards, antipsychotics will be chosen according to treatment-as-usual. The primary outcome will be patients' perceived involvement in the decision-making during the inpatient stay as measured with the SDM-Q-9. We will also assess therapeutic alliance, symptom severity, side-effects, treatment satisfaction, adherence, quality of life, functioning and rehospitalizations as secondary outcomes. Outcomes could be analysed at discharge and at follow-up after three months from discharge. The analysis will be conducted per-protocol using mixed-effects linear regression models for continuous outcomes and logistic regression models using generalised estimating equations for dichotomous outcomes. Barriers and facilitators in the implementation of the intervention will also be examined using a qualitative content analysis. DISCUSSION: This is the first trial to examine a decision assistant specifically designed to facilitate shared-decision making for choosing antipsychotic medications, i.e., SDM-assistant, in acutely ill inpatients with schizophrenia. If the intervention can be successfully implemented, SDM-assistant could advance evidence-based medicine in schizophrenia by putting medical evidence on antipsychotics into the context of patient preferences and values. This could subsequently lead to a higher involvement of the patients in decision-making and better therapy decisions. TRIAL REGISTRATION: German Clinical Trials Register (ID: DRKS00027316 , registration date 26.01.2022).

Concept of the Munich/Augsburg Consortium Precision in Mental Health for the German Center of Mental Health.

The Federal Ministry of Education and Research (BMBF) issued a call for a new nationwide research network on mental disorders, the German Center of Mental Health (DZPG). The Munich/Augsburg consortium was selected to participate as one of six partner sites with its concept "Precision in Mental Health (PriMe): Understanding, predicting, and preventing chronicity." PriMe bundles interdisciplinary research from the Ludwig-Maximilians-University (LMU), Technical University of Munich (TUM), University of Augsburg (UniA), Helmholtz Center Munich (HMGU), and Max Planck Institute of Psychiatry (MPIP) and has a focus on schizophrenia (SZ), bipolar disorder (BPD), and major depressive disorder (MDD). PriMe takes a longitudinal perspective on these three disorders from the at-risk stage to the first-episode, relapsing, and chronic stages. These disorders pose a major health burden because in up to 50% of patients they cause untreatable residual symptoms, which lead to early social and vocational disability, comorbidities, and excess mortality. PriMe aims at reducing mortality on different levels, e.g., reducing death by psychiatric and somatic comorbidities, and will approach this goal by addressing interdisciplinary and cross-sector approaches across the lifespan. PriMe aims to add a precision medicine framework to the DZPG that will propel deeper understanding, more accurate prediction, and personalized prevention to prevent disease chronicity and mortality across mental illnesses. This framework is structured along the translational chain and will be used by PriMe to innovate the preventive and therapeutic management of SZ, BPD, and MDD from rural to urban areas and from patients in early disease stages to patients with long-term disease courses. Research will build on platforms that include one on model systems, one on the identification and validation of predictive markers, one on the development of novel multimodal treatments, one on the regulation and strengthening of the uptake and dissemination of personalized treatments, and finally one on testing of the clinical effectiveness, utility, and scalability of such personalized treatments. In accordance with the translational chain, PriMe's expertise includes the ability to integrate understanding of bio-behavioral processes based on innovative models, to translate this knowledge into clinical practice and to promote user participation in mental health research and care.

Differential psychological response to the COVID-19 pandemic in psychiatric inpatients compared to a non-clinical population from Germany.

The COVID-19 pandemic is an inherently stressful situation, which may lead to adverse psychosocial outcomes in various populations. Yet, individuals may not be affected equally by stressors posed by the pandemic and those with pre-existing mental disorders could be particularly vulnerable. To test this hypothesis, we assessed the psychological response to the pandemic in a case-control design. We used an age-, sex- and employment status-matched case-control sample (n = 216) of psychiatric inpatients, recruited from the LMU Psychiatry Biobank Munich study and non-clinical individuals from the general population. Participants completed validated self-report measures on stress, anxiety, depression, paranoia, rumination, loneliness, well-being, resilience, and a newly developed index of stressors associated with the COVID-19 pandemic. Multiple linear regression analyses were conducted to assess the effects of group, COVID-19-specific stressors, and their interaction on the different psychosocial outcomes. While psychiatric inpatients reported larger mental health difficulties overall, the impact of COVID-19-specific stressors was lower in patients and not associated with worse psychological functioning compared to non-clinical individuals. In contrast, depressive symptoms, rumination, loneliness, and well-being were more strongly associated with COVID-19-specific stressors in non-clinical individuals and similar to the severity of inpatients for those who experienced the greatest COVID-19-specific stressor impact Contrary to expectations, the psychological response to the pandemic may not be worse in psychiatric inpatients compared to non-clinical individuals. Yet, individuals from the general population, who were hit hardest by the pandemic, should be monitored and may be in need of mental health prevention and treatment efforts.

The COVID-19 Pandemic Mental Health Questionnaire (CoPaQ): psychometric evaluation and compliance with countermeasures in psychiatric inpatients and non-clinical individuals.

BACKGROUND: The COVID-19 pandemic has greatly impacted people's lives across a broad spectrum of psychosocial domains. We report the development and psychometric evaluation of the self-report COVID-19 Pandemic Mental Health Questionnaire (CoPaQ), which assesses COVID-19 contamination anxiety, countermeasure necessity and compliance, mental health impact, stressor impact, social media usage, interpersonal conflicts, paranoid ideations, institutional & political trust, conspiracy beliefs, and social cohesion. Further, we illustrate the questionnaire's utility in an applied example investigating if higher SARS-Cov-2 infection rates in psychiatric patients could be explained by reduced compliance with preventive countermeasures. METHODS: A group of 511 non-clinical individuals completed an initial pool of 111 CoPaQ items (Open Science Framework: https://osf.io/3evn9/ ) and additional scales measuring psychological distress, well-being, and paranoia to assess construct validity and lifetime mental health diagnosis for criterion validity. Factor structure was determined by exploratory factor analyses and validated by conducting confirmatory factor analysis in the accompanying longitudinal sample (n = 318) and an independent psychiatric inpatient sample primarily admitted for major depressive-, substance abuse-, personality-, and anxiety disorders (n = 113). Internal consistency was assessed by Cronbach's Alpha and McDonald's Omega. For the applied research example, Welch t-tests and correlational analyses were conducted. RESULTS: Twelve out of 16 extracted subscales were retained in the final questionnaire version, which provided preliminary evidence for adequate psychometric properties in terms of factor structure, internal consistency, and construct and criterion validity. Our applied research example showed that patients exhibited greater support for COVID-19 countermeasures than non-clinical individuals. However, this requires replication in future studies. CONCLUSIONS: We demonstrate that the CoPaQ is a comprehensive and valid measure of the psychosocial impact of the pandemic and could allow to a degree to disentangle the complex psychosocial phenomena of the pandemic as exemplified by our applied analyses.

Parsing the antidepressant effects of non-invasive brain stimulation and pharmacotherapy: A symptom clustering approach on ELECT-TDCS.

BACKGROUND: Transcranial direct current stimulation (tDCS) presents small antidepressant efficacy at group level and considerable inter-individual variability of response. Its heterogeneous effects bring the need to investigate whether specific groups of patients submitted to tDCS could present comparable or larger improvement compared to pharmacotherapy. Aggregate measurements might be insufficient to address its effects. OBJECTIVE: /Hypothesis: To determine the efficacy of tDCS, compared to pharmacotherapy and placebo, in depressive symptom clusters. METHODS: Data from ELECT-TDCS (Escitalopram versus Electrical Direct-Current Therapy for Treating Depression Clinical Study, ClinicalTrials.gov, NCT01894815), in which antidepressant-free, depressed patients were randomized to receive 22 bifrontal tDCS (2 mA, 30 min) sessions (n = 94), escitalopram 20 mg/day (n = 91), or placebo (n = 60) over 10 weeks. Agglomerative hierarchical clustering identified "sleep/insomnia", "core depressive", "guilt/anxiety", and "atypical" clusters that were the dependent measure. Trajectories were estimated using linear mixed regression models. Effect sizes are expressed in raw HAM-D units. P-values were adjusted for multiple comparisons. RESULTS: For core depressive symptoms, escitalopram was superior to tDCS (ES = -0.56; CI95% = -0.94 to -0.17, p = .009), which was superior to placebo (ES = 0.49; CI95% = 0.06 to 0.92, p = .042). TDCS but not escitalopram was superior to placebo in sleep/insomnia symptoms (ES = 0.87; CI95% = 0.22 to 1.52, p = .015). Escitalopram but not tDCS was superior to placebo in guilt/anxiety symptoms (ES = 1.66; CI95% = 0.58 to 2.75, p = .006). No active intervention was superior to placebo for atypical symptoms. CONCLUSIONS: Pharmacotherapy and non-invasive brain stimulation produce distinct effects in depressive symptoms. TDCS or escitalopram could be chosen according to specific clusters of symptoms for a bigger response. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01894815.

Advancing the Understanding of the Factor Structure of Executive Functioning.

There has been considerable debate and interest regarding the factor structure of executive functioning (EF). Therefore, the aim of the current study was to delve into this issue differently, by investigating EF and other cognitive constructs, such as working memory capacity (WMC), relational integration, and divided attention, which may contribute to EF. Here, we examined whether it is possible to provide evidence for a definite model of EF containing the components of updating, shifting, and inhibition. For this purpose, 202 young adults completed a battery of EF, three WMC tests, three relational integration tests, and two divided attention tests. A confirmatory factor analysis on all the cognitive abilities produced a five-factor structure, which included one factor predominately containing shifting tasks, the next factor containing two updating tasks, the third one predominately representing WMC, the fourth factor consisting of relational integration and antisaccade tasks, and finally, the last factor consisting of the divided attention and stop signal tasks. Lastly, a subsequent hierarchical model supported a higher-order factor, thereby representing general cognitive ability.

Educational Intervention Reduced Family Medicine Residents' Intention to Request Diagnostic Tests: Results of a Controlled Trial.

OBJECTIVE: Dealing with uncertainty is a core competence for physicians. To evaluate the impact of an educational intervention on family medicine residents' (FMRs') intention to request diagnostic tests and their attitudes toward uncertainty. METHODS: Nonrandomized controlled trial. Intervention group (IG) FMRs participated in interactive "dealing with uncertainty" seminars comprising statistical lessons and diagnostic reasoning. Control group (CG) FMRs participated in seminars without in-depth diagnostic lessons. FMRs completed the Dealing with Uncertainty Questionnaire (DUQ), comprising the Diagnostic Action and Diagnostic Reasoning scales. The Physicians' Reaction to Uncertainty (PRU) questionnaire, comprising 4 scales (Anxiety Due to Uncertainty, Concern about Bad Outcomes, Reluctance to Disclose Uncertainty to Patients, and Reluctance to Disclose Mistakes to Physicians) was also completed. Follow-up was performed 3 months later. Differences were calculated with repeated-measures analysis of variance. RESULTS: In total, 107 FMRs of the IG and 102 FMRs of the CG participated at baseline and follow-up. The mean (SD) Diagnostic Action scale score decreased from 24.0 (4.8) to 22.9 (5.1) in the IG and increased in the CG from 23.7 (5.4) to 24.1 (5.4), showing significant group difference (P = 0.006). The Diagnostic Reasoning scale increased significantly (P = 0.025) without a significant group difference (P = 0.616), from 19.2 (2.6) to 19.7 (2.4) in the IG and from 18.1 (3.3) to 18.8 (3.2) in the CG. The PRU scale Anxiety Due to Uncertainty decreased significantly (P = 0.029) without a significant group difference (P = 0.116), from 20.5 (4.8) to 18.5 (5.5) in the IG and from 19.9 (5.5) to 19.0 (6.0) in the CG. CONCLUSION: The structured seminar reduced self-rated diagnostic test requisition. The change in Anxiety Due to Uncertainty and Diagnostic Reasoning might be due to an unspecific accompanying effect of the extra-occupational seminars for residents.

Distinct trajectories of response to prefrontal tDCS in major depression: results from a 3-arm randomized controlled trial.

Transcranial direct current stimulation (tDCS) is a safe, effective treatment for major depressive disorder (MDD). While antidepressant effects are heterogeneous, no studies have investigated trajectories of tDCS response. We characterized distinct improvement trajectories and associated baseline characteristics for patients treated with prefrontal tDCS, an active pharmacotherapy (escitalopram), and placebo. This is a secondary analysis of a randomized, non-inferiority, double-blinded trial (ELECT-TDCS, N = 245). Participants were diagnosed with an acute unipolar, nonpsychotic, depressive episode, and presented Hamilton Depression Rating Scale (17-items, HAM-D) scores ≥17. Latent trajectory modeling was used to identify HAM-D response trajectories over a 10-week treatment. Top-down (hypothesis-driven) and bottom-up (data-driven) methods were employed to explore potential predictive features using, respectively, conservatively corrected regression models and a cross-validated stability ranking procedure combined with elastic net regularization. Three trajectory classes that were distinct in response speed and intensity (rapid, slow, and no/minimal improvement) were identified for escitalopram, tDCS, and placebo. Differences in response and remission rates were significant early for all groups. Depression severity, use of benzodiazepines, and age were associated with no/minimal improvement. No significant differences in trajectory assignment were found in tDCS vs. placebo comparisons (38.3, 34, and 27.6%; vs. 23.3, 43.3, and 33.3% for rapid, slow, and no/minimal trajectories, respectively). Additional features are suggested in bottom-up analyses. Summarily, groups treated with tDCS, escitalopram, and placebo differed in trajectory class distributions and baseline predictors of response. Our results might be relevant for designing further studies.

Effects of tDCS on neuroplasticity and inflammatory biomarkers in bipolar depression: Results from a sham-controlled study.

OBJECTIVES: We investigated the role of peripheral biomarkers associated with neuroplasticity and immune-inflammatory processes on the effects of transcranial direct current stimulation (tDCS), a safe, affordable, and portable non-invasive neuromodulatory treatment, in bipolar depression. METHODS: This is an exploratory analysis using a dataset from the sham-controlled study the Bipolar Depression Electrical Treatment Trial (BETTER)(clinicaltrials.govNCT02152878). Participants were 52 adults with type I or II bipolar disorder in a moderate-to-severe depressive episode, randomized to 12 bifrontal active or sham tDCS sessions over a 6-week treatment course. Plasma levels of brain derived neurotrophic factor (BDNF), glial cell derived neurotrophic factor (GDNF), interleukins (IL) 2, 4, 6, 8, 10, 18, 33, 1ß, 12p70, 17a, interferon gamma (IFN), tumor necrosis factor alpha (TNF) and its soluble receptors 1 and 2, ST2, and KLOTHO were investigated at baseline and endpoint. We performed analyses unadjusted for multiple testing to evaluate whether baseline biomarkers were predictive for depression improvement and changed during treatment using linear regression models. RESULTS: A time x group interaction (Cohen's d: -1.16, 95% CI = -1.96 to -0.3, p = .005) was found for IL-8, with greater reductions after active tDCS. Higher baseline IL-6 plasma levels was associated with symptomatic improvement after tDCS (F(1,43) = 5.43; p = .025). Other associations were not significant. CONCLUSIONS: Our exploratory findings suggested that IL-6 is a potential predictor of tDCS response and IL-8 might decrease after tDCS; although confirmatory studies are warranted due to the multiplicity of comparisons.

Predicting personality from patterns of behavior collected with smartphones.

Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals' Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.

Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods.

Exploratory factor analysis is a statistical method commonly used in psychological research to investigate latent variables and to develop questionnaires. Although such self-report questionnaires are prone to missing values, there is not much literature on this topic with regard to exploratory factor analysis-and especially the process of factor retention. Determining the correct number of factors is crucial for the analysis, yet little is known about how to deal with missingness in this process. Therefore, in a simulation study, six missing data methods (an expectation-maximization algorithm, predictive mean matching, Bayesian regression, random forest imputation, complete case analysis, and pairwise complete observations) were compared with respect to the accuracy of the parallel analysis chosen as retention criterion. Data were simulated for correlated and uncorrelated factor structures with two, four, or six factors; 12, 24, or 48 variables; 250, 500, or 1,000 observations and three different missing data mechanisms. Two different procedures combining multiply imputed data sets were tested. The results showed that no missing data method was always superior, yet random forest imputation performed best for the majority of conditions-in particular when parallel analysis was applied to the averaged correlation matrix rather than to each imputed data set separately. Complete case analysis and pairwise complete observations were often inferior to multiple imputation.

One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis.

Determining the number of factors is one of the most crucial decisions a researcher has to face when conducting an exploratory factor analysis. As no common factor retention criterion can be seen as generally superior, a new approach is proposed-combining extensive data simulation with state-of-the-art machine learning algorithms. First, data was simulated under a broad range of realistic conditions and 3 algorithms were trained using specially designed features based on the correlation matrices of the simulated data sets. Subsequently, the new approach was compared with 4 common factor retention criteria with regard to its accuracy in determining the correct number of factors in a large-scale simulation experiment. Sample size, variables per factor, correlations between factors, primary and cross-loadings as well as the correct number of factors were varied to gain comprehensive knowledge of the efficiency of our new method. A gradient boosting model outperformed all other criteria, so in a second step, we improved this model by tuning several hyperparameters of the algorithm and using common retention criteria as additional features. This model reached an out-of-sample accuracy of 99.3% (the pretrained model can be obtained from https://osf.io/mvrau/). A great advantage of this approach is the possibility to continuously extend the data basis (e.g., using ordinal data) as well as the set of features to improve the predictive performance and to increase generalizability. (PsycInfo Database Record (c) 2020 APA, all rights reserved).