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Background Mental disorders in adolescence and young adulthood are major public health concerns. Digital tools such as text-based conversational agents (ie, chatbots) are a promising technology for facilitating mental health assessment. However, the human-like interaction style of chatbots may induce potential biases, such as socially desirable responding (SDR), and may require further effort to complete assessments. Objective This study aimed to investigate the convergent and discriminant validity of chatbots for mental health assessments, the effect of assessment mode on SDR, and the effort required by participants for assessments using chatbots compared with established modes. Methods In a counterbalanced within-subject design, we assessed 2 different constructs—psychological distress (Kessler Psychological Distress Scale and Brief Symptom Inventory-18) and problematic alcohol use (Alcohol Use Disorders Identification Test-3)—in 3 modes (chatbot, paper-and-pencil, and web-based), and examined convergent and discriminant validity. In addition, we investigated the effect of mode on SDR, controlling for perceived sensitivity of items and individuals’ tendency to respond in a socially desirable way, and we also assessed the perceived social presence of modes. Including a between-subject condition, we further investigated whether SDR is increased in chatbot assessments when applied in a self-report setting versus when human interaction may be expected. Finally, the effort (ie, complexity, difficulty, burden, and time) required to complete the assessments was investigated. Results A total of 146 young adults (mean age 24, SD 6.42 years; n=67, 45.9% female) were recruited from a research panel for laboratory experiments. The results revealed high positive correlations (all P Conclusions Our findings suggest that chatbots may yield valid results. Furthermore, an understanding of chatbot design trade-offs in terms of potential strengths (ie, increased social presence) and limitations (ie, increased effort) when assessing mental health were established.

Ward-equivalent treatment (StäB), a form of crisis resolution and home treatment in Germany, has been introduced in 2018 as a new model of mental health service delivery for people with an indication for inpatient care. The rapid progress in the field of information and communication technology offers entirely new opportunities for innovative digital mental health care, such as telemedicine, eHealth, or mHealth interventions.This review aims to provide a comprehensive overview of novel digital forms of service delivery that may contribute to a personalized delivery of StäB and improving clinical and social outcomes as well as reducing direct and indirect costs.This work is based on a narrative review.Four primary digital forms of service delivery have been identified that can be used for personalized delivery of StäB: (1) communication, continuity of care, and flexibility through online chat and video call; (2) monitoring of symptoms and behavior in real-time through ecological momentary assessment (EMA); (3) use of multimodal EMA data to generate and offer personalized feedback on subjective experience and behavioral patterns as well as (4) adaptive ecological momentary interventions (EMI) tailored to the person, moment, and context in daily life.New digital forms of service delivery have considerable potential to increase the effectiveness and cost-effectiveness of crisis resolution, home treatment, and assertive outreach. An important next step is to model and initially evaluate these novel digital forms of service delivery in the context of StäB and carefully investigate their quality from the user perspective, safety, feasibility, initial process and outcome quality as well as barriers and facilitators of implementation.HINTERGRUND: Die stationsäquivalente psychiatrische Behandlung (StäB) wurde 2018 als Krankenhausleistung für Menschen eingeführt, die die Kriterien einer stationären Behandlung erfüllen. Die rasanten Fortschritte im Bereich der Informations- und Kommunikationstechnologie bieten neue Chancen für innovative digitale Versorgungsangebote wie telemedizinische, eHealth- oder mHealth-Verfahren.Diese Übersichtsarbeit soll einen umfassenden Überblick über neue digitale Versorgungsformen geben, die zur Personalisierung der StäB bei schweren psychischen Erkrankungen beitragen und somit klinische und soziale Outcomes verbessern sowie direkte und indirekte Kosten reduzieren könnten.Diese Arbeit basiert auf einer selektiven Literaturrecherche (Narratives Review).Es wurden vier primäre digitale Versorgungsformen identifiziert, die in der StäB gewinnbringend genutzt werden könnten: (1) Kommunikation, Behandlungskontinuität und -flexibilität durch Online-Chat und Videotelefonie, (2) Monitoring von Symptomen und Verhaltensweisen in Echtzeit durch Anwendung des ambulatorischen Assessments („ecological momentary assessment“ [EMA]), (3) Nutzung multimodaler EMA-Daten für die Generierung von personalisiertem Feedback über subjektives Erleben und Verhaltensmuster sowie (4) auf Person, Moment und Kontext zugeschnittene, adaptive ambulatorische Interventionen („ecological momentary interventions“ [EMIs]).Digitale Versorgungsformen haben erhebliches Potenzial die Effektivität und Kosteneffektivität der StäB zu steigern. Ein wichtiger nächster Schritt besteht darin, die Anwendung dieser Versorgungsformen im Bereich der StäB zu modellieren und deren Qualität aus Sicht der Patient*innen, Sicherheit und initiale Prozess- und Ergebnisqualität sowie Implementierungsbedingungen sorgfältig zu untersuchen.

Highly arousing, affective stimuli have adverse effects on cognition and performance. Perception of affective stimuli is, however, highly subjective and may impact on the interaction of emotion and cognition. Here, we tested the impact of high- versus low-threatening stimuli on response inhibition as a function of perceived threat intensity. Response inhibition was probed using a stop-signal paradigm in 62 healthy adults. We used stop-signals that had previously been paired with an unpleasant electrodermal stimulation (i.e., high-threat stimuli) or that had never been paired with electrodermal stimulation (i.e., low-threat stimuli). High-threat stimuli did not affect stopping performance in general. Only participants who perceived the high-threat stimuli as highly painful showed impaired response inhibition on high-threat trials relative to low-threat trials. Participants who perceived the high-threat as mildly painful, however, showed improved response inhibition on high-threat trials. This effect was not moderated by the current anxious state. This suggests that the impact of negative affective stimuli on cognition critically depends on subjective threat perception. Ratings of affective stimuli should be included in studies probing the emotion-cognition interaction because subjective perception might strongly impact on that interaction. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Motivated by the recent replicability crisis we tested replicability of functional magnetic resonance imaging (fMRI) group activations in two independent samples. An identical behavioral and fMRI test battery for the longitudinal investigation of stress resilience mechanisms was developed for the Mainz Resilience Project (MARP) and conducted in a discovery (N = 54) and a replication sample (N = 103). The test battery consisted of a stress reactivity task, a reward sensitivity task, a fear conditioning and extinction paradigm, two volitional reappraisal tasks and an emotional interference inhibition task. Replicability of group activations was tested with the Jaccard index and the Intra Class Correlation (ICC). Overall, we observed good to excellent replicability of activations at the whole brain level. Only a minority of contrasts showed unsatisfactory replicability. Replicability at the level of individual regions of interest (ROIs) was generally lower. Tasks with stronger activation in the discovery sample showed better replicability.

Contains fulltext : 179627.pdf (Publisher’s version ) (Closed access) Consistent failure over the past few decades to reduce the high prevalence of stress-related disorders has motivated a search for alternative research strategies. Resilience refers to the phenomenon of many people maintaining mental health despite exposure to psychological or physical adversity. Instead of aiming to understand the pathophysiology of stress-related disorders, resilience research focuses on protective mechanisms that shield people against the development of such disorders and tries to exploit its insights to improve treatment and, in particular, disease prevention. To fully harness the potential of resilience research, a critical appraisal of the current state of the art - in terms of basic concepts and key methods - is needed. We highlight challenges to resilience research and make concrete conceptual and methodological proposals to improve resilience research. Most importantly, we propose to focus research on the dynamic processes of successful adaptation to stressors in prospective longitudinal studies. 7 p.

In case of uncertainty, predictions that are based on prior, similar experiences guide our decision by processes of generalization. Over-generalization of negative information has been identified as an important feature of several psychopathologies, including anxiety disorders and depression, and might underlie biased interpretation of ambiguous information. Here, we investigated the neural correlates of valence generalization to ambiguous stimuli using a translational affective conditioning task during fMRI. Twenty-five healthy individuals participated in a conditioning procedure with (1) an initial acquisition phase, where participants learned the positive and negative valence of two different tones (reference tones) through their responses and subsequent feedback and (2) a test phase, where participants were presented with the previously learned reference tones and three additional tones with intermediate frequency to the learned reference tones. By recording the responses to these intermediate stimuli we were able to assess the participantsi interpretation of ambiguous tones as either positive or negative. Behavioral results revealed a graded response pattern to the three intermediate tones, which was mirrored on the neural level. More specifically, parametric analyses OF BOLD responses to all five tones revealed a linear effect in bilateral anterior insula and SMA with lowest activation to the negative reference tone and highest activation to the positive negative tone. In addition, a cluster in the SMA showed a reverse-quadratic response, i.e., the strongest response for the most ambiguous tone. These findings suggest overlapping regions in the salience network that mediate valence generalization and decision-making under ambiguity, potentially underlying biased ambiguous cue interpretation.

Deep learning approaches can uncover complex patterns in data. In particular, variational autoencoders (VAEs) achieve this by a non-linear mapping of data into a low-dimensional latent space. Motivated by an application to psychological resilience in the Mainz Resilience Project (MARP), which features intermittent longitudinal measurements of stressors and mental health, we propose an approach for individualized, dynamic modeling in this latent space. Specifically, we utilize ordinary differential equations (ODEs) and develop a novel technique for obtaining person-specific ODE parameters even in settings with a rather small number of individuals and observations, incomplete data, and a differing number of observations per individual. This technique allows us to subsequently investigate individual reactions to stimuli, such as the mental health impact of stressors. A potentially large number of baseline characteristics can then be linked to this individual response by regularized regression, e.g., for identifying resilience factors. Thus, our new method provides a way of connecting different kinds of complex longitudinal and baseline measures via individualized, dynamic models. The promising results obtained in the exemplary resilience application indicate that our proposal for dynamic deep learning might also be more generally useful for other application domains.