The SARS-CoV-2 pandemic is not only a threat to physical health but is also having severe impacts on mental health. Although increases in stress-related symptomatology and other adverse psycho-social outcomes, as well as their most important risk factors have been described, hardly anything is known about potential protective factors. Resilience refers to the maintenance of mental health despite adversity. To gain mechanistic insights about the relationship between described psycho-social resilience factors and resilience specifically in the current crisis, we assessed resilience factors, exposure to Corona crisis-specific and general stressors, as well as internalizing symptoms in a cross-sectional online survey conducted in 24 languages during the most intense phase of the lockdown in Europe (22 March to 19 April) in a convenience sample of N = 15,970 adults. Resilience, as an outcome, was conceptualized as good mental health despite stressor exposure and measured as the inverse residual between actual and predicted symptom total score. Preregistered hypotheses (osf.io/r6btn) were tested with multiple regression models and mediation analyses. Results confirmed our primary hypothesis that positive appraisal style (PAS) is positively associated with resilience (p < 0.0001). The resilience factor PAS also partly mediated the positive association between perceived social support and resilience, and its association with resilience was in turn partly mediated by the ability to easily recover from stress (both p < 0.0001). In comparison with other resilience factors, good stress response recovery and positive appraisal specifically of the consequences of the Corona crisis were the strongest factors. Preregistered exploratory subgroup analyses (osf.io/thka9) showed that all tested resilience factors generalize across major socio-demographic categories. This research identifies modifiable protective factors that can be targeted by public mental health efforts in this and in future pandemics.

The SARS-CoV-2 pandemic is not only a threat to physical health but is also having severe impacts on mental health. While increases in stress-related symptomatology and other adverse psycho-social outcomes as well as their most important risk factors have been described, hardly anything is known about potential protective factors. Resilience refers to the maintenance of mental health despite adversity. In order to gain mechanistic insights about the relationship between described psycho-social resilience factors and resilience specifically in the current crisis, we assessed resilience factors, exposure to Corona crisis-specific and general stressors, as well as internalizing symptoms in a cross-sectional online survey conducted in 24 languages during the most intense phase of the lockdown in Europe (March 22nd to April 19th) in a convenience sample of N=15,970 adults. Resilience, as an outcome, was conceptualized as good mental health despite stressor exposure and measured as the inverse residual between actual and predicted symptom total score. Preregistered hypotheses (osf.io/r6btn) were tested with multiple regression models and mediation analyses. Results confirmed our primary hypothesis that positive appraisal style (PAS) is positively associated with resilience (p&lt;0.0001). The resilience factor PAS also partly mediated the positive association between perceived social support and resilience, and its association with resilience was in turn partly mediated by the ability to easily recover from stress (both p&lt;0.0001). In comparison with other resilience factors, good stress response recovery and positive appraisal specifically of the consequences of the Corona crisis were the strongest factors. Preregistered exploratory subgroup analyses (osf.io/thka9) showed that all tested resilience factors generalize across major socio-demographic categories. This research identifies modifiable protective factors that can be targeted by public mental health efforts in this and in future pandemics.

Background A growing number of psychological interventions are delivered via smartphones with the aim of increasing the efficacy and effectiveness of these treatments and providing scalable access to interventions for improving mental health. Most of the scientifically tested apps are based on cognitive behavioral therapy (CBT) principles, which are considered the gold standard for the treatment of most mental health problems. Objective This review investigates standalone smartphone-based ecological momentary interventions (EMIs) built on principles derived from CBT that aim to improve mental health. Methods We searched the MEDLINE, PsycINFO, EMBASE, and PubMed databases for peer-reviewed studies published between January 1, 2007, and January 15, 2020. We included studies focusing on standalone app-based approaches to improve mental health and their feasibility, efficacy, or effectiveness. Both within- and between-group designs and studies with both healthy and clinical samples were included. Blended interventions, for example, app-based treatments in combination with psychotherapy, were not included. Selected studies were evaluated in terms of their design, that is, choice of the control condition, sample characteristics, EMI content, EMI delivery characteristics, feasibility, efficacy, and effectiveness. The latter was defined in terms of improvement in the primary outcomes used in the studies. Results A total of 26 studies were selected. The results show that EMIs based on CBT principles can be successfully delivered, significantly increase well-being among users, and reduce mental health symptoms. Standalone EMIs were rated as helpful (mean 70.8%, SD 15.3; n=4 studies) and satisfying for users (mean 72.6%, SD 17.2; n=7 studies). Conclusions Study quality was heterogeneous, and feasibility was often not reported in the reviewed studies, thus limiting the conclusions that can be drawn from the existing data. Together, the studies show that EMIs may help increase mental health and thus support individuals in their daily lives. Such EMIs provide readily available, scalable, and evidence-based mental health support. These characteristics appear crucial in the context of a global crisis such as the COVID-19 pandemic but may also help reduce personal and economic costs of mental health impairment beyond this situation or in the context of potential future pandemics.

BackgroundThe field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted.ResultsQuantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences. The package can be used to calculate dozens of characteristics directly from sequences or using physico-chemical properties of amino acids. Besides basic measures, Quantiprot performs quantitative analysis of recurrence and determinism in the sequence, calculates distribution of n-grams and computes the Zipf’s law coefficient.ConclusionsWe propose three main fields of application of the Quantiprot package. First, quantitative characteristics can be used in alignment-free similarity searches, and in clustering of large and/or divergent sequence sets. Second, a feature space defined by quantitative properties can be used in comparative studies of protein families and organisms. Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.

Background: Self-efficacy is associated with positive mental health outcomes. We developed and tested a digital self-efficacy training involving repeated recall of autobiographical self-efficacy memories.Methods: In this randomized controlled trial, we investigated the effects of our digital training on key mental health outcomes, including anxiety, stress, and hopelessness, and on self-efficacy as the target outcome in 93 university students with elevated self-reported stress levels. Results: We found significantly reduced hopelessness (d = 0.29) and trait anxiety (d = 0.79) in the training group compared to the control group at post-assessment. We also found significant effects on self-efficacy at post-assessment across groups when controlling for baseline self-efficacy, with a greater increase in the training group.Conclusions: This digital scalable self-efficacy training had significant positive effects on hopelessness, anxiety, and self-efficacy. Future work is needed to replicate and investigate long-term effects of the training and explore its implementation in clinical groups.