Positive prospective mental imagery characteristics in young adults and their associations with depressive symptoms (preprint)

Background: Positive prospective mental imagery plays an important role in mental well-being, and depressive symptoms have been associated with difficulties in generating positive prospective mental images (PPMIs). We used a mobile app to gather PPMIs generated by young adults during the COVID-19 pandemic and analyzed content, characteristics, and associations with depressive symptoms. Methods: For this longitudinal study, 50 healthy students reported PPMIs at least three times per day for seven consecutive days using a mobile app inducing PPMI generation. We categorized entries into themes and applied linear mixed models to investigate associations between PPMI characteristics and depressive symptom outcomes. Results: We distinguished 25 PPMI themes. The most frequent were related to consuming food and drinks, watching TV/streaming platforms, and doing sports. More vivid PPMIs were easier to generate. Vividness and ease of generation of PPMIs, but not their anticipation or pleasure intensity, were associated with fewer depressive symptoms. Discussion: We identified PPMI themes in young adults and found significant negative associations between depressive symptoms and vividness and generation ease of PPMIs. These results may inform prevention and intervention science, including design of personalized interventions. We discuss implications for future studies and treatment development for individuals experiencing diminished PPMI.

Data-Driven Prediction of COVID-19 Cases in Germany for Decision Making (preprint)

Background: The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitaten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation. Methods: We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. Results: The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible. Conclusions: We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.

Absolute Mortality Risk Assessment of COVID-19 Patients: The Khorshid COVID Cohort (KCC) Study (preprint)

Background: Already at the time of hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. Methods: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on following variables which can simply be measured at hospital admission: gender, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. Results: Cause-specific hazard regression models show that these baseline variables are associated with both hazards, the death as well as the discharge hazard. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC=0.872 [CI 95%: 0.835-0.910]). Conclusions: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.

Using Differentiable Programming for Flexible Statistical Modeling (preprint)

Differentiable programming has recently received much interest as a paradigm that facilitates taking gradients of computer programs. While the corresponding flexible gradient-based optimization approaches so far have been used predominantly for deep learning or enriching the latter with modeling components, we want to demonstrate that they can also be useful for statistical modeling per se, e.g., for quick prototyping when classical maximum likelihood approaches are challenging or not feasible. In an application from a COVID-19 setting, we utilize differentiable programming to quickly build and optimize a flexible prediction model adapted to the data quality challenges at hand. Specifically, we develop a regression model, inspired by delay differential equations, that can bridge temporal gaps of observations in the central German registry of COVID-19 intensive care cases for predicting future demand. With this exemplary modeling challenge, we illustrate how differentiable programming can enable simple gradient-based optimization of the model by automatic differentiation. This allowed us to quickly prototype a model under time pressure that outperforms simpler benchmark models. We thus exemplify the potential of differentiable programming also outside deep learning applications, to provide more options for flexible applied statistical modeling.

Standalone smartphone CBT-based Ecological Momentary Interventions to increase mental health - a narrative review (preprint)

Introduction: A growing number of psychological interventions are delivered via smartphone with the aim to increase the efficacy and effectiveness of these treatments and provide scalable access to interventions for improving mental health. Most of the scientifically tested apps are based on cognitive behavioural therapy principles which are considered as a gold standard for the treatment of many mental health problems. Objective: This review aimed to investigate standalone smartphone-based ‘ecological momentary interventions’ (EMIs) to improve mental health, that were built based on principles derived from cognitive behavioural therapy (CBT). Methods: We searched MEDLINE, PsycINFO, Embase and PubMed databases for peer-reviewed studies published between 1st January 2007 and 15th January 2020. We included studies with a focus on standalone app-based approaches to improve mental health and their feasibility, and/or efficacy and/or effectiveness. Both within- and between-group designs and studies with both healthy and clinical samples were included. Blended interventions, e.g., app-based treatments in combination with psychotherapy, were not included. Selected studies were evaluated in terms of their design, i.e., 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 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 (m=70.8%), and outcomes were satisfying by users (m=72.6%). Conclusions: Study quality was heterogeneous, and feasibility was often not reported in the reviewed studies, hence 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 life. 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.

Psycho-social factors associated with mental resilience in the Corona lockdown (preprint)

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<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.