Motives motivate human behavior. Most behaviors are driven by more than one motive, yet it is unclear how different motives interact and how such motive combinations affect the neural computation of the behaviors they drive. To answer this question, we induced two prosocial motives simultaneously (multi-motive condition) and separately (single motive conditions). After the different motive inductions, participants performed the same choice task in which they allocated points in favor of the other person (prosocial choice) or in favor of themselves (egoistic choice). We used fMRI to assess prosocial choice-related brain responses and drift diffusion modeling to specify how motive combinations affect individual components of the choice process. Our results showed that the combination of the two motives in the multi-motive condition increased participants' choice biases prior to the behavior itself. On the neural level, these changes in initial prosocial bias were associated with neural responses in the bilateral dorsal striatum. In contrast, the efficiency of the prosocial decision process was comparable between the multi-motive and the single-motive conditions. These findings provide insights into the computation of prosocial choices in complex motivational states, the motivational setting that drives most human behaviors .

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.

Background The onset of mental illness such as depression and anxiety disorders in pregnancy and postpartum period is common. The coronavirus induced disease 2019 (COVID-19) pandemic and the resulting public policy responses represent an exceptional situation worldwide and there are hints for adverse psychosocial impact, hence, the study of psychological effects of the pandemic in women during hospitalization for delivery and in the postpartum period is highly relevant. Methods Patients who gave birth during the first wave of the COVID-19 pandemic in Germany (March to June 2020) at the Department of Obstetrics and Gynecology, University of Würzburg, Germany, were recruited at hospital admission for delivery. Biosamples were collected for analysis of SARS-CoV-2 infection and various stress hormones and interleukin-6 (IL-6). In addition to sociodemographic and medical obstetric data, survey questionnaires in relation to concerns about and fear of COVID-19, depression, stress, anxiety, loneliness, maternal self-efficacy and the mother–child bonding were administered at T1 (delivery stay) and T2 (3–6 months postpartum). Results In total, all 94 recruited patients had a moderate concern of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at T1 with a significant rise at T2. This concern correlated with low to low-medium general psychosocial stress levels and stress symptoms, and the women showed a significant increase of active coping from T1 to T2. Anxiety levels were low and the Edinburgh Postnatal Depression Scale showed a medium score of 5 with a significant (T1), but only week correlation with the concerns about SARS-CoV-2. In contrast to the overall good maternal bonding without correlation to SARS-CoV-2 concern, the maternal self-efficiency correlated negatively with the obstetric impairment caused by the COVID-19 pandemic. Conclusion Obstetric patients` concerns regarding SARS-CoV-2 and the accompanying pandemic increased during the course of the pandemic correlating positively with stress and depression. Of note is the increase in active coping over time and the overall good mother–child-bonding. Maternal self-efficacy was affected in part by the restrictions of the pandemic.

A variety of factors contribute to the degree to which a person feels lonely and socially isolated. These factors may be particularly relevant in contexts requiring social distancing, e.g., during the COVID-19 pandemic or in states of immunodeficiency. We present the Loneliness and Isolation during Social Distancing (LISD) Scale. Extending existing measures, the LISD scale measures both state and trait aspects of loneliness and isolation, including indicators of social connectedness and support. In addition, it reliably predicts individual differences in anxiety and depression. Data were collected online from two independent samples in a social distancing context (the COVID-19 pandemic). Factorial validation was based on exploratory factor analysis (EFA; Sample 1, N = 244) and confirmatory factor analysis (CFA; Sample 2, N = 304). Multiple regression analyses were used to assess how the LISD scale predicts state anxiety and depression. The LISD scale showed satisfactory fit in both samples. Its two state factors indicate being lonely and isolated as well as connected and supported, while its three trait factors reflect general loneliness and isolation, sociability and sense of belonging, and social closeness and support. Our results imply strong predictive power of the LISD scale for state anxiety and depression, explaining 33 and 51% of variance, respectively. Anxiety and depression scores were particularly predicted by low dispositional sociability and sense of belonging and by currently being more lonely and isolated. In turn, being lonely and isolated was related to being less connected and supported (state) as well as having lower social closeness and support in general (trait). We provide a novel scale which distinguishes between acute and general dimensions of loneliness and social isolation while also predicting mental health. The LISD scale could be a valuable and economic addition to the assessment of mental health factors impacted by social distancing.