The worldwide spread of a new coronavirus (SARS-CoV-2) since December 2019 has posed a severe threat to individuals’ well-being. While the world at large is waiting that the released vaccines immunize most citizens, public health experts suggest that, in the meantime, it is only through behavior change that the spread of COVID-19 can be controlled. Importantly, the required behaviors are aimed not only at safeguarding one’s own health. Instead, individuals are asked to adapt their behaviors to protect the community at large. This raises the question of which social concerns and moral principles make people willing to do so. We considered in 23 countries (N= 6948) individuals’ willingness to engage in prescribed and discretionary behaviors, as well as country-level and individual-level factors that might drive such behavioral intentions. Results from multilevel multiple regressions, with country as the nesting variable, showed that publicized number of infections were not significantly related to individual intentions to comply with the prescribed measures and intentions to engage in discretionary prosocial behaviors. Instead, psychological differences in terms of trust in government, citizens, and in particular toward science predicted individuals’ behavioral intentions across countries. The more people endorsed moral principles of fairness and care (vs. loyalty and authority), the more they were inclined to report trust in science, which, in turn, statistically predicted prescribed and discretionary behavioral intentions. Results have implications for the type of intervention and public communication strategies that should be most effective to induce the behavioral changes that are needed to control the COVID-19 outbreak.

Background: Emerging aspects of the Covid-19 clinical presentation are its long-term effects, which are characteristic of the so-called “long COVID”. The aim of the present study was to investigate the prevalence of physical, psychological, and sleep disturbances and the quality of life in the general population during the ongoing pandemic. Methods: This study, based on an online survey, collected demographic data, information related to COVID-19, sleep disturbances, and quality of life data from 507 individuals. The level of sleep disturbances and quality of life was assessed through the Insomnia Severity Index (ISI) and the EuroQol-5D (EQ-5D), respectively. Results: In total, 507 individuals (M = 91 and F = 416 women) completed the online survey. The main symptoms associated with “long COVID” were headache, fatigue, muscle aches/myalgia, articular pains, cognitive impairment, loss of concentration, and loss of smell. Additionally, the subjects showed significant levels of insomnia (p < 0.05) and an overall reduced quality of life (p < 0.05). Conclusions: The results of the study appear in line with recent publications, but uncertainty regarding the definition and specific features of “long COVID” remains. Further studies are needed in order to better define the clinical presentation of the “long COVID” condition and related targeted treatments.

The COVID-19 pandemic resulted in an upsurge in the spread of diverse conspiracy theories (CTs) with real-life impact. However, the dynamics of user engagement remain under-researched. In the present study, we leverage Twitter data across 11 months in 2020 from the timelines of 109 CT posters and a comparison group (non-CT group) of equal size. Within this approach, we used word embeddings to distinguish non-CT content from CT-related content as well as analysed which element of CT content emerged in the pandemic. Subsequently, we applied time series analyses on the aggregate and individual level to investigate whether there is a difference between CT posters and non-CT posters in non-CT tweets as well as the temporal dynamics of CT tweets. In this regard, we provide a description of the aggregate and individual series, conducted a STL decomposition in trends, seasons, and errors, as well as an autocorrelation analysis, and applied generalised additive mixed models to analyse nonlinear trends and their differences across users. The narrative motifs, characterised by word embeddings, address pandemic-specific motifs alongside broader motifs and can be related to several psychological needs (epistemic, existential, or social). Overall, the comparison of the CT group and non-CT group showed a substantially higher level of overall COVID-19-related tweets in the non-CT group and higher level of random fluctuations. Focussing on conspiracy tweets, we found a slight positive trend but, more importantly, an increase in users in 2020. Moreover, the aggregate series of CT content revealed two breaks in 2020 and a significant albeit weak positive trend since June. On the individual level, the series showed strong differences in temporal dynamics and a high degree of randomness and day-specific sensitivity. The results stress the importance of Twitter as a means of communication during the pandemic and illustrate that these beliefs travel very fast and are quickly endorsed.

The eyes reveal important social messages, such as emotions and whether a person is aroused and interested or bored and fatigued. A growing body of research has also shown that individuals with large pupils are generally evaluated positively by observers, while those with small pupils are perceived negatively. Here, we examined whether observed pupil size influences approach-avoidance tendencies. Participants performed an Approach-Avoidance Task using faces with large and small pupil sizes. Results showed that pupil size influences the accuracy of arm movements. Specifically, individuals were less prone to approach a face with small pupils than a face with large pupils. Conversely, participants were less prone to avoid a face with large pupils than a face with small pupils. Collectively, these findings suggest that perceivers attend to a facial cue – pupil size – when interacting with others.