The COVID-19 pandemic represents a major stressor for the psychological health of people worldwide. In the UK, the COVID19-Psychological Research Consortium Study (C19PRC) launched to evaluate the psychological impact of COVID-19 in the general population and its implications. The project was then extended to Italy and several other countries. This article provides an overview of the Italian C19PRC study and its replication of two specific findings from the UK C19PRC. In the first part, the relationship between anxiety and somatic symptomatology is examined. In the second part, we analyze the association between several factors and psychological health outcomes: depression/anxiety, traumatic stress, COVID-19 anxiety. In line with the study conducted in the UK, an online survey was administered to the adult Italian general population. The sample included 1038 respondents (age, mean = 49.94, SD = 16.14, 51.15% females) taken from four regions: Lombardia, Veneto, Lazio, and Campania. The relationship between predictors and outcomes was evaluated by means of logistic regression models. Somatic indices showed a positive association with anxiety, worse somatic symptoms were associated with mourning a loss of a beloved one due to COVID-19 and with precarious health conditions. Females showed a higher incidence of psychological issues. No differences in anxiety, depression, and traumatic stress were found across regions but the Campania region showed the most severe somatic symptomatology. In the second analysis, the factors associated with more severe psychological outcomes (i.e., anxiety and/or depression, traumatic stress, and COVID-19 related anxiety) were younger age, the presence of minors in the household, traumatic stressors, and precarious health conditions. No differences across regions emerged. The Italian results correspond to the UK findings for anxiety, depression, and traumatic stress. Both in the UK and Italy, the factors associated with worse psychological health were gender (female), younger age, having children, pre-existing health issues (both for oneself or someone close), and the moderate/high perceived risk of contracting COVID-19 within one month. In Italy, unlike the UK, lower household income and having (had) COVID-19 were not associated with poorer mental health. The psychological impact of COVID-19 can last for months; future research should explore all aspects of the psychological burden of COVID-19 in order to implement psychological interventions and promote psychological health.

In this study, we investigated the effect of global temporal prediction on the brain capability to implicitly adjust proactive motor control. We used the Dynamic Temporal Prediction (DTP), in which local and global predictions of an imperative stimulus were manipulated by using different stimulus-onset asynchronies (SOAs), presented with several distribution probabilities. At a behavioural level, the results show a performance adjustment (reaction time decrease) depending on the implicit use of global prediction. At a neurophysiological level, three separate computational steps underlying motor control were investigated. First, the expectancy implementation was associated with global probability-dependent contingent negative variation (CNV) modulation supported by the recruitment of a frontoparietal network involving the anterior cingulate, the left intraparietal sulcus, the occipital, and the premotor areas. Second, the response implementation was modulated by the global prediction fostering stimulus processing (P3 increase) at the motor response level, as suggested by both oscillatory (beta desynchronization), as well as source analysis (frontal cortical network). Third, the expectancy violation lead to a negativity increase (omission-detection potential) time locked to the global rule violation and additionally, to delta and theta power increase interpreted as inhibitory control and rule violation detection, respectively. The expectancy violation further engaged a left lateralized network including the temporal parietal junction (TPJ) and the motor cortex, suggesting involvement of attentional reorienting and a motor adjustment. Finally, these findings provide new insights on the neurocognitive mechanisms underlying proactive motor control, suggesting an overlapping between implicit and explicit processes.

Exercise addiction (EA) refers to excessive exercise, lack of control, and health risks. The Exercise Addiction Inventory (EAI) is one of the most widely used tools in its assessment. However, the cross-cultural psychometric properties of the EAI could be improved because it misses three pathological patterns, including guilt, exercise despite injury, and experienced harm. Therefore, the present study tested the psychometric properties of the expanded EAI (EAI-3) in a large international sample. The EAI-3 was administered to 1931 physically active adult exercisers speaking five languages (Chinese, German, Italian, Japanese, and Turkish) and other measures for obsessive–compulsive behavior, eating disorders, and personality traits. The assessment structure and reliability of the EAI-3 were tested with factorial analyses and through measurement invariance across languages and sex. Finally, a cutoff point for dysfunction-proneness was calculated. The EAI-3 comprised two factors, reflecting the positive and pathological sides of exercise. The structure had excellent reliability and goodness-of-fit indices and configural and metric invariances of the scale were supported. However, three items caused violations in scalar invariance. The results of partial measurement invariance testing suggested an adequate fit for the data. Following sensitivity and specificity analysis, the EAI-3’s cutoff score was 34 out of a maximum score of 48. This preliminary study suggests that the EAI-3 is a promising tool for screening EA in an international sample, with a robust and reliable structure comparable across languages and sex. In addition, the proposed cutoff could pave the way toward a consensus on a threshold to screen for EA.

Before conducting statistical analyses, scholars and researchers often have specific hypotheses about differences between groups of means. Their hypotheses are frequently tested by applying post hoc comparisons with statistically significant simple or interaction effects. With the exception of exploratory studies, using post hoc comparisons can increase Type I error rates and decrease statistical power. A well-known solution involves planning comparisons before the study. However, the coding of such planned comparisons can be difficult to understand and implement, especially for customized comparisons and interaction effects. In this tutorial, we aim to reduce such difficulties by examining all the possible types of planned comparisons, even the customized ones, for both main and interaction effects. In this tutorial, a Shiny App coded in R and called “appRiori” is presented. appRiori is coded to help in understanding both the logic behind the planned comparisons and the way to interpret them when a model is tested. By using empirical examples on reproducible data, we explain how to code any default planned comparison executable in R. Moreover, through some features of appRiori, the customization of planned comparisons is shown, even on interaction effects, such as the possibility of creating customized contrast through click-and-drop menus. For each step, the R code related to the planned comparison is provided. Implications and fields of use of planned comparisons and appRiori are discussed.