RESUMEN
The COVID-19 pandemic is an unprecedented event entailing long-term consequences on population health and welfare. Those who contracted the coronavirus may have suffered from both physical and mental health issues that unfold the need for tailored intervention strategies. Hence, our study aims to investigate the psychological and social consequences of COVID-19 on a sample of 86 participants, encompassing 43 patients (clinical group; 25 women; mean age = 50.4 ± 10.1 years) recruited from Bari University Hospital, 19 of whom were hospitalized due to the disease. The remaining 43 were individuals not fallen ill with COVID-19 to date (control group; 25 women; mean age = 50.4 ± 10.1 years). The investigation yielded significant gender differences in post-traumatic stress symptoms, depression, and representation of interpersonal distance (IPD), evaluated through the IES-R, the BDI-II, and the IVAS task, respectively. This pattern of results was not replicated in the control group. In general, participants who reported having experienced the most intense post-traumatic symptoms also presented a greater mood deflection and, more specifically, within the clinical group women obtained the highest scores on both scales. Women reported higher IES-R and BDI-II scores compared to men, that could indicate that women who have contracted COVID-19 are more exposed to post-traumatic and depressive symptoms. Our results also showed a significant effect of COVID-19 on IPD with a tendency of disease-experienced individuals to increase their preferred IPD from adults, children, and elderly people. Regarding gender differences in mood and proxemic behavior, a correlation between depressive symptoms and probable PTSD and a further correlation between probable PTSD and greater IPD were found in women from both clinical and control group. Overall, these findings might contribute to a better understanding of gender-based implications of the current pandemic on mental health, also leading to the development of integrated yet personalized intervention strategies.
RESUMEN
INTRODUCTION: Chronic pain (CP) is a complex multidimensional experience severely affecting individuals' quality of life. Multiple cognitive, affective, emotional, and interpersonal factors play a major role in CP. Furthermore, the psychological, social, and physical circumstances leading to CP show high inter-individual variability, thus making it difficult to identify core syndrome characteristics. In a biopsychosocial perspective, we aim at identifying a pattern of psycho-physical impairments that can reliably discriminate between CP individuals and healthy controls (HC) with high accuracy and estimated generalizability using machine learning. METHODS: A total of 118 CP and 86 HC were recruited. All individuals were administered several scales assessing quality of life, physical and mental health, personal functioning, anxiety, depression, beliefs about medical treatments, and cognitive ability. These features were trained to separate CP from HC using support vector classification and repeated nested cross-validation. RESULTS: Our psycho-physical classifier was able to discriminate CP from HC with 86.5% balanced accuracy and significance (p = 0.0001). The most reliable features characterizing CP were anxiety and depression scores, and belief of harm from prolonged pharmacological treatments; for HP, the most reliable features were physical and occupational functioning, and vitality levels. CONCLUSION: Our findings suggest that, using psychological and physical assessments, it is possible to classify CP from HC with high reliability and estimated generalizability via (i) a pattern of psychological symptoms and cognitive beliefs characteristic of CP, and (ii) a pattern of intact physical functioning characteristic of HC. We think that our algorithm enables novel insights into potential individualized targets for CP-related early intervention programs.