Introduction The current study aimed to investigate the rates of anxiety, clinical depression, and suicidality and their changes in health professionals during the COVID-19 outbreak. Materials and methods The data came from the larger COMET-G study. The study sample includes 12,792 health professionals from 40 countries (62.40% women aged 39.76 ± 11.70; 36.81% men aged 35.91 ± 11.00 and 0.78% non-binary gender aged 35.15 ± 13.03). Distress and clinical depression were identified with the use of a previously developed cut-off and algorithm, respectively. Statistical analysis Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses, and Factorial Analysis of Variance (ANOVA) tested relations among variables. Results Clinical depression was detected in 13.16% with male doctors and ‘non-binary genders’ having the lowest rates (7.89 and 5.88% respectively) and ‘non-binary gender’ nurses and administrative staff had the highest (37.50%); distress was present in 15.19%. A significant percentage reported a deterioration in mental state, family dynamics, and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (24.64% vs. 9.62%; p < 0.0001). Suicidal tendencies were at least doubled in terms of RASS scores. Approximately one-third of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop clinical depression was associated with a history of Bipolar disorder (RR = 4.23). Conclusions The current study reported findings in health care professionals similar in magnitude and quality to those reported earlier in the general population although rates of clinical depression, suicidal tendencies, and adherence to conspiracy theories were much lower. However, the general model of factors interplay seems to be the same and this could be of practical utility since many of these factors are modifiable.

Since the COVID-19 pandemic the impact of mental disorders on daily functioning and well-being on the general population and the rising demand for mental health support have been in the limelight. The recent surge in new breakthroughs in artificial intelligence technology has generated interest in the potential to utilize AI in mental health services. New chatbot technologies can utilize these new generations of AI to provide special benefits including accessibility and anonymity and can overcome traditional barriers to therapy. Chatbots facilitate continuous data collection which could enable personalized treatment approaches and improve diagnostic accuracy. While this is promising, the need for human oversight and the importance of ethical data collection and privacy remain an issue. The implementation of AI chatbots into mental health services presents an opportunity to greatly enhance accessibility but careful consideration of their role and limitations in necessary for optimal integration in clinical practice.

Alexithymia is the inability to describe one’s own feelings and is being increasingly researched. According to contemporary psychodynamic theories, negative emotions cannot be adequately named and externalized, but remain trapped in the body. Recent research shows the connection of alexithymia with numerous somatic diseases. Diabetes mellitus type 2 and obesity represent great challenges in treatment, and the psychological profiles in these diseases are being studied more and more often. Therefore, alexithymia enters the focus of some research as a factor that could play a significant role in these diseases, namely as the one that makes a difference. The aim of this paper is a review of the literature with the purpose of understanding the current knowledge about the interconnection between alexithymia, obesity and type 2 diabetes mellitus.