Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.

A total of 1,273 conscripts of the Greek Air Force performed antisaccades and completed self-reporting questionnaires measuring schizotypy and current state-dependent psychopathology. Only 1.0% of variability in antisaccade performance indices was related to psychometric scores in the population and could be attributed more to current state-dependent symptoms such as anxiety rather than to schizotypy. In contrast, a specific increase of error rate and response latency variability and a high correlation of these 2 variables was observed in a group with very high schizotypy scores. This effect was independent of anxiety and depression, suggesting that a specific group of psychosis-prone individuals has a characteristic deviance in antisaccade performance that is not present in the general population.

There is a growing body of evidence highlighting the role of gut microbiota as a biological basis of psychiatric disorders. The existing literature suggest that cognitive and emotional activities can be influenced by microbes through the microbiota–gut–brain axis and implies an association between alterations in the gut microbiome and several psychiatric conditions, such as autism, depression, bipolar disorder and psychosis. The aim of this review is to summarise recent findings and provide concise updates on the latest progress of the role of gut microbiota in the development and maintenance of psychiatric symptoms in schizophrenia and the first episode of psychosis. Despite the lack of consistent findings in regard to specific microbiome changes related to psychosis, the emerging literature reports significant differences in the gut microbiome of schizophrenic subjects compared to healthy controls and increasingly outlines the significance of an altered microbiome composition in the pathogenesis, development, symptom severity and prognosis of psychosis. Further human studies are, however, required, which should focus on identifying the drivers of microbiota changes in psychosis and establish the direction of causality between psychosis and microbiome alterations.