Background:Compared to classical neuroleptics amisulpride has been shown to be more effective in treating negative symptoms of schizophrenia. Moreover, it induces only low or negligible extrapyramidal side effects. Concerning this clinical profile it should be regarded as an atypical neuroleptic. !n vitro studies indicate that amisulpride acts selectively at Dz-and Ds-receptors. Concerning its in vitro receptor profile, no significant anti-adrenergic and anticholinergic properties should be expected in vivo.This study 1); prospectively investigated the effects of amisulpride on autonomic neurocardiac function and ECG time relations and 2); reviewed ECG data available from large pre-clinical and clinical studies of amisulpride.

This 8-week study was designed to explore any correlation between a passive data collection approach using a wearable device (i.e., digital phenotyping), active data collection (patient’s questionnaires), and a traditional clinical evaluation [Montgomery-Åsberg Depression Rating Scale (MADRS)] in patients with major depressive disorder (MDD) treated with trazodone once a day (OAD). Overall, 11 out of 30 planned patients were enrolled. Passive parameters measured by the wearable device included number of steps, distance walked, calories burned, and sleep quality. A relationship between the sleep score (derived from passively measured data) and MADRS score was observed, as was a relationship between data collected actively (assessing depression, sleep, anxiety, and warning signs) and MADRS score. Despite the limited sample size, the efficacy and safety results were consistent with those previously reported for trazodone. The small population in this study limits the conclusions that can be drawn about the correlation between the digital phenotyping approach and traditional clinical evaluation; however, the positive trends observed suggest the need to increase synergies among clinicians, patients, and researchers to overcome the cultural barriers toward implementation of digital tools in the clinical setting. This study is a step toward the use of digital data in monitoring symptoms of depression, and the preliminary data obtained encourage further investigations of a larger population of patients monitored over a longer period of time.