Efficacy of the E-motional Training Program for Improving Social Cognition in People With Dual Pathology in a Clinical and Prison Environment

Drug use, substance-related disorders (SUD), other addictive behaviors, or, from a broader perspective, dual pathology, are problems of enormous socio-health impact that still require a great research effort to improve diagnostic and therapeutic procedures employed in healthcare practice. Since addictive behaviors have been consistently associated with the presence of alterations in cognitive and executive functions, it is necessary to be able to detect, evaluate, and have specific therapies for these dysfunctions and investigate, among other issues, the role they play in the onset and course of evolution. After implementing neuropsychological evaluation techniques for diagnostic improvement, the search addresses procedures that allow working on cognitive and executive deficits as a specific therapeutic target. In a previous project, the investigators studied the presence of social cognition (SC) dysfunctions in a clinical sample of patients treated in drug addiction care facilities. The investigators evaluated the therapeutic efficacy of the ET® program in those who presented difficulties in SC and analyzed molecular patterns in saliva that could be associated with SC dysfunctions and predict therapeutic response. The ET® program is an online self-training program for SC rehabilitation that includes modules for emotion recognition (ER), Theory of Mind (ToM), and attributional style (AS). To replicate the RCT carried out in patients with SUD in patients with dual pathology with difficulties in ER or ToM, in addition to including a gender perspective, tele-neuropsychology, and searching for a biomarker or a pattern of them that predicts the patient profile that will benefit from the training, using advanced LC-ESI proteomics techniques. It is also intended to subsequently improve the instrument (ET®) through the implementation of a gender perspective and big data analysis and machine learning, and the introduction of automated user management. From the perspectiv