Objective: The main aim of our research is to assess the use, satisfaction, and pedagogy of software for neuropsychological rehabilitation by computer, called "Gradior™," to obtain relevant information on the impact of information and communications technology on people with severe and prolonged mental illness. Methods: To evaluate the usability and satisfaction standards, the questionnaire "Usability survey on the use of the cognitive rehabilitation and assessment program by computer" was completed by 83 patients of the Rodríguez Chamorro Hospital. Results: The results of the study show that Gradior has 81.2% acceptance and 83.7% general assessment. This indicates that those who responded to the survey consider that the Gradior program improves cognitive functions and abilities in patients with severe and prolonged mental illness and therefore their quality of life. Conclusion: This research is oriented toward professionals of the Health Area and Systems Engineers, who develop software for neuropsychological rehabilitation with neurocognitive deficit. The purpose is to make the learning process more effective among the people who use it and to improve usability for specific groups. We hope that the reading of the work contributes to the activities, techniques and materials planned are in accordance with the needs of the population affected with cognitive disorders.

Internet of Things (IoT) has emerged as a new paradigm today, connecting a variety of physical and virtual elements integrated with electronic components, sensors, actuators and software to collect and exchange data. IoT is gaining increasing attention as a priority research topic in the Health sector in general and in specific areas such as Mental Health. The main objective of this paper is to show a review of the existing research works in the literature, referring to the main IoT services and applications in Mental Health diseases. The scientific databases used to carry out the review are Google Scholar, IEEE Xplore, PubMed, Science Direct, and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as "IoT OR Internet of Things AND (Application OR Service) AND Mental Health" selecting the most interesting articles. A total of 51 articles were found on IoT-based services and applications in Mental Health, of which 14 have been identified as relevant works in mental health. Many of the publications (more than 60%) found show the applications developed for monitoring patients with mental disorders through sensors and networked devices. The inclusion of the new IoT technology in Health brings many benefits in terms of monitoring, welfare interventions and providing alert and information services. In pathologies such as Mental Health is a vital factor to improve the patient life quality and effectiveness of the medical service.

Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms of Data Mining in Mental Health, specifically in the most prevalent diseases such as: Dementia, Alzheimer, Schizophrenia and Depression. Academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct, Scopus and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as `techniques' AND `Data Mining' AND `Mental Health', `algorithms' AND `Data Mining' AND `dementia' AND `schizophrenia' AND `depression', etc. selecting the papers of greatest interest. A total of 211 articles were found related to techniques and algorithms of Data Mining applied to the main Mental Health diseases. 72 articles have been identified as relevant works of which 32% are Alzheimer's, 22% dementia, 24% depression, 14% schizophrenia and 8% bipolar disorders. Many of the papers show the prediction of risk factors in these diseases. From the review of the research articles analyzed, it can be said that use of Data Mining techniques applied to diseases such as dementia, schizophrenia, depression, etc. can be of great help to the clinical decision, diagnosis prediction and improve the patient's quality of life.