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Lorena Carrascal-Laso,1 Manuel Ángel Franco-Martín,1 Elena Marcos-Vadillo,2 Ignacio Ramos-Gallego,3 Belén García-Berrocal,2 Eduardo Mayor-Toranzo,1 Santiago Sánchez-Iglesias,4 Carolina Lorenzo,4 Alfonso Sevillano-Jiménez,1 Almudena Sánchez-Martín,5 María Jesús García-Salgado,2 María Isidoro-García2,6 1Servicio de Psiquiatría, Hospital Provincial de Zamora, IBSAL, Zamora, 49071, Spain; 2Farmacogenética y Medicina de Precisión, Servicio de Bioquímica, Hospital Universitario de Salamanca, IBSAL, Salamanca, 37007, Spain; 3Departamento de Fisiología y Farmacología, Universidad de Salamanca, Salamanca, 37007, Spain; 4Servicio de Psiquiatría, Hospital Universitario de Salamanca, IBSAL, Salamanca, 37007, Spain; 5Pharmacogenetics Unit, Pharmacy Department, University Hospital Virgen de las Nieves, UGC Provincial de Farmacia de Granada, Granada, 18014, Spain; 6Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, SpainCorrespondence: Manuel Ángel Franco-Martín Email mfrancom@saludcastillayleon.esBackground: Schizophrenia is a severe mental disorder that often manifests within the first three decades of life. Its prognosis is uncertain and may result in a prolonged treatment that could extend throughout the entire lifespan of the patient. Antipsychotic drugs are characterized by a high interindividual variability when considering therapeutic effect and emergence of adverse effects. Such interindividual variability is thought to be associated primarily with pharmacokinetic matters.Objective: The objective of this study was to evaluate the economic impact of the application of the 5-Step Precision Medicine model (5SPM), an approach based on the pharmacogenetic analysis of the primary genes involved in the metabolism of the therapy for each patient, restructuring treatment as necessary.Patients and Methods: One hundred eighty-eight psychiatry patients were analysed for single nucleotide polymorphisms on genes CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5 and ABCB1. Information on patients’ diagnosis, pharmacotherapy, and hospitalizations was collected.Results: We achieved a cost–benefit ratio of 3.31– 3.59 with a reduction of direct cost (hospitalizations plus pharmacotherapy) with a reduction of total cost in 67% of the patients who underwent the clinical intervention.Conclusion: A rational Precision Medicine-based approach to psychiatric patients could result in a reduction on number of drugs required to control exacerbations, and the underlying pathologies, reducing the risk of adverse effects and improving adherence to treatment, leading to a potential decrease in direct costs. This methodology has been shown to be cost-dominant and, being based on a pharmacogenetic analysis, it has a lifelong nature, as the data obtained can be applied to other medical disciplines.Keywords: pharmacogenetics, pharmacoeconomics, psychotic disorders, cytochrome P-450, antipsychotics

We report a case of schizophrenia disorganized in which there is a pronouncedcognitive impairment and particularly intellectually deterioration. The reflection of the background, as well as the monitoring of the quantitative results with the observation qualitative and daily, leading to deepen respect to the need of consider both neuropsychological elements and psychopathological in the interpretations of the results of psychological tests. Moreover in pathologies where the limits and the relationship between the psychopathology and neuropsychology not clear. That is why the emphasis is on the valuation of the individual as a whole and not its dismemberment in parts.

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Objectives: Mental health professionals are becoming increasingly involved in the process of employment rehabilitation of persons with psychiatric disabilities. However, few studies address the attitudes of these professionals toward the employability of those with mental illness. The aim of this research was to identify differences in the attitudes of medical and non-medical mental health professionals, as well as to detect any association between attitude scores and the type of professional., Methods: A sample of 140 employees from public and third sector mental health organizations answered a questionnaire using a scale measuring their attitudes and views on the employability of people with psychiatric disabilities. The psychometric characteristics of the scale are provided together with the variations detected in the professionals' attitudes., Results: This research shows that significant differences in the attitudes between medical and non-medical mental health employees exist and that there is a need for the implementation of educational programs that may help to improve the attitudes of medical professionals toward the employability of people with mental illness., Conclusion: This research indicates the importance of improving the professionals' attitudes to support people attempting to return to work., (© 2022 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

Background: In the era of big data, networks are becoming a popular factor in the field of data analysis. Networks are part of the main structure of BeGraph software, which is a 3D visualization application dedicated to the analysis of complex networks., Objective: The main objective of this research was to visually analyze tendencies of mental health diseases in a region of Spain, using the BeGraph software, in order to make the most appropriate health-related decisions in each case., Methods: For the study, a database was used with 13,531 records of patients with mental health disorders in three acute medical units from different health care complexes in a region of Spain. For the analysis, BeGraph software was applied. It is a web-based 3D visualization tool that allows the exploration and analysis of data through complex networks., Results: The results obtained with the BeGraph software allowed us to determine the main disease in each of the health care complexes evaluated. We noted 6.50% (463/7118) of admissions involving unspecified paranoid schizophrenia at the University Clinic of Valladolid, 9.62% (397/4128) of admissions involving chronic paranoid schizophrenia with acute exacerbation at the Zamora Hospital, and 8.84% (202/2285) of admissions involving dysthymic disorder at the Rio Hortega Hospital in Valladolid., Conclusions: The data analysis allowed us to focus on the main diseases detected in the health care complexes evaluated in order to analyze the behavior of disorders and help in diagnosis and treatment., (©Susel Góngora Alonso, Andrés de Bustos Molina, Beatriz Sainz-De-Abajo, Manuel Franco-Martín, Isabel De la Torre Díez. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 16.06.2021.)

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available-86 registers for the first and 68 for the second-transfer learning techniques were required. The length of the text had no limit from the user's standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

Background: Patients suffering from addiction are a vulnerable group in the midst of COVID-19, so their healthcare is considered essential. In this paper, the measures and responses of the Drug Addiction Assistance Network of Castile and Leon (DAACYL) in Spain during the first 6 weeks of the COVID-19 pandemic are explained. The aim is that this experience could be useful in places where this problem will continue and could help future interventions. Methods: A telephone survey was carried out as the main methodology, to collect information for the subsequent organization and repercussion on professionals and patients. This was carried out by the heads of the 18 DAACYL units. Among the interventions applied, the following stand out: implantation of telemedicine techniques, restriction of daily methadone dispensing, suspension of urine controls and initiation of care programs for the homeless. Results: As a result of these interventions, the professionals observed that patients are less demanding and mostly stable, with a low percentage of relapses. An increase in the consumption of alcohol and benzodiazepines have been reported as more common among people who relapse. Furthermore, the prevalence of COVID-19 infection in the sample is minimal; therefore, different hypotheses should be considered as an explanation (infra-diagnosis, immune system used to aggression, possible anti-inflammatory effect of some psychotropic drugs and a greater perception of danger against infection than the general population). Conclusions: The rapid adaptation and successful implementation of DAACYL have had satisfactory results. On the other hand, the prevention of the possible increase in the development of behavioral addictions and the use of homemade drugs should be considered., (Copyright © 2020 Roncero, Vicente-Hernández, Casado-Espada, Aguilar, Gamonal-Limcaoco, Garzón, Martínez-González, Llanes-Álvarez, Martínez, Franco-Martín and Álvarez-Navares.)

Background: Provision of follow-up and care during treatment of people with suicidal intentions is a challenge for health professionals and experts in information and communications technology (ICT). Therefore, health professionals and ICT experts are making efforts to carry out these activities in collaboration by using mobile apps as a technological resource., Objective: This study aimed to descriptively analyze mobile apps aimed at suicide prevention and to determine relevant factors in their design and development. In addition, it sought to analyze their impact on the support of treatment for patients at risk for suicide., Methods: We considered 20 apps previously listed in the article "Mobile Apps for Suicide Prevention: Review of Virtual Stores and Literature" (de la Torre et al, JMIR mHealth uHealth 2017;5[10]:e130). To find the apps in this list, the most popular app stores (Android and iOS) were searched using the keyword "suicide prevention." The research focused on publicly available app information: language, platform, and user ratings. The results obtained were statistically evaluated using 16 parameters that establish various factors that may affect the choice of the user, and the consequent support that the app can offer to a person at risk for suicide., Results: Of the 20 mobile apps, 4 no longer appeared in the app stores and were therefore excluded. Analysis of the remaining 16 apps sampled showed the following: (1) a high percentage of the apps analyzed in the study (n=13, 82%) are provided in English language; (2) the sampled apps were last updated in 2017, when only 45% of them were updated, but the constant and progressive update of treatments should be reflected in the apps; and (3) the technical quality of these apps cannot be determined on the basis of the distribution of scores, because their popularity indices can be subjective (according to the users). User preference for a particular operating system would require further, more specific research, including study of the differences in the technical and usability aspects between both platforms and the design of medical apps., Conclusions: Although there are positive approaches to the use of apps for suicide prevention and follow-up, the technical and human aspects are yet to be explored and defined. For example, the design and development of apps that support suicide prevention should be strongly supported by health personnel to humanize these apps, so that the effectiveness of the treatments supported by them can be improved., (©Gema Castillo-Sánchez, Ismael Camargo-Henríquez, Juan Luis Muñoz-Sánchez, Manuel Franco-Martín, Isabel de la Torre-Díez. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 13.08.2019.)

Background: Data from primary health care in Spain show a high prevalence of the major depressive disorder. Blended treatment (combination of face-to-face and online components) seems to be a very promising tool for the optimization and dissemination of psychological treatments in a cost-effective form. Although there is growing data that confirm the advantages of blended therapies, few studies have analyzed their application in regular clinical practice. The objective of the present paper is to describe the protocol for a clinical study aimed at exploring the clinical and cost-effectiveness of a blended cognitive behavioral therapy (b-CBT) for depression, compared to treatment as usual (TAU) in a primary health care setting., Methods: A two-arm randomised controlled non-inferiority trial will be carried out, with repeated measures (baseline, 3 months, 6 months, and 12 months) under two conditions: b-CBT and TAU. The b-CBT program will consist in three face-to-face sessions and eight online sessions. The TAU is defined as the routine care delivered by the general practitioner for the treatment of depression in primary care. The primary outcome is a symptomatic change of depressive symptoms on the patient-health questionnaire (PHQ-9). Other secondary outcomes will be considered (e.g., quality of life, treatment preference). All participants must be 18 years of age or older and meet the diagnostic criteria for major depressive disorder according to the Diagnostic and Statistical Manual of Mental disorders 4th edition. 156 participants will be recruited (78 per arm)., Discussion: It is expected that b-CBT is clinically non-inferior when compared to TAU. This is the first study in Spain to use a b-CBT format in primary and specialized care, and this format could be an efficacious and cost-effective therapeutic strategy for the treatment of depression., Trial Registration: ClinicalTrials.gov NCT02361684. Registered on 8 January 2015. Currently recruiting participants.

Background: This study provides an analysis on the use of emerging technologies for the prevention of suicide in 8 different European countries., Objective: The objective of this study was to analyze the potentiality of using emerging technologies in the area of suicide prevention based on the opinion of different professionals involved in suicide prevention., Methods: Opinions of 3 groups of stakeholders (ie, relevant professionals in suicide field) were gathered using a specifically designed questionnaire to explore dimensions underlying perceptions of facilitating factors and barriers in relation to the use of emerging technologies for suicide prevention., Results: Goal 1 involved facilitating factors for the use of emerging technologies in suicide prevention. Northern European countries, except for Belgium, attach greater relevance to those that optimize implementation and benefits. On the other hand, Southern European countries attach greater importance to professionally oriented and user-centered facilitating factors. According to different stakeholders, the analysis of these facilitating factors suggest that professionals in the field of social work attach greater relevance to those that optimize implementation and benefits. However, professionals involved in the area of mental health, policy makers, and political decision makers give greater importance to professionally oriented and user-centered facilitating factors. Goal 2 was related to barriers to the usability of emerging technologies for suicide prevention. Both countries and stakeholders attach greater importance to barriers associated with resource constraints than to those centered on personal limitations. There are no differences between countries or between stakeholders. Nevertheless, there is a certain stakeholders-countries interaction that indicates that the opinions on resource constraints expressed by different stakeholders do not follow a uniform pattern in different countries, but they differ depending on the country., Conclusions: Although all countries and stakeholders agree in identifying resource constraints as the main barrier to the use of emerging technologies, factors facilitating their use in suicide prevention differ among countries and among stakeholders., (©Juan-Luis Muñoz-Sánchez, Carmen Delgado, Esther Parra-Vidales, Manuel Franco-Martín. Originally published in JMIR Mental Health (http://mental.jmir.org), 24.01.2018.)