Digital technologies for innovative mental health rehabilitation

Schizophrenia is a chronic mental illness, characterized by the loss of the notion of reality, failing to distinguish it from the imaginary. It affects the patient in life’s major areas, such as work, interpersonal relationships, or self-care, and the usual treatment is performed with the help of anti- psychotic medication, which targets primarily the hallucinations, delirium, etc. Other symptoms, such as the decreased emotional expression or avolition, require a multidisciplinary approach, including psychopharmacology, cognitive training, and many forms of therapy. In this context, this paper addresses the use of digital technologies to design and develop innovative rehabilitation techniques, particularly focusing on mental health rehabilitation, and contributing for the promotion of well-being and health from a holistic perspective. In this context, serious games and virtual reality allows for creation of immersive environments that contribute to a more effective and lasting recovery, with improvements in terms of quality of life. The use of machine learning techniques will allow the real-time analysis of the data collected during the execution of the rehabilitation procedures, as well as enable their dynamic and automatic adaptation according to the profile and performance of the patients, by increasing or reducing the exercises’ difficulty. It relies on the acquisition of biometric and physiological signals, such as voice, heart rate, and game performance, to estimate the stress level, thus adapting the difficulty of the experience to the skills of the patient. The system described in this paper is currently in development, in collaboration with a health unit, and is an engineering effort that combines hardware and software to develop a rehabilitation tool for schizophrenic patients. A clinical trial is also planned for assessing the effectiveness of the system among negative symptoms in schizophrenia patients.This work is funded by the European Regional Development Fund (ERDF) through the Regional Operational Program North 2020, within the scope of Project GreenHealth - Digital strategies in biological assets to improve well-being and promote green health, Norte-01-0145-FEDER-000042.info:eu-repo/semantics/publishedVersio

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación (España)/ FEDER under the RTI2018-098913-B100 projectConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250 and A-TIC-080-UGR18 project

Web accessibility and mental disorders

Background: Mental disorders are a significant public health issue due to the restrictions they place on participation in all areas of life and the resulting disruption to the families and societies of those affected. People with these disorders often use the Web as an informational resource, platform for convenient self-directed treatment and a means for many other kinds of support. However, some features of the Web can potentially erect barriers for this group that limit their access to these benefits, and there is a lack of research looking into this eventuality. Therefore, it is important to identify gaps in knowledge about “what” barriers exist and “how” they could be addressed so that this knowledge can inform Web professionals who aim to ensure the Web is inclusive to this population. Objective: The objective of this work was to identify the barriers people with mental disorders, especially those with depression and anxiety, experience when using the Web and the facilitation measures used to address such barriers. Methods: This work involved three studies. First, (1) a systematic review of studies that have considered the difficulties people with mental disorders experience when using digital technologies. A synthesis was performed by categorizing data according to the 4 foundational principles of Web accessibility as proposed by the World Wide Web Consortium. Facilitation measures recommended by studies were later summarized into a set of minimal recommendations. This work also relied data triangulation using (2) face-to-face semistructured interview study with participants affected by depression and anxiety and a comparison group, as well as (3) a persona-based expert online survey study with mental health practitioners. Framework analysis was used for study 2 and study 3. Results: A total of 16 publications were included in study 1’s review, comprising 13 studies and 3 international guidelines. Findings suggest that people with mental disorders experience barriers that limit how they perceive, understand, and operate websites. Identified facilitation measures target these barriers in addition to ensuring that Web content can be reliably interpreted by a wide range of user applications. In study 2, 167 difficulties were identified from the experiences of participants in the depression and anxiety group were discussed within the context of 81 Web activities, services, and features. Sixteen difficulties identified from the experiences of participants in the comparison group were discussed within the context of 11 Web activities, services, and features. In study 3, researchers identified 3 themes and 10 subthemes that described the likely difficulties people with depression and anxiety might experience online as reported by mental health practitioners. Conclusions: People with mental disorders encounter barriers on the Web, and attempts have been made to remove or reduce these barriers. This investigation has contributed to a fuller understanding of these difficulties and provides innovative guidance on how to remove and reduce them for people with depression and anxiety when using the Web. More rigorous research is still needed to be exhaustive and to have a larger impact on improving the Web for people with mental disorders

PhD Thesis: PhD Program in Multimedia Engineering: Polytechnic University of Catalonia: Barcelona, February 2011

This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the National Strategic Reference Program and the PROTEC program with the PhD scholarship.A e-Terapia surge no contexto da e-Saúde Mental como uma nova forma de conduzir sessões de terapia através da utilização das Tecnologias da Informação e da Comunicação (TIC). A e-Terapia tem como principal objectivo melhorar a qualidade dos serviços fornecidos e o bem-estar das pessoas, oferecendo serviços e informação através da Internet e de outras TIC. Esta nova forma de ajuda permite melhorar a qualidade de vida das pessoas no seu dia-a-dia e, eventualmente, os seus relacionamentos interpessoais. O trabalho aqui descrito está integrado num projecto de e-Terapia com uma equipa multidisciplinar de profissionais de saúde, engenheiros de software e designers gráficos. O intuito deste projecto consiste na concepção, criação e entrega de uma ferramenta de e-Terapia especialmente dedicada à população dos utentes diagnosticados com esquizofrenia do Hospital Sant Joan de Déu, em Barcelona, Espanha. eSchi é o sistema de e-Terapia que desenvolvemos. Surge como solução que fornece um portal web com um conjunto integrado de ferramentas multimédia que ajudam no processo de reabilitação cognitiva dos utentes com dia- gnóstico de esquizofrenia. Esta aplicação, feita à medida, fornece um ambiente de e- Terapia para os utentes esquizofrénicos e permite aos profissionais de saúde conduzirem sessões de terapia relacionadas com a cognição usando ferramentas multimédia. Este sistema também irá permitir aos seus utilizadores, tanto aos utentes como aos profissionais de saúde, monitorizar e visualizar os resultados obtidos nas diversas sessões. É importante fornecer feedback aos utilizadores finais e dar-lhes a conhecer o seu desempenho e, em que medida, atingiram os objectivos pretendidos para cada sessão. Como resultado, o sistema usa um modelo de monitorização para registar o desempenho dos utentes e um modelo de visualização para apresentar o feedback gráfico e visual adequado para os utilizadores. Resumindo, o principal objectivo é estudar o que se pode aprender quando se registam e visualizam os resultados de utentes diagnosticados com esquizofrenia envolvidos em sessões de e-Terapia. De forma a obter uma resposta para a questão de investigação, adoptámos uma metodologia eficiente para conduzir o trabalho empírico. Elegemos o caso de estudo como meio de abordagem e, mais especificamente, optámos por múltiplos casos de estudo exploratórios. Só depois de obter os dados será possível analisar e explorar a informação. Os resultados destes casos de estudo não são previsíveis. Perante a selecção do método de caso de estudo foi tido em especial consideração o facto de se obter os dados recorrendo a uma grande diversidade de técnicas (entrevistas, observação participatória, trabalho de campo e fontes de dados escritas tais como memos, cartas, relatórios e mensagens de correio electrónico)

Virtual reality assessment for obsessive compulsive disorder: a review

The use of advance computer-based technology is becoming necessary to address the growing complexity of human problems and enhance effective communication. The recent pandemic COVID-19 not only induces many morbidities and mortalities but also intensifies mental health problem worldwide. Due to the increasing benefits of virtual reality (VR) in addressing medical condition, it is believed that VR can be used as a diagnostic tool to assess numerous medical conditions and psychiatric disorders. To date, there is still scarce evidence of VR as a diagnostic tool to assess obsessive compulsive disorder (OCD). In this study, we had conducted a systematic review to investigate the use of VR as a diagnostic tool for OCD and assess its benefits and weaknesses in comparison to computer-assisted tools. Comprehensive searches of electronic databases including PubMed and Google Scholar were undertaken to discover peer review evidence of computer-based simulation tasks in detecting OCD symptoms. Twelve out of 9325 papers were screened and reviewed. Five articles reported on computerised tools and seven articles described VR tools. In comparison to computer-based tasks, VR is a promising assessment tool due to specific virtual environments and high resolutions which are able to induce anxiety symptoms. Despite numerous shortcomings, assessment can be utilised in computerised form to detect and generate a variety of psychiatry diagnoses among the general population. Although computerised assessment task and VR show promising results, the finding are uneven due to study design differences, wide variability content task use, small sample size, several methodological issue with the computerised tasks and lack of appropriate control groups. In conclusion, the choice to use computerisation or VR for OCD assessment will depend on aim, content, technical equipment and budget. More in-depth studies of these issues are required

Web accessibility and mental disorders

Background: Mental disorders are a significant public health issue due to the restrictions they place on participation in all areas of life and the resulting disruption to the families and societies of those affected. People with these disorders often use the Web as an informational resource, platform for convenient self-directed treatment and a means for many other kinds of support. However, some features of the Web can potentially erect barriers for this group that limit their access to these benefits, and there is a lack of research looking into this eventuality. Therefore, it is important to identify gaps in knowledge about “what” barriers exist and “how” they could be addressed so that this knowledge can inform Web professionals who aim to ensure the Web is inclusive to this population. Objective: The objective of this work was to identify the barriers people with mental disorders, especially those with depression and anxiety, experience when using the Web and the facilitation measures used to address such barriers. Methods: This work involved three studies. First, (1) a systematic review of studies that have considered the difficulties people with mental disorders experience when using digital technologies. A synthesis was performed by categorizing data according to the 4 foundational principles of Web accessibility as proposed by the World Wide Web Consortium. Facilitation measures recommended by studies were later summarized into a set of minimal recommendations. This work also relied data triangulation using (2) face-to-face semistructured interview study with participants affected by depression and anxiety and a comparison group, as well as (3) a persona-based expert online survey study with mental health practitioners. Framework analysis was used for study 2 and study 3. Results: A total of 16 publications were included in study 1’s review, comprising 13 studies and 3 international guidelines. Findings suggest that people with mental disorders experience barriers that limit how they perceive, understand, and operate websites. Identified facilitation measures target these barriers in addition to ensuring that Web content can be reliably interpreted by a wide range of user applications. In study 2, 167 difficulties were identified from the experiences of participants in the depression and anxiety group were discussed within the context of 81 Web activities, services, and features. Sixteen difficulties identified from the experiences of participants in the comparison group were discussed within the context of 11 Web activities, services, and features. In study 3, researchers identified 3 themes and 10 subthemes that described the likely difficulties people with depression and anxiety might experience online as reported by mental health practitioners. Conclusions: People with mental disorders encounter barriers on the Web, and attempts have been made to remove or reduce these barriers. This investigation has contributed to a fuller understanding of these difficulties and provides innovative guidance on how to remove and reduce them for people with depression and anxiety when using the Web. More rigorous research is still needed to be exhaustive and to have a larger impact on improving the Web for people with mental disorders