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

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

Digital Healthcare and Expertise

This open access book explores how expertise about bipolar disorder is performed on American and French digital platforms by combining insights from STS, medical sociology and media studies. It addresses topical questions, including: How do different stakeholders engage with online technologies to perform expertise about bipolar disorder? How does the use of the internet for processes of knowledge evaluation and production allow for people diagnosed with bipolar disorder to reposition themselves in relation to medical professionals? How do cultural markers shape the online performance of expertise about bipolar disorder? And what individualizing or collectivity-generating effects does the internet have in relation to the performance of expertise? The book constitutes a critical and nuanced intervention into dominant discourses which approach the internet either as a quick technological fix or as a postmodern version of Pandora’s box, sowing distrust among people and threatening unified conceptualizations and organized forms of knowledge

Digital Healthcare and Expertise

This open access book explores how expertise about bipolar disorder is performed on American and French digital platforms by combining insights from STS, medical sociology and media studies. It addresses topical questions, including: How do different stakeholders engage with online technologies to perform expertise about bipolar disorder? How does the use of the internet for processes of knowledge evaluation and production allow for people diagnosed with bipolar disorder to reposition themselves in relation to medical professionals? How do cultural markers shape the online performance of expertise about bipolar disorder? And what individualizing or collectivity-generating effects does the internet have in relation to the performance of expertise? The book constitutes a critical and nuanced intervention into dominant discourses which approach the internet either as a quick technological fix or as a postmodern version of Pandora’s box, sowing distrust among people and threatening unified conceptualizations and organized forms of knowledge

Designing User-Centered Interfaces to Support Clinical Decision-Making and Patient Engagement

The delivery of most psychotherapies has been constrained by data collected from patient self-report and clinician intuition for the last century. Clinicians who use evidence-based treatments need methods, tools, and data to efficiently track, assess, and respond to mental health needs throughout the treatment process. Patients need tools that provide feedback to optimize their therapeutic exercises and increase engagement. In this dissertation, I explore how interfaces shared by clinicians and patients can be used to support this aim in the context of prolonged exposure (PE) therapy, an evidence-based treatment used in treating post-traumatic stress disorder (PTSD). I focus on the case of designing for United States (US) veterans as well as the clinicians who treat them as US Veterans are disproportionately affected by PTSD due to the nature of their work. In this dissertation, I investigate how to design shared, user-centered interfaces which seek to support clinical decision-making and patient engagement in the context of veterans with post-traumatic stress disorder (PTSD). To lay the groundwork for design, I detail the care ecologies of veterans with PTSD, identifying the human and non-human intermediaries involved in their circles of care as well as barriers to care and future design opportunities. Leveraging this information, I explore how a clinician dashboard for PTSD, sensor-captured patient generated data, and feedback gathered via text message from trusted others (e.g., friends, family) can be designed into a shared interface and support clinical decision-making and/or patient engagement.Ph.D

Foundations of Behavioral Health

Even in the twenty-first century, the public health community continues to face formidable challenges. There is a need for more integrative and collaborative approaches in public health initiatives, considering the complex relationships among the social determinants of health within natural and built environments, population health and health-care systems, and economic, education, and social and community contexts. The continuing changes in the landscape of public health challenge our ability to reconceptualize our approach to how health-care professionals can contribute to health promotion, health education, and disease prevention efforts in communities constantly facing the globalization of communicable and noncommunicable diseases and environmental threats due to man-made and natural disasters

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Designing an architecture for secure sharing of personal health records : a case of developing countries

Includes bibliographical references.While there has been an increase in the design and development of Personal Health Record (PHR) systems in the developed world, little has been done to explore the utility of these systems in the developing world. Despite the usual problems of poor infrastructure, PHR systems designed for the developing world need to conform to users with different models of security and literacy than those designed for developed world. This study investigated a PHR system distributed across mobile devices with a security model and an interface that supports the usage and concerns of low literacy users in developing countries. The main question addressed in this study is: “Can personal health records be stored securely and usefully on mobile phones?” In this study, mobile phones were integrated into the PHR architecture that we/I designed because the literature reveals that the majority of the population in developing countries possess mobile phones. Additionally, mobile phones are very flexible and cost efficient devices that offer adequate storage and computing capabilities to users for typically communication operations. However, it is also worth noting that, mobile phones generally do not provide sufficient security mechanisms to protect the user data from unauthorized access