Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.Consejo Nacional de Ciencia y Tecnologia (CONACyT)Sonora Institute of Technology (ITSON) via the PROFAPI program PROFAPI_2020_0055Spanish Ministry of Science, Innovation and Universities (MICINN) project "Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology and Biomedicine" RTI2018-101674-B-I0

Smartphone-based objective monitoring in bipolar disorder:status and considerations

Abstract In 2001, the WHO stated that: “The use of mobile and wireless technologies to support the achievement of health objectives (mHealth) has the potential to transform the face of health service delivery across the globe”. Within mental health, interventions and monitoring systems for depression, anxiety, substance abuse, eating disorder, schizophrenia and bipolar disorder have been developed and used. The present paper presents the status and findings from studies using automatically generated objective smartphone data in the monitoring of bipolar disorder, and addresses considerations on the current literature and methodological as well as clinical aspects to consider in the future studies

Impaired Theory of Mind in Psychotic and Affective Disorders

Psychotic symptoms in bipolar I disorder during mood episodes has been associated with several negative outcomes raising the question as to whether psychosis is a risk factor for a more severe form of this chronic and debilitating condition. However, relatively little research has been directed at understanding the relationships among social cognitive functioning in bipolar I disorder with and without a history of psychosis. Impaired social cognition has been identified as a putative endophenotypic markers in schizophrenia and the evidence is mounting as to whether similar impairments also exist in bipolar I disorder. Given the plethora of research supporting the presence of social cognitive impairments in schizophrenia researchers have sought to focus on subdomains and component parts of social cognition, such as theory of mind and the processing of naturalistic social exchanges. Compared to healthy controls, research in this area suggests that individuals with schizophrenia struggle to correctly recognize and interpret naturalistic social exchanges involving linguistically inconsistent inferences (e.g., sarcastic) as opposed to consistent inferences that are sincere. Research in this area involving participants with bipolar I disorder has been mixed, which may be explained by heterogeneous bipolar I disorder samples. To date, the theory of mind component involving recognition and interpretation of social exchanges has not been evaluated in individuals with bipolar I disorder with and without a history of psychosis during mood episodes. Hence, the overarching goal of this project was to evaluate whether a history of psychotic symptoms in bipolar I disorder are associated with impaired recognition and interpretation of naturalistic social exchanges, particularly those involving sincere, lie, and sarcastic exchanges

Addressing Variability in Speech when Recognizing Emotion and Mood In-the-Wild

Bipolar disorder is a chronic mental illness, affecting 4% of Americans, that is characterized by periodic mood changes ranging from severe depression to extreme compulsive highs. Both mania and depression profoundly impact the behavior of affected individuals, resulting in potentially devastating personal and social consequences. Bipolar disorder is managed clinically with regular interactions with care providers, who assess mood, energy levels, and the form and content of speech. Recent work has proposed smartphones for automatically monitoring mood using speech. Much of the early work in speech-centered mood detection has been done in the laboratory or clinic and is not reflective of the variability found in real-world conversations and conditions. Outside of these settings, automatic mood detection is hard, as the recordings include environmental noise, differences in recording devices, and variations in subject speaking patterns. Without addressing these issues, it is difficult to move towards a passive mobile health system. My research works to address this variability present in speech so that such a system can be created, allowing for interventions to mitigate the life-changing effects of mood transitions. However detecting mood directly from speech is difficult, as mood varies over the course of days or weeks, while speech fluctuates rapidly. To address this, my thesis explores how an intermediate step can be used to aid in this prediction. For example, one of the major symptoms of bipolar disorder is emotion dysregulation - changes in the way emotions are perceived and a lack of inhibition in their expression. My work has supported the relationship between automatically extracted emotion estimates and mood. Because of this, my thesis explores how to mitigate the variability found when detecting emotion from speech. The remainder of my thesis is focused on employing these emotion-based features, as well as features based on language content, to real-world applications. This dissertation is divided into the following parts: Part I: I address the direct classification of mood from speech. This is accomplished by addressing variability due to recording device using preprocessing and multi-task learning. I then show how both subject-specific and population-general information can be combined to significantly improve mood detection. Part II: I explore the automatic detection of emotion from speech and how to control for the other factors of variability present in the speech signal. I use progressive networks as a method to augment emotion with other paralinguistic data including gender and speaker, as well as other datasets. Additionally, I introduce a novel domain generalization method for cross-corpus detection. Part III: I demonstrate real-world applications of speech mood monitoring using everyday conversations. I show how the previously introduced generalized model can predict emotion from the speech of individuals with suicidal ideation, demonstrating its effectiveness across domains. Furthermore, I use these predictions to distinguish individuals with suicidal thoughts from healthy controls. Lastly, I introduce a novel framework for intervention detection in individuals with bipolar disorder. I then create a natural speech mood monitoring system based on features derived from measures of emotion and automatic speech recognition (ASR) transcripts and show effective intervention detection. I conclude this dissertation with the following future directions: (1) Extending my emotion generalization system to include multiple modalities and factors of variability; (2) Expanding natural speech mood monitoring by including more devices, exploring other data besides speech, and investigating mood rating causality.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153461/1/gideonjn_1.pd

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