A Computational Framework for Planning Therapeutical Sessions aimed to Support the Prevention and Treatment of Mental Health Disorders using Emotional Virtual Agents

[EN] Interaction is defined as the realization of a reciprocal action between two or more people or things. Particularly in computer science, the term interaction refers to the discipline that studies the exchange of information between people and computers, and is generally known by the term Human-Computer Interaction (HCI). Good design decisions and an adequate development of the software is required for efficient HCI to facilitate the acceptability of computer-based applications by the users. In clinical settings it is essential to eliminate any barrier and facilitate the interaction between patients and the system. A smooth communication between the user and the computer-based application is fundamental to maximise the advantages and functionalities offered by the system. The design of these applications must consider the personal and current needs of the user by applying a User-Centered Design methodology. The main purpose of this research work is to contribute in the improvement of HCI-based applications addressed to the clinical context, particularly to enhance computer-based interactive sessions to support people suffering from a mental disorder such as Major Depression (MD). Thanks to the advances in Artificial Intelligence techniques, it is now possible to partially automate complex tasks such as the continuous provision of Cognitive-Behavioural Therapies (CBTs) to patients. These CBTs require good levels of adaptability and variability during the interaction with the patient that facilitates the acceptability in the user, an optimal usability and good level of engagement for a successful mid/long term use of the application and treatment adherence. The modelling of complex deliberative and affective processes in artificial systems can be applied to support the prevention and treatment of mental health related issues, enhancing the continuous and remote assistance of patients, saving some economical and clinical resources and reducing the waiting lists in the health services. In this regard, the efforts of this Thesis have been concentrated on the research of two main lines: (1) the generation and planning of adequate contents in an interactive system to support the prevention and treatment of MD based on characteristics of the user; and (2) the modelling of relevant affective processes able to communicate the contents in an emotional effective way taking into account the importance of the affective conditions associated with the MD in the users. Rule Based Systems and the appraisal theory of emotions have been the roots used to develop the main two modules of the computational Framework presented: the Contents Management and the Emotional Modules. Finally, the obtained Framework was integrated into two interactive systems to evaluate the achievement of the research objectives. The first system has been developed in the context of the Help4Mood European research project and its main aim was to support the remote treatment of patients with MD. The second scenario was a system developed to prevent MD and suicidal thoughts in the University community, which was developed in the context of the local PrevenDep research project. These evaluations have indicated that the proposed Framework has reached good levels of usability and acceptability in the target users thanks to the personalizations and adaptation capabilities of the contents and in the way how these contents are communicated to the user. The research work and the obtained results in this Thesis has contributed to the state of the art in HCI-based systems used as support in therapeutic interventions for the prevention and treatment of MD. This was obtained by the combination of a personalized content management to the patient, and the management of the affective processes associated to these pathologies. The developed work also identifies some research lines that need to be addressed in future works to get better HCI systems used for therapeutic purposes.[ES] Interactuar se define como la realización de una acción recíproca entre dos o más personas o cosas. Particularmente en informática, el término interacción se refiere a la disciplina que estudia el intercambio de información entre las personas y computadoras, y suele conocerse por el término anglosajón Human-Computer Interaction (HCI). Un buen diseño y un adecuado desarrollo del software es necesario para lograr una HCI eficiente que facilite la aceptabilidad del sistema por el usuario. En entornos clínicos es fundamental eliminar cualquier tipo de barrera y facilitar la interacción entre los pacientes y el computador. Es de vital importancia que haya una buena comunicación entre usuario y computador, por este motivo el sistema debe de estar diseñado pensando en las necesidades actuales, cambiantes y personales del usuario, basándose en la metodología de diseño centrado en el usuario. El propósito principal de esta investigación es la identificación de mejoras en HCI aplicada en entornos clínicos, en concreto para dar soporte a personas con trastornos mentales como la Depresión Mayor (DM) y que precisan de terapias psicológicas adecuadas y continuas. Gracias a técnicas de Inteligencia Artificial, es posible automatizar eficientemente ciertas acciones asociadas a los procesos de las terapias cognitivo-conductuales (CBTs, del inglés Cognitive-Behavioural Therapies). Los sistemas de ayuda a la CBT, requieren de una adaptabilidad y variabilidad en la interacción para favorecer la usabilidad del sistema y asegurar la continuidad de la motivación del paciente. Una buena gestión de esta automatización influiría en la aceptabilidad de los pacientes y podría mejorar su adherencia a los tratamientos y por consiguiente mejorar su estado de salud. Adicionalmente, la unión de procesos deliberativos dinámicos pueden liberar recursos clínicos, mejorando el control de los pacientes, y reduciendo los tiempos de espera y los costes económicos. En este sentido, los esfuerzos de esta Tesis se han centrado en la investigación de dos líneas diferentes: (1) la selección y planificación adecuada de los contenidos presentados durante la interacción a través de una planificación dinámica y personalizada, y (2) la adecuación de la comunicación de los contenidos hacia el paciente tomando en cuenta la importancia de los procesos afectivos asociados a estas patologías. Los Sistemas Basados en Reglas (SBR) han sido la herramienta utilizada para dar soporte a los dos módulos principales que componen el Framework presentado en esta Tesis: el módulo de gestión de los contenidos y el módulo emocional. Concluida la fase de diseño, desarrollo y testeo, el Framework fue adaptado e integrado en sistemas reales, para validar la viabilidad y la adecuación del marco de trabajo de esta Tesis. En primer lugar, el sistema se aplicó durante tres años en el tratamiento de la DM en varios centros clínicos europeos en el contexto del Proyecto Europeo de investigación Help4Mood. Finalmente, el sistema fue evaluado en la tarea de prevención de la DM y del suicidio en el Proyecto Local de investigación PrevenDep, de un año de duración. El feedback de estas evaluaciones demostraron que el HCI del Framework tiene unos niveles altos de usabilidad y aceptación, gracias a la personalización, variabilidad y adaptación de los contenidos y de la comunicación de los mismos. Los experimentos computacionales llevados a cabo en esta Tesis han permitido avanzar el estado del arte de sistemas computacionales emocionales aplicados en entornos terapéuticos para la prevención y tratamiento de la DM. Principalmente, gracias a la combinación de una gestión personalizada de los contenidos hacia el paciente tomando en cuenta la importancia de los procesos afectivos asociados a estas patologías. Este trabajo abre nuevas líneas de investigación, como la aplicación de este sistema en otras patologías de salud mental en las qu[CA] Interactuar es defineix com la realització d'una acció recíproca entre dos o més persones o coses. Particularment en informàtica, el terme interacció es refereix a la disciplina que estudia l'intercanvi d'informació entre les persones i computadores, i es sol conèixer pel terme anglosaxó Human-Computer Interaction (HCI). Un bon disseny i un adequat desenvolupament del software és necessari per aconseguir una HCI eficient que faciliti l'acceptabilitat del sistema per l'usuari. En entorns clínics és fonamental eliminar qualsevol tipus de barrera i facilitar la interacció entre els pacients i el computador. És de vital importància que hi hagi una bona comunicació entre l'usuari (o pacient) i el computador, per aquest motiu el sistema ha d'estar dissenyat pensant en les necessitats actuals, cambiants i personals de l'usuari, basant-se en la metodologia de disseny centrat en l'usuari. El propòsit principal d'aquesta investigació és la identificació de millores en HCI aplicada en entorns clínics, en concret per donar suport a persones amb trastorns mentals com la Depressió Major (DM) i que precisen de teràpies psicològiques adequades i contínues. Gràcies a tècniques d'Intel·ligència Artificial, és possible automatitzar eficientment certes accions asociades al processos de les teràpies cognitiu-conductuals. Els sistemes computacionals de ajuda a la CBT, requereixen d'una adaptabilitat i variabilitat en la interacció per afavorir la usabilitat del sistema i assegurar la continuïtat de la motiviació del pacient. Una bona gestió d'aquesta automatització influiria en l'acceptabilitat dels pacients i podria millorar la seva adherència als tractaments i per tant millorar el seu estat de salut. Addicionalment, la unió de processos deliberatius dinàmics poden alliberar recursos clínics, millorant el control dels pacients, i reduint els temps d'espera i els costos econòmics. En aquest sentit, els esforços d'aquesta Tesi s'han centrat en la investigació de dues línies diferents: (1) la selecció i planificació adequada dels continguts presentats durant la interacció a través d'una planificació dinàmica i personalitzada, i (2) l'adequació de la comunicació dels continguts cap al pacient tenint en compte la importància dels processos afectius associats a aquestes patologies. Els Sistemes Basats en Regles (SBR) han estat la eina utilitzada per donar suport als dos mòduls principals que componen el Framework presentat en aquesta Tesi: el mòdul de gestió dels continguts oferits a l'usuari; i el mòdul emocional. Conclosa la fase de disseny, desenvolupament i testeig, el Framework va ser adaptat als dominis corresponents i integrat en sistemes madurs per ser avaluat en dos escenaris reals, per validar la viabilitat i l'adequació del Framework d'aquesta tesi. Primerament, el sistema es va aplicar durant tres anys en el tractament de la DM major en diversos centres clínics europeus en el context del Projecte Europeu d'investigació Help4Mood. Finalment, el sistema va ser avaluat en la tasca de prevenció de la DM i del suïcidi al Projecte Local d'investigació PrevenDep, d'un any de durada. El feedback de les avaluacions han demostrat que el HCI del Framework obté uns nivells alts d'usabilitat i acceptació, gràcies a la personalització, variabilitat i adaptació dels continguts i de la comunicació. Els experiments computacionals duts a terme en aquesta Tesi han permès avançar l'estat de l'art de sistemes computacionals emocionals aplicats en entorns terapèutics per a la prevenció i tractament de la DM. Principalment, gracies a la combinació d'una gestió personalitzada dels continguts cap al pacient tenint en compte la importància dels processos afectius associats a aquestes patologies. Aquest treball obre noves línies d'investigació, com l'aplicació d'aquest sistema en altres patologies de salut mental en què sigui recomanable l'aplicació de sessions terapèutiques.Bresó Guardado, A. (2016). A Computational Framework for Planning Therapeutical Sessions aimed to Support the Prevention and Treatment of Mental Health Disorders using Emotional Virtual Agents [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/64082TESI

Mind over machine? The clash of agency in social media environments

Includes bibliographical references.2022 Fall.Underlying many social media platforms are choice recommendation "nudging" architectures designed to give users instant content and social recommendations to keep them engaged. Powered by complex algorithms, these architectures flush people's feeds and an array of other features with fresh content and create a highly individualized experience tailored to their interests. In a critical realist qualitative study, this research examines how individual agency manifests when users encounter these tools and the suggestions they provide. In interviews and focus groups, 45 participants offered their experiences where they reflected on how they perceived the engines, e.g., their Facebook feed, influenced their actions and behaviors, as well as how the participants felt they controlled it to achieve personal aims. Based on these and other experiences, this study posits the Social Cognitive Machine Agency Dynamic (SCMAD) model, which provides an empirically supported explanatory framework to explain how individual agency can manifest and progress in response to these tools. The model integrates Albert Bandura's social cognitive theory concepts and emergent findings. It demonstrates how users react to the engines through agentic expressions not dissimilar to the real-world, including enacting self-regulatory, self-reflective and intentionality processes, as well as other acts not captured by Bandura's theory. Ultimately, the research and model propose a psycho-environmental explanation of the swerves of agency experienced by users in reaction to the unique conditions and affordances of these algorithmically driven environments. The study is the first known extension of social cognitive theory to this technology context. Implications of the findings are discussed and recommendations for future research provided. The study recommends that future research and media discourse aim for an individual-level psychological evaluation of these powerful technologies. This stance will afford a greater understanding of the technology's impacts and implications on individuals, particularly as it is anticipated to significantly evolve in the coming years

An aesthetics of touch: investigating the language of design relating to form

How well can designers communicate qualities of touch? This paper presents evidence that they have some capability to do so, much of which appears to have been learned, but at present make limited use of such language. Interviews with graduate designer-makers suggest that they are aware of and value the importance of touch and materiality in their work, but lack a vocabulary to fully relate to their detailed explanations of other aspects such as their intent or selection of materials. We believe that more attention should be paid to the verbal dialogue that happens in the design process, particularly as other researchers show that even making-based learning also has a strong verbal element to it. However, verbal language alone does not appear to be adequate for a comprehensive language of touch. Graduate designers-makers’ descriptive practices combined non-verbal manipulation within verbal accounts. We thus argue that haptic vocabularies do not simply describe material qualities, but rather are situated competences that physically demonstrate the presence of haptic qualities. Such competencies are more important than groups of verbal vocabularies in isolation. Design support for developing and extending haptic competences must take this wide range of considerations into account to comprehensively improve designers’ capabilities

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Anti-computing explores forgotten histories and contemporary forms of dissent – moments when the imposition of computational technologies, logics, techniques, imaginaries, utopias have been questioned, disputed, or refused. It also asks why these moments tend to be forgotten. What is it about computational capitalism that means we live so much in the present? What has this to do with computational logics and practices themselves? This book addresses these issues through a critical engagement with media archaeology and medium theory and by way of a series of original studies; exploring Hannah Arendt and early automation anxiety, witnessing and the database, Two Cultures from the inside out, bot fear, singularity and/as science fiction. Finally, it returns to remap long-standing concerns against new forms of dissent, hostility, and automation anxiety, producing a distant reading of contemporary hostility.At once an acute response to urgent concerns around toxic digital cultures, an accounting with media archaeology as a mode of medium theory, and a series of original and methodologically fluid case studies, this book crosses an interdisciplinary research field including cultural studies, media studies, medium studies, critical theory, literary and science fiction studies, media archaeology, medium theory, cultural history, technology history

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Decision Support for Tailored Biopsychosocial Rehabilitation : In Non-specific Low Back Pain

Alaselkäkipu on maailman yleisin toimintakyvyn haittaa aiheuttava oire. Suurin osa alaselkäkivusta on niin sanottua epäspesifiä, eikä sille ole osoitettavissa aukottomasti patoanatomista taustaa. Kivun ja toimintakyvyn haitan kokemukseen ja kroonistumiseen liittyy laajasti erilaisia biopsykososiaalisia tekijöitä, joista osaan voidaan vaikuttaa kohdentamalla interventioita oikea-aikaisesti oikealle potilaalle, ja täten vähentää kivun pitkittymisen riskiä. Kipuun liittyviä biopsykososiaalisia tekijöitä ja niiden välisiä yhteyksiä voidaan ymmärtää paremmin maailman terveysjärjestö WHO:n kansainvälisen toimintakyvyn, toimintarajoitteiden ja terveyden luokituksen (ICF-viitekehys) avulla, joka kuvaa toimintakykyä laaja- alaisena biopsykososiaalisena kokonaisuutena. Tämän artikkeliväitöskirjan päätavoitteena oli kehittää menetelmiä tukemaan yksilöllisen biopsykososiaalisen kuntoutuksen suunnittelua ja toteutusta selkäkipupotilailla. Alatavoitteina oli tuottaa ajankohtaista tietoa tunnistetuista alaselkäkivun kroonistumisen riskitekijöistä, sekä löytää uusia menetelmiä biopsykososiaalisten tekijöiden tunnistamiseen ja näiden tekijöiden avulla sopivan intervention valintaan yksilöllisesti. Alaselkäkivun kroonistumisen riskitekijöistä tehtiin systemaattinen kirjallisuuskatsaus, jossa tutkittiin 25 tutkimuksen tuloksia. Tutkimusten tuli arvioida mahdollista riskitekijää ennen kivun kroonistumisen alkamista (3kk), jotta riskitekijää voitiin pitää ennakoivana tekijänä kroonistumiselle. Biopsykososiaalisten tekijöiden tunnistamiseen kehitettiin sovellus tekoälyalgoritmista, jonka tarkoituksena on tunnistaa toimintakykyyn liittyvää tietoa potilaskertomusteksteistä ICF-viitekehyksen mukaisesti. Sovelluksen tuloksia verrattiin alan asiantuntijan tekemään tunnistamiseen. Selkäpotilaan prosesseja perusterveydenhuollossa ja työterveydessä kehitettiin paremmin tunnistamaan kroonistumisen riskitekijöitä sekä valitsemaan sopivat interventiot yksilöllisesti. Prosessin kehittämisessä oli mukana moniammatillinen työryhmä perusterveydenhuollosta, työterveydestä sekä erikoissairaanhoidosta. Uusien menetelmien kehityksen tueksi kerättiin 93 kroonisen alaselkäkipuisen potilaan aineisto. Aineisto sisälsi vapaata tekstiä potilaskertomusteksteistä sekä numeerista dataa esitietolomakkeiden muodossa. Systemaattisen kirjallisuuskatsauksen mukaan yhteensä 45 erilaista riskitekijää on tunnistettavissa selkäkivun kroonistumisen riskitekijäksi. Riskitekijät jaoteltiin demografisiin ja sairaushistoriaan liittyviin tekijöihin, biomekaanisiin tekijöihin, oireiden ominaisuuksiin liittyviin tekijöihin, psykologisiin ja psykososiaalisiin tekijöihin, sekä elintapatekijöihin. Tunnistetut riskitekijät olivat yhdistettävissä ICF- viitekehyksen toimintakyvyn kuvauksiin, lukuun ottamatta demografisia ja sairaushistoriaan liittyviä tekijöitä. Kehitetty tekoälyalgoritmin sovellus tunnisti toimintakykytietoa potilaskertomusteksteistä 83.1 % herkkyydellä ja 99.84 % tarkkuudella verrattuna alan asiantuntijan tekemään tunnistukseen. Selkäpotilaan prosessin kehityksen tuotoksena syntyi vuokaavio, jonka avulla oikeat ammattilaiset ohjautuvat mukaan prosessiin potilaan tarpeiden mukaisesti, tietävät omat tehtävänsä, sekä pystyvät hyödyntämään paremmin moniammatillista ja monisektorista yhteisöä yksilöllisesti potilaan hyväksi. Tämä artikkeliväitöskirja luo uusia tutkimusmahdollisuuksia sekä selkäpotilaiden että toimintakykytiedon hyödyntämisen alueilla. Kirjallisuuskatsauksen tulokset auttavat kliinikoita paremmin ymmärtämään selkäkivun biospykososiaalista kokonaisuutta ja tutkijoita laajentamaan interventiotutkimusasetelmiaan. Tulevaisuudessa kuntoutusprosessista voidaan tehdä soveltuvuustutkimusta ennen laajempaa interventiota, ja tekoälyalgoritmin sovelluksen hyödyntämistä muille potilasryhmille ja kielille suunnitellaan.Low back pain is globally the most burdensome symptom causing disability. It is most commonly defined as non-specific, which means no pathoanatomical cause can be demonstrated as the cause. Different biopsychosocial factors are widely related to the experience and prolongation of pain and disability. Some of these factors can be affected by targeting timely interventions and decreasing the risk for pain chronicity. Pain related biopsychosocial factors and their connections can be understood more profoundly with the help of the International Classification of Functioning, Disability, and Health (ICF) framework developed by the World Health Organization (WHO), which describes disability from a wide biopsychosocial perspective. The main aim of this dissertation was to develop methods to support the decision-making in the tailored biopsychosocial rehabilitation of patients with non- specific LBP. The secondary aims were to produce a topical summary of the known biopsychosocial risk factors for low back pain chronicity, and to find methods to recognize those factors as well as support the assessment and execution of tailored interventions targeted to the individually recognized factors. A systematic literature review was compiled from the results of 25 different studies on the risk factors associated with low back pain chronicity. The studies had to evaluate the possible risk factor before the chronic phase of pain (3 months) in order to be regarded as a preceding factor for pain. To help the recognition of biopsychosocial factors at the individual level, an artificial intelligence algorithm application was developed that identifies disability information from electronic health records in accordance with the ICF framework. The results of the application were compared to the findings of a domain expert. The processes of patients with low back pain in primary and occupational health care were developed to more comprehensively assess possible risk factors and better tailor interventions to the individuals. A multidisciplinary team was formed from primary, occupational, and special health care professionals for the process design. For the purposes of developing new methods, a patient population of 93 patients with chronic low back pain were gathered. The data comprised free text from electronic health records and quantitative information from medical history forms. According to the systematic review, 45 different factors were identified as being associated with low back pain chronification. The factors were divided into demographical and medical history related factors, biomechanical factors, symptom related factors, psychological and psychosocial factors, and lifestyle factors. The factors were interrelated with the description of disability in the ICF framework, with the exception of the demographic and medical history related factors. The applied artificial intelligence algorithm was able to recognize disability information from the electronic health records with a sensitivity of 83.1% and specificity of 99.84% compared to the results of the domain expert. The rehabilitation process design was presented in a logic model that guides the needed professionals into the process according to the patients’ needs, clearly states the activities of the professionals, and comprehensively exploits a multidisciplinary community over sector boundaries. The findings of this dissertation open new research possibilities in the areas of low back pain and the exploitation of disability information. The results of the systematic review will help clinicians to better understand the biopsychosocial entity of low back pain more competently and researchers to extend their intervention study designs. In future, a feasibility study on the rehabilitation process should be executed before a larger intervention. The benefits of the artificial intelligence algorithm application are planned to be expanded to other patient groups and languages