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

Generic data processing & analysis architecture of a personal health system to manage daily interactive sessions in patients with major depression

[EN] The World Health Organization (WHO) considers Major Unipolar Depression as a significant cause of disability worldwide (globally, more than 350 million people of all ages suffer from depression). This common mental disorder spends many economic and clinical resources and it is sometimes responsible for patient suicides. The work presented in this Master Thesis document describes the design and implementation of a generic Data Processing & Analysis module which has been included within the Personal Health System developed in the Help4Mood Research European Project [FP7 ICT-248765]. The aim of Help4Mood is to develop a computational distributed system to support the treatment of people with Major Depression in the community. It is focused on reducing depressive symptoms, improving functioning, and preventing the recurrence of symptoms in the future. The developed Data Processing & Analysis module is the mechanism responsible of: i) The analysis of the objective and subjective inputs received from the user; ii) The inference of clinical concepts and the production of a set of activities to be suggested by the system during the treatment of depression; iii) The planning of the most appropriate interactive sessions based on the inferred activities; iv) The generation of adequate emotional responses represented in the Help4Mood¿s Virtual Agent to better engages the patient with the use of the system and to facilitate a better adherence to the treatment process; and v) The summarization of the relevant clinical information about the progress of the patient every week. The results of this work suggests that the generic Data Processing & Analysis module is useful for managing flexible and personalised sessions in the treatment of the depression, and it is able to be adapted to other clinical domains. It provides a systematic framework for data collection, processing and analysis which improves the continuous monitoring and treatment of the patients. Additionally, our module improves the communication between patients and clinicians by facilitating the joint reflexion about the evolution and improvements in wellbeing at the different stages of the treatment[ES] La Organización Mundial de la Salud (OMS) considera la Depresión Mayor Unipolar como una causa significativa de discapacidad mundial (más de 350 millones de personas de todas las edades padecen depresión). Esta común enfermedad mental consume muchos recursos económicos y clínicos y en ocasiones es responsable de suicidios de pacientes. El trabajo presentado en esta Tesina de Máster describe el diseño y la implementación de un módulo genérico de Procesado y Análisis de Datos, el cual ha sido incluido en el Sistema Personal de Salud desarrollado en el proyecto de investigación Europeo Help4Mood [FP7 ICT-248765]. El propósito de Help4Mood es el desarrollo de un sistema computacional distribuido que de soporte al tratamiento de personas con Depresión Mayor. Se centra en la reducir los síntomas de la depresión, mejorar el funcionamiento, y en prevenir la futura reaparición de los síntomas. El módulo de Procesado y Análisis de Datos desarrollado es el responsable de: i) El análisis de los datos objetivos y subjetivos recibidos por parte del usuario del sistema; ii) La inferencia de conceptos clínicos y la producción de un conjunto de actividades que serán propuestas por el sistema durante el tratamiento de la depresión; iii) La planificación de la sesión interactiva más apropiada basada en las actividades inferidas; iv) La generación de una respuesta emocional adecuada que el Agente Virtual de Help4Mood muestre para lograr una mayor participación de los usuarios en el uso del sistema y una mejor adherencia al proceso del tratamiento; y v) El resumen de la información clínica relevante sobre el progreso semanal del paciente. Los resultados de este trabajo sugieren que el módulo genérico de Procesado y Análisis de Datos es útil para la gestión flexible y personalizada de sesiones diarias para el tratamiento de la Depresión, además podría ser adaptada a otros dominios clínicos. Este módulo proporciona un marco sistemático para la recopilación, procesamiento y análisis que permite mejorar el control continuo y el tratamiento de los pacientes. Adicionalmente, nuestro módulo mejora la comunicación entre los pacientes y los médicos, facilitando la reflexión conjunta sobre la evolución y las mejoras en el bienestar en las diferentes etapas del tratamiento.Bresó Guardado, A. (2013). Generic data processing & analysis architecture of a personal health system to manage daily interactive sessions in patients with major depression. http://hdl.handle.net/10251/39155Archivo delegad

Robustness and findings of a web-based system for depression assessment in a university work context

[EN] Depression is associated with absenteeism and presentism, problems in workplace relationships and loss of productivity and quality. The present work describes the validation of a web-based system for the assessment of depression in the university work context. The basis of the system is the Spanish version of the Beck Depression Inventory (BDI-II). A total of 185 participants completed the BDI-II web-based assessment, including 88 males and 97 females, 70 faculty members and 115 staff members. A high level of internal consistency reliability was confirmed. Based on the results of our web-based BDI-II, no significant differences were found in depression severity between gender, age or workers' groups. The main depression risk factors reported were: Changes in sleep, Loss of energy, Tiredness or fatigue and Loss of interest. However significant differences were found by gender in Changes in appetite, Difficulty of concentration and Loss of interest in sex; males expressed less loss of interest in sex than females with a statistically significant difference. Our results indicate that the data collected is coherent with previous BDI-II studies. We conclude that the web-based system based on the BDI-II is psychometrically robust and can be used to assess depression in the university working community.Funding for this study was provided by the authors' various departments, and partially by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies [727560]), the MTS4up project (DPI2016-80054-R) and patient-centered pathways of early palliative care, supportive ecosystems and appraisal standard (825750).Asensio-Cuesta, S.; Bresó, A.; Sáez Silvestre, C.; Garcia-Gomez, JM. (2019). Robustness and findings of a web-based system for depression assessment in a university work context. International Journal of Environmental research and Public Health. 16(4):1-17. https://doi.org/10.3390/ijerph16040644S117164Depression [Internet]. World Health Organization http://www.who.int/mediacentre/factsheets/fs369/en/Chang, S. M., Hong, J.-P., & Cho, M. J. (2011). Economic burden of depression in South Korea. Social Psychiatry and Psychiatric Epidemiology, 47(5), 683-689. doi:10.1007/s00127-011-0382-8Greenberg, P. E., Fournier, A.-A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015). The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010). The Journal of Clinical Psychiatry, 76(02), 155-162. doi:10.4088/jcp.14m09298Health and Safety at Work in Europe (1999–2007): A Statistical Portrait. Luxembourg. Publications Office of the European Union https://ec.europa.eu/eurostat/documents/3217494/5718905/KS-31-09-290-EN.PDF/88eef9f7-c229-40de-b1cd-43126bc4a946Lee, Y., Rosenblat, J. D., Lee, J., Carmona, N. E., Subramaniapillai, M., Shekotikhina, M., … McIntyre, R. S. (2018). Efficacy of antidepressants on measures of workplace functioning in major depressive disorder: A systematic review. Journal of Affective Disorders, 227, 406-415. doi:10.1016/j.jad.2017.11.003Schmidt, S., Roesler, U., Kusserow, T., & Rau, R. (2012). Uncertainty in the workplace: Examining role ambiguity and role conflict, and their link to depression—a meta-analysis. European Journal of Work and Organizational Psychology, 23(1), 91-106. doi:10.1080/1359432x.2012.711523Cuijpers, P., & Smit, F. (2004). Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatrica Scandinavica, 109(5), 325-331. doi:10.1111/j.1600-0447.2004.00301.xRihmer, Z. (2001). Can better recognition and treatment of depression reduce suicide rates? A brief review. European Psychiatry, 16(7), 406-409. doi:10.1016/s0924-9338(01)00598-3Nogueira-Martins, L. A., Fagnani Neto, R., Macedo, P. C. M., Cítero, V. A., & Mari, J. J. (2004). The mental health of graduate students at the Federal University of São Paulo: a preliminary report. Brazilian Journal of Medical and Biological Research, 37(10), 1519-1524. doi:10.1590/s0100-879x2004001000011Ibrahim, A. K., Kelly, S. J., Adams, C. E., & Glazebrook, C. (2013). A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research, 47(3), 391-400. doi:10.1016/j.jpsychires.2012.11.015Levecque, K., Anseel, F., De Beuckelaer, A., Van der Heyden, J., & Gisle, L. (2017). Work organization and mental health problems in PhD students. Research Policy, 46(4), 868-879. doi:10.1016/j.respol.2017.02.008Zhong, J., You, J., Gan, Y., Zhang, Y., Lu, C., & Wang, H. (2009). Job Stress, Burnout, Depression Symptoms, and Physical Health among Chinese University Teachers. Psychological Reports, 105(3_suppl), 1248-1254. doi:10.2466/pr0.105.f.1248-1254The International Test Commission. (2006). International Guidelines on Computer-Based and Internet-Delivered Testing. International Journal of Testing, 6(2), 143-171. doi:10.1207/s15327574ijt0602_4Reevy, G. M., & Deason, G. (2014). Predictors of depression, stress, and anxiety among non-tenure track faculty. Frontiers in Psychology, 5. doi:10.3389/fpsyg.2014.00701McLean, L., & Connor, C. M. (2015). Depressive Symptoms in Third‐Grade Teachers: Relations to Classroom Quality and Student Achievement. Child Development, 86(3), 945-954. doi:10.1111/cdev.12344Griffiths, K. M., Christensen, H., Jorm, A. F., Evans, K., & Groves, C. (2004). Effect of web-based depression literacy and cognitive–behavioural therapy interventions on stigmatising attitudes to depression. British Journal of Psychiatry, 185(4), 342-349. doi:10.1192/bjp.185.4.342HASLAM, C., ATKINSON, S., BROWN, S., & HASLAM, R. (2005). Anxiety and depression in the workplace: Effects on the individual and organisation (a focus group investigation). Journal of Affective Disorders, 88(2), 209-215. doi:10.1016/j.jad.2005.07.009Finkelstein, J., & Lapshin, O. (2007). Reducing depression stigma using a web-based program. International Journal of Medical Informatics, 76(10), 726-734. doi:10.1016/j.ijmedinf.2006.07.004BECK, A. T. (1961). An Inventory for Measuring Depression. Archives of General Psychiatry, 4(6), 561. doi:10.1001/archpsyc.1961.01710120031004Montgomery, S. A., & Åsberg, M. (1979). A New Depression Scale Designed to be Sensitive to Change. British Journal of Psychiatry, 134(4), 382-389. doi:10.1192/bjp.134.4.382Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Löwe, B. (2010). The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. General Hospital Psychiatry, 32(4), 345-359. doi:10.1016/j.genhosppsych.2010.03.006ZUNG, W. W. K. (1965). A Self-Rating Depression Scale. Archives of General Psychiatry, 12(1), 63. doi:10.1001/archpsyc.1965.01720310065008Ginting, H., Näring, G., van der Veld, W. M., Srisayekti, W., & Becker, E. S. (2013). Validating the Beck Depression Inventory-II in Indonesia’s general population and coronary heart disease patients. International Journal of Clinical and Health Psychology, 13(3), 235-242. doi:10.1016/s1697-2600(13)70028-0Kojima, M., Furukawa, T. A., Takahashi, H., Kawai, M., Nagaya, T., & Tokudome, S. (2002). Cross-cultural validation of the Beck Depression Inventory-II in Japan. Psychiatry Research, 110(3), 291-299. doi:10.1016/s0165-1781(02)00106-3Kapci, E. G., Uslu, R., Turkcapar, H., & Karaoglan, A. (2008). Beck Depression Inventory II: evaluation of the psychometric properties and cut-off points in a Turkish adult population. Depression and Anxiety, 25(10), E104-E110. doi:10.1002/da.20371Aratake, Y., Tanaka, K., Wada, K., Watanabe, M., Katoh, N., Sakata, Y., & Aizawa, Y. (2007). Development of Japanese Version of the Checklist Individual Strength Questionnaire in a Working Population. Journal of Occupational Health, 49(6), 453-460. doi:10.1539/joh.49.453Kühner, C., Bürger, C., Keller, F., & Hautzinger, M. (2007). Reliabilität und Validität des revidierten Beck-Depressionsinventars (BDI-II). Der Nervenarzt, 78(6), 651-656. doi:10.1007/s00115-006-2098-7Holländare, F., Andersson, G., & Engström, I. (2010). A Comparison of Psychometric Properties Between Internet and Paper Versions of Two Depression Instruments (BDI-II and MADRS-S) Administered to Clinic Patients. Journal of Medical Internet Research, 12(5), e49. doi:10.2196/jmir.1392Potential of the Internet for Personality Research https://www.sciencedirect.com/science/article/pii/B978012099980450006XCarlbring, P., Brunt, S., Bohman, S., Austin, D., Richards, J., Öst, L.-G., & Andersson, G. (2007). Internet vs. paper and pencil administration of questionnaires commonly used in panic/agoraphobia research. Computers in Human Behavior, 23(3), 1421-1434. doi:10.1016/j.chb.2005.05.002Schulenberg, S. E., & Yutrzenka, B. A. (2001). Equivalence of computerized and conventional versions of the Beck Depression Inventory-II (BDI-II). Current Psychology, 20(3), 216-230. doi:10.1007/s12144-001-1008-

A Novel Approach to Improve the Planning of Adaptive and Interactive Sessions for the treatment of Major Depression

[EN] Human Computer Interaction (HCI) is a research field which aims to improve the relationship between users and interactive computer systems. A main objective of this research area is to make the user experience more pleasant and efficient, minimizing the barrier between the users' cognition of what they want to accomplish and the computer's understanding of the user's tasks, by means of userfriendly, useful and usable designs. A bad HCI design is one of the main reasons behind user rejection of computer-based applications, which in turn produces loss of productivity and economy in industrial environments. In the eHealth domain, user rejection of computer-based systems is a major barrier to exploiting the maximum benefit from those applications developed to support the treatment of diseases, and in the worst cases a poor design in these systems may cause deterioration in the clinical condition of the patient. Thus, a high level of personalisation of the system according to users' needs is extremely important, making it easy to use and contributing to the system's efficacy, which in turn facilitates the empowerment of the target users. Ideally, the content offered through the interactive sessions in these applications should be continuously assessed and adapted to the changing condition of the patient. A good HCI design and development can improve the acceptance of these applications and contribute to promoting better adherence levels to the treatment, preventing the patient from further relapses. In this work, we present a mechanism to provide personalised and adaptive daily interactive sessions focused on the treatment of patients with Major Depression. These sessions are able to automatically adapt the content and length of the sessions to obtain personalised and varied sessions in order to encourage the continuous and long-term use of the system. The tailored adaptation of session content is supported by decision-making processes based on: (i) clinical requirements; (ii) the patient's historical data; and (iii) current responses from the patient. We have evaluated our system through two different methodologies: the first one performing a set of simulations producing different sessions from changing input conditions, in order to assess different levels of adaptability and variability of the session content offered by the system. The second evaluation process involved a set of patients who used the system for 14 to 28 days and answered a questionnaire to provide feedback about the perceived level of adaptability and variability produced by the system. The obtained results in both evaluations indicated good levels of adaptability and variability in the content of the sessions according to the input conditions.E. Fuster Garcia acknowledges the financial support from the "Torres Quevedo" program (Spanish Ministry of Economy and Competitiveness) co-funded by the European Social Fund (PTQ-12-05693), and the financial support from the Universitat Politecnica de Valencia under the Grant "Ayudas Para la Contratacion de Doctores para el Acceso al Sistema Espanol de Ciencia, Tecnologia e Innovacion" (PAID-10-14).Bresó Guardado, A.; Martínez Miranda, JC.; Fuster García, E.; García Gómez, JM. (2016). A Novel Approach to Improve the Planning of Adaptive and Interactive Sessions for the treatment of Major Depression. International Journal of Human-Computer Studies. 87:80-91. https://doi.org/10.1016/j.ijhcs.2015.11.003S80918

An HL7-CDA wrapper for facilitating semantic interoperability to rule-based Clinical Decision Support Systems

The success of Clinical Decision Support Systems (CDSS) greatly depends on its capability of being integrated in Health Information Systems (HIS). Several proposals have been published up to date to permit CDSS gathering patient data from HIS. Some base the CDSS data input on the HL7 reference model, however, they are tailored to specific CDSS or clinical guidelines technologies, or do not focus on standardizing the CDSS resultant knowledge. We propose a solution for facilitating semantic interoperability to rule-based CDSS focusing on standardized input and output documents conforming an HL7-CDA wrapper. We define the HL7-CDA restrictions in a HL7-CDA implementation guide. Patient data and rule inference results are mapped respectively to and from the CDSS by means of a binding method based on an XML binding file. As an independent clinical document, the results of a CDSS can present clinical and legal validity. The proposed solution is being applied in a CDSS for providing patient-specific recommendations for the care management of outpatients with diabetes mellitus.We thank Fagor Electrodomesticos S.Coop for their support and funding in the development of this work, specially to Juan Ramon Inurria and Jorge de Antonio Prieto. We also thank the colaboration from Universidad de Mondragon in the design of the general architecture of the telemedicine system, specially, Felix Larrinaga. This work has been partially supported by the Health Institute Carlos III through the RETICS Combiomed, RD07/0067/2001.Sáez Silvestre, C.; Bresó Guardado, A.; Vicente Robledo, J.; Robles Viejo, M.; García Gómez, JM. (2013). An HL7-CDA wrapper for facilitating semantic interoperability to rule-based Clinical Decision Support Systems. Computer Methods and Programs in Biomedicine. 109(3):239-249. doi:10.1016/j.cmpb.2012.10.003S239249109

Fusing actigraphy signals for outpatient monitoring

[EN] Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.This work was partially funded by the European Commission: Help4Mood (Contract No. FP7-ICT-2009-4: 248765). E. FusterGarcia acknowledges Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ-12-05693).Fuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015). Fusing actigraphy signals for outpatient monitoring. Information Fusion. 23:69-80. https://doi.org/10.1016/j.inffus.2014.08.003S69802

Fusing actigraphy signals for outpatient monitoring

Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.Peer Reviewe