Sviluppo e validazione di una App Android per il riconoscimento dell'attivita motoria tramite Smartphones e Smartwatch

In questo progetto di tesi saranno applicate tecniche appartenenti al campo della bioingegneria, indirizzate al riconoscimento delle attività motorie e all’analisi del movimento umano. E' stato definito un protocollo di ricerca necessario per il raggiungimento degli obiettivi finali. Si è quindi implementata un’App Android per l’acquisizione e il salvataggio dei dati provenienti dai principali sensori di Smartwatch e Smartphone, utilizzati secondo le modalità indicate nel protocollo. Successivamente i dati immagazzinati nei dispositivi vengono trasferiti al Pc per effettuarne l’elaborazione off-line, in ambiente Matlab. Per facilitare la seguente procedura di sincronizzazione dei dati intra e inter-device, tutti i sensori sono stati salvati, dall’App Android, secondo uno schema logico definito. Si è perciò verificata la possibilità del riconoscimento del contesto e dell’attività nell’uso quotidiano dei dispositivi. Inoltre si è sviluppato un algoritmo per la corretta identificazione del numero dei passi, indipendentemente dall’orientamento del singolo dispositivo. Infatti è importante saper rilevare in maniera corretta il numero di passi effettuati, soprattutto nei pazienti che, a causa di diverse patologie, non riescono ad effettuare una camminata fluida, regolare. Si è visto come il contapassi integrato nei sistemi commerciali per il fitness più diffusi (Smartwatch), pecca soprattutto in questa valutazione, mentre l’algoritmo, appositamente sviluppato, è in grado di garantire un’analisi accettabile a prescindere dal tipo di attività svolta, soprattutto per i dispositivi posizionati in L5. Infine è stato implementato un algoritmo, che sfrutta il filtro di Kalman e un modello biomeccanico appositamente sviluppato, per estrapolare l’evoluzione dell’angolo Tronco-Coscia. Avere a disposizione tale informazione e perciò conoscere la biomeccanica e la cinematica del corpo umano, rende possibile l’applicazione di questa procedura in svariati campi in ambito clinico e non.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

Salud móvil: avenidas innovadoras para mejorar acceso al aborto seguro

Presentación en la 4ta. Conferencia Regional del CLACAI. Reafirmando el legado de Cairo: Aborto legal y seguro. Lima, 21 y 22 de Agosto de 2014

A model to develop a platform for personalised health applications

Antecedentes Europa vive una situación insostenible. Desde el 2008 se han reducido los recursos de los gobiernos a raíz de la crisis económica. El continente Europeo envejece con ritmo constante al punto que se prevé que en 2050 habrá sólo dos trabajadores por jubilado [54]. A esta situación se le añade el aumento de la incidencia de las enfermedades crónicas, relacionadas con el envejecimiento, cuyo coste puede alcanzar el 7% del PIB de un país [51]. Es necesario un cambio de paradigma. Una nueva manera de cuidar de la salud de las personas: sustentable, eficaz y preventiva más que curativa. Algunos estudios abogan por el cuidado personalizado de la salud (pHealth). En este modelo las prácticas médicas son adaptadas e individualizadas al paciente, desde la detección de los factores de riesgo hasta la personalización de los tratamientos basada en la respuesta del individuo [81]. El cuidado personalizado de la salud está asociado a menudo al uso de las tecnologías de la información y comunicación (TICs) que, con su desarrollo exponencial, ofrecen oportunidades interesantes para la mejora de la salud. El cambio de paradigma hacia el pHealth está lentamente ocurriendo, tanto en el ámbito de la investigación como en la industria, pero todavía no de manera significativa. Existen todavía muchas barreras relacionadas a la economía, a la política y la cultura. También existen barreras puramente tecnológicas, como la falta de sistemas de información interoperables [199]. A pesar de que los aspectos de interoperabilidad están evolucionando, todavía hace falta un diseño de referencia especialmente direccionado a la implementación y el despliegue en gran escala de sistemas basados en pHealth. La presente Tesis representa un intento de organizar la disciplina de la aplicación de las TICs al cuidado personalizado de la salud en un modelo de referencia, que permita la creación de plataformas de desarrollo de software para simplificar tareas comunes de desarrollo en este dominio. Preguntas de investigación RQ1 >Es posible definir un modelo, basado en técnicas de ingeniería del software, que represente el dominio del cuidado personalizado de la salud de una forma abstracta y representativa? RQ2 >Es posible construir una plataforma de desarrollo basada en este modelo? RQ3 >Esta plataforma ayuda a los desarrolladores a crear sistemas pHealth complejos e integrados? Métodos Para la descripción del modelo se adoptó el estándar ISO/IEC/IEEE 42010por ser lo suficientemente general y abstracto para el amplio enfoque de esta tesis [25]. El modelo está definido en varias partes: un modelo conceptual, expresado a través de mapas conceptuales que representan las partes interesadas (stakeholders), los artefactos y la información compartida; y escenarios y casos de uso para la descripción de sus funcionalidades. El modelo fue desarrollado de acuerdo a la información obtenida del análisis de la literatura, incluyendo 7 informes industriales y científicos, 9 estándares, 10 artículos en conferencias, 37 artículos en revistas, 25 páginas web y 5 libros. Basándose en el modelo se definieron los requisitos para la creación de la plataforma de desarrollo, enriquecidos por otros requisitos recolectados a través de una encuesta realizada a 11 ingenieros con experiencia en la rama. Para el desarrollo de la plataforma, se adoptó la metodología de integración continua [74] que permitió ejecutar tests automáticos en un servidor y también desplegar aplicaciones en una página web. En cuanto a la metodología utilizada para la validación se adoptó un marco para la formulación de teorías en la ingeniería del software [181]. Esto requiere el desarrollo de modelos y proposiciones que han de ser validados dentro de un ámbito de investigación definido, y que sirvan para guiar al investigador en la búsqueda de la evidencia necesaria para justificarla. La validación del modelo fue desarrollada mediante una encuesta online en tres rondas con un número creciente de invitados. El cuestionario fue enviado a 134 contactos y distribuido en algunos canales públicos como listas de correo y redes sociales. El objetivo era evaluar la legibilidad del modelo, su nivel de cobertura del dominio y su potencial utilidad en el diseño de sistemas derivados. El cuestionario incluía preguntas cuantitativas de tipo Likert y campos para recolección de comentarios. La plataforma de desarrollo fue validada en dos etapas. En la primera etapa se utilizó la plataforma en un experimento a pequeña escala, que consistió en una sesión de entrenamiento de 12 horas en la que 4 desarrolladores tuvieron que desarrollar algunos casos de uso y reunirse en un grupo focal para discutir su uso. La segunda etapa se realizó durante los tests de un proyecto en gran escala llamado HeartCycle [160]. En este proyecto un equipo de diseñadores y programadores desarrollaron tres aplicaciones en el campo de las enfermedades cardio-vasculares. Una de estas aplicaciones fue testeada en un ensayo clínico con pacientes reales. Al analizar el proyecto, el equipo de desarrollo se reunió en un grupo focal para identificar las ventajas y desventajas de la plataforma y su utilidad. Resultados Por lo que concierne el modelo que describe el dominio del pHealth, la parte conceptual incluye una descripción de los roles principales y las preocupaciones de los participantes, un modelo de los artefactos TIC que se usan comúnmente y un modelo para representar los datos típicos que son necesarios formalizar e intercambiar entre sistemas basados en pHealth. El modelo funcional incluye un conjunto de 18 escenarios, repartidos en: punto de vista de la persona asistida, punto de vista del cuidador, punto de vista del desarrollador, punto de vista de los proveedores de tecnologías y punto de vista de las autoridades; y un conjunto de 52 casos de uso repartidos en 6 categorías: actividades de la persona asistida, reacciones del sistema, actividades del cuidador, \engagement" del usuario, actividades del desarrollador y actividades de despliegue. Como resultado del cuestionario de validación del modelo, un total de 65 personas revisó el modelo proporcionando su nivel de acuerdo con las dimensiones evaluadas y un total de 248 comentarios sobre cómo mejorar el modelo. Los conocimientos de los participantes variaban desde la ingeniería del software (70%) hasta las especialidades médicas (15%), con declarado interés en eHealth (24%), mHealth (16%), Ambient Assisted Living (21%), medicina personalizada (5%), sistemas basados en pHealth (15%), informática médica (10%) e ingeniería biomédica (8%) con una media de 7.25_4.99 años de experiencia en estas áreas. Los resultados de la encuesta muestran que los expertos contactados consideran el modelo fácil de leer (media de 1.89_0.79 siendo 1 el valor más favorable y 5 el peor), suficientemente abstracto (1.99_0.88) y formal (2.13_0.77), con una cobertura suficiente del dominio (2.26_0.95), útil para describir el dominio (2.02_0.7) y para generar sistemas más específicos (2_0.75). Los expertos también reportan un interés parcial en utilizar el modelo en su trabajo (2.48_0.91). Gracias a sus comentarios, el modelo fue mejorado y enriquecido con conceptos que faltaban, aunque no se pudo demonstrar su mejora en las dimensiones evaluadas, dada la composición diferente de personas en las tres rondas de evaluación. Desde el modelo, se generó una plataforma de desarrollo llamada \pHealth Patient Platform (pHPP)". La plataforma desarrollada incluye librerías, herramientas de programación y desarrollo, un tutorial y una aplicación de ejemplo. Se definieron cuatro módulos principales de la arquitectura: el Data Collection Engine, que permite abstraer las fuentes de datos como sensores o servicios externos, mapeando los datos a bases de datos u ontologías, y permitiendo interacción basada en eventos; el GUI Engine, que abstrae la interfaz de usuario en un modelo de interacción basado en mensajes; y el Rule Engine, que proporciona a los desarrolladores un medio simple para programar la lógica de la aplicación en forma de reglas \if-then". Después de que la plataforma pHPP fue utilizada durante 5 años en el proyecto HeartCycle, 5 desarrolladores fueron reunidos en un grupo de discusión para analizar y evaluar la plataforma. De estas evaluaciones se concluye que la plataforma fue diseñada para encajar las necesidades de los ingenieros que trabajan en la rama, permitiendo la separación de problemas entre las distintas especialidades, y simplificando algunas tareas de desarrollo como el manejo de datos y la interacción asíncrona. A pesar de ello, se encontraron algunos defectos a causa de la inmadurez de algunas tecnologías empleadas, y la ausencia de algunas herramientas específicas para el dominio como el procesado de datos o algunos protocolos de comunicación relacionados con la salud. Dentro del proyecto HeartCycle la plataforma fue utilizada para el desarrollo de la aplicación \Guided Exercise", un sistema TIC para la rehabilitación de pacientes que han sufrido un infarto del miocardio. El sistema fue testeado en un ensayo clínico randomizado en el cual a 55 pacientes se les dio el sistema para su uso por 21 semanas. De los resultados técnicos del ensayo se puede concluir que, a pesar de algunos errores menores prontamente corregidos durante el estudio, la plataforma es estable y fiable. Conclusiones La investigación llevada a cabo en esta Tesis y los resultados obtenidos proporcionan las respuestas a las tres preguntas de investigación que motivaron este trabajo: RQ1 Se ha desarrollado un modelo para representar el dominio de los sistemas personalizados de salud. La evaluación hecha por los expertos de la rama concluye que el modelo representa el dominio con precisión y con un balance apropiado entre abstracción y detalle. RQ2 Se ha desarrollado, con éxito, una plataforma de desarrollo basada en el modelo. RQ3 Se ha demostrado que la plataforma es capaz de ayudar a los desarrolladores en la creación de software pHealth complejos. Las ventajas de la plataforma han sido demostradas en el ámbito de un proyecto de gran escala, aunque el enfoque genérico adoptado indica que la plataforma podría ofrecer beneficios también en otros contextos. Los resultados de estas evaluaciones ofrecen indicios de que, ambos, el modelo y la plataforma serán buenos candidatos para poderse convertir en una referencia para futuros desarrollos de sistemas pHealth. ABSTRACT Background Europe is living in an unsustainable situation. The economic crisis has been reducing governments' economic resources since 2008 and threatening social and health systems, while the proportion of older people in the European population continues to increase so that it is foreseen that in 2050 there will be only two workers per retiree [54]. To this situation it should be added the rise, strongly related to age, of chronic diseases the burden of which has been estimated to be up to the 7% of a country's gross domestic product [51]. There is a need for a paradigm shift, the need for a new way of caring for people's health, shifting the focus from curing conditions that have arisen to a sustainable and effective approach with the emphasis on prevention. Some advocate the adoption of personalised health care (pHealth), a model where medical practices are tailored to the patient's unique life, from the detection of risk factors to the customization of treatments based on each individual's response [81]. Personalised health is often associated to the use of Information and Communications Technology (ICT), that, with its exponential development, offers interesting opportunities for improving healthcare. The shift towards pHealth is slowly taking place, both in research and in industry, but the change is not significant yet. Many barriers still exist related to economy, politics and culture, while others are purely technological, like the lack of interoperable information systems [199]. Though interoperability aspects are evolving, there is still the need of a reference design, especially tackling implementation and large scale deployment of pHealth systems. This thesis contributes to organizing the subject of ICT systems for personalised health into a reference model that allows for the creation of software development platforms to ease common development issues in the domain. Research questions RQ1 Is it possible to define a model, based on software engineering techniques, for representing the personalised health domain in an abstract and representative way? RQ2 Is it possible to build a development platform based on this model? RQ3 Does the development platform help developers create complex integrated pHealth systems? Methods As method for describing the model, the ISO/IEC/IEEE 42010 framework [25] is adopted for its generality and high level of abstraction. The model is specified in different parts: a conceptual model, which makes use of concept maps, for representing stakeholders, artefacts and shared information, and in scenarios and use cases for the representation of the functionalities of pHealth systems. The model was derived from literature analysis, including 7 industrial and scientific reports, 9 electronic standards, 10 conference proceedings papers, 37 journal papers, 25 websites and 5 books. Based on the reference model, requirements were drawn for building the development platform enriched with a set of requirements gathered in a survey run among 11 experienced engineers. For developing the platform, the continuous integration methodology [74] was adopted which allowed to perform automatic tests on a server and also to deploy packaged releases on a web site. As a validation methodology, a theory building framework for SW engineering was adopted from [181]. The framework, chosen as a guide to find evidence for justifying the research questions, imposed the creation of theories based on models and propositions to be validated within a scope. The validation of the model was conducted as an on-line survey in three validation rounds, encompassing a growing number of participants. The survey was submitted to 134 experts of the field and on some public channels like relevant mailing lists and social networks. Its objective was to assess the model's readability, its level of coverage of the domain and its potential usefulness in the design of actual, derived systems. The questionnaires included quantitative Likert scale questions and free text inputs for comments. The development platform was validated in two scopes. As a small-scale experiment, the platform was used in a 12 hours training session where 4 developers had to perform an exercise consisting in developing a set of typical pHealth use cases At the end of the session, a focus group was held to identify benefits and drawbacks of the platform. The second validation was held as a test-case study in a large scale research project called HeartCycle the aim of which was to develop a closed-loop disease management system for heart failure and coronary heart disease patients [160]. During this project three applications were developed by a team of programmers and designers. One of these applications was tested in a clinical trial with actual patients. At the end of the project, the team was interviewed in a focus group to assess the role the platform had within the project. Results For what regards the model that describes the pHealth domain, its conceptual part includes a description of the main roles and concerns of pHealth stakeholders, a model of the ICT artefacts that are commonly adopted and a model representing the typical data that need to be formalized among pHealth systems. The functional model includes a set of 18 scenarios, divided into assisted person's view, caregiver's view, developer's view, technology and services providers' view and authority's view, and a set of 52 Use Cases grouped in 6 categories: assisted person's activities, system reactions, caregiver's activities, user engagement, developer's activities and deployer's activities. For what concerns the validation of the model, a total of 65 people participated in the online survey providing their level of agreement in all the assessed dimensions and a total of 248 comments on how to improve and complete the model. Participants' background spanned from engineering and software development (70%) to medical specialities (15%), with declared interest in the fields of eHealth (24%), mHealth (16%), Ambient Assisted Living (21%), Personalized Medicine (5%), Personal Health Systems (15%), Medical Informatics (10%) and Biomedical Engineering (8%) with an average of 7.25_4.99 years of experience in these fields. From the analysis of the answers it is possible to observe that the contacted experts considered the model easily readable (average of 1.89_0.79 being 1 the most favourable scoring and 5 the worst), sufficiently abstract (1.99_0.88) and formal (2.13_0.77) for its purpose, with a sufficient coverage of the domain (2.26_0.95), useful for describing the domain (2.02_0.7) and for generating more specific systems (2_0.75) and they reported a partial interest in using the model in their job (2.48_0.91). Thanks to their comments, the model was improved and enriched with concepts that were missing at the beginning, nonetheless it was not possible to prove an improvement among the iterations, due to the diversity of the participants in the three rounds. From the model, a development platform for the pHealth domain was generated called pHealth Patient Platform (pHPP). The platform includes a set of libraries, programming and deployment tools, a tutorial and a sample application. The main four modules of the architecture are: the Data Collection Engine, which allows abstracting sources of information like sensors or external services, mapping data to databases and ontologies, and allowing event-based interaction and filtering, the GUI Engine, which abstracts the user interface in a message-like interaction model, the Workow Engine, which allows programming the application's user interaction ows with graphical workows, and the Rule Engine, which gives developers a simple means for programming the application's logic in the form of \if-then" rules. After the 5 years experience of HeartCycle, partially programmed with pHPP, 5 developers were joined in a focus group to discuss the advantages and drawbacks of the platform. The view that emerged from the training course and the focus group was that the platform is well-suited to the needs of the engineers working in the field, it allowed the separation of concerns among the different specialities and it simplified some common development tasks like data management and asynchronous interaction. Nevertheless, some deficiencies were pointed out in terms of a lack of maturity of some technological choices, and for the absence of some domain-specific tools, e.g. for data processing or for health-related communication protocols. Within HeartCycle, the platform was used to develop part of the Guided Exercise system, a composition of ICT tools for the physical rehabilitation of patients who suffered from myocardial infarction. The system developed using the platform was tested in a randomized controlled clinical trial, in which 55 patients used the system for 21 weeks. The technical results of this trial showed that the system was stable and reliable. Some minor bugs were detected, but these were promptly corrected using the platform. This shows that the platform, as well as facilitating the development task, can be successfully used to produce reliable software. Conclusions The research work carried out in developing this thesis provides responses to the three three research questions that were the motivation for the work. RQ1 A model was developed representing the domain of personalised health systems, and the assessment of experts in the field was that it represents the domain accurately, with an appropriate balance between abstraction and detail. RQ2 A development platform based on the model was successfully developed. RQ3 The platform has been shown to assist developers create complex pHealth software. This was demonstrated within the scope of one large-scale project, but the generic approach adopted provides indications that it would offer benefits more widely. The results of these evaluations provide indications that both the model and the platform are good candidates for being a reference for future pHealth developments.

A technological framework based on automatic messaging for improving adherence of diabetic patients

En todo el mundo se ha observado un crecimiento exponencial en la incidencia de enfermedades crónicas como la hipertensión y enfermedades cardiovasculares y respiratorias, así como la diabetes mellitus, que causa un número de muertes cada vez mayor en todo el mundo (Beaglehole et al., 2008). En concreto, la prevalencia de diabetes mellitus (DM) está aumentando de manera considerable en todas las edades y representa un serio problema de salud mundial. La diabetes fue la responsable directa de 1,5 millones de muertes en 2012 y 89 millones de años de vida ajustados por discapacidad (AVAD) (OMS, 2014). Uno de los principales dilemas que suelen asociarse a la gestión de EC es la adherencia de los pacientes a los tratamientos, que representa un aspecto multifactorial que necesita asistencia en lo relativo a: educación, autogestión, interacción entre los pacientes y cuidadores y compromiso de los pacientes. Medir la adherencia del tratamiento es complicado y, aunque se ha hablado ampliamente de ello, aún no hay soluciones “de oro” (Reviews, 2002). El compromiso de los pacientes, a través de la participación, colaboración, negociación y a veces del compromiso firme, aumentan las oportunidades para una terapia óptima en la que los pacientes se responsabilizan de su parte en la ecuación de adherencia. Comprometer e involucrar a los pacientes diabéticos en las decisiones de su tratamiento, junto con expertos profesionales, puede ayudar a favorecer un enfoque centrado en el paciente hacia la atención a la diabetes (Martin et al., 2005). La motivación y atribución de poder de los pacientes son quizás los dos factores interventores más relevantes que afectan directamente a la autogestión de la atención a la diabetes. Se ha demostrado que estos dos factores desempeñan un papel fundamental en la adherencia a la prescripción, así como en el fomento exitoso de un estilo de vida sana y otros cambios de conducta (Heneghan et al., 2013). Un plan de educación personalizada es indispensable para proporcionarle al paciente las herramientas adecuadas que necesita para la autogestión efectiva de la enfermedad (El-Gayar et al. 2013). La comunicación efectiva es fundamental para proporcionar una atención centrada en el paciente puesto que influye en las conductas y actitudes hacia un problema de salud ((Frampton et al. 2008). En este sentido, la interactividad, la frecuencia, la temporalización y la adaptación de los mensajes de texto pueden promover la adherencia a un régimen de medicación. Como consecuencia, adaptar los mensajes de texto a los pacientes puede resultar ser una manera de hacer que las sugerencias y la información sean más relevantes y efectivas (Nundy et al. 2013). En este contexto, las tecnologías móviles en el ámbito de la salud (mHealth) están desempeñando un papel importante al conectar con pacientes para mejorar la adherencia a medicamentos recetados (Krishna et al., 2009). La adaptación de los mensajes de texto específicos de diabetes sigue siendo un área de oportunidad para mejorar la adherencia a la medicación y ofrecer motivación a adultos con diabetes. Sin embargo, se necesita más investigación para entender totalmente su eficacia. Los consejos de texto personalizados han demostrado causar un impacto positivo en la atribución de poder a los pacientes, su autogestión y su adherencia a la prescripción (Gatwood et al., 2014). mHealth se puede utilizar para ofrecer programas de asistencia de autogestión a los pacientes con diabetes y, al mismo tiempo, superar las dificultades técnicas y financieras que supone el tratamiento de la diabetes (Free at al., 2013). El objetivo principal de este trabajo de investigación es demostrar que un marco tecnológico basado en las teorías de cambios de conducta, aplicado al campo de la mHealth, permite una mejora de la adherencia al tratamiento en pacientes diabéticos. Como método de definición de una solución tecnológica, se han adoptado un conjunto de diferentes técnicas de conducta validadas denominado marco de compromiso de retroacción conductual (EBF, por sus siglas en inglés) para formular los mensajes, guiar el contenido y evaluar los resultados. Los estudios incorporan elementos del modelo transteórico (TTM, por sus siglas en inglés), la teoría de la fijación de objetivos (GST, por sus siglas en inglés) y los principios de comunicación sanitaria persuasiva y eficaz. Como concepto general, el modelo TTM ayuda a los pacientes a progresar a su próxima fase de conducta a través de mensajes de texto motivados específicos y permite que el médico identifique la fase actual y adapte sus estrategias individualmente. Además, se adoptan las directrices del TTM para fijar objetivos personalizados a un nivel apropiado a la fase de cambio del paciente. La GST encierra normas que van a ponerse en práctica para promover la intervención educativa y objetivos de pérdida de peso. Finalmente, los principios de comunicación sanitaria persuasiva y eficaz aplicados a la aparición de los mensajes se han puesto en marcha para aumentar la efectividad. El EBF tiene como objetivo ayudar a los pacientes a mejorar su adherencia a la prescripción y encaminarlos a una mejora general en la autogestión de la diabetes mediante mensajes de texto personalizados denominados mensajes de retroacción automáticos (AFM, por sus siglas en inglés). Después de una primera revisión del perfil, consistente en identificar características significativas del paciente basadas en las necesidades de tratamiento, actitudes y conductas de atención sanitaria, el sistema elige los AFM personalizados, los aprueba el médico y al final se transfieren a la interfaz del paciente. Durante el tratamiento, el usuario recopila los datos en dispositivos de monitorización de pacientes (PMD, por sus siglas en inglés) de una serie de dispositivos médicos y registros manuales. Los registros consisten en la toma de medicación, dieta y actividad física y tareas de aprendizaje y control de la medida del metabolismo. El compromiso general del paciente se comprueba al estimar el uso del sistema y la adherencia del tratamiento y el estado de los objetivos del paciente a corto y largo plazo. El módulo de análisis conductual, que consiste en una serie de reglas y ecuaciones, calcula la conducta del paciente. Tras lograr el análisis conductual, el módulo de gestión de AFM actualiza la lista de AFM y la configuración de los envíos. Las actualizaciones incluyen el número, el tipo y la frecuencia de mensajes. Los AFM los revisa periódicamente el médico que también participa en el perfeccionamiento del tratamiento, adaptado a la fase transteórica actual. Los AFM se segmentan en distintas categorías y niveles y los pacientes pueden ajustar la entrega del mensaje de acuerdo con sus necesidades personales. El EBF se ha puesto en marcha integrado dentro del sistema METABO, diseñado para facilitar al paciente diabético que controle sus condiciones relevantes de una manera menos intrusiva. El dispositivo del paciente se vincula en una plataforma móvil, mientras que una interfaz de panel médico permite que los profesionales controlen la evolución del tratamiento. Herramientas específicas posibilitan que los profesionales comprueben la adherencia del paciente y actualicen la gestión de envíos de AFM. El EBF fue probado en un proyecto piloto controlado de manera aleatoria. El principal objetivo era examinar la viabilidad y aceptación del sistema. Los objetivos secundarios eran también la evaluación de la eficacia del sistema en lo referente a la mejora de la adherencia, el control glucémico y la calidad de vida. Se reclutaron participantes de cuatro centros clínicos distintos en Europa. La evaluación del punto de referencia incluía datos demográficos, estado de la diabetes, información del perfil, conocimiento de la diabetes en general, uso de las plataformas TIC, opinión y experiencia con dispositivos electrónicos y adopción de buenas prácticas con la diabetes. La aceptación y eficacia de los criterios de evaluación se aplicaron para valorar el funcionamiento del marco tecnológico. El principal objetivo era la valoración de la eficacia del sistema en lo referente a la mejora de la adherencia. En las pruebas participaron 54 pacientes. 26 fueron asignados al grupo de intervención y equipados con tecnología móvil donde estaba instalado el EBF: 14 pacientes tenían T1DM y 12 tenían T2DM. El grupo de control estaba compuesto por 25 pa cientes que fueron tratados con atención estándar, sin el empleo del EBF. La intervención profesional tanto de los grupos de control como de intervención corrió a cargo de 24 cuidadores, entre los que incluían diabetólogos, nutricionistas y enfermeras. Para evaluar la aceptabilidad del sistema y analizar la satisfacción de los usuarios, a través de LimeSurvey, se creó una encuesta multilingüe tanto para los pacientes como para los profesionales. Los resultados también se recopilaron de los archivos de registro generados en los PMD, el panel médico profesional y las entradas de la base de datos. Los mensajes enviados hacia y desde el EBF y los archivos de registro del sistema y los servicios de comunicación se grabaron durante las cinco semanas del estudio. Se entregaron un total de 2795 mensajes, lo que supuso una media de 107,50 mensajes por paciente. Como se muestra, los mensajes disminuyen con el tiempo, indicando una mejora global de la adherencia al plan de tratamiento. Como se esperaba, los pacientes con T1DM recibieron más consejos a corto plazo, en relación a su estado. Del mismo modo, al ser el centro de T2DM en cambios de estilo de vida sostenible a largo plazo, los pacientes con T2DM recibieron más consejos de recomendación, en cuanto a dietas y actividad física. También se ha llevado a cabo una comparación de la adherencia e índices de uso para pacientes con T1DM y T2DM, entre la primera y la segunda mitad de la prueba. Se han observado resultados favorables para el uso. En lo relativo a la adherencia, los resultados denotaron una mejora general en cada dimensión del plan de tratamiento, como la nutrición y las mediciones de inserción de glucosa en la sangre. Se han llevado a cabo más estudios acerca del cambio a nivel educativo antes y después de la prueba, medidos tanto para grupos de control como de intervención. Los resultados indicaron que el grupo de intervención había mejorado su nivel de conocimientos mientras que el grupo de control mostró una leve disminución. El análisis de correlación entre el nivel de adherencia y las AFM ha mostrado una mejora en la adherencia de uso para los pacientes que recibieron los mensajes de tipo alertas, y unos resultados no significativos aunque positivos relacionados con la adherencia tanto al tratamiento que al uso correlacionado con los recordatorios. Por otra parte, los AFM parecían ayudar a los pacientes que no tomaban suficientemente en serio su tratamiento en el principio y que sí estaban dispuestos a responder a los mensajes recibidos. Aun así, los pacientes que recibieron demasiadas advertencias, comenzaron a considerar el envío de mensajes un poco estresante. El trabajo de investigación llevado a cabo al desarrollar este proyecto ofrece respuestas a las cuatro hipótesis de investigación que fueron la motivación para el trabajo. • Hipótesis 1 : es posible definir una serie de criterios para medir la adherencia en pacientes diabéticos. • Hipótesis 2: es posible diseñar un marco tecnológico basado en los criterios y teorías de cambio de conducta mencionados con anterioridad para hacer que los pacientes diabéticos se comprometan a controlar su enfermedad y adherirse a planes de atención. • Hipótesis 3: es posible poner en marcha el marco tecnológico en el sector de la salud móvil. • Hipótesis 4: es posible utilizar el marco tecnológico como solución de salud móvil en un contexto real y tener efectos positivos en lo referente a indicadores de control de diabetes. La verificación de cada hipótesis permite ofrecer respuesta a la hipótesis principal: La hipótesis principal es: es posible mejorar la adherencia diabética a través de un marco tecnológico mHealth basado en teorías de cambio de conducta. El trabajo llevado a cabo para responder estas preguntas se explica en este trabajo de investigación. El marco fue desarrollado y puesto en práctica en el Proyecto METABO. METABO es un Proyecto I+D, cofinanciado por la Comisión Europea (METABO 2008) que integra infraestructura móvil para ayudar al control, gestión y tratamiento de los pacientes con diabetes mellitus de tipo 1 (T1DM) y los que padecen diabetes mellitus de tipo 2 (T2DM). ABSTRACT Worldwide there is an exponential growth in the incidence of Chronic Diseases (CDs), such as: hypertension, cardiovascular and respiratory diseases, as well as diabetes mellitus, leading to rising numbers of deaths worldwide (Beaglehole et al. 2008). In particular, the prevalence of diabetes mellitus (DM) is largely increasing among all ages and constitutes a major worldwide health problem. Diabetes was directly responsible for 1,5 million deaths in 2012 and 89 million Disability-adjusted life year (DALYs) (WHO 2014). One of the key dilemmas often associated to CD management is the patients’ adherence to treatments, representing a multi-factorial aspect that requires support in terms of: education, self-management, interaction between patients and caregivers, and patients’ engagement. Measuring adherence is complex and, even if widely discussed, there are still no “gold” standards ((Giardini et al. 2015), (Costa et al. 2015). Patient’s engagement, through participation, collaboration, negotiation, and sometimes compromise, enhance opportunities for optimal therapy in which patients take responsibility for their part of the adherence equation. Engaging and involving diabetic patients in treatment decisions, along with professional expertise, can help foster a patient-centered approach to diabetes care (Martin et al. 2005). Patients’ motivation and empowerment are perhaps the two most relevant intervening factors that directly affect self-management of diabetes care. It has been demonstrated that these two factors play an essential role in prescription adherence, as well as for the successful encouragement of a healthy life-style and other behavioural changes (Heneghan et al. 2013). A personalised education plan is indispensable in order to provide the patient with the appropriate tools needed for the effective self-management of the disease (El-Gayar et al. 2013). Effective communication is at the core of providing patient-centred care since it influences behaviours and attitudes towards a health problem (Frampton et al. 2008). In this regard, interactivity, frequency, timing, and tailoring of text messages may promote adherence to a medication regimen. As a consequence, tailoring text messages to patients can constitute a way of making suggestions and information more relevant and effective (Nundy et al. 2013). In this context, mobile health technologies (mHealth) are playing significant roles in improving adherence to prescribed medications (Krishna et al. 2009). The tailoring of diabetes-specific text messages remains an area of opportunity to improve medication adherence and provide motivation to adults with diabetes but further research is needed to fully understand their effectiveness. Personalized text advices have proven to produce a positive impact on patients’ empowerment, self-management, and adherence to prescriptions (Gatwood et al. 2014). mHealth can be used for offering self-management support programs to diabetes patients and at the same time surmounting the technical and financial difficulties involved in diabetes treatment (Free et al. 2013). The main objective of this research work is to demonstrate that a technological framework, based on behavioural change theories, applied to mHealth domain, allows improving adherence treatment in diabetic patients. The framework, named Engagement Behavioural Feedback Framework (EBF), is built on top of validated behavioural techniques to frame messages, guide the definition of contents and assess outcomes: elements from the Transtheoretical Model (TTM), the Goal-Setting Theory (GST), Effective Health Communication (EHC) guidelines and Principles of Persuasive Technology (PPT) were incorporated. The TTM helps patients to progress to a next behavioural stage, through specific motivated text messages, and allow clinician’s identifying the current stage and tailor its strategies individually. Moreover, TTM guidelines are adopted to set customised goals at a level appropriate to the patient’s stage of change. The GST was used to build rules to be applied for enhancing educational intervention and weight loss objectives. Finally, the EHC guidelines and the PPT were applied to increase the effectiveness of messages. The EBF aims to support patients on improving their prescription adherence and persuade them towards a general improvement in diabetes self-management, by means of personalised text messages, named Automatic Feedback Messages (AFM). After a first profile screening, consisting in identifying meaningful patient characteristics based on treatment needs, attitudes and health care behaviours, customised AFMs are selected by the system, approved by the professional, and finally transferred into the patient interface. During the treatment, the user collects the data into a Patient Monitoring Device (PMD) from a set of medical devices and from manual inputs. Inputs consist in medication intake, diet and physical activity, metabolic measurement monitoring and learning tasks. Patient general engagement is checked by estimating the usage of the system and the adherence of treatment and patient goals status in the short and the long term period. The Behavioural Analysis Module, consisting in a set of rules and equations, calculates the patient’s behaviour. After behavioural analysis is accomplished, the AFM library and the dispatch setting are updated by the AFM Manager module. Updates include the number, the type and the frequency of messages. The AFMs are periodically supervised by the professional who also participates to the refinement of the treatment, adapted to the current transtheoretical stage. The AFMs are segmented in different categories and levels and patients can adjust message delivery in accordance with their personal needs. The EBF was integrated to the METABO system, designed to facilitate diabetic patients in managing their disease in a less intrusive approach. Patient device corresponds in a mobile platform, while a medical panel interface allows professionals to monitoring the treatment evolution. Specific tools allow professional to check patient adherence and to update the AFMs dispatch management. The EBF was tested in a randomised controlled pilot. The main objective was to examine the feasibility and acceptance of the system. Secondary objectives were also the assessment of the effectiveness of system in terms of adherence improvement, glycaemic control, and quality of life. Participants were recruited from four different clinical centres in Europe. The baseline assessment included demographics, diabetes status, profile information, knowledge about diabetes in general, usage of ICT platforms, opinion and experience about electronic devices and adoption of good practices with diabetes. Acceptance and the effectiveness evaluation criteria were applied to evaluate the performance of the technological framework. The main objective was the assessment of the effectiveness of system in terms of adherence improvement. Fifty-four patients participated on the trials. Twenty-six patients were assigned in the intervention group and equipped with mobile where the EBF was installed: 14 patients were T1DM and 12 were T2DM. The control group was composed of 25 patients that were treated through a standard care, without the usage of the EBF. Professional’s intervention for both intervention and control groups was carried out by 24 care providers, including endocrinologists, nutritionists, and nurses. In order to evaluate the system acceptability and analyse the users’ satisfaction, an online multi-language survey, using LimeSurvey, was produced for both patients and professionals. Results were also collected from the log-files generated in the PMDs, the professional medical panel and the entries of the data base. The messages sent to and from the EBF and the log-files of the system and communication services were recorded over 5 weeks of the study. A total of 2795 messages were submitted, representing an average of 107,50 messages per patient. As demonstrated, messages decrease over time indicating an overall improvement of the care plan’s adherence. As expected, T1DM patients were more loaded with short-term advices, in accordance with their condition. Similarly, being the focus of T2DM on long-term sustainable lifestyle changes, T2DM received more reminders advices, as for diet and physical activity. Favourable outcomes were observed for treatment and usage adherences of the intervention group: for both the adherence indices, results denoted a general improvement on each care plan’s dimension, such as on nutrition and blood glucose input measurements. Further studies were conducted on the change on educational level before and after the trial, measured for both control and intervention groups. The outcomes indicated the intervention group has improved its level of knowledge, while the control group denoted a low decrease. The correlation analysis between the level of adherences and the AFMs showed an improvement in usage adherence for patients who received warnings message, while non-significantly yet even positive indicators related to both treatment and usage adherence correlated with the Reminders. Moreover, the AFMs seemed to help those patients who did not take their treatment seriously enough in the beginning and who were willing to respond to the messages they received. Even though, patients who received too many Warnings, started to consider the message dispatch to be a bit stressful. The research work carried out in developing this research work provides responses to the four research hypothesis that were the motivation for the work: •Hypothesis 1: It is possible to define a set of criteria to measure adherence in diabetic patients. •Hypothesis 2: It is possible to design a technological framework, based on the aforementioned criteria and behavioural change theories, to engage diabetic patients in managing their disease and adhere to care plans. •Hypothesis 3: It is possible to implement the technological framework in the mobile health domain. •Hypothesis 4: It is possible to use the technological framework as a mobile health solution in a real context and have positive effects in terms of diabetes management indicators. The verification of each hypothesis allowed us to provide a response to the main hypothesis: The Main Hypothesis is: It is possible to improve diabetic adherence through a mHealth technological framework based on behavioural change theories. The work carried out to answer these questions is explained in this research work. The framework was developed and applied in the METABO project. METABO is an R&D project, co-funded by the European Commission (METABO 2008) that integrates mobile infrastructure for supporting the monitoring, management, and treatment of type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) patients.

Development of Multisector Alliances (MSAs): Organization and Leadership. ACES Working Papers, 2012

The research team reviewed numerous several multi- sectoral entities and identified nine GGNs that became the subject of our case studies. The research team conducted semi-structured interviews with executives and staff from each of these GNNs and prepared a profile, including a description of the unique evolution of the organization, goals and objectives, organizational structure and governance arrangements for each GGN. The following list provides an overview of the nine GGNs profiled: 1. Every Woman Every Child is an unprecedented global effort that mobilizes and amplifies action by governments, multilaterals, the private sector, research centers, academia and civil society to address life-threatening health challenges facing women and children globally. 2. HERproject catalyzes global partnerships and local Networks to improve female workers’ general and reproductive health in eight emerging economies. 3. R4 Rural Resilience Initiative is a cutting-edge, strategic, large-scale partnership between the public and private sectors to innovate and develop better tools to help the world’s most vulnerable people build resilient livelihoods. 4. Extractive Industry Transparency Initiative is a coalition of governments, companies, civil society groups, investors and international organizations that aims to improve transparency and accountability in the extractives sector. 5. Global Network for Neglected Tropical Diseases works with international partners at the highest level of government, business and society to break down the logistical and financial barriers to delivering existing treatments for the seven most common neglected tropical diseases. 6. Global Alliance for Improved Nutrition is an alliance that supports public-private partnerships to increase access to the missing nutrients in diets necessary for people, communities and economies to be stronger and healthier. 7. Inter-Agency Network For Education in Emergencies is a global Network of individuals and representatives from NGOs, United Nations and donor agencies, governments, academic institutions, schools and affected populations working to ensure all persons have the right to a quality and safe education in emergencies and post- crisis recovery. 8. mHealth Alliance works with diverse partners to advance mobile-based or mobile-enhanced solutions that deliver health through research, advocacy, support for the development of interoperable solutions and sustainable deployment models. 9. The Rainforest Alliance is a global non-profit that focuses on environmental conservation and sustainable development and works through collaborative partnerships with various stakeholders.

Simulazione agent-based di un sistema di m-Health per il self-management del diabete di tipo 1

L'obiettivo della tesi è dimostrare l'utilità e i vantaggi che può fornire il Self-Management del diabete mellito di tipo 1 in un sistema di mobile Health a partire da un modello computazionale Agent-Based. Viene quindi affrontata in maniera approfondita la tematica del mobile Health ed il suo sviluppo nei paesi a basso/medio reddito, illustrando i risultati ottenuti dalla ricerca scientifica fino ad oggi, ed il concetto di Self-Management di malattie croniche, un processo di cura caratterizzato dalla partecipazione autonoma del paziente stesso, fornendo una panoramica degli approcci computazionali sviluppati. Viene quindi studiato il diabete mellito in ogni sua caratteristica, seguito dall'illustrazione di diverse applicazioni per la gestione autonoma della suddetta patologia tutt'ora in commercio. Nel caso di studio vengono effettuate diverse simulazioni, tramite la piattaforma di simulazione MASON, per realizzare varie dinamiche della rete fisiologica di un paziente al fine di stabilire feedback qualitativi per il Self-Management della patologia.

Open-Source Computerized Patient-Reported Outcomes: Case Studies Illustrating Fifteen Years of Evolution

Thesis (Master's)--University of Washington, 2016-06

Childhood obesity treatment: integrating mobile health technology into a paediatric obesity service

Background: The management of childhood obesity is challenging. Aims: Thesis, i) reviews the evidence for lifestyle treatment of obesity, ii) explores cardiometabolic burden in childhood obesity, iii) explores whether changes in body composition predicts change in insulin sensitivity (IS), iv) develops and evaluates a lifestyle obesity intervention; v) develops a mobile health application for obesity treatment and vi) tests the application in a clinical trial. Methods: In Study 1, systematic reviews and meta-analyses of the 12‐month effects of lifestyle and mHealth interventions were conducted. In Study 2, the prevalence of cardiometabolic burden was estimated in a consecutive series of 267 children. In Study 3, body composition was estimated with bioelectrical impedance analysis (BIA) and dual x-ray absorptiometry (DXA) and linear regression analyses were used to estimate the extent to which each methods predicted change in IS. Study 4 describes the development of the Temple Street W82GO Healthy Lifestyle intervention for clinical obesity in children and a controlled study of treatment effect in 276 children is reported. Study 5 describes the development and testing of the Reactivate Mobile Obesity Application. Study 6 outlines the development and preliminary report from a clinical effectiveness trial of Reactivate. Results: In Study 1, meta--‐analyses BMI SDS changed by -0.16 (-0.24,‐0.07, p<0.01) and -0.03 (-0.13, 0.06, p=0.48). In study 2, cardiometabolic comorbidities were common (e.g. hypertension in 49%) and prevalence increased as obesity level increased. In Study 3, BC changes significantly predicted changes in IS. In Study 4, BMI SDS was significantly reduced in W82GO compared to controls (p<0.001). In Study 5, the Reactivate application had good usability indices and preliminary 6‐month process report data from Study 6, revealed a promising effect for Reactivate. Conclusions: W82GO and Reactivate are promising forms of treatment.

Randomized Trial for a Mobile Health Approach to Increase Uptake of Cervical Cancer Screening in Tanzania

Background: Largely due to low availability and uptake of screening in low- and middle-income countries, cervical cancer is the second ranked cancer among women in these countries. This is a tragedy because cervical cancer is one of the most preventable carcinomas. This thesis will investigate behaviour change methods, which capitalize on the recent exponential increase in ownership of mobile phones in Tanzania, to increase uptake of cervical cancer screening (CCS) in the Kilimanjaro region of Tanzania. Objectives: 1) To evaluate the effectiveness of behaviour change messages delivered via short message service (SMS) on the uptake of CCS in the Kilimanjaro region; 2) to evaluate the effectiveness of a transportation eVoucher on the uptake of CCS in the Kilimanjaro region; 3) to explore characteristics associated with CCS uptake in the Kilimanjaro region; and 4) to determine the attitudes towards and perceived benefit of behaviour change SMS messages and eVouchers intended to increase uptake of CCS. Methods: In the Kilimanjaro Region, 853 women participated in a randomized controlled trial. Baseline data was collected through self-report through systematic stratified random sampling. Participants were randomized to one of three groups: a control group, a group receiving behaviour change messages delivered via SMS, or a group receiving a travel eVoucher and identical SMS as the SMS group. A fieldworker recorded participants attending screening at the CCS clinics and administered a post-screening survey. The follow-up period was two months from the time of the participant’s enrolment. Logistic regression (both for the combined and stratified data sets) was used to determine associations between the behaviour change interventions, baseline characteristics and cervical cancer screening uptake. Results: All participants receiving SMS messages (SMS or eVoucher group) were more likely to attend cervical cancer screening in comparison with the control group. 83% of participants who attended screening shared the information contained in the messages with others. Conclusions: Behaviour change messages delivered via SMS and transportation eVouchers have the potential to increase uptake of cervical cancer screening in the Kilimanjaro region of Tanzania. Harnessing this potential will require implementing these interventions alongside other methods to achieve maximum impact.

Evaluation of Mobile Telephone Text Message Reminders for People with Antipsychotic Medication

Nonadherence to treatment is a worldwide problem among people with severe mental disorders. Patient treatment adherence may be supported with simple reminding methods e.g. text message reminders. However, there is limited evidence of its benefits. Intervention evaluation is essential in mHealth research. Therefore, this evaluative study was conducted. This study aimed to evaluate text message reminder use in encouraging patients’treatment adherence among people with antipsychotic medication. The data were collected between September 2011 and December 2013. First, a systematic literature review revealed that text message reminders were widely used in healthcare. However, its impacts were conflicting. Second, a sub-sample (n = 562) analysis showed that patients preferred humorous text message reminders and preferred to receive them in the morning, at the beginning of the week. Age, gender and marital status seemed to have different effects on the preferred amount and timing of the selected reminders. Third, a cross-sectional survey revealed that people with antipsychotic medication (n = 408) expressed overall satisfaction towards the reminder system. Finally, the evaluative design showed that patient recruitment for a randomized controlled trial concerning people with antipsychotic medication was challenging due to low rates of eligible participants. Follow-up drop-out rates varied depending on the data collection method. Participants’ demographic characteristics were associated with the risk of dropping out from the trial. This study suggests that text messages are a potential reminder system in healthcare services among people with antipsychotic medication. More research is needed to gain a comprehensive picture of the impacts and effectiveness of text message reminders.

Exercit@rt mobile : monitorização da reabilitação respiratória na DPOC

A prevalência estimada da Doença Pulmonar Obstrutiva Crónica (DPOC) em Portugal é de 14,2% para indivíduos com idade superior a 45 anos (cerca de 800.000 indivíduos), sendo mais prevalente no sexo masculino (Observatório Nacional das Doenças Respiratórias, 2014). Com o aparecimento de soluções baseadas em novas modalidades de eHealth e mHealth surgiram novas formas de acompanhamento e monitorização das doenças crónicas, nomeadamente da DPOC. É neste contexto que foi desenvolvida a aplicação mobile Exercit@rt, em parceria com a Escola Superior de Saúde da Universidade de Aveiro e em continuidade com outros estudos do MCMM anteriormente desenvolvidos. A aplicação permite monitorizar, em tempo real, através da utilização de um oxímetro Bluetooth, os níveis de batimento cardíaco e saturação de oxigénio dos pacientes com DPOC. Com esta aplicação os pacientes podem realizar diversos exercícios de fisioterapia respiratória assim como atividades físicas de vida diária que podem ser monitorizadas, georreferenciadas e avaliadas. Para além do desenvolvimento da aplicação mobile, a presente investigação integrou ainda uma etapa de validação que contou com a participação de dez pacientes com doenças do foro respiratório – cinco utilizadores que utilizam/têm smartphone (UTS) e cinco utilizadores não utilizam/não têm smartphone (NUNTS). A cada um destes foram propostas tarefas a realizar na aplicação mobile, estando previsto que a aplicação estivesse apta para qualquer participante. A totalidade dos participantes reconheceu a utilidade da aplicação no controlo da sua doença.

Choisir dans l'abondance : les applications mobiles en santé

Colloque organisé par le Regroupement stratégique d’éthique en santé et le Regroupement stratégique de recherche sur les TIC et la santé (Réseau de recherche en santé des populations du Québec) et par le Centre de recherche sur la communication et la santé (Université du Québec à Montréal).

Secure and Privacy-aware Data Collection and Processing in Mobile Health Systems

Healthcare systems have assimilated information and communication technologies in order to improve the quality of healthcare and patient's experience at reduced costs. The increasing digitalization of people's health information raises however new threats regarding information security and privacy. Accidental or deliberate data breaches of health data may lead to societal pressures, embarrassment and discrimination. Information security and privacy are paramount to achieve high quality healthcare services, and further, to not harm individuals when providing care. With that in mind, we give special attention to the category of Mobile Health (mHealth) systems. That is, the use of mobile devices (e.g., mobile phones, sensors, PDAs) to support medical and public health. Such systems, have been particularly successful in developing countries, taking advantage of the flourishing mobile market and the need to expand the coverage of primary healthcare programs. Many mHealth initiatives, however, fail to address security and privacy issues. This, coupled with the lack of specific legislation for privacy and data protection in these countries, increases the risk of harm to individuals. The overall objective of this thesis is to enhance knowledge regarding the design of security and privacy technologies for mHealth systems. In particular, we deal with mHealth Data Collection Systems (MDCSs), which consists of mobile devices for collecting and reporting health-related data, replacing paper-based approaches for health surveys and surveillance. This thesis consists of publications contributing to mHealth security and privacy in various ways: with a comprehensive literature review about mHealth in Brazil; with the design of a security framework for MDCSs (SecourHealth); with the design of a MDCS (GeoHealth); with the design of Privacy Impact Assessment template for MDCSs; and with the study of ontology-based obfuscation and anonymisation functions for health data.

The era of smartphones: Back to our biological makeup?

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