The lack of digital and easily accessible information for home hemodialysis patients leads to a disinclination of using the provided manuals. This problem could potentially be solved with a user-friendly app, where all information and communication with the healthcare providers could take place. Hence, leading to them feeling better prepared to take charge of their own treatment. The purpose of this thesis is to provide a minimum viable product of a mobile application to facilitate treatment for home hemodialysis patients. The app was developed using the programming language SwiftUI. Information was collected from patients with home hemodialysis treatment through interviews conducted in their homes.  The mobile application enables patients to access the manual and failure identification codes through a search bar. It also provides digital checklists, dialysis protocol and symptom follow-up. The healthcare providers can then access the dialysis protocol and symptom follow-up through remote patient monitoring. A chat function allows patients to contact nurses. The mobile application fulfills the clients’ requests and with additional work it could be fully operational. / Bristen på digital och lättåtkomlig information för patienter med hemhemodialys leder till en ovilja att använda manualer och felkoder. En lösning på detta problem skulle kunna vara att utveckla en användarvänlig app som innehåller all nödvändig information, samt ett kommunikationsverktyg mellan patient och vårdpersonal. Syftet med detta examensarbete är att skapa en minsta gångbar produkt i form av en app för att underlätta behandlingen för patienter med hemhemodialys. Appen utvecklades med programmeringsspråket SwiftUI. Information hämtades från patienter genom utförda hemintervjuer.  Den mobila applikationen ger patienter åtkomst till manualen samt felkoderna genom en sökruta. Den innehåller även digitala checklistor, dialysprotokoll och symtomuppföljning. Vårdpersonal kan genom appen få tillgång till patienters dialysprotokoll och symtomuppföljning genom fjärrövervakning. Via chatten kan patienter kontakta sjukvårdspersonal. Applikationen uppfyller uppdragsgivarens önskemål om funktionaliteter. Om fortsatt arbete genomförs kan appen användas för att ge patienterna stöd i sin egenvård. / Nej

Under detta examensarbete har en lösning framtagits som underlättar distansmonitorering av patienter. Då olika medicinska produkter skickar data till sina egna plattformar blir det krångligt för både patienten och vårdgivaren att ta fram och hålla koll på alla vitalparametrar som tas. Därför har en gemensam plattform, LinkWatch, utvecklats. Idag kan flera vitalparametrar mätas och ses i plattformen. En säker och effektiv dataöverföring mellan en blodsockermätare och LinkWatchs webbserver har gjorts för att utöka funktionaliteten på LinkWatch. Målet med projektet var att integrera en blodsockermätare med LinkWatch. Arbetet har utförts i programmeringsspråken Kotlin och Swift. För att få kunskap om dataöverföringen söktes information om bland annat standarden FHIR, Bluetooth Low Energy samt tidigare arbeten. Resultatet av arbetet är två applikationer, en för iOS och en för Android. Mobilapplikationerna kan koppla sig till blodsockermätaren via Bluetooth, överföra dess värden till mobilen samt skicka dessa till LinkWatchs webbserver. Applikationerna har enkla användargränssnitt och enkla layouter för att underlätta patientens användning av applikationerna. Använder patienten LinkWatch, kan patienten se sitt blodsockervärde tillsammans med andra vitalparametrar och på så sätt få en bra överblick över sin hälsa. Projektet är positivt för diabetiker och andra som är intresserade av hälsa. Tack vare den användarvänliga plattformen och mångsidigheten av LinkWatch, kan användare enklare kunna hålla koll på sitt blodsockervärde och göra livsviktiga åtgärder vid uppkomst av symtom. Vidare kan även vårdgivare ta del av information som LinkWatch lämnar ut för att exempelvis se trender i patientens blodsockervärden. Fördelarna med att dela data med vårdgivaren är bland annat reducerade kostnader och ökad trygghet hos patienterna. / During this project, a solution has been developed that promotes remote patient monitoring. As different medical products send data to their own platforms, it becomes troublesome for both the patient and the care provider to take and keep track of all the vital parameters. Hence a common platform, LinkWatch, has been developed. LinkWatch is a platform where all the patient's medical signs can be seen. To increase the functionality of LinkWatch, a secure and efficient data transfer between a blood glucose meter and LinkWatch’s web server has been established. The goal of this project was to integrate a blood glucose meter with LinkWatch. The programming languages Kotlin and Swift were used to develop the mobile applications. To gain knowledge about the data transfer, research was done to obtain information about the FHIR standard, Bluetooth Low Energy and previous work in the field. The results of the project are two applications, one for iOS and one for Android. The mobile applications can connect to the blood glucose meter with Bluetooth, transfer its values to the mobile phone and send these to LinkWatch’s web server. When the records are in LinkWatch’s system, the patient is able to see their glucose levels along with other vital parameters. This project will have a major impact on patients with diabetes and others who are interested in health. Thanks to the user-friendly platform and the flexibility of LinkWatch, users of the application will be able to more easily keep track of their blood sugar value and take early action in the event of symptoms. Furthermore, health care providers can also take part in information that LinkWatch will provide, for example see trends in the patient's blood sugar values. The benefits with sharing data with the health care providers are cost reductions and increased patient satisfaction.

Qu’il s’agisse de suivre des patients à domicile, ou de prévenir l’isolement ou la vulnérabilité de personnes âgées, les systèmes de suivi et d'assistance électroniques qui émergent offrent des opportunités sans précédents. L’enjeu est considérable, et on assiste à un développement technologique important qui permet désormais aux particuliers ou aux établissements hospitaliers ou médico-sociaux d’assurer le diagnostic, la prévention, le contrôle, voire le traitement de patients à distance (mesures de paramètres physiologiques, administration de médicaments, détection de chutes, etc.). Ce suivi à distance est en particulier devenu possible et performant avec l’avènement des objets connectés. On peut ainsi envisager d’exploiter un réseau de capteurs embarqué sur un patient pour mesurer à distance et en temps réel la température, le rythme cardiaque ou la tension artérielle d’un patient. Les données sont transmises (et/ou stockées) au praticien pour réaliser le diagnostic et définir les traitements. Pour autant la conception optimale (choix des technologies de transmission, de stockage, etc.) ainsi que la problématique de la gestion d’énergie constituent des verrous à leur adoption. Le travail proposé dans cette thèse consiste donc à développer un outil d’aide à la conception de réseaux de capteurs médicaux communicants embarqués sur la personne. Il s’agit en particulier de fournir une information sur la faisabilité au plus tôt dans le cycle de conception, et garantir l’obtention d’un circuit « correcte par construction ». L’accent est porté sur la maîtrise (voire la réduction) de la consommation d’énergie Dans ce sens, une simulation fiable et précise permet de contrôler, dès le début du flot de conception, la consommation en énergie du réseau. Elle assure par la suite une meilleure gestion de l’énergie disponible et éventuellement une autonomie plus importante. L’outil, centré sur l’optimisation de la consommation d’énergie, est implémenté dans un environnement Matlab. Basé sur une modélisation de la consommation en énergie d’un nœud de capteur, il se veut générique aussi bien que précis. Il assure une implémentation simple de nouveaux composants à partir de fiches techniques. Ces composants sont construits sous la forme de blocs réutilisables permettant ainsi à l’utilisateur de créer sa propre librairie. En plus de l’estimation de la consommation, cet outil met en œuvre des algorithmes d’optimisation pour guider l’utilisateur vers une conception qui respecte les contraintes énergétiques et médicales de l’application (choix de composants, choix de source d’énergie, configuration du réseau, etc.). Une application est proposée d’abord au niveau d’un nœud de capteur de température corporelle communiquant par Bluetooth Low Energy. Ce même nœud est ensuite placé au sein d’un réseau de capteurs pour la surveillance de cinq paramètres physiologiques différents. Une validation expérimentale des résultats de simulation est également réalisée. / Whether it is to monitor patients at home, or to prevent the isolation and vulnerability of the elderly, the emerging electronic monitoring and assistance systems offer new opportunities. The technological development we have witnessed allows individuals, hospitals, or medical aid organizations to provide the diagnosis, prevention, control or even treatment of patients outside of conventional clinical settings (measurements of physiological parameters, drug administration, fall detection, etc.).Recent developments in connected objects made efficient remote patient monitoring possible. In other words, we are able to use a network of wearable or implantable sensors to remotely obtain real time measurements of a patient’s vital signs (temperature, heart rate, blood pressure, etc.). Data is transmitted (and / or stored) to medical personnel who are able to perform diagnosis and define treatments accordingly. An optimal design (transmission protocols, data storage, etc.) and energy management are the bottlenecks involved in the implementation of such systems. This work proposes to develop a tool to help in the design of medical sensor networks. It aims to provide information regarding feasibility during the early stages of the design thus ensuring that a "well-constructed" circuit is obtained. The emphasis is on the control (or even reduction) of energy consumption. In this regard, an efficient energy consumption simulation at the beginning of the design flow would enable the user to decide on system parameters. This will ensure an optimal management of the available energy and eventually a longer network lifetime. The proposed tool is centered on the optimization of the energy consumption using Matlab environment. It is built over a model of the energy consumption of wireless sensor nodes. It is intended to be generic and accurate. In fact, it enables fast creation of new component description based on the datasheets. These components are reusable thus producing a growing database. In addition to energy consumption estimation, the tool uses optimization routines to guide the user through an energy aware design (picking energy sources, components, network configuration, etc.) that complies with medical requirements. An application to a single Bluetooth Low Energy body temperature sensor is first proposed. The same sensor is then included in a physiological sensor network. A physical implementation is used in order to compare the results obtained through simulation with practical measurements.

The overall aim of this thesis was to develop, deploy and evaluate new IT-based methods for supporting treatment and assessment of treatment of advanced Parkinson’s disease. In this condition a number of different motor and non-motor symptoms occur in episodes of varying frequency, duration and severity. In order to determine outcome of treatment changes, repeated assessments are necessary. Hospitalization for observation is expensive and may not be representative for the situation at home. Paper home diaries have questionable reliability and storage and retrieval of results are problematic. Approaches for monitoring using wearable sensors are unable to address important non-motor symptoms. A test battery system consisting of both self-assessments of symptoms and motor function tests was constructed for a touch screen mobile phone. Tests are performed on several occasions per day during test periods of one week. Data is transmitted over the mobile net to a central server where summaries in different symptom dimensions and an overall test score per patient and test period are calculated. There is a web application that graphically presents the results to treating clinical staff. As part of this work, a novel method for assessment of spiral drawing impairment useful during event-driven sampling was developed. To date, the system has been used by over 100 patients in 10 clinics in Sweden and Italy. Evidence is growing that the test battery is useful, reliable and valid for assessment of symptoms during advanced Parkinson’s disease. Infusion of a levodopa/carbidopa gel into the small intestine has been shown to reduce variation in plasma drug levels and improve clinical response in this patient category. A pharmacokinetic-pharmacodynamic model of this intestinal gel infusion was constructed. Possibly this model can assist the process of individualization of dosage for this treatment through in numero simulations. Results from an exploratory data analysis indicate that severity measures during oral levodopa treatment may be factors to consider when deciding candidates for infusion treatment.

This thesis explores a methodology to quantify trust in wearable medical devices (WMD) by addressing two main challenges: identifying key factors influencing trust and developing a formal framework for precise trust quantification under uncertainty. The work empirically validates trust factors and uses a Bayesian network to quantify trust. The thesis further employs a data-driven approach to estimate Bayesian parameters, facilitating query-based inference and validating the trust model with real and synthetic datasets, culminating in a customizable parameterized trust evaluation prototype for WMD. / Advances in sensor and digital communication technologies have revolutionized the capabilities of wearable medical device (WMD) to monitor patients’ health remotely, raising growing concerns about trust in these devices. There is a need to quantify trust in WMD for their continued acceptance and adoption by different users. Quantifying trust in WMD poses two significant challenges due to their subjective and stochastic nature. The first challenge is identifying the factors that influence trust in WMD, and the second is developing a formal framework for precise quantification of trust while taking into account the uncertainty and variability of trust factors. This thesis proposes a methodology to quantify trust in WMD, addressing these challenges. In this thesis, first, we devise a method to empirically validate dominant factors that influence the trustworthiness of WMD from the perspective of device users. We identified the users’ awareness of trust factors reported in the literature and additional user concerns influencing their trust. These factors are stepping stones for defining the specifications and quantification of trust in WMD. Second, we develop a probabilistic graph using Bayesian network to quantify trust in WMD. Using the Bayesian network, the stochastic nature of trust is viewed in terms of probabilities as subjective degrees of belief by a set of random variables in the domain. We define each random variable in the network by the trust factors that are identified from the literature and validated by our empirical study. We construct the trust structure as an acyclic-directed graph to represent the relationship between the variables compactly and transparently. We set the inter-node relationships, using the goal refinement technique, by refining a high-level goal of trustworthiness to lower-level goals that can be objectively implemented as measurable factors. Third, to learn and estimate the parameters of the Bayesian network, we need access to the probabilities of all nodes, as assuming a uniform Gaussian distribution or using values based on expert opinions may not fully represent the complexities of the factors influencing trust. We propose a data-driven approach to generate priors and estimate Bayesian parameters, in which we use data collected from WMD for all the measurable factors (nodes) to generate priors. We use non-functional requirement engineering techniques to quantify the impacts between the node relationships in the Bayesian network. We design propagation rules to aggregate the quantified relationships within the nodes of the network. This approach facilitates the computation of conditional probability distributions and enables query-based inference on any node, including the high-level trust node, based on the given evidence. The results of this thesis are evaluated through several experimental validations. The factors influencing trust in WMD are empirically validated by an extensive survey of 187 potential users. The learnability, and generalizability of the proposed trust network are validated with a real dataset collected from three users of WMD in two conditions, performing predefined activities and performing regular daily activities. To extend the variability of conditions, we generated an extensive and representative synthetic dataset and validated the trust network accordingly. Finally, to test the practicality of our approach, we implemented a user-configurable, parameterized prototype that allows users of WMD to construct a customizable trust network and effectively compare the trustworthiness of different devices. The prototype enables the healthcare industry to adapt and adopt this method to evaluate the trustworthiness of WMD for their own specific use cases. / Thesis / Doctor of Philosophy (PhD) / In this thesis, two challenges in quantifying trust in wearable medical devices, are addressed. The first challenge is the identification of factors influencing trust which are inherently subjective and vary widely among users. To address this challenge, we conducted an extensive survey to identify and validate the trust factors. These factors are stepping stones for defining the specifications and quantifying trust in wearable medical devices. The second challenge is to develop a precise method for quantification of trust while taking into account the uncertainty and variability of trust factors. We constructed a Bayesian network, that captures the complexities of trust as probabilities of the trust factors (identified from the survey) and developed a data-driven approach to estimate the parameters of the Bayesian network to compute the measure of trust. The findings of this thesis are empirically and experimentally validated across multiple use cases, incorporating real and synthetic data, various testing conditions, and diverse Bayesian network configurations. Additionally, we developed a customizable, parameterized prototype that empowers users and healthcare providers to effectively assess and compare the trustworthiness of different wearable medical devices.