Summary: Although the digital revolution has transformed many niches of human activity, healthcare sector and pharmaceutical drug development has been relatively slow in embracing emerging technologies to optimize health efficacy, especially in Nordic Countries. The topic is of more importance now owing to the present scenario of the corona virus (COVID-19)outbreak, which has caused unparalleled disruption in the conduct of clinical trials and presented challenges as well as opportunities for clinical trialists and data analysts. In this master thesis, the potential opportunity with virtual or digital clinical trials as viable options to enhance drug development efficiency is highlighted that offers diverse patients easier and attractive ways to participate in clinical trials. Special reference is made to wearable devices in clinical trial execution and generating real world data; its acquisition and processing in a virtual trial setting. Issues of patient safety, measurement reliability and validity, and data privacy & integrity are  reviewed, and considerations are put forward for mitigation of underlying regulatory andoperational barriers. The aim of this thesis is to assess the recent wearable technologies that generate Real World Data and to understand the potential of this data to transform Nordic healthcare industry. A systematic review of clinical trials involving wearable patient monitoring technique in North America, Nordic Countries and other European countries was conducted. Out of various innovative wearable technologies, Smartwatches are found to be the most common and it is also observed that these wearable technologies have been able to help in early detection and diagnosis of diseases and modify disease progression by real time monitoring of data and develop precision medicine. thus, it is concluded that Wearable Remote Patient Monitoring is a novel technique that has few barriers;but promises a big transformation in Nordic Countries as well as in entire healthcare industry.

Acute congestive heart failure is a leading cause of morbidity and mortality. Patients often undergo repeated hospitalizations with an annual cost in excess of $32B dollars. Early detection of impending acute congestion allows for pharmaceutical interdiction that prevents hospitalization, improves outcomes, and reduces healthcare spending. A subcutaneous implantable monitoring device that detects impending acute congestive heart failure by using heart sounds may provide a valuable tool that can be used to titrate heart failure medications to prevent acute heart failure requiring hospitalization. The device may be used to measure changes in the intervals between the R-wave and S1 and S2 heart sounds, and to detect evolving S3 and S4 heart sounds consistent with volume overload. The amplitudes of S1 and S3 heart sounds change as heart failure patients' symptoms worsen. Designing a non-invasive, external device, capable of recording heart sounds and EKGs in patients undergoing pharmaceutical regression of acute congestive heart failure in a hospital setting may give important insight into the nature of heart sound and EKG changes that occur in patients during progression of acute heart failure while they lead their day-to-day lives. This thesis details the design of a portable, non-invasive device, worn externally on the left-pectoral muscle, capable of continuously recording human EKG signals and heart sounds (through custom MEMS accelerometer technology) over a period of two days. Hardware testing for the scope of this thesis exclusively involved healthy volunteers.

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.

Η αντιμετώπιση των χρόνιων ασθενών αποτελεί σήμερα για τα συστήματα υγείας και κοινωνικής φροντίδας ένα από τα πιο δύσκολα προβλήματα διεθνώς, τόσο από ιατρικής όσο και από κοινωνικό-οικονομικής πλευράς. Για το λόγο αυτό, τα αντίστοιχα συστήματα στις ΗΠΑ και ΕΕ έχουν αποδυθεί τα τελευταία χρόνια σε ένα εντατικό αγώνα για την αναδόμηση (reengineering) της συνολικής αντιμετώπισής τους, με στόχο τη βελτιστοποίηση των παρεχομένων υπηρεσιών και τον εξορθολογισμό του κόστους. Στρατηγικό εργαλείο για τη παροχή των νέων υπηρεσιών αποτελούν οι νέες Τεχνολογίες Πληροφορικής και Επικοινωνιών (ΤΠΕ). Στο πλαίσιο της παρούσας ερευνητικής εργασίας εξετάζεται η αξιοποίηση σύγχρονων και ευρέως διαθέσιμων τεχνολογιών επικοινωνιών (ευρυζωνικότητα, δίκτυα κινητής τηλεφωνίας κ.ά.), σε συνδυασμό με τη χρήση καινοτόμων προϊόντων, όπως αυτά των «ηλεκτρονικών» υφασμάτων (e-Textiles) για την παροχή καινοτόμων υπηρεσιών παρακολούθησης από απόσταση. Η παρούσα διπλωματική είχε σαν στόχο την εφαρμογή και αξιολόγηση ενός προγράμματος πρώιμης εξόδου από νοσοκομείο και κατ’ οίκον νοσηλείας σε χρόνιους αναπνευστικούς ασθενείς στο Νοσοκομείο «Η Σωτηρία» και διεξήχθη στο πλαίσιο του Ευρωπαϊκού Ερευνητικού Προγράμματος ‘HealthWear’. Η μεθοδολογική προσέγγιση έγινε αφενός με την ευρεία βιβλιογραφική ανασκόπηση αναλόγων παρεμβάσεων και αφετέρου με την αξιολόγηση ενός προγράμματος κλινικής εφαρμογής του σε πραγματικές συνθήκες. Στο πρόγραμμα συμμετείχαν 48 ασθενείς με Χρόνια Αποφρακτική Πνευμονοπάθεια, διαχωρισμένοι τυχαία σε ομάδα ελέγχου (ενδονοσοκομειακή φροντίδα), και ομάδα παρέμβασης (πρώιμη έξοδος και παρακολούθηση με τη χρήση φορετών και φορητών, μη επεμβατικών συσκευών). Παρουσιάζονται αναλυτικά ο σχεδιασμός του συστήματος και της υπηρεσίας, η μεθοδολογία και ο τρόπος παρακολούθησης των ασθενών της ομάδας παρέμβασης καθώς και τα αποτελέσματα της αξιολόγησης. Η εφαρμογή του προγράμματος στην καθ’ ημέρα κλινική πράξη κατέδειξε τη χρήση φορητών και φορετών συστημάτων ως αξιόπιστη εναλλακτική μέθοδο για την πρώιμη έξοδο και συνέχεια της φροντίδας των ασθενών με Χ.Α.Π. Παράλληλα διερευνήθηκαν μελλοντικές προοπτικές εφαρμογών για την εξ αποστάσεως παρακολούθηση κατά τη διάρκεια του ύπνου ή ταυτόχρονα με εκτέλεση άσκησης σε εξωτερικούς χώρους. / Treatment of chronic patients is currently one of the most difficult international issues that health and social care systems need to address, both medically and by socio-economic terms. For this reason, the corresponding systems in the U.S. and EU have engaged the last years a lot of effort, in an intense struggle, for the restructuring (reengineering) of the total care management process, in order to optimize service and streamline costs. Strategic tool for providing new services is Information and Communication Technologies (ICTs). In the current study, the usage of modern and widely available communication technologies (broadband internet, mobile telephony, etc.), combined with innovative products, such as the 'electronic' fabrics (e-Textiles), in order to provide advanced remote monitoring services, were thoroughly examined. This thesis aimed to implement and evaluate a program of early hospital discharge, followed by a home hospitalization program, in chronic respiratory patients of ‘Sotiria’ Hospital in the region of Attica - Greece, conducted within the framework of a European Research Project named 'HealthWear'. The methodological approach followed was first to establish an in-depth background for this type of interventions, through a broad, thorough systematic literature review, and secondly to evaluate a clinical trial, in the real everyday life of a public hospital. The program involved 48 patients with Chronic Obstructive Pulmonary Disease (COPD), separated randomly into control group (traditional care) and intervention group (early discharge and follow up by using wearable, portable, non-invasive devices). A comprehensive presentation of the ICT system used, the clinical protocol of the service and the methodology for the remote monitoring the intervention group patients, are followed by the assessment results of the trial. Our experience from this trial allows the prediction that wearable and wireless systems can be proved as new era’s tools in patients’ remote follow up and personalized care, especially valuable in early discharge, as well as in home based monitoring during sleep and outdoor activities.