BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canad

Status and recommendations of technological and data-driven innovations in cancer care:Focus group study

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects. Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations

The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study

Background: This paper examines the development of the Connected Health research landscape with a view on providing a historical perspective on existing Connected Health research. Connected Health has become a rapidly growing research field as our healthcare system is facing pressured to become more proactive and patient centred. Objective: We aimed to identify the extent and coverage of the current body of knowledge in Connected Health. With this, we want to identify which topics have drawn the attention of Connected health researchers, and if there are gaps or interdisciplinary opportunities for further research. Methods: We used a systematic mapping study that combines scientific contributions from research on medicine, business, computer science and engineering. We analyse the papers with seven classification criteria, publication source, publication year, research types, empirical types, contribution types research topic and the condition studied in the paper. Results: Altogether, our search resulted in 208 papers which were analysed by a multidisciplinary group of researchers. Our results indicate a slow start for Connected Health research but a more recent steady upswing since 2013. The majority of papers proposed healthcare solutions (37%) or evaluated Connected Health approaches (23%). Case studies (28%) and experiments (26%) were the most popular forms of scientific validation employed. Diabetes, cancer, multiple sclerosis, and heart conditions are among the most prevalent conditions studied. Conclusions: We conclude that Connected Health research seems to be an established field of research, which has been growing strongly during the last five years. There seems to be more focus on technology driven research with a strong contribution from medicine, but business aspects of Connected health are not as much studied

Computerized methodologies for virus typing

The continuous mutations that take place in the genetic material of a certain virus constantly trigger the creation of new types of the virus. Because of these mutations, the new virus types obtain features that differentiate the severity of their infection (e.g., the human papillomavirus which appears in both oncogenic and non-oncogenic types). In these cases the identification of the exact type(s) of a virus that have infected a patient, namely the task of typing the virus, has acquired great importance with regards to the efficient treatment of its infection. Due to the importance of virus typing, several molecular biology methods that aim at efficiently discriminating among the various types of a virus based on their genotypic differences have been developed during the last decades. Among them, the molecular method called PCR-RFLP gel electrophoresis is currently employed widely over the world. However, the procedure of virus typing via the discussed method remains, in contrast to the other methods, heavily manual. This shortcoming of the PCR-RFLP method with respect to automation makes the conventional typing protocol of the method error-prone – especially in complex cases of multiple infections – and laborious for the molecular biologists who execute it. The aforementioned issues of the PCR-RFLP method can be tackled with the help of digital signal processing techniques along with the estimation and decision theory. For this purpose, the present thesis develops a series of novel computational methodologies that undertake the entire task of virus typing via the PCR-RFLP method in a consistent and effective manner. In order to ensure the correctness of the typing decisions, the proposed methodologies employ additional information that has been ignored by the conventional typing protocol, namely the concentration of the viral genetic material. The introduction of a factual observation model of the PCR-RFLP examination product sets the ground for the proposed methodologies to employ the additional information. On top of this model, the present thesis develops an integrated computational typing methodology (the static methodology), which analyzes the examination product and reaches typing decisions. The performance of the static methodology is evaluated thoroughly through theoretic investigations and experiments on real as well as simulated typing data. Moreover, the static methodology is implemented in its entirety as a graphical software application. Apart from the static methodology, the present thesis introduces also the dynamic typing methodology, which is capable of making typing decisions while the PCR-RFLP examination is still in progress. Dynamic typing is achieved through the establishment of communication between the previously isolated phases of the conventional typing protocol and it tackles several open issues associated with the static approach. An integrated hardware-software system is designed in order to implement the dynamic methodology. The feasibility of the dynamic typing approach is validated through an experiment that emulates the operation of the aforementioned system. Both the proposed typing methodologies are complemented by a novel algorithm (the fast algorithm) which undertakes the most demanding part of the typing procedure in terms of computational complexity, namely the task of testing the possible combinations of virus types. The fast algorithm parcels the aforementioned task into a set of subproblems; this way, the typing procedure is significantly accelerated without essentially harassing the typing accuracy. The fast algorithm is evaluated extensively on a well-sized set of real typing data. Before exploiting the concentration information as suggested by the proposed typing methodologies, the background intensity of the images that result from the PCR-RFLP examination needs to be efficiently removed. For this purpose, a novel method that models the background intensity of an examination image as a polynomial of the space coordinates and then successfully removes it from the image is introduced in the present thesis.Οι αέναες μεταλλάξεις που λαμβάνουν χώρα στο γενετικό υλικό ενός γνωστού ιού οδηγούν στη συνεχή γέννηση νέων τύπων του εν λόγω ιού. Εξαιτίας των μεταλλάξεων αυτών οι νεότευκτοι τύποι του ιού αποκτούν χαρακτηριστικά τα οποία μεταβάλλουν την επικινδυνότητα της μόλυνσής τους (π.χ. ο ιός των ανθρωπίνων θηλωμάτων ο οποίος διαθέτει καρκινογόνους και μη καρκινογόνους τύπους). Σε τέτοιες περιπτώσεις η αναγνώριση του συγκεκριμένου τύπου ή τύπων ενός ιού οι οποίοι έχουν προσβάλει έναν ασθενή, δηλαδή η τυποποίηση του ιού, αποκτά τεράστια σημασία αναφορικά με την αποτελεσματική θεραπεία του. Ως συνέπεια της κρισιμότητας της τυποποίησης, τις τελευταίες δεκαετίες έχει αναπτυχθεί μία σειρά μεθόδων μοριακής βιολογίας οι οποίες αποσκοπούν να διακρίνουν αποτελεσματικά τους διάφορους τύπους ενός ιού βάσει των γονοτυπικών διαφορών τους. Μεταξύ αυτών, η μοριακή μέθοδος PCR-RFLP ηλεκτροφόρησης γέλης χρησιμοποιείται σήμερα ευρέως ανά τον κόσμο. Ωστόσο, η τυποποίηση με την εν λόγω μέθοδο παραμένει, σε αντιδιαστολή με τις υπόλοιπες μεθόδους, έντονα χειροκίνητη. Αυτή η υστέρηση της μεθόδου PCR-RFLP στον τομέα της αυτοματοποίησης καθιστά το συμβατικό πρωτόκολλο τυποποίησης της μεθόδου επιρρεπές σε λάθη – ιδιαίτερα σε πολύπλοκες περιπτώσεις πολλαπλών μολύνσεων – και κοπιώδες για τους μοριακούς βιολόγους που το διεκπεραιώνουν. Τα προαναφερθέντα προβλήματα της μοριακής μεθόδου PCR-RFLP μπορούν να αντιμετωπιστούν με τη βοήθεια της ψηφιακής επεξεργασίας σήματος και της θεωρίας εκτίμησης και απόφασης. Έτσι, στο πλαίσιο της παρούσας διατριβής αναπτύχθηκαν πρωτότυπες υπολογιστικές μεθοδολογίες οι οποίες αναλαμβάνουν με συστηματικό και αποτελεσματικό τρόπο την ολοκληρωμένη τυποποίηση ιών μέσω της μεθόδου PCR-RFLP. Προκειμένου να διασφαλιστεί η ορθότητα της τυποποίησης, οι προτεινόμενες μεθοδολογίες χρησιμοποιούν την πληροφορία συγκέντρωσης του ιικού γενετικού υλικού, η οποία αγνοείται από το συμβατικό πρωτόκολλο τυποποίησης. Το κατάλληλο υπόβαθρο για τη χρησιμοποίηση της εν λόγω πληροφορίας παρέχεται με την εισαγωγή ενός τεκμηριωμένου μοντέλου παρατήρησης του προϊόντος της εργαστηριακής εξέτασης PCR-RFLP. Πάνω στο μοντέλο αυτό θεμελιώνεται μία ολοκληρωμένη υπολογιστική μεθοδολογία τυποποίησης (η στατική μεθοδολογία), η οποία αναλύει το προϊόν της εργαστηριακής εξέτασης και καταλήγει σε αποφάσεις τυποποίησης. Η επίδοση της στατικής μεθοδολογίας αξιολογείται εξονυχιστικά μέσω θεωρητικών μελετών και πειραμάτων σε πραγματικά καθώς και σε προσομοιωμένα δεδομένα τυποποίησης. Επιπλέον, η στατική μεθοδολογία τυποποίησης υλοποιείται στο σύνολό της ως εφαρμογή λογισμικού γραφικού περιβάλλοντος. Πέραν της στατικής μεθοδολογίας, η παρούσα διατριβή εισάγει επίσης την πρότυπη δυναμική μεθοδολογία τυποποίησης, η οποία είναι ικανή να λαμβάνει αποφάσεις τυποποίησης ενόσω η εργαστηριακή εξέταση βρίσκεται σε εξέλιξη. Η δυναμική τυποποίηση επιτυγχάνεται μέσω της εγκαθίδρυσης επικοινωνίας μεταξύ των απομονωμένων φάσεων εκτέλεσης του συμβατικού πρωτοκόλλου τυποποίησης και επιλύει ορισμένα άλυτα προβλήματα της στατικής προσέγγισης. Ένα ολοκληρωμένο σύστημα υλικού-λογισμικού σχεδιάζεται προκειμένου να υλοποιήσει τη δυναμική μεθοδολογία. Η σκοπιμότητα της δυναμικής προσέγγισης τυποποίησης επαληθεύεται μέσω ενός πειράματος εξομοίωσης της λειτουργίας του προαναφερθέντος συστήματος. Οι δύο προτεινόμενες μεθοδολογίες τυποποίησης συμπληρώνονται από ένα πρωτότυπο αλγόριθμο (ο ταχύς αλγόριθμος) ο οποίος αναλαμβάνει να εκτελέσει το πλέον απαιτητικό από υπολογιστικής άποψης κομμάτι της διαδικασίας τυποποίησης, δηλαδή τη δοκιμή των πιθανών συνδυασμών από τύπους του ιού. Ο ταχύς αλγόριθμος κατακερματίζει το εν λόγω πρόβλημα σε ένα σύνολο υποπροβλημάτων επιταχύνοντας σημαντικά τη διαδικασία χωρίς ωστόσο να θυσιάζει την ακρίβεια των αποφάσεων τυποποίησης. Ο ταχύς αλγόριθμος αξιολογείται εκτενώς σε ένα ευμεγέθες σύνολο πραγματικών δεδομένων τυποποίησης. Η αποτελεσματική εξάλειψη της φωτεινότητας υποβάθρου από τις εικόνες-προϊόντα της εργαστηριακής εξέτασης PCR-RFLP είναι αναγκαία για τη χρησιμοποίηση της πληροφορίας συγκέντρωσης που υιοθετείται από τις προτεινόμενες μεθοδολογίες τυποποίησης. Για το σκοπό αυτό η παρούσα διατριβή εισάγει μία πρωτότυπη μέθοδο αντιμετώπισης του προβλήματος, η οποία μοντελοποιεί τη φωτεινότητα υποβάθρου ως πολυώνυμο των χωρικών συντεταγμένων της εικόνας και εν τέλει την εξαλείφει επιτυχώς

Co-Designing Smartphone Notifications According to the Clinical Routine of Cancer Patients

Although smartphone-based notifications offer a promising tool to support patient engagement and data collection via mobile health apps, attention must be given to the burden caused by frequent notifications and notification timing. This study presents a personalized mobile notification scheme, designed and developed to optimize reachability, and thus data collection from patients. Engineers, psychologists, oncologists, and patients were involved in various stages of a co-design approach and the presented implementation is currently used in the context of a clinical study

Predicting Real-Life Eating Behaviours Using Single School Lunches in Adolescents

Large portion sizes and a high eating rate are associated with high energy intake and obesity. Most individuals maintain their food intake weight (g) and eating rate (g/min) rank in relation to their peers, despite food and environmental manipulations. Single meal measures may enable identification of “large portion eaters„ and “fast eaters,„ finding individuals at risk of developing obesity. The aim of this study was to predict real-life food intake weight and eating rate based on one school lunch. Twenty-four high-school students with a mean (±SD) age of 16.8 yr (±0.7) and body mass index of 21.9 (±4.1) were recruited, using no exclusion criteria. Food intake weight and eating rate was first self-rated (“Less,„ “Average„ or “More than peers„), then objectively recorded during one school lunch (absolute weight of consumed food in grams). Afterwards, subjects recorded as many main meals (breakfasts, lunches and dinners) as possible in real-life for a period of at least two weeks, using a Bluetooth connected weight scale and a smartphone application. On average participants recorded 18.9 (7.3) meals during the study. Real-life food intake weight was 327.4 g (±110.6), which was significantly lower (p = 0.027) than the single school lunch, at 367.4 g (±167.2). When the intra-class correlation of food weight intake between the objectively recorded real-life and school lunch meals was compared, the correlation was excellent (R = 0.91). Real-life eating rate was 33.5 g/min (±14.8), which was significantly higher (p = 0.010) than the single school lunch, at 27.7 g/min (±13.3). The intra-class correlation of the recorded eating rate between real-life and school lunch meals was very large (R = 0.74). The participants’ recorded food intake weights and eating rates were divided into terciles and compared between school lunches and real-life, with moderate or higher agreement (κ = 0.75 and κ = 0.54, respectively). In contrast, almost no agreement was observed between self-rated and real-life recorded rankings of food intake weight and eating rate (κ = 0.09 and κ = 0.08, respectively). The current study provides evidence that both food intake weight and eating rates per meal vary considerably in real-life per individual. However, based on these behaviours, most students can be correctly classified in regard to their peers based on single school lunches. In contrast, self-reported food intake weight and eating rate are poor predictors of real-life measures. Finally, based on the recorded individual variability of real-life food intake weight and eating rate, it is not advised to rank individuals based on single recordings collected in real-life settings

Developing a smartphone application to support smoking behavior change through social comparison

Abstract The growing field of mHealth has often dealt with the modification of harmful behaviors, such as smoking, that are associated with medical conditions. Smoking behavior has been targeted by numerous mHealth smoking cessation interventions with the help of a wide range of behavior change support (BCS) techniques. However, the exploitation of the established BCS technique of social comparison by mHealth research on smoking cessation has been limited. Based on up-to-date BCS theory and following a user-centered design, we have developed a novel smartphone application, namely QuitIT!, for smoking behavior modification with the help of social comparison. This paper presents the development of QuitIT! as well as its preliminary evaluation through a small pilot study. The latter has yield encouraging initial results concerning the feasibility and the effectiveness of QuitIT! as an mHealth tool for smoking BCS