Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress

[EN] Cancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, much data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities. One approach for providing health professionals with access to clinical data is to create the infrastructure and interface for a clinical tool to make data accessible in a relevant manner. In this sense, results should be understandable and valuable for cancer experts to evaluate and optimize cancer processes. This work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical relevance for healthcare professionals for monitoring cancer patients¿ care processes in the context of the LifeChamps project. Following a co-creation and interactive approach thanks to the Interactive Process Mining paradigm, the tool presents patients¿ progress over time for different clinical models and a graphical and navigable Process Indicator in the context of prostate cancer patients.This work was partially funded by the European Union¿s Horizon 2020 research and innovation program under Grant Agreement No 875329.Valero Ramon, Z.; Fernández Llatas, C.; Collantes, G.; Valdivieso, F.; Billis, A.; Bamidis, P.; Traver Salcedo, V. (2023). Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress. Frontiers in Oncology. 12:1-19. https://doi.org/10.3389/fonc.2022.10434111191

A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform

Purpose: This study is to evaluate healthcare needs, preferences, and expectations in supportive cancer care as perceived by cancer survivors, family caregivers, and healthcare professionals. Methods: Key stakeholders consisted of cancer survivors diagnosed with breast cancer, prostate cancer, or melanoma; adult family caregivers; and healthcare professionals involved in oncology. Recruitment was via several routes, and data were collected via either online surveys or telephone interviews in Greece, Spain, Sweden, and the UK. Framework analysis was applied to the dataset. Results: One hundred and fifty-five stakeholders participated: 70 cancer survivors, 23 family caregivers, and 62 healthcare professionals (13 clinical roles). Cancer survivors and family caregivers’ needs included information and support on practical/daily living, as frustration was apparent with the lack of follow-up services. Healthcare professionals agreed on a multidisciplinary health service with a “focus on the patient” and availability closer to home. Most healthcare professionals acknowledged that patient-reported outcomes may provide “better individualised care”. Cancer survivors and family caregivers generally felt that the digital platform would be useful for timely personalised support and aided communication. Healthcare professionals were supportive of the “proactive” functionality of the platform and the expected advantages. Anticipated challenges were integration obstacles such as workload/infrastructure and training/support in using the new technology. Conclusions: Obtaining key stakeholders’ insights provided a foundation for action to further co-create the LifeChamps digital platform to meet needs and priorities and deliver enhanced supportive care to “older” cancer survivors. Implications for cancer survivors: Co-creation provided insight into gaps where digital support may enhance health and well-being

Gains in cognition through combined cognitive and physical training: the role of training dosage and severity of neurocognitive disorder

Physical as well as cognitive training interventions improve specific cognitive functions but effects barely generalize on global cognition. Combined physical and cognitive training may overcome this shortcoming as physical training may facilitate the neuroplastic potential which, in turn, may be guided by cognitive training. This study aimed at investigating the benefits of combined training on global cognition while assessing the effect of training dosage and exploring the role of several potential effect modifiers. In this multi-center study, 322 older adults with or without neurocognitive disorders (NCDs) were allocated to a computerized, game-based, combined physical and cognitive training group (n = 237) or a passive control group (n = 85). Training group participants were allocated to different training dosages ranging from 24 to 110 potential sessions. In a pre-post-test design, global cognition was assessed by averaging standardized performance in working memory, episodic memory and executive function tests. The intervention group increased in global cognition compared to the control group, p = 0.002, Cohen's d = 0.31. Exploratory analysis revealed a trend for less benefits in participants with more severe NCD, p = 0.08 (cognitively healthy: d = 0.54; mild cognitive impairment: d = 0.19; dementia: d = 0.04). In participants without dementia, we found a dose-response effect of the potential number and of the completed number of training sessions on global cognition, p = 0.008 and p = 0.04, respectively. The results indicate that combined physical and cognitive training improves global cognition in a dose-responsive manner but these benefits may be less pronounced in older adults with more severe NCD. The long-lasting impact of combined training on the incidence and trajectory of NCDs in relation to its severity should be assessed in future long-term trials

Digital biomarkers as ecologically valid measures for the remote and longitudinal assessment of older adults health

Ageing consists one of the biggest societal challenges worldwide. Older adults’ desire of staying healthy, staying at their own home consists an important motive for the development of assistive technologies that will realize it through remote monitoring technologies. Proliferation of low-cost, off-the-shelf IoT devices has led to the implementation of the so called smart homes projects, which are solutions that are mixing the older adults’ physical spaces with monitoring and computing capabilities, allowing the collection of high-frequency daily life data. Methods: This stydy consist of two parts. An experimental one that takes place in a lab space and a real life application of sensor monitoring technology. In particular, an ecologically valid space was created within the Thessaloniki Active & Healthy Ageing Living, where fifteen (15) older adults participated in the pilot testing of the technology. Participants visited the ecologically valid space for almost two weeks, covering eight (8) 1 hour-sessions in total. There, they followed a protocol of typical daily life activities, where several unobtrusive scenario of monitoring were interweaved. No restrictions were imposed with respect to the execution of tasks, thus making sure that the data collection follows an ecologically valid paradigm. Subsequently, senor data collected from 10 participants were statistically correlated to clinical assessment tools. Then, a series of focus groups openly discussing about technology and obtrusiveness were carried out with thirteen participants. Moving to the second part of the study, some adjustments with respect to the technology had to be considered according to the Living Lab study findings. Favorable technology was installed to 5 older womens’ homes for more than a year time resulting to hundreds of thousands data points to be collected. Different modeling techniques were employed, such as statistical modeling, and machine learning, in order to explore clinical added value of digital biomarkers. As an application scenario, the study of modelling emotional disturbances and in particular the one of geriatric depression was carried out. In addition, a longitudinal study about sensor monitoring unobtrusiveness was conducted to examine end-user acceptance. A follow-up interview was carried out with each one of the participants. Finally, we explore the possibility of mapping the results of the data-driven modelling approaches with expert-driven models and knowledge representation schemata, such as the Fuzzy Cognitive Maps (FCMs) in a way that is transparent to the clinicians. Results: With respect to the first part of the study quite a few statistical significant correlations between sensor data and clinical tools were found, e.g. mobility and affective characteristics linked to emotional health and quality of life. With respect to the results of the focus groups these were transcribed and analysed with qualitative methods in order to extract the most significant themes and to categorize them to one of the obtrusiveness framework axes. For the second part o fthe study, a generalized linear mixed prediction model of PHQ-9 was developed utilizing information about TV usage patterns. Random Forests achieved mean accuracy score >80% when given to classify between healthy and depressive cases, while another RF classifier achieved an AUC>90% when having to deal with all sorts of depressive synptoms severity (from mild to severe). The initial FCM model also achieved a high classification rate up to 96% given some synthetic cases provided by experts. Finally, older adults’ longitudinal attitudes revealed a negative stance towards the use of the mirror camera and the smart watch, as well as the Kinect device. The answers to the questionnaire of the lady that left the study at month 12, were compared against the mean value of the rest four particpants to check for any particular reasons of her decision. Significant differences from the mean value of the rest four were found for the Kinect device, the smart watch and the mirror camera. Conclusions: Clinical value of digital biomarkers has been been revealed in many publications, yet their application in longitudinal studies is absent. A great challenge nowadays remains the robust operation of such technologies under real life circumstances, without any restrictions (in the wild) as well as their acceptance from older people, and their doctors, if at any time in the future we wish to integrate such data in the clinical practice.Η γήρανση του πληθυσμού αποτελεί μία από τις μεγαλύτερες κοινωνικές προκλήσεις σε ολόκληρο τον πλανήτη. Η επιθυμία των ηλικιωμένων για καλή γήρανση, παραμένοντας στο σπίτι τους αποτελεί σημαντικό παράγοντα για την ανάπτυξη υποστηρικτικών τεχνολογιών που θα προσφέρουν αυτήν την δυνατότητα μέσω τεχνολογιών απομακρυσμένης παρακολούθησης. Η εξάπλωση φθηνών έξυπνων συσκευών άμεσα διαθέσιμων στο εμπόριο και με την δυνατότητα σύνδεσης στο λεγόμενο Διαδίκτυο των Πραγμάτων (Internet of Things - IoT) έχει στρέψει την ερευνητική κοινότητα στην ανάπτυξη των λεγόμενων έξυπνων σπιτιών. Τα έξυπνα σπίτια αποτελούν λύσεις που συνδυάζουν τεχνολογίες αισθητήρων ενσωματώνοντάς τες στον φυσικό περιβάλλοντα χώρο των σπιτιών των ηλικιωμένων, επιτρέποντας την συνεχή και λεπτομερή παρακολούθηση των καθημερινών δραστηριότητων τους. Μέθοδοι: Η μελέτη μας αποτελείται από δύο σκέλη. Ένα πειραματικό σε εργαστηριακό χώρο και μια εφαρμογή της τεχνολογίας σε πραγματικό περιβάλλον. Συγκεκριμένα, δημιουργήθηκε ένας οικολογικά έγκυρος χώρος στα πλαίσια του Ζωντανού Εργαστηρίου Ενεργού και Υγιούς Γήρανσης, όπου δέκα πέντε (15) ηλικιωμένοι συμμετέχοντες έλαβαν μέρος στην πιλοτική δοκιμή της τεχνολογίας. Οι συμμετέχοντες επισκέπτονταν τον οικολογικά έγκυρο χώρο για περίπου 2 εβδομάδες (8 συνολικά συνεδρίες) όπου ακολουθούσαν ένα πρωτόκολλο δραστηριοτήτων, που περιελάμβαναν ορισμένα διακριτικά σενάρια παρακολούθησης, χωρίς αυστηρούς περιορισμούς επιτρέποντας την συλλογή ρεαλιστικών συμπεριφορικών δεδομένων από το δίκτυο αισθητήρων. Στην συνέχεια τα δεδομένα που συλλλέχθηκαν από 10 συμμετέχοντες αναλύθηκαν στατιστικά με τα κλινικά τεστ αξιολόγησης της υγείας των ηλικιωμένων ώστε να βρεθούν τυχόν συσχετίσεις και δείκτες υγείας. Μετά το πέρας αυτών των πιλοτικών δοκιμών πραγματοποιήθηκε μια σειρά από ομάδες εστιασμένης συζήτησης όπου πραγματοποιήθηκε κουβέντα γύρω από την παρεμβατικότητα της τεχνολογίας με την υιοθέτηση ενός θεωρητικού πλαισίου ορισμού από την βιβλιογραφία. Στο δεύτερο μέρος της μελέτης, μελετήθηκαν τα αποτελέσματα τόσο από την ποιοτική ανάλυση των ομάδων εστίασης, όσο και από τα μελή της ερευνητικής ομάδας και πραγματοποιήθηκε η εγκατάσταση ενός ελαφρώς τροποποιημένου τεχνολογικού συστήματος σε 5 σπίτια ηλικιωμένων για ένα διάστημα πλέον του ενός έτους, συλλέγοντας με αυτόν τον τρόπο εκατοντάδες χιλιάδες ψηφιακά στιγμιότυπα της καθημερινότητας των ηλικιωμένων. Στην συνέχεια επιχειρήθηκε η ανάπτυξη τόσο στατιστικών μοντέλων, όσο και μοντέλων μηχανικής μάθησης για την διερεύνηση της κλινικής αξίας των ψηφιακών βιοδεικτών. Σαν μελέτη εφαρμογής αυτών των μοντέλων ήταν η πρόβλεψη συναισθηματικών διαταραχών καθώς και της καταθλιπτικής συμπτωματολογίας. Παράλληλα με αυτές τις μελέτες, διενεργήθηκε και μια μακροπρόθεσμη (Longitudinal) μελέτη της αποδοχής των αισθητήρων από τους ηλικιωμένους με έμφαση και πάλι στον βαθμό παρεμβατικότητας. Η μελέτη διενεργήθηκε με την μορφή προσωπικών συνεντεύξεων με κάθε μία από τις πέντε συμμετέχουσες. Τέλος, με την πρόταση και δημιουργία ενός εμπειρικού μοντέλου (expert model) υποστήριξης της διάγνωσης της γηριατρικής κατάθλιψης (Fuzzy Cognitive Maps), αποκρυσταλλώνοντας την γνώση των ειδικών σε ένα σχήμα αναπαράστασης γνώσης προσιτό σε αυτούς, επιχειρείται η αντιστοίχηση της νέας γνώσης που παράγεται από τους αισθητήρες με την υπάρχουσα κλινική γνώση. Αποτελέσματα: Όσον αφορά το πρώτο σκέλος της διατριβής βρέθηκαν αρκετές στατιστικά σημαντικές συσχετίσεις ανάμεσα στις επιμέρους κατηγορίες ψηφιακών δεικτών, όπως κινητικοί, συναισθηματικοί και φυσιολογικοί και των επιμέρους κατηγοριών κλινικών αξιολογήσεων της υγείας των ηλικιωμένων. Όσον αφορά τα αποτελέσματα από τις ομάδες εστιασμένης συζήτησης αυτά αναλύθηκαν με ποιοτικές μεθόδους για να εξαχθούν τα πιο βασικά θέματα που προέκυψαν από τις συζητήσεις και να κατηγοριοποιηθούν σε κάθε ένα από τους άξονες του πλαισίου παρεμβατικότητας (obtrusiveness framework). Για το δεύτερο σκέλος της διατριβής, δημιουργήθηκε ένα γραμμικό μοντέλο πρόβλεψης του PHQ-9 σκορ από τα μοτίβα λειτουργίας της τηλεόρασης. Εκεί βρέθηκαν συσχετίσεις με κάποια από τα συμπτώματα της κατάθλιψης. Το μοντέλο μηχανικής μάθησης και συγκεκριμένα, Τυχαία Δάση (Random Forests, RF) πέτυχαν με ακρίβεια άνω του 80% να διακρίνουν μεταξύ καταθλιπτικών και υγιών καθημερινών στιγμυοτύπων συμπεριφοράς, ενώ το μοντέλο ταξινόμησης καθημερινών προτύπων συμπεριφοράς σε ήπια, μέτρια και σοβαρά καταθλιπτικά περιστατικά πέτυχε εμβαδόν επιφάνειας ROC >90%. Επίσης, η αρχική αξιολόγηση του μοντέλου υποστήριξης της διάγνωσης της κατάθλιψης, με βάση το σχήμα αναπαράστασης γνώσης FCM είχε μέση ακρίβεια περίπου 96%. Τέλος, η σύγκριση των μοτίβων αντίληψης των ηλικιωμένων όσον αφορά την παρεμβατικότητα των τεχνολογιών που είχαν εγκατεστημένες στο σπίτι τους, απεκάλυψε την αρνητική τους στάση απέναντι στο έξυπνο ρολόι και τον καθρέφτη-κάμερα. Επίσης, καθώς μία από τις 5 κυρίες απεχώρησε οικιοθελώς από την μελέτη, συγκρίθηκαν οι απαντήσεις της με τον μέσο όρο των υπολοίπων ηλικιωμένων γυναικών. ώστε να αποκαλυφθούν οι αιτίες της αποχώρησης από την μελέτη. Βασικοί λόγοι αποδείχθηκαν το έξυπνο ρολόι, ο καθρέφτης με την ενσωματωμένη κάμερα αλλά και η συσκευή Kinect λόγω και της οποίας αναγκάστηκε να αλλάξει την καθημερινή ρουτίνα της. Συμπεράσματα: Η κλινική αξία των ψηφιακών βιοδεικτών έχει αναδειχθεί σε πλήθος δημοσιεύσεων, όμως η χρήση και αξιολόγησή τους σε μακροπρόθεσμες μελέτες απουσιάζει. Μεγάλη πρόκληση αποτελεί στις μέρες μας η εύρωστη λειτουργία τέτοιων τεχνολογιών υπό πραγματικές συνθήκες χωρίς πριορισμούς (in the wild) αλλά και η αποδοχή τους τόσο από τους ηλικιωμένους, όσο και από τους γιατρούς αν κάποια στιγμή στο μέλλον θελήσουμε να χρησιμοποιήσουμε τέτοιου είδους δεδομένα στην κλινική πράξη

Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.

Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.

Risk Assessment of COVID-19 Cases in Emergency Departments and Clinics With the Use of Real-World Data and Artificial Intelligence: Observational Study

BackgroundThe recent COVID-19 pandemic has highlighted the weaknesses of health care systems around the world. In the effort to improve the monitoring of cases admitted to emergency departments, it has become increasingly necessary to adopt new innovative technological solutions in clinical practice. Currently, the continuous monitoring of vital signs is only performed in patients admitted to the intensive care unit. ObjectiveThe study aimed to develop a smart system that will dynamically prioritize patients through the continuous monitoring of vital signs using a wearable biosensor device and recording of meaningful clinical records and estimate the likelihood of deterioration of each case using artificial intelligence models. MethodsThe data for the study were collected from the emergency department and COVID-19 inpatient unit of the Hippokration General Hospital of Thessaloniki. The study was carried out in the framework of the COVID-X H2020 project, which was funded by the European Union. For the training of the neural network, data collection was performed from COVID-19 cases hospitalized in the respective unit. A wearable biosensor device was placed on the wrist of each patient, which recorded the primary characteristics of the visual signal related to breathing assessment. ResultsA total of 157 adult patients diagnosed with COVID-19 were recruited. Lasso penalty function was used for selecting 18 out of 48 predictors and 2 random forest–based models were implemented for comparison. The high overall performance was maintained, if not improved, by feature selection, with random forest achieving accuracies of 80.9% and 82.1% when trained using all predictors and a subset of them, respectively. Preliminary results, although affected by pandemic limitations and restrictions, were promising regarding breathing pattern recognition. ConclusionsThis study represents a novel approach that involves the use of machine learning methods and Edge artificial intelligence to assist the prioritization and continuous monitoring procedures of patients with COVID-19 in health departments. Although initial results appear to be promising, further studies are required to examine its actual effectiveness

A New Approach for Ageing at Home: The CAPTAIN System

Our work exhibits how previous projects on the Active and Healthy Ageing field have advanced to the conception of CAPTAIN, a radically new approach towards increased enduser acceptance. The goal is to create intuitive technology that does not require specific skills for interaction and blends in with real life. CAPTAIN will be co-designed by all types of stakeholders, including older adults, involved in all stages, from the initial design to delivery of the final syste