Continuous evolution of digital health technologies is enabling and improving health data exchange and storage using tools for remote data capture and data sharing of relevant information across the health ecosystem. In this landscape, the integration of Real World Data (RWD) into our daily lives through mobile health (mHealth) Apps becomes pivotal to reach such aims, thus increasing the value of person-generated digital health data and extracted real-world evidence (RWE). This PhD thesis aims to: 1) Explore and understand this scenario, conducting a non-systematic scoping review to explore the pivotal role of mHealth apps in the realm of RWD collection within the context of digital health, by specifically focusing on two main aspects: the practical implementation of RWD management, and the development of such ecosystem centred around mHealth apps for RWD gathering; 2) Design and develop a mHealth-based RWD platform to provide to the end-users health & wellness services, by exploiting concepts and best practices extracted from the starting analysis; 3) Evaluate the developed solution by a usability validation of the mHealth app in a real-world use case. The Food & Drugs Administration defined RWD in 2017 as “data related to patient health status and/or the delivery of health care routinely collected from Electronic Health Records (EHRs), claims and billing data, data from product and disease registries, patient-generated data including home-use settings, and data gathered from other sources that can inform on health status, such as mobile devices”. Mobile Health Apps (mHealth Apps) are considered a prominent source of RWD within the broader digital health landscape, thanks to their nature to run on smartphones or tablets, imbuing the RWD collection process with the property of ubiquity. They can range from targeting specific medical conditions to more general health and wellness improvement, and crucially, mHealth Apps may fall or not, based on the manufacturer’s claims, under the category of "Software as medical device" (SaMD), subject to additional regulations, such as the EU's Medical Device Regulation 2017/745 (MDR). The conceptualization of RWD involves understanding its life cycle, that encompasses several key steps, each contributing to the transformation of raw data into valuable RWE for clinical decision-making: 1) Data Creation and Collection (Acquisition Process); 2) Data Aggregation and Enrichment (Data Management Process); 3) Data Maintenance; 4) Data Analysis; and 5) Data Usage (Real World Evidence -RWE). In this context, quality becomes an intrinsic property crucial for the effective use of RWD. Indeed, high-quality RWD can be defined as data that are intrinsically good, contextually appropriate, clearly represented, and accessible to data consumers, based on different key features: Intrinsic, Contextual (or Extrinsic), and Technical Features. Assessing quality of RWD necessitates robust tools and frameworks. For example, the Control Objectives for Information and related Technology (COBIT), and standard ISO 27001, or the Harmonized Intrinsic DQ framework (HIDQF). Different works suggest that one of the most critical factors influencing the quality of mHealth Apps is their usability. Usability hinges on how effectively end-users, be they citizens, patients, caregivers, or healthcare professionals, can navigate and derive utility from the application. mHealth quality can be evaluated with different tools, such as questionnaires as System Usability Scale (SUS), or Mobile App Rating Scale (MARS), or frameworks, such as the National Frameworks and Guidelines, APA Framework, or standards, such as the CEN-ISO/TS 823904-2. Moreover, ensuring mHealth apps align with established medical device guidelines and regulations, such as the MDR, is of paramount importance for cultivating such a quality perspective. For the effective development, different frameworks and solutions were found to be useful: Patient and Public Involvement (PPI) approach, International Comparison of Evaluation Criteria (comparing mHealth app evaluation criteria from different countries), the TeleWear-AF Project Workflow, the SNApp Framework for Behavioral Health Research, the Waterfall Framework for Systematic Development, and the CeHRes Roadmap for Human-Centered Design proposed by The University of Twente. These definitions, tools and best practices extracted from the literature review were then exploited to design and develop a RWD mobile-based ecosystem, the SynCare platform, a patient-centered mHealth-based system to collect and manage RWD, ecosystem for remote patient monitoring designed and developed executive research, with the aim to: 1) facilitate patient’s self-monitoring and remote patient monitoring in chronic diseases; 2) support therapeutic alliance between patients and HPs; 3) improve patients’ engagement and therapeutic adherence for chronic patients, or patients performing at-home therapies. The main design goals were: 1) to build up secure channels for data sharing, in compliance with General Data Protection Regulation (GDPR); 2) to support the patients in the management of their own health data; 3) to clearly define the digital health services, circumscribing the doctor-patient relationship, to guarantee the healthcare professional to report the provided services referring to specific tariffs, and defining the connected responsibilities; 4) as LifeCharger srl, to provide the above-mentioned services/ecosystem for the subscribed end-users, acting as a normal third party, without the need to view users’ data without a user explicit consent. Access to patients’ data is regulated through the creation of informed, traceable, transparent, withdrawable and not tampered digital consents, that are saved as smart contracts into the Ethereum public blockchain. The patient can decide to share data with third parties, such as a healthcare professionals or an informal caregiver, by signing digital consents through the mobile app. In the evolution of this platform from version 1.0 to 2.0, the main difference relied on the creation of the Data Analytics module. All data stored by the LC ecosystem in these repositories is maintained in a pseudo-anonymized form, that are sent from the app to the repository for feeding the Data Analytics module, to be subsequently processed. Such module consists of a series of Python scripts triggered by various routines, executed on LifeCharger’s local server to 1) Retrieve the data from the Cloud database; 2) Process the data according to the different objectives and services offered by the platform; and 3) Write back the analyzed results—still in pseudo-anonymized form—to the same Cloud database.In this way, both the clinician—via the Dashboard—and the user themselves retrieve these results for display. The usability and acceptability validation of the designed and developed platform was implemented through a real-world use case, the e-BRAVE study, where the end-users were intended to use autonomously the mobile app based upon the developed SynCare ecosystem, in their real-world environments, thus, obtaining a usability evaluation and validation in a real-world scenario. The e-BRAVE study was approved by the Istituto Nazionale Tumori (INT) ethics committee Act N° 30/24, and the recruitment, through the mobile BRCApp registration, started on June 28th, 2024, and it is currently undergoing, letting the volunteers download the app from Apple and Google Play Stores and begin the e-Brave study in every moment. The BRCApp is a mobile app that represents the main end-user front-end, as part of the developed innovative digital ecosystem designed to gather cohort information, enhancing synergy between project participants and researchers. The BRCApp platform aims to support women’s engagement, adherence to treatment, intervention plans, and self-empowerment, complemented by activity tracking and monitoring. Thus, the BRCApp through its Ecosystem becomes able to create clusters of users, applying the related journey including activities to be performed in app (physiologic and body parameters to be measured, questionnaires to be filled in and informative contents to read). After at least 7 days of BRCApp use, the Mobile Application Rating Scale (uMARS) questionnaire was administered, digitally in-App, to the e-Brave’s users, to evaluate the perceived usability. The uMARS assesses different dimensions of engagement, functionality, aesthetics, information, and subjective quality by a 5-point scale, and it has been largely used to evaluate different mHealth apps, making possible comparisons in literature between different usability studies and results. To obtain the overall usability uMARS score, the mean of each objective subscale was derived, and their means and medians computed. This score represents the objective quality of the app, while the means and medians of the two dimensions E and F represent the user’s perceived quality and the future impact of this technology on the users themselves, respectively. To evaluate possible correlations between the obtained scores of the different uMARS sections and the effective usage of the app, to demonstrate the impact of the app usability on its effective real-world usage, the Spearman’s Correlation was used. Considering a threshold (TH) representing the minimum days of usage, users were stratified into subgroups, where a value of TH=1 means including all the users independently from continuous usage. The days of use were computed by analyzing the BRCApp Logs, from the user registration date to the date of data download for the analysis. For each subgroup the Spearman’s correlation was calculated, repeating it for the different section scores: the obtained Overall uMARS score (the mean of the main four sections), for the Section A (Engagement), for the Section B (Functionality), for the Section C (Aesthetics), and for the Section D (Information). In addition, the usability evaluation was conducted also by comparing two sub-groups of users extracted from the entire sample, the control and trial groups. , with a TH equal to 7. From the beginning of the study (28th June 2024), 1546 users registered to the BRCApp, of which 81 accounts were testers, which were not considered, and 147 accounts were found as duplicates of users, that deleted and recreated their credentials, thus resulting in a total number of 1318 unique recruited volunteers. The usability study started on 18th February 2025, and it is currently undergoing. The data for the presented analysis were extracted on 30th December 2025. Among all the registered e-Brave study participants, 110 users have filled-in the uMARS questionnaire, and 59 of them were randomly assigned to control group, while 36 assigned to the trial. The BRCApp information and contents, as planned, turned out to be the main means to improve end-user’s eBrave study outcomes, such as the monitoring and improvement of weight and abdominal circumference, following diets and physical exercises suggestions. Indeed, the uMARS information section obtained the highest scores, both for medians, 4(3;5), for single uMARS question mean score, with a median of 5(5;5) (Item 17: reliability of the sources), and correlations with Days of Activity, thus representing a good starting point to obtain positive final outcomes. Finally, the correlation between the app’s days of use and the section’s uMARS score was used to evaluate the effective influence of a real app use (not just a first interaction) on the obtained usability scores. This is important to objectively evaluate the results, without taking conclusions that are decoupled from the effective usage. Indeed, this consideration is highlighted by significant correlation for Section D (Information) > 0.7 for the trial group, showing that correlations were in general statistically significant after at least seven days of use (TH=7). This matches the idea that the perceived usability is effectively influenced by a real app usage. This thesis addressed key challenges in understanding RWD characteristics in order to develop a mobile-based RWD platform that can collect, manage and exploit these types of data, to provide to the end-users the best health & wellness service possible while maintaining a certain data quality and security during the whole life cycle, using best practices and frameworks. RWD are crucial for obtaining RWE that can be used as insights to improve the outcome of the provided health & wellness services, through the mHealth apps in the end-user’s hands. By leveraging on different development frameworks and innovative technologies such as the blockchain, a secure mHealth-based RWD platform was developed and secured, to provide to the end-users different types of health & wellness services, enhanced and fed by their own personal self-reported data. The architecture was designed to be scalable, flexible, and applicable in different real-world use cases. Indeed, the platform has demonstrated the ability to be used in a prospective cohort study, the e-Brave study, by collecting different types of self-reported data, such as questionnaires and physiological parameters, monitoring the end-user’s behaviours and trends, during app utilization. In addition, the informed digital consents were signed directly through the mHealth app and saved as a smart contract on the Ethereum public blockchain, creating a secure and not tamperable consent ledger, recoverable in each moment, ensuring that the mHealth app could share sensitive data for that purpose.
L’evoluzione continua delle tecnologie di salute digitale sta abilitando e migliorando lo scambio e l’archiviazione dei dati sanitari, attraverso strumenti per la raccolta remota dei dati e la condivisione di informazioni rilevanti all’interno dell’ecosistema sanitario. In questo contesto, l’integrazione dei Real World Data (RWD) nella vita quotidiana tramite le applicazioni di mobile health (mHealth) diventa cruciale per raggiungere tali obiettivi, aumentando il valore dei dati sanitari digitali generati dalle persone e delle evidenze real-world (RWE) che ne derivano. Questa tesi di dottorato si propone di: 1. Esplorare e comprendere questo scenario, conducendo una scoping review non sistematica per analizzare il ruolo centrale delle app mHealth nella raccolta di RWD nell’ambito della salute digitale, focalizzandosi in particolare su due aspetti principali: l’implementazione pratica della gestione dei RWD e lo sviluppo di un ecosistema centrato sulle app mHealth per la raccolta di tali dati; 2. Progettare e sviluppare una piattaforma RWD basata su mHealth per fornire servizi di salute e benessere agli utenti finali, sfruttando concetti e best practice emersi dall’analisi iniziale; 3. Valutare la soluzione sviluppata attraverso una validazione di usabilità dell’app mHealth in un caso d’uso reale. La Food & Drug Administration ha definito i RWD nel 2017 come “dati relativi allo stato di salute dei pazienti e/o all’erogazione dell’assistenza sanitaria, raccolti routinariamente da cartelle cliniche elettroniche (EHR), dati amministrativi e di fatturazione, registri di prodotti e malattie, dati generati dai pazienti anche in contesti domestici, e dati provenienti da altre fonti che possono informare sullo stato di salute, come i dispositivi mobili”. Le applicazioni di Mobile Health (mHealth) sono considerate una fonte rilevante di RWD nel più ampio panorama della salute digitale, grazie alla loro capacità di funzionare su smartphone o tablet, conferendo ubiquità al processo di raccolta dei dati. Possono essere orientate a specifiche condizioni mediche o al miglioramento generale della salute e del benessere e, in base alle dichiarazioni del produttore, possono rientrare o meno nella categoria di “Software come dispositivo medico” (SaMD), soggetta a ulteriori regolamentazioni, come il Regolamento Europeo sui Dispositivi Medici 2017/745 (MDR). La concettualizzazione dei RWD richiede la comprensione del loro ciclo di vita, che comprende diverse fasi chiave, ciascuna delle quali contribuisce alla trasformazione dei dati grezzi in RWE utili per il processo decisionale clinico: 1. Creazione e raccolta dei dati (processo di acquisizione); 2. Aggregazione e arricchimento dei dati (processo di gestione); 3. Manutenzione dei dati; 4. Analisi dei dati; 5. Utilizzo dei dati (Real World Evidence - RWE). In questo contesto, la qualità diventa una proprietà intrinseca fondamentale per l’uso efficace dei RWD. Infatti, dati RWD di alta qualità possono essere definiti come dati intrinsecamente validi, contestualmente appropriati, chiaramente rappresentati e accessibili ai fruitori, secondo diverse caratteristiche chiave: intrinseche, contestuali (o estrinseche) e tecniche. La valutazione della qualità dei RWD richiede strumenti e framework robusti, come il Control Objectives for Information and related Technology (COBIT), lo standard ISO 27001 o l’Harmonized Intrinsic Data Quality Framework (HIDQF). Diversi studi suggeriscono che uno dei fattori più critici che influenzano la qualità delle app mHealth è la loro usabilità. L’usabilità dipende da quanto efficacemente gli utenti finali—cittadini, pazienti, caregiver o professionisti sanitari—riescono a utilizzare l’applicazione e trarne beneficio. La qualità delle app mHealth può essere valutata tramite diversi strumenti, come questionari quali il System Usability Scale (SUS) o il Mobile App Rating Scale (MARS), oppure framework come i National Frameworks and Guidelines, l’APA Framework, o standard come il CEN-ISO/TS 82304-2. Inoltre, garantire che le app mHealth siano conformi alle linee guida e normative sui dispositivi medici, come il MDR, è fondamentale per assicurare elevati standard di qualità. Per uno sviluppo efficace, diversi framework e approcci si sono rivelati utili: il Patient and Public Involvement (PPI), il confronto internazionale dei criteri di valutazione, il workflow del progetto TeleWear-AF, il framework SNApp per la ricerca in salute comportamentale, il modello Waterfall per lo sviluppo sistematico e la CeHRes Roadmap per la progettazione human-centered proposta dall’Università di Twente. Queste definizioni, strumenti e best practice, emersi dalla revisione della letteratura, sono stati utilizzati per progettare e sviluppare un ecosistema mobile basato su RWD, la piattaforma SynCare: un sistema mHealth centrato sul paziente per la raccolta e gestione dei RWD, pensato per il monitoraggio remoto dei pazienti. Gli obiettivi principali sono: 1. facilitare l’automonitoraggio e il monitoraggio remoto nelle malattie croniche; 2. supportare l’alleanza terapeutica tra pazienti e professionisti sanitari; 3. migliorare il coinvolgimento e l’aderenza terapeutica dei pazienti cronici o sottoposti a terapie domiciliari. Gli obiettivi progettuali principali sono stati: 1. creare canali sicuri per la condivisione dei dati, conformi al GDPR; 2. supportare i pazienti nella gestione dei propri dati sanitari; 3. definire chiaramente i servizi di salute digitale e il rapporto medico-paziente, garantendo la tracciabilità delle prestazioni e delle responsabilità; 4. consentire a LifeCharger srl di operare come terza parte, senza accedere ai dati degli utenti senza consenso esplicito. L’accesso ai dati dei pazienti è regolato tramite consensi digitali informati, tracciabili, trasparenti, revocabili e non alterabili, salvati come smart contract sulla blockchain pubblica Ethereum. Il paziente può decidere di condividere i propri dati con terze parti, come professionisti sanitari o caregiver, firmando consensi digitali tramite app. Nell’evoluzione dalla versione 1.0 alla 2.0, la principale innovazione è stata l’introduzione del modulo Data Analytics. Tutti i dati memorizzati sono mantenuti in forma pseudo-anonimizzata e utilizzati per alimentare tale modulo, composto da script Python eseguiti su server locale per: 1. recuperare i dati dal database cloud; 2. elaborarli secondo gli obiettivi della piattaforma; 3. scrivere i risultati analizzati, sempre in forma pseudo-anonimizzata, nel database cloud. I risultati sono poi resi disponibili sia ai clinici (tramite dashboard) sia agli utenti. La validazione di usabilità e accettabilità della piattaforma è stata condotta tramite un caso d’uso reale: lo studio e-BRAVE, in cui gli utenti hanno utilizzato autonomamente l’app nel proprio contesto quotidiano. Lo studio è stato approvato dal comitato etico dell’Istituto Nazionale Tumori (INT) (Atto n. 30/24). Il reclutamento è iniziato il 28 giugno 2024 ed è tuttora in corso, consentendo ai volontari di scaricare l’app dagli store Apple e Google Play. La BRCApp rappresenta il front-end principale per gli utenti finali, progettato per raccogliere dati di coorte e migliorare la sinergia tra partecipanti e ricercatori. Supporta il coinvolgimento, l’aderenza terapeutica e l’autoefficacia delle donne, integrando monitoraggio di attività e parametri fisiologici, questionari e contenuti informativi. Dopo almeno 7 giorni di utilizzo, agli utenti è stato somministrato il questionario uMARS in-app per valutare l’usabilità percepita. Questo strumento valuta engagement, funzionalità, estetica, informazioni e qualità soggettiva su scala a 5 punti. Il punteggio complessivo uMARS è stato calcolato come media delle sottoscale oggettive. Sono state inoltre analizzate correlazioni tra usabilità e utilizzo reale tramite correlazione di Spearman. Gli utenti sono stati stratificati in sottogruppi in base ai giorni di utilizzo (TH). Dal 28 giugno 2024, 1546 utenti si sono registrati, di cui 1318 unici. L’analisi (30 dicembre 2025) ha incluso 110 utenti che hanno compilato il questionario, suddivisi in gruppo controllo (59) e trial (36). La sezione “informazioni” dell’uMARS ha ottenuto i punteggi più elevati (mediana 4(3;5)), con punteggio massimo per l’affidabilità delle fonti (item 17). Sono state osservate correlazioni significative tra utilizzo dell’app e punteggi di usabilità, in particolare per la sezione informativa (correlazione > 0.7 nel gruppo trial per TH=7). Questo evidenzia come l’usabilità percepita sia influenzata dall’uso reale dell’applicazione. La tesi affronta le principali sfide nella comprensione dei RWD e nello sviluppo di una piattaforma mobile per raccoglierli, gestirli e utilizzarli, garantendo qualità e sicurezza lungo tutto il ciclo di vita. I RWD risultano fondamentali per generare RWE utili a migliorare i servizi di salute e benessere. Grazie all’uso di framework di sviluppo e tecnologie innovative come la blockchain, è stata sviluppata una piattaforma mHealth sicura e scalabile, in grado di fornire servizi basati sui dati auto-riportati dagli utenti. L’architettura è risultata flessibile e applicabile a diversi contesti reali, dimostrando efficacia nello studio e-BRAVE, con raccolta di dati, monitoraggio dei comportamenti e gestione dei consensi digitali tramite blockchain, garantendo sicurezza, tracciabilità e integrità nella condivisione dei dati sensibili.
Design, implementation and usability evaluation of a mHealth app-based Real World Data (RWD) platform secured by blockchain
PIGHINI, CLAUDIO
2025/2026
Abstract
Continuous evolution of digital health technologies is enabling and improving health data exchange and storage using tools for remote data capture and data sharing of relevant information across the health ecosystem. In this landscape, the integration of Real World Data (RWD) into our daily lives through mobile health (mHealth) Apps becomes pivotal to reach such aims, thus increasing the value of person-generated digital health data and extracted real-world evidence (RWE). This PhD thesis aims to: 1) Explore and understand this scenario, conducting a non-systematic scoping review to explore the pivotal role of mHealth apps in the realm of RWD collection within the context of digital health, by specifically focusing on two main aspects: the practical implementation of RWD management, and the development of such ecosystem centred around mHealth apps for RWD gathering; 2) Design and develop a mHealth-based RWD platform to provide to the end-users health & wellness services, by exploiting concepts and best practices extracted from the starting analysis; 3) Evaluate the developed solution by a usability validation of the mHealth app in a real-world use case. The Food & Drugs Administration defined RWD in 2017 as “data related to patient health status and/or the delivery of health care routinely collected from Electronic Health Records (EHRs), claims and billing data, data from product and disease registries, patient-generated data including home-use settings, and data gathered from other sources that can inform on health status, such as mobile devices”. Mobile Health Apps (mHealth Apps) are considered a prominent source of RWD within the broader digital health landscape, thanks to their nature to run on smartphones or tablets, imbuing the RWD collection process with the property of ubiquity. They can range from targeting specific medical conditions to more general health and wellness improvement, and crucially, mHealth Apps may fall or not, based on the manufacturer’s claims, under the category of "Software as medical device" (SaMD), subject to additional regulations, such as the EU's Medical Device Regulation 2017/745 (MDR). The conceptualization of RWD involves understanding its life cycle, that encompasses several key steps, each contributing to the transformation of raw data into valuable RWE for clinical decision-making: 1) Data Creation and Collection (Acquisition Process); 2) Data Aggregation and Enrichment (Data Management Process); 3) Data Maintenance; 4) Data Analysis; and 5) Data Usage (Real World Evidence -RWE). In this context, quality becomes an intrinsic property crucial for the effective use of RWD. Indeed, high-quality RWD can be defined as data that are intrinsically good, contextually appropriate, clearly represented, and accessible to data consumers, based on different key features: Intrinsic, Contextual (or Extrinsic), and Technical Features. Assessing quality of RWD necessitates robust tools and frameworks. For example, the Control Objectives for Information and related Technology (COBIT), and standard ISO 27001, or the Harmonized Intrinsic DQ framework (HIDQF). Different works suggest that one of the most critical factors influencing the quality of mHealth Apps is their usability. Usability hinges on how effectively end-users, be they citizens, patients, caregivers, or healthcare professionals, can navigate and derive utility from the application. mHealth quality can be evaluated with different tools, such as questionnaires as System Usability Scale (SUS), or Mobile App Rating Scale (MARS), or frameworks, such as the National Frameworks and Guidelines, APA Framework, or standards, such as the CEN-ISO/TS 823904-2. Moreover, ensuring mHealth apps align with established medical device guidelines and regulations, such as the MDR, is of paramount importance for cultivating such a quality perspective. For the effective development, different frameworks and solutions were found to be useful: Patient and Public Involvement (PPI) approach, International Comparison of Evaluation Criteria (comparing mHealth app evaluation criteria from different countries), the TeleWear-AF Project Workflow, the SNApp Framework for Behavioral Health Research, the Waterfall Framework for Systematic Development, and the CeHRes Roadmap for Human-Centered Design proposed by The University of Twente. These definitions, tools and best practices extracted from the literature review were then exploited to design and develop a RWD mobile-based ecosystem, the SynCare platform, a patient-centered mHealth-based system to collect and manage RWD, ecosystem for remote patient monitoring designed and developed executive research, with the aim to: 1) facilitate patient’s self-monitoring and remote patient monitoring in chronic diseases; 2) support therapeutic alliance between patients and HPs; 3) improve patients’ engagement and therapeutic adherence for chronic patients, or patients performing at-home therapies. The main design goals were: 1) to build up secure channels for data sharing, in compliance with General Data Protection Regulation (GDPR); 2) to support the patients in the management of their own health data; 3) to clearly define the digital health services, circumscribing the doctor-patient relationship, to guarantee the healthcare professional to report the provided services referring to specific tariffs, and defining the connected responsibilities; 4) as LifeCharger srl, to provide the above-mentioned services/ecosystem for the subscribed end-users, acting as a normal third party, without the need to view users’ data without a user explicit consent. Access to patients’ data is regulated through the creation of informed, traceable, transparent, withdrawable and not tampered digital consents, that are saved as smart contracts into the Ethereum public blockchain. The patient can decide to share data with third parties, such as a healthcare professionals or an informal caregiver, by signing digital consents through the mobile app. In the evolution of this platform from version 1.0 to 2.0, the main difference relied on the creation of the Data Analytics module. All data stored by the LC ecosystem in these repositories is maintained in a pseudo-anonymized form, that are sent from the app to the repository for feeding the Data Analytics module, to be subsequently processed. Such module consists of a series of Python scripts triggered by various routines, executed on LifeCharger’s local server to 1) Retrieve the data from the Cloud database; 2) Process the data according to the different objectives and services offered by the platform; and 3) Write back the analyzed results—still in pseudo-anonymized form—to the same Cloud database.In this way, both the clinician—via the Dashboard—and the user themselves retrieve these results for display. The usability and acceptability validation of the designed and developed platform was implemented through a real-world use case, the e-BRAVE study, where the end-users were intended to use autonomously the mobile app based upon the developed SynCare ecosystem, in their real-world environments, thus, obtaining a usability evaluation and validation in a real-world scenario. The e-BRAVE study was approved by the Istituto Nazionale Tumori (INT) ethics committee Act N° 30/24, and the recruitment, through the mobile BRCApp registration, started on June 28th, 2024, and it is currently undergoing, letting the volunteers download the app from Apple and Google Play Stores and begin the e-Brave study in every moment. The BRCApp is a mobile app that represents the main end-user front-end, as part of the developed innovative digital ecosystem designed to gather cohort information, enhancing synergy between project participants and researchers. The BRCApp platform aims to support women’s engagement, adherence to treatment, intervention plans, and self-empowerment, complemented by activity tracking and monitoring. Thus, the BRCApp through its Ecosystem becomes able to create clusters of users, applying the related journey including activities to be performed in app (physiologic and body parameters to be measured, questionnaires to be filled in and informative contents to read). After at least 7 days of BRCApp use, the Mobile Application Rating Scale (uMARS) questionnaire was administered, digitally in-App, to the e-Brave’s users, to evaluate the perceived usability. The uMARS assesses different dimensions of engagement, functionality, aesthetics, information, and subjective quality by a 5-point scale, and it has been largely used to evaluate different mHealth apps, making possible comparisons in literature between different usability studies and results. To obtain the overall usability uMARS score, the mean of each objective subscale was derived, and their means and medians computed. This score represents the objective quality of the app, while the means and medians of the two dimensions E and F represent the user’s perceived quality and the future impact of this technology on the users themselves, respectively. To evaluate possible correlations between the obtained scores of the different uMARS sections and the effective usage of the app, to demonstrate the impact of the app usability on its effective real-world usage, the Spearman’s Correlation was used. Considering a threshold (TH) representing the minimum days of usage, users were stratified into subgroups, where a value of TH=1 means including all the users independently from continuous usage. The days of use were computed by analyzing the BRCApp Logs, from the user registration date to the date of data download for the analysis. For each subgroup the Spearman’s correlation was calculated, repeating it for the different section scores: the obtained Overall uMARS score (the mean of the main four sections), for the Section A (Engagement), for the Section B (Functionality), for the Section C (Aesthetics), and for the Section D (Information). In addition, the usability evaluation was conducted also by comparing two sub-groups of users extracted from the entire sample, the control and trial groups. , with a TH equal to 7. From the beginning of the study (28th June 2024), 1546 users registered to the BRCApp, of which 81 accounts were testers, which were not considered, and 147 accounts were found as duplicates of users, that deleted and recreated their credentials, thus resulting in a total number of 1318 unique recruited volunteers. The usability study started on 18th February 2025, and it is currently undergoing. The data for the presented analysis were extracted on 30th December 2025. Among all the registered e-Brave study participants, 110 users have filled-in the uMARS questionnaire, and 59 of them were randomly assigned to control group, while 36 assigned to the trial. The BRCApp information and contents, as planned, turned out to be the main means to improve end-user’s eBrave study outcomes, such as the monitoring and improvement of weight and abdominal circumference, following diets and physical exercises suggestions. Indeed, the uMARS information section obtained the highest scores, both for medians, 4(3;5), for single uMARS question mean score, with a median of 5(5;5) (Item 17: reliability of the sources), and correlations with Days of Activity, thus representing a good starting point to obtain positive final outcomes. Finally, the correlation between the app’s days of use and the section’s uMARS score was used to evaluate the effective influence of a real app use (not just a first interaction) on the obtained usability scores. This is important to objectively evaluate the results, without taking conclusions that are decoupled from the effective usage. Indeed, this consideration is highlighted by significant correlation for Section D (Information) > 0.7 for the trial group, showing that correlations were in general statistically significant after at least seven days of use (TH=7). This matches the idea that the perceived usability is effectively influenced by a real app usage. This thesis addressed key challenges in understanding RWD characteristics in order to develop a mobile-based RWD platform that can collect, manage and exploit these types of data, to provide to the end-users the best health & wellness service possible while maintaining a certain data quality and security during the whole life cycle, using best practices and frameworks. RWD are crucial for obtaining RWE that can be used as insights to improve the outcome of the provided health & wellness services, through the mHealth apps in the end-user’s hands. By leveraging on different development frameworks and innovative technologies such as the blockchain, a secure mHealth-based RWD platform was developed and secured, to provide to the end-users different types of health & wellness services, enhanced and fed by their own personal self-reported data. The architecture was designed to be scalable, flexible, and applicable in different real-world use cases. Indeed, the platform has demonstrated the ability to be used in a prospective cohort study, the e-Brave study, by collecting different types of self-reported data, such as questionnaires and physiological parameters, monitoring the end-user’s behaviours and trends, during app utilization. In addition, the informed digital consents were signed directly through the mHealth app and saved as a smart contract on the Ethereum public blockchain, creating a secure and not tamperable consent ledger, recoverable in each moment, ensuring that the mHealth app could share sensitive data for that purpose.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/256157