A selection of peer-reviewed publications in digital health, eHealth and agent-based systems. Each entry links to its DOI for verification.
Article2017Revista de la Sociedad Española del Dolor
Painometer v2®: Una aplicación móvil certificada para monitorizar a los pacientes con dolor
Miró, Jordi; De la Vega, Rocío; Roset Mayals, Roman; Castarlenas, Elena; Sánchez-Rodríguez, Elisabet
The use of smart mobile devices and apps for assessing and treating patients with chronic pain is becoming more widespread. The significant benefits of these technological alternatives contribute to this trend, including making treatment more accessible, even from remote and resource-scarce areas, aiding in enhancing clinical procedures, as well as reducing costs.
I took the lead in spearheading the eLearning strategy implementation to adapt a previously in-person cognitive-behavioral treatment to a remote format. Additionally, I directed the technical development of the Painometer v2® mobile application for Android, utilizing Java, Android SDK, and Firebase.
Fibroline: A mobile app for improving the quality of life of young people with fibromyalgia
De la Vega, Rocío; Roset Mayals, Roman; Galán, Santiago; Miró, Jordi
Fibroline is a mobile application with a self-administered cognitive behavioral treatment for young people with fibromyalgia or chronic widespread pain, designed to reduce pain and other common negative symptoms and improve quality of life. Our aims are to report on the usability and feasibility protocols used to assess the app. Two usability cycles were implemented. A group of patients followed the cognitive behavioral treatment intervention to test its feasibility. Qualitative data were collected and content analyses were conducted. The results demonstrated that the app is error-free, easy to use, liked by the users, and acceptable.
I focused on crafting eLearning strategies to transition a cognitive-behavioral treatment from in-person to remote delivery. I also headed the development of the Painometer v2® application for Android, utilizing Java, Android SDK, and Firebase.
On the electronic measurement of pain intensity: Can we use different pain intensity scales interchangeably?
Sánchez-Rodríguez, Elisabet; Castarlenas, Elena; De la Vega, Rocío; Roset Mayals, Roman; Miró, Jordi
The objective of this work was to study the agreement between four pain intensity scales when administered electronically: the Numerical Rating Scale-11, the Faces Pain Scale-Revised, the Visual Analogue Scale and the Coloured Analogue Scale. In all, 180 schoolchildren between 12 and 19 years old participated in the study. They had to report the maximum intensity of their most frequent pain using the electronic versions of the four scales. Agreement was calculated using the Bland–Altman method. Results show that the electronic versions of Numerical Rating Scale-11, Coloured Analogue Scale and Visual Analogue Scale can be used interchangeably.
My contribution involved digitizing scales for chronic pain, transforming them from paper-based to mobile format. Technologies such as HTML5 and JavaScript were employed. Ultimately, this digital version was integrated into a native app to ensure its efficiency and adaptability.
AN APP for the Assessment of Pain Intensity: Validity Properties and Agreement of Pain Reports When Used with Young People
Sánchez-Rodríguez, Elisabet; De la Vega, Rocío; Castarlenas, Elena; Roset Mayals, Roman; Miró, Jordi
Painometer is a mobile application that includes four pain intensity scales: the Numerical Rating Scale, the Faces Pain Scale-Revised, the mechanical visual analogue scale and the Colored Analogue Scale. The aim of this study was to analyze the validity and agreement of the intensity reports provided by these scales and their traditional counterparts.
I digitized chronic pain scales, shifting from paper to mobile format using HTML5 and JavaScript. Subsequently, the solution was integrated into a native app to ensure functionality and adaptability.
Development and Testing of Painometer: A Smartphone App to Assess Pain Intensity
De la Vega, Rocío; Roset Mayals, Roman; Castarlenas, Elena; Sánchez-Rodríguez, Elisabet; Solé, Ester; Miró, Jordi
Electronic and information technologies are increasingly being used to assess pain. This study aims to 1) introduce Painometer, a smartphone app that helps users to assess pain intensity, and 2) report on its usability (ie, user performance and satisfaction) and acceptability (ie, the willingness to use it) when it is made available to health care professionals and nonprofessionals.
In this article, I actively participated in testing the application, implementing various versions, and providing user support. Moreover, I led the development of Painometer v2® for Android, leveraging Java, Android SDK, and Firebase for data management.
Agreement Between Verbal and Electronic Versions of the Numerical Rating Scale (NRS-11) when Used to Assess Pain Intensity in Adolescents
Castarlenas, Elena; Sánchez-Rodríguez, Elisabet; De la Vega, Rocío; Roset Mayals, Roman; Miró, Jordi
Objectives: Electronic pain measures are becoming common tools in the assessment of pediatric pain intensity. The aims of this study were (1) to examine the agreement between the verbal and the electronic versions of the 11-point Numerical Rating Scale (NRS-11)(vNRS-11 and eNRS-11, respectively) when used to assess pain intensity in adolescents; and (2) to report participants preferences for each of the 2 alternatives.
I contributed to the application development and technical design of the research on pain assessment in adolescents.
Currently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way.The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis.
Within the project, I implemented an ontology for categorizing brain tumors and an API to connect distributed agents with this ontology. I used Java, with support from technologies like RDF, SPARQL, and GraphQL. I also integrated libraries to enhance communication between the agents.
In this paper we present our experience of representing the knowledge behind HealthAgents (HA), a distributed decision support system for brain tumour diagnosis. Our initial motivation came from the distributed nature of the information involved in the system and has been enriched by clinicians’ requirements and data access restrictions. We present in detail the steps we have taken towards building our ontology starting from knowledge acquisition to data access and reasoning. We motivate our representational choices and show our results using domain examples used by clinical partners in HA.
In this article, my primary responsibility was implementing an ontology designed by collaborators. This implementation was carried out using Java, SPARQL, and RDF. The main objective of this task was to facilitate the use of the ontology by distributed agents, ensuring effective and efficient integration in distributed computational environments.
This paper focuses on the problem of representing, in a meaningful way, the knowledge involved in the HealthAgents project. Our work is motivated by the complexity of representing electronic healthcare records in a consistent manner. We present HADOM (HealthAgents domain ontology) which conceptualises the required HealthAgents information and propose describing the sources knowledge by the means of conceptual graphs (CGs). This allows to build upon the existing ontology permitting for modularity and flexibility. The novelty of our approach lies in the ease with which CGs can be placed above other formalisms and their potential for optimised querying and retrieval.
In the article, I provided a development tool for the project that visualizes the ontology and supports queries with SPARQL and GraphQL. A key feature is presenting responses as visual graphs. I employed technologies like Java, SPARQL, GraphQL, RDF, and OWL to create it.
We have developed a program to determine the number, length and position of exact consecutive repeats of short sequences in DNA fragments or whole genomes. The program also gives the statistical significance of results by comparing them with those expected for a random sequence generated according to a Markovian model.
In my final degree project in Computer Engineering, I presented this article where I developed an optimized algorithm to detect recurring patterns in genomic sequences, surpassing other solutions in efficiency. For its implementation, I used C, and for the user interface, I opted for Perl and HTML.
In this paper we present several web-based tools to identify conserved patterns in sequences. In particular we present details on the functionality of PROMO version 2.0, a program for the prediction of transcription factor binding site in a single sequence or in a group of related sequences and, of MALGEN, a tool to visualize sequence correspondences among long DNA sequences. The web tools and associated documentation can be accessed at http://www.lsi.upc.es/~alggen (RESEARCH link).
I contributed to the development of tools for identifying patterns in biological sequences, primarily using Python and Perl.