Beatriz Sainz de Abajo

Universidad de Valladolid

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Isabel de la Torre Díez

Fundació ACE

Carlos de Castro Lozano

University of Córdoba

Miguel López-Coronado

Universidad de Valladolid

Enrique García Salcines

University of Córdoba

Begonya García-Zapirain

Universidad de Deusto

Manuel Franco

Universidade de São Paulo

Miguel López Coronado

Federico Santa María Technical University

Francisco Javier Burón Fernández

University of Córdoba

Susel Góngora Alonso

Universidad de Valladolid

Francisco Javier Díaz Pernas

Universidad de Valladolid

Cooperative Institutions

Universidad de Valladolid

University of Córdoba

Université Sultan Moulay Slimane

Hospital Universitario Río Hortega

Hospital Clínico Universitario de Valladolid

University of Carabobo

Quaid-i-Azam University

Universitat de València

Khwaja Fareed University of Engineering and Information Technology

Software (Spain)

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Aplicación plural de herramientas para gamificar. Análisis y comparativa

Beatriz Sainz de Abajo Isabel de la Torre Díez Miguel López-Coronado Javier Aguiar Pérez Carlos de Castro Lozano

Los beneficios de la gamificación, como instrumento pedagógico para la motivación de los alumnos en su proceso de aprendizaje, están sobradamente demostrados. Este enfoque lúdico, basado en la competitividad que ofrece el juego, persigue el interés del discente por la materia, colocándole en un rol más activo y estimulándole a través de recompensas. Son muchas las herramientas que nos ofrecen para gamificar. El objetivo de este artículo es el análisis comparativo de algunas de las aplicaciones más conocidas para la realización de test en línea como KAHOOT!, SOCRATIVE, QUIZIZZ o GOOGLEFORMS. Ello nos permite considerar la más adecuada en cada caso para los contenidos evaluados en el aula.

10.4995/inred2019.2019.10467

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Citations (8)

Analysis of Mental Health Disease Trends Using BeGraph Software in Spanish Health Care Centers: Case Study (Preprint)

Susel Góngora Alonso A. Bustos Beatriz Sainz de Abajo Manuel Franco Isabel de la Torre Díez

BACKGROUND In the era of big data, networks are becoming a popular factor in the field of data analysis. Networks are part of the main structure of BeGraph software, which is a 3D visualization application dedicated to the analysis of complex networks. OBJECTIVE The main objective of this research was to visually analyze tendencies of mental health diseases in a region of Spain, using the BeGraph software, in order to make the most appropriate health-related decisions in each case. METHODS For the study, a database was used with 13,531 records of patients with mental health disorders in three acute medical units from different health care complexes in a region of Spain. For the analysis, BeGraph software was applied. It is a web-based 3D visualization tool that allows the exploration and analysis of data through complex networks. RESULTS The results obtained with the BeGraph software allowed us to determine the main disease in each of the health care complexes evaluated. We noted 6.50% (463/7118) of admissions involving unspecified paranoid schizophrenia at the University Clinic of Valladolid, 9.62% (397/4128) of admissions involving chronic paranoid schizophrenia with acute exacerbation at the Zamora Hospital, and 8.84% (202/2285) of admissions involving dysthymic disorder at the Rio Hortega Hospital in Valladolid. CONCLUSIONS The data analysis allowed us to focus on the main diseases detected in the health care complexes evaluated in order to analyze the behavior of disorders and help in diagnosis and treatment.

Paranoid schizophrenia

10.2196/preprints.15527

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Wiki Tool for Adaptive, Accesibility, Usability and Colaborative Hypermedia Courses: MediaWikiCourse

Lecture notes in computer science (2011)

Carlos de Castro Lozano Enrique García Salcines José Miguel Ramírez F. J. Burón Beatriz Sainz de Abajo

Adaptive hypermedia

10.1007/978-3-642-20810-2_3

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Citations (1)

FoodScan: Food Monitoring App by Scanning the Groceries Receipts

IEEE Access (2020)

Beatriz Sainz de Abajo José García-Alonso Javier Berrocal Sergio Laso Isabel de la Torre Díez

In the mobile device market there is a large number of applications to help people monitor intake or provide suggestions to lose weight and manage a healthy diet. However, the vast majority of these apps consume a lot of time by having to introduce food one by one. This paper presents the work to develop and pilot test a new Android application, FoodScan, aimed at people over 70, specially those from rural environments or with limited technical knowledge, to manage their food from the items that appear on their grocery receipts, avoiding the obligation to introduce one by one those foods, and generating recommendations. To achieve this final objective, specific objectives have been completed as indicated in the methods section. We conducted a review of current calorie control applications to learn about their weaknesses and strengths. Different algorithms were tested to expedite the introduction of food into the application and the most suitable for the FoodScan application was selected. Likewise, several options were taken into account to create the knowledge base of food, taking into account dietary recommendations for people over 70 years. Once developed, a pilot evaluation was carried out with a convenience sample of 109 volunteers in rural areas of Caceres and Valladolid (Spain) and Alentejo (Portugal). They tested FoodScan for a month after which they completed a user satisfaction survey. 93 % (101/109) believed that the app was easy to download and install, 66 % (72/109) thought that it was easy to use, 47 % (51/109) noted that the charts with the recommendations helped them with diet control and 49 % (53/109) indicated that FoodScan helped them improve healthy eating habits. One-month pilot evaluation data suggested that most users found the app somewhat helpful for monitoring food intake, easy to download and easy to use.

Download

10.1109/access.2020.3046031

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Citations (16)

Development, Technical, and User Evaluation of a Web Mobile Application for Self-Control of Diabetes

Telemedicine Journal and e-Health (2016)

Begonya García-Zapirain Isabel de la Torre Díez Beatriz Sainz de Abajo Miguel López-Coronado

Objective:The main objective of this research was to develop and evaluate a Web-based mobile application (app) known as "Diario Diabetes" on both a technical and user level, by means of which individuals with diabetes may monitor their illness easily at any time and in any place using any device that has Internet access.Methods:The technologies used to develop the app were HTML, CSS, JavaScript, PHP, and MySQL, all of which are an open source. Once the app was developed, it was evaluated on a technical level (by measuring loading times) and on a user level, through a survey.Results:Different loading times for the application were measured, with it being noted that under no circumstances does this exceed 2 s. Usability was evaluated by 150 users who initially used the application. A majority (71%) of users used a PC to access the app, 83% considered the app's design to be attractive, 67% considered the tasks to be very useful, and 67% found it very easy to use.Conclusions:Although applications exist for controlling diabetes both at mobile virtual shops or on a research level, our app may help to improve the administration of these types of patients and they are the ones who will ultimately opt for one or the other. According to the results obtained, we can state that all users would recommend the app's use to other users.

10.1089/tmj.2015.0233

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Citations (14)

Impact of COVID-19 on the psychological health of university students in Spain and their attitudes toward Mobile mental health solutions

International Journal of Medical Informatics (2020)

Gonçalo Marques Nidal Drissi Isabel de la Torre Díez Beatriz Sainz de Abajo Sofía Ouhbi

Pandemic

Mental Health Care

Depression

10.1016/j.ijmedinf.2020.104369

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Citations (70)

Health Care Management Models for the Evolution of Hospitalization in Acute Inpatient Psychiatry Units: Comparative Quantitative Study (Preprint)

Susel Góngora Alonso Beatriz Sainz de Abajo Isabel de la Torre Díez Manuel Franco

BACKGROUND Mental health disorders are a problem that affects patients, their families, and the professionals who treat them. Hospital admissions play an important role in caring for people with these diseases due to their effect on quality of life and the high associated costs. In Spain, at the Healthcare Complex of Zamora, a new disease management model is being implemented, consisting of not admitting patients with mental diseases to the hospital. Instead, they are supervised in sheltered apartments or centers for patients with these types of disorders. OBJECTIVE The main goal of this research is to evaluate the evolution of hospital days of stay of patients with mental disorders in different hospitals in a region of Spain, to analyze the impact of the new hospital management model. METHODS For the development of this study, a database of patients with mental disorders was used, taking into account the acute inpatient psychiatry unit of 11 hospitals in a region of Spain. SPSS Statistics for Windows, version 23.0 (IBM Corp), was used to calculate statistical values related to hospital days of stay of patients. The data included are from the periods of 2005-2011 and 2012-2015. RESULTS After analyzing the results, regarding the days of stay in the different health care complexes for the period between 2005 and 2015, we observed that since 2012 at the Healthcare Complex of Zamora, the total number of days of stay were reduced by 64.69%. This trend is due to the implementation of a new hospital management model in this health complex. CONCLUSIONS With the application of a new hospital management model at the Healthcare Complex of Zamora, the number of days of stay of patients with mental diseases as well as the associated hospital costs were considerably reduced.

10.2196/preprints.15776

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Telematics Platform for People with Cognitive Problems: Experience with Healthcare Professionals (Preprint)

Susel Góngora Alonso José Miguel Toribio Guzmán Beatriz Sainz de Abajo Juan Luis Sánchez Manuel Franco

BACKGROUND The integration of new technologies in the Mental Health field helps to prevent the cognitive impairment of patients and improve their quality of life. OBJECTIVE The main objective of our paper is to analyze the usability of the Long Lasting Memories program applied to Mental Health professionals. METHODS The study sample consisted of 23 participants, Mental Health professionals, of which 52.2% are psychologists and 47.8% are qualified assistants. Participants were given a usability questionnaire that included different variables once the intervention was concluded with the program. RESULTS The results obtained from the questionnaire applied to the 23 participants of the study were analyzed taking into account the evaluation of use ease of the program, satisfaction and sustainability. CONCLUSIONS From the results collected through the program it can be concluded that, from the professional’s point of view in charge of the intervention, the usability degree is high.

Telematics

Health Professionals

10.2196/preprints.16115

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Approach to teaching communications systems by collaborative learning. Student perceptions in the application of problem-based learning: Analysis of results

Beatriz Sainz de Abajo Isabel de la Torre Díez Miguel López-Coronado Francisco Javier Díaz Pernas J. F. Díez-Higuera

This document reflects the results of the study at the end of the course, which justify how the application of problem-based learning and collaborative learning help the student to take on board in the most appropriate way the study material. An analysis of the results of the surveys carried out amongst those students learning Communication Systems in Industrial Technical Engineering was undertaken, in order to evaluate whether the application of Problem-Based Learning (PBL) methodology together with Collaborative Learning (CL) was likely to improve the rate of development of their ability indispensable in today's business world, and also to achieve those objectives set out in the course. By far the majority of students showed a very positive attitude towards this methodology, their objections being very limited. A comparison was carried out with the results of those courses of a similar nature at the University of Cordoba, as was the attitude of the students towards this new methodology, with very comparable results.

Problem-Based Learning

10.1109/educon.2010.5492376

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Citations (5)

Predicting bankruptcy of firms using earnings call data and transfer learning

PeerJ Computer Science (2023)

Hafeez Ur Rehman Siddiqui Beatriz Sainz de Abajo Isabel de la Torre Díez Furqan Rustam Muhammad Amjad Raza

Business collapse is a common event in economies, small and big alike. A firm's health is crucial to its stakeholders like creditors, investors, partners, etc. and prediction of the upcoming financial crisis is significantly important to devise appropriate strategies to avoid business collapses. Bankruptcy prediction has been regarded as a critical topic in the world of accounting and finance. Methodologies and strategies have been investigated in the research domain for predicting company bankruptcy more promptly and accurately. Conventionally, predicting the financial risk and bankruptcy has been solely achieved using the historic financial data. CEOs also communicate verbally via press releases and voice characteristics, such as emotion and tone may reflect a company's success, according to anecdotal evidence. Companies' publicly available earning calls data is one of the main sources of information to understand how businesses are doing and what are expectations for the next quarters. An earnings call is a conference call between the management of a company and the media. During the call, management offers an overview of recent performance and provides a guide for the next quarter's expectations. The earning calls summary provided by the management can extract CEO's emotions using sentiment analysis. This article investigates the prediction of firms' health in terms of bankruptcy and non-bankruptcy based on emotions extracted from earning calls and proposes a deep learning model in this regard. Features extracted from long short-term memory (LSTM) network are used to train machine learning models. Results show that the models provide results with a high score of 0.93, each for accuracy and F1 when trained on LSTM extracted feature from synthetic minority oversampling technique (SMOTE) balanced data. LSTM features provide better performance than traditional bag of words and TF-IDF features.

Bankruptcy Prediction

10.7717/peerj-cs.1134

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Citations (1)