Artículos (Tecnología Electrónica)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11417

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The role of digital health in growth hormone therapy: perspectives from Gulf Cooperation Council pediatric endocrinologists
(Frontiers Media, 2025-10-15) Kaplan, Walid; Alherbish, Abdullah; Aljnaibi, Abdullah; Alsagheir, Afaf; Almutair, Angham; Farooque, Aqeel; Deeb, Asma; Abbas, Bassam Bin; Jubeh, Jamal Al; Attia, Najya; Thalange, Nandu; Salem Al Remeithi, Sareea; Dimitri, Paul; Rivera-Romero, Octavio; Koledova, Ekaterina; Tecnología Electrónica; TIC277: Informática de la Salud Participativa y Personalización
Background: With the increasing use of digital health tools patient-generated health data play a crucial role in clinical decision-making, particularly for monitoring treatment adherence. However, integrating data into routine practice remains challenging, especially for chronic conditions such as growth disorders requiring growth hormone therapy (GHT). Integrating these data is essential to improve treatment adherence and growth outcomes in pediatric patients on GHT. Aim: To explore perspectives of pediatric endocrinologists in the Gulf Cooperation Council (GCC) region on patient-generated health data for improving GHT adherence and identified strategies for integrating such data into clinical practice. Methods: A participatory workshop was conducted on March 2, 2024, in Dubai, United Arab Emirates, using the nominal group technique. Twelve pediatric endocrinologists from the GCC region, one chairman, and two moderators participated in the session. The session centered on three clinical scenarios: GHT naïve (recently diagnosed), poorly adherent, and poor responders. Through two structured voting rounds, experts individually identified, discussed, and ranked the top five most relevant and useful patient-generated health data factors. The first round prioritized key factors, while the second round allowed participants to reassess and refine their selections to reach consensus. The final discussion focused on how identified factors could integrate into clinical practice.Results: Twenty-two influencing factors were identified, representing the most relevant and useful types of patient-generated health data for integration into clinical practice. Top factors in the first ranking round included demographic data (21 points: age, income level, familiarity with technology); patient’s feelings about treatments and satisfaction (19 points); and social background (17 points: family support, insurance, caregiving responsibilities). Other considerations included reasons for missed injections and educational needs (15 points each). In the second round, social background (35 points) ranked highest, followed by injection context (34 points: timing, comfort, administration support) and patient’s feelings about treatments and satisfaction (30 points) emphasized motivational and emotional aspects of adherence. Conclusion: The study highlights the significant role of social background, injection contexts, and patient satisfaction as key patient-generated health data factors for pediatric endocrinologists in the GCC region. These findings highlight their potential integration into GHT workflows to enhance clinical decision-making.
A Comparative Study of BERT-Based Models for Teacher Classification in Physical Education
(MDPI, 2025-09-28) Martín-Hoz, Laura; Yanes Luis, Samuel; Huerta Cejudo, Jerónimo; Gutiérrez Reina, Daniel; Franco Álvarez, Evelia; Tecnología Electrónica; Ingeniería Electrónica; Junta de Andalucía; TIC201: ACE-TI
Assessing teaching behavior is essential for improving instructional quality, particularly in Physical Education, where classroom interactions are fast-paced and complex. Traditional evaluation methods such as questionnaires, expert observations, and manual discourse analysis are often limited by subjectivity, high labor costs, and poor scalability. These challenges underscore the need for automated, objective tools to support pedagogical assessment. This study explores and compares the use of Transformer-based language models for the automatic classification of teaching behaviors from real classroom transcriptions. A dataset of over 1300 utterances was compiled and annotated according to the teaching styles proposed in the circumplex approach (Autonomy Support, Structure, Control, and Chaos), along with an additional category for messages in which no style could be identified (Unidentified Style). To address class imbalance and enhance linguistic variability, data augmentation techniques were applied. Eight pretrained BERT-based Transformer architectures were evaluated, including several pretraining strategies and architectural structures. BETO achieved the highest performance, with an accuracy of 0.78, a macro-averaged F1-score of 0.72, and a weighted F1-score of 0.77. It showed strength in identifying challenging utterances labeled as Chaos and Autonomy Support. Furthermore, other BERT-based models purely trained with a Spanish text corpus like DistilBERT also present competitive performance, achieving accuracy metrics over 0.73 and and F1-score of 0.68. These results demonstrate the potential of leveraging Transformer-based models for objective and scalable teacher behavior classification. The findings support the feasibility of leveraging pretrained language models to develop scalable, AI-driven systems for classroom behavior classification and pedagogical feedback.
Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning
(IEEE, 2025-05-28) Mendoza Barrionuevo, Alejandro; Yanes Luis, Samuel; Gutiérrez Reina, Daniel; Toral, S. L.; Ingeniería Electrónica; Tecnología Electrónica; Junta de Andalucía; TIC201: ACE-TI
This letter presents a model-free deep reinforcement learning framework for informative path planning with heterogeneous fleets of autonomous surface vehicles to locate and collect plastic waste. The system employs two teams of vehicles: scouts and cleaners. Coordination between these teams is achieved through a deep reinforcement approach, allowing agents to learn strategies to maximize cleaning efficiency. The primary objective is for the scout team to provide an up-to-date contamination model, while the cleaner team collects as much waste as possible following this model. This strategy leads to heterogeneous teams that optimize fleet efficiency through inter-team cooperation supported by a tailored reward function. Different trainings of the proposed algorithm are compared with other state-of-the-art algorithms in three distinct scenarios, one with moderate convexity, another with narrow corridors and challenging access, and the last one larger, more complex and with more difficult to access shape. According to the obtained results, it is demonstrated that deep reinforcement learning based algorithms outperform baselines, exhibiting superior adaptability. In addition, training with examples of actions from other algorithms further improves performance, especially in scenarios where the search space is larger.
Computational Analysis of the Long Horizon FCS-MPC Problem for Power Converters
(2024-06) Zafra, Eduardo; Vázquez Pérez, Sergio; Geyer, Tobías; Aguilera, Ricardo P.; Freire Macías, Emilio; García Franquelo, Leopoldo; Ingeniería Electrónica; Matemática Aplicada II; Ministerio de Ciencia e Innovación (MICIN). España; TIC109: Tecnología
Abstract—Long prediction horizon finite control set model predictive control (LPH-FCS-MPC) for power converters can be reformulated as a box-constrained integer-least squares (ILS) problem to find the optimal control action without requiring an exhaustive search. Instead, the solution can be found by means of a sphere decoding method that still presents several intricacies regarding its complexity and its variable computational cost. This paper provides a study of the computational behavior of this ap- proach. Special emphasis is placed on how the generator matrix is calculated, either as a lower or an upper triangular structure. This choice decides whether the switching sequences are explored forward- or backward-in-time during the optimization process. In this work, it is explained how this selection holds a great impact on the computational burden of the algorithm. Similarly, it is also analyzed how the tuning of the FCS-MPC and system parameters also drastically impacts the computational cost.
Reduction in Prepulse Inhibition Following Acute Stress in Male and Female Wistar Rats
(Elsevier, 2025) Santos Carrasco, Daniel; Cintado García, María de los Ángeles; Casa Rivas, Luis Gonzalo de La; Psicología Experimental; Tecnología Electrónica
Prepulse inhibition (PPI) of the startle response is widely recognized as an operational index of sensorimotor gating. While its use in psychopharmacological studies has increased, it is essential to examine how different modulatory factors, such as emotional variables highlighted in human studies, influence PPI. To this end, this study aimed to assess the impact of acute stress on PPI and the startle response. We hypothesized that acute stress would reduce PPI and increase the startle response, potentially in a sex-dependent manner. To test this, male and female Wistar rats (n = 48) were exposed to acute stress via either forced swim test or inescapable footshocks treatment, with an untreated group serving as control. Immediately after stress exposure, PPI and the acoustic startle response were measured. Results revealed a significant reduction in PPI following both stressors, with no sex differences, suggesting that acute stress impairs sensorimotor gating regardless of sex. The startle response was reduced, again regardless of sex, in those animals subjected to the forced swim test as compared to those that received inescapable shock and those in the control group. These results may contribute to a deeper understanding of stress-induced alterations in sensorimotor gating and suggest a potential value for PPI as a translational measure in stress-related neuropsychiatric research. However, given the limitations of our current findings, further research is necessary to fully elucidate the specific mechanisms and the extent of PPI's translational utility in this context.
Evaluating the effectiveness of integrating biofeedback in the treatment of aggressive outbursts (BRET-IA2): A study protocol
(PUBLIC LIBRARY SCIENCE, 2025) Molina Cantero, Alberto Jesús; Rojas-Pérez, Isabel; Gómez de Terreros Guardiola, Montserrat; Gómez González, Isabel María; Vidosa-Batllés, José C; Bermejo-González, Teresa de Jesús; Merino Monge, Manuel; Personalidad, Evaluación y Tratamiento Psicológicos; Tecnología Electrónica; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
Introduction: This study provides a comprehensive overview of the materials and methods used to evaluate the effectiveness of the use of biofeedback in the treatment of aggressive episodes in children and adolescents. Background: Aggressive episodes are common in various disorders and are associated with deficits in emotional processing and impulse control, primarily due to dysfunctions in the amygdala and prefrontal cortex (PFC). These brain regions also regulate physiological arousal, influencing heart rate and other autonomic functions even before aggression manifests. These early signals can be shown to the person (biofeedback) reinforcing therapeutic skills to enhance emotional regulation and reduce aggression. Methods: A total of 70 participants will be recruited for a randomized controlled trial (RCT). All participants will receive therapy, although only the intervention group will incorporate biofeedback. The experimental study will be split into three blocks: (1) Home Monitoring: Physiological signals will be recorded using a smartwatch, and aggressive episodes will be captured with a camera; (2) Laboratory Assessment: Participants will attend three sessions, where therapists will induce aggressive reactions, using the video clips recorded at home. Simultaneously, real-time physiological signals will be measured. These sessions will also include relaxation periods before and after the provoked outburst; (3) Therapeutic Intervention: Similar to the laboratory assessment block, therapists will induce aggressive responses in three sessions; however, in this block, participants will receive therapy. Additionally, participants who belong to the intervention group, will include biofeedack in the therapy. Biofeedback is focused on heart rate (HR), heart rate variability (HRV), and skin conductance level (SCL). The CACIA, the Stroop, and other pre- and post-experimental tests. will be used to assess the differences between the control and intervention groups. Discussion: Emotions play a fundamental role in decision-making, social interactions, and mental health. Emotional dysregulation often leads to aggression, irritability, and anxiety. Showing physiological responses to patients, such as heart rate variability and skin conductance, may improve emotional awareness and regulation. This study aims to verify the effectiveness of including biofeedback in such therapy.
Healthcare Professionals’ Perceptions on Advances in Digital Health Devices for Growth Hormone Therapy: Clinical Expert Panel Discussion in the UK and France
(IOS Press, 2025-05) Rivera-Romero, Octavio; Atterbury, Abigail; Banerjee, Indraneel; Cochet, Solenn; Kapoor, Ritika R.; Mathew, Verghese; Perge, Kevin; Shah, Pratik; Tollerfield, Sally; Trouvin, Marie-Agathe; Koledova, Ekaterina; Keiser, Matthew; Tecnología Electrónica; TIC150: Tecnología Electrónica e Informática Industrial
Assessing healthcare professionals’ (HCPs’) perceptions on the appropriateness, ease of use and reliability of connected digital health technologies can help to understand acceptance and their recommendations to patients/caregivers for personalization of growth hormone (GH) therapy. Two study cases representing different versions of a digital device were used to facilitate expert panel discussions in the UK and France involving 19 paediatric HCPs. Panel members commented that the new functionalities embodied user-friendly technological progression. The evolution of the easypod device should sustain clinical decision support and enhance personalized approaches to care and management of patients receiving GH therapy.
Cognitive Accessibility in User Experience Assessment of Mobile Health: A Review
(IOS Press, 2025) Montoya, Laura; Rivera-Romero, Octavio; Dorronzoro Zubiete, Enrique; Tecnología Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); TIC150: Tecnología Electrónica e Informática Industrial
With an increasingly aging population, cognitive impairment (CI) prevalence is a major concern in healthcare. The international standard for health technologies specifies requirements to design cognitively accessible products aimed to support people with CI regardless of age. While User Experience (UX) design frameworks like the Honeycomb define accessibility as a dimension to be considered, little is known about how cognitive accessibility (CA) is being regarded in the UX assessment of mobile health (mHealth). We conducted a secondary analysis from a broader review on UX assessment of interactive mHealth technologies focusing on CA consideration in those targeted to people with, or at risk of CI. Nine papers were analyzed. Most common adaptations of the UX assessment procedure were to involve caregivers, conducting interviews in person and in naturalistic settings and time considerations. Only one study utilized an adapted validated version of mHealth technology questionnaire. Considerations of CA included means of motivation, simple design considerations and means of representation and understanding. While some guidelines of CA are being regarded, results of the assessments show a gap between what is important for patients and what was considered in the evaluation instruments. Developing mHealth specific UX questionnaires could help in understanding the needs of people with CI and guide the design of accessible technologies.
Detailed Assessment of Hardware Implementations, Attacks and Countermeasures for the Ascon Authenticated Cipher
(Wiley, 2025) Martín González, M.; Tena Sánchez, Erica; Potestad Ordóñez, Francisco Eugenio; Acosta Jiménez, Antonio José; Tecnología Electrónica; Electrónica y Electromagnetismo; Ministerio de Ciencia e Innovación (MICIN). España; European Union (UE). H2020; European Union (UE)
The design and implementation of lightweight-oriented ciphers on hardware has turned into an urgent matter with the expansive field of Internet of Things (IoT) and the ever increasing presence of small electronic devices that require fast and secure communication in our modern world. In 2023, the Ascon cipher was selected as the new standard authenticated encryption with associated data (AEAD) algorithm for lightweight environments by the National Institute of Standards and Technology (NIST). This paper provides a full comparison and joint evaluation of the hardware implementations, attacks and countermeasures that have been proposed for Ascon since it was published, aiming to shed light on some open development paths in addition to enable the hardware designer to make better informed decisions. All in all, Ascon implementations tend to achieve great performance while staying lightweight, but unprotected implementations are vulnerable to hardware attacks, and some attacks can even dodge counter measures. The very promising Ascon cipher will surely thrive in the field of lightweight cryptography, but further work into the design of secure implementations is still needed, being this paper a great starting point for researchers and designers alike.
Use of artificial intelligence techniques in characterization of vibration signals for application in agri-food engineering
(Springer Nature, 2025-03-15) Luque Sendra, Amalia; Campos Olivares, Daniel; Mazzoleni, Mirko; Ferramosca, Antonio; Previdi, Fabio; Carrasco Muñoz, Alejandro; Ingeniería del Diseño; Tecnología Electrónica; Universidad de Sevilla; TEP990: Proyectos de Ingeniería
Bottling machinery is a critical component in agri-food industries, where maintaining operational efficiency is key to ensuring productivity and minimizing economic losses. Early detection of faulty conditions in this equipment can significantly improve maintenance procedures and overall system performance. This research focuses on health monitoring of gripping pliers in bottling plants, a crucial task that has traditionally relied on analyzing raw vibration signals or using narrowly defined, application-specific features. However, these methods often face challenges related to limited robustness, high computational costs, and sensitivity to noise. To address these limitations, we propose a novel approach based on generic features extracted through basic signal processing techniques applied to vibration signals. These features are then classified using a random forest algorithm, enabling an effective analysis of health states. The proposed method is evaluated against traditional approaches and demonstrates clear advantages, including higher accuracy in detecting and classifying faulty conditions, greater robustness against random perturbations, and a reduced computational cost. Additionally, the method requires fewer training instances to achieve reliable performance. This study highlights the potential of artificial intelligence and signal processing techniques in predictive maintenance, offering a scalable and efficient solution for fault detection in manufacturing processes, particularly within the agri-food sector.
Cost-Effective Operation of Microgrids: A MILP-Based Energy Management System for Active and Reactive Power Control
(Elsevier, 2025-04) García Caro, Sebastián; Bracco, Stefano; Parejo Matos, Antonio; Fresia, Matteo; Guerrero Alonso, Juan Ignacio; León de Mora, Carlos; Tecnología Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission. Fondo Social Europeo (FSO); TIC150: Tecnología Electrónica e Informática Industrial
Microgrids (MGs) have emerged as a potential solution for the integration of Distributed Energy Resources (DERs) into the distribution network. In this sense, to effectively manage MGs, it is essential to implement Energy Management Systems (EMSs). This entails not only performing the unit commitment but also considering the voltage and reactive power technical constraints and managing ancillary services. This paper contributes with a comprehensive EMS for the optimal management of active and reactive power of a generic grid-tied MG composed of Renewable Energy Sources (RESs), Battery Energy Storage Systems (BESSs), Diesel Generator (DGs) units and loads, with the goal of reducing the operating costs of the facility. The EMS includes models for the power electronics units to apply reactive power management and a generic formulation for the management of the startup and shutdown cycles of dispatchable units. Furthermore, a detailed modeling of BESS and DG units is presented, reflecting the actual behavior of the devices. The MG is modeled as a multi-busbar network, with the application of the power flow equations to establish the link between power flows and nodal voltages. All the constraints are linearized to formulate the EMS as a Mixed-Integer Linear Programming (MILP) optimization problem. The EMS is validated in a real facility: the CATEPS Microgrid Living-Lab. The results demonstrate the operational effectiveness of the EMS in different seasons, exhibiting a reduction in costs ranging from 21.84 % in summer to a 5.69 % in winter compared to a scenario with RES production but without energy management. In addition, a comprehensive examination of reactive power and voltage management is presented. Furthermore, an empirical assessment of the power flow equations linearization demonstrated minimal discrepancy in the results when compared with those obtained with the non-linear equations, exhibiting a mean absolute error of 8.8e-5 p.u. and 3.2e-5 rad in voltage magnitude and phase angle, respectively, in the most unfavorable scenario. A sensitivity analysis of the startup and shutdown cycles management of the BESS reveals a negligible effect on operational costs, yet it provides a mechanism for managing the battery stress by reducing the number of startups in a complete week from 28 to 16 in summer and from 37 to 24 in winter. The dependence between the maximum charging and discharging power on the state of charge of the BESS is also assessed in the use case.
A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
(Elsevier, 2025-06) García Caro, Sebastián; Fresia, Matteo; Mora-Merchán, Javier María; Carrasco Muñoz, Alejandro; Personal Vázquez, Enrique; León de Mora, Carlos; Tecnología Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission. Fondo Social Europeo (FSO); TIC150: Tecnología Electrónica e Informática Industrial
A comprehensive knowledge of topology is of great importance for the effective operation and maintenance of distribution networks. This paper contributes with a novel data-driven topology identification method for low-voltage distribution networks based on the wavelet transform. The method uses only energy measurements from smart meters, being compatible with the current European smart meter capabilities. The method identifies the feeder and phase topology of single and three-phase customers, even in unbalanced situations. A computationally-efficient methodology to link customers' time-frequency features with their network connection is proposed. The performance of the method is assessed on eleven non-synthetic networks, with a robustness assessment of factors such as network observability, dataset size, measurement errors, and Renewable Energy Sources (RES) penetration. Accuracy rates exceeding 95 % are obtained in most cases, outperforming an energy-conservation approach. A 98 % accuracy can be achieved with a 30-day hourly dataset if at least 80 % of network observability is provided. For lower observability levels, 45 or 60 days of data are needed to reach similar rates. The sensitivity analysis of measurement error demonstrated that it had a negligible influence on the results. The method showed favorable results even in scenarios with high-RES penetration, with accuracy values exceeding 95 %.
Key Components of Participatory Design Workshops for Digital Health Solutions: Nominal Group Technique and Feasibility Study
(Springer, 2025-05-14) Denecke, Kerstin; Rivera-Romero, Octavio; Giunti, Guido; Holten, Karin van; Gabarrón, Elia; Tecnología Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); European Union (UE). H2020; TIC150: Tecnología Electrónica e Informática Industrial
Participatory design (PD) is an essential method in the development of digital health solutions since it promises to increase acceptance, usability, and trust in the developed solution. Although careful planning and preparation is crucial for the success of PD workshops, a framework of key components to consider is still missing. The objective of this work is to develop such framework enriched with examples for aspects to be considered when planning and conducting PD workshops for designing and developing digital interventions in healthcare. We applied the nominal group technique with four participants with backgrounds in computer science, health informatics, psychology, and social anthropology to identify key components of PD workshops. The resulting framework was applied by an expert in PD to a case of a digital health solution for fatigue self-management for multiple sclerosis. The feasibility and applicability of the framework and its shortcomings were assessed. As a result, a framework consisting of five main categories and a total of 36 factors were assigned and defined in relation to the categories. The categories are participatory process, involved persons and their roles, workshop definition, setting, privacy and ethics, including regulations. The application of the framework to the test case demonstrated the feasibility and applicability of the suggested framework as well as the shortcomings of the analyzed PD process. This framework provides practical guidance while highlighting the complexity of PD workshops, encouraging their broad adoption, critical reflection, and continuous refinement. It has potential to improve the conduct of PD workshops and, in this way, potential to improve usability, acceptance, and usefulness of digital health solutions. In future work, the user perspective could be used to extend the framework.
The Unexpected Harms of Artificial Intelligence in Healthcare: Reflections on Four Real-World Cases
(IOS Press, 2025-05) Denecke, Kerstin; Lopez-Campos, Guillermo; Rivera-Romero, Octavio; Gabarrón, Elia; Tecnología Electrónica; TIC150: Tecnología Electrónica e Informática Industrial
Rapid advances in Artificial Intelligence (AI), especially with large language models, present both opportunities and challenges in healthcare. This article analyzes real-world AI-related harms in healthcare. Methods: We selected four recent AI-related incidents from the AIAAIC Repository. Results: The incidents discussed include: Whisper’s harmful hallucinations; UNOS’s algorithm delaying transplants for black patients; the WHO’s S.A.R.A.H. chatbot providing inaccurate health information; and Character AI’s chatbot promoting disordered eating among teens. Discussion and conclusion: These incidents highlight diverse risks, from misinformation to safety concerns, involving both industry and institutional providers. The article emphasizes the need for systematic reporting of AI-related harms, concerns about security, privacy, and ethics, and calls for a centralized health-specific database to enhance patient safety and understanding.
Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review
(JMIR Publications, 2024-01-01) Gabarrón, Elia; Larbi, Dillys; Rivera-Romero, Octavio; Denecke, Kerstin; Tecnología Electrónica; TIC150: Tecnología Electrónica e Informática Industrial
Background: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes. Objective: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA. Methods: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases—PubMed, Embase, and IEEE Xplore—and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Results: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence. Conclusions: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI’s impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.
Healthcare professionals’ perspectives towards the digitalisation of paediatric growth hormone therapies: expert panels in Italy and Korea
(Frontiers Media, 2024-07-10) Rivera-Romero, Octavio; Chae, Hyun Wook; Faienza, Maria Felicia; Vergani, Edoardo; Cheon, Chong Kun; Di Mase, Raffaella; Frasca, Francesco; Lee, Hae Sang; Giavoli, Claudia; Kim, Jihyun; Klain, Antonella; Moon, Jung Eun; Iezzi, Maria Laura; Yeh, James; Aversa, Antonio; Rhie, Young Jun; Koledova, Ekaterina; Tecnología Electrónica; TIC150: Tecnología Electrónica e Informática Industrial
Introduction: To analyse the perspectives of healthcare professionals (HCPs) regarding the acceptance of digital health solutions for growth hormone (GH) deficiency care. This study identified factors impacting HCPs’ intent to use and recommend digital solutions supporting recombinant-human growth hormone (r-hGH) therapy in Italy and Korea with a use case of connected drug delivery system (Aluetta® with Smartdot™) integrated in a platform for GH treatment support (the Growzen™ digital health ecosystem). Methods: Participatory workshops were conducted in Rome, Italy, and Seoul, Korea, to collect the perspectives of 22 HCPs on various predefined topics. HCPs were divided into two teams, each moderated by a facilitator. The workshops progressed in five phases: introduction of the project and experts, capturing views on the current context of digitalisation, perceived usefulness and ease of use of Aluetta® with Smartdot™, exploration of the perception of health technology evolution, and combined team recommendations. Data shared by HCPs on technology acceptance were independently analysed using thematic analysis, and relevant findings were shared and validated with experts. Results: HCPs from both Italy and Korea perceived Aluetta® with Smartdot™ and the Growzen™ based digital health ecosystem as user-friendly, intuitive, and easy-to-use solutions. These solutions can result in increased adherence, a cost-effective healthcare system, and medication self-management. Although technology adoption and readiness may vary across countries, it was agreed that using digital solutions tailored to the needs of users may help in data-driven clinical decisions and strengthen HCP–patient relationships. Conclusion: HCPs’ perspectives on the digitalisation in paediatric GH therapies suggested that digital solutions enable automatic, real-time injection data transmission to support adherence monitoring and evidence-based therapy, strengthen HCP–patient relationships, and empower patients throughout the GH treatment process.
Digital Health Program to Support Family Caregivers of Children Undergoing Growth Hormone Therapy: Qualitative Feasibility Study
(JMIR Publications, 2025-02-05) Jiménez-Díaz, Alba; Pierantonelli, Maitena; Coscolín, Patricia Morte; Salinas-Uhalte, Amaia; Quer-Palomas, Silvia; Rivera-Romero, Octavio; Herrero, Rocío; Fernández Luque, Luis; Baños, Rosa; Berrios, Ricardo C.; Arriba, Antonio de; Tecnología Electrónica; Universidad de Sevilla; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission. Fondo Social Europeo (FSO); TIC150: Tecnología Electrónica e Informática Industrial
Background: Caregivers of children with growth hormone deficiency often face emotional challenges (eg, stress) associated with their children’s health conditions. This psychological burden might affect children’s adherence to treatment and hinder their health-related quality of life (HrQoL). This assumption is leading to seriously considering multidimensional clinical approaches to pediatric health conditions where the emotional well-being of caregivers should be accounted for to optimize children’s health outcomes. Novel mobile health (mHealth) solutions based on emotional and behavioral change techniques can play a promising role because they are increasingly used within different health areas to support adaptive psychological functioning. However, whether and how mHealth solutions of this type of emotional well-being support caregivers of children with growth-related problems is an issue that needs to be clarified. Objective: This study aimed to gather qualitative information to better understand individualized experiences of caregiving of children undergoing growth hormone therapy (GHt) and perceived barriers or facilitators for the adoption of an mHealth solution called Adhera Caring Digital Program (ACDP).Methods: A total of 10 family caregivers were recruited at Miguel Servet Children’s Hospital, and they engaged with the ACDP for 1 month. The ACDP is a mobile-based digital intervention focused on promoting the overall well-being of family caregivers which provides access to personalized education, motivational mobile-based messages, and mental well-being exercises such as mindfulness or respiratory exercises. Subsequently, an individual semistructured interview was performed to gather qualitative user experience information. Results: The digital intervention was well-received. The ACDP was found to be useful, easy to use, and understandable, addressing all the difficulties expressed by caregivers. It was also noted to be particularly helpful at the beginning of the treatment and, for some families, became a natural tool that strengthened the parent-child relationship. Conclusions: The ACDP is a promising and well-accepted tool that enhances the experience of patients and caregivers. It improves the management of growth hormone deficiency and promotes the overall well-being of family caregivers.
Gestión óptima en microrredes con soporte fotovoltaico e hidrógeno verde
(Comité Español de Automática, 2025) Moliner Heredia, Rubén; Vivas Venegas, Carlos; Rodríguez Rubio, Francisco; Tecnología Electrónica; Ingeniería de Sistemas y Automática; Agencia Estatal de Investigación. España
La gestión de la energía es esencial para un correcto control de una microrred. Es importante que dicha gestión tenga en cuenta la optimización de la vida útil de los componentes de la microrred. Uno de los posibles comportamientos que se desean evitar es el apagado y encendido continuo de determinados componentes, tales como las celdas de combustible y los electrolizadores. En este artículo se ha propuesto un algoritmo usando control predictivo basado en modelo (MPC) utilizando diversas restricciones de tiempos mínimos de activación de dichos componentes para evitar efectos perjudiciales sobre los equipos de hidrógeno verde. Además, se ha propuesto un método de transmisión entre iteraciones del algoritmo para que la aplicación de dichas restricciones sea compatible con errores en las predicciones del algoritmo y de perturbaciones en el sistema. Para validar el algoritmo propuesto, se ha desarrollado un modelo simplificado de la microrred, y se han realizado simulaciones y comparaciones con varios algoritmos utilizando Matlab.
Low-Cost Full Correlated-Power-Noise Generator to Counteract Side-Channel Attacks
(MDPI, 2025-03-12) Tena Sánchez, Erica; Potestad Ordóñez, Francisco Eugenio; Zúñiga González, Virginia; Acosta Jiménez, Antonio José; Tecnología Electrónica; Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Junta de Andalucía; European Union (UE). H2020; Ministerio para la Transformacion Digital y Función Pública; European Commission. Fondo Social Europeo (FSO); TIC180: Diseño de Circuitos Integrados Digitales y Mixtos
Considerable attention has been given to addressing side-channel attacks to improve the security of cryptographic hardware implementations. These attacks encourage the exploration of various countermeasures across different levels of abstraction, through masking and hiding techniques, mainly. In this paper, we introduce a novel hiding countermeasure designed to mitigate Correlation Power Analysis (CPA) attacks without significant overhead. The new countermeasure interferes with the processed data, minimizing the power correlation with the secret key. The proposed method involves using a Correlated-Power-Noise Generator (CPNG). This study is supported by experimental results using CPA attacks on a SAKURA-G board with a SPARTAN-6 Xilinx FPGA. An Advanced Encryption Standard (AES) cipher with 128/256-bit key size is employed for this purpose. The proposed secure design of AES has an area overhead of 29.04% compared to unprotected AES. After conducting a CPA attack, the acquisition of information about the private key has been reduced drastically by 44.5%.
Assessing the impact of vaccines on COVID-19 efficacy in survival rates: a survival analysis approach for clinical decision support
(Frontiers Media, 2024-11) González Rodríguez, Juan Luis; Oprescu, Andreea M.; Muñoz Lezcano, Sergio; Cordero Ramos, Jaime; Romero Cabrera, Juan Luis; Armengol de la Hoz, Miguel Ángel; Estella, Ángel; Tecnología Electrónica
Background: The global COVID-19 pandemic, caused by the SARS-CoV-2 virus, has presented significant challenges to healthcare systems worldwide. Objective: This study, based on an analysis of a cohort from the Public Health System of Andalusia (Spain), aims to evaluate how vaccination affects case-fatality rate in patients hospitalized due to COVID-19 infection in Andalusia. Methods: The cohort consists of 37,274 individuals after applying the inclusion criteria. We conducted survival analyses employing the Cox proportional hazards models and generated adjusted survival curves to examine the outcomes. The analyses were performed from three perspectives: vaccinated vs. unvaccinated patients, vaccinated and unvaccinated patients grouped by age, and stratified by vaccination status. Results: Results indicate a substantial correlation between vaccination and a 20% reduction in the risk of case-fatality. Age-specific effects reveal varying degrees of protection across different age groups. Conclusion: These findings emphasize the pivotal role of vaccination status in COVID-19 risk assessment, supporting the development of a clinical decision support system for accurate predictions and optimizing healthcare management at admission.

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