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OBJECTIVE: To analyze the opinions of nursing professionals on the current limitations and future potential of digital tools in healthcare. DESIGN: Qualitative and descriptive study. LOCATION: The study took place during an asynchronous MOODLE course on the use of ICT in healthcare, specifically aimed at nursing professionals. PARTICIPANTS: The number of nurses enrolled in the course was 150. METHODS: A qualitative study was conducted focusing on the positive and negative aspects that telenursing can offer in the context of a Moodle training in new technologies for nurses. A thematic analysis was carried out following the method proposed by Braun and Clarke. RESULTS: In the end 68 nurses participated in the forum. Their statements, opinions and perceptions were analyzed and 28 descriptive codes were obtained and subsequently categorized into positive and negative aspects. CONCLUSIONS: Nurses positively value the usefulness of digital tools and identify a wide range of benefits of telenursing in daily practice. At the same time, they point out crucial limitations that may slow down the adoption of telenursing, pointing to areas for improvement such as training and digital literacy of both patients and professionals. They consider that telenursing can humanise care, but insist on the need to prevent its use from increasing health inequalities.
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Actitud del Personal de Salud , Atención Primaria de Salud , Investigación Cualitativa , Humanos , Femenino , Masculino , Teleenfermería , Adulto , Persona de Mediana Edad , Enfermería , Telemedicina/métodosRESUMEN
Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.
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Atención a la Salud , Hospitales , HumanosRESUMEN
BACKGROUND: Video is used daily for various purposes, such as leisure, culture, and even learning. Currently, video is a tool that is available to a large part of the population and is simple to use. This audio-visual format has many advantages such as its low cost, speed of dissemination, and possible interaction between users. For these reasons, it is a tool with high dissemination and educational potential, which could be used in the field of health for learning about and management of chronic diseases by adult patients. OBJECTIVE: The following review determines whether the use of health educational videos by adult patients with chronic diseases is effective for their self-management according to the literature. METHODS: An electronic literature search of the PubMed, CINAHL, and MEDLINE (via the EBSCOhost platform) databases up to April 2020 was conducted. The systematic scoping review followed the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. RESULTS: After reviewing 1427 articles, 12 were selected as the most consistent with the proposed inclusion criteria. After their review, it was found that the studies showed that video is effective as a tool for improving care related to chronic diseases. CONCLUSIONS: Video is effective in improving the care and quality of life for patients with chronic diseases, whether the initiative for using video came from their health care professionals or themselves.
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Personal de Salud , Calidad de Vida , Adulto , Enfermedad Crónica , Humanos , Aprendizaje , Poder PsicológicoRESUMEN
Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients' dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients' unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.
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Diabetes Mellitus , Manejo de la Enfermedad , Hipertensión , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Enfermedad Crónica , Atención a la Salud , Humanos , Hipertensión/diagnóstico , Hipertensión/terapia , Persona de Mediana Edad , Adulto JovenRESUMEN
BACKGROUND: To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. METHODS: The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. RESULTS: Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. CONCLUSIONS: By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.
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Sistemas de Apoyo a Decisiones Clínicas , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/etiología , Adulto , Anciano , Simulación por Computador , Femenino , Humanos , Masculino , Tamizaje Masivo , Persona de Mediana Edad , Medición de Riesgo , Programas Informáticos , TelemedicinaRESUMEN
Heterogeneity of people with diabetes makes maintaining blood glucose control and achieving therapy adherence a challenge. It is fundamental that patients get actively involved in the management of the disease in their living environments. The objective of this paper is to evaluate the use and acceptance of a self-management system for diabetes developed with User Centered Design Principles in community settings. Persons with diabetes and health professionals were involved the design, development and evaluation of the self-management system; which comprised three iterative cycles: scenario definition, user archetype definition and system development. A comprehensive system was developed integrating modules for the management of blood glucose levels, medication, food intake habits, physical activity, diabetes education and messaging. The system was adapted for two types of principal users (personas): Type 1 Diabetes user and Type 2 Diabetes user. The system was evaluated by assessing the use, the compliance, the attractiveness and perceived usefulness in a multicenter randomized pilot study involving 20 patients and 24 treating professionals for a period of four weeks. Usage and compliance of the co-designed system was compared during the first and the last two weeks of the study, showing a significantly improved behaviour of patients towards the system for each of the modules. This resulted in a successful adoption by both type of personas. Only the medication module showed a significantly different use and compliance (p= 0.01) which can be explained by the different therapeutic course of the two types of diabetes. The involvement of patients to make their own decisions and choices form design stages was key for the adoption of a self-management system for diabetes.
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Diabetes Mellitus Tipo 1/terapia , Diabetes Mellitus Tipo 2/terapia , Intercambio de Información en Salud/estadística & datos numéricos , Telemedicina/métodos , Automonitorización de la Glucosa Sanguínea , Diabetes Mellitus Tipo 1/psicología , Diabetes Mellitus Tipo 2/psicología , Humanos , Sistemas de Información/organización & administración , Sistemas Recordatorios/estadística & datos numéricosRESUMEN
Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 ± 0.0035 s (max = 0.2504 s), whereas the Raspberry Pi yields a delay of 0.0175 ± 0.149 s (max = 0.294 s) for N = 300. Our analysis confirms that portable devices ( p < < 0 . 01 ) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems.
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Internet , Telemedicina/métodos , Dispositivos Electrónicos Vestibles , Atención a la Salud , Humanos , Reproducibilidad de los Resultados , Telemedicina/tendenciasRESUMEN
Symptom checkers are software tools that allow users to submit a set of symptoms and receive advice related to them in the form of a diagnosis list, health information or triage. The heterogeneity of their potential users and the number of different components in their user interfaces can make testing with end-users unaffordable. We designed and executed a two-phase method to test the respiratory diseases module of the symptom checker Erdusyk. Phase I consisted of an online test with a large sample of users (n=53). In Phase I, users evaluated the system remotely and completed a questionnaire based on the Technology Acceptance Model. Principal Component Analysis was used to correlate each section of the interface with the questionnaire responses, thus identifying which areas of the user interface presented significant contributions to the technology acceptance. In the second phase, the think-aloud procedure was executed with a small number of samples (n=15), focusing on the areas with significant contributions to analyze the reasons for such contributions. Our method was used effectively to optimize the testing of symptom checker user interfaces. The method allowed kept the cost of testing at reasonable levels by restricting the use of the think-aloud procedure while still assuring a high amount of coverage. The main barriers detected in Erdusyk were related to problems understanding time repetition patterns, the selection of levels in scales to record intensities, navigation, the quantification of some symptom attributes, and the characteristics of the symptoms.
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Sistemas Hombre-Máquina , Sistemas de Apoyo a Decisiones Clínicas , Humanos , Análisis Multivariante , Análisis de Componente PrincipalRESUMEN
Google Glass is a wearable sensor presented to facilitate access to information and assist while performing complex tasks. Despite the withdrawal of Google in supporting the product, today there are multiple applications and much research analyzing the potential impact of this technology in different fields of medicine. Google Glass satisfies the need of managing and having rapid access to real-time information in different health care scenarios. Among the most common applications are access to electronic medical records, display monitorizations, decision support and remote consultation in specialties ranging from ophthalmology to surgery and teaching. The device enables a user-friendly hands-free interaction with remote health information systems and broadcasting medical interventions and consultations from a first-person point of view. However, scientific evidence highlights important technical limitations in its use and integration, such as failure in connectivity, poor reception of images and automatic restart of the device. This article presents a technical study on the aforementioned limitations (specifically on the latency, reliability and performance) on two standard communication schemes in order to categorize and identify the sources of the problems. Results have allowed us to obtain a basis to define requirements for medical applications to prevent network, computational and processing failures associated with the use of Google Glass.
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Telemedicina , Dispositivos Electrónicos Vestibles , Comunicación , Procesamiento de Imagen Asistido por Computador , Factores de TiempoRESUMEN
The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015.
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Redes de Comunicación de Computadores , Computación en Informática Médica , Dispositivo de Identificación por Radiofrecuencia/métodos , Algoritmos , Diseño de Equipo , HumanosRESUMEN
INTRODUCTION: Norway has a high use of e-health. METHODS: This paper summarizes and discusses the published data from the Tromsø 7 Study, conducted between 2015 and 2016, focusing on e-health utilization in the Norwegian population aged 40 and above. RESULTS: More than half of the participants reported using the Internet for health purposes. The main channels for obtaining information were search engines, apps, social media platforms, and online videos. The respondents frequently acted upon the information obtained online, and online health information influenced decisions regarding healthcare utilization and treatment management. Most respondents indicated a positive reaction to the information found online. CONCLUSIONS: The Tromsø 7 Study highlights the widespread utilization of e-health in Norway. The study also emphasizes the significant impact of e-health on individuals' decision-making processes related to their health. The findings suggest that the use of e-health overall does not replace the use of traditional health services, but rather functions as a supplement. Most respondents report positive reactions to online health information, highlighting the importance and relevance of e-health in modern healthcare practices.
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Internet , Noruega , Humanos , Adulto , Persona de Mediana Edad , Conducta en la Búsqueda de Información , Información de Salud al Consumidor , Anciano , Medios de Comunicación Sociales , Telemedicina , Masculino , Motor de Búsqueda , FemeninoRESUMEN
The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection.
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Minería de Datos , Casas de Salud , Algoritmos , Humanos , Monitoreo FisiológicoRESUMEN
BACKGROUND: Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE: This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS: A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS: A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS: These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.
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Introduction: Cancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, a large quantity of data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities. Methods: Following a co-creation, an interactive approach thanks to the Interactive Process Mining paradigm, and data from a tertiary hospital, we developed an exploratory tool to present cancer patients' progress over time. Results: This work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical relevance for healthcare professionals for monitoring cancer patients' care processes in the context of the LifeChamps project together with a graphical and navigable Process Indicator in the context of prostate cancer patients. Discussion: The tool presented includes Process Mining techniques to infer actual processes and present understandable results visually and navigable, looking for different types of patients, trajectories, and behaviors.
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BACKGROUND: Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people's health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. OBJECTIVE: To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. METHODS: A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. RESULTS: The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. CONCLUSION: Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Inteligencia Artificial , Cobertura Universal del Seguro de Salud , Urgencias Médicas , Promoción de la Salud , Humanos , Reproducibilidad de los Resultados , Revisiones Sistemáticas como AsuntoRESUMEN
People with intellectual disabilities have more sedentary lifestyles than the general population. Regular physical activity is of both medical and social importance, reducing the risk of cardiovascular disease and promoting functioning in everyday life. Exergames have been envisioned for promoting physical activity; however, most of them are not user-friendly for individuals with intellectual disabilities. In this paper, we report the design, development, and user acceptance of a mobile health solution connected to sensors to motivate physical activity. The system is mounted on an indoor stationary bicycle and an ergometer bike tailored for people with intellectual disabilities. The development process involved the application of user-centered design principles to customize the system for this group. The system was pilot-tested in an institutional house involving six end-users (intervention group) and demonstrated/self-tested to relatives of persons with ID and staff (supervision group). A System Usability Scale and open-ended interview in the supervision group were used to assess the user acceptance and perceived usefulness. Results indicate that the users with an intellectual disability enjoyed using the system, and that respondents believed it was a useful tool to promote physical activity for the users at the institution. The results of this study provide valuable information on beneficial technological interventions to promote regular physical activity for individuals with intellectual disabilities.
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Discapacidad Intelectual , Ciclismo , Ejercicio Físico , Videojuego de Ejercicio , HumanosRESUMEN
Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the "4+1 architectural model".
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Supervivientes de Cáncer , Nombres , Neoplasias , Inteligencia Artificial , Macrodatos , Humanos , InteligenciaRESUMEN
BACKGROUND: Patients with chronic disease represent an at-risk group in the face of the COVID-19 crisis as they need to regularly monitor their lifestyle and emotional management. Coping with the illness becomes a challenge due to supply problems and lack of access to health care facilities. It is expected these limitations, along with lockdown and social distancing measures, have affected the routine disease management of these patients, being more pronounced in low- and middle-income countries with a flawed health care system. OBJECTIVES: The purpose of this study is to describe a protocol for a randomized controlled trial to test the efficacy of the Adhera® MejoraCare Digital Program, an mHealth intervention aimed at improving the quality of life of patients with chronic diseases during the COVID-19 outbreak in Paraguay. METHOD: A two-arm randomized controlled trial will be carried out, with repeated measures (baseline, 1-month, 3-month, 6-month, and 12-month) under two conditions: Adhera® MejoraCare Digital Program or waiting list. The primary outcome is a change in the quality of life on the EuroQol 5-Dimensions 3-Levels Questionnaire (EQ-5D-3L). Other secondary outcomes, as the effect on anxiety and health empowerment, will be considered. All participants must be 18 years of age or older and meet the criteria for chronic disease. A total of 96 participants will be recruited (48 per arm). CONCLUSIONS: It is expected that the Adhera® MejoraCare Digital Program will show significant improvements in quality of life and emotional distress compared to the waiting list condition. Additionally, it is hypothesized that this intervention will be positively evaluated by the participants in terms of usability and satisfaction. The findings will provide new insights into the viability and efficacy of mHealth solutions for chronic disease management in developing countries and in times of pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT04659746.
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COVID-19 , Telemedicina , Humanos , Adolescente , Adulto , COVID-19/epidemiología , Calidad de Vida , SARS-CoV-2 , Paraguay/epidemiología , Control de Enfermedades Transmisibles , Enfermedad Crónica , Ensayos Clínicos Controlados Aleatorios como AsuntoRESUMEN
The importance of e-health to citizens, patients, health providers, governments, and other stakeholders is rapidly increasing. E-health services have a range of advantages. For instance, e-health may improve access to services, reduce costs, and improve self-management. E-health may allow previously underserved populations to gain access to services. Services utilizing apps, social media, or online video are rapidly gaining ground in most countries. In this special issue, we present a range of up-to-date studies from around the world, providing important insights into central topics relating to e-health services.
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Accesibilidad a los Servicios de Salud , Telemedicina , Servicios de Salud , Humanos , Área sin Atención Médica , Poblaciones VulnerablesRESUMEN
Objective To analyze the opinions of nursing professionals on the current limitations and future potential of digital tools in healthcare. Design Qualitative and descriptive study. Location The study took place during an asynchronous MOODLE course on the use of ICT in healthcare, specifically aimed at nursing professionals. Participants The number of nurses enrolled in the course was 150. Methods A qualitative study was conducted focusing on the positive and negative aspects that telenursing can offer in the context of a Moodle training in new technologies for nurses. A thematic analysis was carried out following the method proposed by Braun and Clarke. Results In the end 68 nurses participated in the forum. Their statements, opinions and perceptions were analyzed and 28 descriptive codes were obtained and subsequently categorized into positive and negative aspects. Conclusions Nurses positively value the usefulness of digital tools and identify a wide range of benefits of telenursing in daily practice. At the same time, they point out crucial limitations that may slow down the adoption of telenursing, pointing to areas for improvement such as training and digital literacy of both patients and professionals. They consider that telenursing can humanise care, but insist on the need to prevent its use from increasing health inequalities. (AU)
Objetivo Analizar las opiniones de los profesionales de enfermería sobre las limitaciones actuales y el potencial futuro de las herramientas digitales en la atención sanitaria. Diseño Estudio cualitativo y descriptivo. Lugar El estudio se desarrolló durante un curso MOODLE asíncrono sobre el uso de las TIC en la atención sanitaria, dirigido específicamente a profesionales de enfermería. Participantes El número de enfermeras inscritas en el curso fue de 150. MétodosSe realizó un estudio cualitativo centrado en los aspectos positivos y negativos que puede ofrecer la teleenfermería en el contexto de una formación Moodle en nuevas tecnologías para enfermeras. Se realizó un análisis temático siguiendo el método propuesto por Braun y Clarke. Resultados Finalmente participaron en el foro 68 enfermeras. Se analizaron las declaraciones, las opiniones y las percepciones de las mismas, obteniéndose 28 códigos descriptivos que posteriormente se categorizaron en aspectos positivos y negativos. Conclusiones Las enfermeras valoran positivamente la utilidad de las herramientas digitales e identifican una amplia gama de beneficios de la teleenfermería en la práctica diaria. Al mismo tiempo, señalan limitaciones cruciales que pueden ralentizar la adopción de la teleenfermería, señalando áreas de mejora como la formación y la alfabetización digital tanto de pacientes como de profesionales. Consideran que la teleenfermería puede humanizar la asistencia, pero insisten en la necesidad de evitar que su uso aumente las desigualdades en salud. (AU)