Propuesta de Arquitectura de Referencia de Sistemas de e-Salud y e-Inclusión

Las organizaciones necesitan adaptarse cada vez más de una manera más flexible a un entorno en el que cambian permanentemente los requisitos del usuario y los objetivos de negocio y especialmente, en un ámbito como el sociosanitario. Esto requiere capacidad de influir en todas las actividades del ciclo de vida de un sistema TIC (Tecnologías de la Información y Comunicación), desde la organización estructural hasta la infraestructura de redes [DOE03]. Para ello, es esencial la existencia de una metodología y una arquitectura que permita la descripción y visualización del sistema desde diferentes dominios y sus relaciones con los actores en los ámbitos de la e-salud y la e-inclusión . Actualmente, grandes y medianas organizaciones son ya conscientes del problema que supone carecer de un marco arquitectural de referencia para sus sistemas. La tesis pretende resolver este problema con la propuesta de una metodología, una arquitectura marco de referencia y una serie de herramientas para uso de los diferentes actores en las diversas fases por las que pasa un sistema durante su ciclo de vida. Para ello, se presentan los resultados de esta tesis, plasmados en: o un estudio del estado del arte alrededor de la e-salud y la e-inclusión, sus sistemas software y sus arquitecturas; o una propuesta de marco de referencia arquitectural en el ámbito de los sistemas sociosanitarios, que incluye las plantillas para documentar dicha arquitectura; o una descripción de dos sistemas, HEALTHMATE y CONFIDENT haciendo uso de la metodología propuesta; o y una evaluación del marco de referencia y de las descripciones arquitecturales La existencia de la arquitectura software de un sistema de e-salud o de e-inclusión plasmada en las diferentes vistas permite también: una documentación del sistema que facilita la comunicación entre los diferentes actores, la detección de fallos en la arquitectura en sus etapas iniciales y la respuesta ante cambios en cualquier módulo del sistema, con la cTraver Salcedo, V. (2005). Propuesta de Arquitectura de Referencia de Sistemas de e-Salud y e-Inclusión [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1846Palanci

Evaluating the Social Media Performance of Hospitals in Spain: A Longitudinal and Comparative Study

[EN] BACKGROUND: Social media is changing the way in which citizens and health professionals communicate. Previous studies have assessed the use of Health 2.0 by hospitals, showing clear evidence of growth in recent years. In order to understand if this happens in Spain, it is necessary to assess the performance of health care institutions on the Internet social media using quantitative indicators. OBJECTIVES: The study aimed to analyze how hospitals in Spain perform on the Internet and social media networks by determining quantitative indicators in 3 different dimensions: presence, use, and impact and assess these indicators on the 3 most commonly used social media - Facebook, Twitter, YouTube. Further, we aimed to find out if there was a difference between private and public hospitals in their use of the aforementioned social networks. METHODS: The evolution of presence, use, and impact metrics is studied over the period 2011- 2015. The population studied accounts for all the hospitals listed in the National Hospitals Catalog (NHC). The percentage of hospitals having Facebook, Twitter, and YouTube profiles has been used to show the presence and evolution of hospitals on social media during this time. Usage was assessed by analyzing the content published on each social network. Impact evaluation was measured by analyzing the trend of subscribers for each social network. Statistical analysis was performed using a lognormal transformation and also using a nonparametric distribution, with the aim of comparing t student and Wilcoxon independence tests for the observed variables. RESULTS: From the 787 hospitals identified, 69.9% (550/787) had an institutional webpage and 34.2% (269/787) had at least one profile in one of the social networks (Facebook, Twitter, and YouTube) in December 2015. Hospitals' Internet presence has increased by more than 450.0% (787/172) and social media presence has increased ten times since 2011. Twitter is the preferred social network for public hospitals, whereas private hospitals showed better performance on Facebook and YouTube. The two-sided Wilcoxon test and t student test at a CI of 95% show that the use of Twitter distribution is higher (P<.001) for private and public hospitals in Spain, whereas other variables show a nonsignificant different distribution. CONCLUSIONS: The Internet presence of Spanish hospitals is high; however, their presence on the 3 main social networks is still not as high compared to that of hospitals in the United States and Western Europe. Public hospitals are found to be more active on Twitter, whereas private hospitals show better performance on Facebook and YouTube. This study suggests that hospitals, both public and private, should devote more effort to and be more aware of social media, with a clear strategy as to how they can foment new relationships with patients and citizens.The authors wish to acknowledge the ITACA Institute (Universitat Politècnica de València) for making possible the publication of this paper through the Excellence Support program for the publication in high-impact international journals.S11119

Estimating Patient Empowerment and Nurses' Use of Digital Strategies: eSurvey Study

[EN] Patient empowerment is seen as the capability to understand health information and make decisions based on it. It is a competence that can improve self-care, adherence and overall health. The COVID-19 pandemic has increased the need for information and has also reduced the number of visits to health centers. Nurses have had to adapt in order to continue offering quality care in different environments such as the digital world, but this entails assessing the level of their patients¿ empowerment and adapting material and educational messages to new realities. The aim of this study is, on the one hand, to assess nurses¿ use of digital resources to provide reinforcing information to their patients and, on the other hand, to evaluate how they assess the level of empowerment of their patients. To perform the study, 850 nurses answered 21 questions related to their own digital literacy and patients¿ empowerment. The ability to make decisions is the characteristic most selected by nurses (70%) as useful in measuring patient empowerment, whereas 9.19% do not measure it in any way. Printed material is most often used by nurses to offer additional information to patients (71.93%), mobile applications are the least used option (21.58%), and elder nurses are those who most recommend digital resources. In this study, younger nurses make little or no use of technology as a resource for training and monitoring patients. In spite of some limitations concerning the study, digital health needs to be promoted as an indisputable tool in the nurse¿s briefcase in the future to ensure that older patients can manage electronic resources in different fields.Navarro Martínez, O.; Igual García, J.; Traver Salcedo, V. (2021). Estimating Patient Empowerment and Nurses' Use of Digital Strategies: eSurvey Study. International Journal of Environmental research and Public Health. 18(18):1-16. https://doi.org/10.3390/ijerph18189844S116181

Empowering Patients Living With Chronic Conditions Using Video as an Educational Tool: Scoping Review

[EN] 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.This study was supported in part by Universidad Catolica San Vicente Martir.Navarro, O.; Escrivá, M.; Faubel, R.; Traver Salcedo, V. (2021). Empowering Patients Living With Chronic Conditions Using Video as an Educational Tool: Scoping Review. JOURNAL OF MEDICAL INTERNET RESEARCH. 23(7):1-9. https://doi.org/10.2196/26427S1923

Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

[EN] 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.This research was partially funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement no. 727560.Valero Ramon, Z.; Fernández Llatas, C.; Valdivieso, B.; Traver Salcedo, V. (2020). Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining. Sensors. 20(18):1-25. https://doi.org/10.3390/s20185330S1252018Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. 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The Lancet, 388(10053), 1659-1724. doi:10.1016/s0140-6736(16)31679-8Gómez, J., Oviedo, B., & Zhuma, E. (2016). Patient Monitoring System Based on Internet of Things. Procedia Computer Science, 83, 90-97. doi:10.1016/j.procs.2016.04.103Harvey, A., Brand, A., Holgate, S. T., Kristiansen, L. V., Lehrach, H., Palotie, A., & Prainsack, B. (2012). The future of technologies for personalised medicine. New Biotechnology, 29(6), 625-633. doi:10.1016/j.nbt.2012.03.009Larry Jameson, J., & Longo, D. L. (2015). Precision Medicine—Personalized, Problematic, and Promising. Obstetrical & Gynecological Survey, 70(10), 612-614. doi:10.1097/01.ogx.0000472121.21647.38Collins, F. S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793-795. doi:10.1056/nejmp1500523Glasgow, R. E., Kwan, B. M., & Matlock, D. D. (2018). Realizing the full potential of precision health: The need to include patient-reported health behavior, mental health, social determinants, and patient preferences data. Journal of Clinical and Translational Science, 2(3), 183-185. doi:10.1017/cts.2018.31Whittemore, A. S. (2010). Evaluating health risk models. Statistics in Medicine, 29(23), 2438-2452. doi:10.1002/sim.3991Reynolds, B. C., Roem, J. L., Ng, D. K. S., Matsuda-Abedini, M., Flynn, J. T., Furth, S. L., … Parekh, R. S. (2020). Association of Time-Varying Blood Pressure With Chronic Kidney Disease Progression in Children. JAMA Network Open, 3(2), e1921213. doi:10.1001/jamanetworkopen.2019.21213Campbell, H., Hotchkiss, R., Bradshaw, N., & Porteous, M. (1998). Integrated care pathways. BMJ, 316(7125), 133-137. doi:10.1136/bmj.316.7125.133Schienkiewitz, A., Mensink, G. B. M., & Scheidt-Nave, C. (2012). Comorbidity of overweight and obesity in a nationally representative sample of German adults aged 18-79 years. BMC Public Health, 12(1). doi:10.1186/1471-2458-12-658Must, A. (1999). The Disease Burden Associated With Overweight and Obesity. JAMA, 282(16), 1523. doi:10.1001/jama.282.16.1523Audureau, E., Pouchot, J., & Coste, J. (2016). Gender-Related Differential Effects of Obesity on Health-Related Quality of Life via Obesity-Related Comorbidities. Circulation: Cardiovascular Quality and Outcomes, 9(3), 246-256. doi:10.1161/circoutcomes.115.002127Everhart, J. E., Pettitt, D. J., Bennett, P. H., & Knowler, W. C. (1992). Duration of Obesity Increases the Incidence of NIDDM. Diabetes, 41(2), 235-240. doi:10.2337/diab.41.2.235Wannamethee, S. G. (2005). Overweight and obesity and weight change in middle aged men: impact on cardiovascular disease and diabetes. Journal of Epidemiology & Community Health, 59(2), 134-139. doi:10.1136/jech.2003.015651Ziegelstein, R. C. (2018). Perspectives in Primary Care: Knowing the Patient as a Person in the Precision Medicine Era. The Annals of Family Medicine, 16(1), 4-5. doi:10.1370/afm.2169Tricoli, A., Nasiri, N., & De, S. (2017). Wearable and Miniaturized Sensor Technologies for Personalized and Preventive Medicine. Advanced Functional Materials, 27(15), 1605271. doi:10.1002/adfm.201605271Saponara, S., Donati, M., Fanucci, L., & Celli, A. (2016). An Embedded Sensing and Communication Platform, and a Healthcare Model for Remote Monitoring of Chronic Diseases. Electronics, 5(4), 47. doi:10.3390/electronics5030047Alvarez, C., Rojas, E., Arias, M., Munoz-Gama, J., Sepúlveda, M., Herskovic, V., & Capurro, D. (2018). Discovering role interaction models in the Emergency Room using Process Mining. Journal of Biomedical Informatics, 78, 60-77. doi:10.1016/j.jbi.2017.12.015Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1-2), 79-133. doi:10.1016/s0004-3702(96)00025-2Orphanou, K., Stassopoulou, A., & Keravnou, E. (2016). DBN-Extended: A Dynamic Bayesian Network Model Extended With Temporal Abstractions for Coronary Heart Disease Prognosis. IEEE Journal of Biomedical and Health Informatics, 20(3), 944-952. doi:10.1109/jbhi.2015.2420534Spruijt-Metz, D., Hekler, E., Saranummi, N., Intille, S., Korhonen, I., Nilsen, W., … Pavel, M. (2015). Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Translational Behavioral Medicine, 5(3), 335-346. doi:10.1007/s13142-015-0324-1Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Yoo, S., Cho, M., Kim, E., Kim, S., Sim, Y., Yoo, D., … Song, M. (2016). Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital. International Journal of Medical Informatics, 88, 34-43. doi:10.1016/j.ijmedinf.2015.12.018Ibanez-Sanchez, G., Fernandez-Llatas, C., Martinez-Millana, A., Celda, A., Mandingorra, J., Aparici-Tortajada, L., … Traver, V. (2019). Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case. International Journal of Environmental Research and Public Health, 16(10), 1783. doi:10.3390/ijerph16101783Chambers, D. A., Feero, W. G., & Khoury, M. J. (2016). Convergence of Implementation Science, Precision Medicine, and the Learning Health Care System. JAMA, 315(18), 1941. doi:10.1001/jama.2016.3867Cameranesi, M., Diamantini, C., Mircoli, A., Potena, D., & Storti, E. (2020). Extraction of User Daily Behavior From Home Sensors Through Process Discovery. 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BMJ Open, 7(3), e012493. doi:10.1136/bmjopen-2016-01249

Special Issue on E-Health Services

[EN] 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.Wynn, R.; Gabarron, E.; Johnsen, JK.; Traver Salcedo, V. (2020). Special Issue on E-Health Services. International Journal of Environmental research and Public Health (Online). 17(8):1-6. https://doi.org/10.3390/ijerph17082885S16178Risling, T., Martinez, J., Young, J., & Thorp-Froslie, N. (2017). Evaluating Patient Empowerment in Association With eHealth Technology: Scoping Review. Journal of Medical Internet Research, 19(9), e329. doi:10.2196/jmir.7809Wynn, R., Oyeyemi, S. O., Budrionis, A., Marco-Ruiz, L., Yigzaw, K. Y., & Bellika, J. G. (2020). Electronic Health Use in a Representative Sample of 18,497 Respondents in Norway (The Seventh Tromsø Study - Part 1): Population-Based Questionnaire Study. JMIR Medical Informatics, 8(3), e13106. doi:10.2196/13106Pagliari, C., Sloan, D., Gregor, P., Sullivan, F., Detmer, D., Kahan, J. P., … MacGillivray, S. (2005). What Is eHealth (4): A Scoping Exercise to Map the Field. Journal of Medical Internet Research, 7(1), e9. doi:10.2196/jmir.7.1.e9Oyeyemi, S. O., & Wynn, R. (2014). Giving cell phones to pregnant women and improving services may increase primary health facility utilization: a case–control study of a Nigerian project. Reproductive Health, 11(1). doi:10.1186/1742-4755-11-8Oyeyemi, S. O., & Wynn, R. (2015). The use of cell phones and radio communication systems to reduce delays in getting help for pregnant women in low- and middle-income countries: a scoping review. Global Health Action, 8(1), 28887. doi:10.3402/gha.v8.28887Acharibasam, J. W., & Wynn, R. (2018). Telemental Health in Low- and Middle-Income Countries: A Systematic Review. International Journal of Telemedicine and Applications, 2018, 1-10. doi:10.1155/2018/9602821Kummervold, P. E., & Wynn, R. (2012). Health Information Accessed on the Internet: The Development in 5 European Countries. International Journal of Telemedicine and Applications, 2012, 1-3. doi:10.1155/2012/297416Andreassen, H. K., Bujnowska-Fedak, M. M., Chronaki, C. E., Dumitru, R. C., Pudule, I., Santana, S., … Wynn, R. (2007). European citizens’ use of E-health services: A study of seven countries. BMC Public Health, 7(1). doi:10.1186/1471-2458-7-53Gabarron, E., & Wynn, R. (2016). Use of social media for sexual health promotion: a scoping review. Global Health Action, 9(1), 32193. doi:10.3402/gha.v9.32193Gabarron, E., Luque, L. F., Schopf, T. R., Lau, A. Y. S., Armayones, M., Wynn, R., & Serrano, J. A. (2017). Impact of Facebook Ads for Sexual Health Promotion Via an Educational Web App. International Journal of E-Health and Medical Communications, 8(2), 18-32. doi:10.4018/ijehmc.2017040102Marco-Ruiz, L., Wynn, R., Oyeyemi, S. O., Budrionis, A., Yigzaw, K. Y., & Bellika, J. G. (2020). Impact of Illness on Electronic Health Use (The Seventh Tromsø Study - Part 2): Population-Based Questionnaire Study. Journal of Medical Internet Research, 22(3), e13116. doi:10.2196/13116Oyeyemi, S. O., Gabarron, E., & Wynn, R. (2014). Ebola, Twitter, and misinformation: a dangerous combination? BMJ, 349(oct14 5), g6178-g6178. doi:10.1136/bmj.g6178Gabarron, E., Serrano, J. A., Wynn, R., & Lau, A. Y. (2014). Tweet Content Related to Sexually Transmitted Diseases: No Joking Matter. Journal of Medical Internet Research, 16(10), e228. doi:10.2196/jmir.3259Wynn, R., Oyeyemi, S. O., Johnsen, J.-A. K., & Gabarron, E. (2017). Tweets are not always supportive of patients with mental disorders. International Journal of Integrated Care, 17(3), 149. doi:10.5334/ijic.3261Del Hoyo, J., Nos, P., Faubel, R., Bastida, G., Muñoz, D., Valero-Pérez, E., … Aguas, M. (2020). Adaptation of TECCU App Based on Patients´ Perceptions for the Telemonitoring of Inflammatory Bowel Disease: A Qualitative Study Using Focus Groups. International Journal of Environmental Research and Public Health, 17(6), 1871. doi:10.3390/ijerph17061871Li, Z., & Xu, X. (2020). Analysis of Network Structure and Doctor Behaviors in E-Health Communities from a Social-Capital Perspective. International Journal of Environmental Research and Public Health, 17(4), 1136. doi:10.3390/ijerph17041136Simonÿ, C., Riber, C., Bodtger, U., & Birkelund, R. (2019). Striving for Confidence and Satisfaction in Everyday Life with Chronic Obstructive Pulmonary Disease: Rationale and Content of the Tele-Rehabilitation Programme >C☺PD-Life>>. International Journal of Environmental Research and Public Health, 16(18), 3320. doi:10.3390/ijerph16183320López-Liria, R., López-Villegas, A., Enebakk, T., Thunhaug, H., Lappegård, K. T., & Catalán-Matamoros, D. (2019). Telemonitoring and Quality of Life in Patients after 12 Months Following a Pacemaker Implant: the Nordland Study, a Randomised Trial. International Journal of Environmental Research and Public Health, 16(11), 2001. doi:10.3390/ijerph16112001Tang, Y., Yang, Y.-T., & Shao, Y.-F. (2019). Acceptance of Online Medical Websites: An Empirical Study in China. International Journal of Environmental Research and Public Health, 16(6), 943. doi:10.3390/ijerph16060943A. Rahim, A., Ibrahim, M., A. Salim, F., & Ariffin, M. (2019). Health Information Engagement Factors in Malaysia: A Content Analysis of Facebook Use by the Ministry of Health in 2016 and 2017. International Journal of Environmental Research and Public Health, 16(4), 591. doi:10.3390/ijerph16040591Kolasa, K., & Kozinski, G. (2020). How to Value Digital Health Interventions? A Systematic Literature Review. International Journal of Environmental Research and Public Health, 17(6), 2119. doi:10.3390/ijerph17062119Almeida, A. F., Rocha, N. P., & Silva, A. G. (2020). Methodological Quality of Manuscripts Reporting on the Usability of Mobile Applications for Pain Assessment and Management: A Systematic Review. International Journal of Environmental Research and Public Health, 17(3), 785. doi:10.3390/ijerph17030785Reiners, F., Sturm, J., Bouw, L. J. W., & Wouters, E. J. M. (2019). Sociodemographic Factors Influencing the Use of eHealth in People with Chronic Diseases. International Journal of Environmental Research and Public Health, 16(4), 645. doi:10.3390/ijerph16040645Menon, A., Fatehi, F., Bird, D., Darssan, D., Karunanithi, M., Russell, A., & Gray, L. (2019). Rethinking Models of Outpatient Specialist Care in Type 2 Diabetes Using eHealth: Study Protocol for a Pilot Randomised Controlled Trial. International Journal of Environmental Research and Public Health, 16(6), 959. doi:10.3390/ijerph16060959Obstfelder, A., Engeseth, K. H., & Wynn, R. (2007). Characteristics of successfully implemented telemedical applications. Implementation Science, 2(1). doi:10.1186/1748-5908-2-25Littauer, H., Sexton, H., & Wynn, R. (2005). Qualities clients wish for in their therapists. Scandinavian Journal of Caring Sciences, 19(1), 28-31. doi:10.1111/j.1471-6712.2005.00315.xWynn, R., & Wynn, M. (2006). Empathy as an interactionally achieved phenomenon in psychotherapy. Journal of Pragmatics, 38(9), 1385-1397. doi:10.1016/j.pragma.2005.09.00

Performance assessment of a closed-loop system for diabetes management

Telemedicine systems can play an important role in the management of diabetes, a chronic condition that is increasing worldwide. Evaluations on the consistency of information across these systems and on their performance in a real situation are still missing. This paper presents a remote monitoring system for diabetes management based on physiological sensors, mobile technologies and patient/ doctor applications over a service-oriented architecture that has been evaluated in an international trial (83,905 operation records). The proposed system integrates three types of running environments and data engines in a single serviceoriented architecture. This feature is used to assess key performance indicators comparing them with other type of architectures. Data sustainability across the applications has been evaluated showing better outcomes for full integrated sensors. At the same time, runtime performance of clients has been assessed spotting no differences regarding the operative environmentThe authors wish to acknowledge the consortium of the METABO project (funded by the European Commission, Grant nr. 216270) for their commitment during concept development and trial execution.Martínez Millana, A.; Fico, G.; Fernández Llatas, C.; Traver Salcedo, V. (2015). Performance assessment of a closed-loop system for diabetes management. Medical and Biological Engineering and Computing. 53(12):1295-1303. doi:10.1007/s11517-015-1245-3S129513035312Bellazzi R, Larizza C, Montani A et al (2002) A telemedicine support dor diabetes management: the T-IDDM project. Comput Methods Programs Biomed 69:147–161Boloor K, Chirkova R, Salo T, Viniotis Y (2011) Analysis of response time percentile service level agreements in soa-based applications. 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Public Health Innovations Program tailored to Master on Telecommunications’ Students

[EN] Developed and under-developed countries are facing several challenges related to public health and sustainability of health care systems. New challenges demand of the collaborative action of multiple stakeholders with different backgrounds. In the late years, telecommunication engineers are involved in a wide range of companies and institutions to help designing and building innovative and efficient solutions, among which public health is a paradigmatic example. In this paper authors introduce a program for teaching public health principles and tools focused at telecommunications master students. The program is presented in five practices of three hours duration (fifteen hours overall). The sessions are structured in the classic problem-solving methodology in which the students must respond to concrete and general questions by the application of knowledge, practice and reasoning. Each practice includes theoretical framework introduction, provision of tools and use of open repositories to complete the assignments. The covered topics are: mobile health and usability, open data, data mining, Internet of Things and wearable and process mining.Martínez Millana, A.; Martínez Mateu, L.; Guillem Sánchez, MS.; Traver Salcedo, V. (2021). Public Health Innovations Program tailored to Master on Telecommunications’ Students. En Proceedings INNODOCT/20. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 137-144. https://doi.org/10.4995/INN2020.2020.11860OCS13714

A Semantic layer for Embedded Sensor Networks

Sensor Networks progressively assumed the critical role of bridges between the real world and information systems, through always more consolidated and efficient sensor technologies that enable advanced heterogeneous sensor grids. Sensor data is commonly used by advanced systems and intelligent applications in order to archive complex goals. Processes that build high-level knowledge from sensor data are commonly considered as the key core concept. This paper proposes a semantic layer that would optimally support the knowledge building in sensor systems as well as it enables semantic interaction model at different levels (module, subsystem, system). The semantic layer proposed in the paper is currently used by several architectures and applications in the context of different domains.Pileggi, SF.; Fernández Llatas, C.; Traver Salcedo, V. (2011). A Semantic layer for Embedded Sensor Networks. ARPN Journal of Systems and Software. 1(3):101-107. http://hdl.handle.net/10251/63174S1011071

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

[EN] Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents' health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident's activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.Shirali, M.; Bayo-Monton, JL.; Fernández Llatas, C.; Ghassemian, M.; Traver Salcedo, V. (2020). Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment. Sensors. 20(24):1-25. https://doi.org/10.3390/s20247167S1252024Lutz, W., Sanderson, W., & Scherbov, S. (2001). The end of world population growth. 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Journal of Network and Computer Applications, 97, 48-65. doi:10.1016/j.jnca.2017.08.017Palipana, S., Pietropaoli, B., & Pesch, D. (2017). Recent advances in RF-based passive device-free localisation for indoor applications. Ad Hoc Networks, 64, 80-98. doi:10.1016/j.adhoc.2017.06.007Chen, G., Wang, A., Zhao, S., Liu, L., & Chang, C.-Y. (2017). Latent feature learning for activity recognition using simple sensors in smart homes. Multimedia Tools and Applications, 77(12), 15201-15219. doi:10.1007/s11042-017-5100-4Tewell, J., O’Sullivan, D., Maiden, N., Lockerbie, J., & Stumpf, S. (2019). Monitoring meaningful activities using small low-cost devices in a smart home. Personal and Ubiquitous Computing, 23(2), 339-357. doi:10.1007/s00779-019-01223-2Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 10, 138-154. doi:10.1016/j.pmcj.2012.07.003Wang, A., Chen, G., Wu, X., Liu, L., An, N., & Chang, C.-Y. (2018). Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors, 18(11), 3629. doi:10.3390/s18113629Liu, Y., Wang, X., Zhai, Z., Chen, R., Zhang, B., & Jiang, Y. (2019). Timely daily activity recognition from headmost sensor events. ISA Transactions, 94, 379-390. doi:10.1016/j.isatra.2019.04.026Viani, F., Robol, F., Polo, A., Rocca, P., Oliveri, G., & Massa, A. (2013). Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation. Proceedings of the IEEE, 101(11), 2381-2396. doi:10.1109/jproc.2013.2266858Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011). Discovering Activities to Recognize and Track in a Smart Environment. IEEE Transactions on Knowledge and Data Engineering, 23(4), 527-539. doi:10.1109/tkde.2010.148Samsung SmartThings http://www.smartthings.com/Apple HomeKit https://www.apple.com/ios/home/Vera3 Advanced Smart Home Controller http://getvera.com/controllers/vera3/AndroidThings https://developer.android.com/things/index.htmlTeleAlarm Assisted Living http://www.telealarm.com/en/products/assisted-livingBirdie—Connected Sensors around the Home https://birdie.care/AllJoyn Framework https://identity.allseenalliance.org/developersCook, D. J., Crandall, A. S., Thomas, B. L., & Krishnan, N. C. (2013). CASAS: A Smart Home in a Box. Computer, 46(7), 62-69. doi:10.1109/mc.2012.328Skubic, M., Alexander, G., Popescu, M., Rantz, M., & Keller, J. (2009). A smart home application to eldercare: Current status and lessons learned. Technology and Health Care, 17(3), 183-201. doi:10.3233/thc-2009-0551Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y., & Jansen, E. (2005). The Gator Tech Smart House: a programmable pervasive space. Computer, 38(3), 50-60. doi:10.1109/mc.2005.107Doctor, F., Hagras, H., & Callaghan, V. (2005). A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(1), 55-65. doi:10.1109/tsmca.2004.838488Abowd, G. D., & Mynatt, E. D. (2005). Designing for the Human Experience in Smart Environments. Smart Environments, 151-174. doi:10.1002/047168659x.ch7Technology Integrated Health Management (TIHM) Project https://www.sabp.nhs.uk/tihmAhvar, E., Daneshgar-Moghaddam, N., Ortiz, A. M., Lee, G. M., & Crespi, N. (2016). On analyzing user location discovery methods in smart homes: A taxonomy and survey. Journal of Network and Computer Applications, 76, 75-86. doi:10.1016/j.jnca.2016.09.012Milenkovic, M., & Amft, O. (2013). Recognizing Energy-related Activities Using Sensors Commonly Installed in Office Buildings. Procedia Computer Science, 19, 669-677. doi:10.1016/j.procs.2013.06.089Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Dogan, O., Bayo-Monton, J.-L., Fernandez-Llatas, C., & Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors, 19(3), 557. doi:10.3390/s19030557Schmitter-Edgecombe, M., & Cook, D. J. (2009). Assessing the Quality of Activities in a Smart Environment. Methods of Information in Medicine, 48(05), 480-485. doi:10.3414/me0592Alberdi Aramendi, A., Weakley, A., Aztiria Goenaga, A., Schmitter-Edgecombe, M., & Cook, D. J. (2018). Automatic assessment of functional health decline in older adults based on smart home data. 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