Um Modelo para Assistência Educacional Ubíqua orientado a Doenças Crônicas Não Transmissíveis
Resumo
A educação ubíqua pode contribuir para a prevenção das doenças crônicas não transmissíveis (DCNTs) através de uma assistência educacional contextualizada e personalizada para controle dos fatores de risco. Este artigo apresenta o SALUS, um modelo computacional para assistência educacional ubíqua orientado a DCNTs. SALUS utiliza perfis dinâmicos, análise de similaridade e análise de padrões de históricos de contextos. Uma avaliação foi conduzida através de um ambiente simulado usando dados do MIMIC III para a formação de grupos e recomendação de conteúdos. Cinco grupos foram formados em cada um dos três critérios utilizados. Ao menos 65% dos indivíduos possuem similaridade entre as variáveis analisadas.
Palavras-chave:
Educação Ubíqua, Doenças Crônicas Não Transmissíveis, SALUS, MIMIC III, Prevenção
Referências
Abaza, H., Marschollek, M. (2017) SMS education for the promotion of diabetes self-management in low & middle income countries: a pilot randomized controlled trial in Egypt. BMC Public Health, 17(962).
Alotaibi, M. (2015) A Mobile Diabetes Educational System for Fasting Type-2 Diabetics in Saudi Arabia. In Proceedings of the 2nd ICITACEE, pages 173-176.
Cárdenas-Robledo, L. A., Peña-Ayala, A. (2018) Ubiquitous learning: A systematic review. Telematics and Informatics, 35(5):1097-1132.
Dey, A. K. (2001) Understanding and Using Context. Journal Personal and Ubiquitous Computing, 5(1):4-7.
Dupont, D., Barbosa, J. L. V., Alves, B. M. (2019) CHSPAM: a multi-domain model for sequential pattern discovery and monitoring in contexts histories. Pattern Analysis and Applications, pages 1-10.
Goldberger, A. L. et al. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23):215-220.
Goldschmidt, R., Passos, E. (2005) Data mining: um guia prático. RJ: Elsevier.
Guo, S., Chang, H. -K., Lin, C. -Y. (2015) Impact of Mobile Diabetes Self-Care System on patients’ knowledge, behavior and efficacy. Computers in Industry, 69:22-29.
Haeng-Kon, K. (2014). Convergence agent model for developing u-healthcare systems. Future Generation Computer Systems, 35:39-48.
Hidalgo, J. I., Maqueda, E., Risco-Martín, J. L., Cuesta-Infante, A., Colmenar, J. M., Nobel, J. (2014) glUCModel: A monitoring and modeling system for chronic diseases applied to diabetes. Journal of Biomedical Informatics, 48:183-192.
Johnson, A. E. W. et al. (2016) MIMIC-III, a freely accessible critical care database. Scientific Data, 3(160035).
Johnson, A., Pollard, T., Mark, R. (2019) MIMIC-III Clinical Database Demo. PhysioNet. Disponível em: [link]. Acesso em: 7 de Julho de 2019.
Kaufman, L., Rousseeuw, P.J. (1990) Finding Groups in Data: An Introduction to Cluster Analysis. New Jersey: John Wiley & Sons, Inc.
Kim, H., Xie, B. (2017) Health Literacy in the eHealth era: A Systematic review of the literature. Patient Education and Counseling, 100:1073-1082.
Larentis, A. V., Barbosa, D. N. F., da Silva, C. R., Barbosa, J. L. V. (2019) Applied Computing to Education on Noncommunicable Chronic Diseases: A Systematic Mapping Study. Telemedicine and e-Health.
Mendes Neto, F. M. et al. (2014) Content’s personalized recommendation for implementing ubiquitous learning in health 2.0. IEEE Latin America Transactions, 12(8):1515-1522.
Partridge, H., Shaban, C., Weiss, M. (2017) Innovating structured education for people with type 1 diabetes: Www.Bertieonline.org.UK. Journal of Diabetes Nursing, 21(7): 255-258.
Rosa, J. H., Barbosa, J. L. V., Ribeiro, G. D. (2016) ORACON: An Adaptive Model for Context Prediction. Expert Systems with Applications, 45(1):56–70.
TAM (2007) Standardized Technical Architecture Modeling - Conceptual and Design Level. Disponível em: [link]. Acesso em: 7 de Julho de 2019.
Wagner, A., Barbosa, J. L. V., Barbosa, D. N. F. (2014) A model for profile management applied to ubiquitous learning environments. Expert Systems with Applications, 41(4):2023-2034.
WHO (2013) Global Action Plan for the Prevention and Control of noncommunicable diseases 2013-2020. Disponível em: [link]. Acesso em: 7 de Julho de 2019.
WHO (2018) World Health Statistics 2018: Monitoring Health for the SDGs, sustainable development goals. Disponível em: [link]. Acesso em: 7 de Julho de 2019.
Wiedemann, T., Barbosa, J. L. V., Rigo, S. J., Barbosa, D. N. F. (2016) RecSim: A Model for Learning Objects Recommendation using Similarity of Sessions. Journal of Universal Computer Science, 22(8):1175–1200.
Alotaibi, M. (2015) A Mobile Diabetes Educational System for Fasting Type-2 Diabetics in Saudi Arabia. In Proceedings of the 2nd ICITACEE, pages 173-176.
Cárdenas-Robledo, L. A., Peña-Ayala, A. (2018) Ubiquitous learning: A systematic review. Telematics and Informatics, 35(5):1097-1132.
Dey, A. K. (2001) Understanding and Using Context. Journal Personal and Ubiquitous Computing, 5(1):4-7.
Dupont, D., Barbosa, J. L. V., Alves, B. M. (2019) CHSPAM: a multi-domain model for sequential pattern discovery and monitoring in contexts histories. Pattern Analysis and Applications, pages 1-10.
Goldberger, A. L. et al. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23):215-220.
Goldschmidt, R., Passos, E. (2005) Data mining: um guia prático. RJ: Elsevier.
Guo, S., Chang, H. -K., Lin, C. -Y. (2015) Impact of Mobile Diabetes Self-Care System on patients’ knowledge, behavior and efficacy. Computers in Industry, 69:22-29.
Haeng-Kon, K. (2014). Convergence agent model for developing u-healthcare systems. Future Generation Computer Systems, 35:39-48.
Hidalgo, J. I., Maqueda, E., Risco-Martín, J. L., Cuesta-Infante, A., Colmenar, J. M., Nobel, J. (2014) glUCModel: A monitoring and modeling system for chronic diseases applied to diabetes. Journal of Biomedical Informatics, 48:183-192.
Johnson, A. E. W. et al. (2016) MIMIC-III, a freely accessible critical care database. Scientific Data, 3(160035).
Johnson, A., Pollard, T., Mark, R. (2019) MIMIC-III Clinical Database Demo. PhysioNet. Disponível em: [link]. Acesso em: 7 de Julho de 2019.
Kaufman, L., Rousseeuw, P.J. (1990) Finding Groups in Data: An Introduction to Cluster Analysis. New Jersey: John Wiley & Sons, Inc.
Kim, H., Xie, B. (2017) Health Literacy in the eHealth era: A Systematic review of the literature. Patient Education and Counseling, 100:1073-1082.
Larentis, A. V., Barbosa, D. N. F., da Silva, C. R., Barbosa, J. L. V. (2019) Applied Computing to Education on Noncommunicable Chronic Diseases: A Systematic Mapping Study. Telemedicine and e-Health.
Mendes Neto, F. M. et al. (2014) Content’s personalized recommendation for implementing ubiquitous learning in health 2.0. IEEE Latin America Transactions, 12(8):1515-1522.
Partridge, H., Shaban, C., Weiss, M. (2017) Innovating structured education for people with type 1 diabetes: Www.Bertieonline.org.UK. Journal of Diabetes Nursing, 21(7): 255-258.
Rosa, J. H., Barbosa, J. L. V., Ribeiro, G. D. (2016) ORACON: An Adaptive Model for Context Prediction. Expert Systems with Applications, 45(1):56–70.
TAM (2007) Standardized Technical Architecture Modeling - Conceptual and Design Level. Disponível em: [link]. Acesso em: 7 de Julho de 2019.
Wagner, A., Barbosa, J. L. V., Barbosa, D. N. F. (2014) A model for profile management applied to ubiquitous learning environments. Expert Systems with Applications, 41(4):2023-2034.
WHO (2013) Global Action Plan for the Prevention and Control of noncommunicable diseases 2013-2020. Disponível em: [link]. Acesso em: 7 de Julho de 2019.
WHO (2018) World Health Statistics 2018: Monitoring Health for the SDGs, sustainable development goals. Disponível em: [link]. Acesso em: 7 de Julho de 2019.
Wiedemann, T., Barbosa, J. L. V., Rigo, S. J., Barbosa, D. N. F. (2016) RecSim: A Model for Learning Objects Recommendation using Similarity of Sessions. Journal of Universal Computer Science, 22(8):1175–1200.
Publicado
11/11/2019
Como Citar
LARENTIS, Andrêsa Vargas; BARBOSA, Débora Nice Ferrari; DA SILVA, Carla Rosana; BARBOSA, Jorge Luis Victória.
Um Modelo para Assistência Educacional Ubíqua orientado a Doenças Crônicas Não Transmissíveis. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 439-448.
DOI: https://doi.org/10.5753/cbie.sbie.2019.439.
