Exploring mobile health applications for self-management of Diabetes Mellitus
Abstract
Patients with diabetes mellitus (DM) needs to deal with many data and consider diverse factors in the treatment routine. In this regard, this master’s thesis presents an app for monitoring the linkage among treatment factors of Type 1 DM with an interactive data visualization approach. Initially, we systematically reviewed the literature to investigate DM app’s features, the basis for its design and testing. Next, an app was prototyped, preliminarily assessed with 76 patients, and deployed along with a website dashboard. Afterward, we conducted a pilot trial with 4 patients, and a satisfaction assessment with 97 patients and 9 health professionals.
References
American Diabetes Association (2018). 8. Pharmacologic Approaches to Glycemic Treatment: Standards of Medical Care in Diabetes—2018. Diabetes Care, v. 41, n. S1.
Bellei, E. A., Biduski, D., Cechetti, N. P. and De Marchi, A. C. B. (2018). Diabetes Mellitus m-Health Applications: A Systematic Review of Features and Fundamentals. Telemed J E Health, v. 24, n. 11.
Bellei, E. A., Biduski, D., Lisboa, H. R. K. and De Marchi, A. C. B. (2020). Development and Assessment of a Mobile Health Application for Monitoring the Linkage Among Treatment Factors of Type 1 Diabetes Mellitus. Telemed J E Health, v. 26, n. 2.
Bellei, E. A. (2019). Exploring mobile health applications for self-management of Diabetes Mellitus. Master’s Thesis. University of Passo Fundo. Disponível em http://tede.upf.br/jspui/handle/tede/1668
Bui, A. A. T., Aberle, D. R. and Kangarloo, H. (2007). TimeLine: Visualizing Integrated Patient Records. IEEE Trans Inf Technol Biomed, v. 11, n. 4.
Cechetti, N. P., Bellei, E. A., Biduski, D., et al. (2019). Developing and implementing a gamification method to improve user engagement: A case study with an m-Health application for hypertension monitoring. Telemat Informatics, v. 41.
Chomutare, T., Fernandez-Luque, L., Årsand, E. and Hartvigsen, G. (2011). Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current Applications Compared Against Evidence-Based Guidelines. J Med Internet Res, v. 13, n. 3.
Debon, R., Coleone, J. D., Bellei, E. A., and De Marchi, A. C. B. (2019). Mobile health applications for chronic diseases: A systematic review of features for lifestyle improve-ment. Diabetes Metab Syndr Clin Res Rev, v. 13, n. 4.
Gay, V. and Leijdekkers, P. (2015). Bringing Health and Fitness Data Together for Connected Health Care: Mobile Apps as Enablers of Interoperability. J Med Internet Res, v. 17, n. 11.
Hill-Briggs, F. and Gemmell, L. (2007). Problem Solving in Diabetes Self-management and Control. Diabetes Educ, v. 33, n. 6.
Hirsch I.B. (2005) Insulin Analogues. N Engl J Med, v. 352 n. 2.
Ko, I. and Chang, H. (2018). Interactive data visualization based on conventional statistical findings for antihypertensive prescriptions using National Health Insurance claims data. Int J Med Inform, v. 116.
Mamykina, L., Heitkemper, E. M., et al. (2017). Personal discovery in diabetes selfmanagement: Discovering cause and effect using self-monitoring data. J Biomed Inform, v. 76.
Mummah, S. A., Robinson, T. N., King, A. C., Gardner, C. D. and Sutton, S. (2016). IDEAS (integrate, design, assess, and share): A framework and toolkit of strategiesfor the development of more effective digital interventions to change health behavior. J Med Internet Res, v.18, n. 12.
Rodriguez, J. P. M., De Marchi, A. C. B., Bellei, E. A., Biduski, D., and Lisboa, H.R. K. (2019). Uma abordagem de visualização gráfica para compreensão da relação entre os fatores do tratamento de diabetes mellitus tipo 1. In Anais Estendidos do XIX Simpósio Brasileiro de Computação Aplicada à Saúde (CTIC-SBCAS 2019).
Skrøvseth, S. O., Årsand, E., Godtliebsen, F. and Joakimsen, R. M. (2015). Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial. Diabetes Technol Ther, v. 17, n. 7.
Van Velthoven, M. H., Wyatt, J. C., Meinert, E., Brindley, D. and Wells, G. (2018). How standards and user involvement can improve app quality: A lifecycle approach. Int J Med Inform, v. 118, p. 54–57.
Wong, J. C., Neinstein, A. B., Look, H., et al. (2017). Pilot Study of a Novel Application for Data Visualization in Type 1 Diabetes. J Diabetes Sci Technol, v. 11, n. 4.
Wu, Y., Yao, X., Vespasiani, G., et al. (2017). Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy. JMIR mHealth and uHealth, v. 5, n. 3.
Bellei, E. A., Biduski, D., Cechetti, N. P. and De Marchi, A. C. B. (2018). Diabetes Mellitus m-Health Applications: A Systematic Review of Features and Fundamentals. Telemed J E Health, v. 24, n. 11.
Bellei, E. A., Biduski, D., Lisboa, H. R. K. and De Marchi, A. C. B. (2020). Development and Assessment of a Mobile Health Application for Monitoring the Linkage Among Treatment Factors of Type 1 Diabetes Mellitus. Telemed J E Health, v. 26, n. 2.
Bellei, E. A. (2019). Exploring mobile health applications for self-management of Diabetes Mellitus. Master’s Thesis. University of Passo Fundo. Disponível em http://tede.upf.br/jspui/handle/tede/1668
Bui, A. A. T., Aberle, D. R. and Kangarloo, H. (2007). TimeLine: Visualizing Integrated Patient Records. IEEE Trans Inf Technol Biomed, v. 11, n. 4.
Cechetti, N. P., Bellei, E. A., Biduski, D., et al. (2019). Developing and implementing a gamification method to improve user engagement: A case study with an m-Health application for hypertension monitoring. Telemat Informatics, v. 41.
Chomutare, T., Fernandez-Luque, L., Årsand, E. and Hartvigsen, G. (2011). Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current Applications Compared Against Evidence-Based Guidelines. J Med Internet Res, v. 13, n. 3.
Debon, R., Coleone, J. D., Bellei, E. A., and De Marchi, A. C. B. (2019). Mobile health applications for chronic diseases: A systematic review of features for lifestyle improve-ment. Diabetes Metab Syndr Clin Res Rev, v. 13, n. 4.
Gay, V. and Leijdekkers, P. (2015). Bringing Health and Fitness Data Together for Connected Health Care: Mobile Apps as Enablers of Interoperability. J Med Internet Res, v. 17, n. 11.
Hill-Briggs, F. and Gemmell, L. (2007). Problem Solving in Diabetes Self-management and Control. Diabetes Educ, v. 33, n. 6.
Hirsch I.B. (2005) Insulin Analogues. N Engl J Med, v. 352 n. 2.
Ko, I. and Chang, H. (2018). Interactive data visualization based on conventional statistical findings for antihypertensive prescriptions using National Health Insurance claims data. Int J Med Inform, v. 116.
Mamykina, L., Heitkemper, E. M., et al. (2017). Personal discovery in diabetes selfmanagement: Discovering cause and effect using self-monitoring data. J Biomed Inform, v. 76.
Mummah, S. A., Robinson, T. N., King, A. C., Gardner, C. D. and Sutton, S. (2016). IDEAS (integrate, design, assess, and share): A framework and toolkit of strategiesfor the development of more effective digital interventions to change health behavior. J Med Internet Res, v.18, n. 12.
Rodriguez, J. P. M., De Marchi, A. C. B., Bellei, E. A., Biduski, D., and Lisboa, H.R. K. (2019). Uma abordagem de visualização gráfica para compreensão da relação entre os fatores do tratamento de diabetes mellitus tipo 1. In Anais Estendidos do XIX Simpósio Brasileiro de Computação Aplicada à Saúde (CTIC-SBCAS 2019).
Skrøvseth, S. O., Årsand, E., Godtliebsen, F. and Joakimsen, R. M. (2015). Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial. Diabetes Technol Ther, v. 17, n. 7.
Van Velthoven, M. H., Wyatt, J. C., Meinert, E., Brindley, D. and Wells, G. (2018). How standards and user involvement can improve app quality: A lifecycle approach. Int J Med Inform, v. 118, p. 54–57.
Wong, J. C., Neinstein, A. B., Look, H., et al. (2017). Pilot Study of a Novel Application for Data Visualization in Type 1 Diabetes. J Diabetes Sci Technol, v. 11, n. 4.
Wu, Y., Yao, X., Vespasiani, G., et al. (2017). Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy. JMIR mHealth and uHealth, v. 5, n. 3.
Published
2020-09-15
How to Cite
BELLEI, Ericles A.; LISBOA, Hugo R. K.; DE MARCHI, Ana Carolina.
Exploring mobile health applications for self-management of Diabetes Mellitus. In: THESIS AND DISSERTATION CONTEST - BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTHCARE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 68-73.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas.2020.11560.
