Abstract
Diabetes is a chronic disease that is diagnosed by observing raised levels of glucose in the blood. High levels of glucose in the blood damage many tissues in the body, thus bringing life-threating and disabling health complications. According to the World Health Organization, the number of people with diabetes is around 422 million, and the diabetes prevalence has been raising more rapidly in middle and low-income countries. People with diabetes must have periodic contact with healthcare professionals. However, it is necessary for them to have the skills, attitude, and support for self-management. In other words, people with diabetes should be active participants in the treatment. In this work, we present a system for diabetes self-management. This system deals with different subjects related to the control and management of glucose levels in the blood, such as diet, physical activity, mood, medication, and treatment. Furthermore, this system implements the collaborative filtering recommendation algorithm for generating health recommendations. This module was evaluated to measure its effectiveness providing such recommendations obtaining encouraging results. This evaluation involved the participation of real patients with diabetes and healthcare professionals.
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References
WHO | Diabetes. World Heal. Organ (2016)
Ren, L., Han, W., Yang, H., Sun, F., Xu, S., Hu, S., Zhang, M., He, X., Hua, J., Peng, S.: Autophagy stimulated proliferation of porcine PSCs might be regulated by the canonical Wnt signaling pathway. Biochem. Biophys. Res. Commun. 479, 537–543 (2016)
World Health Organization: Global report on diabetes. World Health Organization (2016)
Salas-Zárate, M.P., Medina-Moreira, J., Lagos-Ortiz, K., Luna-Aveiga, H., Rodríguez-García, M.Á., Valencia-García, R.: Sentiment analysis on tweets about diabetes: an aspect-level approach. Comput. Math. Methods Med. 2017, 1–9 (2017)
Funnell, M.M., Brown, T.L., Childs, B.P., Haas, L.B., Hosey, G.M., Jensen, B., Maryniuk, M., Peyrot, M., Piette, J.D., Reader, D., Siminerio, L.M., Weinger, K., Weiss, M.A.: National standards for diabetes self-management education. Diabetes Care 33(Suppl 1), S89–S96 (2010)
Clement, S.: Diabetes self-management education. Diabetes Care 18, 1204–1214 (1995)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011)
Colombo-Mendoza, L.O., Valencia-García, R., Rodríguez-González, A., Alor-Hernández, G., Samper-Zapater, J.J.: RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes. Expert Syst. Appl. 42, 1202–1222 (2015)
Yuan, N.J., Zheng, Y., Zhang, L., Xie, X.: T-Finder: a recommender system for finding passengers and Vacant Taxis. IEEE Trans. Knowl. Data Eng. 25, 2390–2403 (2013)
Tejeda-Lorente, Á., Porcel, C., Peis, E., Sanz, R., Herrera-Viedma, E.: A quality based recommender system to disseminate information in a university digital library. Inf. Sci. (Ny) 261, 52–69 (2014)
Zheng, Z., Ma, H., Lyu, M.R., King, I.: WSRec: a collaborative filtering based web service recommender system. In: 2009 IEEE International Conference on Web Services, pp. 437–444. IEEE (2009)
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4, 81–173 (2011)
Paradiso, R.: Wearable health care system for vital signs monitoring. In: 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, pp. 283–286. IEEE (2003)
Chawla, N.V., Davis, D.A.: Bringing big data to personalized healthcare: a patient-centered framework. J. Gen. Intern. Med. 28, 660–665 (2013)
Duan, L., Street, W.N., Xu, E.: Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterp. Inf. Syst. 5, 169–181 (2011)
Kirwan, M., Vandelanotte, C., Fenning, A., Duncan, M.J.: Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trial. J. Med. Internet Res. 15, e235 (2013)
El-Gayar, O., Timsina, P., Nawar, N., Eid, W.: Mobile applications for diabetes self-management: status and potential. J. Diabetes Sci. Technol. 7, 247–262 (2013)
Gao, C., Zhou, L., Liu, Z., Wang, H., Bowers, B.: Mobile application for diabetes self-management in China: do they fit for older adults? Int. J. Med. Inform. 101, 68–74 (2017)
Goyal, S., Morita, P., Lewis, G.F., Yu, C., Seto, E., Cafazzo, J.A.: The systematic design of a behavioural mobile health application for the self-management of type 2 diabetes. Can. J. Diabetes 40, 95–104 (2016)
Schwaber, K., Beedle, M.: Agile software development with Scrum. Prentice Hall, Upper Saddle River (2002)
Colberg, S.R., Sigal, R.J., Yardley, J.E., Riddell, M.C., Dunstan, D.W., Dempsey, P.C., Horton, E.S., Castorino, K., Tate, D.F.: Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes Care 39, 2065–2079 (2016)
Colberg, S.R., Sigal, R.J., Fernhall, B., Regensteiner, J.G., Blissmer, B.J., Rubin, R.R., Chasan-Taber, L., Albright, A.L., Braun, B.: American College of Sports Medicine, American Diabetes Association: exercise and type 2 diabetes: the American College of Sports Medicine and the American Diabetes Association: joint position statement. Diabetes Care 33, e147–e167 (2010)
LaMonte, M.J., Blair, S.N., Church, T.S.: Physical activity and diabetes prevention. J. Appl. Physiol. 99, 1205–1213 (2005)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. Eighteenth Natl. Conf. Artif. Intell. 1034, 187–192 (2002)
Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth.” In: Proceedings of the SIGCHI conference on Human factors in computing systems—CHI 1995, pp. 210–217. ACM Press, New York (1995)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Raghavan, V., Bollmann, P., Jung, G.S.: A critical investigation of recall and precision as measures of retrieval system performance. ACM Trans. Inf. Syst. 7, 205–229 (1989)
Fang, B., Liao, S., Xu, K., Cheng, H., Zhu, C., Chen, H.: A novel mobile recommender system for indoor shopping. Expert Syst. Appl. 39, 11992–12000 (2012)
European Association for the Study of the Liver: Electronic address: easloffice@easloffice.eu: EASL Recommendations on Treatment of Hepatitis C 2016. J. Hepatol. 66, 153–194 (2017)
Badreddin, O., Castillo, R., Lessard, L., Albanese, M.: Towards improved performance and compliance in healthcare using wearables and bluetooth technologies (2015). http://dl.acm.org/citation.cfm?id=2886482
Klucken, J.: Mobile Healthcare Technologies—“Wearables” for objective measures of motor symptoms in Parkinson’s disease. Basal Ganglia 8, 119–121 (2017)
Acknowledgments
This work has been funded by the Universidad de Guayaquil (Ecuador) through the project entitled “Tecnologías inteligentes para la autogestión de la salud”. Finally, this work has been also partially supported by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER / ERDF) through project KBS4FIA (TIN2016-76323-R).
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Medina-Moreira, J., Apolinario, O., Luna-Aveiga, H., Lagos-Ortiz, K., Paredes-Valverde, M.A., Valencia-García, R. (2017). A Collaborative Filtering Based Recommender System for Disease Self-management. In: Valencia-García, R., Lagos-Ortiz, K., Alcaraz-Mármol, G., Del Cioppo, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2017. Communications in Computer and Information Science, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-67283-0_5
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DOI: https://doi.org/10.1007/978-3-319-67283-0_5
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