Factors influencing the acceptance of telemedicine for diabetes management
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Abstract
Telemedicine service is effective intervention in blood glucose management and reducing the progression of diabetic complications. While telemedicine service for the enhanced management of diabetes has been known for its usefulness, there is little understanding regarding which factors should be considered when diabetic patients accept telemedicine. Thus, this study aimed to examine the factors that influence the acceptance of telemedicine service for the enhanced management of diabetes mellitus based on the Unified Theory of Acceptance and Use of Technolog (UTAUT) model. Data were collected from a paper-based survey of 116 diabetic patients who were outpatients in six different university hospitals. This study used partial least squares regression to determine the causal relationship between the five variables. Demographic variables, such as age and gender, as moderating variables for behavioral intention to use were analyzed. The results indicate that facilitating factors have effects on the behavioral intention to use telemedicine service through the performance expectancy (\(p<0.05\)). In addition, facilitating factors have effects on the behavioral intention to use telemedicine service through the effort expectancy (\(p<0.05\)). This study also found that performance expectancy, effort expectancy and social influence have positive effects on behavioral intentions to use telemedicine service, as predicted using the UTAUT model (\(p<0.05\)). Finally, gender and age were found to be moderators between PE and behavioral intention to use telemedicine service as predicted using the UTAUT model. Our results showed that telemedicine service for diabetes mellitus management should facilitate infrastructure methods such as continuous assistance service and service guideline education. Therefore, the capacity of telemedicine service providers is more important for telemedicine success than the competence of the individuals receiving telemedicine service care. In addition, performance expectancy, effort expectancy and social influence are influencing factors for the acceptance of telemedicine service for diabetes management. Accordingly, in order to raise service usage, telemedicine service providers’ variety support is important.
Keywords
Telemedicine Diabetes mellitus Facilitating conditions UTAUT model Behavioral intention to useNotes
Acknowledgments
This study was supported by a grant of the Korea Health technology R&D Project, Ministry of Health & Welfare, Republic of Korea (A112022).
References
- 1.Aggelidis, V.P., Chatzoglou, P.D.: Using a modified technology acceptance model in hospitals. Int. J. Med. Inform. 78, 115–126 (2009)CrossRefGoogle Scholar
- 2.Ahn, Y.H.: Characteristics of subgroups on patients with hypertension for hypertension management—based on knowledge, attitudes, and behavior related to medication and health lifestyle. J. Korean Acad. Community Health Nurs. 18, 112–122 (2007)Google Scholar
- 3.Aldosari, B.: User acceptance of a picture archiving and communication system (PACS) in a Saudi Arabian hospital radiology department. BMC Med. Inf. Decis. Making. 12(44), (2012).Google Scholar
- 4.Bakken, S., Grullon-Figueroa, L., Izquierdo, R., Lee, N.J., Morin, P., Palmas, W., Teresi, J., Weinstock, R.S., Shea, S., Starren, J.: IDEATel consortium: development, validation, and use of English and Spanish versions of the telemedicine satisfaction and usefulness Basoglu questionnaire. J. Am. Med. Inform. Assoc. 13, 660–667 (2006)CrossRefGoogle Scholar
- 5.Basoglu, N., Daim, T.U., Topacan, U.: Determining patient preferences for remote monitoring. J. Med. Syst. 36, 1389–1401 (2010)CrossRefGoogle Scholar
- 6.Bellazzi, R., Arcelloni, M., Ferrari, P., Decata, P., Hernando, M.E., García, A., Gazzaruso, C., Gómez, E.J., Larizza, C.: Management of patients with diabetes through information technology: tools for monitoring and control of the patients’ metabolic behavior, pietro fratino, and mario stefanelli diabetes. Tech. Therapeutics. 6, 567–578 (2004)CrossRefGoogle Scholar
- 7.Bellazzi, R., Larizza, S., Montani, S., Riva, A., Stefanelli, M., d’Annunzio, G., Lorini, R., Gomez, E.J., Hernando, E., Brugues, E., Cermeno, J., Corcoy, R., de Leiva, A., Cobelli, C., Nucci, G., Del Prato, S., Maran, A., Kilkki, E., Tuominen, J.: A telemedicine support for diabetes management: the T-IDDM project. Comput. Methods Progr. Biomed. 69, 147–161 (2002)CrossRefGoogle Scholar
- 8.Chang, I.C., Hsu, H.M.: Predicting medical staff intention to use an online reporting system with modified unified theory of acceptance and use of technology. Telemed. J. E. Health. 8, 67–73 (2012)CrossRefGoogle Scholar
- 9.Chang, I.C., Hwang, H.G., Hung, W.F., Li, Y.C.: Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Syst. Appl. 33, 296–303 (2007)CrossRefGoogle Scholar
- 10.Chau, P.Y.K., Hu, P.J.H.: Examining a model of information technology acceptance by individual professionals: an exploratory study. J. Manag. Inf. Syst. 18, 191–229 (2002)Google Scholar
- 11.Chau, P.Y.K., Hu, P.J.H.: Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Inf. Manage. 39, 297–311 (2002)CrossRefGoogle Scholar
- 12.Chin, W.W.: The partial least squares approach to structural equation modeling. In: Marcoulides, GA. (eds.) Moderns Methods for Business Research Mahwahm, pp. 295–336. Lawrence Erlbaum Associates, NJ (1998).Google Scholar
- 13.Choi, I.Y., Kim, S.K., Kwon, Y.D.: Key aspects of using web-based diabetes telemedicine systems in multiple clinical settings. J. Kor. Soc. Med. Inf. 13(4), 375–383 (2007)Google Scholar
- 14.Duyck, P., Pynoo, B., Devolder, P., Voet, T., Adang, L., Ovaere, D.: Monitoring the PACS implementation process in a large university hospital-discrepancies between radiologists and physicians. J. Digit. Imagin. 23, 73–80 (2010)CrossRefGoogle Scholar
- 15.Holden, R.J., Karsh, B.T.: The technology acceptance model: its past and its future in health care. J Biomed. Inf. 43, 159–172 (2010)Google Scholar
- 16.Hsu, C.L., Tseng, K.C., Chuang, Y.H.: Predictors of future use of telehomecare health services by middle-aged people in Taiwan. J. Soc. Behav. Pers. 39, 1251–1261 (2011)CrossRefGoogle Scholar
- 17.Im, I., Hong, S.T., Kang, M.S.: An international comparison of technology adoption: testing the UTAUT model. Inf. Manage. 48, 1–8 (2011)CrossRefGoogle Scholar
- 18.Jung, E.Y., Kim, J.H., Chung, K.Y., Park, D.K.: Home health gateway based healthcare services through U-Health platform. Wirel. Pers. Commun. 73(2), 207–218 (2013)CrossRefGoogle Scholar
- 19.Jung, E.Y., Kim, J.H., Chung, K.Y., Park, D.K.: Mobile healthcare application with EMR interoperability for diabetes patients. Cluster Comput. (2013). doi: 10.1007/s10586-013-0315-2
- 20.Jung, H., Chung, K.Y.: Mining based associative image filtering using harmonic mean. Cluster Comput. (2013). doi: 10.1007/s10586-013-0318-z
- 21.Kijsanayotina, B., Pannarunothaib, S., Speedie, S.M.: Factors influencing health information technology adoption in Thailand’s community health centers: applying the UTAUT model. Int. J. Med. Inf. 78, 404–416 (2009)CrossRefGoogle Scholar
- 22.Kim, C., Mirusmonov, M., Lee, I.: An empirical examination of factors influencing the intention to use mobile payment. Comput. Human. Behav. 26, 310–322 (2010)CrossRefGoogle Scholar
- 23.Korea Institute for Health and Social Affairs: Korea’s Health and Welfare Trends 2010. 2010–28 (2010).Google Scholar
- 24.Korean Health and Society Research Center: A Report of Korea Health Panel Survey 2008(1), 2009–28 (2008)Google Scholar
- 25.Krupinski, E., Nypaver, M., Poropatich, R., Ellis, D., Safwat, R., Sapci, H.: Clinical applications in telemedicine/telehealth. Telemed. J. e-Health. 8, 13–34 (2002)CrossRefGoogle Scholar
- 26.Lee, J.B., Rho, M.J.: The perception of influencing factors on acceptance of mobile health monitoring service: a comparison between users and non-users. Healthc. Inf. Res. 19(3), 167–176 (2013)CrossRefGoogle Scholar
- 27.Lee, S.B., Baik, Y.J., Nam, K.C., Ahn, J.H., Lee, Y.J., Oh, S.S., Kim, K.S.: Developing a cognitive evaluation method for serious game engineers. Cluster Comput. (2013). doi: 10.1007/s10586-013-0289-0
- 28.Lindenmeyer, A., Whitlock, S., Sturt, J., Griffiths, F.: Patient engagement with a diabetes self-management intervention. Chronic Illn. 6, 306–316 (2010)CrossRefGoogle Scholar
- 29.Mair, F.S., Goldstein, P., May, C., Angus, R., Shiels, C., Hibbert, D., O’Connor, J., Boland, A., Roberts, C., Haycox, A., Capewell, S.: Patient and provider perspectives on home telecare: preliminary results from a randomized controlled trial. J. Telemed. Telecare. 11, 95–97 (2005)CrossRefGoogle Scholar
- 30.Nunnally, J.C.: Psychometric Theory. McGraw-Hill, New York (1978)Google Scholar
- 31.Oh, S.Y., Chung, K.Y.: Target speech feature extraction using non-parametric correlation coefficient. Cluster Comput. (2013). doi: 10.1007/s10586-013-0284-5
- 32.Okazaki, S., Mendez, F.: Exploring convenience in mobile commerce: moderating effects of gender. Comput. Human. Behav. 29, 1234–1242 (2013)CrossRefGoogle Scholar
- 33.Park, E.J.: Medication Compliance: Factors and Interventions. Health and welfare policy forum. 82–91 (2011).Google Scholar
- 34.Park, H.Y., Chon, Y.C., Lee, J.S., Choi, I.J., Yoon, K.H.: Service design attributes affecting diabetic patient preferences of telemedicine in South Korea. Telemed. J. E. Health. 17, 442–451 (2011)CrossRefMATHGoogle Scholar
- 35.Preston, D.S.: Karahanna E. antecedents of IS strategic alignment: a nomological network. Inf. Syst. Res. 20, 159–179 (2009)Google Scholar
- 36.Schrijver, G.J.: The User of Video-Telephony in the Care Process of ALS Patients. Master’s thesis. University of Twente. (2008).Google Scholar
- 37.Tenenhaus, M.: Component-based structural equation modelling. Qual. Manag. Bus. Excell. 19, 871–886 (2008)Google Scholar
- 38.Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Quart. 27, 425–478 (2003)Google Scholar
- 39.Venkatesh, V., Sykes, T.A., Zhang, X.: Just what the doctor ordered: a revised UTAUT for EMR system adoption and use by Doctors. in: Proceedings of the 44th Hawaii International Conference on System Sciences 2011 (2011). Google Scholar
- 40.Ward, R., Stevens, C., Brentnall, P., Briddon, J.: The attitudes of health care staff to information technology: a comprehensive review of the research literature. Health. Info. Libr. J. 25, s81–97 (2008)CrossRefGoogle Scholar
- 41.Wetzels, M., Odekerken-Schroder, G., Oppen, C.V.: Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quart. 33, 177–195 (2009)Google Scholar
- 42.Wright, E.W.: The Rx for Electronic Healthcare Records: Time, Not Incentives, Case Western Reserve University. USA. Sprouts: Working Papers on Information Systems. 5 (2005).Google Scholar
- 43.Wu, J., Wang, S.C., Lin, L.M.: Mobile computing acceptance factors in the healthcare industry: a structural equation model. Int. J. Med. Inf. 76, 66–77 (2007)CrossRefGoogle Scholar
- 44.Yu, P., Li, H., Gagnon, M.P.: Health IT acceptance factors in long-term care facilities: a cross-sectional survey. Int. J. Med. Inf. 78, 219–229 (2009)CrossRefGoogle Scholar