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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.
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Aggelidis, V.P., Chatzoglou, P.D.: Using a modified technology acceptance model in hospitals. Int. J. Med. Inform. 78, 115–126 (2009) CrossRef
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)
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).
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) CrossRef
Basoglu, N., Daim, T.U., Topacan, U.: Determining patient preferences for remote monitoring. J. Med. Syst. 36, 1389–1401 (2010) CrossRef
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) CrossRef
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) CrossRef
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) CrossRef
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) CrossRef
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)
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) CrossRef
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).
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)
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) CrossRef
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)
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) CrossRef
Im, I., Hong, S.T., Kang, M.S.: An international comparison of technology adoption: testing the UTAUT model. Inf. Manage. 48, 1–8 (2011) CrossRef
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) CrossRef
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
Jung, H., Chung, K.Y.: Mining based associative image filtering using harmonic mean. Cluster Comput. (2013). doi: 10.1007/s10586-013-0318-z
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) CrossRef
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) CrossRef
Korea Institute for Health and Social Affairs: Korea’s Health and Welfare Trends 2010. 2010–28 (2010).
Korean Health and Society Research Center: A Report of Korea Health Panel Survey 2008(1), 2009–28 (2008)
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) CrossRef
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) CrossRef
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
Lindenmeyer, A., Whitlock, S., Sturt, J., Griffiths, F.: Patient engagement with a diabetes self-management intervention. Chronic Illn. 6, 306–316 (2010) CrossRef
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) CrossRef
Nunnally, J.C.: Psychometric Theory. McGraw-Hill, New York (1978)
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
Okazaki, S., Mendez, F.: Exploring convenience in mobile commerce: moderating effects of gender. Comput. Human. Behav. 29, 1234–1242 (2013) CrossRef
Park, E.J.: Medication Compliance: Factors and Interventions. Health and welfare policy forum. 82–91 (2011).
Preston, D.S.: Karahanna E. antecedents of IS strategic alignment: a nomological network. Inf. Syst. Res. 20, 159–179 (2009)
Schrijver, G.J.: The User of Video-Telephony in the Care Process of ALS Patients. Master’s thesis. University of Twente. (2008).
Tenenhaus, M.: Component-based structural equation modelling. Qual. Manag. Bus. Excell. 19, 871–886 (2008)
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)
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).
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) CrossRef
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)
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).
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) CrossRef
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) CrossRef
- Factors influencing the acceptance of telemedicine for diabetes management
Mi Jung Rho
Hun Sung Kim
In Young Choi
- Springer US
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