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http://dx.doi.org/10.5953/JMJH.2022.29.1.50

Patient Experiences with Artificial Intelligence-Based Smartwatch for Diabetes Medication Monitoring Service  

Lee, Mi Sun (Department of Nursing, College of Health and Welfare, Gangneung-Wonju National University)
Jeong, Suyong (Department of Nursing, College of Health and Welfare, Gangneung-Wonju National University)
Lee, Hwiwon (InHandPlus)
Publication Information
Journal of muscle and joint health / v.29, no.1, 2022 , pp. 50-59 More about this Journal
Abstract
Purpose: This qualitative study aimed to explore the experiences of patients with diabetes provided with medication monitoring using an artificial intelligence-based smartwatch. Methods: Giorgi's descriptive phenomenological methodology was applied to collect and analyze data from November 9 to December 23, 2021. The study samples were recruited by convenience sampling, and even patients with diabetes participated in in-depth interviews via video conference and telephone calls or face-to-face visits. Results: Ten sub-themes and four themes were finally revealed. The four themes were as follows: journey with unfamiliar devices, a less-than-acceptable smartwatch, insufficient functions and content for patients with diabetes to use, and efforts for regular medication behaviors and daily monitoring of patient's health conditions. Conclusion: To effectively manage diabetic conditions using digital healthcare technologies, nursing interventions were needed to identify personal needs and consider technological, psychological, aesthetic, and socioeconomic aspects of wearable devices.
Keywords
Diabetes mellitus; Medication adherence; Artificial intelligence; Wearable electronic devices;
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1 Lee, J. W., Choi, J. H., & Park, J. W. (2014). An empirical study on the individual and device characteristics affecting user's intention to use smart watch. Korean Institute of Information Technology, 12(11), 201-214.
2 Li, J., Huang, J., Zheng, L., & Li, X. (2020). Application of artificial intelligence in diabetes education and management: Present status and promising prospect. Frontiers in Public Health, 8, 173. https://doi.org/10.3389/fpubh.2020.00173   DOI
3 Seo, S. M., Han, S. H., & Park, Y. J. (2008). The impact of diabetes fear of self-injecting (FSI) and fear of self-testing (FST) on glycemic control and diabetes self-management. Korean Journal of Family Medicine, 29(10), 768-780.
4 Shim, S. (2014). White book 2014 for wearable device industry. (Digieco Report, 1). Seoul: KT Economic Management Institute.
5 Suh, C. K., & Seong, S. J. (2004). Individual characteristics affecting user's intention to use internet shopping mall. Asia Pacific Journal of Information Systems, 14(3), 1-22.   DOI
6 Yoon, S. J . (2017). The relationships among health literacy, medication adherence and self-care performance of diabetes mellitus patients. Health & Nursing, 29(1), 27-38.
7 Chang, B. L., Bakken, S., Brown, S. S., Houston, T. K., Kreps, G. L., Kukafka, R., et al. (2004). Bridging the digital divide: Reaching vulnerable populations. Journal of the American Medical Informatics Association, 11(6), 448-457. https://doi.org/10.1197/jamia.M1535   DOI
8 Dong, J. Y., Lee, C. H., & Song, Y. J. (2019). User experience analysis trend of smart wearable devices. Korea Institute of Communication Sciences, 36(4), 3-9.
9 Fagherazzi, G., & Ravaud, P. (2019). Digital diabetes: Perspectives for diabetes prevention, management and research. Diabetes & Metabolism, 45(4), 322-329. https://doi.org/10.1016/j.diabet.2018.08.012   DOI
10 Jeong, S. Choi, H., Gwon, S. H., & Kim, J. (2018). Telephone support and telemonitoring for low-income older adults. Research in Gerontological Nursing, 11(4), 198-206. https://doi.org/10.3928/19404921-20180502-01   DOI
11 Jeong, S. Y., Lee, H. W., Yoo, S. P., Lee, K. J., & Heo, S. P. (2020). Artificial intelligence-based medication behavior monitoring system using smartwatch. Journal of Korean Institute of Information Technology, 18(8), 125-133. https://doi.org/10.14801/jkiit.2020.18.8.125   DOI
12 Korean Diabetes Association. (2020). Diabetes fact sheet in Korea 2020. Retrieved November 21, 2021, from https://www.diabetes.or.kr/pro/news/admin.php?category=A&code=admin&number=1972&mode=view
13 Lee, J. K., Kang, J. H., Kim, H. B., Ahn, E. S., Oh, M. J., & Jo, H. (2016). Influencing factors on intention to adopt of wearable device: Focusing on the smart watch. Korea Internet Electronic Commerce Research, 16(1), 195-213.
14 Lee, J. O., Whang, J. H., Lee, S. R., & Kang, S. R. (2006). Extended TAM for accepting mobile devices including functional attributes: The case of cellular phone. Journal of Information Technology Applications and Management, 13(1), 39-66.
15 Park, Y. I., Lee, K. Y., Kim, D. O., Uhm, D. C., & Kim, J. H. (2014). Medication status and the effects of a medication management education program for the elderly in a community. Journal of Korean Academy of Community Health Nursing, 25 (3), 170-179. https://doi.org/10.12799/jkachn.2014.25.3.170   DOI
16 Milosavljevic, A., Aspden, T., & Harrison, J. (2018). Community pharmacist-led interventions and their impact on patients' medication adherence and other health outcomes: A systematic review. International Journal of Pharmacy Practice, 26(5), 387-397. https://doi.org/10.1111/ijpp.12462   DOI
17 Ministry of Economy and Finance. (2020). Comprehensive plan for New Deal policy in South Korea. Retrieved November 21, 2021, from https://english.moef.go.kr/pc/selectTbPressCenterDtl.do?boardCd=N0001&seq=4948
18 Musacchio, N., Giancaterini, A., Guaita, G., Ozzello, A., Pellegrini, M. A., Ponzani, P., et al. (2020). Artificial intelligence and big data in diabetes care: A position statement of the Italian Association of Medical Diabetologists. Journal of Medical Internet Research, 22(6), e16922. https://doi.org/10.2196/16922   DOI
19 Ross, D. A., & Blasch, B. B. (2000, November). Wearable interfaces for orientation and wayfinding. In proceedings of the fourth international ACM conference on assistive technologies (pp. 193-200). https://doi.org/10.1145/354324.354380   DOI
20 Shim, K. H., & Hwang, M. S. (2013). Effect of self-monitoring of blood glucose based diabetes self-management education on glycemic control in type 2 diabetes. Journal of Korean Academic Society of Nursing Education, 19(2), 127-136. https://doi.org/10.5977/jkasne.2013.19.2.127   DOI
21 Emanuel, E. J., & Wachter, R. M. (2019). Artificial intelligence in health care: Will the value match the hype?. Journal of the American Medical Association, 321(23), 2281-2282. https://doi.org/10.1001/jama.2019.4914   DOI
22 Kim, B. H., Kim, G. J., Park, I. S., Lee, G. J., Kim, J. K., Hong, J. J., et al. (1999). A comparative study of phenomenological research methods: Focusing on Giorgi, Colaizzi, Van Kaam method. Journal of Korean Academy of Nursing, 29(6), 1208-1220.   DOI
23 Giorgi, A. (1997). The theory, practice, and evaluation of the phenomenological method as a qualitative research procedure. Journal of Phenomenological Psychology, 28(2), 235-260.   DOI
24 Verloo, H., Chiolero, A., Kiszio, B., Kampel, T., & Santschi, V. (2017). Artificial intelligence and big data in diabetes care: A position statement of the Italian Association of Medical Diabetologists. Age and Ageing, 46(5), 747-754. https://doi.org/10.1093/ageing/afx076   DOI
25 American Diabetes Association. (2013). Economic costs of diabetes in the US in 2012. Diabetes Care, 36(4), 1033-1046. https://doi.org/10.2337/dc12-2625   DOI
26 Byun, M. S., Lee, H. J., & Lee, J. W. (2014). A study on the mental model of smart watch purchasing consumers. Journal of Korean Society for the Scientific Study of Subjectivity, 29, 77-98.
27 Dankwa-Mullan, I., Rivo, M., Sepulveda, M., Park, Y., Snowdon, J., & Rhee, K. (2019). Transforming diabetes care through artificial intelligence: The future is here. Population Health Management, 22(3), 229-242. https://doi.org/10.1089/pop.2018.0129   DOI
28 Do, Y. I., Geum, S. E., Lee, S. U., & Lee, J. M. (2014). An exploratory study on factors affecting the usability and durability of wearable devices for health care: A review of user experience from the perspective of technology, psychology, and interaction convergence. Communications of the Korean Institute of Information Scientists and Engineers, 32(11), 37-45.
29 Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. London: SAGE Publications.
30 Greenhalgh, T., Koh, G. C. H., & Car, J. (2020). Covid-19: A remote assessment in primary care. British Medical Journal, 368. https://doi.org/10.1136/bmj.m1182   DOI