Comprehensive Analysis: Diabetes and the Impact of Wearable Technology

This report provides an overview of the current research on wearable technology in diabetes management. It synthesizes findings from various studies, including those by Hardy et al. (2018), Cheng and Mitomo (2017), Schwartz et al. (2018), and others, to assess the impact of wearable devices on patient monitoring, glucose level prediction, and overall patient safety. The studies highlight the benefits of smart devices in providing real-time data, improving treatment, and enhancing patient outcomes, while also touching upon the challenges and factors influencing the adoption of such technologies in healthcare settings. The report emphasizes the increasing prevalence of chronic illnesses and the role of wearable technology in addressing the need for continuous patient monitoring, improved healthcare delivery, and strategies for enhancing patient safety. This report underscores the significance of wearable devices in diabetes care, addressing their potential to transform healthcare by providing efficient monitoring and promoting proactive patient management.

Running head: DIABETES AND WEARABLE TECHNOLOGY
DIABETES AND WEARABLE TECHNOLOGY
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DIABETES AND WEARABLE TECHNOLOGY
To understand the association between the Health and places a research has been
conducted by Hardy et al. (2018). In the study, a 28 day field is performed, where 21 participants
were tracked by the aid of two tracking devices, location enabled smart phone and GPS watch.
The finding suggests that the use of the smart phone for tracking is easier to handle provides
more efficient data. In the study conducted by Cheng and Mitomo (2017), 647 respondents have
been selected and surveyed. Their response regarding the use and applicability of the smart
wearable devices are recorded and observed from which concludes that the smart wearable
devices is one of the fast growing technology for the disaster application. In another study
conducted by Schwartz, Marlin and Bunescu (2018), the effectiveness of the smart monitoring
devices in the patient suffering from diabetes are evaluated. From the study, it can be concluded
that, the wearable monitoring devices is effective providing benefits such as, improved algorithm
of pump controller, more accurate prediction of blood glucose level and enhanced patient safety.
Rendering to the study conducted by the Tarakci, Kulkarni and Ozdemir (2018), in the health
care system 24 hour monitoring of the patient suffering from chronic illness is required for which
wearable device is provided to the patient. By observing the patient for a given period of time, it
can be concluded that the smart wearable device provides benefit in monitoring the real time and
accurate information, which can be used to deliver restorative treatments to the patient.
According to the research performed by Chen, Zdorova and Nathan-Roberts (2017), wearable
devices helps in the efficient monitoring of the physiological changes in the body, but instated of
that they are not widely used in the healthcare sector. To enhance the use nurse should be
educated about its applicability. Conferring to the study performed by Kerr et al. (2018), regular
consultation with the practitioner for many of the diabetic patient is not possible which increases
the risk of further deterioration. Therefore to avoid such outcomes, non-invasive glucose

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monitoring systems is developed which helps the patient to track their glucose level. As the
prevalence rate of chronic illness is increasing day by day. Nasir and Yurder (2015), has
conducted a research to discuss about the strategies which enhances the patient safety. From the
findings of the study, it can be stated that wearable technology can be used for the accurate
monitoring of the patient’s health condition. Wang et al. (2015), conducted a research aiming to
evaluate factors which enhances the adoption of wearable devices by the help of protection
motivation theory and privacy calculus theory. The findings advocate that factors which
responsible to adopt wearable includes, functional congruence, perceived privacy risk, social
influence, self-efficacy and perceived severity. In the study done by Yildirim and Ali-Eldin
(2018), factors which influence the employee’s aim to use wearable device is evaluated among
the 76 participant. The result shows that, perceived usefulness of the device is one of the most
motivating factors to adopt such device for monitoring their health.

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Reference:
Chen, K., Zdorova, M., & Nathan-Roberts, D. (2017, September). Implications of wearables,
fitness tracking services, and quantified self on healthcare. In Proceedings of the Human
Factors and Ergonomics Society Annual Meeting (Vol. 61, No. 1, pp. 1066-1070). Sage
CA: Los Angeles, CA: SAGE Publications.
Cheng, J. W., & Mitomo, H. (2017). The underlying factors of the perceived usefulness of using
smart wearable devices for disaster applications. Telematics and Informatics, 34(2), 528-
539.
Hardy, J., Veinot, T. C., Yan, X., Berrocal, V. J., Clarke, P., Goodspeed, R., ... & Vydiswaran,
V. V. (2018). User acceptance of location-tracking technologies in health research:
implications for study design and data quality. Journal of biomedical informatics, 79, 7-
19.
Kerr, D., Axelrod, C., Hoppe, C., & Klonoff, D. C. (2018). Diabetes and technology in 2030: a
utopian or dystopian future?. Diabetic Medicine, 35(4), 498-503.
Nasir, S., & Yurder, Y. (2015). Consumers’ and physicians’ perceptions about high tech
wearable health products. Procedia-Social and Behavioral Sciences, 195, 1261-1267.
Schwartz, F. L., Marling, C. R., & Bunescu, R. C. (2018). The promise and perils of wearable
physiological sensors for diabetes management. Journal of diabetes science and
technology, 12(3), 587-591.

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Tarakci, H., Kulkarni, S., & Ozdemir, Z. D. (2018). The impact of wearable devices and
performance payments on health outcomes. International Journal of Production
Economics, 200, 291-301.
Wang, X., White, L., Chen, X., Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of
wearable technology acceptance in healthcare. Industrial Management & Data Systems.
Yildirim, H., & Ali-Eldin, A. M. (2018). A model for predicting user intention to use wearable
IoT devices at the workplace. Journal of King Saud University-Computer and
Information Sciences.