Exploration of Healthcare Workers' Experiences in Using Wearable Technology for Chronic Disease Monitoring in Hospitals: A Phenomenological Exploration
Main Article Content
Abstract
The growing use of wearable technology in healthcare has revolutionized the management of chronic diseases by enabling continuous monitoring of patients' health. However, there is limited understanding regarding how healthcare professionals experience and perceive the integration of these technologies in clinical settings. This study specifically seeks to answer the research question: How do healthcare providers perceive and experience the integration of wearable technology for chronic disease monitoring in their clinical practice? A phenomenological approach was employed to explore these healthcare professionals' perspectives, uncovering the challenges and benefits they perceive in incorporating wearable technology into their practices. Through in-depth interviews with 12 healthcare providers, the study found that while wearable devices offer substantial benefits in continuous health monitoring, issues such as data reliability and integration with existing medical systems remain significant barriers. The findings highlight the need for improved training, better device integration, and more robust data management systems to enhance the effectiveness of wearable technologies. These insights contribute to a deeper understanding of the complex dynamics between technology and healthcare professionals, paving the way for future research into more effective implementation strategies and the role of healthcare providers in technology adoption.
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Albahri, O. S., Albahri, A. S., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Mohsin, A. H., Mohammed, K. I., Alamoodi, A. H., Nidhal, S., Enaizan, O., Chyad, M. A., Abdulkareem, K. H., Almahdi, E. M., Al Shafeey, G. A., Baqer, M. J., Jasim, A. N., Jalood, N. S., & Shareef, A. H. (2019). Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors. IEEE Access, 7, 50052–50080. https://doi.org/10.1109/ACCESS.2019.2910411
Azbeg, K., Ouchetto, O., & Jai Andaloussi, S. (2022). BlockMedCare: A healthcare system based on IoT, Blockchain and IPFS for data management security. Egyptian Informatics Journal, 23(2), 329–343. https://doi.org/10.1016/j.eij.2022.02.004
Bent, B., Wang, K., Grzesiak, E., Jiang, C., Qi, Y., Jiang, Y., Cho, P., Zingler, K., Ogbeide, F. I., Zhao, A., Runge, R., Sim, I., & Dunn, J. (2021). The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data. Journal of Clinical and Translational Science, 5(1). https://doi.org/10.1017/cts.2020.511
Binyamin, S. S., & Hoque, M. R. (2020). Understanding the drivers of wearable health monitoring technology: An extension of the unified theory of acceptance and use of technology. Sustainability, 12(22), 1–20. https://doi.org/10.3390/su12229605
Chong, Y.-W., Ismail, W., Ko, K., & Lee, C.-Y. (2019). Energy Harvesting for Wearable Devices: A Review. IEEE Sensors Journal, 19(20), 9047–9062. https://doi.org/10.1109/JSEN.2019.2925638
Chung, K., & Park, R. C. (2019). Chatbot-based healthcare service with a knowledge base for cloud computing. Cluster Computing, 22, 1925–1937. https://doi.org/10.1007/s10586-018-2334-5
Ed-daoudy, A., & Maalmi, K. (2019). A new Internet of Things architecture for real-time prediction of various diseases using machine learning on big data environment. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0271-7
Hasan, M. K., Shahjalal, M., Chowdhury, M. Z., & Jang, Y. M. (2019). Real-time healthcare data transmission for remote patient monitoring in patch-based hybrid OCC/BLE networks. Sensors, 19(5). https://doi.org/10.3390/s19051208
He, K., Liu, Z., Wan, C., Jiang, Y., Wang, T., Wang, M., Zhang, F., Liu, Y., Pan, L., Xiao, M., Yang, H., & Chen, X. (2020). An On-Skin Electrode with Anti-Epidermal-Surface-Lipid Function Based on a Zwitterionic Polymer Brush. Advanced Materials, 32(24). https://doi.org/10.1002/adma.202001130
He, T., & Lee, C. (2021). Evolving Flexible Sensors, Wearable and Implantable Technologies towards BodyNET for Advanced Healthcare and Reinforced Life Quality. IEEE Open Journal of Circuits and Systems, 2, 702–720. https://doi.org/10.1109/OJCAS.2021.3123272
Kalasin, S., Sangnuang, P., & Surareungchai, W. (2022). Intelligent Wearable Sensors Interconnected with Advanced Wound Dressing Bandages for Contactless Chronic Skin Monitoring: Artificial Intelligence for Predicting Tissue Regeneration. Analytical Chemistry, 94(18), 6842–6852. https://doi.org/10.1021/acs.analchem.2c00782
Li, W., Chai, Y., Khan, F., Jan, S. R. U., Verma, S., Menon, V. G., & Li, X. (2021). A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System. Mobile Networks and Applications, 26(1), 234–252. https://doi.org/10.1007/s11036-020-01700-6
Nasr, M., Islam, M. M., Shehata, S., Karray, F., & Quintana, Y. (2021). Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects. IEEE Access, 9, 145248–145270. https://doi.org/10.1109/ACCESS.2021.3118960
Punj, R., & Kumar, R. (2019). Technological aspects of WBANs for health monitoring: A comprehensive review. Wireless Networks, 25(3), 1125–1157. https://doi.org/10.1007/s11276-018-1694-3
Sankhala, D., Sardesai, A. U., Pali, M., Lin, K.-C., Jagannath, B., Muthukumar, S., & Prasad, S. (2022). A machine learning-based on-demand sweat glucose reporting platform. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-06434-x