Factors Influencing the Continuous Use of Wearable Health Technologies for Personal Health Management

  • Authors

    • Shubham Naresh Bhoir Assistant Manager, Volo Health Services, RH No.9, Villa Delphine, Opp Infant Jesus School, Mahada Colony, Ambarnath East, Mumbai, India. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-V9I4P101

    Published 10-03-2026

  • Wearable Health Technologies, Internet of Things (IoT), Artificial Intelligence (AI), Remote Patient Monitoring, Health Monitoring Systems

    Issue

    Vol. 9 No. 4 (2026)

    Section

    Articles

  • Abstract

    Wearable technology has recently played a huge role in the healthcare industry. Wearable technology (WT) is transforming the personal healthcare landscape around preventive healthcare, real-time health monitoring and personalized health management.  Chronic Disease Management and Behavior Change through devices with sensors are becoming more common. Wearable technology is a great tool in the field of preventive and remote healthcare because of the rapid development of the field of artificial intelligence (AI), the Internet of Things (IoT), mobile health (mHealth) and sensor technologies. The aim of this review is to give a general overview of wearable health technologies, their applications in health care and other sectors, strengths and weaknesses, and possible considerations for adoption and continued adoption of these technologies in health care. This paper also reviews the latest research progress on these areas including real-time physiological monitoring, stress detection, cardiovascular health assessment, localization and long-term health behavior enhancement. The review also covers critical issues on data privacy, security, interoperability, sensor measurement accuracy, battery life limitations, and regulatory requirements obstructing the widespread use of wearable technology. Finally, the review demonstrates that there is significant potential for wearable health technologies to transform the provision of health services. They offer a way to connect with patients, keep track of their health over time, and provide evidence to help doctors make decisions, but they also have to deal with some technical and moral problems right now.

  • References

    Alsulami, A. N. M., Aljuaid, T. H., Alamri, B. J., Alharbi, M. A., Alarfi, M. D. M., Albather, M. H., Raea, S. M., & Alyamani, A. M. A. (2019). Wearable technology and the future of personal healthcare. International Journal of Health Sciences, 3(S1), 560–581. https://doi.org/10.53730/ijhs.v3nS1.15431

    [2] Awoyemi, B. O., Idem, U. J., Awoyemi, J. O., Oluwagbade, I. O., & Yahaya, M. J. (2024). The Roles of Digital Health Technologies-Mobile Health Applications and Wearable Health Devices in Nigerian Health Performance. 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON), 1–5. https://doi.org/10.1109/NIGERCON62786.2024.10927022

    [3] Bove, L. A. (2019). Increasing Patient Engagement Through the Use of Wearable Technology. The Journal for Nurse Practitioners, 15(8), 535–539. https://doi.org/10.1016/j.nurpra.2019.03.018

    [4] Dantuluri, A. V. S. R. (2026). A Scalable Deep Learning Analytics Pipeline for Converting Longitudinal Real-World Data Into Predictive Disease Trajectories. IEEE Access, 14, 24871–24878. https://doi.org/10.1109/ACCESS.2026.3663571

    [5] Dharmavaram, S., & Bhanushali, P. (2026). AI-Driven Blood Glucose Forecasting in Real-World Diabetes Care: Evaluating Wearable-Based Predictive Models. International Journal of Emerging Research in Engineering and Technology, 7(2), 176–182. https://doi.org/10.63282/3050-922X.IJERET-V7I2P122

    [6] Gregório, I. G., Da Silva, H. P., Dos Santos, A. F. A., & Vieira, A. C. L. (2026). Early-Stage Decision-Making for Wearable and Invisible Digital Health Technologies: A Staged Multi-Criteria Value Framework. IEEE Journal of the Electron Devices Society, 1–1. https://doi.org/10.1109/JEDS.2026.3695296

    [7] Gu, X., & Chang, X. (2024). Long-term Effects of Wearable Health Technology and Future MHealth Usage for Older Adults. 2024 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 618–622. https://doi.org/10.1109/IEEM62345.2024.10857169

    [8] Kang, M. J., & Hwang, Y. C. (2022). Exploring the Factors Affecting the Continued Usage Intention of IoT-Based Healthcare Wearable Devices Using the TAM Model. Sustainability, 14(19), 12492. https://doi.org/10.3390/su141912492

    [9] Kumar, V., Madishetty, S. K., & Maddipatla, G. N. (2023). Intelligent Surgical Tables: Real-Time Patient Monitoring and Autonomous Adjustment Using IoT, Cloud Computing, and Cybersecurity. Journal of Computational Analysis and Applications, 31(02). https://doi.org/10.48047/jocaaa.2023.31.02.50

    [10] Kyytsönen, M., Vehko, T., Anttila, H., & Ikonen, J. (2023). Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults. PLOS Digital Health, 2(5), e0000245. https://doi.org/10.1371/journal.pdig.0000245

    [11] Mahmud, S. (2025). AI And Data Analytics For Enhancing Home Healthcare: Optimizing Patient Outcomes And Resource Allocation. Frontiers in Applied Engineering and Technology, 2(01), 78–100. https://doi.org/10.70937/faet.v2i01.61

    [12] Mahmud, S., Mohammed, K. K., Jain, V. R., & Shah, S. A. (2026). Artificial Intelligence-Driven Predictive Models for Identifying Risk Factors of Chronic Diseases. 14th International Symposium on Digital Forensics and Security (ISDFS), 01–06, April. https://doi.org/10.1109/ISDFS69419.2026.11459003

    [13] McLeish, K., Hill, D., Roche, J., Au-Yeung, W.-T., & Kaye, J. (2024). Validating Room Location within the Home for Functional Measures from Digital Health Technologies. 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1–4. https://doi.org/10.1109/EMBC53108.2024.10782820

    [14] Mittal, D. V. A. J. R. M. (2025). AI-Driven Clinical Decision Support Systems: Evaluating Impact on Diagnosis and Treatment Accuracy. International Journal of Engineering Applications of Artificial Intelligence (IJEAAI), 3(1), 10–29.

    [15] Mittal, M. (2024). The Transformative Role of CRM Systems in Modern Healthcare: Bridging the Provider-Patient Gap. International Journal For Multidisciplinary Research, 6(6), 1–9. https://doi.org/10.36948/ijfmr.2024.v06i06.34280

    [16] Mukherjee, S. (2026a). Early Detection and Prediction of Depression Based on Data-Driven Machine Learning Techniques in Mental Healthcare. 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), 1–6, February. https://doi.org/10.1109/ICAIC67076.2026.11395670

    [17] Mukherjee, S. (2026b). Machine Learning-Enabled Classification of Diabetes Using Clinical via Lifestyle Health Data. 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), 1–6. https://doi.org/10.1109/ICAIC67076.2026.11395709

    [18] Patel, N. K. S. (2026). Enterprise AI Operations in Healthcare IT Infrastructure. Journal of Information Systems Engineering and Management, 11(3), 2508–2522.

    [19] Patil, V., Singhal, D., Naik, N., Hameed, B., Shah, M., Ibrahim, S., Smriti, K., Chatterjee, G., Kale, A., Sharma, A., Paul, R., Chłosta, P., & Somani, B. (2022). Factors Affecting the Usage of Wearable Device Technology for Healthcare among Indian Adults: A Cross-Sectional Study. Journal of Clinical Medicine, 11(23), 7019. https://doi.org/10.3390/jcm11237019

    [20] Prashar, S., & Kataria, P. (2025). Rise and Future of Smart Wearable Technology for Real-Time Health Monitoring. 2025 International Conference on Intelligent and Secure Engineering Solutions (CISES), 1073–1077. https://doi.org/10.1109/CISES66934.2025.11265417

    [21] Prince, D., Prasanna Kumar, R., Jose, D., & Srijan, R. (2025). Enhancing Mental Health by Stress Monitoring using Smart Wearable Technology. 2025 11th International Conference on Smart Computing and Communications (ICSCC), 1–5. https://doi.org/10.1109/ICSCC66177.2025.11233716

    [22] Somula, S. R. (2025). Legacy system modernization: Strategic imperatives and transformation metrics in digital evolution. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., 11.

    [23] Venkata, K., & Koppireddy, R. K. (2025). Revolutionizing Identity Verification: AI-Driven Digital identity solutions for a secure and seamless future. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 2814–2824.

    [24] Warrier, A. (2020). Real-Time Healthcare Event Processing: Stream Analytics for Clinical Decision Support. International Journal of Emerging Research in Engineering and Technology, 1(4, Decmber), 47–54. https://doi.org/10.63282/3050-922X.IJERET-V1I4P106

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