Operational Efficacy and Adoption Challenges of Wearable Health Technologies in Chronic Disease Management: A Multi-Stakeholder Investigation Across Selected Hospitals of Jaipur, Rajasthan

References

[1] World Health Organization (WHO). (2023). Noncommunicable diseases: Key facts. Geneva: WHO Press. Retrieved from https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases

[2] Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., et al. (2019). Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas (9th ed.). Diabetes Research and Clinical Practice, 157, 107843. https://doi.org/10.1016/j.diabres.2019.107843

[3] Indian Council of Medical Research (ICMR), Public Health Foundation of India (PHFI), & Institute for Health Metrics and Evaluation (IHME). (2017). India: Health of the Nation’s States. New Delhi: ICMR.

[4] Prabhakaran, D., Jeemon, P., Sharma, M., Roth, G. A., Johnson, C., Harikrishnan, S., et al. (2018). The changing patterns of cardiovascular diseases and their risk factors in the States of India. Lancet Global Health, 6(12), e1339–e1351.

[5] Majumder, S., Mondal, T., & Deen, M. J. (2017). Wearable sensors for remote health monitoring. Sensors, 17(1), 130. https://doi.org/10.3390/s17010130

[6] Dunn, J., Runge, R., & Snyder, M. (2018). Wearables and the medical revolution. Personalized Medicine, 15(5), 429–448. https://doi.org/10.2217/pme-2018-0044

[7] Larsen, P., Wettstein, R., Sedaghat-Hamedani, F., Frey, N., & Katus, H. A. (2024). A remote patient monitoring system with feedback mechanisms using a smartwatch: Active DCM randomised controlled trial. European Heart Journal — Digital Health, 5(1), 45–58.

[8] Sopic, D., Aminifar, A., & Atienza, D. (2017). Real-time event-driven classification technique for early detection and prevention of myocardial infarction on wearable devices. IEEE Transactions on Biomedical Circuits and Systems, 12(3), 668–679.

[9] Latinus, E., & Pereira, R. (2023). Involvement of human volunteers in the development and evaluation of wearable devices designed to improve medication adherence: A scoping review. Patient Preference and Adherence, 17, 2401–2418.

[10] National Family Health Survey — 5 (NFHS-5): India Report. (2021). International Institute for Population Sciences (IIPS), Mumbai. Ministry of Health and Family Welfare, Government of India.

[11] Anjana, R. M., Deepa, M., Pradeepa, R., Mahanta, J., Narain, K., Das, H. K., et al. (2017). Prevalence of diabetes and prediabetes in 15 States of India: Results from the ICMR-INDIAB population-based cross-sectional study. Lancet Diabetes & Endocrinology, 5(8), 585–596.

[12] Prinja, S., Chauhan, A. S., Karan, A., Kaur, G., & Kumar, R. (2019). Impact of publicly financed health insurance schemes on healthcare utilisation and financial risk protection in India. PLOS ONE, 14(2), e0211017.

[13] National Health Authority, Government of India. (2022). Ayushman Bharat Digital Health Mission: Operational Guidelines (Version 2.0). New Delhi: NHA.

[14] Ministry of Health and Family Welfare, Government of India. (2019). National Digital Health Blueprint. New Delhi: MoHFW.

[15] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

[16] Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: Free Press.

[17] Wagner, E. H. (1998). Chronic disease management: What will it take to improve care for chronic illness? Effective Clinical Practice, 1(1), 2–4.

[18] Ibrahim, T., & Ali, H. (2023). The impact of wearable IoT devices on early disease detection and prevention. International Journal of Applied Health Care Analytics (IJAHA), 8(2), 14–29.

[19] Gong, X. Y., Cheng, J., Wu, Y. T., & Li, H. (2024). Effectiveness of home-based cardiac telerehabilitation based on monitoring devices in heart failure patients: A randomised controlled trial. JAMA Cardiology, 9(3), 212–221.

[20] Zaher, M. A., Eldakhly, N. M., Yahia, N. A., & Saleh, A. (2024). Framework using wearable devices for personalised health management using health IoT. Future Generation Computer Systems, 154, 134–148.

[21] Piet, A., Jablonski, L., Daniel, C., Villoing, B., & Thiessard, F. (2023). Non-invasive wearable devices for monitoring vital signs in patients with type 2 diabetes: A systematic review. Diabetes Technology & Therapeutics, 25(11), 773–789.

[22] Taylor, L., Ding, X., & Clifton, D. A. (2023). Wearable vital signs monitoring for patients with asthma: A review. IEEE Journal of Biomedical and Health Informatics, 27(5), 2148–2161.

[23] Coutu, F. A., Iorio, O. C., & Maltais, F. (2023). Remote patient monitoring strategies and wearable technology in chronic obstructive pulmonary disease: A systematic review. COPD: Journal of Chronic Obstructive Pulmonary Disease, 20(1), 189–203.

[24] Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers, of health behaviour change. JAMA, 313(5), 459–460. https://doi.org/10.1001/jama.2014.14781

[25] Wiebe, M., Mackay, M., & Krupa, T. (2023). Feasibility characteristics of wrist-worn fitness trackers in health status monitoring: A mixed-methods study. Digital Health, 9, Article 20552076231168743.

[26] Maric, I., Sahu, S., & Misra, A. (2019). Wearable device adoption in urban Indian healthcare: Infrastructure limitations and patient education as determinants. Journal of Diabetes Science and Technology, 13(4), 760–768.

[27] Chung, C. F., Dew, K., Cole, A., Zia, J., Fogarty, J., Kientz, J. A., & Munson, S. A. (2016). Boundary negotiating artefacts in personal informatics: Patient–provider collaboration with patient-generated data. Proceedings of the ACM CSCW, 770–786.

[28] Abed, A. H., & Nasr, M. (2022). Integration of business intelligence and data mining techniques with electronic health records for clinical decision support. Health Informatics Journal, 28(3), Article 14604582221105690.

[29] Abdolkhani, R., Gray, K., Borda, A., & DeSouza, R. (2023). Recommendations for quality management of patient-generated health data in remote monitoring systems. International Journal of Medical Informatics, 171, Article 104992.

[30] Vogels, M. S., & Kaptchuk, G. (2022). COVID-19 contact tracing and privacy: A longitudinal study of public opinion. Journal of the American Medical Informatics Association, 29(4), 706–714.

[31] Mahmood, H., & Kuo, C. F. (2023). Artificial intelligence in healthcare: Applications, challenges, and future prospects. Nature Medicine, 29(8), 1955–1966.

[32] Shin, K., Kim, J., Park, J., Han, S., Cho, S. W., & Kim, J. H. (2023). A machine learning-assisted system to predict thyrotoxicosis using wearable devices. Thyroid, 33(5), 601–611.

[33] El Khatib, M. M., & Gouher Ahmed, A. (2019). Management of artificial intelligence enabled smart wearable devices for early disease detection. International Journal of Engineering and Advanced Technology, 9(1), 4067–4072.

[34] Amaral, J., & Nguyen, T. (2023). Enhancing health monitoring and active ageing in the elderly population: A study on wearable technology and technology-assisted care. Gerontechnology, 22(2), 1–14.

[35] Buisset, P., Moreau, Y. E., & Gilmore, M. (2020). Wearable solutions for patients with Parkinson’s disease and neurocognitive disorder: A systematic review. npj Parkinson’s Disease, 6(1), 1–11.

[36] Figueroa, S. S., Toral, R., Jimeno, G., Luque, J., González, I., & Petrovic, M. (2021). Potential applications of mobile and wearable devices for psychological support during the COVID-19 pandemic: A review. IEEE Sensors Journal, 21(22), 24877–24897.

[37] Stoppa, A., Milella, A., Ehrmann, M. F., & Zielinski, M. (2021). The e-textile for biomedical applications: A systematic review of literature. Sensors, 21(4), 1048.

[38] Sharma, A., Badea, M., Tiwari, S., & Marty, J. L. (2021). Wearable biosensors: An alternative and practical approach in healthcare and disease monitoring. Molecules, 26(3), 748.

[39] Yang, C., Shang, L., Yao, S., & Li, J. (2023). Cost, time savings, and effectiveness of wearable devices for remote patient monitoring. npj Digital Medicine, 6(1), 109.

[40] Holtz, B. E., Urban, F. A., & Oestreich, J. (2024). The promise of remote patient monitoring. Telemedicine and e-Health, 30(3), 578–585.

[41] Ometov, A., Shubina, V., Klus, L., Škibińska, J., Saafi, S., Pascacio, P., et al. (2021). A survey on wearable technology: History, state-of-the-art and current challenges. Computer Networks, 193, 108074.

[42] Pantelopoulos, A., & Bourbakis, N. G. (2010). A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics — Part C, 40(1), 1–12.

[43] Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4, Article 50.

[44] Creswell, J. W., & Plano Clark, V. L. (2017). Designing and Conducting Mixed Methods Research (3rd ed.). Thousand Oaks, CA: SAGE Publications.

[45] Slovin, E. (1960). Slovin’s formula for sampling technique. Cited in: Tejada, J. J., & Punzalan, J. R. B. (2012). On the misuse of Slovin’s formula. Philippine Statistician, 61(1), 129–136.

[46] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

[47] Kwiatkowska, M., Rehman, P., Walaszczyk, E., Wojciechowska, A., & Czekierda, L. (2022). Telemedicine technologies selection for post-hospital patient care after total hip arthroplasty using multi-criteria decision making. Decision Support Systems, 155, 113728.

[48] Bent, B., Wang, K., Grzesiak, E., Jiang, C., Qi, Y., Jiang, Y., et al. (2020). The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data. JMIR mHealth and uHealth, 8(1), e15790.

@article{1716925,
      author = {Ankita Mulchandani, Dr. Dipti Sethi},
      title = {Operational Efficacy and Adoption Challenges of Wearable Health Technologies in Chronic Disease Management: A Multi-Stakeholder Investigation Across Selected Hospitals of Jaipur, Rajasthan},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {3140-3156},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716925.pdf},
      abstract = {Healthcare delivery in urban India is at a turning point. The simultaneous rise of lifestyle-linked chronic illnesses and the accelerating penetration of sensor-embedded wearable technologies have opened new clinical possibilities for continuous, patient-centred disease monitoring outside conventional hospital environments. This paper investigates the operational efficacy, deployment barriers, and multi-stakeholder adoption dynamics of wearable health devices across ten purposively selected tertiary-care and multispecialty hospitals in Jaipur, Rajasthan. Drawing on a structured synthesis of ninety peer-reviewed publications alongside a proposed cross-sectional mixed-method field study involving 391 respondents — comprising chronic disease patients, treating clinicians, and hospital information-technology personnel — the study constructs and validates a conceptual framework that positions four independent variable classes (device usage patterns; operational-technical infrastructure; patient socio-behavioural factors; and institutional readiness) as joint determinants of three outcome domains: clinical improvement, patient engagement, and operational efficiency. Preliminary findings from the literature synthesis reveal that wearable devices demonstrate consistent clinical utility in diabetes, hypertension, cardiovascular disease, and chronic obstructive pulmonary disease management, yet their translation into sustained operational practice within Indian urban hospitals is constrained by fragmented EHR interoperability, heterogeneous patient digital literacy, inadequate staff training, and immature data governance architecture. An original five-component Wearable Device Operational Integration Model (WDOIM) is proposed to guide hospital administrators and health-technology policymakers in designing context-sensitive, scalable deployment programmes. The paper fills an identified tri-level research gap — theoretical, contextual, and methodological — and advances a research agenda grounded in the specific institutional, demographic, and infrastructural realities of Rajasthan’s evolving digital health ecosystem.},
      keywords = {Wearable Health Devices; Chronic Non-Communicable Disease; Digital Health Management; Patient Adherence; EHR Integration; Urban India; Jaipur; Mhealth; Iot Biosensors; Implementation Science.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716925}
  }