WEARABLE BIOSENSORS IN REMOTE CHRONIC DISEASE MONITORING: A STRUCTURED REVIEW OF IMPLEMENTATION BARRIERS AND HEALTH EQUITY IMPLICATIONS
DOI:
https://doi.org/10.31435/ijitss.4(52).2026.6394Keywords:
Wearable Biosensors, Remote Patient Monitoring, Health Equity, Implementation Barriers, Chronic Disease Management, Digital DivideAbstract
Background Clinical-grade wearable biosensors offer significant potential for tracking chronic diseases outside the hospital. However, while the hardware itself is highly accurate, the medical field still struggles with the logistical reality of deploying these tools. The structural barriers preventing widespread clinical use, and the ways these hurdles impact overall health equity, lack rigorous synthesis in the current literature.
Objective Moving beyond pure hardware accuracy, this analysis maps the practical realities of continuous patient surveillance. It specifically investigates the institutional workflow bottlenecks, hardware usability hurdles, and underlying economic disparities that dictate how monitoring programs actually function for populations managing heart failure, diabetes, or COPD.
Methodology Peer-reviewed literature spanning 2018 to 2026 was extracted from PubMed/MEDLINE and Google Scholar. The inclusion criteria deliberately filtered out purely technical validation studies. Instead, the analysis targeted data documenting the logistical friction, long-term adherence failures, and unequal access surrounding continuous glucose monitors, ambulatory cardiac patches, and multi-parameter tracking devices.
Results The synthesized data show that severe workflow friction and poor electronic health record interoperability actively block hospital networks from scaling these programs. On the patient side, a distinct "compliance paradox" emerges. High initial motivation frequently collapses into device abandonment, usually triggered by physical skin irritation, convoluted software interfaces, and low technical literacy. The literature also exposes a severe digital divide. A patient's income bracket, geographic location, and advanced age strictly gatekeep their ability to access continuous medical monitoring.
Conclusion Healthcare networks cannot rely solely on precise hardware to improve chronic disease management. Deploying continuous monitors successfully requires a fundamental shift toward repairing broken clinical workflows and addressing systemic exclusion. Without structural interventions targeting financial and technical accessibility, digital health initiatives will simply reinforce existing medical inequalities.
References
Bent, B., Goldstein, B. A., Kibbe, W. A., & Dunn, J. P. (2020). Investigating sources of inaccuracy in wearable optical heart rate sensors. Npj Digital Medicine, 3(1), 18. https://doi.org/10.1038/s41746-020-0226-6
Brewer, L. C., Fortuna, K. L., Jones, C., Walker, R., Hayes, S. N., Patten, C. A., & Cooper, L. A. (2020). Back to the future: Achieving health equity through health informatics and digital health. JMIR mHealth and uHealth, 8(1), e14512. https://doi.org/10.2196/14512
Brotherdale, R., Berry, K., & Bucci, S. (2024). A qualitative study exploring the digital therapeutic alliance with fully automated smartphone apps. DIGITAL HEALTH, 10, 20552076241277712. https://doi.org/10.1177/20552076241277712
Crawford, A., & Serhal, E. (2020). Digital health equity and COVID-19: The innovation curve cannot reinforce the social gradient of health. Journal of Medical Internet Research, 22(6), e19361. https://doi.org/10.2196/19361
Dinh-Le, C., Chuang, R., Chokshi, S., & Mann, D. (2019). Wearable health technology and electronic health record integration: Scoping review and future directions. JMIR mHealth and uHealth, 7(9), e12861. https://doi.org/10.2196/12861
Eberle, C., & Stichling, S. (2021). Clinical improvements by telemedicine interventions managing type 1 and type 2 diabetes: Systematic meta-review. Journal of Medical Internet Research, 23(2), e23244. https://doi.org/10.2196/23244
Gautam, N., Ghanta, S., Mueller, J., Mansour, M., Chen, Z., Puente, C., Ha, Y., Tarun, T., Dhar, G., Sivakumar, K., Zhang, Y., Halimeh, A., Nakarmi, U., Al-Kindi, S., DeMazumder, D., & Al’Aref, S. (2022). Artificial intelligence, wearables and remote monitoring for heart failure: Current and future applications. Diagnostics, 12(12), 2964. https://doi.org/10.3390/diagnostics12122964
Laranjo, L., Ding, D., Heleno, B., Kocaballi, B., Quiroz, J. C., Tong, H. L., Chahwan, B., Neves, A. L., Gabarron, E., Dao, K. P., Rodrigues, D., Neves, G. C., Antunes, M. L., Coiera, E., & Bates, D. W. (2021). Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression. British Journal of Sports Medicine, 55(8), 422–432. https://doi.org/10.1136/bjsports-2020-102892
Lupton, D. (2018). How do data come to matter? Living and becoming with personal data. Big Data & Society, 5(2), 2053951718786314. https://doi.org/10.1177/2053951718786314
Malouin-Lachance, A., Capolupo, J., Laplante, C., & Hudon, A. (2025). Does the digital therapeutic alliance exist? Integrative review. JMIR Mental Health, 12, e69294–e69294. https://doi.org/10.2196/69294
Marra, C., Gordon, W. J., & Stern, A. D. (2021). Use of connected digital products in clinical research following the COVID-19 pandemic: A comprehensive analysis of clinical trials. BMJ Open, 11(6), e047341. https://doi.org/10.1136/bmjopen-2020-047341
Nazi, K. M., Newton, T., & Armstrong, C. M. (2024). Unleashing the potential for patient-generated health data (PGHD). Journal of General Internal Medicine, 39(S1), 9–13. https://doi.org/10.1007/s11606-023-08461-4
Nundy, S., Cooper, L. A., & Mate, K. S. (2022). The quintuple aim for health care improvement: A new imperative to advance health equity. JAMA, 327(6), 521. https://doi.org/10.1001/jama.2021.25181
Odugbesan, O., Mungmode, A., Rioles, N., Buckingham, D., Nelson, G., Agarwal, S., Grant, A., Wright, T., Hess, E., Ebekozien, O., & T1D Exchange Quality Improvement Collaborative. (2024). Increasing continuous glucose monitoring use for non-Hispanic Black and Hispanic people with type 1 diabetes: Results from the T1D Exchange Quality Improvement Collaborative Equity Study. Clinical Diabetes, 42(1), 40–48. https://doi.org/10.2337/cd23-0050
Reid, D., Mehta, J., Anis, K., & Mehta, S. (2023). Impact of remote patient monitoring platform on patients with moderate to severe persistent asthma: Observational study. JMIR Formative Research, 7, e51065. https://doi.org/10.2196/51065
Rodriguez, J. A., Clark, C. R., & Bates, D. W. (2020). Digital health equity as a necessity in the 21st Century Cures Act era. JAMA, 323(23), 2381. https://doi.org/10.1001/jama.2020.7858
Sana, F., Isselbacher, E. M., Singh, J. P., Heist, E. K., Pathik, B., & Armoundas, A. A. (2020). Wearable devices for ambulatory cardiac monitoring. Journal of the American College of Cardiology, 75(13), 1582–1592. https://doi.org/10.1016/j.jacc.2020.01.046
Sim, I. (2019). Mobile devices and health. New England Journal of Medicine, 381(10), 956–968. https://doi.org/10.1056/NEJMra1806949
Sjoding, M. W., Dickson, R. P., Iwashyna, T. J., Gay, S. E., & Valley, T. S. (2020). Racial bias in pulse oximetry measurement. New England Journal of Medicine, 383(25), 2477–2478. https://doi.org/10.1056/NEJMc2029240
Smuck, M., Odonkor, C. A., Wilt, J. K., Schmidt, N., & Swiernik, M. A. (2021). The emerging clinical role of wearables: Factors for successful implementation in healthcare. Npj Digital Medicine, 4(1), 45. https://doi.org/10.1038/s41746-021-00418-3
Steinhubl, S. R., Waalen, J., Edwards, A. M., Ariniello, L. M., Mehta, R. R., Ebner, G. S., Carter, C., Baca-Motes, K., Felicione, E., Sarich, T., & Topol, E. J. (2018). Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: The mSToPS randomized clinical trial. JAMA, 320(2), 146. https://doi.org/10.1001/jama.2018.8102
Thakur, M., Maurer, E. W., Tran, K. N., Tholkes, A., Rajamani, S., & Dwivedi, R. (2025). Enhancing health equity and patient engagement in diabetes care: Technology-aided continuous glucose monitoring pilot implementation project. JMIR Diabetes, 10, e68324. https://doi.org/10.2196/68324
Veinot, T. C., Mitchell, H., & Ancker, J. S. (2018). Good intentions are not enough: How informatics interventions can worsen inequality. Journal of the American Medical Informatics Association, 25(8), 1080–1088. https://doi.org/10.1093/jamia/ocy052
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Copyright (c) 2026 Jakub Juskowiak, Zuzanna Julia Komin, Maryan Liasota, Eliza Zofia Kabat, Michalina Justyna Kozłowska, Krzysztof Konopka, Maciej Kamil Majchrzak, Faustyna Magdalena Kołodziej, Alicja Kwiatkowska, Mateusz Lis
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