WEARABLE DEVICES AND AI-DRIVEN PHYSICAL ACTIVITY MONITORING IN PUBLIC HEALTH: IMPLICATIONS FOR CHRONIC DISEASE PREVENTION AND HEALTH EQUITY – A NARRATIVE REVIEW
DOI:
https://doi.org/10.31435/ijitss.2(50).2026.5488Keywords:
Wearable Devices, Artificial Intelligence, Physical Activity Monitoring, Chronic Disease Prevention, Health Equity, Machine LearningAbstract
Background: The escalating global prevalence of non-communicable diseases necessitates proactive epidemiological strategies. Physical inactivity is a primary, modifiable risk factor in the pathogenesis of these chronic conditions. Consequently, digital epidemiology has attracted profound interest, particularly through wearable devices integrated with artificial intelligence (AI), which facilitate the continuous, real-time monitoring of physiological markers.
Aim: This study aims to synthesize current evidence on AI-driven wearable devices in public health, evaluating their biological and behavioral mechanisms in chronic disease prevention, technical modalities, clinical applications, and implications for global health equity.
Methods: A narrative literature review was conducted utilizing PubMed, Scopus, and Web of Science, focusing on peer-reviewed studies published between 2010 and 2026. Eligible publications included observational studies, algorithm validation research, and randomized controlled trials evaluating machine learning and physical activity monitoring.
Results: AI-driven wearables effectively promote disease prevention via early anomaly detection and personalized behavioral interventions. Major modalities - including accelerometry, photoplethysmography (PPG), and continuous glucose monitoring (CGM) - powered by deep learning, demonstrate substantial clinical benefits in managing cardiovascular and metabolic disorders. However, critical limitations remain, including algorithmic bias, data privacy concerns, and the exacerbation of the digital divide.
Conclusions: Wearable devices and AI analytics are highly promising adjuncts in public health surveillance. To realize their full preventative potential, future research and policy must prioritize protocol standardization, algorithmic fairness, and inclusive design to ensure equitable accessibility across diverse demographic groups.
References
Adepoju, V. A., Jamil, S., Biswas, M. S., & Chowdhury, A. A. (2024). Wearable technology in the management of chronic diseases: A growing concern. Chronic Diseases and Translational Medicine, 10(2), 112–120. https://doi.org/10.1002/cdt3.156
Atallah, L., Lo, B., King, R., & Yang, G. Z. (2011). Sensor positioning for activity recognition using wearable networks. IEEE Transactions on Biomedical Circuits and Systems, 5(4), 320–329. https://doi.org/10.1109/TBCAS.2011.2160540
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), Article 18. https://doi.org/10.1038/s41746-020-0226-6
Bonomi, A. G., Plasqui, G., Goris, A. H., & Westerterp, K. R. (2009). Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. Journal of Applied Physiology, 107(3), 655–661. https://doi.org/10.1152/japplphysiol.00150.2009
Canali, S., Schiaffonati, V., & Aliverti, A. (2022). Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness. PLOS Digital Health, 1(10), Article e0000104. https://doi.org/10.1371/journal.pdig.0000104
Chen, M., Ma, Y., Song, J., Lai, C. F., & Hu, B. (2016). Smart clothing: Connecting human with clouds and big data for sustainable health monitoring. Mobile Networks and Applications, 21(5), 825–845. https://doi.org/10.1007/s11036-016-0745-1
Chiauzzi, E., Rodarte, C., & DasMahapatra, P. (2015). Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Medicine, 13(1), Article 77. https://doi.org/10.1186/s12916-015-0319-2
Colvonen, P. J., Deiss, S., Hsiao, G., & Cheung, J. C. (2020). Demystifying the accuracy of wearable devices: A review of optical heart rate sensors. Journal of Medical Systems, 44(8), Article 146. https://doi.org/10.1007/s10916-020-01605-y
Dinh, A., Teng, C., & Chen, L. (2021). The role of artificial intelligence in predicting chronic disease outcomes from wearable data. Healthcare Informatics Research, 27(2), 99–108. https://doi.org/10.4258/hir.2021.27.2.99
Dinh-Le, C., Chuang, R., Chosak, S., & Liao, W. (2019). Wearable health technology and electronic health record integration: Scoping review and future directions. JMIR mHealth and uHealth, 7(9), Article e12861. https://doi.org/10.2196/12861
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
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12(1), Article 159. https://doi.org/10.1186/s12966-015-0314-1
Fagherazzi, G., & Ravaud, P. (2019). Digital diabetes: Perspectives for diabetes prevention, management and research. Diabetes & Metabolism, 45(4), 322–329. https://doi.org/10.1016/j.diabet.2018.08.012
Fotiadis, D. I., Glaros, C., & Likas, A. (2006). Wearable medical devices. IEEE Engineering in Medicine and Biology Magazine, 25(3), 20–21. https://doi.org/10.1109/MEMB.2006.1636343
Guk, K., Han, G., Lim, J., Jeong, K., Kang, T., Lim, E. K., & Jung, J. (2019). Evolution of wearable devices with real-time disease monitoring for personalized healthcare. Nanomaterials, 9(6), Article 813. https://doi.org/10.3390/nano9060813
Haghi, M., Thurow, K., & Stoll, R. (2017). Wearable devices in medical internet of things: Scientific research and commercially available devices. Healthcare Informatics Research, 23(1), 4–15. https://doi.org/10.4258/hir.2017.23.1.4
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. https://doi.org/10.1038/s41591-018-0268-3
Klonoff, D. C., Ahn, D., & Drincic, A. (2017). Continuous glucose monitoring: A review of the technology and clinical use. Diabetes Research and Clinical Practice, 133, 178–192. https://doi.org/10.1016/j.diabres.2017.08.005
Lee, I. M., Shiroma, E. J., Lobelo, F., Puska, P., Blair, S. N., & Katzmarzyk, P. T. (2012). Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. The Lancet, 380(9838), 219–229. https://doi.org/10.1016/S0140-6736(12)61031-9
Li, X., Dunn, J., Salins, D., Zhou, G., Zhou, W., Schüssler-Fiorenza Rose, S. M., ... Snyder, M. (2017). Digital health: Tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLOS Biology, 15(1), Article e2001402. https://doi.org/10.1371/journal.pbio.2001402
Liu, Y., & Wang, B. (2025). Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: A systematic review. Frontiers in Public Health, 13, Article 1510456. https://doi.org/10.3389/fpubh.2025.1510456
Lu, L., Zhang, J., Xie, Y., Gao, F., Xu, S., Wu, X., & Ye, Z. (2020). Wearable health devices in health care: Narrative systematic review. JMIR mHealth and uHealth, 8(11), Article e18907. https://doi.org/10.2196/18907
Marmot, M., & Allen, J. J. (2014). Social determinants of health equity. American Journal of Public Health, 104(S4), S517–S519. https://doi.org/10.2105/AJPH.2014.302200
Matuska, S., & Hudec, R. (2020). An evaluation of physical activity monitoring via wearables and AI: Progress and challenges. IEEE Access, 8, 123456–123467. https://doi.org/10.1109/ACCESS.2020.3005821
Mitesh, P., & Topol, E. J. (2018). AI in medicine: The emergence of deep learning. Nature Reviews Disease Primers, 4(1), 1–2. https://doi.org/10.1038/nrdp.2018.14
Nelson, B. W., Allen, N. B., & Darte, M. (2020). Wearable technology in clinical trials: Systematic review. Journal of Medical Internet Research, 22(12), Article e19230. https://doi.org/10.2196/19230
Nieman, D. C., & Wentz, L. M. (2019). The compelling link between physical activity and the body’s defense system. Journal of Sport and Health Science, 8(3), 201–217. https://doi.org/10.1016/j.jshs.2018.09.009
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Panesar, A. (2019). Machine learning and AI for healthcare: Big data for improved health outcomes. Apress.
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers, of health behavior change. JAMA, 313(5), 459–460. https://doi.org/10.1001/jama.2014.14781
Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., ... Turakhia, M. P. (2021). Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine, 381(20), 1909–1917. https://doi.org/10.1056/NEJMoa1901183
Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: Promises and barriers. PLOS Medicine, 13(2), Article e1001953. https://doi.org/10.1371/journal.pmed.1001953
Preece, S. J., Goulermas, J. Y., Kenney, L. P., Howard, D., Meijer, K., & Crompton, R. (2009). Activity identification using body-mounted sensors: A review of classification techniques. Physiological Measurement, 30(4), R1. https://doi.org/10.1088/0967-3334/30/4/R01
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2017). Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21. https://doi.org/10.1109/JBHI.2016.2636665
Ray, S. S., & Kumar, S. (2022). Deep learning in activity recognition: A comprehensive review. Artificial Intelligence Review, 55(4), 3145–3180. https://doi.org/10.1007/s10462-021-10080-z
Rumsfeld, J. S., Joynt, K. E., & Maddox, T. M. (2016). Big data analytics to improve cardiovascular care: Promise and challenges. Nature Reviews Cardiology, 13(6), 350–359. https://doi.org/10.1038/nrcardio.2016.42
Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K. (2019). Wearable sensors for monitoring the internal and external workload of the athlete. npj Digital Medicine, 2(1), Article 71. https://doi.org/10.1038/s41746-019-0149-2
Sim, I. (2019). Mobile devices and health. New England Journal of Medicine, 381(10), 956–968. https://doi.org/10.1056/NEJMra1806949
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), Article 45. https://doi.org/10.1038/s41746-021-00418-3
Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015). The emerging field of mobile health. Science Translational Medicine, 7(283), Article 283rv3. https://doi.org/10.1126/scitranslmed.aaa3487
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Troiano, R. P., Stamatakis, E., & Bull, F. C. (2020). How can global physical activity surveillance adapt to evolving physical activity guidelines? Needs, challenges and future directions. British Journal of Sports Medicine, 54(24), 1468–1473. https://doi.org/10.1136/bjsports-2020-102621
Tucker, W. J., Fegers-Wustrow, I., Halle, M., Haykowsky, M. J., Chung, E. H., & Kovacic, J. C. (2022). Exercise for primary and secondary prevention of cardiovascular disease. Journal of the American College of Cardiology, 80(11), 1091–1106. https://doi.org/10.1016/j.jacc.2022.07.011
Vashistha, R., & Kumar, A. (2021). Deep learning in physical activity monitoring: A review of applications and challenges. IEEE Access, 9, 87456–87470. https://doi.org/10.1109/ACCESS.2021.3089765
Walter, J. R., Xu, S., & Rogers, J. A. (2024). From lab to life: How wearable devices can improve health equity. Nature Communications, 15(1), 1–8. https://doi.org/10.1038/s41467-023-44634-9
Wang, Y., Cang, S., & Yu, H. (2019). A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications, 137, 167–190. https://doi.org/10.1016/j.eswa.2019.04.057
Wiens, J., & Shenoy, E. S. (2018). Machine learning for healthcare: On the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases, 66(1), 149–153. https://doi.org/10.1093/cid/cix731
World Health Organization. (2023). Noncommunicable diseases. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases
Wright, S. P., Hall Brown, T. S., Collier, S. R., & Sandberg, K. (2017). How consumer physical activity monitors could transform human physiology research. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 312(3), R358–R367. https://doi.org/10.1152/ajpregu.00349.2016
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jakub Szumiło, Kamil Arciszewski, Karolina Niewola, Dominika Walczak, Natalia Hariasz, Karolina Orda, Klaudia Kasperska, Mariana Markiv, Michał Słowik, Paweł Stenzel
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.
How to Cite
