Exploring the Lived Experience of Using Wearable Cardiac Monitoring Devices Among Older Adults Living Independently at Home in Urban Java, Indonesia
Main Article Content
Abstract
The integration of wearable health technologies has transformed patient care by enabling real-time monitoring and promoting self-management, especially among older adults. However, current research has primarily focused on clinical outcomes and usability metrics, offering limited insight into the lived experiences of elderly users. Despite the growing adoption of cardiac monitoring devices, little is known about how older adults interpret and emotionally engage with these technologies in daily life. This study adopts a descriptive phenomenological approach to explore how older individuals experience wearable cardiac monitoring devices at home. Using Colaizzi’s method, in-depth interviews were conducted with _eight older adult participants aged 65 and above_, recruited from urban and semi-urban communities in Indonesia. The findings revealed four central themes: emotional vulnerability in solitary use, cautious navigation of technology, privacy concerns, and evolving empowerment through continued use. These results illustrate how wearable devices become embedded not just in health routines but in users’ identities, fears, and sense of agency. The study highlights the importance of user-centered design that addresses emotional and existential concerns of elderly populations. These insights contribute to a deeper understanding of aging with technology and suggest directions for future research in human-centered digital health innovation.
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. Scopus. https://doi.org/10.1109/ACCESS.2019.2910411
Amoon, M., Altameem, T., & Altameem, A. (2020). Internet of things sensor assisted security and quality analysis for health care data sets using artificial intelligent based heuristic health management system. Measurement: Journal of the International Measurement Confederation, 161. Scopus. https://doi.org/10.1016/j.measurement.2020.107861
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. Scopus. https://doi.org/10.1016/j.eij.2022.02.004
Baldassano, S. N., Roberson, S. W., Balu, R., Scheid, B., Bernabei, J. M., Pathmanathan, J., Oommen, B., Leri, D., Echauz, J., Gelfand, M., Bhalla, P. K., Hill, C. E., Christini, A., Wagenaar, J. B., & Litt, B. (2020). IRIS: A modular platform for continuous monitoring and caretaker notification in the intensive care unit. IEEE Journal of Biomedical and Health Informatics, 24(8), 2389–2397. Scopus. https://doi.org/10.1109/JBHI.2020.2965858
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). Scopus. 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 (Switzerland), 12(22), 1–20. Scopus. https://doi.org/10.3390/su12229605
Blonigen, D., Hyde, J., McInnes, D. K., Yoon, J., Byrne, T., Ngo, T., & Smelson, D. (2023). Integrating data analytics, peer support, and whole health coaching to improve the health outcomes of homeless veterans: Study protocol for an effectiveness-implementation trial. Contemporary Clinical Trials, 125. Scopus. https://doi.org/10.1016/j.cct.2022.107065
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. Scopus. https://doi.org/10.1109/JSEN.2019.2925638
Chung, K., & Park, R. C. (2019). Chatbot-based heathcare service with a knowledge base for cloud computing. Cluster Computing, 22, 1925–1937. Scopus. https://doi.org/10.1007/s10586-018-2334-5
Clair, R. P. (2003). Expressions of ethnography: Novel approaches to qualitative methods (hlm. 303). State University of New York Press; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896556900&partnerID=40&md5=d14cc6ba1608309f0398c418b0c86e4b
Daly, K. J. (2007). Qualitative methods for family studies & human development (hlm. 293). SAGE Publications Inc.; Scopus. https://doi.org/10.4135/9781452224800
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). Scopus. https://doi.org/10.1186/s40537-019-0271-7
Fahlevi, H., Irsyadillah, I., Indriani, M., & Oktari, R. S. (2022). DRG-based payment system and management accounting changes in an Indonesian public hospital: Exploring potential roles of big data analytics. Journal of Accounting and Organizational Change, 18(2), 325–345. Scopus. https://doi.org/10.1108/JAOC-10-2020-0179
Fenton, N. E., & Baxter, J. (2016). Practicing Qualitative Methods in Health Geographies (hlm. 266). Taylor and Francis; Scopus. https://doi.org/10.4324/9781315601946
Fife, W. (2020). Counting as a Qualitative Method: Grappling with the Reliability Issue in Ethnographic Research (hlm. 140). Springer International Publishing; Scopus. https://doi.org/10.1007/978-3-030-34803-8
Fleming, M. D., Safaeinili, N., Knox, M., & Brewster, A. L. (2024). Organizational and community resilience for COVID-19 and beyond: Leveraging a system for health and social services integration. Health Services Research, 59(S1). Scopus. https://doi.org/10.1111/1475-6773.14250
Gous, N., Nyaruhirira, A. U., Cunningham, B., & Macek, C. (2020). Driving the usage of tuberculosis diagnostic data through capacity building in low- And middle-income countries. African Journal of Laboratory Medicine, 9(2). Scopus. https://doi.org/10.4102/AJLM.V9I2.1092
Gramaje, A., Thabtah, F., Abdelhamid, N., & Ray, S. K. (2021). Patient Discharge Classification Using Machine Learning Techniques. Annals of Data Science, 8(4), 755–767. Scopus. https://doi.org/10.1007/s40745-019-00223-6
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 (Switzerland), 19(5). Scopus. 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). Scopus. 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. Scopus. 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. Scopus. https://doi.org/10.1021/acs.analchem.2c00782
Kawamura, Y. (2020). DOING RESEARCH IN FASHION AND DRESS: An Introduction to Qualitative Methods, 2nd edition (hlm. 166). Bloomsbury Publishing Plc.; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188589040&partnerID=40&md5=b3db406659cd1ea5b20e05664bec39a3
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. Scopus. https://doi.org/10.1007/s11036-020-01700-6
Maddeh, M., Hajjej, F., Alazzam, M. B., Otaibi, S. A., Turki, N. A., & Ayouni, S. (2023). Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring. Sensors, 23(10). Scopus. https://doi.org/10.3390/s23104614
Metz, M., Smith, R., Mitchell, R., Duong, Y. T., Brown, K., Kinchen, S., Lee, K., Ogollah, F. M., Dzinamarira, T., Maliwa, V., Moore, C., Patel, H., Chung, H., Mtengo, H., & Saito, S. (2021). Data Architecture to Support Real-Time Data Analytics for the Population-Based HIV Impact Assessments. Journal of Acquired Immune Deficiency Syndromes, 87, S28–S35. Scopus. https://doi.org/10.1097/QAI.0000000000002703
Minopoulos, G. M., Memos, V. A., Stergiou, C. L., Stergiou, K. D., Plageras, A. P., Koidou, M. P., & Psannis, K. E. (2022). Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System. Applied Sciences (Switzerland), 12(12). Scopus. https://doi.org/10.3390/app12125859
Mulrooney, M., Smith, M., Lewis, K., Vuernick, E., Anderson, D., Channamsetty, V., & Giannotti, T. (2022). Practical implementation insights from 2 population health pharmacist project approaches to improve blood pressure control. Journal of the American Pharmacists Association, 62(1), 270–280. Scopus. https://doi.org/10.1016/j.japh.2021.07.012
Murphy, E., & Dingwall, R. (2017). Qualitative methods and health policy research (hlm. 230). Taylor and Francis; Scopus. https://doi.org/10.4324/9781315127873
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. Scopus. 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. Scopus. https://doi.org/10.1007/s11276-018-1694-3
Role, J., Chao, H., Rosario, C., Ho, P., & Hodgkins, M. (2021). Inpatient Staffing Dashboard: A Nursing-Information Technology Collaborative Project. CIN - Computers Informatics Nursing, 39(11), 772–779. Scopus. https://doi.org/10.1097/CIN.0000000000000778
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). Scopus. https://doi.org/10.1038/s41598-022-06434-x
Sharma, N., Mangla, M., Mohanty, S. N., Gupta, D., Tiwari, P., Shorfuzzaman, M., & Rawashdeh, M. (2021). A smart ontology-based IoT framework for remote patient monitoring. Biomedical Signal Processing and Control, 68. Scopus. https://doi.org/10.1016/j.bspc.2021.102717
Sirasakamol, O., Ariya, P., Nadee, W., & Puritat, K. (2022). Development of a Mobile-Healthcare Application for Safety and Prevention in Emergency Assistance at Marathon Events: A Case Study in CMU Marathon. International journal of online and biomedical engineering, 18(6), 65–81. Scopus. https://doi.org/10.3991/ijoe.v18i06.29515
Taghavi, M., Johnston, G., Urquhart, R., Henderson, D., Tschupruk, C., & Tupala, B. (2021). Workforce Planning for Community-Based Palliative Care Specialist Teams Using Operations Research. Journal of Pain and Symptom Management, 61(5), 1012-1022.e4. Scopus. https://doi.org/10.1016/j.jpainsymman.2020.09.009
Tran, V.-T., Riveros, C., & Ravaud, P. (2019). Patients’ views of wearable devices and AI in healthcare: Findings from the ComPaRe e-cohort. npj Digital Medicine, 2(1). Scopus. https://doi.org/10.1038/s41746-019-0132-y
Wurster, F., Beckmann, M., Cecon-Stabel, N., Dittmer, K., Jes Hansen, T., Jaschke, J., Köberlein-Neu, J., Okumu, M.-R., Rusniok, C., Pfaff, H., & Karbach, U. (2024). The Implementation of an Electronic Medical Record in a German Hospital and the Change in Completeness of Documentation: Longitudinal Document Analysis. JMIR Medical Informatics, 12(1). Scopus. https://doi.org/10.2196/47761
Xie, Y., Lu, L., Gao, F., He, S.-J., Zhao, H.-J., Fang, Y., Yang, J.-M., An, Y., Ye, Z.-W., & Dong, Z. (2021). Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Current Medical Science, 41(6), 1123–1133. Scopus. https://doi.org/10.1007/s11596-021-2485-0
Zhang, H., Deng, K., Li, H., Albin, R. L., & Guan, Y. (2020). Deep Learning Identifies Digital Biomarkers for Self-Reported Parkinson’s Disease. Patterns, 1(3). Scopus. https://doi.org/10.1016/j.patter.2020.100042