Pemanfaatan Teknologi dan Intervensi Digital dalam Pencegahan Risiko Jatuh pada Lansia
Abstract
The aim of this research is to evaluate digital interventions aimed at preventing falls among individuals aged 65 and older. The methodology employed involves a literature review assessing the effectiveness of various technologies in reducing fall risks, as well as identifying the strengths and weaknesses of each approach. The study includes elderly participants using digital technology or interventions compared to a control group receiving standard care or no intervention. Outcomes considered include reduced fall risk, fall incidents, or severity of fall-related injuries. Studies meeting inclusion criteria encompass experimental designs (randomized controlled trials, controlled trials) or observational studies with adequate controls, available in English. PubMed, Scopus, and IEEE Xplore databases were utilized up to June 19, 2024, with additional reference searches. Research findings indicate that out of 608 identified articles, 10 studies met inclusion criteria following screening processes. These studies utilized a variety of technologies such as mobile applications, virtual reality (VR), augmented reality (AR), machine learning, and robotics. Conducted in various countries, these studies demonstrate diverse applications; for instance, UK studies used mobile apps and 3D games for fall awareness, Chinese studies employed VR for rehabilitation, and German studies explored robotics for fall prevention. The research concludes by highlighting the potential of technologies like VR, AR, machine learning, and robotics in reducing fall risks among the elderly, thereby enhancing healthcare practices and quality of life.
Keywords: Elderly, Falls Risk, Prevention, Technology
References
Chen, M., Tang, Q., Xu, S., Leng, P., & Pan, Z. (2020). Design and Evaluation of An Augmented Reality-Based Exergame System to Reduce Fall Risk in The Elderly. International Journal of Environmental Research and Public Health, 17(19). https://doi.org/10.3390/ijerph17197208
Chen, P. J., Penn, I. W., Wei, S. H., Chuang, L. R., & Sung, W. H. (2020). Augmented Reality-Assisted Training with Selected Tai-Chi Movements Improves Balance Control and Increases Lower Limb Muscle Strength in Older Adults: A Prospective Randomized Trial. Journal of Exercise Science and Fitness, 18(3). https://doi.org/10.1016/j.jesf.2020.05.003
Chen, S. H., Lee, C. H., Jiang, B. C., & Sun, T. L. (2021). Using A Stacked Autoencoder for Mobility and Fall Risk Assessment Via Time–Frequency Representations of The Timed Up and Go Test. Frontiers in Physiology, 12. https://doi.org/10.3389/fphys.2021.668350
Chung, H. L., Chin, K. Y., & Wang, C. S. (2021). Development of a Head-Mounted Mixed Reality Museum Navigation System. 4th IEEE International Conference on Knowledge Innovation and Invention 2021, ICKII 2021. https://doi.org/10.1109/ICKII51822.2021.9574731
Dewi, S. K. (2024). Validitas dan reliabilitas Two-Level Quick-Steadi Algorithm untuk Skrining Risiko Jatuh Lansia di Indonesia: Studi Observasional Analitik. Majalah Ilmiah Fisioterapi Indonesia, 12(1), 81. https://doi.org/10.24843/mifi.2024.v12.i01.p14
Diener, J., Rayling, S., Bezold, J., Krell-Roesch, J., Woll, A., & Wunsch, K. (2022). Effectiveness and Acceptability of E- And M-Health Interventions to Promote Physical Activity and Prevent Falls in Nursing Homes—A Systematic Review. in Frontiers in Physiology (Vol. 13). https://doi.org/10.3389/fphys.2022.894397
Haescher, M., Chodan, W., Höpfner, F., Bieber, G., Aehnelt, M., Srinivasan, K., & Murphy, M. A. (2020). Automated fall Risk Assessment of Elderly Using Wearable Devices. Journal of Rehabilitation and Assistive Technologies Engineering, 7. https://doi.org/10.1177/2055668320946209
Hamm, J., Money, A. G., & Atwal, A. (2019). Enabling Older Adults to Carry Out Paperless Falls-Risk Self-Assessments Using Guidetomeasure-3D: A Mixed Methods Study. Journal of Biomedical Informatics, 92. https://doi.org/10.1016/j.jbi.2019.103135
Hassannataj Joloudari, J., Mojrian, S., Nodehi, I., Mashmool, A., Kiani Zadegan, Z., Khanjani Shirkharkolaie, S., Alizadehsani, R., Tamadon, T., Khosravi, S., Akbari Kohnehshari, M., Hassannatajjeloudari, E., Sharifrazi, D., Mosavi, A., Loh, H. W., Tan, R. S., & Acharya, U. R. (2022). Application of Artificial Intelligence Techniques for Automated Detection of Myocardial Infarction: A Review. In Physiological Measurement (Vol. 43, Issue 8). https://doi.org/10.1088/1361-6579/ac7fd9
Iida, H., Seki, T., Takegami, Y., Osawa, Y., Kato, D., Takemoto, G., Ando, K., Ishizuka, S., Hasegawa, Y., & Imagama, S. (2024). Association Between Locomotive Syndrome and Fall Risk in The Elderly Individuals in Japan: The Yakumo Study. Journal of Orthopaedic Science, 29(1). https://doi.org/10.1016/j.jos.2022.11.023
Lyu, Z., Wang, L., Gao, X., & Ma, Y. (2023). The Identification of Elderly People with High Fall Risk Using Machine Learning Algorithms. Healthcare (Switzerland), 11(1). https://doi.org/10.3390/healthcare11010047
Money, A. G., Atwal, A., Boyce, E., Gaber, S., Windeatt, S., & Alexandrou, K. (2019). Falls Sensei: A serious 3D Exploration Game to Enable The Detection of Extrinsic Home Fall Hazards for Older Adults. BMC Medical Informatics and Decision Making, 19(1). https://doi.org/10.1186/s12911-019-0808-x
Morat, T., Snyders, M., Kroeber, P., De Luca, A., Squeri, V., Hochheim, M., Ramm, P., Breitkopf, A., Hollmann, M., & Zijlstra, W. (2023). Evaluation of A Novel Technology-Supported Fall Prevention Intervention – Study Protocol of A Multi-Centre Randomised Controlled Trial in Older Adults at Increased Risk of falls. BMC Geriatrics, 23(1). https://doi.org/10.1186/s12877-023-03810-8
Nurhasanah, A., Nurdahlia, Hartini, T., & Maryam, R. S. (2022). Edukasi Multimedia Interaktif dan Pendampingan Keluarga Meningkatkan Kemampuan Keluarga dalam Deteksi Dini Risiko Jatuh pada Lansia. Prosiding Seminar Nasional Poltekkes Jakarta III.
Oh-Park, M., Doan, T., Dohle, C., Vermiglio-Kohn, V., & Abdou, A. (2021). Technology Utilization in Fall Prevention. in American Journal of Physical Medicine And Rehabilitation, 100(1). https://doi.org/10.1097/PHM.0000000000001554
Ruiz, J. F. B., Chaparro, J. D., Romero, M. J. S., Molina, F. J. V., García, X. D. T., Peño, C. B., Solano, H. L., Colantonio, S., Flórez-Revuelta, F., & López, J. C. (2022). Bedtime Monitoring for Fall Detection and Prevention in Older Adults. International Journal of Environmental Research and Public Health, 19(12). https://doi.org/10.3390/ijerph19127139
Tonga, E., Srikesavan, C., Williamson, E., & Lamb, S. E. (2022). Components, Design and Effectiveness of Digital Physical Rehabilitation Interventions for Older People: A Systematic Review. Journal of Telemedicine and Telecare, 28(3). https://doi.org/10.1177/1357633X20927587
Wibowo, H., & Prastowo, B. (2024). Program Preventif dan Kuratif untuk Menurunkan Risiko Jatuh pada Lansia oleh Persatuan Wredatama Republik Indonesia, Lamongan. Jurnal Inovasi Dan Pengabdian Masyarakat Indonesia, 3(1). https://doi.org/10.26714/jipmi.v3i1.205
World Health Organization. (2020). Falls. https://www.who.int/news-room/fact-sheets/detail/falls
Zhao, R., Zhao, X., Guan, J., Zhang, C., & Zhu, K. (2023). The Effect of Virtual Reality Technology on Anti-Fall Ability and Bone Mineral Density of The Elderlywith Osteoporosis in An Elderly Care Institution. European Journal of Medical Research, 28(1). https://doi.org/10.1186/s40001-023-01165-9
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