การพัฒนานวัตกรรมแจ้งเตือนหกล้มในผู้สูงอายุ

Sarinya Chansuk; Sattjaphan Jingmark; Kritchai Boonsiwanon

Authors

Sarinya Chansuk Faculty of Nursing, Udon Thani Rajabhat University
Sattjaphan Jingmark Faculty of Engineer, Pitchayabundit College
Kritchai Boonsiwanon Faculty of Technology and Engineer, Udon thani Rajaphat University

Keywords:

older adults, fall alert, sensor technology, health innovation

Abstract

Introduction: Falls are a primary cause of morbidity and mortality among older adults. Therefore, development of innovations to prevent falls in this population is essential to improve their quality of life.

Research objectives: This study aimed to develop an innovative fall alert system for older adults.

Research methodology: This developmental research comprised three phases: knowledge synthesis, device design and development, performance testing and user satisfaction assessment. The sample included 30 older adults aged 60 years and above from Sam Phrao Subdistrict, Mueang District, Udon Thani Province. Participants were selected through purposive sampling based on a Thai Falls Risk Assessment Test (Thai-FRAT) score of ≥4. The data were collected using the Belt Fall Alert (fall-warning belt) device, a form for recording the performance of the innovation, and a user satisfaction questionnaire. Data were analyzed using descriptive statistics, including frequency, percentage, mean, and standard deviation.

Results: The Belt Fall Alert accurately detected changes in body tilt angles in all instances (100%) and successfully triggered alerts in every test. Overall user satisfaction was at a high level (M = 3.99, SD = .56).

Conclusions: The developed innovation serves as an effective fall prevention alert system for older adults. Users also reported high satisfaction with its use.

Implications: Further studies should examine the impact of this innovation on reducing fall incidence, consider developing a smaller and more convenient device, explore the integration of AI for deeper analysis of older adults’ behavior, and evaluate cost-effectiveness to reduce production costs and support commercial production.

Downloads

Download data is not yet available.

References

National Statistical Office, Ministry of Digital Economy and Society. The 2024 survey of the older persons in Thailand and provincial level. Bangkok: Statistical Information Dissemination and Service Group, Statistical Forecasting Division, National Statistical Office; 2024. (in Thai)

Department of Disease Control. Falls among older adults: A common problem [Internet]. 2019 [cited 2025 Nov 19]. Available from: https://www.ddc.moph.go.th/doe/journal_detail.php?publish=5996&utm_source=chatgpt.com (in Thai)

Srichang N, Singthimat S, Lertchirakarn P, Homhuan S, Riangthaisong K. Epidemiology of elderly injured by falls based on the case study in Regional Health 9. Academic Journal of Community Public Health 2024;10(3):46–56. (in Thai)

Pannurat N, Thiemjarus S, Nantajeewarawat E. Automatic fall monitoring: A review. sensors 2017;17(1):199. (in Thai)

Takahashi T. Fall risk assessment using wearable sensors. Healthcare 2020;8(3):219.

Lord SR, Sherrington C, Menz HB. Falls in older people: Risk factors and strategies for prevention. Cambridge: Cambridge University Press; 2021.

Rashidi P, Mihailidis A. A survey on ambient-assisted living tools for older adults. IEEE Journal of Biomedical and Health Informatics 2019;17(3):579–90.

Udonthani Statistical Office. Elderly analysis Udon Thani 2024 [Internet]. 2024 [cited 2025 Nov 19]. Available from: https://udon.nso.go.th/reports-publications/ebook/indicators-analysis-basic-provincial-information/11-elderly-analysis-udon-thani.html?utm_source=chatgpt.com. (in Thai)

Subramaniam S, Faisal AI, Deen MJ. Wearable sensor systems for fall risk assessment: A review. Frontiers in Digital Health 2022;4:921506. doi: 10.3389/fdgth.2022.921506.

Thiamwong L, Thamarpirat J, Maneesriwongul W, Jitapunkul S. Thai falls risk assessment test (Thai-FRAT) developed for community-dwelling Thai elderly. The Journal of the Medical Association of Thailand 2008;91(12):1823-31. (in Thai)

World Health Organization. Falls [Internet]. 2021 [cited 2025 Nov 19]. Available from: https://www.who.int/news-room/fact-sheets/detail/falls

Thaniwattananon P, Kumkong M. Falls in the elderly: Prevention and care in COVID-19 situations. Journal of Medical and Health Sciences 2023;30(1):166–78. (in Thai)

Hopewell S, Adedire O, Copsey B, Boniface G, Sherrington C, Clemson L. Multifactorial interventions for preventing falls in older people living in the community. Cochrane Database Systematic Review 2019;(7):CD012221. doi: 10.1136/bjsports-2019-100732.

Centers for Disease Control and Prevention. Older adult falls: Data and statistics [Internet]. 2024. [cited 2025 Nov 19]. Available from: https://www.cdc.gov/falls

Guirguis-Blake JM, Michael YL, Perdue LA, Coppola EL, Beil TL. Interventions to prevent falls in older adults: Updated evidence report and systematic review for the US Preventive Services Task Force JAMA 2024;331(3):234–50.

Salari N, Darvishi N, Ahmadipanah M, Shohaimi S, Mohammadi M. Global prevalence of falls in the older adults: A comprehensive systematic review and meta-analysis. Journal of Orthopaedic Surgery and Research 2022;17(1):334. doi: 10.1186/s13018-022-03222-1.

Thawornwong N, Akarasatthung A, & Makasorn P. Designing of falls detection for elderly by using tilt sensor. Naresuan University Journal 2011; Special Issue: 43-6. (in Thai)

Argañarás JG, Wong YT, Begg R, Karmakar NC. State-of-the-art wearable sensors and possibilities for Radar in fall prevention. Sensors (Basel) 2021;21(20):6836. doi: 10.3390/s21206836.

Lin HC, Chen MJ, Lee CH, Kung LC, Huang JT. Fall recognition based on an IMU wearable device and fall verification through a smart speaker and the IoT. Sensors (Basel) 2023;23(12):5472. doi: 10.3390/s23125472.