IJGPNInternational Journal of General Practice Nursing2981-94152981-9423Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ijgpn.v3iSpecial.13921ArticleFall Assessment Methods in the Elderly: A Reviewhttps://artdesignp.com/journal/IJGPN/3/0/10.26689/ijgpn.v3iSpecial.13921HuangShiqi2025302025-12-31Falls among the elderly have become a critical global public health issue, threatening physical and psychological well-being and impeding the development of age-friendly societies. Accurate fall risk assessment is essential for effective prevention and intervention. This study systematically analyzes the technical characteristics, application status, and core limitations of existing fall assessment methods for the elderly and puts forward targeted optimization suggestions that integrate community nursing needs and technological trends. Three main method types were reviewed: traditional scales (Morse Fall Scale [MFS] and its Chinese Version [CMFS]), functional tests (Timed Up and Go Test [TUGT]), and wearable device-based fall detection algorithms. Key findings include: MFS/CMFS are widely used for convenience but have limitations in reliability and validity; TUGT facilitates rapid screening but has limited predictive efficacy and is not suitable for non-independent individuals; wearable technologies offer high real-time accuracy but face challenges in acceptance, battery life, and cost. Existing methods suffer from three core contradictions: convenience and accuracy, static assessment and dynamic risks, as well as universality and particularity. To address these, an integrated framework of combining static screening, dynamic monitoring, and special group adaptation is recommended. This framework aims to support precise fall prevention and control for the elderly.

WHO, 2021, Falls, viewed November 5, 2025, https://www.who.int/news-room/fact-sheets/detail/falls

Hicks C, Tiedemann A, Sherrington C, et al., 2025, Fall Prevention, in: Mooren FC, Schmitz B, (eds) Encyclopedia of Exercise Medicine in Health and Disease, Springer, Berlin, Heidelberg, 1–4.

Adoud A, Gana W, Poitau F, et al., 2023, High Prevalence of Geriatric Conditions Among Older Adults With Cardiovascular Disease. J Am Heart Assoc, 12(2): e026850.

Svensson M, Ekström H, Elmståhl S, et al., 2024, Association of Polypharmacy with Occurrence of Loneliness and Social Isolation among Older Adults. Arch Gerontol Geriatr, 116: 105158.

Ye PP, Er YL, Wang HD, et al., 2021, Burden of Falls Among People Aged 60 Years and Older in China, 1990—2019: Findings from the Global Burden of Disease Study 2019. Lancet Public Health, 6(12): e907–e918.

Lu JY, Guo DX, Li FW, et al., 2020, Status and the Influence Factors of Falls among the Elderly in China. J Zhengzhou Univ(Med Sci), 55(5): 662–667.

Chinese Center for Disease Control and Prevention, National Center for Chronic and Noncommunicable Disease Control and Prevention, Natl Health Family Plann Commiss Stat Inf Center, et al., 2016, China Death Cause Surveillance Dataset 2015, China Sci Technol Press, Beijing.

Li FW, 2020, Analysis of Influencing Factors of Falls among Elderly in China, thesis, Zhengzhou University.

Zhang TT, Feng ZQ, Wang WC, et al., 2022, A Study on the Status Quo and Influencing Factors of Falls among the Elderly in China. Chin J Dis Control Prev, 26(5): 502–507.

Demircioglu KA, Sahin UK, Dag O, et al., 2025, Fear of Falling is a Top Issue for Older Adults with a History of Falling: Multidimensional Perspective. Psychogeriatrics, 25(3): 1–8.

Tinetti ME, Powell L, 1993, Fear of Falling and Low Self-Efficacy: A Cause of Dependence in Elderly Persons. J Gerontol, 48: 35–38.

Chang HT, Chen HC, Chou P, 2016, Factors Associated with Fear of Falling Among Community-Dwelling Older Adults in the Shih-Pai Study in Taiwan. PLoS One, 11(3): e0150612.

Wang H, Zhao M, Duan LL, et al., 2015, Analysis on the Characteristics and Factors Associated with Avoidance of Activity Induced by Fear of Falling in the Community-Dwelling Elderly. Chin J Epidemiol, 36(8): 794–798.

Hong JT, Wang JJ, Li YS, et al., 2025, Sedentary Behavior and Lower-Limb Muscle Strength in Community-Dwelling Older Adults: The Mediating and Moderating Effects of Fear of Falling and Age. Chin J Tissue Eng Res, 17.

Dong HQ, Wang GL, Tang SL, 2024, Study on the Social Isolation, Loneliness and Health Inequalities in Old Age from Crossing the “Social Dilemma” Trap. Chin Health Serv Manag, 41(4): 453–458.

Chung MC, McKee KJ, Austin C, 2009, Posttraumatic Stress Disorder in Older People After a Fall. Int J Geriatr Psychiatry, 24(9): 24.

Martins HO, Bernardo KMA, Martins MS, et al., 2016, Controle Postural e o Medo de Cair Em Idosos Fragilizados e o Papel de um Programa de Prevenção de Quedas. Acta Fisiátrica, 23(3): 113–119.

Morse JM, Morse RM, Tylko SJ, 1987, Development of a Scale to Identify the Fall-Prone Patient. Can J Aging, 8(4): 366–377.

Morse JM, Black C, Oberle K, et al., 1989, A Prospective Study to Identify the Fall-Prone Patient. Soc Sci Med, 28: 81–86.

Zhou JG, 2010, Preliminary Study on Morse Fall Assessment Scale for Elderly Inpatients, thesis, Southern Med University.

Cumming RG, Sherrington C, Lord SR, et al., 2008, Cluster Randomised Trial of a Targeted Multifactorial Intervention to Prevent Falls Among Older People in Hospital. BMJ, 336(7647): 758–760.

Liu DX, Ding F, He XZ, et al., 2014, Investigation on Clinical Application of Chinese Version Morse Fall Assessment Table. J Nurs Sci, 29(19): 37–39.

Zhou JG, Li YJ, Fan JZ, 2014, Study on Reliability and Validity of Chinese Version of Morse Fall Assessment Scale in Elderly Inpatients in China. Chin J Rehabil Med, 27(3): 244–247.

McFarlane-Kolb H, 2004, Falls Risk Assessment, Multitargeted Interventions and the Impact on Hospital Falls. Int J Nurs Pract, 10(5): 199–206.

Eagle DJ, Salama S, Whitman D, et al., 1999, Comparison of Three Instruments in Predicting Accidental Falls in Selected Inpatients in a General Teaching Hospital. J Gerontol Nurs, 25(7): 40–45.

McCollam ME, 1995, Evaluation and Implementation of a Research-Based Falls Assessment Innovation. Nurs Clin North Am, 30(3): 507–514.

McFarlane-Kolb H, 2004, Falls Risk Assessment, Multitargeted Intervention and the Impact on Hospital Falls. Int J Nurs Pract, 10(5): 199–206.

Zhou JG, 2010, Research on the Application of Chinese Version Morse Fall Scale in Evaluating the Fall Risk of Senile Patients in Hospital, thesis, Southern Med University.

Wang WL, 2014, Revision of Risk Assessment Scale of Inpatients’ Fall and Its Reliability and Validity. Chin Nurs Manag, 14(9): 5.

Mathias S, Nayak USL, Isaacs B, 1986, Balance in Elderly Patients: The “Get-Up and Go” Test. Arch Phys Med Rehabil, 67(6): 387–389.

Podisadle D, Richardson S, 1991, The Timed “Up and Go”: A Test of Basic Functional Mobility for Frail Elderly Persons. J Am Geriatr Soc, 39(2): 142–148.

Lin JQ, Zhang CM, Zheng YF, et al., 2019, A Predictive Value of the Timed “Up and Go” Test on the Fall Risk Assessment of Inpatients with Stroke. Chin J Pract Nurs, 35(21): 1607–1612.

Kang L, 2017, The value of Timed Up and Go Test in the Elderly Fall Prediction and Risk Assessment, thesis, Tianjin Med University.

Kurosawa C, Shimazu N, Yamamoto S, 2020, Where Do Healthy Older Adults Take More Time During the Timed Up and Go test? J Phys Ther Sci, 32(10): 663–668.

Hu LS, Wang SZ, Chen YQ, et al., 2018, Fall Detection Algorithms Based on Wearable Device: A Review. J Zhejiang Univ(Eng Sci), 52(9): 1717–1728.

Wang J, Chen R, Sun X, et al., 2012, Generative Models for Automatic Recognition of Human Daily Activities from a Single Triaxial Accelerometer, Proc Int Joint Conf Neural Netw, IEEE, Brisbane, 1–6.

Natthapon P, Surapa T, Ekawit N, 2014, Automatic Fall Monitoring: A Review. Sensors, 14(7): 12900–12936.

Koshmak G, Loutfi A, Linden M, 2016, Challenges and Issues in Multisensor Fusion Approach for Fall Detection. J Sens, 2016: 6931789.

Kostopoulos P, Nunes T, Salvi K, et al., 2015, F2D: A Fall Detection System Tested with Real Data from Daily Life of Elderly People, Proc Int Conf E-health Netw Appl Serv, IEEE, Boston, 397–403.

Igual R, Medrano C, Plaza I, 2013, Challenges, Issues and Trends in Fall Detection Systems. Biomed Eng Online, 12(1): 1–24.