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Comparing predictive performance of two composite and four single fall risk assessment tools in community-dwelling older adults: A prospective study
Journal article   Peer reviewed

Comparing predictive performance of two composite and four single fall risk assessment tools in community-dwelling older adults: A prospective study

Weiqiang Li, Yanhong Fu, Min Zhao, David C Schwebel, Peishan Ning, Li Li, Peixia Cheng, Jiaqi Huang, Zhenzhen Rao and Guoqing Hu
American journal of preventive medicine, 108532
08/01/2026
DOI: 10.1016/j.amepre.2026.108532
PMID: 42542274

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Abstract

Many tools have been developed to assess fall risk and support fall prevention for older adults, but the authors are unaware of any study that compares the performance of predictive algorithms based on major composite assessment tools and commonly used single assessment tools. This study compares the predictive performance of the algorithms for two composite fall risk assessment tools and four single fall risk assessment tools among community-dwelling Chinese older adults. A 12-month prospective study was conducted between April 2023 and June 2024 in Changsha, China. Two major composite assessment tools (Stopping Elderly Accidents, Deaths & Injuries [STEADI] and World Falls Guidelines [WFG] algorithms) and four single assessment tools (Stay Independent Brochure Questionnaire [SIB], Falls Efficacy Scale International [FES-I], Home Falls and Accidents Screening Tool [HOME FAST], and Timed Up and Go Test [TUGT]) were included. Primary performance measures included area under the receiver operating characteristic curves (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Among the 1,428 enrolled older adults, 1,237 participants completed the study. All ten algorithms from the two composite assessment tools showed notably low AUC (0.527-0.575), sensitivity (11.3-36.5%) and PPV (12.7-31.7%), but acceptably high specificity (78.9-96.2%) and NPV (87.2-92.9%) to predict both falls and fall-related injuries throughout the 12-month follow-up period. Among the ten algorithms, WFG algorithm 3 exhibited the best performance in predicting both falls (AUC = 0.573, 95% CI: 0.544, 0.600) and fall-related injuries (AUC = 0.575, 95% CI: 0.546, 0.602). Compared to five algorithms based on four single assessment tools, WFG algorithm 3 did not demonstrate significantly superior performance (p>0.05). The performances of the optimal algorithm in predicting 12-month falls and fall-related injuries among older adults from the two composite risk assessment tools were not better than those from four common single risk assessment tools.
 China Falls Risk assessment tools Older adults Fall-related injuries Predictive performance

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