Background: About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls.
Objective: The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up.
Methods: FRAT-up is based on the assumption that a subject's fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators.
Results: The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration.
Conclusions: FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface.
Trial Registration: ClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study); http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR).
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http://dx.doi.org/10.2196/jmir.4064 | DOI Listing |
Geriatr Gerontol Int
January 2025
Division of Acute Care Surgery, Department of Surgery, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, Iowa, USA.
Aim: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of machine learning, we aimed to identify a "signature" (combination of clinical variables) that could predict which older adults are at risk of fall-related hospital admission. We hypothesized that frailty, measured using the 5-item modified Frailty Index, could be utilized in combination with other factors as a predictor of admission for fall-related injuries.
View Article and Find Full Text PDFInj Prev
January 2025
The University of Queensland, Brisbane, Queensland, Australia.
Background: Given that fall injury is a critical public health concern in Australia, understanding the economic implications of falls among older adults is crucial to allocating healthcare resources efficiently to reduce falls and improve quality of life. This study therefore aimed to estimate the cost and identify factors associated with fall-related injuries within residential aged care (RAC).
Methods: A cohort analysis from the healthcare system perspective based on data from a double-blinded randomised controlled trial-the Opti-Med trial.
J Biomech
January 2025
Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802, USA. Electronic address:
Most often, gait biomechanics is studied during straight-ahead walking. However, real-life walking imposes various lateral maneuvers people must navigate. Such maneuvers challenge people's lateral balance and can induce falls.
View Article and Find Full Text PDFQJM
January 2025
Tallaght hospital, Dept. of Age Related Healthcare; Trinity College Dublin, Dept. of Medical Gerontology.
Background: Falls are frequently reported within the HSE. The Irish Longitudinal Study on Ageing(TILDA) found that 40% of over 50 s experience a fall in a two year period, with 20% requiring hospital attendance (1). It has been estimated that the cost of injuries related to falls in older people will increase exponentially over the coming years (2).
View Article and Find Full Text PDFGait Posture
January 2025
Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chungli District, Taoyuan, Taiwan. Electronic address:
Background: The use of inertial measurement units (IMUs) in assessing fall risk is often limited by subject discomfort and challenges in data interpretation. Additionally, there is a scarcity of research on attitude estimation features. To address these issues, we explored novel features and representation methods in the context of sit-to-stand transitions.
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