A multivariate logistic regression model for fall risk prediction based on physical performance in community-dwelling elderly
DOI:
https://doi.org/10.47197/retos.v75.117848Keywords:
Fall Risk Prediction, Elderly, Lower Limb Strength, Timed Up and Go Test, Power Sit-to-Stand Test, Multivariate Logistic RegressionAbstract
Introduction: Falls among older adults lead to significant morbidity and increased healthcare costs. Physical performance measures like strength, mobility, and balance are crucial for fall risk prediction, but existing assessments often rely on single measures, which may not fully capture fall risk.
Objective: To develop a multivariate logistic regression model for fall risk prediction, integrating the lower limb muscle strength-to-body weight (LS/BW) ratio, the Timed Up and Go Test (TTUGT), and the Power Sit-to-Stand Test (PSTS).
Methodology: Data from 108 community-dwelling elderly individuals (mean age 65.59 ± 4.32 years) were analyzed. Participants underwent physical performance assessments, including TTUGT, MVIC of lower limb strength, LS/BW ratio, and PSTS. A binary logistic regression model was used to analyze the relationship between these variables and fall risk, with model performance evaluated using AUC of the receiver operating characteristic (ROC).
Results: The model, incorporating LS/BW ratio, TTUGT, and PSTS, explained 70.6% of the variance in fall risk. Significant predictors included lower LS/BW ratio (adj.OR = 0.980), longer TTUGT times (adj.OR = 1.617), and lower PSTS values (adj.OR = 0.953). The Fall Risk Score equation provides a practical tool for identifying high-risk individuals.
Conclusion: The multivariate model effectively predicts fall risk, offering a reliable tool for fall risk screening and prevention in older adults, supporting personalized interventions to reduce fall incidence.
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