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Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults.

Huanghe Zhang, Chuanyan Wu, Yulong Huang

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |March 3, 2025
    PubMed
    Summary

    Instrumented footwear accurately predicts fall risk in older adults. This novel system using gait data offers superior fall prediction compared to traditional methods, aiding in prevention and care.

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    Area of Science:

    • Gerontology
    • Biomedical Engineering
    • Rehabilitation Science

    Background:

    • Falls are a significant risk for institutionalized older adults, leading to injury and decreased quality of life.
    • Accurate fall risk assessment is crucial for effective prevention strategies.
    • Traditional methods for fall risk assessment have limitations in precision and real-world applicability.

    Purpose of the Study:

    • To develop and validate a novel framework for predicting fall risk in institutionalized older adults.
    • To evaluate the efficacy of instrumented footwear in capturing gait parameters for fall risk prediction.
    • To compare the predictive performance of instrumented footwear data against traditional mobility tests and electronic walkway data.

    Main Methods:

    • Collected gait data from 95 institutionalized older adults using instrumented footwear, electronic walkway, and traditional timed mobility tests.
    • Utilized a brute-force search method for optimal feature selection from gait data.
    • Developed predictive models using AdaBoost algorithms and validated them with cross-validation techniques.
    • Categorized participants into fallers and non-fallers based on retrospective, prospective, and combined fall history.

    Main Results:

    • Models employing instrumented footwear gait data demonstrated superior fall risk prediction compared to traditional tests (AUC 0.47) and electronic walkway data (AUC 0.66).
    • Instrumented footwear models achieved an AUC of 0.80 for predicting prospective falls.
    • Model sensitivity improved when trained on combined retrospective and prospective fall data.

    Conclusions:

    • Instrumented footwear offers a highly effective tool for assessing fall risk in elderly populations.
    • The developed framework shows significant potential for enhancing fall prevention strategies and improving care for older adults.
    • Gait analysis using instrumented footwear provides superior predictive insights over conventional assessment methods.