Abstract
As people in developed countries live longer, assessing the fall risk becomes more important. A major contributor to the risk of elderly people falling is postural instability. This study aimed to use the multiscale entropy (MSE) analysis to evaluate postural stability during a timed-up-and-go (TUG) test. This test was deemed a promising method for evaluating fall risk among the elderly in a community. The MSE analysis of postural instability can identify the elderly prone to falling, whereupon early medical rehabilitation can prevent falls. Herein, an objective approach is developed for assessing the postural stability of 85 community-dwelling elderly people (aged 76.12 ± 6.99 years) using the short-form Berg balance scale. Signals were collected from the TUG test using a triaxial accelerometer. A segment-based TUG (sTUG) test was designed, which can be obtained according to domain knowledge, including “Sit-to-Walk (STW),” “Walk,” “Turning,” and “Walk-to-Sit (WTS)” segments. Employing the complexity index (CI) of sTUG can reveal information about the physiological dynamics’ signal for postural stability assessment. Logistic regression was used to assess the fall risk based on significant features of CI related to sTUG. MSE curves for subjects at risk of falling (n = 19) exhibited different trends from those not at risk of falling (n = 66). Additionally, the CI values were lower for subjects at risk of falling than those not at risk of falling. Results show that the area under the curve for predicting fall risk among the elderly subjects with complexity index features from the overall TUG test is 0.797, which improves to 0.853 with the sTUG test. For the elderly living in a community, early assessment of the CI for sTUG using MSE can help predict the fall risk.
Highlights
Preventing falls is always an important issue in clinical preventive medicine
A subject was considered at risk of falling if her/his SFBBS score was less than 23 points and not at risk of falling if the score was greater than 23 points
We used multiscale entropy (MSE) analysis and calculated complexity index (CI), which checks for significant features related to a favourable fall-risk outcome and obtained multivariable logistic regression models
Summary
Preventing falls is always an important issue in clinical preventive medicine. Falls for the elderly are likely to lead to major injuries, long-term disability, reduced activity or mobility, or even death. According to a report by the World Health Organization, a person aged 65–70 years has a risk of 28%–35% of falling, increasing to 32%–42% for those aged over 70 years [1]. Entropy 2019, 21, 1076 risk of future falls [3,4]. Correct assessment of postural stability is considered an important predictor of fall risk [5,6]. An effective fall prevention programme first identifies the elderly at risk of falling, and determines the most appropriate interventions, thereby reducing or eliminating preventable falls
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