Abstract
Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME (p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers.
Highlights
Falls among older persons are an important cause of loss of independence, reduced quality of life, and admission to hospitals or nursing homes
A significantly (p < 0.0004) lower phase-dependent generalized multiscale entropy (PGME) was found at 40% of the step cycle for q between −1 and 0 and scale between 1 and 3, where the differences disappeared for q > 0 and scale k > 3
For a single fall within the 6-month follow-up period, the predictive ability of PGME at 60% of the step phase at scale k = 2 to 4 and q = 0.5 to 0.8 was ranked highest in the Partial Least Squares (PLS) prediction model by the TP-loadings
Summary
Falls among older persons are an important cause of loss of independence, reduced quality of life, and admission to hospitals or nursing homes. Metrics of daily-life walking have been shown to improve early fall prediction in community-dwelling older persons beyond the ability of clinical screening tools for fall risk (Weiss et al, 2013; Rispens et al, 2015; van Schooten et al, 2016). These metrics can be assessed by analysis of 3D acceleration data from body-worn sensors that could be self-managed by the older adults through smart technology (Shany et al, 2012)
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