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Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking

2018; Frontiers Media; Volume: 10; Linguagem: Inglês

10.3389/fnagi.2018.00044

1663-4365

Espen A. F. Ihlen, Kimberley S. van Schooten, Sjoerd M. Bruijn, Jaap H. van Dieën, Beatrix Vereijken, Jorunn L. Helbostad, Mirjam Pijnappels,

Context-Aware Activity Recognition Systems

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 seconds walking epochs in a re-analysis of one week of daily-life activity data from the FARAO study, originally described by Van Schooten et al. (2016). The re-analysed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a six 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.