Improved prediction of falls in community-dwelling older adults through phase-dependent entropy of daily-life walking

  • E.A.F. I
  • K.S. V
  • S.M. B
  • et al.
N/ACitations
Citations of this article
2Readers
Mendeley users who have this article in their library.
Get full text

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.Copyright © 2018 Ihlen, van Schooten, Bruijn, van Dieen, Vereijken, Helbostad and Pijnappels.

Cite

CITATION STYLE

APA

E.A.F., I., K.S., van S., S.M., B., J.H., van D., B., V., & J.L., H. (2018). Improved prediction of falls in community-dwelling older adults through phase-dependent entropy of daily-life walking. Frontiers in Aging Neuroscience, 10(MAR), 44. https://doi.org/http://dx.doi.org/10.3389/fnagi.2018.00044

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free