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

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

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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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.

Original languageEnglish
Article number44
Pages (from-to)1-12
Number of pages12
JournalFrontiers in Aging Neuroscience
Volume10
Issue numberMARCH
DOIs
Publication statusPublished - 5 Mar 2018

Keywords

  • Accelerometry
  • Accidental falls
  • Aged
  • Complexity
  • Fall prediction
  • Fall risk
  • Gait assessment
  • Physical activity

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