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Front. Aging Neurosci. | doi: 10.3389/fnagi.2018.00044

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

  • 1Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Norway
  • 2Department of Biomedical Kinesiology and Physiology, Simon Fraser University, Canada
  • 3Centre for Hip Health and Mobility, University of British Columbia, Canada
  • 4Department of Human Movement Sciences, VU University Amsterdam, Netherlands

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.

Keywords: activity monitoring, accelerometry, Complexity, Gait assessment, physical activity, Aged, Fall prediction, fall risk, Accidental Falls

Received: 22 Jun 2017; Accepted: 12 Feb 2018.

Edited by:

Changiz Geula, Northwestern University, United States

Reviewed by:

Andrés Soto-Varela, Complejo Hospitalario Universitario de Santiago, Spain
Renae L. Smith-Ray, Walgreens (United States), United States  

Copyright: © 2018 Ihlen, Van Schooten, Bruijn, Van Dieen, Helbostad, Vereijken and Pijnappels. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Espen Alexander Fürst Ihlen, E.A.F.I.., Norwegian University of Science and Technology, Department of Neuromedicine and Movement Science, Trondheim, Norway, espen.ihlen@ntnu.no