Frailty assessment based on trunk kinematic parameters during walking

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Date
2015Author
Version
Acceso abierto / Sarbide irekia
Type
Artículo / Artikulua
Version
Versión publicada / Argitaratu den bertsioa
Impact
|
10.1186/s12984-015-0040-6
Abstract
Background: Physical frailty has become the center of attention of basic, clinical and demographic research due to
its incidence level and gravity of adverse outcomes with age. Frailty syndrome is estimated to affect 20 % of the
population older than 75 years. Thus, one of the greatest current challenges in this field is to identify parameters
that can discriminate between vulnerable and robust s ...
[++]
Background: Physical frailty has become the center of attention of basic, clinical and demographic research due to
its incidence level and gravity of adverse outcomes with age. Frailty syndrome is estimated to affect 20 % of the
population older than 75 years. Thus, one of the greatest current challenges in this field is to identify parameters
that can discriminate between vulnerable and robust subjects. Gait analysis has been widely used to predict frailty.
The aim of the present study was to investigate whether a collection of parameters extracted from the trunk
acceleration signals could provide additional accurate information about frailty syndrome.
Methods: A total of 718 subjects from an elderly population (319 males, 399 females; age: 75.4 ± 6.1 years, mass:
71.8 ± 12.4 kg, height: 158 ± 6 cm) volunteered to participate in this study. The subjects completed a 3-m walk test
at their own gait velocity. Kinematic data were acquired from a tri-axial inertial orientation tracker.
Findings: The spatio-temporal and frequency parameters measured in this study with an inertial sensor are related
to gait disorders and showed significant differences among groups (frail, pre-frail and robust). A selection of those
parameters improves frailty classification obtained to gait velocity, compared to classification model based on gait
velocity solely.
Interpretation: Gait parameters simultaneously used with gait velocity are able to provide useful information for a
more accurate frailty classification. Moreover, this technique could improve the early detection of pre-frail status,
allowing clinicians to perform measurements outside of a laboratory environment with the potential to prescribe a
treatment for reversing their physical decline. [--]
Subject
Gait analysis,
Frailty,
Accelerometer,
Gyroscope,
Inertial sensors
Publisher
BioMed Central
Published in
Journal of Neuroengineering and Rehabilitation 2015, 12:48
Departament
Universidad Pública de Navarra. Departamento de Matemáticas /
Nafarroako Unibertsitate Publikoa. Matematika Saila
Publisher version
Sponsorship
This work was supported in part by the Spanish Department of Health and
Institute Carlos III of the Government of Spain [Spanish Net on Aging and
frailty; (RETICEF)], and Economy and Competitivity Department of the
Government of Spain, under grants numbered RD12/043/0002, and
DEP2011-24105, respectively.
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