Highlights
- •Falls are a common public health concern among persons with multiple sclerosis and can substantially decrease quality of life
- •Forward walking and balance measures exhibit limited sensitivity and predictive accuracy for falls
- •Backward walking has been linked to falls in older adults and may provide additional clues about fall risk in multiple sclerosis
- •Backward walking velocity is the strongest descriptor of retrospective fallers in our sample of 38 individuals with multiple sclerosis
- •Backward walking may be a useful tool to sensitively identify multiple sclerosis fallers along with current clinical methods
Abstract
Background
Individuals with multiple sclerosis experience deficits in mobility resulting in injurious
falls. Fall detection has proved challenging; the majority of clinical measures rely
on forward walking and balance measures, yet these measures have poor sensitivity
and predictive value for differentiating between fallers and non-fallers. Backward
walking better differentiates fallers from non-fallers in the elderly and other neurodegenerative
diseases; therefore, the objective of this study was to examine both forward and backward
walking to determine the strongest, unique contributor that differentiates fallers
from non-fallers in persons with multiple sclerosis.
Methods
In a single session, spatiotemporal measures of forward and backward walking and fall
history were collected. For the subsequent six months, individuals recorded falls
in a fall diary. Discriminant function analysis was used to determine what variables
most strongly and uniquely differentiate multiple sclerosis fallers from non-fallers.
Results
Thirty-eight individuals with multiple sclerosis participated. Forward and backward
velocity, stride length, and double support time as well as age, disease severity,
and symptom duration were included in the models. Together, the variables differentiated
between fallers and non-fallers (Wilk's lambda χ2 (8, N = 36) = 0.497, p<0.001) and
in rank order, backward walking velocity was the strongest unique predictor. Repeating
the analysis with a stepwise approach yielded that backward walking velocity in the
first step (χ2 (1, 34) = 0.68, F = 15.96, p<0.001) and symptom duration in the second
step (χ2 = 0.59, F (2, 33) = 11.46; p<0.001) most strongly differentiated retrospective
fallers and non-fallers. This stepwise model with backward walking velocity and symptom
duration accurately classified 76.3% of cases. Addition of forward walking measures
did not significantly improve the models, and indeed the accuracy of classification
was reduced to 71.1%. Exploratory analysis showed that backward walking velocity was
the best predictor of prospectively reported fallers and non-fallers (χ2 (1, 7) = 0.43,
F = 9.20, p = 0.02).
Conclusion
Backward walking velocity exhibits the highest effect magnitude and specificity in
differentiating fallers from non-fallers in individuals with MS and demonstrates potential
as clinically feasible and efficient fall detection tool.
Keywords
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Article info
Publication history
Published online: July 09, 2020
Accepted:
July 9,
2020
Received in revised form:
July 2,
2020
Received:
March 27,
2020
Footnotes
Preliminary data from this manuscript was presented at the American Physical Therapy Association Combined Sections Meeting in January, 2018.
Identification
Copyright
© 2020 Elsevier B.V. All rights reserved.
