http://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-016-0176-z
- Michiel PuntEmail author,
- Sjoerd M. Bruijn,
- Kimberley S. van Schooten,
- Mirjam Pijnappels,
- Ingrid G. van de Port,
- Harriet Wittink and
- Jaap H. van Dieën
Journal of NeuroEngineering and Rehabilitation201613:67
DOI: 10.1186/s12984-016-0176-z
© The Author(s). 2016
Received: 9 December 2015
Accepted: 17 July 2016
Published: 27 July 2016
Abstract
Background
Falls in stroke survivors can
lead to serious injuries and medical costs. Fall risk in older adults
can be predicted based on gait characteristics measured in daily life.
Given the different gait patterns that stroke survivors exhibit it is
unclear whether a similar fall-prediction model could be used in this
group. Therefore the main purpose of this study was to examine whether
fall-prediction models that have been used in older adults can also be
used in a population of stroke survivors, or if modifications are
needed, either in the cut-off values of such models, or in the gait
characteristics of interest.
Methods
This study investigated gait
characteristics by assessing accelerations of the lower back measured
during seven consecutive days in 31 non fall-prone stroke survivors, 25
fall-prone stroke survivors, 20 neurologically intact fall-prone older
adults and 30 non fall-prone older adults. We created a binary logistic
regression model to assess the ability of predicting falls for each gait
characteristic. We included health status and the interaction between
health status (stroke survivors versus older adults) and gait
characteristic in the model.
Results
We found four significant
interactions between gait characteristics and health status. Furthermore
we found another four gait characteristics that had similar predictive
capacity in both stroke survivors and older adults.
Conclusion
The interactions between gait
characteristics and health status indicate that gait characteristics are
differently associated with fall history between stroke survivors and
older adults. Thus specific models are needed to predict fall risk in
stroke survivors.
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