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Gait Stability and Its Influencing Factors in Older Adults

A stable gait pattern is a prerequisite to successfully master various activities of daily living. Furthermore, reduced gait stability is associated with a higher risk of falling. To provide specific intervention strategies to improve gait stability, gaining detailed knowledge of the underlying mech...

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Detalles Bibliográficos
Autores principales: Hamacher, Daniel, Liebl, Dominik, Hödl, Claudia, Heßler, Veronika, Kniewasser, Christoph K., Thönnessen, Thomas, Zech, Astrid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354563/
https://www.ncbi.nlm.nih.gov/pubmed/30733686
http://dx.doi.org/10.3389/fphys.2018.01955
Descripción
Sumario:A stable gait pattern is a prerequisite to successfully master various activities of daily living. Furthermore, reduced gait stability is associated with a higher risk of falling. To provide specific intervention strategies to improve gait stability, gaining detailed knowledge of the underlying mechanism and influencing factors is of utmost importance. The effects of relevant influencing factors on gait stability are poorly examined, yet. Therefore, the aim of the current study was to quantify the effects of various influencing factors on gait stability. In a cross-sectional study, we assessed dynamic gait stability and relevant influencing factors in 102 older adults (age >65 years). In addition to dynamic gait stability (largest Lyapunov exponent [LLE] and gait variability measures) during normal over-ground (single-task: ST) and dual-task (DT) walking, we registered the following influencing factors: health status (SF12), pain status (painDETECT, SES), fear of falling (falls efficacy scale), depression (CES-D), cognition performance (Stroop test), physical activity (Freiburger Fragebogen zur körperlichen Aktivität), proprioception (joint position sense), peripheral sensation (mechanical and vibration detection threshold), balance performance (static balance on force plate) and muscular fitness (instrumented sit-to-stand test). We used a principal components regression to link the identified principal components with the gait stability and gait variability responses. The four principal components “strength and gender” (e.g., p = 0.001 for LLE during ST), “physical activity” (e.g., p = 0.006 for LLE during ST), “pain” (e.g., p = 0.030 for LLE during DT) and “peripheral sensation” (e.g., p = 0.002 for LLE during ST) were each significantly associated with at least two of the analyzed gait stability/variability measures. The dimension “balance” was a significant predictor in only one gait measure. While “proprioception” tends to correlate with a gait variability measure, we did not find a dependency of mental health on any gait measure. In conclusion, the participants' ability to recover from small perturbations (as measured with the largest Lyapunov exponent) seems to be related to gender and strength, the amount of physical activity the participants spent every week, peripheral sensation and pain status. Since the explained variance is still rather low, there could be more relevant factors that were not addressed, yet.