Could Local Dynamic Stability Serve as an Early Predictor of Falls in Patients with Moderate Neurological Gait Disorders? A Reliability and Comparison Study in Healthy Individuals and in Patients with Paresis of the Lower Extremities

Abstract

Falls while walking are frequent in patients with muscular dysfunction resulting from neurological disorders. Falls induce injuries that may lead to deconditioning and disabilities, which further increase the risk of falling. Therefore, an early gait stability index would be useful to evaluate patients in order to prevent the occurrence of future falls. Derived from chaos theory, local dynamic stability (LDS), defined by the maximal Lyapunov exponent, assesses the sensitivity of a dynamic system to small perturbations. LDS has already been used for fall risk prediction in elderly people. The aim of the present study was to provide information to facilitate future researches regarding gait stability in patients with neurological gait disorders. The main objectives were 1) to evaluate the intra-session repeatability of LDS in patients and 2) to assess the discriminative power of LDS to differentiate between healthy individuals and neurological patients. Eighty-three patients with mild to moderate neurological disorders associated with paresis of the lower extremities and 40 healthy controls participated in the study. The participants performed 2×30 s walking wearing a 3D accelerometer attached to the lower back, from which 2×35 steps were extracted. LDS was defined as the average exponential rate of divergence among trajectories in a reconstructed state-space that reflected the gait dynamics. LDS assessed along the medio-lateral axis offered the highest repeatability and discriminative power. Intra-session repeatability (intraclass correlation coefficient between the two repetitions) in the patients was 0.89 and the smallest detectable difference was 16%. LDS was substantially lower in the patients than in the controls (33% relative difference, standardized effect size 2.3). LDS measured in short over-ground walking tests seems sufficiently reliable. LDS exhibits good discriminative power to differentiate fall-prone individuals and opens up the possibility of future clinical applications for better prediction of fall risk in neurological patients.

Modular organization of murine locomotor pattern in the presence and absence of sensory feedback from muscle spindles

“…Increased MLEs correspond to a more chaotic and unstable dynamical system (Kantz & Schreiber, 2004;Bradley & Kantz, 2015). In humans, previous studies found an increase in the MLE during walking in patients with focal cerebellar lesion (Hoogkamer et al 2015) and with moderate neurological gait disorders (Reynard et al 2014). We also showed that increased MLE is detectable when inexperienced barefoot runners transition from shod to barefoot running (Ekizos et al 2017) and when healthy adults walk and run over uneven surfaces (Santuz et al 2018a).…”

“…) and with moderate neurological gait disorders (Reynard et al . ). We also showed that increased MLE is detectable when inexperienced barefoot runners transition from shod to barefoot running (Ekizos et al .…”

Effects of noxious stimulation to the back or calf muscles on gait stability

“…As a result of impaired sensorimotor control, reduced gait stability is associated with aging (Kang and Dingwell, 2008) and neurological disorders (Ijmker and Lamoth, 2012;Reynard et al, 2014). Musculoskeletal pain has been associated with fall risk (Asai et al, 2015;de Zwart et al, 2015;Kitayuguchi et al, 2015) and may also impact stability.…”

Quantifying Muscle Fatigue of the Low Back during Repetitive Load Lifting Using Lyapunov Analysis

“…Lyapunov exponent analysis is becoming an increasingly popular measure for examining the response of the neuromuscular system to small perturbations and therefore it could be a reliable tool for injury prevention and for the design of the work/rest ratio. Scientists have used the kinematic trajectory divergence technique as an early identification tool for individuals who are at risk of falling and to study the ability to resist perturbations in passive dynamic walking models [36][37][38][39]. However, the possible relationship between the low back trajectories divergence and muscle fatigue has not been widely investigated during repetitive lightweight lifting tasks.…”