Department of Natural Sciences in Kinanthropology, Faculty of Physical Culture, Palacky University Olomouc, Olomouc, Czech Republic.
Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy.
PLoS One. 2018 May 10;13(5):e0197091. doi: 10.1371/journal.pone.0197091. eCollection 2018.
Computing the local dynamic stability using accelerometer data from inertial sensors has recently been proposed as a gait measure which may be able to identify elderly people at fall risk. However, the assumptions supporting this potential were concluded as most studies implement a retrospective fall history observation. The aim of this study was to evaluate the potential of local dynamic stability for fall risk prediction in a cohort of subjects over the age of 60 years using a prospective fall occurrence observation. A total of 131 elderly subjects voluntarily participated in this study. The baseline measurement included gait stability assessment using inertial sensors and clinical examination by Tinetti Balance Assessment Tool. After the baseline measurement, subjects were observed for a period of one year for fall occurrence. Our results demonstrated poor multiple falls predictive ability of trunk local dynamic stability (AUC = 0.673). The predictive ability improved when the local dynamic stability was combined with clinical measures, a combination of trunk medial-lateral local dynamic stability and Tinetti total score being the best predictor (AUC = 0.755). Together, the present findings suggest that the medial-lateral local dynamic stability during gait combined with a clinical score is a potential fall risk assessment measure in the elderly population.
使用惯性传感器的加速度计数据计算局部动态稳定性最近被提出作为一种步态测量方法，可能能够识别有跌倒风险的老年人。然而，支持这种潜力的假设被认为是大多数研究采用回顾性跌倒史观察得出的。本研究旨在评估局部动态稳定性在使用前瞻性跌倒发生观察的 60 岁以上受试者队列中进行跌倒风险预测的潜力。共有 131 名老年人自愿参加了这项研究。基线测量包括使用惯性传感器进行步态稳定性评估和使用 Tinetti 平衡评估工具进行临床检查。基线测量后，对受试者进行了为期一年的跌倒发生观察。我们的结果表明，躯干局部动态稳定性对多次跌倒的预测能力较差（AUC = 0.673）。当局部动态稳定性与临床测量相结合时，预测能力得到改善，躯干前后向局部动态稳定性与 Tinetti 总分的组合是最佳预测指标（AUC = 0.755）。总之，这些发现表明，步态时的前后向局部动态稳定性与临床评分相结合是老年人跌倒风险评估的潜在指标。
