Mobbs Ralph Jasper, Natarajan Pragadesh, Fonseka R Dineth, Betteridge Callum, Ho Daniel, Mobbs Redmond, Sy Luke, Maharaj Monish
Faculty of Medicine, University of New South Wales, Sydney, Australia.
NeuroSpine Surgery Research Group (NSURG), Sydney, Australia.
BMC Musculoskelet Disord. 2022 Mar 29;23(1):304. doi: 10.1186/s12891-022-05211-1.
Musculoskeletal disorders can contribute to injurious falls and incur significant societal and healthcare burdens. Identification of fallers from non-fallers through wearable-based gait analysis can facilitate timely intervention to assist mobility and prevent falls whilst improving care and attention for high fall-risk patients. In this study, we use wearable sensor-based gait analysis to introduce a novel variable to assess walking stability in fallers and non-fallers - the Walking Orientation Randomness Metric. The WORM score quantifies the stability, or 'figure-of-eight' motion of a subject's trunk during walking as an indicator of a falls-predictive (pathological) gait.
WORM is calculated as the 'figure-of-eight' oscillation mapped out in the transverse-plane by the upper body's centre-point during a walking bout. A sample of patients presenting to the Prince of Wales Hospital (Sydney, Australia) with a primary diagnosis of "falls for investigation" and age-matched healthy controls (non-fallers) from the community were recruited. Participants were fitted at the sternal angle with the wearable accelerometer, MetaMotionC (Mbientlab Inc., USA) and walked unobserved (at self-selected pace) for 5-50 m along an obstacle-free, carpeted hospital corridor.
Participants comprised of 16 fallers (mean age: 70 + 17) and 16 non-fallers (mean age: 70 + 9) based on a recent fall(s) history. The (median) WORM score was 17-fold higher (p < 0.001) in fallers (3.64 cm) compared to non-fallers (0.21 cm). ROC curve analyses demonstrate WORM can discriminate fallers from non-fallers (AUC = 0.97). Diagnostic analyses (cut-off > 0.51 cm) show high sensitivity (88%) and specificity (94%).
In this pilot study we have introduced the WORM score, demonstrating its discriminative performance in a preliminary sample size of 16 fallers. WORM is a novel gait metric assessing walking stability as measured by truncal way during ambulation and shows promise for objective and clinical evaluation of fallers.
肌肉骨骼疾病可导致跌倒受伤，并给社会和医疗保健带来沉重负担。通过基于可穿戴设备的步态分析将跌倒者与非跌倒者区分开来，有助于及时进行干预，以辅助行动并预防跌倒，同时改善对高跌倒风险患者的护理和关注。在本研究中，我们使用基于可穿戴传感器的步态分析引入一种新变量——步行方向随机性指标，以评估跌倒者和非跌倒者的步行稳定性。WORM评分量化了受试者在行走过程中躯干的稳定性或“8字形”运动，作为跌倒预测（病理性）步态的指标。
WORM通过行走过程中上身中心点在横平面上绘制出的“8字形”振荡来计算。招募了一组在威尔士亲王医院（澳大利亚悉尼）就诊、初步诊断为“因跌倒待查”的患者样本，以及来自社区的年龄匹配的健康对照者（非跌倒者）。参与者在胸骨角处佩戴可穿戴加速度计MetaMotionC（美国Mbientlab公司），并在无障碍物、铺有地毯的医院走廊上以自定速度行走5至50米，期间无人观察。
根据近期跌倒史，参与者包括16名跌倒者（平均年龄：70±17岁）和16名非跌倒者（平均年龄：70±9岁）。跌倒者的（中位数）WORM评分为3.64厘米，是非跌倒者（0.21厘米）的17倍（p<0.001）。ROC曲线分析表明，WORM能够区分跌倒者和非跌倒者（AUC=0.97）。诊断分析（临界值>0.51厘米）显示出高敏感性（88%）和特异性（94%）。
在这项初步研究中，我们引入了WORM评分，在16名跌倒者的初步样本量中展示了其区分性能。WORM是一种新的步态指标，通过步行过程中躯干方式测量步行稳定性，对跌倒者的客观和临床评估具有前景。
