Department of Neurology, Oregon Health & Science University, Portland, OR, USA.
Department of Neurology, Oregon Health & Science University, Portland, OR, USA.
Gait Posture. 2022 Jan;91:186-191. doi: 10.1016/j.gaitpost.2021.10.029. Epub 2021 Oct 26.
Telemedicine has the advantage of expanding access to care for patients with Parkinson's Disease (PD). However, rigidity and postural instability in PD are difficult to measure remotely, and are important measures of functional impairment and fall risk.
Can measures from wearable sensors be used as future surrogates for the MDS-UPDRS rigidity and Postural Instability and Gait Difficulty (PIGD) subscores?
Thirty-one individuals with mild to moderate PD wore 3 inertial sensors at home for one week to measure quantity and quality of gait and turning in daily life. Separately, we performed a clinical assessment and balance characterization of postural sway with the same wearable sensors in the laboratory (On medication). We then first performed a traditional correlation analysis between clinical scores and objective measures of gait and balance followed by multivariable linear regression employing a best subset selection strategy.
The number of walking bouts and turns correlated significantly with the rigidity subscore, while the number of turns, foot pitch angle, and sway area while standing correlated significantly with the PIGD subscore (p < 0.05). The multivariable linear regression showed that rigidity subscore was best predicted by the number of walking bouts while the PIGD subscore was best predicted by a combination of number of walking bouts, gait speed, and postural sway.
The correlation between objective sensor data and MDS-UPDRS rigidity and PIGD scores paves the way for future larger studies that evaluate use of objective sensor data to supplement remote MDS-UPDRS assessment.
远程医疗具有扩大帕金森病(PD)患者获得医疗服务的优势。然而,PD 中的僵硬和姿势不稳难以远程测量,且是衡量功能障碍和跌倒风险的重要指标。
可穿戴传感器的测量值能否作为未来 MDS-UPDRS 僵硬和姿势不稳及步态运动困难(PIGD)子评分的替代指标?
31 名轻度至中度 PD 患者在家中佩戴 3 个惯性传感器一周,以测量日常生活中的步态和转弯的数量和质量。另外,我们使用相同的可穿戴传感器在实验室(服药时)进行临床评估和姿势摆动平衡特征描述。然后,我们首先对临床评分与步态和平衡的客观测量值之间进行传统相关性分析,然后采用最佳子集选择策略进行多变量线性回归。
行走回合次数和转弯次数与僵硬子评分显著相关,而转弯次数、脚距角和站立时的摆动面积与 PIGD 子评分显著相关(p<0.05)。多变量线性回归显示,行走回合次数最能预测僵硬子评分,而 PIGD 子评分则最好由行走回合次数、步态速度和姿势摆动的组合来预测。
客观传感器数据与 MDS-UPDRS 僵硬和 PIGD 评分之间的相关性为未来更大规模的研究铺平了道路,这些研究将评估使用客观传感器数据来补充远程 MDS-UPDRS 评估。