Tian Tian, Wang Cheng, Xu Yuan, Bai Yuzhi, Wang Jing, Long Zhou, Wang Xiangdong, Zhou Lichun
General Practice Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China.
Institute of Digital Economy Industry, ICT, Hangzhou, People's Republic of China.
Diabetes Metab Syndr Obes. 2021 Apr 23;14:1799-1808. doi: 10.2147/DMSO.S305102. eCollection 2021.
Previous studies have shown that the gait of patients with type-2 diabetes mellitus is abnormal compared with the healthy group. Currently, a three-dimensional motion analyzer system is commonly used for gait analysis. However, it is challenging to collect data and use in clinical study due to extensive experimental conditions and high price. In this study, we used a wearable gait analysis system (Gaitboter) to investigate the spatial and temporal parameters, and kinematic data of gait in diabetic patients, especially those with peripheral neuropathy. The aim of the study is to evaluate the wearable gait analysis system in diabetic study.
We conducted a case-control study to analyze the gait of type 2 diabetes mellitus. Gaitboter was used to detect and collect gait data in the ward of Beijing Chao-yang Hospital, Capital Medical University from June 2018 to October 2018. We collected the gait data of participants (N= 146; 73 patients with type 2 diabetes, 16 with peripheral neuropathy and 57 without peripheral neuropathy, and 73 matched controls). The gait data (stance phase, swing phase, double-foot stance phase, single-foot stance phase, walking cadence, stride length, walking speed, off-ground angle, landing angle, maximum swing angle, minimum swing angle, and foot progression angle) in diabetic patients were recorded and compared with controls. SPSS 22.0 statistical software was used to analyzed the gait parameter data.
We found that the landing angle and the maximum swing angle of diabetes patients with or without peripheral neuropathy were significantly less than those of the control group (P < 0.05). The walking speed of diabetes patients with peripheral neuropathy is significantly less than those of the control group (P < 0.05).
This study confirms that the wearable gait analysis system (Gaitboter) is an ideal system to identify abnormal gait in patients with type 2 diabetes and provides a new device and method for diabetes-related gait research.
以往研究表明，2型糖尿病患者的步态与健康组相比存在异常。目前，三维运动分析系统常用于步态分析。然而，由于实验条件繁杂且价格高昂，在临床研究中收集数据并使用该系统具有挑战性。在本研究中，我们使用了一种可穿戴步态分析系统（Gaitboter）来研究糖尿病患者，尤其是伴有周围神经病变患者的步态时空参数和运动学数据。本研究的目的是评估该可穿戴步态分析系统在糖尿病研究中的应用。
我们进行了一项病例对照研究，以分析2型糖尿病患者的步态。2018年6月至2018年10月期间，在北京朝阳医院病房使用Gaitboter检测并收集步态数据。我们收集了参与者（N = 146；73例2型糖尿病患者，其中16例伴有周围神经病变，57例不伴有周围神经病变，以及73例匹配的对照）的步态数据。记录糖尿病患者的步态数据（支撑期、摆动期、双足支撑期、单足支撑期、步行节奏、步长、步行速度、离地角度、着地角度、最大摆动角度、最小摆动角度和足前进角度）并与对照组进行比较。使用SPSS 22.0统计软件分析步态参数数据。
我们发现，伴有或不伴有周围神经病变的糖尿病患者的着地角度和最大摆动角度均显著小于对照组（P < 0.05）。伴有周围神经病变的糖尿病患者的步行速度显著低于对照组（P < 0.05）。
本研究证实，可穿戴步态分析系统（Gaitboter）是识别2型糖尿病患者异常步态的理想系统，并为糖尿病相关步态研究提供了一种新的设备和方法。
