ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2021
A Wearable Gait Analysis System Used in Type 2 Diabetes Mellitus Patients: A Case-Control Study.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 8 citations in OpenAlex.
- Transcranial direct current stimulation enhances the efficacy of wearable transcutaneous electrical nerve stimulation for mild knee osteoarthritis in the middle-aged person: a randomized controlled trial.BMC musculoskeletal disorders · 2025Trial
- Lower limb intelligent rehabilitation robot based on human-gait coupling, spatiotemporal gait sensing, and fuzzy PID control.Scientific reports · 2025Article
- Reliability of Gait Analysis Using ORPHE ANALYTICS in Patients with Type 2 Diabetes: A Single-center Observational Study.JMA journal · 2025Article
- Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning.Frontiers in neuroinformatics · 2025Article
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Authors and funding
8 authors at 4 institutions in 1 country.
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Abstract
backgroundPrevious 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. MATERIALS AND
methodsWe 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.
resultsWe 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).
conclusionThis 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.
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