Institute of Neuroscience, Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK.
Institute of Neuroscience, Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK; School of Biomedical Sciences, Newcastle University, UK.
Gait Posture. 2020 Feb;76:372-376. doi: 10.1016/j.gaitpost.2019.12.028. Epub 2019 Dec 28.
There are unique signatures of gait impairments in different dementia disease subtypes, such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease (PDD). This suggests gait analysis is a useful differential marker for dementia disease subtypes, but this has yet to be assessed using inexpensive wearable technology.
This study aimed to assess whether a single accelerometer-based wearable could differentiate dementia disease subtypes through gait analysis.
80 people with mild cognitive impairment or dementia due to AD, DLB or PD performed six ten-metre walks. An accelerometer-based wearable (Axivity) assessed gait. Data was processed using algorithms validated in other neurological disorders and older adults. Fourteen spatiotemporal characteristic were computed, that broadly represent pace, variability, rhythm, asymmetry and postural control features of gait. One way analysis of variance and Kruskall Wallis tests identified significant between-group differences, and post-hoc independent t-tests and Mann Whitney U's established where differences lay. Receiver Operating Characteristics and Area Under the Curve (AUC) demonstrated overall accuracy for single gait characteristics.
The wearable was able to differentiate dementia disease subtypes (p ≤ .05) and demonstrated significant differences between the groups in 7 gait characteristics with modest accuracy. For reference the instrumented walkway showed 2 between-group differences in gait characteristics.
This study found that a wearable device can be used to differentiate dementia disease subtypes. This provides a foundation for future research to investigate the application of wearable technology as a clinical tool to aid diagnostic accuracy, allowing the correct treatment and care to be applied. Wearable technology may be particularly useful as its use is less restricted to context, making it easier to implement.
不同痴呆疾病亚型(如阿尔茨海默病(AD)、路易体痴呆(DLB)和帕金森病(PDD))的步态损伤具有独特特征。这表明步态分析是一种有用的痴呆疾病亚型鉴别标志物,但这尚未通过廉价的可穿戴技术进行评估。
本研究旨在评估基于单个加速度计的可穿戴设备是否可以通过步态分析来区分痴呆疾病亚型。
80 名轻度认知障碍或 AD、DLB 或 PD 引起的痴呆患者进行了 6 次 10 米步行。基于加速度计的可穿戴设备(Axivity)评估步态。使用在其他神经障碍和老年人中经过验证的算法处理数据。计算了 14 个时空特征,这些特征广泛代表步态的步速、变异性、节奏、不对称性和姿势控制特征。单因素方差分析和 Kruskal-Wallis 检验确定了组间的显著差异,事后独立 t 检验和 Mann-Whitney U 检验确定了差异所在。受试者工作特征和曲线下面积(AUC)表明了单个步态特征的整体准确性。
可穿戴设备能够区分痴呆疾病亚型(p≤.05),并在 7 个步态特征上表现出具有中等准确性的显著组间差异。作为参考,仪器化步道在步态特征上显示出 2 个组间差异。
本研究发现,可穿戴设备可用于区分痴呆疾病亚型。这为未来的研究提供了基础,以探讨可穿戴技术作为一种临床工具来提高诊断准确性的应用,从而可以进行正确的治疗和护理。可穿戴技术可能特别有用,因为它的使用对环境的限制较少,因此更容易实施。