Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany; Computer Science Department, Faculty of Computers and Information, Assiut University, Egypt.
Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany.
Int J Med Inform. 2023 Sep;177:105145. doi: 10.1016/j.ijmedinf.2023.105145. Epub 2023 Jul 7.
Gait and cognition impairments are common problems among People with Multiple Sclerosis (PwMS). Previous studies have investigated cross-sectional associations between gait and cognition. However, there is a lack of evidence regarding the longitudinal association between these factors in PwMS. Therefore, the objective of this study was to explore this longitudinal relationship using smartphone-based data from the Floodlight study.
Using the publicly available Floodlight dataset, which contains smartphone-based longitudinal data, we used a linear mixed model to investigate the longitudinal relationship between cognition, measured by the Symbol Digit Modalities Test (SDMT), and gait, measured by the 2 Minute Walking test (2 MW) step count and Five-U-Turn Test (FUTT) turning speed. Four mixed models were fitted to explore the association between: 1) SDMT and mean step count; 2) SDMT and variability of step count; 3) SDMT and mean FUTT turning speed; and 4) SDMT and variability of FUTT turningt speed.
After controlling for age, sex, weight, and height, there were significant correlations between SDMT and the variability of 2 MW step count, the mean of FUTT turning speed. No significant correlation was observed between SDMT and the 2 MW mean step count.
Our findings support the evidence that gait and cognition are associated in PwMS. This may support clinicians to adjust treatment and intervention programs that address both gait and cognitive impairments.
步态和认知障碍是多发性硬化症患者(PwMS)常见的问题。先前的研究已经调查了步态和认知之间的横断面关联。然而,关于 PwMS 中这些因素之间的纵向关联,证据不足。因此,本研究的目的是使用 Floodlight 研究中的基于智能手机的数据来探索这种纵向关系。
使用公开的 Floodlight 数据集,其中包含基于智能手机的纵向数据,我们使用线性混合模型来研究认知(通过符号数字模态测试(SDMT)测量)和步态(通过 2 分钟步行测试(2MW)步数和 5 转 U 形转弯测试(FUTT)转弯速度测量)之间的纵向关系。拟合了四个混合模型来探索以下四个方面的关联:1)SDMT 和平均步数;2)SDMT 和步数的可变性;3)SDMT 和平均 FUTT 转弯速度;4)SDMT 和 FUTT 转弯速度的可变性。
在控制年龄、性别、体重和身高后,SDMT 与 2MW 平均步数、FUTT 平均转弯速度的变异性之间存在显著相关性。SDMT 与 2MW 平均步数之间没有显著相关性。
我们的研究结果支持了步态和认知在 PwMS 中相关的证据。这可能支持临床医生调整治疗和干预方案,以解决步态和认知障碍。