Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors

Kelin Xu; Yingzhe Wang; Yanfeng Jiang; Yawen Wang; Peixi Li; Heyang Lu; Chen Suo; Ziyu Yuan; Qi Yang; Qiang Dong; Li Jin; Mei Cui; Xingdong Chen

doi:10.1016/j.cmpb.2024.108162

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors

Comput Methods Programs Biomed. 2024 Apr 13:250:108162. doi: 10.1016/j.cmpb.2024.108162. Online ahead of print.

Authors

Kelin Xu¹, Yingzhe Wang², Yanfeng Jiang³, Yawen Wang¹, Peixi Li⁴, Heyang Lu⁴, Chen Suo⁵, Ziyu Yuan⁶, Qi Yang⁴, Qiang Dong⁴, Li Jin³, Mei Cui⁴, Xingdong Chen⁷

Affiliations

¹ Department of Biostatistics, Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China.
² State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
³ State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, China.
⁴ Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
⁵ Fudan University Taizhou Institute of Health Sciences, Taizhou, China; Department of Epidemiology, Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China.
⁶ Fudan University Taizhou Institute of Health Sciences, Taizhou, China.
⁷ State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, China. Electronic address: xingdongchen@fudan.edu.cn.

PMID: 38631129
DOI: 10.1016/j.cmpb.2024.108162

Abstract

Background and objectives: Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored.

Methods: We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden.

Results: The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden.

Conclusions: We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

Keywords: Cerebral small vessel disease; Community-based; Gait; Magnetic resonance imaging; Sparse sliced inverse regression.