Gait Disturbances in Older Adults With Cerebral Small Vessel Disease: Mixed Methods Study Using Smartphone Sensors and Video Analysis

TL;DR

This study uses smartphones and video to detect gait issues in older adults with brain small vessel disease, revealing measurable differences in walking patterns and posture.

Contribution

The study introduces an integrative method combining smartphone sensors and video analysis to detect gait disturbances in cerebral small vessel disease.

Findings

CSVD patients showed 15%–23% lower step regularity compared to controls during walking tasks.

Patients with CSVD had a 4°–7° more pronounced forward head posture than controls during all walking tasks.

Dual-task walking revealed the most significant differences in head posture between CSVD patients and controls.

Abstract

Cerebral small vessel disease (CSVD) significantly impacts motor functions, particularly gait dynamics. However, its analysis often lacks the integration of comprehensive tools that capture the multifaceted nature of gait disturbances. Traditional methods may not fully address the complexity of CSVD’s impact on gait, underscoring the need for a detailed exploration of gait characteristics through advanced technological means.

This study aims to identify the distinct gait patterns and postural adaptations present in patients with CSVD compared to a healthy older population, using an integrative analysis combining sensor and video data to provide a holistic understanding of gait dynamics in CSVD.

This study involved 90 participants older than 50 years (mean age 68.85, SD 9.74 years; 47 males and 43 females), with 24 categorized as normal controls (mean age 66.42, SD 7.51 years) and 66 diagnosed with CSVD (mean age 69.74, SD 10.37 years). Participants performed three walking tasks: normal walking, dual-task walking (with concurrent mental arithmetic), and fast walking. Gait parameters were collected through video data for image posture parameters using the OpenPose BODY_25 key point model, and the “Pocket Gait Test” smartphone app for sensor-based parameters sampled at approximately 40 Hz. Data analysis included 5 sensor-based parameters (step frequency, root mean square (RMS), step variability, step regularity, and step symmetry) and 6 key video-based parameters (including knee angle, ankle angle, elbow angle, body trunk angles, and head posture).

Among the 29 participants with complete sensor and video data (10 normal controls and 19 patients with CSVD), significant differences were observed in step regularity (normal walking: mean 0.76, SD 0.09 vs mean 0.61, SD 0.25; P<.003 and dual-task: mean 0.74, SD 0.13 vs mean 0.57 SD 0.24; P<.005), RMS (normal walking: mean 1.64, SD 0.45 vs mean 1.43, SD 0.42; P<.006), and forward head posture angles (head-to-body angle during normal walking: mean 132.96, SD 7.78 vs mean 128.07, SD 7.99; P<.02 and head-to-ground angle: mean 134.11, SD 8.28 vs mean 128.40, SD 9.75; P<.008) between the CSVD and control groups. The CSVD group exhibited a more pronounced forward head posture across all walking tasks, with the greatest difference observed during dual-task walking (head-to-ground angle: mean 134.43, SD 8.29 vs mean 125.02, SD 8.42; P<.02).

The study provides compelling evidence of distinct gait disturbances in patients with CSVD, characterized by reduced step regularity (15%‐23% lower than controls), altered acceleration patterns, and significant postural adaptations, particularly forward head positioning (4°‐7° more pronounced than controls). These quantifiable differences, detectable through accessible smartphone and video technology, offer potential biomarkers for early CSVD detection and monitoring. The integration of sensor and video analysis provides a more comprehensive assessment approach that could be implemented in both clinical and home settings for longitudinal monitoring of disease progression and rehabilitation outcomes.

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