Summary

Background

: Gait disturbances are common in patients undergoing hemodialysis and are associated with increased fall risk, mobility decline, and adverse health outcomes. Prior research suggests that hemodialysis may impact gait parameters such as speed, stride length, and variability; however, findings are inconsistent.

Objectives:

To evaluate acute changes in gait metrics before and after hemodialysis using an artificial intelligence (AI) based video gait analysis system.

Methods

: We initially enrolled 38 hemodialysis patients, two were excluded due to clothing interference with video analysis (27.8% female, 72.2% male). AI-driven gait analysis was performed immediately before and after dialysis. The system extracted spatiotemporal gait and joint range of motion. Statistical analyses included the Shapiro-Wilk test for normality, Wilcoxon signed-rank tests for non-normally distributed data, and paired t-tests for normally distributed data (P < 0.05).

Results

: Gait speed (0.59 m/sec pre-dialysis) remained unchanged post-dialysis (P = 0.876), as did cycle length and time. However, step length significantly decreased post-dialysis (P = 0.001), suggesting a more conservative gait pattern. Knee flexion and extension increased slightly but did not reach statistical significance.

Conclusions

: Dialysis does not acutely affect overall gait speed but significantly reduces stride length. Post-dialysis fatigue or hemodynamic shifts may alter walking patterns, highlighting the need for fall prevention strategies and physical rehabilitation interventions in dialysis care. AI-based gait analysis may provide a practical tool for monitoring mobility changes in hemodialysis patients.