Objective Camptocormia contributes to vertical gait instability and, at times, may also lead to forward instability in experimental settings in Parkinson’s disease (PD) patients. However, these aspects, along with compensatory mechanisms, remain largely unexplored. This study comprehensively investigated gait instability and compensatory strategies in PD patients with camptocormia (PD+CC).
Methods Ten PD+CC patients, 30 without camptocormia (PD-CC), and 27 healthy controls (HCs) participated. Self-paced gait tasks were analyzed using three-dimensional motion capture systems to assess gait stability as well as spatiotemporal and kinematic parameters. Unique cases with pronounced forward gait stability or instability were first identified, followed by group comparisons. Correlation analysis was performed to examine associations between trunk flexion angles (lower/upper) and gait parameters. The significance level was set at 0.05.
Results Excluding one unique case, the PD+CC group presented a significantly lower vertical center of mass (COM) position (p=0.019) increased mediolateral COM velocity (p=0.004) and step width (p=0.013), compared to the PD-CC group. Both PD groups presented greater anterior‒posterior margins of stability than did the HCs (p<0.001). Significant correlations were found between lower/upper trunk flexion angles and a lower vertical COM position (r=-0.690/-0.332), as well as increased mediolateral COM velocity (r=0.374/0.446) and step width (r=0.580/0.474).
Conclusion Most PD+CC patients presented vertical gait instability, increased fall risk, and adopted compensatory strategies involving greater lateral COM shift and a wider base of support, with these trends intensifying as trunk flexion angles increased. These findings may guide targeted interventions for gait instability in PD+CC patients.
Objective This study aims to develop an automated and objective tool to evaluate postural abnormalities in Parkinson’s disease (PD) patients.
Methods We applied a deep learning-based pose-estimation algorithm to lateral photos of prospectively enrolled PD patients (n = 28). We automatically measured the anterior flexion angle (AFA) and dropped head angle (DHA), which were validated with conventional manual labeling methods.
Results The automatically measured DHA and AFA were in excellent agreement with manual labeling methods (intraclass correlation coefficient > 0.95) with mean bias equal to or less than 3 degrees.
Conclusion The deep learning-based pose-estimation algorithm objectively measured postural abnormalities in PD patients.
Citations
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