Validating gait profile score for post-stroke gait: Correlation with observational scales and clinical outcomes

Introduction Compared to traditional gait analysis, computer-generated gait metrics derived from motion capture provide enhanced precision in differentiating individual performances. The Gait Profile Score (GPS) evaluates deviations from healthy gait kinematics and can strengthen clinical assessment. This study aimed to establish GPS’s convergent validity by correlating it with clinical outcome measures in post-stroke individuals. Methods A total of 46 post-stroke participants recruited from a rehabilitation centre completed the Berg Balance Scale (BBS), Fugl-Meyer Assessment for Lower Extremities (FMA-LE), Timed Up-and-Go (TUG), Wisconsin Gait Score (WGS), Tinetti Gait Score (TGS), and walking speed assessments. GPS was then calculated using motion capture data to quantify gait deviations. Results Results showed moderate correlations with FMA-LE (rho = 0.34), BBS (rho = 0.50), WGS (rho = 0.55), TGS (rho = 0.57), TUG (r = 0.66), and walking speed (r = 0.66). Multiple regression analyses indicated that GPS predicted TGS (R 2 = 0.86, p < 0.001) and WGS (R 2 = 0.85, p < 0.001), demonstrating its potential in identifying gait impairments that align with observational measures. However, logistic regression revealed that GPS did not predict fall risk (odds ratio 0.925, p = 0.801). Conclusion These findings support the convergent validity of GPS with established clinical measures, highlighting the close relationship between gait abnormalities and functional outcomes. As an objective measure, GPS complements clinical gait analysis and provides a robust approach to quantifying post-stroke walking performance. Further research is needed to clarify its role in predicting fall risk and guiding therapeutic interventions.