Quantitative Analysis of Timed Up and Go Metrics Across Parkinson's Disease Severity and Their Clinical Correlations

Background: Parkinson's disease (PD) diagnosis is often delayed until signature motor symptoms manifest, at which point profound dopaminergic neuron loss has already occurred, necessitating advanced motor diagnostic biomarkers. Quantitative gait analysis is a promising tool, but phase-specific kinematic parameters remain underexplored. This study aims to identify novel, stage-divided Timed Up and Go (TUG) biomarkers not only to differentiate healthy controls (HCs) from patients with PD but also to objectively monitor and track disease progression across advancing severity stages, which are further validated against conventional clinical motor scales. Methods: A total of 81 participants (48 PD, 33 HCs) performed a 3 m TUG test using MotionCore (JEIOS Inc., Busan, Republic of Korea). The test was subdivided into three movement phases (Stage 1, sit-to-walk; Stage 2, turning; Stage 3, walk-to-sit). Results: PD patients exhibited significantly prolonged durations and altered turning metrics compared to HCs. Turning parameters including turning radius (ETR), area (EMA), and turning stability (FN) demonstrated strong correlations with disease severity and clinical scales. Notably, stage-specific analyses revealed that step counts, time, and speed metrics across Stages 1, 2, and 3 effectively differentiated disease severity, with transitional decelerating and seating metrics in Stage 3 showing the most pronounced clinical correlations. Discussion: This study confirms that the TUG test systematically deteriorates with increasing PD severity. The robust correlations with clinical scales (UPDRS, FOG-Q, BBS) validate TUG metrics as objective measures of motor and balance impairments. Utilizing novel, staging-specific indices significantly enhances the TUG test's clinical utility for supporting diagnosis, accurate staging, and monitoring disease progression. Although the overall group comparisons demonstrated statistical significance, a data overlap remains between mild PD and HCs, underscoring the need for large-scale longitudinal studies to validate these metrics for early detection.