Psychometric reliability of patient-reported visual analogue scales in STN-DBS programming for Parkinson’s disease
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Background: Subthalamic nucleus deep brain stimulation (STN-DBS) is an established therapy for Parkinson’s disease (PD), yet programming relies heavily on subjective feedback. Visual analogue scales (VAS) have been proposed to structure patient-reported outcome measures during programming, but their psychometric reliability has not been systematically assessed. Objective: To evaluate the reliability and contextual stability of VAS ratings in STN-DBS programming. Methods: Fifteen patients with bilateral STN-DBS underwent four structured experiments: (i) test–retest consistency, (ii) effect of stimulation duration (15, 60, 120 s), (iii) impact of unilateral DBS withdrawal intervals (0, 10, 30 min), and (iv) contralateral stimulation ON versus OFF. Across experiments, patients provided >3,000 VAS ratings. Reliability was analyzed using correlation, regression, and Bland–Altman methods, with subgroup analyses by motor phenotype, cognition, and disease burden. Results: VAS ratings showed strong test–retest reliability (r = 0.70, R² = 0.53), with 83% of repeated scores within ±2 points. Reliability was reduced in tremor-onset compared to non-tremor patients (p = 0.04), but unaffected by cognition or quality of life. Stimulation duration influenced absolute scores, with 15 s ratings systematically lower than 60–120 s (p < 0.001), though relative scaling was preserved. DBS withdrawal intervals did not affect group means but increased trial-level variability. Contralateral stimulation ON versus OFF yielded modest correspondence (r = 0.31, R² = 0.13), suggesting hemispheric interactions in subjective perception. Conclusions: VAS ratings provide reproducible, quantifiable feedback during STN-DBS programming, though reliability depends on motor phenotype, stimulation duration, and bilateral context. Incorporating structured VAS feedback may enhance programming workflows, remote care models, and future multimodal closed-loop DBS strategies.
