Environmental and Medication Effects on Real-World Gait in Parkinson’s Disease: Implications for Context-Aware Movement Biomarkers

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Abstract Real-world movement assessments enable continuous monitoring of gait impairments in people with Parkinson’s disease (PD), but their interpretation is challenging due to fluctuations arising from the context-dependent nature of daily-life walking. This study analysed real-world gait outcomes and contextual data (medication intake, weather, and location) collected from twenty people with PD. Machine learning regression models were used to quantify the contribution of contextual factors to variability in gait outcomes. Contextual factors explained 30–40% of the variability in real-world gait outcomes, with the highest predictive performance observed for gait speed (R² = 0.39) and the lowest for cadence (R² = 0.30). Feature importance analysis indicated that bout-specific characteristics, start time of the bout, and time since medication intake were the primary predictors. These findings demonstrate that real-world gait in people with PD is substantially influenced by contextual factors, highlighting the need for context-aware analysis when interpreting gait outcomes.

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