Kenichi Yamamura
Transgenic Group, Inc., Fukuoka, Japan
Publications
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Research Article
Walking Asymmetry in Parkinson’s Disease Requires Multi-Layered Interpretation: A Longitudinal Single-Subject Analysis Using Apple Health Data
Author(s): Zhenghua Li and Kenichi Yamamura*
Background: Consumer-device gait metrics offer a promising means of continuously monitoring Parkinsonian gait in daily life. However, interpretation of algorithm-derived parameters such as walking asymmetry remains difficult because the absence of a recorded value may reflect either the absence of measurable asymmetry or failure of the walking episode to satisfy algorithmic recording conditions. In addition, our recent work has shown that Apple HealthKit-based digital phenotyping can detect pharmacological interference in Parkinsonian gait, demonstrating that this platform is useful not only for long-term monitoring but also for objective assessment of treatment-related motor deterioration. Objective: To re-evaluate the meaning of walking asymmetry data in Parkinson’s disease (PD) by separating the signal into three components: non-zero asymmetry values, days wi.. Read More»