Objective Longitudinal Assessment of Walking Function Using Apple Health Data in Parkinson’s Disease
Abstract
Zhenghua Li, Jingling Shen, Haruhisa Ishida and Kenichi Yamamura
Accurate longitudinal assessment of walking function is clinically important in Parkinson’s disease (PD). However, conventional evaluation often depends on brief clinical observation and patient-reported impressions, which may not fully capture long-term change in daily-life walking performance. Consumer health data collected through smartphones and wearable devices may provide an opportunity for objective and repeated assessment outside the clinic. We investigated whether Apple Health–derived walking metrics can be used for objective longitudinal assessment of walking function in PD. Longitudinal walking-related data were obtained from Apple Health in a patient with PD. Multiple gait-related metrics, including walking speed, step length, double support percentage, walking asymmetry percentage, step count, and walking steadiness, were analyzed over 2 years observation periods. Apple Health data enabled repeated and objective assessment of walking-related parameters over time in daily life. Longitudinal analysis demonstrated measurable temporal variation across multiple gait metrics, indicating that these data can detect changes in walking function that may be difficult to appreciate through conventional episodic or subjective assessment alone. Apple Health–derived walking metrics may provide a practical approach for objective longitudinal assessment of walking function in PD. Consumer health data may complement conventional clinical evaluation by enabling repeated real-world monitoring of gait-related changes over time.