Objective: To determine gait parameters that differentiate between ON and OFF medication states using mobile health technology (one device at the lower back) in a group of early Parkinson’s disease (PD, Hoehn&Yahr stage <= 2) patients.

Background: Early PD patients often show slight motor impairment that is difficult to evaluate by clinicians. Mobile health technology may have the potential to detect these changes and allow easy disease monitoring1. We investigated the potential of mobile health technology-derived straight and circular walking parameters to differentiate between ON and OFF medication state.

Method: A group of 45 PD patients (20 females) underwent clinical (MDS-UPDRS-III) and mobile device (Hasomed, Magdeburg, Germany)-based assessment under ON and OFF medication conditions. They performed a straight (20m, preferred pace) and circular walking task (1080° around a circle2). The following straight and circular walking parameters were analyzed using validated algorithms: step, stride, stance, swing and double support times, variability and asymmetry.

Results: The mean age of the group was 67 (±9) years and the mean disease duration was 7 (±5) years. Mean MDS-UPDRS-III score during OFF was 25 (±10) and 14 (±8) during ON medication condition. None of the straight walking parameters differentiated significantly between the ON and OFF conditions. In the circular walking condition, stride (p=0.007), stance (p=0.009) and swing time (p=0.006) were significantly different between the conditions. The ROC curve analysis including these parameters showed an area under the curve (AUC) of 0.63 for the differentiation between ON and OFF.

Conclusion: Our preliminary results show that especially parameters extracted from circular walking by use of a single mobile device at the lower back are useful for determining treatment effects in early PD. We will investigate these effects in more detail in a growing sample and also test whether a combination of straight and circular walking parameters may further improve the AUC for the differentiation between ON and OFF medication conditions.

References: 1 Matias R, et al. A Perspective on Wearable Sensor Measurements and Data Science for Parkinson’s Disease. Front Neurol. 2017;8:677. 2 Micó-Amigo ME, et al. Potential Markers of Progression in Idiopathic Parkinson’s Disease Derived from assessment of circular gait with a single-body-fixed-sensor: a 5 years longitudinal study. Front in Human Neurosc. 2019;13(59).

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MDS Abstracts - https://www.mdsabstracts.org/abstract/quantitative-straight-and-circular-walking-parameters-for-detecting-on-and-off-medication-states-in-early-pd/