Identification and quantitative assessment of motor complications in Parkinson's disease using the Parkinson's KinetiGraph™

Yan Qu; Tingting Zhang; Yunyan Duo; Liling Chen; Xiaohong Li

doi:10.3389/fnagi.2023.1142268

Identification and quantitative assessment of motor complications in Parkinson's disease using the Parkinson's KinetiGraph™

Front Aging Neurosci. 2023 Aug 1:15:1142268. doi: 10.3389/fnagi.2023.1142268. eCollection 2023.

Authors

Yan Qu^#¹, Tingting Zhang^#², Yunyan Duo¹, Liling Chen¹, Xiaohong Li¹

Affiliations

¹ Department of Neurology, Affiliated Dalian Municipal Friendship Hospital of Dalian Medical University, Dalian, China.
² Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian, China.

^# Contributed equally.

Abstract

Introduction: Effective management and therapies for the motor complications of Parkinson's disease (PD) require appropriate clinical evaluation. The Parkinson's KinetiGraph™ (PKG) is a wearable biosensor system that can record the motion characteristics of PD objectively and remotely.

Objective: The study aims to investigate the value of PKG in identifying and quantitatively assessing motor complications including motor fluctuations and dyskinesia in the Chinese PD population, as well as the correlation with the clinical scale assessments.

Methods: Eighty-four subjects with PD were recruited and continuously wore the PKG for 7 days. Reports with 7-day output data were provided by the manufacturer, including the fluctuation scores (FS) and dyskinesia scores (DKS). Specialists in movement disorders used the Movement Disorder Society-Unified Parkinson's Disease Rating Scale-IV (MDS-UPDRS IV), the wearing-off questionnaire 9 (WOQ-9), and the unified dyskinesia rating scale (UDysRS) for the clinical assessment of motor complications. Spearman correlation analyses were used to evaluate the correlation between the FS and DKS recorded by the PKG and the clinical scale assessment results. Receiver operating characteristic (ROC) curves were generated to analyze the sensitivity and specificity of the FS and DKS scores in the identification of PD motor complications.

Results: The FS was significantly positively correlated with the MDS-UPDRS IV motor fluctuation (items 4.3-4.5) scores (r = 0.645, p < 0.001). ROC curve analysis showed a maximum FS cut-off value of 7.5 to identify motor fluctuation, with a sensitivity of 74.3% and specificity of 87.8%. The DKS was significantly positively correlated with the UDysRS total score (r = 0.629, p < 0.001) and the UDysRS III score (r = 0.634, p < 0.001). ROC curve analysis showed that the maximum DKS cut-off value for the diagnosis of dyskinesia was 0.7, with a sensitivity of 83.3% and a specificity of 83.3%.

Conclusion: The PKG assessment of motor complications in the PD population analyzed in this study has a significant correlation with the clinical scale assessment, high sensitivity, and high specificity. Compared with clinical evaluations, PKG can objectively, quantitatively, and remotely identify and assess motor complications in PD, providing a good objective recording for managing motor complications.

Keywords: Parkinson’s KinetiGraph™; Parkinson’s disease; dyskinesia; motor complications; motor fluctuations.

Grants and funding

This research was funded by the National Key Research and Development Program of China (2016YFC1306500). Global Kinetics Corporation, which supported the study and provided the Parkinson’s KinetiGraph™ (PKG) devices free of charge for trial, had no role in the design, execution, interpretation, or writing of the study.