Dyskinesia and impulsive compulsive behaviour in Parkinson's disease are not related: Insights from a study with a wearable sensor.

INTRODUCTION: Previous studies have suggested an association between Impulsive Compulsive Behaviour (ICB) and dyskinesia in Parkinson's disease (PD). However, none of these studies have employed an objective home-based measure of dyskinesia. OBJECTIVES: To evaluate in advanced PD the relationship between ICB and dyskinesia, objectively measured with a wearable device. METHODS: In this cross-sectional study, ICB and other neuropsychiatric symptoms were assessed by means of structured clinical interview and specific screening instruments. Presence and severity of motor fluctuations and dyskinesia were rated with patient's and clinician's based rating instruments. Motor fluctuations and dyskinesia were also measured at home for 5-days using a validated wearable devise, the Parkinson's KinetiGraph™(PKG). RESULTS: We included 89 subjects with PD (29 females, 62 ± 7 years, disease duration 10.3 ± 4.5), of whom 36 (40%) had ICB. Patients with and without ICB did not differ by presence and severity of dyskinesia measured by clinical scales and PKG. There was no association between the presence of ICB and dyskinesia in the whole sample. CONCLUSION: Our data suggest that ICB and dyskinesia are common but unrelated disorders in advanced PD.

Khodakarami, H., et al., Prediction of the Levodopa Challenge Test in Parkinson's Disease Using Data from a Wrist-Worn Sensor. Sensors 2019, 19, 5153.

The authors wish to make the following erratum to this paper [...].

Prediction of the Levodopa Challenge Test in Parkinson's Disease Using Data from a Wrist-Worn Sensor.

The response to levodopa (LR) is important for managing Parkinson's Disease and is measured with clinical scales prior to (OFF) and after (ON) levodopa. The aim of this study was to ascertain whether an ambulatory wearable device could predict the LR from the response to the first morning dose. The ON and OFF scores were sorted into six categories of severity so that separating Parkinson's Kinetigraph (PKG) features corresponding to the ON and OFF scores became a multi-class classification problem according to whether they fell below or above the threshold for each class. Candidate features were extracted from the PKG data and matched to the class labels. Several linear and non-linear candidate statistical models were examined and compared to classify the six categories of severity. The resulting model predicted a clinically significant LR with an area under the receiver operator curve of 0.92. This study shows that ambulatory data could be used to identify a clinically significant response to levodopa. This study has also identified practical steps that would enhance the reliability of this test in future studies.