Evaluation of a sensor algorithm for motor state rating in Parkinson's disease. (July 2019)

• Record Type: Journal Article
• Authors: Johansson, Dongni; Thomas, Ilias; Ericsson, Anders; Johansson, Anders; Medvedev, Alexander; Memedi, Mevludin; Nyholm, Dag; Ohlsson, Fredrik; Senek, Marina; Spira, Jack; Westin, Jerker; Bergquist, Filip
• Is Part Of: Parkinsonism & related disorders. Volume 64(2019)
• Page Start: 112
• Page End: 117
• Publication Date: 2019-07
• Subjects: Levodopa challenge test -- Independent evaluation -- Wearable sensors -- Parkinson's disease -- Machine learning algorithms; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2019.03.022
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Introduction: A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models. Methods: Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III. Results: As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS ( r s = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found ( r s = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state ( P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used. Conclusion: The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness. Highlights: A previously developed objective sensor index for treatment effects was evaluated in a new PD population. The objective sensor index performed better in patients with acute levodopa response and clear motor fluctuations. The objective sensor index shows promise for constructing individual dose-response models.