Introduction The coronavirus disease 2019 (COVID-19) pandemic has disrupted everyday life of Parkinson's disease (PD) patients, but its clinical impact has not been illustrated. In this study, we investigated the change in physical activity and subsequently clinical symptoms of PD during the COVID-19 pandemic. Methods We enrolled PD patients who were able to ambulate independently and had visited our clinic at Samsung Medical Centre from December 2019 to January 2020 (baseline) and in May 2020 (follow-up during the COVID-19 crisis), and divided them into either ‘the sustained exercise group’ or ‘the reduced exercise group’. Then, we assessed the change in the exercise and clinical features between these two groups over the study period. Results A total of 100 subjects were recruited. During the COVID-19 pandemic, the amount, duration and frequency of exercise were reduced. There was decrease in number of patients who do indoor-solo exercise and increase in that of patients who do not exercise. One third reported subjective worsening of both motor and non-motor features, although Unified PD Rating Scale (UPDRS) part 3 score was similar. Additionally, the reduced exercise group reported more motor and non-motor aggravation than the sustained exercise group, despite lack of significant difference in the UPDRS part 3 score. Conclusion The COVID-19 pandemic had a clear impact on exercise and subjective symptoms in PD patients, with reduced exercise being related to a subjective increase in both motor and non-motor symptoms of PD. Maintaining exercise should therefore be emphasized even in situations like the COVID-19 pandemic.
Objectives: We aimed to validate the accuracy of blood pressure (BP) measurement using a smartwatch in patients with Parkinson's disease (PD).Materials and Methods: We compared 168 pairs of BP (n = 56) measurements acquired by a smartwatch (SM-R850) with those measured by a sphygmomanometer (reference device).Results: Differences between the smartwatch BP and reference BP measurements were compared. The mean and standard deviation of the differences systolic BP (SBP) and diastolic BP (DBP), measured by smartwatch and reference device, fulfilled both criterion 1 (0.4 ± 4.6 and 1.1 ± 4.5 mm Hg for DBP and SBP, respectively) and criterion 2 (0.2 ± 2.5 and 0.9 ± 2.4 mm Hg for DBP and SBP, respectively) of the BP validation criterion of the International Organization for Standardization.Conclusion: BP measurement using a smartwatch with a photoplethysmography sensor is an accurate and reliable method in patients with PD.
Dysarthria may present during the natural course of many degenerative neurological conditions. Hypokinetic and ataxic dysarthria are common in movement disorders and represent the underlying neuropathology. We developed an artificial intelligence (AI) model to distinguish ataxic dysarthria and hypokinetic dysarthria from normal speech and differentiate ataxic and hypokinetic speech in parkinsonian diseases and cerebellar ataxia. We screened 804 perceptual speech analyses performed in the Samsung Medical Center Neurology Department between January 2017 and December 2020. The data of patients diagnosed with parkinsonian disorders or cerebellar ataxia were included. Two speech tasks (numbering from 1 to 50 and reading nine sentences) were analyzed. We adopted convolutional neural networks and developed a patch-wise wave splitting and integrating AI system for audio classification (PWSI-AI-AC) to differentiate between ataxic and hypokinetic speech. Of the 395 speech recordings for the reading task, 76, 112, and 207 were from normal, ataxic dysarthria, and hypokinetic dysarthria subjects, respectively. Of the 409 recordings of the numbering task, 82, 111, and 216 were from normal, ataxic dysarthria, and hypokinetic dysarthria subjects, respectively. The reading and numbering task recordings were classified with 5-fold cross-validation using PWSI-AI-AC as follows: hypokinetic dysarthria vs. others (area under the curve: 0.92 ± 0.01 and 0.92 ± 0.02), ataxia vs. others (0.93 ± 0.04 and 0.89 ± 0.02), hypokinetic dysarthria vs. ataxia (0.96 ± 0.02 and 0.95 ± 0.01), hypokinetic dysarthria vs. none (0.86 ± 0.03 and 0.87 ± 0.05), and ataxia vs. none (0.87 ± 0.07 and 0.87 ± 0.09), respectively. PWSI-AI-AC showed reliable performance in differentiating ataxic and hypokinetic dysarthria and effectively augmented data to classify the types even with limited training samples. The proposed fully automatic AI system outperforms neurology residents. Our model can provide effective guidelines for screening related diseases and differential diagnosis of neurodegenerative diseases.
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