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Background: Exercise is crucial for the well-being of people with Parkinson's disease (PD). Although there are challenges to exercising with PD, mobile apps are seen as potential solutions, though their impact is not yet fully understood. We developed a mobile app and a home-based exercise program specialised for people with PD and investigated the effect of the mobile exercise app for the people with PD. Methods: Participants from the Movement Disorder Clinic were prompted to download and actively use our app for a duration of 2 weeks. Before commencing, we assessed their self-rated smartphone proficiency. Both at the start and after the 2-week period, we employed the International Physical Activity Questionnaire-Short Form and the PD Questionnaire-39 (PDQ-39) to evaluate their physical activity and overall quality of life (QoL). Exercise metrics were quantified in terms of metabolic equivalent minutes per week (MET-min/week). Furthermore, we gathered feedback on user satisfaction with the app at the end of the study. Results: Out of 41 recruited patients, 25 completed the 2-week program and 16 dropped out. Median MET-min/week rose from 1386.0 to 3210.0 (P = 0.009), primarily in moderate activities (P = 0.049) and walking (P = 0.002). Median PDQ-39 scores showed improvement from 17.2 to 8.5 (P = 0.005) after the program. Conclusion: The mobile app holds potential to enhance exercise and QoL for people with PD. For optimal benefits, future studies should focus on e-health literacy education, app quality enhancements, and a broader exercise program variety. Regular exercise can greatly benefit those with Parkinson's disease, and our pilot study shows that a tailored mobile app can help. By participating in a home exercise program using the app for 2 weeks, patients saw a notable increase in activity and an enhancement in their overall well-being. This approach could offer a supportive step toward improved health management for those with Parkinson's disease. [ABSTRACT FROM AUTHOR]

This article discusses the potential electromagnetic interference (EMI) that can occur when deep brain stimulation (DBS) impulse generators (IPG) and cardiac implantable electronic devices (CIEDs) are cohabitated. The case study presented involves a 48-year-old woman who underwent DBS surgery while having pre-existing CIEDs. The study explores how changing the DBS parameter settings can influence CIED function and highlights the importance of avoiding severe interference. The article also mentions previous cases involving patients with CIEDs undergoing DBS and provides recommendations for future studies. [Extracted from the article]

Introduction. Fatigue and orthostatic hypotension (OH) are common and disabling nonmotor symptoms (NMSs) of Parkinson's disease (PD), but none of the studies have reported on the longitudinal association between fatigue and OH. Methods. Drug-naïve PD patients were recruited from a hospital-based cohort and evaluated with the Parkinson Fatigue Scale (PFS), head-up tilt test, Unified PD Rating Scale, Hoehn and Yahr stage, Montreal Cognitive Assessment, Scale for Outcomes in PD-Autonomic (SCOPA-AUT), Beck Depression Inventory (BDI), Beck Anxiety Inventory, PD Sleep Scale, and medications at the baseline and follow-up visits. Results. A total of 80 patients were included, and the mean ages were 66.6 and 63.8 years in the fatigue and nonfatigue groups, respectively. The prevalence of fatigue was 17.5% (14/80) at the baseline and follow-up (mean follow-up: 23.3 ± 9.9 months). The prevalence of OH in the fatigue group was 57.1%, and it was significantly higher than that of the nonfatigue group. Six of the 14 patients (42.9%) in the fatigue group had persistent fatigue at the follow-up, and eight of them (57.1%) converted to the nonfatigue group. Logistic regression analysis demonstrated that the changes of BDI and the presence of OH at the baseline were the predictors for fatigue in drug-naïve PD. Conclusion. Fatigue is a common NMS in PD but can vary depending on the disease course. OH and depression are the most relevant predictors for the development of fatigue in drug-naïve PD. The present study suggests that the management of autonomic symptoms and depression might be helpful for managing fatigue in PD. [ABSTRACT FROM AUTHOR]

Background: The "motor reserve" is an emerging concept based on the discrepancy between the severity of parkinsonism and dopaminergic degeneration; however, the related brain structures have not yet been elucidated. Objective: We investigated brain structures relevant to the motor reserve in Parkinson's disease (PD) in this study. Methods: Patients with drug-naïve, early PD were enrolled, who then underwent dopamine transporter (DAT) scan and diffusion tensor imaging (DTI). The severity of motor symptoms was evaluated with the Unified Parkinson's Disease Rating Scale score of bradykinesia and rigidity on the more affected side and dopaminergic degeneration of DAT uptake of the more affected putamen. Individual motor reserve estimate (MRE) was evaluated based on the discrepancy between the severity of motor symptoms and dopaminergic degeneration. Using DTI and the Brainnetome atlas, brain structures correlated with MRE were identified. Results: We enrolled 193 patients with drug-naïve PD (mean disease duration of 15.6±13.2 months), and the MRE successfully predicted the increase of levodopa equivalent dose after two years. In the DTI analysis, fractional anisotropy values of medial, inferior frontal, and temporal lobes, limbic structures, nucleus accumbens, and thalamus were positively correlated with the MRE, while no brain structures were correlated with mean diffusivity. Additionally, degree centrality derived from the structural connectivity of the frontal and temporal lobes and limbic structures was positively correlated with the MRE. Conclusion: Our results show empirical evidence for MR in PD and brain structures relevant to MR, particularly, the extra-basal ganglia system including the limbic and frontal structures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]