Exploring the Impact of Parkinson's Disease on Driving: A Population-Based Survey.

Background: Persons with Parkinson's disease (PD) experience progressive motor and non-motor symptoms which may influence their ability to drive a car. This is experienced as a massive challenge by many affected individuals, for whom being able to drive a car is vital to maintain functional independence.

Objectives: We assessed how the diagnosis of PD affected the possession of a driving license, how people with PD had adapted their driving style, and to what extent they had communicated about their driving ability with their healthcare professionals.

Respiratory Dysfunction and Abnormal Hypoxic Ventilatory Response in Mild to Moderate Parkinson's Disease.

Background: Respiratory dysfunction is an important contributor to morbidity and mortality in advanced Parkinson's disease (PD), but it is unclear what parameters are sensitive to diagnose and monitor respiratory dysfunction across disease phases.

Objectives: We aimed to characterize respiratory dysfunction in mild to moderate PD.

Methods: In 20 individuals without cardiopulmonary comorbidity, pulmonary and inspiratory muscle function testing were performed ON-medication.

Validation of computer vision technology for analyzing bradykinesia in outpatient clinic videos of people with Parkinson's disease.

Background: Current diagnosis and monitoring of Parkinson's disease (PD) is based on subjective clinical assessments. Objective measures of motor functioning could support clinical acumen. Computer vision (CV) technology is a promising contactless technique but requires further validation.

Implementing Advance Care Planning and Care Coordination in the Care for People With Parkinson Disease: A Feasibility Study.

BACKGROUND: For people with a moderate stage of Parkinson disease (PD), dedicated care coordination combined with advance care planning (ACP) is highly needed. However, evidence is lacking. The objective of this study was to assess the feasibility and acceptability of the study processes to inform a larger randomized controlled trial, aiming the effectiveness of a combined intervention on ACP and care coordination for people with PD.

Application of physics encoded neural networks to improve predictability of properties of complex multi-scale systems.

Predicting physical properties of complex multi-scale systems is a common challenge and demands analysis of various temporal and spatial scales. However, physics alone is often not sufficient due to lack of knowledge on certain details of the system. With sufficient data, however, machine learning techniques may aid.