Background: Rehabilitation becomes increasingly important in the more advanced stages of Parkinson's Disease. As the disease reaches its more debilitating stages and pharmacological or surgical treatment becomes less relevant, non-pharmacological interventions including rehabilitation become key. Existing systematic interventions typically focus on individuals in the early to mid-stages of the disease. The objective of this scoping review was to identify and map the available evidence on non-pharmacological rehabilitation interventions for people living with advanced Parkinson's disease.
Methods: This scoping review was conducted following the methodology for scoping reviews developed by the Joanna Briggs Institute. A systematic search was conducted in PubMed, EMBASE, CINAHL, and Cochrane. Studies published in English from 2000 to May 2024 were considered eligible and screened for relevance.
Results: Thirteen studies were included. The majority of the interventions were experimental; one had a focus on feasibility and one had a mixed focus on effect and feasibility. Most interventions were referred to as either rehabilitation, training, or therapy, with the two feasibility interventions focusing on comprehensive assessment and referrals. The majority used modalities concerned with levels of functioning. Studies focusing on stage 4 (H&Y) Parkinson's disease were prominent.
Conclusions: This scoping review provides a foundational overview of existing non-pharmacological rehabilitation interventions for advanced Parkinson's disease, revealing a small yet diverse range of approaches, from single-disciplinary to multidisciplinary interventions. It offers initial insights that can point to areas where further research can solidify and expand effective, targeted care strategies for people living with advanced Parkinson's disease.
The current era of high-throughput analysis-driven research offers invaluable insights into disease etiologies, accurate diagnostics, pathogenesis, and personalized therapy. In the field of movement disorders, investigators are facing an increasing growth in the volume of produced patient-derived datasets, providing substantial opportunities for precision medicine approaches based on extensive information accessibility and advanced annotation practices. Integrating data from multiple sources, including phenomics, genomics, and multi-omics, is crucial for comprehensively understanding different types of movement disorders. Here, we explore formats and analytics of big data generated for patients with movement disorders, including strategies to meaningfully share the data for optimized patient benefit. We review computational methods that are essential to accelerate the process of evaluating the increasing amounts of specialized data collected. Based on concrete examples, we highlight how bioinformatic approaches facilitate the translation of multidimensional biological information into clinically relevant knowledge. Moreover, we outline the feasibility of computer-aided therapeutic target evaluation, and we discuss the importance of expanding the focus of big data research to understudied phenotypes such as dystonia.