Adopting a multidisciplinary telemedicine approach for falls prevention in Parkinson’s disease (2579)

Abstract

Objective: This multidisciplinary study aims to evaluate the effects of a home-based intervention programme delivered by a multidisciplinary health team using telemedicine, and wearable sensors to reduce the incidence of falls in PD

Background: Falls in Parkinson’s Disease (PD) are very frequent with often devastating consequences, increasing comorbidity, mortality, decreased quality of life (QoL), and increased socio-health costs. Healthcare services for chronically ill patients, including PD patients, have been compromised during the COVID-19 pandemic, owing to limited access to multidisciplinary care.

Design/Methods: Ongoing, longitudinal, two-group randomized controlled trial, which will be including 38, non-demented patients with idiopathic PD, high risk of falling and restricted access to multidisciplinary care. Multidisciplinary telemedicine visits including neurologists, nurses, occupathional therapists and psychologists in addition to wearable sensors (study group) will be compared to in-office visits best medical practice (control group). Falls reduction, PDQ-39, motor (MDS-UPDRS) and non-motor symptoms (NMSS) severity and balance impairment will be compared.

Results: Up to date, 12 PD patients without access to non-pharmacological interventions with a mean age 69.0 ± 7.4,50% males, median Hoehn Yhar of 2.0, were included (6 patients in the telemedicine group, 6 patients in the control group). At baseline, patients had a mean MDS-UPDRS-III 45.3 ± 6.4, NMSS score 73.5 ± 6.7, PDQ-39 52.8 ± 23.4, 2.5 ± 0.7 falls in the past 6 months, and abnormal balance (Cognitive-TUG). Comparison of control vs. study groups outcomes and multivariate analyses will be performed.

Conclusions: The findings of this study will allow us to study the feasibility of remote health care to prevent falls in patients with PD, to provide direction for health and lifestyle interventions, allowing equity in the distribution and access to specialized, multidisciplinary health care. The current findings will be specially relevant in the context of Covid-19 pandemic and the unusual situation of inaccessibility of in-person health services.

José Luis Garrido-Labrador

Assistant Lecturer in Computer Languages and Systems

PhD in Machine Learning, researching in semi-supervised learning and restricted set classification. Assistant Lecturer in Computer Languages and Systems at Universidad de Burgos.