A multidisciplinary telemedicine approach for managing frailty in Parkinson’s disease. A longitudinal, case-control study

Abstract

Introduction: The interaction between frailty and Parkinson’s disease (PD) is still unknown. This study aimed to study the effectiveness of a multidisciplinary telemedicine program in reducing frailty in Parkinson’s disease. Methods: Longitudinal, randomized, case-control study. All participants in the office were evaluated at baseline, four, and eight months (V0, V1, and V2). Patients included in the telemedicine program received additional multidisciplinary care with nurse, neurologist, and occupational therapist interventions from V0 to V1. PD motor, non-motor symptoms, frailty and health-related quality of life (HR-QoL) were assessed using recommended PD rating scales. Results: Fifty patients were included, 25 patients in the telemedicine group, and 25 patients in the control group. Frailty was highly correlated with performance in activities of daily living, and freezing of gait, balance, gait speed, and motor impairment, moderately correlated with hand grip strength, number of daily steps, and HR-QoL, and slightly correlated with age and level of physical fatigue. Frailty was reduced in the telemedicine group, compared to the control group, from V0 to V1 (p = .0001) and from V0 to V2 (p = .007). In addition, gait freezing, balance, gait speed, fatigue, non-motor symptoms, and HR-QoL were also improved in the telemedicine group (p values < .05). Conclusion: By leveraging multidisciplinary telemedicine interventions in addition to in-office visits, healthcare providers can deliver patient-centric care, improving frailty, non-motor symptoms, gait impairment, and quality of life in Parkinson’s disease. These hybrid interventions could solve current barriers to health systems with limited capacity.

Publication
Parkinsonism & Related Disorders 130:107215
José Luis Garrido-Labrador
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.