RESUMEN
The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson's disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.
RESUMEN
BACKGROUND: Approximately 40-70% of people with Parkinson's disease (PD) fall each year, causing decreased activity levels and quality of life. Current fall-prevention strategies include the use of pharmacological and non-pharmacological therapies. To increase the accessibility of this vulnerable population, we developed a multidisciplinary telemedicine program using an Information and Communication Technology (ICT) platform. We hypothesized that the risk for falling in PD would decrease among participants receiving a multidisciplinary telemedicine intervention program added to standard office-based neurological care. OBJECTIVE: To determine the feasibility and cost-effectiveness of a multidisciplinary telemedicine intervention to decrease the incidence of falls in patients with PD. METHODS: Ongoing, longitudinal, randomized, single-blinded, case-control, clinical trial. We will include 76 non-demented patients with idiopathic PD with a high risk of falling and limited access to multidisciplinary care. The intervention group (n = 38) will receive multidisciplinary remote care in addition to standard medical care, and the control group (n = 38) standard medical care only. Nutrition, sarcopenia and frailty status, motor, non-motor symptoms, health-related quality of life, caregiver burden, falls, balance and gait disturbances, direct and non-medical costs will be assessed using validated rating scales. RESULTS: This study will provide a cost-effectiveness assessment of multidisciplinary telemedicine intervention for fall reduction in PD, in addition to standard neurological medical care. CONCLUSION: In this challenging initiative, we will determine whether a multidisciplinary telemedicine intervention program can reduce falls, as an alternative intervention option for PD patients with restricted access to multidisciplinary care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04694443.
