A Paradigm Shift in the Management of Patients with Parkinson’s Disease

Abstract

<b><i>Background:</i></b> Technological evolution leads to the constant enhancement of monitoring systems and recording symptoms of diverse disorders. <b><i>Summary:</i></b> For Parkinson’s disease, wearable devices empowered with machine learning analysis are the main modules for objective measurements. Software and hardware improvements have led to the development of reliable systems that can detect symptoms accurately and be implicated in the follow-up and treatment decisions. <b><i>Key Messages:</i></b> Among many different devices developed so far, the most promising ones are those that can record symptoms from all extremities and the trunk, in the home environment during the activities of daily living, assess gait impairment accurately, and be suitable for a long-term follow-up of the patients. Such wearable systems pave the way for a paradigm shift in the management of patients with Parkinson’s disease.

Paradigm shift in Parkinson's disease: using continuous telemonitoring to improve symptoms control. Results from a 2-years journey

“…Telemonitoring offers clinicians an opportunity to evaluate patient symptomatology throughout the day during activities of daily living, to assess response to therapy during long periods of months and years, and to improve follow-up care ( 10 ). The concept of continuous telemonitoring for symptoms aligns with the existing standard of care and is not a novel concept in the context of PD ( 11 ). Such transformative approaches for PD management pave the way for a paradigm shift aimed at actively managing and potentially improving symptoms control.…”

Editorial: Remote monitoring technologies in neurodegenerative movement disorders

“…Combining, these technologies could theoretically provide 24/7 insight into the condition of patients with movement disorders in a real-life context, and advances in artificial intelligence (AI) and data analytics could extract useful insights from these vast amounts of data to support the development of treatment plans optimized for individual patients ( 6 ).…”