2017

Conference Protocol IEEE Engineering in Medicine and Biology Society 2016 Aug; 2016:663-666

PD_Manager: an mHealth platform for Parkinson’s disease patient management

Jorge Cancela, Samanta Villanueva Mascato, Dimitrios Gatsios, George Rigas, Andrea Marcante, Giovanni Gentile, Roberta Biundo, Manuela Giglio, Maria Chondrogiorgi, Robert Vilzmann, Spyros Konitsiotis, Angelo Antonini, Maria T. Arredondo and Dimitrios I. Fotiadis

PD_manager Consortium

Schlagwörter

Parkinson, mHealth, Mobile devices

Parkinson, mHealth, Mobile devices

Zusammenfassung

Parkinson's disease (PD) is a complex, chronic disease that many patients live with for many years. In this work we propose a mHealth approach based on a set of unobtrusive, simple-in-use, off-the-self, co-operative, mobile devices that will be used for motor and non-motor symptoms monitoring and evaluation, as well as for the detection of fluctuations along with their duration through a waking day. Ideally, a multidisciplinary and integrated care approach involving several professionals working together (neurologists, physiotherapists, psychologists and nutritionists) could provide a holistic management of the disease increasing the patient's independence and Quality of Life (QoL). To address these needs we describe also an ecosystem for the management of both motor and non-motor symptoms on PD facilitating the collaboration of health professionals and empowering the patients to self-manage their condition. This would allow not only a better monitoring of PD patients but also a better understanding of the disease progression.

Moticon's Schlussfolgerungen

Parkinson’s disease (PD) is a prevalent neurodegenerative disease, requiring complex management. The PD_Manager mHealth platform aims to improve PD management through an integrated, cloud-based ecosystem utilizing lightweight sensors, including a wristband and Moticon sensor insoles, for continuous, unobtrusive monitoring. Methods included real-time data capturing, remote symptom assessment, and the use of a decision support system (DSS) for clinicians. The study demonstrated high accuracy in detecting PD symptoms such as tremor, dyskinesia, and bradykinesia, with validation against clinical assessments. Notably, the platform effectively estimated gait impairments and freezing of gait (FoG) events, providing comprehensive data for clinician review. Results indicated that 78% of patients found the platform beneficial, citing improved treatment personalization. The DSS enhanced disease progression monitoring and medication adherence evaluation, suggesting medication adjustments based on patient data.

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