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2016

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

Monitoring of motor and non-motor symptoms of Parkinson’s disease through a mHealth platform

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

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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) affects over one million Europeans, with significant economic and personal impacts. Traditional symptom monitoring methods are subjective and inadequate. The PD_manager project aimed to develop a comprehensive mHealth platform to objectively monitor both motor and non-motor PD symptoms, involving 20 patients initially and expanding to 200. Utilizing wearable sensors, including Moticon sensor insoles, and smart algorithms, the system collects data on motor functions (e.g., bradykinesia, dyskinesia, gait) and non-motor functions (e.g., cognitive abilities, speech). This data feeds into a Decision Support System (DSS) for personalized treatment adjustments. Early results show promise in creating an automatic, objective patient diary to aid clinicians in medication management.

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2016

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

Monitoring of motor and non-motor symptoms of Parkinson’s disease through a mHealth platform

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

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Parkinson, mHealth, Mobile devices

Parkinson, mHealth, Mobile devices

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