title: Free-living monitoring of Parkinson's disease: Lessons from the field.
abstract: Wearable technology comprises miniaturized sensors (eg, accelerometers) worn on the body and/or paired with mobile devices (eg, smart phones) allowing continuous patient monitoring in unsupervised, habitual environments (termed free-living). Wearable technologies are revolutionizing approaches to health care as a result of their utility, accessibility, and affordability. They are positioned to transform Parkinson's disease (PD) management through the provision of individualized, comprehensive, and representative data. This is particularly relevant in PD where symptoms are often triggered by task and free-living environmental challenges that cannot be replicated with sufficient veracity elsewhere. This review concerns use of wearable technology in free-living environments for people with PD. It outlines the potential advantages of wearable technologies and evidence for these to accurately detect and measure clinically relevant features including motor symptoms, falls risk, freezing of gait, gait, functional mobility, and physical activity. Technological limitations and challenges are highlighted, and advances concerning broader aspects are discussed. Recommendations to overcome key challenges are made. To date there is no fully validated system to monitor clinical features or activities in free-living environments. Robust accuracy and validity metrics for some features have been reported, and wearable technology may be used in these cases with a degree of confidence. Utility and acceptability appears reasonable, although testing has largely been informal. Key recommendations include adopting a multidisciplinary approach for standardizing definitions, protocols, and outcomes. Robust validation of developed algorithms and sensor-based metrics is required along with testing of utility. These advances are required before widespread clinical adoption of wearable technology can be realized. © 2016 International Parkinson and Movement Disorder Society.
authors.affiliation: Institute of Neuroscience, Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK.
authors.forename: Silvia
authors.initials: S
authors.lastname: Del Din
authors.affiliation: Institute of Neuroscience, Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK.
authors.forename: Alan
authors.initials: A
authors.lastname: Godfrey
authors.affiliation: Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK.|INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK.
authors.forename: Claudia
authors.initials: C
authors.lastname: Mazzà
authors.affiliation: Institute of Neuroscience, Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK.
authors.forename: Sue
authors.initials: S
authors.lastname: Lord
authors.affiliation: Institute of Neuroscience, Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK. lynn.rochester@ncl.ac.uk.
authors.forename: Lynn
authors.initials: L
authors.lastname: Rochester
mesh_terms: D006801:Humans; D018670:Monitoring, Ambulatory / Q000592:standards*; D010300:Parkinson Disease / Q000175:diagnosis*
keywords: Parkinson's disease; free-living assessment; remote monitoring; wearable technology