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Review
. 2016 Mar 10;6(2):279-87.
doi: 10.3233/JPD-150740.

A Viewpoint on Wearable Technology-Enabled Measurement of Wellbeing and Health-Related Quality of Life in Parkinson's Disease

Janet M T van Uem  1   2 , Tom Isaacs  3 , Alan Lewin  3 , Eros Bresolin  3 , Dina Salkovic  1   2 , Alberto J Espay  4 , Helen Matthews  3 , Walter Maetzler  1   2
Affiliations
Free PMC article
Review

A Viewpoint on Wearable Technology-Enabled Measurement of Wellbeing and Health-Related Quality of Life in Parkinson's Disease

Janet M T van Uem et al. J Parkinsons Dis. .
Free PMC article

Abstract

In this viewpoint, we discuss how several aspects of Parkinson's disease (PD) - known to be correlated with wellbeing and health-related quality of life-could be measured using wearable devices ('wearables'). Moreover, three people with PD (PwP) having exhaustive experience with using such devices write about their personal understanding of wellbeing and health-related quality of life, building a bridge between the true needs defined by PwP and the available methods of data collection. Rapidly evolving new technologies develop wearables that probe function and behaviour in domestic environments of people with chronic conditions such as PD and have the potential to serve their needs. Gathered data can serve to inform patient-driven management changes, enabling greater control by PwP and enhancing likelihood of improvements in wellbeing and health-related quality of life. Data can also be used to quantify wellbeing and health-related quality of life. Additionally these techniques can uncover novel more sensitive and more ecologically valid disease-related endpoints. Active involvement of PwP in data collection and interpretation stands to provide personally and clinically meaningful endpoints and milestones to inform advances in research and relevance of translational efforts in PD.

Keywords: Body fixed sensors; Parkinson’s disease; health-related quality of life; wearable devices; wellbeing.

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Figures

Fig.1
Fig.1
Use of different technical equipment in combination with health-related ‘meta algorithms’ may have the potential to offer novel insights into one’s behaviour including participation, PD-associated features, and the interaction thereof.
See this image and copyright information in PMC

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