JMIR Publications

ABSTRACT

Background:

The prevalence of Parkinson’s disease is becoming of increasing concern due to the United Kingdom’s ageing population. Wearable devices have the potential to improve clinical care for patients with Parkinson’s disease while reducing costs in healthcare. Thus, wearable devices have been exploited within United Kingdom secondary care settings. Exploring the features of these wearable devices is important to identify limitations and further areas of investigation of how wearable devices were currently used in clinical care.

Objective:

The scoping review aims to explore the features of wearable devices used for Parkinson’s disease in hospitals within the United Kingdom.

Methods:

A scoping review of the current research was undertaken and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis-extension for scoping reviews (PRISMA-ScR) guidelines. Publications were obtained from Medline/PubMed, Embase, and Cochrane Library. One reviewer reviewed the eligibility of the studies. Eligible publications were initially screened by the Title and Abstract. Publications that passed the initial screening underwent a full review. The final publications included in the study were reviewed and discussed with the first and second authors. One reviewer then extracted appropriate data from the included studies, and the evidence was then synthesised using a narrative approach.

Results:

Of the 4543 publications identified, 39 publications underwent a full review, and 20 publications were included in the scoping review. Most studies were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust with study sample sizes ranging from 10 to 418. The majority of study participants were male with a mean age ranging from 57.7 to 78.0 years old. The AX3 was the most popular device brand used and commercially manufactured by Axivity. The majority of wearable device types included body-worn sensors, inertial measurement units, and smartwatches which often used accelerometers and gyroscopes as the wearable device sensor to measure Parkinson’s disease symptoms or biomarkers. The overwhelming majority of wearable device primary measures involved the measure of Parkinson’s disease motor symptoms such as gait, bradykinesia, and dyskinesia. The most common wearable device placement was the lumbar, head, and wrist. 65% of studies used artificial intelligence or machine learning to support Parkinson’s disease data analysis.

Conclusions:

The scoping review provides insight into how wearable devices are being utilised in UK secondary care settings to improve Parkinson’s disease care. The current preliminary research demonstrated promise in quantifying and providing a detailed characterisation of Parkinson’s disease symptoms. Using machine learning, wearable devices could also differentiate Parkinson’s disease from other neurodegenerative diseases. However, the scoping review has also identified limitations that have currently been overlooked and areas of interest that may warrant further research. Currently, there is little investigation surrounding machine learning optimisation, wearable device data security, and information governance. Future research is also needed to evaluate how wearable devices can be utilised in large-scale, routine clinical practice using larger sample sizes.