Affiliations: [a] Institute of Neuroscience/Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK
| [b] Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| [c] Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| [d] Oxford Parkinson’s Disease Centre, University of Oxford, Oxford, UK
| [e] Institute of Clinical Neurosciences, University of Bristol, Bristol, UK
Correspondence:
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Correspondence to: Professor Lynn Rochester, Institute of Neuroscience/Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne Hospitals NHS Foundation Trust Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK. Tel.: +44 (0) 191 208 1291; Fax: +44 (0) 191 208 1251; E-mail: lynn.rochester@ncl.ac.uk.
Abstract: Background:Patients with REM sleep behavior disorder (RBD) have a high risk of developing PD, and thus can be used to study prodromal biomarkers. RBD has been associated with changes in gait; quantifying these changes using wearable technology is promising; however, most data are obtained in clinical settings precluding pragmatic application. Objective:We aimed to investigate if wearable-based, real-world gait monitoring can detect early gait changes and discriminate individuals with RBD from controls, and explore relationships between real-world gait and clinical characteristics. Methods:63 individuals with RBD (66±10 years) and 34 controls recruited in the Oxford Parkinson’s Disease Centre Discovery Study were assessed. Data were collected using a wearable device positioned on the lower back for 7 days. Real-world gait was quantified in terms of its Macrostructure (volume, pattern and variability (S2)) and Microstructure (14 characteristics). The value of Macro and Micro gait in discriminating RBD from controls was explored using ANCOVA and ROC analysis, and correlation analysis was performed between gait and clinical characteristics. Results:Significant differences were found in discrete Micro characteristics in RBD with reduced gait velocity, variability and rhythm (p≤0.023). These characteristics significantly discriminated RBD (AUC≥0.620), with swing time as the single strongest discriminator (AUC=0.652). Longer walking bouts discriminated best between the groups for Macro and Micro outcomes (p≤0.036). Conclusions:Our results suggest that real-world gait monitoring may have utility as “risk” clinical marker in RBD participants. Real-world gait assessment is low-cost and could serve as a pragmatic screening tool to identify gait impairment in RBD.
Keywords: Free-living, gait, prodromal, REM sleep behavior disorder, wearables
