Rapid eye movement sleep behavior disorder: a narrative review from a technological perspective

• Published in: Sleep (New York, N.Y.) Vol. 46; no. 6; p. 1
• Main Authors: Gnarra, Oriella, Wulf, Marie-Angela, Schäfer, Carolin, Nef, Tobias, Bassetti, Claudio L A
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 13.06.2023
• Subjects: Algorithms; Behavior disorders; Biomarkers; Eye movements; Humans; Machine learning; Medical research; Medicine, Experimental; Nervous system diseases; Parkinson Disease; Polysomnography; REM Sleep Behavior Disorder - diagnosis; Risk factors; Sensors; Synucleinopathies; Technology application; neurodegenerative diseases; home monitoring; digital biomarkers; sleep movement disorders; wearable sensors; nearable sensors; machine learning; Parkinson’s disease; REM sleep behavior disorder
• ISSN: 0161-8105; 1550-9109
• DOI: 10.1093/sleep/zsad030

Abstract Study objectives Isolated rapid eye movement sleep behavior disorder (iRBD) is a parasomnia characterized by dream enactment. It represents a prodromal state of α-synucleinopathies, like Parkinson’s disease. In recent years, biomarkers of increased risk of phenoconversion from iRBD to overt α-synucleinopathies have been identified. Currently, diagnosis and monitoring rely on self-reported reports and polysomnography (PSG) performed in the sleep lab, which is limited in availability and cost-intensive. Wearable technologies and computerized algorithms may provide comfortable and cost-efficient means to not only improve the identification of patients with iRBD but also to monitor risk factors of phenoconversion. In this work, we review studies using these technologies to identify iRBD or monitor phenoconversion biomarkers. Methods A review of articles published until May 31, 2022 using the Medline database was performed. We included only papers in which participants with RBD were part of the study population. The selected papers were divided into four sessions: actigraphy, gait analysis systems, computerized algorithms, and novel technologies. Results In total, 25 articles were included in the review. Actigraphy, wearable accelerometers, pressure mats, smartphones, tablets, and algorithms based on PSG signals were used to identify RBD and monitor the phenoconversion. Rest–activity patterns, core body temperature, gait, and sleep parameters were able to identify the different stages of the disease. Conclusions These tools may complement current diagnostic systems in the future, providing objective ambulatory data obtained comfortably and inexpensively. Consequently, screening for iRBD and follow-up will be more accessible for the concerned patient cohort.