Safer Screening for REM Sleep Behavior Disorder (RBD)

REM-Sleep behavior disorder (RBD), with pathological and often violent motor and verbal dream enactment, has become a highly attractive research target in neurology, sleep medicine and neurodegeneration due to an increasing body of evidence that up to 80% of those initially diagnosed with idiopathic RBD will develop a synucleinopathy or other neurodegenerative disorder over time. Polysomnography (PSG) is required for a definite and accurate diagnosis of RBD, and demonstrates, in addition to a history/video documentation of potentially violent or disruptive behaviors, the presence of disturbed atonia during REM sleep, with increased tonic or phasic EMG activity in the chin or phasic EMG activity in the extremity muscles. Several questionnaires have been designed to screen for probable RBD, and although initially showing good validation results, there is now an increasing body of evidence that the application of these questionnaires in unselected clinical cohorts or healthy subjects may lead to an unexpected high rate of false positives PSG is costly, time-intensive, requiring specific skills, therefore, easy-access, low threshold instruments are needed to improve diagnostic certainty of suspected RBD which can be used both in epidemiologic studies as well as unselected large clinical cohorts or countries with restricted access to full PSG. This bilateral project aims to improve diagnostic screening for RBD based on questionnaire by adding simple and widely available additional tools such as actigraphy, home video or smartphone apps. The project will last 3 years and include 80 patients (20 RBD, 20 RLS/PLMS, 20 sleep apnea, 20 healthy controls). All participants will undergo a battery of different RBD questionnaires, and bilateral wrist actigraphy will be performed. Participating subjects will be encouraged to perform and bring different kinds of home videos or recordings made with sleep specific smartphone apps as accessible. PSG is performed in all subjects to confirm or reject diagnosis of RBD as diagnostic gold standard. Evaluation of actigraphy and video material will be performed by blinded experts after extensive training for homologization and checks for interrater reliability. The results of this study are expected to contribute valuable new information to the timely and highly pressing question, how an improved diagnosis of probable RBD can be reached with more specificity even in the absence of PSG, or to make a better selection which patients should undergo the more costly and cumbersome full PSG. Due to the very high risk of idiopathic RBD patients to convert into neurodegerative disease, the results of this study will also help to build better future disease-modifying strategy studies, as they rely heavily on the diagnostic accuracy of RBD. 1

In this bilateral project (Austria-Argentina), we aimed to develop better screening methods for isolated REM sleep behaviour disorder (iRBD). IRBD is characterized by intense dreams, violent movements/vocalisations and muscle jerks during REM sleep. The physiological muscle inactivity ("atonia"), which prevails during healthy REM sleep, is absent in RBD. If unrecognized or untreated, iRBD can lead to sleep disruption, and injuries of both the patients and their bedpartners. Moreover, iRBD has been recognized as the most sensitive and specific indicator of high risk risk to develop a full-blown neurodegenerative disease, most often Parkinson disease or Dementia with Lewy Bodies. Video-polysomnography is obligatory to make the diagnosis of RBD, which often prevents affected patients to be diagnosed in time or at all. Therefore, in this project, we aimed to develop low-threshold, cost-effective and easy screening methods to detect iRBD among other sleep disorders. Several questionnaires, and actigraphy, were compared to video-polysomnography. In addition, patients were encouraged to use sleep apps available on their mobile phones. Ninety patients participated in this study. Results: Visual analysis of actigraphy proved useful to identify subjects with iRBD, and to distinguish iRBD from other movements during sleep, due to the typical burst pattern and temporal distribution of movements during the night. This was achieved by sleep experts, who were unaware of the patient's diagnosis (Stefani A. et al, SLEEP 2018). In addition to visual analysis-based pattern recognition, advanced computerized analysis of the rest-activity cycle data allowed to shape another measure to distinguish iRBD from other motor disorders during sleep and from controls: The I