Empatica E4 wristband assessment of probable REM sleep behavior disorder in people with Parkinson's disease. Results from the DIGI.PARK study.

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BackgroundParkinson's disease is frequently accompanied by Rapid Eye Movement (REM) sleep behavior disorder (RBD), causing individuals to physically act out their dreams. The REM Sleep Behavior Disorder Questionnaire (RBDSQ) is a 13-items self-report tool to identify individuals with probable REM Sleep Behavior Disorder (pRBD). While self-report of symptoms is limited by inaccuracies in recall and subjective interpretation, some of the RBDSQ items concerns nocturnal motor behavior that could be suitable for digital assessment. Therefore, we examined the potential of the Empatica E4 wristband to objectively support RBD assessment alongside the self-reported RBDSQ.MethodsTo capture nocturnal motor behavior (e.g., number, total sleep time, magnitude) and heart rate variability, data from 149 nights were recorded continuously from 14 people with Parkinson's disease. Data were analyzed by visual inspection, movement classification, and the Cole-Kripke algorithm. Participants also completed the RBDSQ. Cronbach's alpha was used to determine how consistently the clinical and digital data points were measuring the same underlying construct of nocturnal motor behavior and RBDSQ defined pRBD.ResultsWe identified four RBDSQ items that assessed nocturnal motor behavior and there were discrepancies between self-reported RBDSQ items and sensor data for these items. Accelerometry data showed higher frequency of nocturnal motor activity in individuals with RBDSQ defined pRBD in crude models, which was not fully captured in the RBDSQ scores. We explored the potential of integrating sensor data into selected RBDSQ items, and evaluation with Cronbach's alfa indicated high internal consistency (α = 0.87).ConclusionSupplementing self-reported questionnaires with wearable sensor data could provide a more objective and reliable method for assessing RBDSQ defined pRBD in people with Parkinson's disease. This approach could improve symptom assessment accuracy by reducing the subjective biases inherent in self-reported data and by capturing symptoms that are fluctuating, underreported or unrecognized.

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