BackgroundIsolated rapid-eye-movement sleep behavior disorder (iRBD) is in most cases a prodrome of neurodegenerative synucleinopathies, affecting 1-2% of middle-aged and older adults, however accurate ambulatory diagnostic methods are lacking. Questionnaires lack specificity in non-clinic populations. Wrist actigraphy can detect characteristic features in individuals with RBD, however high frequency actigraphy has rarely been used.ObjectivesTo develop a machine learning classifier using high frequency (1-second resolution) actigraphy and a short patient survey for detecting iRBD with high accuracy and precision.MethodsAnalysis of ≥7 nights home actigraphy data and 9-item questionnaire (RBD Innsbruck inventory and 3 synucleinopathy prodromes of subjective hyposmia, constipation and orthostatic dizziness) in a dataset including 42 patients with iRBD, 21 sleep clinic patients with other sleep disorders, and 21 community controls.ResultsThe actigraphy classifier achieved 95.2% (95% CI: 88.3 - 98.7) sensitivity and 90.9% (95% CI: 82.1 - 95.8) precision. The questionnaire classifier achieved 90.6% accuracy and 92.7% precision, exceeding performance of RBD-I and prodromal questionnaire alone. Concordant predictions between actigraphy and questionnaire reached specificity and precision of 100% (95% CI: 95.7 - 100.0) with 88.1% sensitivity (95% CI: 79.2 - 94.1) and outperformed any combination of actigraphy plus single question on RBD or prodromal symptoms.ConclusionsActigraphy detected iRBD with high accuracy in a mixed clinical and community cohort. This cost-effective fully remote procedure can be used to diagnose iRBD in specialty outpatient settings and has potential for large scale screening of iRBD in the general population.
