Ambulatory Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Combining Actigraphy and Questionnaire

Andreas Brink-Kjaer, Niraj Gupta, Eric Marin, Jennifer Zitser, Oliver Sum-Ping, Anahid Hekmat, Flavia Bueno, Ana Cahuas, James Langston, Poul Jennum, Helge B D Sorensen, Emmanuel Mignot, Emmanuel During*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Background: Isolated rapid-eye-movement sleep behavior disorder (iRBD) is in most cases a prodrome of neurodegenerative synucleinopathies, affecting 1% to 2% of middle-aged and older adults; however, accurate ambulatory diagnostic methods are not available. Questionnaires lack specificity in nonclinical populations. Wrist actigraphy can detect characteristic features in individuals with RBD; however, high-frequency actigraphy has been rarely used.

Objective: The aim was to 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.

Methods: The method involved analysis of home actigraphy data (for seven nights and more) and a nine-item questionnaire (RBD Innsbruck inventory and three synucleinopathy prodromes of subjective hyposmia, constipation, and orthostatic dizziness) in a data set comprising 42 patients with iRBD, 21 sleep clinic patients with other sleep disorders, and 21 community controls.

Results: The actigraphy classifier achieved 95.2% (95% confidence interval [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 the performance of the Innsbruck RBD Inventory and prodromal questionnaire alone. Concordant predictions between actigraphy and questionnaire reached a 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 and a single question on RBD or prodromal symptoms.

Conclusions: Actigraphy 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.

Original languageEnglish
JournalMovement Disorders
Number of pages11
ISSN0885-3185
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • Actigraphy
  • Rapid-eye-movement sleep behavior disorder
  • Parkinson’s disease
  • Machine learning

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