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Diagnosing isolated REM Sleep Behavior Disorder (iRBD) at home is feasible using lumbar wearable sensors. These devices detect distinct nocturnal motor patterns, aiding early identification of neurodegenerative disease risk.

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Area of Science:

  • Neurology
  • Sleep Medicine
  • Biomedical Engineering

Background:

  • Isolated REM Sleep Behavior Disorder (iRBD) predicts synucleinopathies.
  • Current diagnosis relies on resource-intensive polysomnography (vPSG) in sleep labs.
  • Home-based diagnostics are needed due to limited vPSG access and sleep habit variations.

Purpose of the Study:

  • Evaluate feasibility of home-based iRBD identification using lumbar wearable sensors.
  • Assess night-to-night variability in sensor data.
  • Explore potential for a staged screening approach.

Main Methods:

  • Seventy-three participants (15 iRBD, 58 controls) underwent vPSG.
  • Participants wore a lumbar inertial measurement unit for six nights at home.
  • Machine learning models analyzed mobility features for iRBD classification.

Main Results:

  • iRBD participants exhibited distinct nocturnal mobility patterns compared to controls.
  • Machine learning models achieved high sensitivity and moderate specificity for iRBD detection.
  • Diagnostic performance improved with data from up to five nights, then plateaued.
  • Significant differences were observed between lab and home-recorded data.

Conclusions:

  • Lumbar-mounted wearables can sensitively detect iRBD-associated motor patterns at home.
  • Multi-night home monitoring offers a viable alternative to lab-based vPSG.
  • This technology could support staged screening and cohort enrichment for neurodegenerative disease research.