Actigraphy-based detection of isolated REM sleep behavior disorder: multicenter validation across devices and populations.

ABSTRACT: Scalable home-based detection of isolated REM sleep behavior disorder (iRBD) is essential for early care, prevention trials, and identifying candidates for neuroprotective interventions against synucleinopathies. We previously showed that high-resolution wrist actigraphy (Axivity AX6) could identify iRBD based on abnormal sleep (AUC of 0.916) and rest-activity-rhythms (RAR, AUC of 0.856) using machine learning. Here, we aimed to assess generalizability across: (1) other actigraphs using lower resolutions; (2) different populations. We tested the analysis pipeline directly in cohorts from the International RBD Study Group using: Axivity AX6 (50-100 Hz), Philips Actiwatch (60-second epoch), and MicroMini-Motionlogger (30-second epoch). The cohorts included a total of 352 iRBD and 258 non-RBD

SUBMITTER: Zhou L 

PROVIDER: S-EPMC12572274 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature