ArticleNPJ digital medicine2025
Actigraphy-based detection of isolated REM sleep behavior disorder: multicenter validation across devices and populations.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- A gradual and sustainable approach to diagnosing sleep and circadian disturbances in dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- ActiTect: a generalizable machine learning pipeline for REM sleep behavior disorder screening through standardized actigraphy.NPJ digital medicine · 2026Article
- A Two-Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort.Annals of clinical and translational neurology · 2026Article
- Comparison of EMG, Video, and Actigraphy Signals for Detecting Motor Activity in REM Sleep Behavior Disorder.Diagnostics (Basel, Switzerland) · 2026Article
- Digital Technology for Sleep Symptoms in Parkinson's Disease: A Scoping Review.Journal of movement disorders · 2026Article
- Home-Based REM Sleep Without Atonia in Patients with Parkinson's Disease: A Post Hoc Analysis of the ZEAL Study.NeuroSci · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
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 participants from 4 centers (Mount Sinai, Oxford, Hong Kong, and Innsbruck). Two conversion pipelines were created to map activity counts from Actiwatch and MicroMini-Motionlogger to AX6 from 14 volunteers co-wearing two devices. In addition to the actigraphy analysis, four synucleinopathy prodromes-RBD symptoms, hyposmia, constipation, orthostatic hypotension-were tested in a two-stage screening approach. The sleep model achieved AUCs of 0.838-0.865 across centers, and the RAR model 0.520-0.818. Screening based on prodromes followed by actigraphy achieved sensitivities, specificities, and positive predictive values (PPVs) of 59.4-78.3%, 84.1-98.2%, and 56.0-98.6% (RBD symptoms), 46.5%, 99.0%, and 98.9% (hyposmia), 25.8-43.3%, 95.5-98.8%, 96.3-98.0% (constipation), and 11.6-36.8%, 96.0-100%, and 96.2-100% (orthostatic hypotension), respectively. Resolution (high versus low) did not affect the performance. After adjusting for a real-world iRBD prevalence of 1.5%, the corresponding PPVs would range from 6.3% to 100.0% depending on the prodromes. This multicenter study shows that the original actigraphy-based detection model of iRBD using sleep features but not RAR features generalizes well across independent cohorts and devices. Combined with key prodromes of synucleinopathies, it could enable precise, scalable population-level screening.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.