A Two-Stage Questionnaire and Actigraphy Screening for Isolated REM Sleep Behavior Disorder in a Multicenter Cohort
Massimi, C. A.; Ricciardiello Mejia, G.; Metzger, A.; Ryu, K. H.; Marwaha, S.; Grzegorczyk, E.; Zhou, L.; Jacobs, E.; Gilyadov, B.; Kunney, C.; Ncube, L.; Parekh, A.; Mignot, E.; Elahi, F. M.; Winer, J.; Poston, K.; Brink-Kjaer, A.; During, E.
Show abstract
ObjectiveIsolated rapid-eye-movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video-polysomnography. We assessed a two-stage screening strategy combining a brief questionnaire on rapid-eye-movement sleep behavior disorder symptoms and other prodromes with wrist actigraphy across multiple case-control cohorts. MethodsParticipants aged 40-80 without neurodegenerative disease were recruited from five cohorts; all cases were confirmed by video-polysomnography. The questionnaire was administered to 289 participants, and 236 underwent [≥]14 nights of home wrist actigraphy. The wearable-based algorithm was built on four movement features (mean motor activity, activity index, short or long immobile bouts, twitch activity). Models were trained with nested cross-validation using XGBoost. ResultsThe full retrospective cohort included 396 participants (99 cases, 297 controls; mean age 64 {+/-} 11; 55% male). The dream enactment question alone achieved an area under the curve of 0.85, which improved to 0.86 using the four-item questionnaire. Actigraphy alone achieved 82% sensitivity and 84% specificity. In the subgroup completing both assessments (75 cases, 54 controls), the two-stage protocol--questionnaire followed by actigraphy--yielded 68% sensitivity and 100% specificity using the dream-enactment question alone, and 73% sensitivity and 100% specificity using the four-item questionnaire. InterpretationA two-stage protocol combining questionnaire and actigraphy demonstrated high specificity and good sensitivity for detecting isolated rapid-eye-movement sleep behavior disorder in this multicenter cohort. This low-cost, scalable strategy is compatible with widely used wearable devices and warrants validation in community-based populations.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality 94%
- Unobtrusive inference of diurnal rhythms from smartphone data 92%
- Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge 92%
Similar papers in this journal
- RBDAct: Home screening of REM sleep behaviour disorder based on wrist actigraphy in Parkinson’s patients 96%
- Age-related differences in the association between REM sleep and the polygenic risk for Parkinson's disease 94%
- Propranolol reduces Parkinson’s tremor and inhibits tremor-related activity in the motor cortex: a placebo-controlled crossover trial 90%
Similar papers in this journal
- An Interpretable Machine Learning Tool for In-Home Screening of Agitation Episodes in People Living with Dementia 94%
- Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal EEG investigation 92%
- Characterizing Long COVID in an International Cohort: 7 Months of Symptoms and Their Impact 91%
Similar papers in this journal
- EEG-based Machine Learning Models for the Prediction of Phenoconversion Time and Subtype in iRBD 94%
- The Aging Slow Wave: A Shifting Amalgam of Distinct Slow Wave and Spindle Coupling Subtypes Define Slow Wave Sleep Across the Human Lifespan 94%
- Evaluation of Dreem headband for sleep staging and EEG spectral analysis in people living with Alzheimer’s and older adults 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.