ActiTect: a generalizable machine learning pipeline for REM sleep behavior disorder screening through standardized actigraphy

Topic: Brain proteins, microglia and lab models

Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of α-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. While wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts by capturing abnormal nocturnal movements, they require a reliable and efficient analysis pipeline. This study presents ActiTect, a fully automated, open-source machine learning tool to identify RBD from actigraphy recordings. To ensure generalizability across heterogeneous acquisition settings, our pipeline includes robust preprocessing and automated sleep-wake detection to harmonize multi-device data and extract physiologically interpretable motion features. Model development was conducted on a cohort of 78 individuals, yielding strong discrimination under nested cross-validation (AUROC = 0.95). Generalization was confirmed on a blinded local test set (n = 31, AUROC = 0.86) and two independent external cohorts (n = 113, AUROC = 0.84; n = 57, AUROC = 0.94). To assess robustness, leave-one-dataset-out cross-validation across cohorts demonstrated consistent performance (AUROC range = 0.84-0.89). Complementary stability analysis showed that predictive features remained reproducible across datasets, supporting the pooled multi-center pre-trained model for broader deployment. As an open-source, easy-to-use tool, ActiTect promotes adoption, independent validation, and collaborative improvements, thereby advancing generalizable wearable-based RBD detection.

Key findings

• While wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts by capturing abnormal nocturnal movements, they require a reliable and efficient analysis pipeline.
• To assess robustness, leave-one-dataset-out cross-validation across cohorts demonstrated consistent performance (AUROC range = 0.84-0.89).
• Complementary stability analysis showed that predictive features remained reproducible across datasets, supporting the pooled multi-center pre-trained model for broader deployment.

Why it matters

The ActiTect machine learning pipeline identified REM sleep behavior disorder, a prodromal marker of α-synucleinopathies, from wrist-worn actigraphy recordings with strong discrimination across multiple cohorts. This is a screening tool for a risk marker and does not treat, cure, or prevent Alzheimer's disease.

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Study details

• Participants31
• Evidence strengthModerate

Read the original

• DOI: 10.1038/s41746-026-02738-8
• PubMed: 42286243
• Original source →
• Free full text: npj Digital Medicine →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{bertram2026-42286243,
  author  = {Bertram, David and Ophey, Anja and Röttgen, Sinah and Kufer, Konstantin and Merten, Nele and Fink, Gereon R. and Kalbe, Elke and Hansen, Clint and others},
  title   = {{ActiTect: a generalizable machine learning pipeline for REM sleep behavior disorder screening through standardized actigraphy}},
  journal = {npj Digital Medicine},
  year    = {2026},
  doi     = {10.1038/s41746-026-02738-8},
  pmid    = {42286243},
  url     = {https://doi.org/10.1038/s41746-026-02738-8}
}

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