{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T19:00:39Z","timestamp":1790622039560,"version":"4.1.0"},"reference-count":42,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sleep Research"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Isolated rapid eye movement (REM) sleep behaviour disorder (RBD) is an early stage of alpha\u2010synucleinopathies, such as Parkinson's disease (PD). Actigraphy is a promising tool for assessing rest\u2010activity patterns, however its diagnostic or predictive value in these disorders is understudied. This study evaluated AI\u2010based models applied to actigraphy data to improve the detection of PD and RBD, additionally exploring their capability to capture early neurodegenerative patterns in isolated RBD. We trained and evaluated machine learning models with extracted actigraphy features, a convolutional neural network and a pre\u2010trained transformer model\u2014PAT\u2014on two classification tasks: (1) distinguishing patients with PD from non\u2010neurodegenerative controls and (2) distinguishing patients with PD and RBD (PD\u2010RBD) from those without (PD\u2010noRBD). We tested the best\u2010performing model on patients with isolated RBD. PD\u2010RBD showed significantly altered actigraphy features compared to PD\u2010noRBD and controls. In PD versus control classification, the PAT model performed best (AUC\u2009=\u20090.937, sensitivity\u2009=\u200980.5%, specificity\u2009=\u200992.9%) followed by the neural network (AUC\u2009=\u20090.863) and machine learning (AUC\u2009=\u20090.840). In the PD\u2010RBD versus PD\u2010noRBD classification task, the PAT model again outperformed the other models (AUC\u2009=\u20090.956, sensitivity\u2009=\u200984.4% and specificity\u2009=\u200992.9%). For both tasks, the scores of isolated RBD patients fell between those of the comparison groups. AI\u2010based actigraphy models can distinguish PD patients from controls and identify subtypes with high sensitivity and specificity. This shows their potential as non\u2010invasive, scalable diagnostic tools and their relevance for identifying prodromal motor features, with future applications in predicting isolated RBD progression to PD. Validation in longitudinal cohorts remains necessary.<\/jats:p>","DOI":"10.1111\/jsr.70396","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T14:09:41Z","timestamp":1783433381000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Actigraphy Meets\n                    <scp>AI<\/scp>\n                    : A Digital Biomarker for Parkinson's Disease and Isolated\n                    <scp>REM<\/scp>\n                    Sleep Behaviour Disorder"],"prefix":"10.1111","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9536-6691","authenticated-orcid":false,"given":"Leonor","family":"Lopes","sequence":"first","affiliation":[{"name":"Department of Nuclear Medicine, Inselspital, Bern University Hospital University of Bern  Bern Switzerland"},{"name":"Graduate School for Cellular and Biomedical Sciences University of Bern  Bern Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6852-4191","authenticated-orcid":false,"given":"Jan D.","family":"Warncke","sequence":"additional","affiliation":[{"name":"Schlaf\u2010Wach\u2010Epilepsie Zentrum, Department of Neurology, Inselspital, Bern University Hospital University of Bern  Bern Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9486-1017","authenticated-orcid":false,"given":"Irina","family":"Filchenko","sequence":"additional","affiliation":[{"name":"Schlaf\u2010Wach\u2010Epilepsie Zentrum, Department of Neurology, Inselspital, Bern University Hospital University of Bern  Bern Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8714-3084","authenticated-orcid":false,"given":"Kuangyu","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Inselspital, Bern University Hospital University of Bern  Bern Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4535-0245","authenticated-orcid":false,"given":"Claudio L. 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