Actigraphy Meets AI: A Digital Biomarker for Parkinson's Disease and Isolated REM Sleep Behaviour Disorder

Isolated rapid eye movement (REM) sleep behaviour disorder (RBD) is an early stage of alpha-synucleinopathies, such as Parkinson's disease (PD). Actigraphy is a promising tool for assessing rest-activity patterns, however its diagnostic or predictive value in these disorders is understudied. This study evaluated AI-based 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-trained transformer model-PAT-on two classification tasks: (1) distinguishing patients with PD from non-neurodegenerative controls and (2) distinguishing patients with PD and RBD (PD-RBD) from those without (PD-noRBD). We tested the best-performing model on patients with isolated RBD. PD-RBD showed significantly altered actigraphy features compared to PD-noRBD and controls. In PD versus control classification, the PAT model performed best (AUC = 0.937, sensitivity = 80.5%, specificity = 92.9%) followed by the neural network (AUC = 0.863) and machine learning (AUC = 0.840). In the PD-RBD versus PD-noRBD classification task, the PAT model again outperformed the other models (AUC = 0.956, sensitivity = 84.4% and specificity = 92.9%). For both tasks, the scores of isolated RBD patients fell between those of the comparison groups. AI-based actigraphy models can distinguish PD patients from controls and identify subtypes with high sensitivity and specificity. This shows their potential as non-invasive, 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.