Association between wrist-worn actigraphy and the MDS-UPDRS Parkinson's disease rating scale through machine learning: an exploratory study.

INTRODUCTION

Parkinson's disease (PD) is typically assessed during short clinical visits using rating scales such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). These assessments provide only a snapshot of symptom severity and may not capture fluctuations in daily life. In this study, we examined whether wrist-worn actigraphy can be used to estimate MDS-UPDRS scores in people with Parkinson's disease (PwP).

METHODS

Continuous accelerometer recordings at 25 Hz were collected over up to 28 days using GeneActiv devices. From these recordings, three feature representations were derived: non-embedding actigraphy features, self-supervised accelerometer embeddings, and a combined feature set. A small set of regression models was evaluated using strict leave-one-participant-out cross-validation (LOPO-CV).

RESULTS

Estimation performance varied across targets and feature sets. The strongest result was observed for MDS-UPDRS Part IV, where non-embedding features with Elastic Net achieved a mean absolute error (MAE) of 1.6 and a correlation of 0.83 between estimated and actual values. The combined feature set performed best for Part I (MAE = 3.0, r = 0.60), Part III (MAE = 8.2, r = 0.47), and the total MDS-UPDRS score (MAE = 13.3, r = 0.49), whereas non-embedding features performed best for Part II (MAE = 2.7, r = 0.61). Embedding-only models were competitive for some outcomes, but were not the best overall.

DISCUSSION: Overall, the results show that month-long wrist-worn actigraphy contains information related to PD severity in daily life, although estimation accuracy remains limited and depends on the MDS-UPDRS target. Wearable-derived measures may therefore provide complementary information to clinical assessments, particularly for motor complications.