Digital outcome measures from smartwatch data relate to non-motor features of Parkinson’s disease

Schalkamp, Ann-Kathrin, Harrison, Neil A., Peall, Kathryn J. ORCID: https://orcid.org/0000-0003-4749-4944 and Sandor, Cynthia 2024. Digital outcome measures from smartwatch data relate to non-motor features of Parkinson’s disease. npj Parkinson's Disease 10 (1) , 110. 10.1038/s41531-024-00719-w

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Abstract

Monitoring of Parkinson’s disease (PD) has seen substantial improvement over recent years as digital sensors enable a passive and continuous collection of information in the home environment. However, the primary focus of this work has been motor symptoms, with little focus on the non-motor aspects of the disease. To address this, we combined longitudinal clinical non-motor assessment data and digital multi-sensor data from the Verily Study Watch for 149 participants from the Parkinson’s Progression Monitoring Initiative (PPMI) cohort with a diagnosis of PD. We show that digitally collected physical activity and sleep measures significantly relate to clinical non-motor assessments of cognitive, autonomic, and daily living impairment. However, the poor predictive performance we observed, highlights the need for better targeted digital outcome measures to enable monitoring of non-motor symptoms.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Professional Services > Advanced Research Computing @ Cardiff (ARCCA) Schools > Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	Nature Research
Date of First Compliant Deposit:	30 May 2024
Date of Acceptance:	8 May 2024
Last Modified:	06 Jun 2025 09:29
URI:	https://orca.cardiff.ac.uk/id/eprint/169280

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