Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease

Anat Mirelman*, Jana Volkov, Amit Salomon, Eran Gazit, Alice Nieuwboer, Lynn Rochester, Silvia Del Din, Laura Avanzino, Elisa Pelosin, Bastiaan R. Bloem, Ugo Della Croce, Andrea Cereatti, Avner Thaler, Daniel Roggen, Claudia Mazza, Julia Shirvan, Jesse M. Cedarbaum, Nir Giladi, Jeffrey M. Hausdorff

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Background: Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression. Objectives: The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale. Methods: Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I–III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time. Results: Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19–0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04–0.12]). Conclusions: Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care.

Original languageEnglish
Pages (from-to)328-338
Number of pages11
JournalMovement Disorders
Issue number2
StatePublished - Feb 2024


FundersFunder number
Aufzien Academic Center in Tel Aviv University
Batsheva Cohen
FRESCO foundation
IMI2 JU853981
IRCCS Policlinico San Martino5X1000
Liat Yahimovich
NHS Foundation Trust
Northumberland and Tyne and Wear
Sieratzki Family Foundation
Wellcome Trust Clinical Research Facility
National Institutes of Health
U.S. Department of DefenseW81XWH2010468
Michael J. Fox Foundation for Parkinson's Research
National Parkinson Foundation
Canine Research Foundation
Seventh Framework ProgrammeV‐TIME‐278169
EU Joint Programme – Neurodegenerative Disease Research
European Federation of Pharmaceutical Industries and Associations
UK Research and Innovation
Verily Life Sciences
Aligning Science Across Parkinson's
Engineering and Physical Sciences Research Council
National Institute for Health and Care ResearchEP/W031590/1
Newcastle University
European Commission
Ministero della Salute
Ministero dell’Istruzione, dell’Università e della RicercaPRIN 2022
Newcastle upon Tyne Hospitals NHS Foundation Trust
Israel Science Foundation
Innovative Medicines Initiative820820
NIHR Newcastle Biomedical Research Centre
NIHR Imperial Biomedical Research Centre


    • Parkinson's disease
    • digital mobility measures
    • disease progression
    • wearable sensors


    Dive into the research topics of 'Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease'. Together they form a unique fingerprint.

    Cite this