@article{6875bc2313c44ec1a6e19f2e341b6ee4,
title = "Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease",
abstract = "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.",
keywords = "Parkinson's disease, digital mobility measures, disease progression, wearable sensors",
author = "Anat Mirelman and Jana Volkov and Amit Salomon and Eran Gazit and Alice Nieuwboer and Lynn Rochester and {Del Din}, Silvia and Laura Avanzino and Elisa Pelosin and Bloem, {Bastiaan R.} and {Della Croce}, Ugo and Andrea Cereatti and Avner Thaler and Daniel Roggen and Claudia Mazza and Julia Shirvan and Cedarbaum, {Jesse M.} and Nir Giladi and Hausdorff, {Jeffrey M.}",
note = "Publisher Copyright: {\textcopyright} 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.",
year = "2024",
month = feb,
doi = "10.1002/mds.29689",
language = "אנגלית",
volume = "39",
pages = "328--338",
journal = "Movement Disorders",
issn = "0885-3185",
publisher = "John Wiley & Sons Inc.",
number = "2",
}