In:
npj Digital Medicine, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2024-09-06)
Abstract:
Parkinson’s disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data’s utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.
Type of Medium:
Online Resource
ISSN:
2398-6352
DOI:
10.1038/s41746-024-01236-z
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2024
detail.hit.zdb_id:
2925182-5