ArticleNPJ digital medicine2024
Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Altered Neuromuscular Time-Frequency Organization During Sit-to-Walk in Parkinson's Disease: An Explainable Artificial Intelligence Approach Using Surface Electromyography.Sensors (Basel, Switzerland) · 2026Article
- Dual-Model Artificial Intelligence Framework Integrating AI-Based Markerless Motion Capture for Dynamic Gait Prediction and Health-Status Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Quantitative Analysis of Timed Up and Go Metrics Across Parkinson's Disease Severity and Their Clinical Correlations.Diagnostics (Basel, Switzerland) · 2026Article
- Advances in biomarkers for Parkinson's disease: from molecular pathology to precision diagnostics.Journal of Zhejiang University. Science. B · 2026Review
- Enhancing Parkinson's Disease Staging: An Integrative Deep Learning Framework for Multimodal Feature Selection.Journal of molecular neuroscience : MN · 2026Article
- Effects of anti-parkinsonian medication on gait during internal and external rhythmic auditory cueing in PD.Frontiers in human neuroscience · 2026Article
- Metabolomics biomarkers for precision psychiatry.Frontiers in psychiatry · 2026Review
- Clinical and neuroimaging correlates of disease related gait patterns in patients with multiple system atrophy cerebellar type.Scientific reports · 2025Article
- Brain age gap as predictor of disease progression in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition strategies.Genes & nutrition · 2025Review
- Objective monitoring of motor symptom severity and their progression in Parkinson's disease using a digital gait device.Scientific reports · 2025Article
- Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review.NPJ digital medicine · 2025Article
- Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait.Journal of medical Internet research · 2025Article
- 80N as the Optimal Assistive Threshold for Wearable Exoskeleton-Mediated Gait Rehabilitation in Parkinson's Disease: A Prospective Biomarker Validation Study.Healthcare (Basel, Switzerland) · 2025Article
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- Machine learning-guided analysis of metabolomic alterations in Parkinson's disease with comorbid symptoms.Frontiers in aging neuroscience · 2025Article
- A comprehensive review on computational metabolomics: Advancing multiscale analysis throughComputational and structural biotechnology journal · 2025Review
- Reduced composite dietary antioxidant index increases the risk of Parkinson's disease and all-cause mortality in Parkinson's disease patients: evidence from the NHANES database.Frontiers in aging neuroscience · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.