ReviewNPJ digital medicine2022
Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms.
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 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.
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Who cites it
55 citing papers in PubMed, 106 citations in OpenAlex.
- Wearable Electronics for Precision Diagnosis Through Advanced Manufacturing and Integration.Nano-micro letters · 2026Review
- What's in a name? reframing advanced Parkinson's disease as a multidimensional state.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Multimodal Intelligent Monitoring of Parkinson Disease: Scoping Review of Progress and Translational Challenges.Journal of medical Internet research · 2026Article
- Harnessing artificial intelligence: Revolutionizing clinical care for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Digital Technologies for Symptom Monitoring in Parkinson Disease.Current neurology and neuroscience reports · 2026Review
- Smartphone videos are a scalable tool for gait evaluation in Parkinson's disease.NPJ digital medicine · 2026Article
- Evolution of disease modifying therapy clinical trial design and therapeutic endpoints for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Video-based machine learning models for predicting deep brain stimulation outcomes in Parkinson's disease patients.NPJ Parkinson's disease · 2026Article
- Uncertainty-aware personalized estimation of Parkinson's disease severity from longitudinal speech.PloS one · 2026Article
- Non-motor symptoms as critical predictors of quality of life in Parkinson's disease: a machine learning approach.Health and quality of life outcomes · 2025Observational
- An Explainable Ensemble and Deep Learning Framework for Accurate and Interpretable Parkinson's Disease Detection from Voice Biomarkers.Diagnostics (Basel, Switzerland) · 2025Article
- Minimal Clinically Important Difference of Average Daily Steps Measured Through a Consumer Smartwatch in People With Mild-to-Moderate Parkinson Disease: Cross-Sectional Study.JMIR mHealth and uHealth · 2025Article
- Objective monitoring of motor symptom severity and their progression in Parkinson's disease using a digital gait device.Scientific reports · 2025Article
- Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.Journal of neuroengineering and rehabilitation · 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
- [Research progress in motor assessment of neurodegenerative diseases driven by motion capture data].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- OAB-14 Attenuated Glymphatic System Disorder, Neuroinflammation and Dyskinesia in Parkinson's Disease Model Mice Induced by Rotenone.Neurochemical research · 2025Article
- Modernizing the Staging of Parkinson Disease Using Digital Health Technology.Journal of medical Internet research · 2025Article
- Accelerometry-derived features of physical activity, sleep and circadian rhythm relate to non-motor symptoms in individuals with isolated REM sleep behavior disorder.Journal of neurology · 2025Article
- Freezing of gait detection: The effect of sensor type, position, activities, datasets, and machine learning model.Journal of Parkinson's disease · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.
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.