ReviewFrontiers in neurology2024
Commercial symptom monitoring devices in Parkinson's disease: benefits, limitations, and trends.
Review in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Wearable device for tremor-like motion acquisition: a low-cost device using 3D printing for research and education.HardwareX · 2026Article
- What's in a name? reframing advanced Parkinson's disease as a multidimensional state.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- The future digital outpatient clinic for movement disorders: from technological innovation to ethical and systemic implementation-a position paper.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- AI-Enabled Flexible Sensing Ecosystems for Parkinson's Disease: Advancing Digital Biomarkers and Closed-Loop Interventions.Sensors (Basel, Switzerland) · 2026Review
- A computational pharmacokinetic-pharmacodynamic framework for simulating and comparing individualized levodopa dosing in Parkinson's disease.Frontiers in pharmacology · 2026Article
- A Preliminary Mechanics-Informed Machine Learning Framework for Objective Assessment of Parkinson's Disease and Rehabilitation Outcomes.Diagnostics (Basel, Switzerland) · 2025Article
- L-Dopa Comparably Improves Gait and Limb Movements in Parkinson's Disease: A Wearable Sensor Analysis.Biomedicines · 2025Article
- PARKA AI: A Sensor-Integrated Mobile Application for Parkinson's Disease Monitoring and Self-Management.Bioengineering (Basel, Switzerland) · 2025Article
- Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson's Disease Diagnosis and Monitoring.Biomedicines · 2025Review
- A Low-Cost, Open-Source Wireless Platform for Real-Time Tremor Monitoring in Parkinson's Disease.International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that significantly impacts patients' quality of life. Managing PD requires accurate assessment of motor and non-motor symptoms, often complicated by the subjectivity in symptom reporting and the limited availability of neurologists. To address these challenges, commercial wearable devices have emerged to continuously monitor PD symptoms outside the clinical setting. The main devices include PKG™, Kinesia 360™, Kinesia U™, PDMonitor™, and STAT-ON™. These devices utilize advanced technologies such as accelerometers, gyroscopes, and specific algorithms to provide objective data on motor symptoms like tremors, dyskinesia, and bradykinesia. Despite their potential, the adoption of these devices is limited due to concerns about their accuracy, complexity of use, and lack of independent validation. The correlation between these devices' measurements and traditional clinical observations varies, and patient usability and adherence remain critical areas for improvement. To optimize their utility and improve patient outcomes, it is essential to conduct validation and usability studies with a sufficient number of patients, develop standardized protocols, and ensure integration with hospital information systems.
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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.