ArticleNPJ Parkinson's disease2019
Monitoring Parkinson's disease symptoms during daily life: a feasibility study.
Article in NPJ Parkinson's disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 5 of them syntheses that pooled it.
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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
38 citing papers in PubMed, 5 syntheses or guidelines pooled it, 87 citations in OpenAlex.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Assessing the usability of wearable devices to measure gait and physical activity in chronic conditions: a systematic review.Journal of neuroengineering and rehabilitation · 2021Pooled it
- Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.PloS one · 2021Pooled it
- Gait Kinematic Parameters in Parkinson's Disease: A Systematic Review.Journal of Parkinson's disease · 2020Pooled it
- Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving.JMIR human factors · 2024Trial
- Home Health Management of Parkinson Disease Deep Brain Stimulation: A Randomized Clinical Trial.JAMA neurology · 2021Trial
- Exploring the mid-term impact of telemonitoring in Parkinson's disease: insights from adoption into clinical practice.Neurodegenerative disease management · 2026Article
- Wearable multimodal sensing for quantifying the cardiovascular autonomic effects of levodopa in parkinsonism.Frontiers in network physiology · 2025Article
- Deep Learning for Parkinson's Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein-Peptide Datasets.Diagnostics (Basel, Switzerland) · 2024Article
- Data-Driven Discovery of Movement-Linked Heterogeneity in Neurodegenerative Diseases.Nature machine intelligence · 2024Article
- Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review.NPJ Parkinson's disease · 2024Article
- Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.Sensors (Basel, Switzerland) · 2024Article
- Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Distinguishing features of Parkinson's disease fallers based on wireless insole plantar pressure monitoring.NPJ Parkinson's disease · 2024Article
- Numerosity estimation of virtual humans as a digital-robotic marker for hallucinations in Parkinson's disease.Nature communications · 2024Article
- Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial.JMIR human factors · 2024Article
- Home-based monitoring of persons with advanced Parkinson's disease using smartwatch-smartphone technology.Scientific reports · 2024Article
- Understanding societal challenges: a NeurotechFrontiers in neuroscience · 2024Article
- A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease symptoms are most often charted using the MDS-UPDRS. Limitations of this approach include the subjective character of the assessments and a discrepant performance in the clinic compared to the home situation. Continuous monitoring using wearable devices is believed to eventually replace this golden standard, but measurements often lack a parallel ground truth or are only tested in lab settings. To overcome these limitations, this study explores the feasibility of a newly developed Parkinson's disease monitoring system, which aims to measure Parkinson's disease symptoms during daily life by combining wearable sensors with an experience sampling method application. Twenty patients with idiopathic Parkinson's disease participated in this study. During a period of two consecutive weeks, participants had to wear three wearable sensors and had to complete questionnaires at seven semi-random moments per day on their mobile phone. Wearable sensors collected objective movement data, and the questionnaires containing questions about amongst others Parkinson's disease symptoms served as parallel ground truth. Results showed that participants wore the wearable sensors during 94% of the instructed timeframe and even beyond. Furthermore, questionnaire completion rates were high (79,1%) and participants evaluated the monitoring system positively. A preliminary analysis showed that sensor data could reliably predict subjectively reported OFF moments. These results show that our Parkinson's disease monitoring system is a feasible method to use in a diverse Parkinson's disease population for at least a period of two weeks. For longer use, the monitoring system may be too intense and wearing comfort needs to be optimized.
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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.