ArticleNPJ digital medicine2020
Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07555847 (Effect of Kinesiotaping on Hand Tremor in Patients With Parkinson's Disease), which is not on this map. Cited by 72 papers, 2 of them syntheses that pooled it.
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.
Effect of Kinesiotaping on Hand Tremor in Patients With Parkinson's Disease: A Randomized Controlled Trial
Who cites it
72 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advancements in Wearable Sensor Technologies for Health Monitoring in Terms of Clinical Applications, Rehabilitation, and Disease Risk Assessment: Systematic Review.JMIR mHealth and uHealth · 2026Pooled it
- Movement Disorders and Smart Wrist Devices: A Comprehensive Study.Sensors (Basel, Switzerland) · 2025Pooled it
- Flexible and Self-Powered Wearable Sensors for Tremor Monitoring in Parkinson'S Disease: Recent Advances in Materials and Device Architectures.Advanced healthcare materials · 2026Review
- Device-embedded accelerometry complements neural signals for tracking parkinsonian motor states.bioRxiv : the preprint server for biology · 2026Article
- Perioperative quantification of MDS-UPDRS-III tremor measurements in patients with Parkinson's disease using accelerometry.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- A framework of digital biomarkers for neurodegenerative diseases.Nature reviews bioengineering · 2026Article
- Automatic and explainable assessment for Parkinson's disease by video-based human motion understanding.Journal of neuroengineering and rehabilitation · 2026Article
- AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.Journal of medical Internet research · 2026Review
- Navigating the Choice Between Wrist-Worn Research- and Consumer-Grade Wearables to Monitor Movement Behaviours: A Perspective.International journal of public health · 2026Article
- Wavelet-Based Tremor Quantification From Wrist-Worn Sensor Data in Home-Dwelling People With Parkinson's Disease.IEEE journal of translational engineering in health and medicine · 2026Article
- Task-specific machine learning system for automatic assessment of multi-limb bradykinesia in Parkinson's disease.Frontiers in neuroscience · 2026Article
- Added value of a wrist-worn device for assessing tremor in Parkinson's disease: reliability and validity of tremor evaluation at home.NPJ Parkinson's disease · 2025Article
- PARKA AI: A Sensor-Integrated Mobile Application for Parkinson's Disease Monitoring and Self-Management.Bioengineering (Basel, Switzerland) · 2025Article
- A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.Journal of neuroengineering and rehabilitation · 2025Article
- Systematic review of wearables assessing medication effect on motor function and symptoms in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Innovations and ongoing challenges in digital technologies for Parkinson's disease.NPJ Parkinson's disease · 2025Article
- How can patients shape digital medicine? A rapid review of patient and public involvement and engagement in the development of digital health technologies for neurological conditions.Expert review of pharmacoeconomics & outcomes research · 2025Review
- Integrating Wearable Sensor Signal Processing with Unsupervised Learning Methods for Tremor Classification in Parkinson's Disease.Bioengineering (Basel, Switzerland) · 2025Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective assessment of Parkinson's disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.
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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.