ArticleJournal of Parkinson's disease2023
Exploring the Complex Phenotypes of Impaired Finger Dexterity in Mild-to-moderate Stage Parkinson's Disease: A Time-Series Analysis.
Article in Journal of Parkinson's disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Bridging the global Parkinson's divide: Technology as a structural solution for equitable and brain health-integrated care.Journal of Parkinson's disease · 2026Review
- Analyzing the 'Bradykinesia Complex' in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Unlocking the daily impact of parkinson's challenges through a comprehensive assessment using the ChulaPD ADL questionnaire.Scientific reports · 2025Article
- Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.Journal of neuroengineering and rehabilitation · 2025Article
- Parkinson's disease functional movement battery a comprehensive test set to evaluate of motor abilities in persons with Parkinson's disease.Scientific reports · 2025Article
- A Usability Pilot Study of a Sensor-Guided Interactive System for Dexterity Training in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- The Relationship Between Reduced Hand Dexterity and Brain Structure Abnormality in Older Adults.Geriatrics (Basel, Switzerland) · 2024Article
- User-centred design, validation and clinical testing of an anti-choking mug for people with Parkinson's disease.Scientific reports · 2024Article
- Old problems, new solutions: harnessing technology and innovation in Parkinson's disease-evidence and experiences from Thailand.Journal of neural transmission (Vienna, Austria : 1996) · 2024Review
- Article
- Identifying Subtle Motor Deficits Before Parkinson's Disease is Diagnosed: What to Look for?Journal of Parkinson's disease · 2024Review
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundImpaired dexterity is an early motor symptom in Parkinson's disease (PD) that significantly impacts the daily activity of patients; however, what constitutes complex dexterous movements remains controversial.
objectiveTo explore the characteristics of finger dexterity in mild-to-moderate stage PD.
methodsWe quantitatively assessed finger dexterity in 48 mild-to-moderate stage PD patients and 49 age-matched controls using a simple alternating two-finger typing test for 15 seconds. Time-series analyses of various kinematic parameters with machine learning were compared between sides and groups.
resultsBoth the more and less affected hands of patients with PD had significantly lower typing frequency and slower typing velocity than the non-dominant and the dominant hands of controls (p = 0.019, p = 0.016, p < 0.001, p < 0.001). The slope of the typing velocity decreased with time, indicating a sequence effect in the PD group. A typing duration of 6 seconds was determined sufficient to discriminate PD patients from controls. Typing error, repetition, and repetition rate were significantly higher in the more affected hands of patients with PD than in the non-dominant hand of controls (p < 0.001, p = 0.03, p < 0.001). The error rate was constant, whereas the repetition rate was steep during the initiation of typing. A predictive model of the more affected hand demonstrated an accuracy of 70% in differentiating PD patients from controls.
conclusionOur study demonstrated complex components of impaired finger dexterity in mild-to-moderate stage PD, namely bradykinesia with sequence effects, error, and repetition at the initiation of movement, suggesting that multiple neural networks may be involved in dexterity deficits in PD.
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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.