ArticleScientific reports2016
Computer keyboard interaction as an indicator of early Parkinson's disease.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 2 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
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment.Journal of Parkinson's disease · 2020Pooled it
- Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases: Machine-Learning Pilot Feasibility Study.JMIR formative research · 2026Article
- Diagnosing Parkinson's disease via behavioral biometrics of keystroke dynamics.Science advances · 2025Article
- Screening for Parkinson's disease using "computer vision".PloS one · 2025Article
- Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Exploring Asymmetric Fine Motor Impairment Trends in Early Parkinson's Disease via Keystroke Typing.Movement disorders clinical practice · 2023Article
- Keystroke Biometrics as a Tool for the Early Diagnosis and Clinical Assessment of Parkinson's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- A scoping review of neurodegenerative manifestations in explainable digital phenotyping.NPJ Parkinson's disease · 2023Article
- Keystroke-Dynamics for Parkinson's Disease Signs Detection in an At-Home Uncontrolled Population: A New Benchmark and Method.IEEE transactions on bio-medical engineering · 2023Article
- Detection of Mental Fatigue in the General Population: Feasibility Study of Keystroke Dynamics as a Real-world Biomarker.JMIR biomedical engineering · 2022Article
- Morphological Engineering of Sensing Materials for Flexible Pressure Sensors and Artificial Intelligence Applications.Nano-micro letters · 2022Review
- Remote Assessments of Hand Function in Neurological Disorders: Systematic Review.JMIR rehabilitation and assistive technologies · 2022Review
- A novel framework to estimate cognitive impairment via finger interaction with digital devices.Brain communications · 2022Article
- Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease.Frontiers in neurology · 2022Review
- Quantitative Digitography Measures Motor Symptoms and Disease Progression in Parkinson's Disease.Journal of Parkinson's disease · 2022Article
- Individual differences in information-seeking.Nature communications · 2021Observational
- Sharing Practices for Datasets Related to Accessibility and Aging.ASSETS. Annual ACM Conference on Assistive Technologies · 2021Article
- Touchscreen-based finger tapping: Repeatability and configuration effects on tapping performance.PloS one · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).
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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.