ArticleScientific reports2022
Parkinson's disease severity clustering based on tapping activity on mobile device.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Smartphone-Based Hand Function Assessment: Systematic Review.Journal of medical Internet research · 2024Pooled 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
- Smartphone-Based Multimodal Digital Biomarker Integration for Parkinson's Disease Screening and Diagnostic Support.Neurology and therapy · 2026Article
- Modelling and Correcting the Effects of Tremor on Touchscreen Interaction.Sensors (Basel, Switzerland) · 2026Article
- Development and clinical translation of smartphone-based digital biomarkers for Parkinson's disease: a structured review.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- In-clinic validation of a smartphone-based finger tapping test for use in neurodegenerative and neurological populations.medRxiv : the preprint server for health sciences · 2026Article
- Diagnostic potential of a panel of acoustic/aerodynamic voice markers in patients with dysphonia: a cluster analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Longitudinal cohort study on the dynamic changes of atherogenic index of plasma and the risk of cardiometabolic multimorbidity in middle - aged and elderly people.BMC cardiovascular disorders · 2026Article
- Parkinson's disease severity clustering based on gait activity from mobile device.Scientific reports · 2025Article
- Handwriting, touchscreen dexterity and bradykinesia measures in Parkinson's disease: a feature selection study.Journal of neurology · 2025Article
- Modernizing the Staging of Parkinson Disease Using Digital Health Technology.Journal of medical Internet research · 2025Article
- Association between atherogenic index of plasma and future risk of cardiovascular disease in individuals with cardiovascular-kidney-metabolic syndrome stages 0-3: a nationwide prospective cohort study.Cardiovascular diabetology · 2025Article
- Integrating Wearable Sensor Signal Processing with Unsupervised Learning Methods for Tremor Classification in Parkinson's Disease.Bioengineering (Basel, Switzerland) · 2025Article
- Motor symptoms of Parkinson's disease: critical markers for early AI-assisted diagnosis.Frontiers in aging neuroscience · 2025Review
- Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease.Journal of big data · 2024Article
- Identifying Subtle Motor Deficits Before Parkinson's Disease is Diagnosed: What to Look for?Journal of Parkinson's disease · 2024Review
- Development and implementation of the frog-in-maze game to study upper limb movement in people with Parkinson's disease.Scientific reports · 2023Article
- A smartphone-based tapping task as a marker of medication response in Parkinson's disease: a proof of concept study.Journal of neural transmission (Vienna, Austria : 1996) · 2023Article
- A scoping review of neurodegenerative manifestations in explainable digital phenotyping.NPJ Parkinson's disease · 2023Article
- WiFi-Based Detection of Human Subtle Motion for Health Applications.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we investigated the relationship between finger tapping tasks on the smartphone and the MDS-UPDRS I-II and PDQ-8 using the mPower dataset. mPower is a mobile application-based study for monitoring key indicators of PD progression and diagnosis. Currently, it is one of the largest, open access, mobile Parkinson's Disease studies. Data from seven modules with a total of 8,320 participants who provided the data of at least one task were released to the public researcher. The modules comprise demographics, MDS-UPDRS I-II, PDQ-8, memory, tapping, voice, and walking. Finger-tapping is one of the tasks that easy to perform and has been analyzed for the quantitative measurement of PD. Therefore, participants who performed both the tapping activity and MDS-UPDRS I-II rating scale were selected for our analysis. Note that the MDS-UPDRS mPower Survey only contains parts of the original scale and has not been clinimetrically tested for validity and reliability. We obtained a total of 1851 samples that contained the tapping activity and MDS-UPDRS I-II for the analysis. Nine features were selected to represent tapping activity. K-mean was applied as an unsupervised clustering algorithm in our study. For determining the number of clusters, the elbow method, Sihouette score, and Davies-Bouldin index, were employed as supporting evaluation metrics. Based on these metrics and expert opinion, we decide that three clusters were appropriate for our study. The statistical analysis found that the tapping features could separate participants into three severity groups. Each group has different characteristics and could represent different PD severity based on the MDS-UPDRS I-II and PDQ-8 scores. Currently, the severity assessment of a movement disorder is based on clinical observation. Therefore, it is highly dependant on the skills and experiences of the trained movement disorder specialist who performs the procedure. We believe that any additional methods that could potentially assist with quantitative assessment of disease severity, without the need for a clinical visit would be beneficial to both the healthcare professionals and patients.
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