ArticleAnnals of clinical and translational neurology2019
Predicting motor, cognitive & functional impairment in Parkinson's.
Article in Annals of clinical and translational neurology, 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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 5 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Digital Outcomes as Biomarkers of Disease Progression in Early Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2025Pooled it
- Early detection of diseases causing dementia using digital navigation and gait measures: A systematic review of evidence.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Pooled it
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review.Journal of medical Internet research · 2022Pooled it
- Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review.Journal of medical systems · 2021Pooled it
- Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson's disease.Scientific reports · 2022Trial
- Towards the Prevention of Freezing of Gait in Parkinson's Disease.The European journal of neuroscience · 2026Review
- A Neurosurgeon's Guide to Mobile Health Application Development With a Case Study for Cervical Myelopathy.Neurosurgery · 2026Article
- Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- Identifying and predicting fast versus slow Parkinson's disease motor progressors using clinical and digital data.BMJ neurology open · 2026Article
- The North American Prodromal Synucleinopathy study: protocol for a multi-site, longitudinal, observational study of idiopathic/isolated rapid eye movement sleep behavior disorder.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Smartphone-based prediction of dopaminergic deficit in prodromal and manifest Parkinson's disease.NPJ digital medicine · 2025Article
- Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- Systematic review of prognostic models in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Improving Prediction of Falls and Cognitive Impairment in Parkinson Disease: Protocol for a Decentralized Observational Study.JMIR research protocols · 2025Article
- Stakeholder Perspectives on Trustworthy AI for Parkinson Disease Management Using a Cocreation Approach: Qualitative Exploratory Study.Journal of medical Internet research · 2025Article
- Exploratory digital outcome measures of motor sign progression in Parkinson's disease patients treated with prasinezumab.NPJ digital medicine · 2025Article
- Systematic review of wearables assessing medication effect on motor function and symptoms in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- A Call for Change: Updating the Operational Definition for Dementia in Parkinson's Disease.Movement disorders clinical practice · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveWe recently demonstrated that 998 features derived from a simple 7-minute smartphone test could distinguish between controls, people with Parkinson's and people with idiopathic Rapid Eye Movement sleep behavior disorder, with mean sensitivity/specificity values of 84.6-91.9%. Here, we investigate whether the same smartphone features can be used to predict future clinically relevant outcomes in early Parkinson's.
methodsA total of 237 participants with Parkinson's (mean (SD) disease duration 3.5 (2.2) years) in the Oxford Discovery cohort performed smartphone tests in clinic and at home. Each test assessed voice, balance, gait, reaction time, dexterity, rest, and postural tremor. In addition, standard motor, cognitive and functional assessments and questionnaires were administered in clinic. Machine learning algorithms were trained to predict the onset of clinical outcomes provided at the next 18-month follow-up visit using baseline smartphone recordings alone. The accuracy of model predictions was assessed using 10-fold and subject-wise cross validation schemes.
resultsBaseline smartphone tests predicted the new onset of falls, freezing, postural instability, cognitive impairment, and functional impairment at 18 months. For all outcome predictions AUC values were greater than 0.90 for 10-fold cross validation using all smartphone features. Using only the 30 most salient features, AUC values greater than 0.75 were obtained.
interpretationWe demonstrate the ability to predict key future clinical outcomes using a simple smartphone test. This work has the potential to introduce individualized predictions to routine care, helping to target interventions to those most likely to benefit, with the aim of improving their outcome.
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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.