Evidence map›Paper›PMID 42404082›Full record

ArticleBMJ neurology open2026

Identifying and predicting fast versus slow Parkinson's disease motor progressors using clinical and digital data.

Timothee Aubourg, Katarina M Gunter, Christine Lo, Jessica Welch, Karolien Groenewald, Johannes C Klein, Jamil Razzaque, Ludo Van Hillegondsberg, Pietro Luca Ratti, Adriana Nastasa and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ neurology open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Timothee AubourgNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-0151-6397
Katarina M GunterNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Christine LoNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Jessica WelchNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Karolien GroenewaldNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Johannes C KleinNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-8553-2801
Jamil RazzaqueNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Ludo Van HillegondsbergNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Pietro Luca RattiNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Adriana NastasaNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Grace AuldComprehensive Clinical Trials Unit, University College London, London, UK.
Rachel MccomishComprehensive Clinical Trials Unit, University College London, London, UK.
Alexa KingComprehensive Clinical Trials Unit, University College London, London, UK.
Kashfia ChowdhuryComprehensive Clinical Trials Unit, University College London, London, UK.
Nirosen VijiaratnamDepartment of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK.
Christine GirgesDepartment of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK.
Abigail PatrickUniversity Hospitals Plymouth, Plymouth, UK.
Jemma InchesUniversity Hospitals Plymouth, Plymouth, UK.
Camille B CarrollUniversity Hospitals Plymouth, Plymouth, UK.
Thomas FoltynieDepartment of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK.
Siddharth Arora *Said Business School, University of Oxford, Oxford, UK.
Michele T Hu *Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0001-6382-5841

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Digital health technologies offer high-frequency, objective measures for Parkinson's disease (PD) yet individual stratification is lacking. This study aimed to identify and predict fast versus slow PD motor progressors by integrating longitudinal clinical evaluation with smartphone motor testing. Methods: In this 96-week subgroup analysis of participants in the Exenatide-PD3 trial, motor progression was measured using Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III OFF scores and smartphone testing (baseline, weeks 24, 48, 72, 96). Fast versus slow progressors were identified using data-driven subtyping; linear mixed-effects models assessed longitudinal OFF scores. Baseline clinical and digital features (in-clinic and at-home) were used to predict 96-week motor progression. Model performance was quantified by the area under the receiver operating curve (AUC). Results: Data-driven clustering identified 26.5% (26/98) as fast progressors, with higher baseline OFF scores (+18.16, 95% CI 13.70 to 22.62; adjusted p<0.001) and greater 96-week progression (+8.17, 95% CI 2.75 to 13.60; adjusted p=0.019), undetected by prespecified approaches. Baseline prediction models combining smartphone features, alone or with clinical scores, consistently outperformed the clinical model alone (best AUC=0.78 vs 0.53). Translational value of smartphone assessments was strengthened by high user acceptability (>96% support future in-clinic use) and predictive value in a deployment scenario (AUC 0.80, 95% CI 0.79 to 0.82). Conclusion: Data-driven clustering identified one in four early-to-mid stage PD patients as fast motor progressors. Integrating smartphone-derived features with clinical scores improved baseline motor prediction. High user acceptability supports this approach for individual-level PD stratification, addressing heterogeneity in motor progression that may obscure treatment trial effects.

Indexed as

CLINICAL NEUROLOGYMOVEMENT DISORDERSPARKINSON'S DISEASE

Identifiers

PMID42404082
PMCPMC13331057

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.