Evidence map›Paper›PMID 41326766›Full record

ArticleNPJ digital medicine2025

Smartphone-based prediction of dopaminergic deficit in prodromal and manifest Parkinson's disease.

Katarina M Gunter, Karolien Groenewald, Timothee Aubourg, Christine Lo, Jessica Welch, Jamil Razzaque, Ludo van Hillegondsberg, Adriana Nastasa, Pietro-Luca Ratti, Beatrice Orso and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

20 authors.

Katarina M GunterNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK. katarina.gunter@ndcn.ox.ac.uk.
Karolien GroenewaldNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Timothee AubourgNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Christine LoDepartment of Clinical Neurology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
Jessica WelchNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Jamil RazzaqueNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Ludo van HillegondsbergNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Adriana NastasaNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Pietro-Luca RattiNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Beatrice OrsoDepartment of Neuroscience, University of Genoa, Genoa, Italy.
Pietro MattioliDepartment of Neuroscience, University of Genoa, Genoa, Italy.
Matteo PardiniDepartment of Neuroscience, University of Genoa, Genoa, Italy.
Stefano RaffaNuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Federico MassaDepartment of Neuroscience, University of Genoa, Genoa, Italy.
Daniel R McGowanDepartment of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Kevin M BradleyDepartment of Nuclear Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Dario ArnaldiDepartment of Neuroscience, University of Genoa, Genoa, Italy.
Johannes C KleinNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Siddharth Arora *Somerville College, University of Oxford, Oxford, UK.
Michele T Hu *Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dopamine transporter (DaT) SPECT can confirm dopaminergic deficiency in Parkinson's disease (PD) but remains costly and inaccessible. We investigated whether brief smartphone-based motor assessments could predict DaT scan results as a scalable alternative. Data from Oxford and Genoa cohorts included individuals with iRBD, PD, and controls. Machine learning models trained on smartphone-derived features classified DaT scan status and predicted striatal binding ratios, compared with MDS-UPDRS-III benchmarks. Among 100 DaT scans, the smartphone-only XGBoost model achieved AUC = 0.80, improving to 0.82 when combined with MDS-UPDRS-III (AUC's gender-corrected). A simpler logistic regression model performed better with MDS-UPDRS-III alone (AUC = 0.83) versus smartphone features, with slightly higher performance when combined (AUC = 0.85). Regression models predicted binding ratios with modest error (RMSE = 0.49, R² = 0.56). Gait, tremor, and dexterity features were most predictive. These findings support smartphone-based assessments complementing clinical evaluations, though larger independent validation remains essential.

Identifiers

PMID41326766
PMCPMC12738540

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Registered trials

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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.