Evidence map›Paper›PMID 40640229›Full record

ArticleNPJ Parkinson's disease2025

A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson's disease.

Nienke A Timmermans, Roberta Terranova, Diogo C Soriano, Hayriye Cagnan, Yordan P Raykov, Ioan Gabriel Bucur, Bastiaan R Bloem, Rick C Helmich, Luc J W Evers

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
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

9 authors.

Nienke A Timmermans *Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands. Nienke.timmermans@radboudumc.nl.
Roberta Terranova *Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
Diogo C SorianoMRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Hayriye CagnanMRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Yordan P RaykovSchool of Mathematical Sciences, University of Nottingham, Nottingham, UK.
Ioan Gabriel BucurInstitute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
Bastiaan R BloemDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
Rick C HelmichDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.
Luc J W EversDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable sensors can objectively and continuously monitor daily-life tremor in Parkinson's Disease (PD). We developed an open-source algorithm for real-life monitoring of PD tremor which achieves generalizable performance across different wrist-worn devices. We achieved this using a unique combination of two independent, complementary datasets. The first was a small, but extensively video-labeled gyroscope dataset collected during unscripted activities at home (n = 24 PD; n = 24 controls). We used this to train and validate a logistic regression tremor detector based on cepstral coefficients. The second was a large, unsupervised dataset (n = 517 PD; n = 50 controls, data collected for 2 weeks with a different device), used to externally validate the algorithm. Results show that our algorithm can reliably quantify real-life PD tremor (sensitivity of 0.61 (0.20) and specificity of 0.97 (0.05)). Weekly aggregated tremor time and power showed excellent test-retest reliability and moderate correlation to MDS-UPDRS rest tremor scores. This opens possibilities to support clinical trials and individual tremor management with wearable technology.

Identifiers

PMID40640229
PMCPMC12246068

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.