Evidence map›Paper›PMID 40011957›Full record

ArticleJournal of neuroengineering and rehabilitation2025

Quantifying arm swing in Parkinson's disease: a method accounting for arm activities during free-living gait.

Erik Post, Twan van Laarhoven, Yordan P Raykov, Max A Little, Jorik Nonnekes, Tom M Heskes, Bastiaan R Bloem, Luc J W Evers

Abstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Erik PostDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands. erik.post@radboudumc.nl.
Twan van LaarhovenInstitute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
Yordan P RaykovUniversity of Nottingham, Nottingham, UK.
Max A LittleUniversity of Birmingham, Birmingham, UK.
Jorik NonnekesDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
Tom M HeskesInstitute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
Bastiaan R BloemDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
Luc J W EversDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Funding

Dutch Ministry of Economic Affairs LSHM20090-H048Dutch Research Counsil SDI.2020.060Dutch Research Counsil Long-term Program KICH3.LTP.20.006Michael J. Fox Foundation for Parkinson's Research 20425
6 · The paper itself

Abstract

backgroundAccurately measuring hypokinetic arm swing during free-living gait in Parkinson's disease (PD) is challenging due to other concurrent arm activities. We developed a method to isolate gait segments without these arm activities.

methodsWrist accelerometer and gyroscope data were collected from 25 individuals with PD and 25 age-matched controls while performing unscripted activities in their home environment. This was done after overnight withdrawal of dopaminergic medication ('pre-medication') and approximately one hour after intake ('post-medication'). Using video annotations as ground truth, we trained and evaluated two classifiers: one for detecting gait and one for detecting gait segments without other arm activities. Based on the filtered gait segments, arm swing was quantified using the median and 95th percentile range of motion (RoM). These arm swing parameters were evaluated in three ways: (1) the agreement between predicted and video-annotated gait segments without other arm activities, (2) the sensitivity to differences between PD and controls, and (3) the sensitivity to the effects of dopaminergic medication.

resultsOn the most affected side, the mean (SD) balanced accuracy for detecting gait without other arm activities was 0.84 (0.10) pre-medication and 0.88 (0.09) post-medication. The agreement between arm swing parameters of predicted and video-annotated gait segments without other arm activities was high irrespective of medication state (intra-class correlation coefficients: median RoM: 0.99; 95th percentile RoM: 0.97). Both the median and 95th percentile RoM were smaller in PD pre-medication compared to controls (median:

conclusionsFiltering out gait segments with other concurrent arm activities is feasible and increases the change in arm swing parameters following dopaminergic medication in free-living conditions. This approach may be used to monitor treatment effect and disease progression in daily life.

Indexed as

ArmGaitGait Disorders, NeurologicParkinson DiseaseAccelerometryAgedBiomechanical PhenomenaFemaleHumansIndependent LivingMaleMiddle AgedArm swingDigital biomarkersGaitHypokinesiaParkinson’s diseaseWearablesWrist-worn sensors

Identifiers

PMID40011957
PMCPMC11863854

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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.