Evidence map›Paper›PMID 39860708›Full record

ArticleSensors (Basel, Switzerland)2025

Biomechanics Parameters of Gait Analysis to Characterize Parkinson's Disease: A Scoping Review.

Michela Russo, Marianna Amboni, Noemi Pisani, Antonio Volzone, Danilo Calderone, Paolo Barone, Francesco Amato, Carlo Ricciardi, Maria Romano

Abstract readScoping Review
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  7. Gait analysis for Parkinson's disease using multiscale entropy.Neurodegenerative disease management · 2025
    Review
  8. Article
  9. Article
  10. Review
  11. 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

9 authors.

Michela RussoDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0002-5874-6441
Marianna AmboniCenter for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, 84131 Salerno, Italy.ORCID 0000-0002-6988-8197
Noemi PisaniDepartment of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy.ORCID 0000-0002-2412-9058
Antonio VolzoneCenter for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, 84131 Salerno, Italy.
Danilo CalderoneDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.
Paolo BaroneCenter for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, 84131 Salerno, Italy.
Francesco AmatoDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0002-9053-3139
Carlo RicciardiDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0001-7290-6432
Maria RomanoDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.ORCID 0000-0003-1133-1115

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) is characterized by a slow, short-stepping, shuffling gait pattern caused by a combination of motor control limitations due to a reduction in dopaminergic neurons. Gait disorders are indicators of global health, cognitive status, and risk of falls and increase with disease progression. Therefore, the use of quantitative information on the gait mechanisms of PD patients is a promising approach, particularly for monitoring gait disorders and potentially informing therapeutic interventions, though it is not yet a well-established tool for early diagnosis or direct assessment of disease progression. Over the years, many studies have investigated the spatiotemporal parameters that are altered in the PD gait pattern, while kinematic and kinetic gait parameters are more limited. A scoping review was performed according to the PRISMA guidelines. The Scopus and PubMed databases were searched between 1999 and 2023. A total of 29 articles were included that reported gait changes in PD patients under different gait conditions: single free walking, sequential motor task, and dual task. The main findings of our review highlighted the use of optoelectronic systems for recording kinematic parameters and force plates for measuring kinetic parameters, due to their high accuracy. Most gait analyses in PD patients have been conducted at self-selected walking speeds to capture natural movement, although studies have also examined gait under various conditions. The results of our review indicated that PD patients experience alterations in the range of motion of the hip, knee, and ankle joints, as well as a reduction in the power generated/absorbed and the extensor/flexor moments. These findings suggest that the PD gait pattern may be more effectively understood using kinematic and kinetic parameters.

Indexed as

GaitGait AnalysisParkinson DiseaseBiomechanical PhenomenaHumansRange of Motion, ArticularWalkingbiomechanicsgait analysiskinematicskineticsParkinson’s disease

Identifiers

PMID39860708
PMCPMC11769234

What OpenQuestion holds

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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.