Evidence map›Paper›PMID 41069430›Full record

ArticleFrontiers in neurology2025

Gait characteristics and factors associated with fall risk in patients with dementia with Lewy bodies.

Zhou Su, Mengran Liu, Jun Kuai, Tingting Yi, Yuechang Zheng, Congcong Wang, Junyu Peng, Xiaojun Tian

Abstract read
In one paragraph

Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Zhou Su *Department of Neurology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
Mengran Liu *School of Education, University of Bristol, Bristol, United Kingdom.
Jun KuaiDepartment of Gastroenterology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
Tingting YiDepartment of Neurology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
Yuechang ZhengDepartment of Neurology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
Congcong WangDepartment of Neurology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
Junyu PengDepartment of Neurology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
Xiaojun TianDepartment of Neurology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the association between gait parameters and cognitive decline in patients with dementia with Lewy bodies (DLB) and evaluate the impact of gait abnormalities on fall risk. Methods: This cross-sectional study enrolled 63 DLB patients. Gait analysis, including gait speed, stride length, gait symmetry, and swing time, was performed using a pressure-sensing walkway. Cognitive function was assessed using the MoCA and MMSE. Spearman correlation analysis and multiple linear regression models were used to examine the relationship between gait parameters and cognitive function. Logistic regression models, adjusted for potential confounders, were employed to analyze the effect of gait abnormalities on fall risk. Results: Gait speed showed significant positive correlations with MoCA score ( Conclusion: Gait abnormalities are associated with cognitive decline, and reduced gait speed and gait asymmetry are markers of cognitive decline and increased fall risk in patients with established DLB.

Indexed as

cognitive functiondementia with Lewy bodiesfall riskgait analysisgait parameters

Identifiers

PMID41069430
PMCPMC12504080

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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.