Evidence map›Paper›PMID 41258602›Full record

ArticleAging clinical and experimental research2025

Classification and influencing factors of fear of falling in elderly stroke patients: A latent profile analysis.

Yue Yang, Meiying Li, Xiaoge Ding, Lei Zhang, Meixiang Jin, Yinji Jin

Abstract read
In one paragraph

Article in Aging clinical and experimental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Longitudinal Trajectories and Determinants of Fear of Falling in Post-Stroke Patients.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2026
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yue Yang *School of Nursing, Yanbian University, 977 Gongyuan Road, Yanji City Jilin, 133002, China.ORCID http://orcid.org/0009-0009-5255-5474
Meiying Li *School of Nursing, Yanbian University, 977 Gongyuan Road, Yanji City Jilin, 133002, China.
Xiaoge DingSchool of Nursing, Yanbian University, 977 Gongyuan Road, Yanji City Jilin, 133002, China.
Lei ZhangSchool of Nursing, Yanbian University, 977 Gongyuan Road, Yanji City Jilin, 133002, China.
Meixiang JinSchool of Nursing, Yanbian University, 977 Gongyuan Road, Yanji City Jilin, 133002, China.
Yinji JinSchool of Nursing, Yanbian University, 977 Gongyuan Road, Yanji City Jilin, 133002, China. kimeunhee1004@gmail.com.ORCID http://orcid.org/0000-0003-3073-5916

Funding

The Education Department of Jilin Province JJKH20240707KJ
6 · The paper itself

Abstract

objectiveTo explore the potential categories of fear of falling in elderly stroke patients and analyze the differences in characteristics and influencing factors among patients in different categories.

methodsAA total of 386 elderly stroke patients hospitalized in the Department of Neurology of a tertiary grade A general hospital in Jilin Province from March 2024 to June 2024 were selected as research subjects using the convenience sampling method. A general information questionnaire, Modified Falls Efficacy Scale (MFES), Simplified Coping Style Questionnaire (SCSQ), and Social Support Rating Scale (SSRS) were used for the survey. Mplus 8.3 software was applied to conduct latent profile analysis (LPA) on fear of falling in elderly stroke patients to identify potential categories, and multivariate logistic regression was used to further explore the influencing factors of each category.

resultsThere were 3 potential categories of fear of falling in elderly stroke patients: the high fear of falling group (21.8%), moderate fear of falling group (38.3%), and low fear of falling group (39.9%). Multivariate logistic regression analysis showed that gender, age, type of stroke diagnosis, visual status, hearing status, limb strength, coping style, and social support were the influencing factors for the potential categories of fear of falling in elderly stroke patients.

conclusionFear of falling in elderly stroke patients has obvious categorical characteristics. Medical staff should implement targeted interventions based on the characteristics and influencing factors of different potential categories to reduce patients' fear of falling.

Indexed as

Accidental FallsFearStrokeAdaptation, PsychologicalAgedAged, 80 and overFemaleHumansLogistic ModelsMaleRisk FactorsSocial SupportSurveys and QuestionnairesElderlyFear of fallingLatent profile analysisStroke

Identifiers

PMID41258602
PMCPMC12630168

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LicenceCC BY-NC-ND
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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.