Evidence map›Paper›PMID 40933385›Full record

Observational studyVascular health and risk management2025

Identification of Proactive Health Behavior Clusters in Atrial Fibrillation-Related Ischemic Stroke Patients: A Multi-Center Latent Class Analysis.

Lina Guo, Yuying Guo, Jed Montayre, Wenjing Ning, Genoosha Namassevayam, Mengyu Zhang, Yuying Xie, Xinxin Zhou, Peng Zhao, Juanjuan Wang and 1 more

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in Vascular health and risk management, 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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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Lina Guo *Department of Neurology, National Advanced Stroke Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.ORCID 0000-0001-9843-6627
Yuying Guo *Department of Neurology, National Advanced Stroke Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
Jed MontayreSchool of Nursing, the Hong Kong Polytechnic University, Hong Kong, SAR, People's Republic of China.
Wenjing NingSchool of Nursing, the Hong Kong Polytechnic University, Hong Kong, SAR, People's Republic of China.
Genoosha NamassevayamDepartment of Supplementary Health Sciences, Faculty of Health-Care Sciences, Eastern University, Batticaloa, Sri Lanka.
Mengyu ZhangDepartment of Neurology, National Advanced Stroke Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
Yuying XieSchool of Nursing and Health, Zhengzhou University, Zhengzhou, People's Republic of China.
Xinxin ZhouSchool of Nursing and Health, Zhengzhou University, Zhengzhou, People's Republic of China.ORCID 0009-0002-8164-0431
Peng ZhaoSchool of Nursing and Health, Zhengzhou University, Zhengzhou, People's Republic of China.
Juanjuan WangDepartment of Neurology, National Advanced Stroke Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
Ruiqing DiDepartment of Nursing, the first Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to identify latent classes of proactive health behavior and to explore the predictive factors associated with various clusters of proactive health behavior among patients with atrial fibrillation-related ischemic stroke. Methods: A multi-center cross-sectional study was conducted, recruiting a total of 1,250 participants through cluster random sampling from January 2023 to May 2024. Latent class analysis was performed to identify classes of proactive health behavior within the sample of atrial fibrillation-related ischemic stroke patients. Additionally, multinomial regression analyses were utilized to investigate the predictive factors associated with the different latent classes identified. This study adhered to the STROBE checklist. Results: Out of the 1,250 participants, 1,196 (91.6%) completed the survey, including 809 males and 387 females, with 71% of them reporting moderate or lower levels of proactive health behavior. The findings revealed three latent classes: (1) low proactive health behavior with health responsibility deficiency (n=426, 35.6%); (2) moderate proactive health behavior with stress and coping disorder (n=464, 38.7%); and (3) high proactive health behavior with light physical activity (n=306, 25.5%). Factors correlated with the latent classes of proactive health behavior were identified. Protective factors included a high level of stroke knowledge, strong awareness of health beliefs, and better environmental and social support (all p < 0.05). Conversely, risk factors for the latent classes of proactive health behavior included low education, being unmarried, lack of thrombolysis, and low household income (all p < 0.05). Conclusion: This study successfully identified three different latent classes of proactive health behaviors and their related predictors in Chinese atrial fibrillation-related ischemic stroke patients. These findings provide theoretical guidance and practical insights for the development of targeted intervention programs aimed at improving proactive health behaviors in patients with atrial fibrillation-related ischemic stroke patients.

Indexed as

Atrial FibrillationHealth BehaviorHealthy LifestyleIschemic StrokeRisk Reduction BehaviorAdaptation, PsychologicalAgedAged, 80 and overChinaCross-Sectional StudiesExerciseFemaleHealth Knowledge, Attitudes, PracticeHumansLatent Class AnalysisMaleatrial fibrillationischemic strokelatent class analysismulti-center studyproactive health behavior

Identifiers

PMID40933385
PMCPMC12417708

What Socratic holds

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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 Socratic graph.