Evidence map›Paper›PMID 41466390›Full record

ArticleBMC pulmonary medicine2025

Latent classes of self-management and associated factors in patients with COPD.

Jie Yu, Jianmei Fu, Shujuan Zhao, Shuting Zhang, Qing Li, Dan Zhang, Yulan Li

Abstract read
In one paragraph

Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

7 authors.

Jie Yu *Department of Respiratory and Critical Care Medicine, Hebei CNPC Central Hospital, No. 51, Xinkai Road, Guangyang District, Langfang, 065000, Hebei, China.ORCID http://orcid.org/0009-0003-8550-0505
Jianmei Fu *Department of Respiratory and Critical Care Medicine, Hebei CNPC Central Hospital, No. 51, Xinkai Road, Guangyang District, Langfang, 065000, Hebei, China.ORCID http://orcid.org/0009-0005-5050-6098
Shujuan ZhaoDepartment of Respiratory and Critical Care Medicine, Hebei CNPC Central Hospital, No. 51, Xinkai Road, Guangyang District, Langfang, 065000, Hebei, China.ORCID http://orcid.org/0009-0004-4875-1302
Shuting ZhangDepartment of Respiratory and Critical Care Medicine, Hebei CNPC Central Hospital, No. 51, Xinkai Road, Guangyang District, Langfang, 065000, Hebei, China.ORCID http://orcid.org/0009-0001-5274-6766
Qing LiDepartment of Respiratory and Critical Care Medicine, Hebei CNPC Central Hospital, No. 51, Xinkai Road, Guangyang District, Langfang, 065000, Hebei, China.ORCID http://orcid.org/0009-0002-6115-9413
Dan ZhangDepartment of Respiratory and Critical Care Medicine, Hebei CNPC Central Hospital, No. 51, Xinkai Road, Guangyang District, Langfang, 065000, Hebei, China.ORCID http://orcid.org/0009-0000-7569-3874
Yulan LiDepartment of Respiratory and Critical Care Medicine, Hebei CNPC Central Hospital, No. 51, Xinkai Road, Guangyang District, Langfang, 065000, Hebei, China. yulanli_yulan@163.com.ORCID http://orcid.org/0009-0008-0581-1300

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEffective self-management is crucial for Chronic Obstructive Pulmonary Disease (COPD) patients, yet significant heterogeneity exists in their self-management behaviors. Understanding this variation is key to providing personalized care. This study aimed to identify distinct latent classes of self-management behaviors among COPD patients and explore the factors associated with class membership.

methodsA cross-sectional survey was conducted with 315 COPD inpatients from a tertiary hospital in Hebei, China. Data were collected using the COPD Self-Management Scale, the modified Medical Research Council Dyspnea Scale (mMRC), the Fatigue Severity Scale (FSS), the Hospital Anxiety and Depression Scale (HADS), and a sociodemographic/clinical questionnaire. Latent Class Analysis (LCA) was used to identify unobserved subgroups based on self-management patterns. Factors influencing class membership were identified using multivariate logistic regression analysis.

resultsLCA revealed three distinct latent classes of self-management: “Comprehensive Weakness” (45.4%), characterized by poor performance across all domains; “Symptom-Disconnected” (35.9%), showing adequate symptom management but poor daily life/emotion management; and ‘Balanced Management” (18.7%), demonstrating strong performance in all areas. Multivariate analysis indicated that higher education level, longer disease duration, and higher social support were significant predictors of belonging to the “Balanced Management” class compared to the “Comprehensive Weakness” class. Anxiety was a significant negative factor distinguishing the classes.

conclusionsThis study identifies three distinct profiles of self-management behavior in COPD patients, highlighting the need for tailored interventions rather than a one-size-fits-all approach. Factors such as education, disease duration, and social support are key determinants of better self-management. Clinicians should prioritize assessing and addressing anxiety and leverage social support to help patients transition towards more balanced and effective self-management practices.

trial registrationNot applicable. This study is an observational, cross-sectional survey and does not involve a health care intervention.

Indexed as

Pulmonary Disease, Chronic ObstructiveSelf-ManagementAgedAnxietyChinaCross-Sectional StudiesDepressionDyspneaFatigueFemaleHumansLatent Class AnalysisLogistic ModelsMaleMiddle AgedMultivariate AnalysisChronic obstructive pulmonary diseaseInfluencing factorsLatent class analysisSelf-management

Identifiers

PMID41466390
PMCPMC12859867

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.