Evidence map›Paper›PMID 40221695›Full record

ArticleBMC public health2025

Predicting poor self-management behaviors in adults with newly diagnosed COPD: based on the information-motivation-behavioral skills model.

Xiaomei Chen, Jia Liu, Yuxuan He, Li Wei, Menghui Deng, Rui Zhang, Huiqin Song, Yanni Yang

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Article in BMC public health, 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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5 · Who and what money

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8 authors.

Xiaomei ChenDepartment of Nursing, Chengdu Wenjiang District People's Hospital, Chengdu, China.
Jia LiuSchool of Nursing, Army Medical University, No.30 Gaotanyan Street, Chongqing, 400038, People's Republic of China.
Yuxuan HeSchool of Nursing, Army Medical University, No.30 Gaotanyan Street, Chongqing, 400038, People's Republic of China.
Li WeiSchool of Nursing, Army Medical University, No.30 Gaotanyan Street, Chongqing, 400038, People's Republic of China.
Menghui DengSchool of Nursing, Army Medical University, No.30 Gaotanyan Street, Chongqing, 400038, People's Republic of China.
Rui ZhangThoracic Surgery, Medical Center Hospital of Qionglai, Chengdu, China.
Huiqin SongDepartment of Nursing, Medical Center Hospital of Qionglai, Chengdu, China.
Yanni YangSchool of Nursing, Army Medical University, No.30 Gaotanyan Street, Chongqing, 400038, People's Republic of China. yangyanni@tmmu.edu.cn.

Funding

National Social Science Funding Project 20BRK039
6 · The paper itself

Abstract

backgroundSelf-management is an important measure to control the development of chronic obstructive pulmonary disease (COPD), but the self-management ability of newly diagnosed COPD patients can not be evaluated. Therefore, this study aims to develop and verify a risk prediction model based on the information-motivation-behavioral skills (IMB) model to predict poor self-management behaviors in newly diagnosed COPD patients.

methodsIn this prospective cohort study, a total of 331 adults with COPD were recruited from a general hospital in Chengdu, China. Data were collected at baseline based on the IMB model, such as cognitive function, social support, frailty, depressive and anxiety symptoms, and patient activation. Self-management behaviors were evaluated as the outcome variable after one-year follow up. Multivariate logistic regression was used to develop a risk prediction model to predict poor self-management behaviors. The nomogram was used to perform and visualise the predictive model and the receiver operator characteristic (ROC) curve, external validation were applied to evaluate the prediction performance of the model.

resultsA total of 331 patients completed follow-up (222 in the development cohort and 109 in the validation cohort). 68.3% of the participants occurred poor self-management behaviors. Cognitive function, patient activation, and depression were independent predictors for poor self-management behaviors for COPD patients. A nomogram was established based on regression analysis, and the AUC of this nomogram was 0.945. The sensitivity and specificity were 89.68% and 91.04% respectively. The AUC of the validation cohort was 0.898 and the Hosmer-Lemeshow test indicated good model prediction.

conclusionsThe risk prediction model based on IMB model and a nomogram including 3 easily available prediction factors (cognitive function, patient activation and depression) on poor self-management behaviors for newly diagnosed COPD patients was established, which showed good discrimination, and calibration. It can be used to screen out high- risk population with poor self-management behaviors for newly diagnosed COPD patients early.

Indexed as

MotivationPulmonary Disease, Chronic ObstructiveSelf-ManagementAdultAgedChinaFemaleHumansInformation Motivation Behavioral Skills ModelMaleMiddle AgedProspective StudiesRisk AssessmentCOPDNewly diagnosedRisk prediction modelSelf-management

Identifiers

PMID40221695
PMCPMC11992811

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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.