Chen Xiaomei, Liu Jia, He Yuxuan, Wei Li, Deng Menghui, Zhang Rui, Song Huiqin, Yang Yanni
Department of Nursing, Chengdu Wenjiang District People's Hospital, Chengdu, China.
School of Nursing, Army Medical University, No.30 Gaotanyan Street, Chongqing, 400038, People's Republic of China.
BMC Public Health. 2025 Apr 12;25(1):1384. doi: 10.1186/s12889-025-22569-8.
Self-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.
In 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.
A 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.
The 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.
自我管理是控制慢性阻塞性肺疾病（COPD）进展的重要措施，但新诊断的COPD患者的自我管理能力无法得到评估。因此，本研究旨在基于信息-动机-行为技能（IMB）模型开发并验证一个风险预测模型，以预测新诊断的COPD患者的不良自我管理行为。
在这项前瞻性队列研究中，共从中国成都的一家综合医院招募了331例成年COPD患者。基于IMB模型在基线时收集数据，如认知功能、社会支持、虚弱、抑郁和焦虑症状以及患者激活情况。在一年随访后，将自我管理行为作为结局变量进行评估。采用多因素逻辑回归开发一个风险预测模型，以预测不良自我管理行为。使用列线图来执行和可视化预测模型，并应用受试者工作特征（ROC）曲线进行外部验证，以评估模型的预测性能。
共有331例患者完成随访（开发队列222例，验证队列109例）。68.3%的参与者出现了不良自我管理行为。认知功能、患者激活和抑郁是COPD患者不良自我管理行为的独立预测因素。基于回归分析建立了列线图，该列线图的AUC为0.945。敏感性和特异性分别为89.68%和91.04%。验证队列的AUC为0.898，Hosmer-Lemeshow检验表明模型预测良好。结论：建立了基于IMB模型的风险预测模型以及包含3个易于获得的预测因素（认知功能、患者激活和抑郁）的列线图，用于预测新诊断的COPD患者的不良自我管理行为，该模型具有良好的区分度和校准度。它可用于早期筛查出新诊断的COPD患者中自我管理行为不良的高危人群。
