Purpose: To examine the effectiveness of a nurse-led self-management program on outcomes of patients with chronic obstructive pulmonary disease (COPD). Design: A randomized controlled, single-blind trial, carried out from October 2017 to December 2018, included 154 participants admitted with COPD to the Affiliated Hospital of Zunyi Medical University in Guizhou, (randomized into intervention (n = 77) and control groups (n = 77)). Materials and Methods: Participants in the intervention group underwent a nurseled self-management program in addition to routine care, and participants of the control group received only routine care. The main outcome measures were COPDrelated readmission and emergency department visits, the 6-minute walk distance (6MWD) test for measurement of exercise capacity, the St George Respiratory Questionnaire (SGRQ) for measurement of health-related quality of life, and the COPD Transitional Care Patient Satisfaction Questionnaire (CTCPSQ) for measurement of satisfaction. Data collection was conducted at baseline (T1) and after 3 (T2), 6 (T3) and 12 mo (T4). FindingsCompared to the control group, participants in the intervention group showed significantly fewer COPD-related hospital admissions (P = 0.03) and emergency department visits (P = 0.001) and higher total CTCPSQ scores (P = 0.001) at 12 mo.Meanwhile, analysis of variance showed a significantly greater improvement in exercise capacity and health status over time in the nurse-led program group than in the control group, P < 0.001. Conclusions: This study demonstrated that the nurse-led self-management program was effective in decreasing hospital readmissions and emergency department visits and improving exercise capacity, health-related quality of life and satisfaction for patients with COPD.
Introduction Fatigue is important, but ignored symptom among COPD patients. At present, there is very limited data are available for the prevalence of fatigue and its risk factors among COPD patients in China. Objective The purpose of this study is to determine the prevalence of fatigue and to investigate the factors associated with fatigue among clinically stable patients with COPD in China. Methods This is a cross‐sectional study using a questionnaire to collect data on sociodemographic, related to COPD disease, and exercise habits. Multidimensional fatigue inventory (MFI‐20) was used to assess the prevalence of fatigue. Independent samples t test, bivariate correlation, one‐way ANOVA test, and test for several independent samples were used to compare the sociodemographic factors with MFI‐20 scores of COPD patients. Multiple stepwise linear regression was performed to estimate influencing factors related to the MFI‐20 of COPD patients. Results Among the participants, the prevalence of fatigue was 88.62%. Negative correlations were found between FEV1% and multidimensional fatigue (r = −0.40, p < 0.01), general fatigue (r = −0.20, p < 0.05), reduced activity (r = −0.20, p < 0.01), and physical fatigue (r = −0.10, p < 0.01). A multiple linear regression models revealed that age (p < 0.05), BMI (p < 0.05), FEV1% (p < 0.01), exercise times (p < 0.01), and the times of hospitalization in the past 12 months (p < 0.05) were associated with multidimensional fatigue scores. Conclusions The prevalence of fatigue is high among clinically stable patients with COPD. Keeping exercise, prevention obesity, and exacerbation should be an effective intervention strategy to reduce COPD‐related fatigue.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.