2.50
Hdl Handle:
http://hdl.handle.net/10755/159536
Type:
Presentation
Title:
Predictors of Fatigue in Patients with Chronic Lung Disease
Abstract:
Predictors of Fatigue in Patients with Chronic Lung Disease
Conference Sponsor:Midwest Nursing Research Society
Conference Year:2002
Author:Kim, Eui-Geum
P.I. Institution Name:Yonsei University
Title:Assistant Professor
Contact Address:College of Nursing, 134 Shinchon-dong, Sodaemoon-gu, Seoul, 120-740, Korea
Purpose: to identify predictors of fatigue in patients with chronic lung disease. Design: cross sectional, descriptive-correlation study. Setting: outpatient respiratory clinic at large university hospital in Korea. Sample: total 128 patients (age=64.2(11.3), FEV1 percent predicted=64.4%(28.8) were participated in the study. Most were diagnosed as having COPD (n=106, 82.8%) by their physician. Methods: Fatigue was measured with the Multidimensional Fatigue Inventory including five subscales of fatigue: general, physical and mental fatigue, reduced activity and reduced motivation. Spirometric measure of pulmonary function (FEV1 % predicted), pulmonary symptoms (wheezing, shortness of breath, chest tightness, sputum), dyspnea (by Baseline Dyspnea Index), and functional status (by Sickness Impact Profile-68) were measured as physiological variables. For psychological variables, mood (POMS-modified: excluding fatigue category) and stress were measured. Sleep quality component in the Pittsburgh Sleep Quality Index was measured as a situational variable. Potential predictors in the regression analysis were physiological variables (FEV1 percent predicted value, pulmonary symptoms, dyspnea, functional status), psychological variables (mood, stress), situational variable (sleep quality), and others (age, sex, years since diagnosis). Results: Predictors of overall fatigue were dyspnea (beta=.31, t=2.42,p=.02), pulmonary symptoms (beta=.18, t=- 2.06, p=.04), mood (beta=.28, t=2.69, p < .01), and functional status (beta=.42, t=2.42, p=.02). These variables explained 55.0% of the variances in overall fatigue (F (10, 93)=11.49, p < .001). Conclusions: These data suggest that fatigue is influencing by both physiological and psychological variables. These data confirmed also the relationship between dyspnea, mood, and fatigue in this oversea patient population.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Midwest Nursing Research Society

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titlePredictors of Fatigue in Patients with Chronic Lung Diseaseen_GB
dc.identifier.urihttp://hdl.handle.net/10755/159536-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Predictors of Fatigue in Patients with Chronic Lung Disease</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Midwest Nursing Research Society</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2002</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Kim, Eui-Geum</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Yonsei University</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Assistant Professor</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">College of Nursing, 134 Shinchon-dong, Sodaemoon-gu, Seoul, 120-740, Korea</td></tr><tr><td colspan="2" class="item-abstract">Purpose: to identify predictors of fatigue in patients with chronic lung disease. Design: cross sectional, descriptive-correlation study. Setting: outpatient respiratory clinic at large university hospital in Korea. Sample: total 128 patients (age=64.2(11.3), FEV1 percent predicted=64.4%(28.8) were participated in the study. Most were diagnosed as having COPD (n=106, 82.8%) by their physician. Methods: Fatigue was measured with the Multidimensional Fatigue Inventory including five subscales of fatigue: general, physical and mental fatigue, reduced activity and reduced motivation. Spirometric measure of pulmonary function (FEV1 % predicted), pulmonary symptoms (wheezing, shortness of breath, chest tightness, sputum), dyspnea (by Baseline Dyspnea Index), and functional status (by Sickness Impact Profile-68) were measured as physiological variables. For psychological variables, mood (POMS-modified: excluding fatigue category) and stress were measured. Sleep quality component in the Pittsburgh Sleep Quality Index was measured as a situational variable. Potential predictors in the regression analysis were physiological variables (FEV1 percent predicted value, pulmonary symptoms, dyspnea, functional status), psychological variables (mood, stress), situational variable (sleep quality), and others (age, sex, years since diagnosis). Results: Predictors of overall fatigue were dyspnea (beta=.31, t=2.42,p=.02), pulmonary symptoms (beta=.18, t=- 2.06, p=.04), mood (beta=.28, t=2.69, p &lt; .01), and functional status (beta=.42, t=2.42, p=.02). These variables explained 55.0% of the variances in overall fatigue (F (10, 93)=11.49, p &lt; .001). Conclusions: These data suggest that fatigue is influencing by both physiological and psychological variables. These data confirmed also the relationship between dyspnea, mood, and fatigue in this oversea patient population.</td></tr></table>en_GB
dc.date.available2011-10-26T22:06:19Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T22:06:19Z-
dc.description.sponsorshipMidwest Nursing Research Societyen_GB
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