2.50
Hdl Handle:
http://hdl.handle.net/10755/165436
Category:
Abstract
Type:
Presentation
Title:
Symptom Clusters Predict Fatigue Severity in Oncology Outpatients
Author(s):
West, Claudia; Paul, S.; Miaskowski, Christine; Dodd, M.; Lee, K.
Author Details:
Claudia West, University of California-San Francisco, School of Nursing, San Francisco, California, USA, email: claudia.west@nursing.ucsf.edu; S. Paul; Christine Miaskowski; M. Dodd; K. Lee
Abstract:
Recent work suggests that the presence of one or more symptoms (specifically pain, fatigue, or sleep disturbance) can influence outcomes in oncology outpatients. The purpose of this study was to determine whether the number of symptoms an oncology outpatient reported effects fatigue severity. Oncology outpatients (n=117) who were receiving active treatment for their disease were recruited from four sites. The majority of the patients were female (75.2%) and Caucasian (86.2%) with a mean age of 59.6 years. The patients completed a demographic questionnaire, a numeric rating scale for worst pain, the Lee Fatigue Scale (LFS), the General Sleep Disturbance Scale (GSDS), and the Center for Epidemiological Studies - Depression Scale (CES-D). Patients were classified into one of 4 symptom groupings [i.e., 0, 1, 2, or 3 symptoms] based on pre-established cutoffs for pain, fatigue, and sleep disturbance. A linear stepwise multiple regression analysis was used to determine which of the following ten variables were significant, independent predictors of fatigue: age, gender, years of education, living arrangements, hematocrit, Karnofsky Performance Status score, CES-D score, quality of sleep score, excessive daytime sleepiness score, and total number of symptoms. The optimal regression equation included only four of these ten variables and explained 56.7% of the total variance in fatigue (F(4,92)=30.06, p=0.000). The significant, unique contributions of these four variables were: 7.84% for number of symptoms (p=0.000), 7.45% for excessive daytime sleepiness (p=0.000), 5.06% for depression (p=0.001) and 2.79% for quality of sleep (p=0.017). These findings suggest that increased levels of fatigue occur in patients who are experiencing multiple symptoms, are depressed, have poorer sleep quality, and report excessive daytime sleepiness.
Repository Posting Date:
27-Oct-2011
Date of Publication:
27-Oct-2011
Conference Date:
2003
Conference Name:
28th Annual Oncology Nursing Society Congress
Conference Host:
Oncology Nursing Society
Conference Location:
Denver, Colorado, USA
Note:
This is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.

Full metadata record

DC FieldValue Language
dc.type.categoryAbstracten_US
dc.typePresentationen_GB
dc.titleSymptom Clusters Predict Fatigue Severity in Oncology Outpatientsen_GB
dc.contributor.authorWest, Claudiaen_US
dc.contributor.authorPaul, S.en_US
dc.contributor.authorMiaskowski, Christineen_US
dc.contributor.authorDodd, M.en_US
dc.contributor.authorLee, K.en_US
dc.author.detailsClaudia West, University of California-San Francisco, School of Nursing, San Francisco, California, USA, email: claudia.west@nursing.ucsf.edu; S. Paul; Christine Miaskowski; M. Dodd; K. Leeen_US
dc.identifier.urihttp://hdl.handle.net/10755/165436-
dc.description.abstractRecent work suggests that the presence of one or more symptoms (specifically pain, fatigue, or sleep disturbance) can influence outcomes in oncology outpatients. The purpose of this study was to determine whether the number of symptoms an oncology outpatient reported effects fatigue severity. Oncology outpatients (n=117) who were receiving active treatment for their disease were recruited from four sites. The majority of the patients were female (75.2%) and Caucasian (86.2%) with a mean age of 59.6 years. The patients completed a demographic questionnaire, a numeric rating scale for worst pain, the Lee Fatigue Scale (LFS), the General Sleep Disturbance Scale (GSDS), and the Center for Epidemiological Studies - Depression Scale (CES-D). Patients were classified into one of 4 symptom groupings [i.e., 0, 1, 2, or 3 symptoms] based on pre-established cutoffs for pain, fatigue, and sleep disturbance. A linear stepwise multiple regression analysis was used to determine which of the following ten variables were significant, independent predictors of fatigue: age, gender, years of education, living arrangements, hematocrit, Karnofsky Performance Status score, CES-D score, quality of sleep score, excessive daytime sleepiness score, and total number of symptoms. The optimal regression equation included only four of these ten variables and explained 56.7% of the total variance in fatigue (F(4,92)=30.06, p=0.000). The significant, unique contributions of these four variables were: 7.84% for number of symptoms (p=0.000), 7.45% for excessive daytime sleepiness (p=0.000), 5.06% for depression (p=0.001) and 2.79% for quality of sleep (p=0.017). These findings suggest that increased levels of fatigue occur in patients who are experiencing multiple symptoms, are depressed, have poorer sleep quality, and report excessive daytime sleepiness.en_GB
dc.date.available2011-10-27T12:18:30Z-
dc.date.issued2011-10-27en_GB
dc.date.accessioned2011-10-27T12:18:30Z-
dc.conference.date2003en_US
dc.conference.name28th Annual Oncology Nursing Society Congressen_US
dc.conference.hostOncology Nursing Societyen_US
dc.conference.locationDenver, Colorado, USAen_US
dc.description.noteThis is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.-
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