Show simple item record

dc.contributor.advisorKluding, Patricia Men_US
dc.contributor.authorTseng, Benjamin Yichen
dc.date.accessioned2009-08-07T22:21:32Z
dc.date.available2009-08-07T22:21:32Z
dc.date.issued2009-01-29en_US
dc.date.submitted2009en_US
dc.identifier.otherhttp://dissertations.umi.com/ku:10197en_US
dc.identifier.urihttp://hdl.handle.net/1808/5390en_US
dc.description.abstractPURPOSE: Fatigue is a commonly neglected issue despite the high incidence rate reported in people with chronic stroke. It can impact daily functions and quality of life and has been linked with a higher mortality rate after stroke because of its association with sedentary lifestyle. Previous studies have acknowledged the multidimensional nature of post-stroke fatigue but have not distinguished the types of fatigue using quantifiable outcome measures as predictors. PARTICIPANTS: Twenty-one people post-stroke (8 females, 12 males, age = 59.5 ± 10.3 years; post-stroke time = 4.1 ± 3.5 years) participated in the study. METHODS: At rest, participants were asked to report their level of fatigue using the Visual Analog Fatigue Scale (VAFS). Next, they underwent a standardized fatigue-inducing exercise on a recumbent stepper. Immediately after the exercise, the VAFS was administered again to assess the level of fatigue at the moment. Exertion fatigue (EF) was calculated by subtracting the VAFS score at-rest from the VAFS score post-exercise. In addition, chronic fatigue (CF) was measured using Fatigue Severity Scale (FSS). The predictor variables included aerobic fitness, motor control, and depressive symptoms measured by VO2peak, Fugl-Meyer (FM) test, and Geriatric Depression Scale (GDS), respectively. RESULTS: Using stepwise multiple regression, we found that that VO2peak was an independent predictor of EF (p=.006) and explained 30.5% of variance in EF (R2=.305); we also found that GDS was an independent predictor of CF (p=.002) and explained 37.8% of variance in CF (R2=.378). CONCLUSIONS: Our results indicate that exertion fatigue and chronic fatigue are 2 distinct constructs in people post-stroke. We found that aerobic fitness may be a good predictor for exertion fatigue; while depressive symptoms may be used to predict chronic fatigue. This finding merits future investigations to determine the effect of individualized therapeutic interventions on different types of fatigue in people with chronic stroke.
dc.format.extent165 pagesen_US
dc.language.isoen_USen_US
dc.publisherUniversity of Kansasen_US
dc.rightsThis item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.en_US
dc.subjectHealth sciences
dc.subjectRehabilitation and therapy
dc.subjectHealth sciences
dc.subjectHealth care management
dc.subjectHealth sciences
dc.subjectPublic health
dc.subjectFatigue
dc.subjectStroke
dc.titlePREDICTORS OF POST-STROKE FATIGUE
dc.typeDissertationen_US
dc.contributor.cmtememberGajewski, Byron
dc.contributor.cmtememberLiu, Wen
dc.contributor.cmtememberBurns, Jeff
dc.contributor.cmtememberRadel, Jeff
dc.thesis.degreeDisciplinePhysical Therapy & Rehabilitation Sciences
dc.thesis.degreeLevelPh.D.
kusw.oastatusna
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
kusw.bibid6857461
dc.rights.accessrightsopenAccessen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record