dc.contributor.advisor | Kluding, Patricia M | en_US |
dc.contributor.author | Tseng, Benjamin Yichen | |
dc.date.accessioned | 2009-08-07T22:21:32Z | |
dc.date.available | 2009-08-07T22:21:32Z | |
dc.date.issued | 2009-01-29 | en_US |
dc.date.submitted | 2009 | en_US |
dc.identifier.other | http://dissertations.umi.com/ku:10197 | en_US |
dc.identifier.uri | http://hdl.handle.net/1808/5390 | en_US |
dc.description.abstract | PURPOSE: 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.extent | 165 pages | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | University of Kansas | en_US |
dc.rights | This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author. | en_US |
dc.subject | Health sciences | |
dc.subject | Rehabilitation and therapy | |
dc.subject | Health sciences | |
dc.subject | Health care management | |
dc.subject | Health sciences | |
dc.subject | Public health | |
dc.subject | Fatigue | |
dc.subject | Stroke | |
dc.title | PREDICTORS OF POST-STROKE FATIGUE | |
dc.type | Dissertation | en_US |
dc.contributor.cmtemember | Gajewski, Byron | |
dc.contributor.cmtemember | Liu, Wen | |
dc.contributor.cmtemember | Burns, Jeff | |
dc.contributor.cmtemember | Radel, Jeff | |
dc.thesis.degreeDiscipline | Physical Therapy & Rehabilitation Sciences | |
dc.thesis.degreeLevel | Ph.D. | |
kusw.oastatus | na | |
kusw.oapolicy | This item does not meet KU Open Access policy criteria. | |
kusw.bibid | 6857461 | |
dc.rights.accessrights | openAccess | en_US |