Article Details

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Citation:  Benedict, R.H.B., Wahlig, E., Bakshi, R., Fishman, I., Munschauer, F., Zivadinov, R., Weinstock-Guttman, B. (2005). Predicting quality of life in multiple sclerosis: Accounting for physical disability, fatigue, cognition, mood disorder, personality and behavior change. Journal of Neurological Sciences, 231 29-34.
Title:  Predicting quality of life in multiple sclerosis: Accounting for physical disability, fatigue, cognition, mood disorder, personality and behavior change
Authors:  Benedict, R.H.B., Wahlig, E., Bakshi, R., Fishman, I., Munschauer, F., Zivadinov, R., Weinstock-Guttman, B.
Year:  2005
Journal/Publication:  Journal of Neurological Sciences
Publisher:  Elsevier
DOI:  https://doi.org/10.1016/j.jns.2004.12.009
Full text:  http://proxy.library.vcu.edu/login?url=http://www.sciencedirect.com...   
Peer-reviewed?  Yes
NIDILRR-funded?  No

Structured abstract:

Background:  Clinicians are interested in ways to improve health-related quality of life (HQOL) for individuals with multiple sclerosis (MS). Poor HQOL has been associated with progressive disease course, physical disability, self reported fatigue, depression, and cognitive impairment. Past research did not consider multiple predictors at the same time. Considering all predictors at the same time should help determine the core disease and clinical features associated with HQOL.
Purpose:  The purpose of the study was to find out which domain is most closely associated with HQOL. The researchers predicted that when all clinical domains are accounted for that depression would most strongly predict self reported indices of HQOL and vocational status would be predicted by cognitive and physical capacity.
Setting:  The study took place in a clinical setting.
Study sample:  The participants were 120 patients with multiple sclerosis and 40 volunteers. The average age was 44 years. The majority or 71% were females. Ninety three percent were Caucasians. The average level of education was 14.4 years. The average length of disease duration was 11.8 years.
Intervention:  There was no intervention.
Control or comparison condition:  Healthy volunteers served as controls and were matched to patients based on age, education, race and gender.
Data collection and analysis:  Neuropsychological evaluations provide summary scores for the MS Quality of Life 54. This included the physical health composite, mental health composite and overall index. Physical disability was measured by the Expanded Disability Status Scale. Fatigue was measured using the Fatigue Severity Scale. For cognitive function an adapted version of the Minimal Assessment of Cognitive Function in MS battery was administered. Patient self report and informant reports were used and behavior disorder was measured using the Neuropsychiatric Inventory. The two groups (MS patients versus control) were compared using a one-way analysis of variance and chi square tests. Linear and logistic regression models were used to predict outcome measures.
Findings:  There were no group differences on demographic factors. MS patients reported lower quality of life than controls. Group differences were found in all predictors except for personality. There were no significant correlations between the outcome measures and demographics within the MS patient group. Physical HQOL was predicted by fatigue, depression, and physical disability. Mental HQOL related to depression and fatigue. Vocational status was predicted by three cognitive tests, conscientiousness, and disease duration.
Conclusions:  Self report HQOL indices are predicted by measures of depression. Vocational status is predicted by objective measures of cognitive functioning. Early identification of problems can help improve clinical practices and inform areas in need of additional research.

Disabilities served:  Multiple sclerosis
Populations served:  Gender: Female and Male
Race: White / Caucasian