OBJECTIVE: To examine the relationship of objectively assessed cognitive functioning to self-reported quality of life. DESIGN: Correlational, multiple regression, and factor analytic comparisons of a new self-report quality of life inventory with neuropsychological tests of cognition and mood. SUBJECTS: Two hundred fifty-seven patients with epilepsy. SETTING: Twenty-five epilepsy centers and neurology clinics across the United States. MEASURES: A recently developed self-report (ie, Quality of Life in Epilepsy-89 inventory) and objective tests of memory, verbal abilities, spatial functions, psychomotor and cognitive processing speed, cognitive flexibility, and mood. RESULTS: Factors that assessed mood, psychomotor speed, verbal memory, and language correlated significantly with selected scales of the Quality of Life in Epilepsy-89 inventory (P < .0001) and were predictive of overall quality of life (P < .002 to P < .0001). The mood factor showed the highest correlations (r = -.20 to r = -.73) and was the strongest predictor of quality of life in regression analyses (46.7% explained variance, P < .0001). CONCLUSIONS: Mood may be adversely affected by diminished quality of life, or perceived quality of life may be affected by mood disturbance. Quantitative quality of life assessments can be used in conjunction with formal neuropsychological testing of mood and cognition when evaluating patients with epilepsy.
OBJECTIVE: To examine the relationship of objectively assessed cognitive functioning to self-reported quality of life. DESIGN: Correlational, multiple regression, and factor analytic comparisons of a new self-report quality of life inventory with neuropsychological tests of cognition and mood. SUBJECTS: Two hundred fifty-seven patients with epilepsy. SETTING: Twenty-five epilepsy centers and neurology clinics across the United States. MEASURES: A recently developed self-report (ie, Quality of Life in Epilepsy-89 inventory) and objective tests of memory, verbal abilities, spatial functions, psychomotor and cognitive processing speed, cognitive flexibility, and mood. RESULTS: Factors that assessed mood, psychomotor speed, verbal memory, and language correlated significantly with selected scales of the Quality of Life in Epilepsy-89 inventory (P < .0001) and were predictive of overall quality of life (P < .002 to P < .0001). The mood factor showed the highest correlations (r = -.20 to r = -.73) and was the strongest predictor of quality of life in regression analyses (46.7% explained variance, P < .0001). CONCLUSIONS: Mood may be adversely affected by diminished quality of life, or perceived quality of life may be affected by mood disturbance. Quantitative quality of life assessments can be used in conjunction with formal neuropsychological testing of mood and cognition when evaluating patients with epilepsy.
Authors: Lucyna M Lach; Gabriel M Ronen; Peter L Rosenbaum; Charles Cunningham; Michael H Boyle; Shauna Bowman; David L Streiner Journal: Qual Life Res Date: 2006-09-13 Impact factor: 4.147
Authors: Jeffrey R Binder; David S Sabsevitz; Sara J Swanson; Thomas A Hammeke; Manoj Raghavan; Wade M Mueller Journal: Epilepsia Date: 2008-04-24 Impact factor: 5.864
Authors: Krystyna A Mathiak; Małgorzata Luba; Klaus Mathiak; Katarzyna Karzel; Tomasz Wolańczyk; Elzbieta Szczepanik; Paweł Ostaszewski Journal: BMC Neurol Date: 2010-08-17 Impact factor: 2.474