The Relationship between Quality of Life and Psycho-Socio-Spiritual Characteristics in Male Patients with Alcohol Dependence / 신경정신의학

OBJECTIVES: This study was designed to evaluate the relationship between quality of life (QOL) and psycho-socio-spiritual characteristics in male patients with alcohol dependence. METHODS: The sample consisted of 109 men with alcohol dependence defined by DSM-IV criteria. We assessed QOL by the WHO QOL assessment instrument-BREF (WHOQOL) and SmithKline Beecham QOL (SBQOL). Sociodemographic and alcohol related data were collected, and 7 questionnaires were administered: MAST, BDI, STAI, Drinker Inventory of Consequences (DrInC), Scale of Social Support (SSS), Religious Beliefs and Behaviors (RBB) and Spiritual Well-being Scale (SWBS). The correlations between each QOL score and other variables were examined, and stepwise multiple regression analysis was performed. RESULTS: The WHOQOL score positively correlated with education level, SSS (support) and RBB and negatively correlated with MAST, DrInC, BDI, STAI (trait) and SSS (conflict) scores. In stepwise regression analysis, the scores on the STAI and BDI contributed to the score on the WHOQOL. The SBQOL score correlated with the income level, and negatively correlated with BDI and STAI score. STAI score was a weak predictor of SBQOL score. CONCLUSION: The significant predictors of QOL in patients with alcohol dependence were psychological factors such as anxiety and depression.

A preliminary Study for Nonlinear Dynamic Analysis of EEG in Patients with Dementia of Alzheimer

The changes of electroencephalogram(EEG) in patients with dementia of Alzheimer's type are most commonly studied by analyzing power or magnitude in traditionally defined frequency bands. However because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to the chaos theory, irregular signals of EEG can be also resulted from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the largest Lyapunov exponent(L1). The authors have analyzed EEG epochs from three patients with dementia of Alzheimer's type and three matched control subjects. The largest L1 is calculated from EEG epochs consisting of 16.384 data points per channel in 15 channels. The results showed that patients with dementia of Alzheimer's type had significantly lower L1 than non-demented controls on 8 channels. Topographic analysis showed that the L1 were significantly lower in patients with Alzheimer's disease on all the frontal, temporal, central, and occipital head regions. These results show that brains of patients with dementia of Alzheimer's type have a decreased chaotic quality of electrophysiological behavior. We conclude that the nonlinear analysis such as calculating the L1 can be a promising tool for detecting relative changes in the complexity of brain dynamics.