Analysis of factors associated with family disease burden and correlation with social support among family caregivers of patients with severe mental illnesses.

BACKGROUND: Caregivers are responsible for the home care of family members with severe mental illnesses (SMIs) and their lives are often subject to changes that can create stress and burden. The purpose of this study was to explore the current state of family disease burden and its correlation with social support among family caregivers of SMIs patients. METHODS: Using a random sampling method, a total of 1,108 family caregivers of SMIs patients in community health service centers were selected. A general information questionnaire of family caregivers and patients, the Family Disease Burden Scale, and the Social Support Rating Scale were used. RESULTS: The score of the Family Disease Burden Scale of 1108 family caregivers was 16.57 ± 10.65. Family disease burden was negatively correlated with social support (p < .05). The main influencing factors of family disease burden were average annual family income, duration of illness, distance to medical care, risk of unpredictable behavior, social support, caregiver-patient relationship, gender, and comorbid chronic conditions (p < .05). CONCLUSION: Family caregivers of SMIs patients have a relatively low level of caregiver-perceived social support. Interventions to enhance perceived social support could help maintain the health of family caregivers and improve the quality of family care.

A quantitative assessment of T-wave morphology in LQT1, LQT2, and healthy individuals based on Holter recording technology.

BACKGROUND: The clinical course and the precipitating risk factors in the congenital long QT syndrome (LQTS) are genotype specific. OBJECTIVES: The goal of this study was to develop a computer algorithm allowing for electrocardiogram (ECG)-based identification and differentiation of LQT1 and LQT2 carriers. METHODS: Twelve-lead ECG Holter monitor recordings were acquired in 49 LQT1 carriers, 25 LQT2 carriers, and 38 healthy subjects as controls. The cardiac beats were clustered based on heart-rate bin method. Scalar and vectorial repolarization parameters were compared for similar heart rates among study groups. The Q to Tpeak (QTpeak), the Tpeak to Tend interval, T-wave magnitude and T-loop morphology were automatically quantified using custom-made algorithms. RESULTS: QTpeak from lead II and the right slope of the T-wave were the most discriminant parameters for differentiating the 3 groups using prespecified heart rate bin (75.0 to 77.5 beats/min). The predictive model utilizing these scalar parameters was validated using the entire spectrum of heart rates. Both scalar and vectorcardiographic models provided very effective identification of tested subjects in heart rates between 60 and 100 beats/min, whereas they had limited performance during tachycardia and slightly better discrimination in bradycardia. In the 60 to 100 beats/min heart rate range, the best 2-variable model identified correctly 89% of healthy subjects, 84% of LQT1 carriers, and 92% of LQT2 carriers. A model including 3 parameters based purely on scalar ECG parameters could correctly identify 90% of the population (89% of healthy subjects, 90% of LQT1 carriers, and 92% of LQT2 carriers). CONCLUSION: Automatic algorithm quantifying T-wave morphology discriminates LQT1 and LQT2 carriers and healthy subjects with high accuracy. Such computerized ECG methodology could assist physicians evaluating subjects suspected for LQTS.