BACKGROUND AND OBJECTIVES: Heart rate variability has been known to be a prognostic factor of heart disease. However, determinants of heart rate variability in general korean population without clinical heart disease have not been studied. Objectives of this study were to measure heart rate variability in general population and to investigate clinical determinants of heart rate variability. METHODS:Heart rate variability measures were obtained by LRR-03TM and MemCalcTM software (GMS, Tokyo, Japan) from public officials in a district of Seoul and their families(n=69). Predictors of heart rate variability included age, gender, heart rate, smoking status, systolic blood pressure, diastolic blood pressure, serum total cholesterol, HDL-cholesterol. Univariate analysis and analysis of variance of low frequency power(0.04-0.15 Hz), high frequency power(0.15-0.30 Hz), and total power spectrum in relation to explanatory variables were done. In order to select determinants of heart rate variability, multiple linear regression model of each heart rate variability measure was created and stepwise selection method was applied. RESULTS: Analysis of variance showed that older age, higher heart rate, body mass index > or =27, systolic blood pressure > or =140 mmHg, diastolic blood pressure > or =90 mmHg, and serum total cholesterol > or =240 mg/dl were negatively associated with one or more heart rate variability measures. Serum HDL-cholesterol > or =35 mg/dl was positively associated with low and high frequency power. Multiple linear regression analyses showed that age and heart rate were the major determinants, gender and cardiovascular risk factors such as diastolic blood pressure, HDL-cholesterol, and smoking contributed to one or more heart rate variability measures. CONCLUSIONS: Age, heart rate, gender, and cardiovascular risk factors must be considered when evaluating heart rate variability.
Blood Pressure ; Body Mass Index ; Cholesterol ; Heart Diseases ; Heart Rate* ; Heart* ; Linear Models ; Risk Factors ; Seoul ; Smoke ; Smoking

Effect of shift work on worker's health, family and social life was investigated at a automobile manufacturing plant in Inchon. In total, 2488 shift workers and 599 non-shift workers completed self administered questionnaire in their sleep pattern, subjective gastrointestinal symptoms, prevalent chronic disease status, general well-being schedule, family and social life. More shift workers complained of sleep disturbance, poor sleep quality, tiredness at awakening, and sleepiness in work than non-shift workers. More shift workers also complained of gastrointestinal disturbance than non-shift workers. Worker's mental health was assessed by General Well-Being Schedule(GWB) questionnaire developed for the U.S. Health and Nutrition Examination Surveys(HANES I). The percentage of severely distressed shift workers was significantly higher than that of non-shift workers(p<0.01). Among subscores in General Well-Being Schedule, anxiety, depression, positive well-being, vitality, and general health subscale of shift workers were lower than those of non-shift workers (p<0.05). In terms of family and social life, there was no significant difference between two groups. Multiple logistic regression analysis was done for discrete variables which showed statistically significant difference between shift and non-shift group. The variables included in analysis were sleep disturbance symptoms, gastrointestinal complaints, and distress level calculated from GWB score. Age, tenure, smoking, alcohol drinking, and exercise were adjusted as confounding factors and odds ratios for above symptoms due to shift work were calculated. Odds ratios (ORs) for sleep disturbance symptoms ranged from 0.52 to 3.59. ORs for gastrointestinal complaints ranged from 1.19 to 1.34. OR for distress level was 1.31. We concluded that shift workers are suffered from physical and psychological ailments due to shift work and interventional methods for preventing worker's health from adverse effects of shift work are needed.
Alcohol Drinking ; Anxiety ; Appointments and Schedules ; Automobiles* ; Chronic Disease ; Depression ; Family Health* ; Humans ; Incheon ; Logistic Models ; Mental Health ; Odds Ratio ; Plants* ; Questionnaires ; Smoke ; Smoking