• Research in Community and Public Health Nursing
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• v.5 no.1
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• pp.81-96
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• 1994

The primary purpose of this study was to identify factors affecting health promoting lifestyles of college students on the basis of Pender's Health Promotion Model. The subjects were 1,159 students of one university in Seoul. These data were collected by self reporting questionnaire from April 19 to May 3, 1993. This study examined health promoting lifestyles, cognitive-percep-tual factors: perceived health status, perceived importance of health, health locus of control, and health conception, and modifying factors: sex, grade, major, residence type, and attendance at a health care course. The instruments used in this study were Health Promoting Lifestyle Profile by Walker et al. (1987), Health Value Scale by Wallston et al. (1976), Health Locus of Control by Wallston et al. (1978), and Health Conception Scale by Laffrey(1986). The data were analyzed by Cronbach's $\alpha$, mean, standard deviation, percentage, t-test, ANOVA, Pearson's Correlation Coefficient, and Stepwise Multiple Regression with SPSS PC+ Program. The results were as follows : 1. The means of health promoting lifestyles revealed total 3.33, self actualization 3.74, health responsibility 2.72, exercise 2.80, nutrition 3.55, interpersonal surpport 3.76, and stress management 2.96. Interpersonal support showed the highest score and health responsibility showed the lowest score. 2. No significant differences between sex, grade, major, and residence type, and health promoting lifestyles Were founded(p>.05). Attendants at a health care course showed a significant higher score than nonattendants (p<.001). Male showed a significant higher score in exercise subscale, female showed significant higher scores in health responsibility, nutrition, interpersonal support, stress management subscales(p<.001), residence type showed a significant difference in nutrition subscale(p<.001). 3. No significant differences between perceived health status and sex, grade, major, residence type, and attendance at a health care course were founded(p>.05). Perceived importance of health showed no differences among sex, grade, major, and residence type(p>.05), showed only in attendance at a health care course (p<.001). Attendants at a health care course showed a significant higher score than nonattendants(p<.001). No significant differences between health conception and sex, grade, major, and residence type were founded (p>.05), only significant difference between health conception and attendance at a health care course was founded(p<.05). Nonattendants showed a significant higher score in clinical health conception, attendants showed a significant higher score in wellbeing health conception(p<.05). 4. A significant positive correlation between health promoting lifestyles and perceived health status was founded(r=.2415, p<.001). A significant positive correlation between health promoting lifestyles and perceived importance of health was founded (r=.1475, p<.001). The health promoting lifestyles revealed significant positive correlations in internal and powerful others locus of control (r=.3187, p<.001: r=.1475, p<.001), but revealed a significant negative correlation in chance locus of control(r=-.997, p<.001). A significant positive correlation between health promoting lifestyles and clinical health conception and wellbeing health conception were founded (r=.1241, p<.001 : r=.3047, p<.001). 5. Perceived health status was the highest factor predicting health promoting lifestyles of college students(R=.3415, $R^2=11.62$). Perceived health' status, perceived improtance of health, internal locus of control, wellbeing health conception, powerful others locus of control accounted for 28.19% in health promoting lifestyle patterns. In conclusion, college students who reported more helath promoting lifestyles evaluated their health positively, perceived importance of health highly, perceived their health as affected by theirselves, powerful others but not by chance or luck, and accepted health as high level wellness rather than merely the absence of illness. Those who attending at a health care course had healthier lifestyle patterns. And attendance at a health care course had significant. correlations in these cognitive-perceptual factors. Further studies are required to identify reasons of attending a health care course, and to compare health promoting lifestyles pre-post attending a course related to health care.

• Journal of Nutrition and Health
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• v.41 no.8
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• pp.733-741
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• 2008

In the present study, we comprehensively examined the associations of plasma levels of total adiponectin and high molecular weight (HMW) adiponectin with the features of cardiometabolic risks including body fat distribution, dyslipidemia, insulin resistance and inflammatory markers in a cross-sectional study of 110 treated hypertensive patients. Blood lipid profiles, high sensitivity C-reactive protein (hsCRP) and homeostasis model assessment of insulin resistance (HOMA- IR) derived from fasting glucose and insulin concentrations were determined. Plasma levels of tumor necrosis factor-${\alpha}$ (TNF-${\alpha}$), interleukin-6 (IL-6) and intercellular adhesion molecule-1 (ICAM-1) were analyzed using ELISA. The results showed that plasma levels of HMW-adiponectin were negatively associated with body mass index (BMI, r = - 0.203, p < 0.05) and waist circumference (r = -0.307, p < 0.01), which was not shown in total adiponectin. Plasma levels of HMW-adiponectin were negatively associated with triglyceride (r = -0.223, p < 0.05) and positively associated with HDL-cholesterol (r = 0.228, p < 0.05). Plasma levels of adiponectin were positively associated with HDL-cholesterol (r = 0.224, p < 0.05). Plasma levels of HMW-adiponectin were negatively associated with hsCRP (r = -0.276, p < 0.01) and IL-6 (r = -0.272, p < 0.01). In addition, there were weak associations between plasma levels of HMWadiponectin and TNF-${\alpha}$ (r = -0.163, p = 0.07) and ICAM-1 (r = -0.158, p = 0.09). However, there were no significant associations of total adiponectin with inflammatory markers except hsCRP (r = -0.203, p < 0.05). Stepwise multiple linear regression analysis showed that only plasma levels of HMW-adiponectin was an independent factor influencing serum levels of hsCRP, a marker of systemic low grade inflammation, after adjusting for age, gender, BMI, waist circumference, alcohol intake, smoking status, blood lipids, total adiponectin and drug use (p < 0.01). These results suggest that HMW-adiponectin, rather than total adiponectin, is likely to be closely associated with the features of cardiometabolic risks in treated hypertensive patients and might be effective biomarker for the prediction of cardiovascular disease.

• Sleep Medicine and Psychophysiology
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• v.21 no.1
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• pp.5-13
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• 2014

Objectives: Depression, sleep complaints and cognitive impairments are commonly observed in the elderly. Elderly subjects with depressive symptoms have been found to show both poor cognitive performances and sleep disturbances. However, the relationship between sleep complaints and cognitive dysfunction in elderly depression is not clear. The aim of this study is to identify the association between sleep disturbances and cognitive decline in late-life depression. Methods: A total of 282 elderly people who underwent nocturnal polysomnography in a sleep laboratory were enrolled in the study. The Korean version of the Neuropsychological Assessment Battery developed by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD-K) was applied to evaluate cognitive function. Depressive symptoms were assessed with the geriatric depression scale (GDS) and subjective sleep quality was measured using the Pittsburg sleep quality index (PSQI). Results: The control group ($GDS{\leq}9$) when compared with mild ($10{\leq}GDS{\leq}16$) and severe ($17{\leq}GDS$) depression groups, had significantly different scores in the Trail making test part B (TMT-B), Benton visual retention test part A (BVRT-A), and Stroop color and word test (SCWT)(all tests p<0.05). The PSQI score, REM sleep duration, apnea-hypopnea index and oxygen desaturation index were significantly different across the three groups (all indices, p<0.05). A stepwise multiple regression model showed that educational level, age and GDS score were predictive for both TMT-B time (adjusted $R^2$=35.6%, p<0.001) and BVRT-A score (adjusted $R^2$=28.3%, p<0.001). SCWT score was predicted by educational level, age, apnea-hypopnea index (AHI) and GDS score (adjusted $R^2$=20.6%, p<0.001). Poor sleep quality and sleep structure alterations observed in depression did not have any significant effects on cognitive deterioration. Conclusion: Older adults with depressive symptoms showed mild sleep alterations and poor cognitive performances. However, we found no association between sleep disturbances (except sleep apnea) and cognitive difficulties in elderly subjects with depressive symptoms. It is possible that the impact of sleep disruptions on cognitive abilities was hindered by the confounding effect of age, education and depressive symptoms.