1.The Influence of Physical Activity and Depression on Sleep Quality in Community-dwelling Older Adults: A Comparison between Young-old and Old-old.
Journal of Korean Biological Nursing Science 2015;17(4):287-296
PURPOSE: The purpose of this study was to identify the influence of physical activity and depression on sleep quality among the young-old and old-old community-dwelling elderly. METHODS: Participants were 216 community-dwelling older adults in Korea aged 65 or above. Data were collected using structured questionnaires with face-to-face interviews that included demographic and health-related characteristics, International Physical Activity Questionnaires (IPAQ), the Short Form Geriatric Depression Scale (SGDS) and the Pittsburgh Sleep Quality Index (PSQI). A hierarchical multiple regression was conducted to examine whether physical activity and depression would predict sleep quality under other controlled factors. RESULTS: There were differences in demographic and health-related characteristics, physical activity, and depression by age groups, but not in sleep quality. In the young-old elderly, physical activity (beta=-0.22, p=.043) and depression (beta=0.31, p=.002) were significantly associated with sleep quality (F=4.46, p=.001, Adjusted R2=.16). In the old-old elderly, physical activity (beta=-0.29, p=.001) and depression (beta=0.41, p<.001) were significantly associated with sleep quality (F=10.79, p<.001, Adjusted R2=.29). CONCLUSION: These finding highlight physical activity and depression as important contributors to sleep quality in both young-old and old-old elderly.
Adult*
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Aged
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Depression*
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Humans
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Korea
;
Motor Activity*
2.Influencing Factors for and Medical Expenditures of Metabolic Syndrome among Public Officials.
Ahrin KIM ; Chanyeong KWAK ; Eun Shil YIM
Korean Journal of Occupational Health Nursing 2012;21(3):209-220
PURPOSE: This study examined the influencing factors for metabolic syndrome and the annual medical expenditures of metabolic syndrome among public officials. METHODS: The National Health Insurance data in 2009 were collected for 364,932 public officials and the heath examination results and annual medical expenditures were analyzed using PASW 18.0 program. RESULTS: The prevalence of metabolic syndrome is 17.6%, and it was higher in male officials than that of females in all age groups. In men, the influencing factors for metabolic syndrome were: age, family history of stroke, cardiovascular disease, hypertension, and diabetes mellitus, smoking, alcohol consumption, exercise, and obesity. However, in women, health-related behaviors such as smoking, alcohol consumption and exercise did not affect metabolic syndrome. People who had metabolic syndrome showed significantly higher medical expenditures than those without metabolic syndrome. The odds ratios of having the highest quartile in medical expenditures were 1.372 (95% CI 1.252~1.504, p<.001) in women with metabolic syndrome and 1.213 (95% CI: 1.184~1.243, p<.001) in men. CONCLUSION: The results implied that health-related behaviors were associated with metabolic syndrome, and resulted in higher medical expenditures. In order not only to decrease the risk of metabolic syndrome but also reduce medical expenditures, nurses should plan health promotion strategies to educate public officials about healthy life strategies.
Alcohol Drinking
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Cardiovascular Diseases
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Diabetes Mellitus
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Female
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Health Behavior
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Health Care Costs
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Health Expenditures
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Health Promotion
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Humans
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Hypertension
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Male
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Metabolic Syndrome X
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National Health Programs
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Obesity
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Odds Ratio
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Prevalence
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Risk Factors
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Smoke
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Smoking
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Stroke
3.Effects of medication adherence interventions for older adults with chronic illnesses: a systematic review and meta-analysis
Hae Ok JEON ; Myung-Ock CHAE ; Ahrin KIM
Osong Public Health and Research Perspectives 2022;13(5):328-340
This systematic review and meta-analysis aimed to understand the characteristics of medication adherence interventions for older adults with chronic illnesses, and to investigate the average effect size by combining the individual effects of these interventions. Data from studies meeting the inclusion criteria were systematically collected in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The results showed that the average effect size (Hedges’ g) of the finally selected medication adherence interventions for older adults with chronic illnesses calculated using a random-effects model was 0.500 (95% confidence interval [CI], 0.342−0.659). Of the medication adherence interventions, an implementation intention intervention (using face-to-face meetings and telephone monitoring with personalized behavioral strategies) and a health belief model–based educational program were found to be highly effective. Face-to-face counseling was a significantly effective method of implementing medication adherence interventions for older adults with chronic illnesses (Hedges’ g= 0.531, 95% CI, 0.186−0.877), while medication adherence interventions through education and telehealth counseling were not effective. This study verified the effectiveness of personalized behavioral change strategies and cognitive behavioral therapy based on the health belief model, as well as face-to-face meetings, as medication adherence interventions for older adults with chronic illnesses.
4.A Structural Model for Premenstrual Coping in University Students: Based on Biopsychosocial Model.
Myung Ock CHAE ; Hae Ok JEON ; Ahrin KIM
Journal of Korean Academy of Nursing 2017;47(2):257-266
PURPOSE: The aims of this study were to construct a hypothetical structural model which explains premenstrual coping in university students and to test the fitness with collected data. METHODS: Participants were 206 unmarried women university students from 3 universities in A and B cities. Data were collected from March 29 until April 30, 2016 using self-report structured questionnaires and were analyzed using IBM SPSS 23.0 and AMOS 18.0. RESULTS: Physiological factor was identified as a significant predictor of premenstrual syndrome (t=6.45, p<.001). This model explained 22.1% of the variance in premenstrual syndrome. Psychological factors (t=-2.49, p=.013) and premenstrual syndrome (t=8.17, p<.001) were identified as significant predictors of premenstrual coping. Also this model explained 30.9% of the variance in premenstrual coping in university students. A physiological factors directly influenced premenstrual syndrome (β=.41, p=.012). Premenstrual syndrome (β=.55, p=.005) and physiological factor (β=.23, p=.015) had significant total effects on premenstrual coping. Physiological factor did not have a direct influence on premenstrual coping, but indirectly affected it (β=.22, p=.007). Psychological factors did not have an indirect or total effect on premenstrual coping, but directly affected it (β=-.17, p=.036). CONCLUSION: These findings suggest that strategies to control physiological factors such as menstrual pain should be helpful to improve premenstrual syndrome symptoms. When developing a program to improve premenstrual coping ability and quality of menstrual related health, it is important to consider psychological factors including perceived stress and menstrual attitude and premenstrual syndrome.
Dysmenorrhea
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Female
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Humans
;
Models, Structural*
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Premenstrual Syndrome
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Psychology
;
Single Person
;
Young Adult
5.Factors Influencing Medication Adherence and Status of Medication Use of the Elderly with Chronic Disease Taking Non-opioid Analgesics.
Hae Ok JEON ; Bockryun KIM ; Haesook KIM ; Myung Ock CHAE ; Myeong Ae KIM ; Ahrin KIM
Journal of Korean Biological Nursing Science 2017;19(1):18-29
PURPOSE: This study investigates the status of medication use of the elderly with chronic disease taking non-opioid analgesics and attempts to identify factors influencing medication adherence. METHODS: Data were collected from September 1 to October 19, 2016. A structured questionnaire was used for face-to-face interview with a convenience sample of 161, elderly people with chronic disease taking non-opioid analgesics. The survey included questions about status of medication use, medication adherence, symptom experience, depression and family function. Data were analyzed using descriptive statistics, t-tests, ANOVA, Pearson's correlation coefficients, and stepwise multiple regression with IBM SPSS 23.0 program. RESULTS: The mean score of medication adherence of the elderly with chronic disease was 4.48±2.35. Experiences of side effects (β=.31, p<.001), use of over-the-counter pain medication (β=.19, p=.009), and family function (β=.16, p=.031) were identified as significant predictors. The final model explained 18.0% of the variation of medication adherence of the elderly with chronic disease taking non-opioid analgesics (F=12.30, p<.001). CONCLUSION: Therefore, as a strategy to improve medication adherence of the elderly with chronic disease, therapeutic intervention should be developed to improve family function and to manage with personalized plans considering experiences of side effects and use of over-the-counter pain medication.