repository
Trajectory of health behaviors and cardiovascular disease
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 이순영 | - |
| dc.contributor.author | 한진아 | - |
| dc.date.accessioned | 2022-11-29T02:33:08Z | - |
| dc.date.available | 2022-11-29T02:33:08Z | - |
| dc.date.issued | 2022-02 | - |
| dc.identifier.other | 31786 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/20606 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :의학과,2022. 2 | - |
| dc.description.tableofcontents | Ⅰ. INTRODUCTION 1 A. Study background 1 B. Study objectives 4 C. Literature review 5 1. Life-course approach 5 2. Health behavior factors and cardiovascular disease 8 3. Measurement of change in health behaviors and risk of CVD 10 Ⅱ. STUDY METHODS 15 A. Research hypothesis 15 B. Study model 16 C. Materials 17 1. Data source 17 2. Study subjects 19 D. Definition of variables 20 1. Socioeconomic characteristics of study subjects 20 2. Health behaviors 22 3. Morbidity of chronic disease 24 E. Study analysis 26 1. Group-based trajectory modeling analysis 26 2. The association between the trajectory of health behaviors and the incidence of CVD 28 3. Validity of the trajectory of health behaviors 29 Ⅲ. RESULTS 30 A. Characteristics of study subjects 30 1. Socioeconomic characteristics of study subjects 30 2. Health behavior characteristics of study subjects 33 3. Predisposing disease for cardiovascular disease 35 B. Trajectory of heath behaviors 37 1. Trajectory of heath behaviors 37 2. Characteristics of vulnerable trajectory in health behaviors by age groups 56 3. Trajectory of heath behaviors and predisposing disease for CVD 68 C. Trajectory of health behaviors and cumulative incidence rate of CVD 70 D. Risk factors for CVD in trajectory of health behavior 76 E. Validity of the trajectory of health behaviors 82 Ⅳ. DISCUSSION 86 Ⅴ. CONCLUSION 91 REFERENCE 92 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Trajectory of health behaviors and cardiovascular disease | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 의학과 | - |
| dc.date.awarded | 2022. 2 | - |
| dc.description.degree | Doctoral | - |
| dc.identifier.localId | 1244994 | - |
| dc.identifier.uci | I804:41038-000000031786 | - |
| dc.identifier.url | https://dcoll.ajou.ac.kr/dcollection/common/orgView/000000031786 | - |
| dc.subject.keyword | CVD | - |
| dc.subject.keyword | Korean Medical Panel Survey | - |
| dc.subject.keyword | group-based trajectory model | - |
| dc.subject.keyword | health behaviors | - |
| dc.subject.keyword | trajectory | - |
| dc.description.alternativeAbstract | Cardiovascular disease (CVD) impose a great personal, social, and economic burden. The incidence of CVD is mainly affected by health behavior. Despite various efforts to improve the health behavior of Korean adults over the past decade, there have been limited effects. We attempted to confirm the trajectory of Korean adults’ health behaviors over the past 10 years. We tried to understand their characteristics by selecting vulnerable trajectories. Furthermore, we assessed the risk of heart and cerebrovascular disease according to each health behavior trajectory. 1. We aimed to confirm the trajectory of health behaviors by age group. Using data from the 2009-2018 Korean Medical Panel Survey, 7,142 adults aged over 18 who had completed all 10 surveys were selected as study subjects. Based on the subjects’ age at baseline, they were classified into four groups : 18-29, 30-49, 50-64, and over 65 years. We applied the Group-Based Trajectory Model (GBTM) to confirm the trajectories of smoking, drinking, physical activity, body mass index (BMI), sleep duration, and sedentary. The results are as follows. - Smoking was classified into four to five trajectories, depending on the age group. The trajectories of smoking for those aged 18-29 years shows no significant change from the level of smoking at baseline. Moreover, the initial value of each trajectory was lower than that of other age groups. In most other age groups, we found trajectories whereby the average daily smoking amount decreased. Notably, the initial value of trajectories for those aged 30-49 years was higher than that of other age groups. - Drinking was classified into five to six trajectories, depending on the age group. The trajectories of drinking for those aged 18-29, and 30-49 years shows slightly increases or decreases from the frequency of drinking at baseline. The trajectories of drinking for those aged 50-64, and over 65 years shows significant decrease from the frequency of drinking at baseline. - Physical activity was classified into three to four trajectories, depending on the age group. The trajectories of physical activity for those aged 18-29, and 30-49 years shows no significant change from the level of physical activity at baseline. Notably, the highest level trajectory for those aged 30-49 years was higher than that of other age groups. - BMI was classified five trajectories in all age group. The trajectories of BMI for those aged 18-29, and 30-49 years shows increases from the BMI at baseline. The trajectories for BMI for those aged 50-64 and over 65 years shows no significant change from the BMI at baseline. - Sleep duration was classified into three to five trajectories, depending on the age group. We found three trajectories for those aged 18-29 and five trajectories for the other age groups. - Sedentary was classified into three to four trajectories, depending on the age group. We found three trajectories for those aged 18-29 and four trajectories for the other age groups. 2. We attempted to understand the socioeconomic characteristics of trajectories vulnerable in health behaviors. Vulnerable trajectories included: a) decreased in quantity, but still maintained a relatively high level of smoking, b) decreased in frequency, but still maintained a relatively high level of drinking, c) maintained inactivity, d) maintained a relatively high level of BMI, e) maintained a relatively short sleep duration, f) maintained a relatively long sedentary. Using the chi-square test to examine their socioeconomic characteristics, we found that trajectories vulnerable to smoking or drinking were mainly male or occupational, while those vulnerable to physical activity, BMI, sleep duration, and sedentary were mainly female, low income, low education, or being jobless. 3. To examine the effect of the trajectory of health behaviors on the occurrence of CVD, logistic regression analysis was performed using data from 6,828 who had no experience in diagnosing heart or cerebrovascular disease at baseline. The socioeconomic factors and the prevalence of predisposing disease for CVD at baseline were adjusted. - The 10-year cumulative incidence of heart disease was 4.7%. Compared to the “Steadying non-smoking”, the risk of heart disease in the “Steadying low level”, “Steadying moderate level”, and “High level” was 1.9, 1.6, and 1.7 times higher, respectively. Compared to the “Steadying moderate level”, the risk of heart disease in the “Steadying low level” and “Steadying inactivity” was 1.8 and 2.2 times higher, respectively. “Steadying high in normal”, “Steadying overweight”, and “Steadying obese” had 1.8, 2.2, and 2.3 times higher than the “Steadying low level”, respectively. - The 10-year cumulative incidence of cerebrovascular disease was 3.6%. Compared to the “Steadying non-smoking”, the risk of cerebrovascular disease in the “Steadying low level” was 1.6 times higher. 4. The trajectories of health behaviors used as major variables of interest in the study were derived in optimal numbers and forms through the Akaike information criterion (AIC) and Bayesian information criterion (BIC) indexes calculated by the GBTM analysis. In addition, the explanatory power of the model using the trajectory variable of health behavior was significantly higher than that of the model using the level of health behavior at one point in time. The accuracy of each health behavior trajectory"s prediction of CVD occurrence was higher than 60%. The level of health behavior once formed has remained unchanged over the past decade. The level of health behavior in the early stages of life seems to be very important. Therefore, active intervention is needed to select healthy behavior and improve these behaviors. We found that an individual’s accumulated experiences, such as smoking, maintained high level BMI, or inactivity were acted as a risk factor in the occurrence of CVD. Furthermore, we showed that relatively higher daily average smoking amount, low physical activity level, or BMI level were accompanied by a higher risk of CVD. Active and efficient services are needed by focusing on groups still vulnerable to change despite of various efforts over the past decade. Finally, this study is meaningful in that within one study model, various health behavior by age group were considered simultaneously. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Medicine > 4. Theses(Ph.D)
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
-
- License
- STATISTICS
- Total Visit :3,739,872
- Total Download :1,818
- Today View :17,530
