1. Introduction
Diabetes is one of the diseases with the most rapid increases in prevalence worldwide; approximately every 10 s, three individuals develop diabetes. The number of patients diagnosed with diabetes is projected to increase to approximately 59 million by 2035 [1]. In South Korea, approximately one in seven adults aged 30 years or older and approximately three out of ten elderly people aged 65 years or older are currently diagnosed with diabetes [2]. However, the proportion of patients achieving an HbA1c of <6.5% stands at 28.3%, and the proportion of those requiring active treatment (HbA1c of ≥8%) is at 19%. This indicates a lack of or inadequate diabetes management and suggests that the existing intervention programs are not sufficient to induce and sustain behavioral changes in patients diagnosed with the disease.
As diabetes is associated with a high incidence of fatal complications, such as cardiovascular disease and diabetic kidney disease, as well as having a high mortality rate, the effective health management of diabetes is imperative [2,3]. Diabetes is a particularly strong independent risk factor for cardiovascular disease [4], with men with diabetes having a 2–3 times increased risk and women a 3–4 times increased risk of cardiovascular disease compared to that in the general healthy population. Patients with diabetes also develop cardiovascular disease 15 years earlier on average [5], and approximately 20% of patients with diabetes die due to cardiovascular complications [2]. Health management is, therefore, essential for patients as well as for individuals at a high risk who have not yet developed diabetes.
Health management is affected by an individual’s subjective health status, which represents the self-evaluation of one’s overall health [6]. This subjective perception of one’s health determines attitudes that do or do not lead to health behaviors [7]. It has been reported that those who perceive themselves as being in good health practice more health behaviors, indicating that subjective health status is an important factor in inducing individual behavior changes [8,9]. In patients with diabetes in particular, subjective health status is considered a reliable measure of an individuals’ health [10], and, in Europe, patients with diabetes who perceive their health status as bad were reported to have a higher mortality [11].
Studies investigating subjective health perception among patients with diabetes to date have focused on quality of life [12,13], depression [14], influential factors [15], mediating effects [16], HbA1c [17], suicidal behavior [18], and mental health [19]. However, no study so far has reported on health behavior compliance and cardiovascular indices related to subjective health perception in patients with diabetes.
The aim of this study was, therefore, to investigate health behavior compliance and the relationship between cardiovascular disease-related indices and subjective health perception in patients with prediabetes and diabetes. We suggest that our results be used as the basis for the development of intervention strategies to reduce the prevalence of diabetes and the incidence of complications related to the disease.
2. Methods
2.1. Study Design
This descriptive research study used a secondary analysis of the raw data from the 7th (2016–2018) and 8th (2019) Korea National Health and Nutrition Examination Surveys to investigate cardiovascular disease-related risk factors as well as the effects of subjective health perception on health behavior in patients with prediabetes and diabetes.
2.2. Participants and Data Collection
We selected among a total of 32,379 people who participated in the 7th (2016–2018) and 8th (2019) National Health and Nutrition Survey, 19,235 people over the age of 40. The following respondents were then excluded: (1) those (n = 6908) for whom HbA1c levels were not available or those who had reported normal HbA1c levels (<5.6); (2) those who had not responded to the subjective health status item or had responded with “normal” (n = 23,310); (3) those who were pregnant or breastfeeding at the time of the survey (n = 1); (4) those undergoing dietary therapy due to disease at the time of the survey (n = 594); (5) those with cancer (n = 390); and (6) those who had not responded to the health behavior item (n = 1809). A total of 2,485 individuals were finally selected as participants for this study and were classified into a group with diabetes (HbA1c ≥ 6.5%) and a group with prediabetes (HbA1c 5.7–6.4%) [20]. Subsequently, they were cross-classified according to their subjective health status into those who perceived their health as “good” and those who considered themselves as being in “bad” health and divided into the following four groups: prediabetes stage with good self-perceived health status (“Good/PDM”, 1123 individuals), prediabetes stage with bad self-perceived health status (“Bad/PDM”, 904), diabetes stage with good self-perceived health status (“Good/DM”, 151), and diabetes stage with bad self-perceived health status (“Bad/DM”, 307).
2.3. Instruments
2.3.1. Subjective Health Status
Subjective health status in this study was rated on a 5-point Likert scale, with the levels “very good”, “good”, “normal”, “bad”, and “very bad”. Respondents had been asked the question “How do you usually think about your health status?” Responses were classified as good (“very good” and “good”), bad (“bad” and “very bad”), and “normal”.
2.3.2. Health-Related Behaviors
We used the seven health-related behaviors referred to as the “Alameda 7”, (non-smoking, moderate drinking, adequate sleep, maintaining a desirable weight, exercising, eating breakfast, and avoiding snacks), but replaced “avoiding snacks” with “eating out rarely”. Non-smoking was defined as having never smoked in one’s lifetime or having smoked in the past but not currently. Moderate drinking was defined as not drinking throughout one’s lifetime or drinking less than twice a week within the past year. A desirable weight was defined as having a body mass index (BMI) of ≥18.5 kg/m2 to 25 kg/m2 based on the World Health Organization BMI classification for the Asian Population [21]. Adequate sleep was defined as sleeping 7 to 8 h per night. Exercising was defined as a health behavior if a person reported exercising for 150 min or longer per week [21]. Having breakfast was defined as a health behavior if a person had breakfast 5–7 times a week. Eating out rarely was defined as a health behavior if a person reported eating out less than 5 times a week.
2.3.3. Cardiovascular Disease Risk-Related Indicators
The Framingham Risk Score (FRS) was developed based on data obtained from the long-term Framingham Heart Study conducted in the USA and is used to estimate the 10-year cardiovascular risk of individuals aged 30–74 years without cardiovascular disease by scoring for age, total cholesterol, high-density cholesterol (HDL), blood pressure levels, smoking status, and the presence or absence of diabetes. A higher score indicates a greater risk of cardiovascular disease [22].
2.3.4. Body and Blood Indices
Systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), HDL, cholesterol, and fasting blood glucose levels were examined according to the goal of integrated management of patients with diabetes as recommended in the Clinical Practice Guidelines for Diabetes 2021 [2].
3. Data Analysis
Statistical analyses were performed using SAS ver. 9.4 (SAS Institute Inc, Cary, NC, USA). Complex sample data analysis was performed using weights in accordance with the Korea Disease Control and Prevention Agency’s guidelines for the use of raw data from the Korea National Health and Nutrition Examination Survey. The participants were divided into four groups by cross-classifying subjective health status (good/bad) and diabetes status (prediabetes/diabetes), and the general characteristics of the participants were analyzed using a crossover analysis. Then, a crossover analysis was performed to compare health behaviors between groups, and a general linear model analysis was conducted to determine differences in blood indices between groups.
Ethical Considerations
This study represents a secondary analysis of national survey data available through the Korea National Health and Nutrition Examination Survey website and was conducted after receiving approval from the institutional review board of S University (SSWUIRB-2022-024). Because the raw data analyzed for this study contain no personally identifiable information, anonymity and confidentiality were guaranteed.
4. Results
4.1. General Characteristics of the Participants
The proportion of men was highest in the Good/PDM group (53.1%), followed by the Bad/PDM (28.6%), Bad/DM (10.5%), and Good/DM (7.8%) groups, while the proportion of women was highest in the Good/PDM group (43.5%), followed by the Bad/PDM (39.8%), Bad/DM (12.1%), and Good/DM (4.6%) groups. In both disease groups, that is, among individuals with prediabetes and as well as those with diabetes, more men than women perceived their subjective health status as good, while more women than men perceived their health status as bad.
Body weight was highest in the Good/DM group (mean = 68.7 kg), followed by the Bad/DM group (67.3 kg), the Good/PDM group (65.3 kg), and then the Bad/PDM group (63.4 kg). BMI was highest in the Bad/DM group (26.4 kg/m2), followed by the Good/DM (26.0 kg/m2), Bad/PDM (24.8 kg/m2), and Good/PDM (24.3 kg/m2) groups. These findings indicate that body weight and BMI were higher in both DM groups overall, but that BMI was higher in those individuals with poor subjective health.
Regarding age, the majority of adults (40–59 year) fell into the Good/PDM category (58.7%), followed by Bad/PDM (27.6%), Bad/DM (11.3%), and Good/DM (6.1%). For elderly people (>60 year), the percentage of those in the Bad/PDM group was higher (40.9%), followed by the Good/PDM (38.1%), Bad/DM (14.3%), and Good/DM (6.7%) groups. Overall, the proportion of individuals with a good subjective health status and prediabetes (Good/PDM) was approximately 20% higher in adults than in the elderly age group. In terms of marital status, the subsample of survey participants who reported having a spouse had the highest proportion of individuals who fell into the Good/PDM category, at 52.9%. A higher household income was also associated with a higher proportion of individuals in the Good/PDM group, as was a higher education level. Among women, the proportion of respondents in the Good/PDM group was higher, at 62.4%, in non-menopausal women (see Table 1).
4.2. Health Behaviors
Among the seven assessed health behaviors, the differences between the ratios of individuals reporting a normal weight, an appropriate sleep time, an appropriate exercise time, and a low frequency of eating out were statistically significant among the groups.
The proportion of those with a normal weight was the highest in the Good/PDM group (33.1%), followed by the Bad/PDM (29%), Bad/DM (21.7%), and Good/DM (16.1%) groups. The proportion of those reporting adequate sleep was the highest in the Good/PDM group (32.9%), followed by the Good/DM (31.8%), Good/PDM (28.7%), and Bad/DM (20%) groups. The proportion of those exercising for an adequate time was the highest in the Good/PDM group (47.4%), followed by the Bad/PDM (30.4%), Good/DM (30.4%), and Bad/DM (26.5%) groups. The proportion of those eating out less was the highest in the Bad/PDM group (76.3%), followed by the Bad/DM (74.3%), Good/DM (64.2%), and Good/PDM (58.2%) groups.
These findings indicate that the proportion of individuals with a normal weight or those exercising for an adequate time was higher in the group with prediabetes. The proportion of individuals reporting an adequate sleep time was higher among those with a good subjective health status, while the proportion of those eating out several times per week was higher among those with poor subjective health (see Table 2 & Figure 1).
4.3. Cardiovascular Disease-Related Indices
Mean systolic blood pressure was the highest in the Bad/DM group (128.88) and the lowest in the Good/PDM group (120.88). The mean diastolic blood pressure was the highest in the Good/DM group (77.92) and the lowest in the Bad/DM group (75.84). The mean total cholesterol level was the highest in the Good/PDM group (202.61) and the lowest in the Bad/DM group (181.6), while the mean HDL level was the highest in the Good/PDM group (50.18) and the lowest in the Good/DM group (43.87). The mean LDL level was the highest in the Good/PDM group (122.43) and the lowest in the Bad/DM group (105.44). The mean CVD score was the lowest in the Good/PDM group (10.75) and the highest in the Bad/DM group (13.41). The mean triglyceride level was the highest in the Good/DM group (199.56) and the lowest in the Good/PDM group (141.99). The mean fasting blood glucose level was the highest in the Bad/DM group (15.054) and the lowest in the Good/PDM group (100.55) (see Table 3).
5. Discussion
One in 10 adults worldwide have diabetes [23], which is associated with an increased risk of diseases, such as cardiovascular disease and mortality [24]. Therefore, there is an emerging need for social efforts to prevent chronic disease early and reduce social costs due to health deterioration. Study results have revealed that glycemic control through various interventions in patients with diabetes increases the quality of life [25] and reduces complications, medical expenses [25,26], and the prevalence of the cardiovascular disease [27].
Based on the results of studies [8,9,15] indicating that a higher perceived subjective health is, in general, associated with better health behavior, we investigated whether such findings can be applied to patients with diabetes as well.
Our findings show that, first, among those with prediabetes and diabetes, men perceive their health as better overall than women and that in those with prediabetes, adults perceived their health as better than elderly people. These results are consistent with the results of a previous study showing that female elderly people perceived their own health to be worse compared to male elderly people in terms of sex-specific subjective health statuses [28]. Our findings are, however, contradictory to the results of a study in which elderly patients with diabetes were reported to have a positive subjective health perception [17] and wherein subjective health perception became more positive with increasing age [29]. Considering the results of other studies indicating that subjective health perception in elderly subjects is affected by life satisfaction and mood state [30] and that an individual’s experience with illness can affect their evaluation of their health status negatively [31], more research is needed to draw clear conclusions. Furthermore, the factors affecting subjective health status and health-related behaviors differ by sex [15,17]. In addition, even when the same health-related behaviors are implemented, the effects on health indicators vary by sex as well [32], and this factor should be given priority in interventions for diabetic patients.
Second, we found that a better subjective health status is associated with a higher proportion of individuals with a normal weight, an adequate sleep time, an adequate exercise time, and eating out only a few times per week. These results are similar to the results of a study revealing that insomnia, depression, and stress affect subjective health status and that those with insomnia in particular, who find it difficult to fall and stay asleep, perceive their subjective health status to be poor [29]. Moreover, other studies have shown that stress-related symptoms impair subjective health perception [32,33]. In addition, those who exercise regularly were reported to feel more subjectively healthy than those who do not [34], and menopausal women with a high participation in exercise were reported to have a good subjective health status and a significantly lower incidence of menopausal symptoms [35]. The results of a meta-analysis of studies regarding various exercise interventions in patients with diabetes indicate that exercise interventions had the most positive effects on blood glucose control in patients with diabetes [36]. These findings suggest that exercise can help improve disease condition, thereby enhancing subjective health perception.
We found that the individuals in the group with a good subjective health reported eating out more often than those in other groups. This suggests that people who tend to be confident about their health may eat out more, and there may, thus, be a difference between what is perceived as socially acceptable for patients with prediabetes and diabetics and what are recommendations by medical practitioners. In addition, we found that individuals in the Bad/DM group reported the lowest percentage of eating out. We assume that this stems from the fact that dietary management is an important factor for glycemic control in diabetic patients and that patients with diabetes refrain from eating out due to limited food choices and the generally high content of sodium and sugar in the available foods.
Most of the assessed health-related behavior variables were found to have a significant effect in the Good/PDM group. This is consistent with the results of studies showing that subjective health perception positively affects health behavior [8,9,15], that the prevalence of diabetes is higher in those with poor than in those with good subjective health [37], that subjective health is a factor affecting the prevalence of diabetes in elderly people [38], and that those who evaluate their own physical, mental, and cognitive functions more positively are more likely to practice health-promoting behaviors [39].
Overall, patients with diabetes tend to perceive their subjective health as poor compared to those in the general population [40]. To sum up the results of this study and put them in context with earlier research, it seems that for patients with diabetes, perceiving their health as better helps their physical health to some extent. Therefore, when developing intervention strategies for patients with diabetes, it is necessary to carefully assess and manage their own perception of their health. In addition, it is necessary to develop strategies that can improve patients’ positive perception of their health, involving their weight, sleep time, and exercise factors that affect subjective health status, and emphasize dietary habit changes with regard to eating out.
Finally, the results of our analysis of blood pressure and blood indices related to cardiovascular disease risk (FRS) reveal that a better subjective health status was associated with a lower risk of developing cardiovascular disease in all indices except for blood pressure. The FRS estimates the 10-year risk of developing coronary heart disease in adults aged 30–74 years according to sex by scoring for age, smoking status, the presence or absence of diabetes, systolic and diastolic blood pressure, total cholesterol, and HDL cholesterol levels [22]. As regards diabetes and smoking, the participants in this study were patients with prediabetes/diabetes, and we assessed their relationship with smoking through their health behaviors. Our results show that the FRS was the lowest in the Good/PDM group. We then compared the relative FRS differences between groups to investigate cardiovascular disease risk according to subjective health status. Since the FRS was measured without the variables diabetes and smoking, we cannot assume that it accurately determined cardiovascular disease risk. However, it does indicate that the subjective health status in patients with prediabetes and diabetes is significantly associated with indices related to cardiovascular disease as a major complication of diabetes and that a better subjective health status is associated with a reduced risk of cardiovascular disease.
Because previous studies regarding cardiovascular disease according to subjective health status are scarce, a direct comparison between the results of this study and other studies cannot be made. However, it has been reported that better objective medical indices in patients with chronic obstructive pulmonary disease are associated with better subjective health [41], that a better subjective health status in patients with type 2 diabetes is associated with lower HbA1c and blood glucose levels [17], and that a better subjective health status is associated with a higher health-related quality of life [42,43], in line with the results of this study. On the other hand, the lower the illness perception, the different from the results of studies [44], in which show a good control of blood sugar. Continuous research on the effects of self-health and the awareness of one’s disease on health management is necessary.
6. Conclusions
This study shows that in both patients with prediabetes and those with diabetes, male patients have a better subjective health perception than female patients and that those with a good subjective health perception also practiced more good health behaviors (related to sleep, weight, and exercise). This suggests that enhancing the subjective health perception in patients with prediabetes and diabetes increases the probability of also improving their diabetes. Although various studies regarding methods for improving glycemic control in patients with prediabetes/diabetes have been conducted, most are related to basic diabetes guidelines on dietary control and exercise. For more systematic and efficient managements of patients with prediabetes/diabetes, it is necessary to first identify the subjective health perception at an individualized level and provide customized programs for individual patients that can improve their subjective health perception and be applied to public health system management.
This study is significant in that it investigated the effects of subjective health perception on health behaviors and cardiovascular risk factors in patients with prediabetes/diabetes and provides basic data for the development of programs for improving diabetes. Because our study used a nationally representative large-scale sample in South Korea, it is possible to generalize the results of this study. However, this study has the limitation in that causal relationships cannot be assumed, due to its cross-sectional nature. In addition, because we were not able to include other factors affecting individuals’ behavior such as attitude and values, large-scale studies considering these factors in the future are needed.
Based on the results of this study, efforts to develop and apply intervention programs to public health system management that can improve positive subjective health perceptions in patients with prediabetes/diabetes should be undertaken for the prevention and continuous management of diseases in patients with prediabetes/diabetes.
Conceptualization, S.K., S.B. and J.S.; methodology, S.B. and S.K.; software, S.B. and J.S.; validation, J.S.; formal analysis, J.S.; resources, S.K., S.B., J.S. and Y.L.; data curation, S.K. and S.B.; writing—original draft preparation, S.K.; writing—review and editing, S.B., J.S. and Y.L.; visualization, Y.L.; supervision, S.B. and J.S.; project administration, S.B. and J.S. All authors have read and agreed to the published version of the manuscript.
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Sungshin Women University Institutional Review Board (approval number: SSWUIRB-2022-024).
Informed consent was obtained from all subjects involved in the study.
Not applicable.
The author declares no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Chracteristics of subjects.
| Good/PDM | Bad/PDM | Good/DM | Bad/DM | Total | χ2/F | p | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | n | % | ||||
| Sex | M | 524 | 53.1 | 312 | 28.6 | 88 | 7.8 | 131 | 10.5 | 1055 | 100.0 | 35.061 | <0.001 |
| F | 599 | 43.5 | 592 | 39.8 | 63 | 4.6 | 176 | 12.1 | 1430 | 100.0 | |||
| Total | 1123 | 48.1 | 904 | 34.5 | 151 | 6.1 | 307 | 11.3 | 2485 | 100.0 | |||
| Weight | 65.3 ± 0.4 (1) | 63.4 ± 0.5 | 68.7 ± 1.1 | 67.3 ± 1.0 | 64.0 ± 0.1 | 10.36 (2) | <0.001 | ||||||
| BMI | 24.3 ± 0.1 | 24.8 ± 0.1 | 26.0 ± 0.3 | 26.4 ± 0.3 | 24.2 ± 0.1 | 26.06 (2) | <0.001 | ||||||
| Age | 40–59 year | 558 | 58.7 | 265 | 27.6 | 52 | 5.5 | 84 | 8.2 | 959 | 100.0 | 80.523 | <0.001 |
| >60 year | 565 | 38.1 | 639 | 40.9 | 99 | 6.7 | 223 | 14.3 | 1526 | 100.0 | |||
| Total | 1123 | 48.1 | 904 | 34.5 | 151 | 6.1 | 307 | 11.3 | 2485 | 100.0 | |||
| Marital status | Single | 28 | 34.7 | 38 | 47.5 | 3 | 3.2 | 11 | 14.6 | 80 | 100.0 | 51.754 | <0.001 |
| Married (living together) | 911 | 52.9 | 602 | 31.0 | 121 | 6.1 | 202 | 10.0 | 1836 | 100.0 | |||
| Married (living apart) | 184 | 33.5 | 264 | 44.4 | 27 | 6.5 | 94 | 15.6 | 569 | 100.0 | |||
| Total | 1123 | 48.1 | 904 | 34.5 | 151 | 6.1 | 307 | 11.3 | 2485 | 100.0 | |||
| Household income | Q1 | 161 | 22.9 | 356 | 51.5 | 43 | 6.7 | 144 | 18.9 | 704 | 100.0 | 193.434 | <0.001 |
| Q2 | 268 | 48.1 | 229 | 34.1 | 32 | 5.1 | 73 | 12.7 | 602 | 100.0 | |||
| Q3 | 319 | 58.7 | 170 | 28.5 | 36 | 5.2 | 49 | 7.6 | 574 | 100.0 | |||
| Q4 | 372 | 62.3 | 145 | 24.6 | 38 | 6.7 | 39 | 6.4 | 594 | 100.0 | |||
| Total | 1120 | 48.2 | 900 | 34.5 | 149 | 6.0 | 305 | 11.3 | 2474 | 100.0 | |||
| Employment | Yes | 736 | 57.3 | 412 | 28.2 | 96 | 6.7 | 116 | 7.8 | 1360 | 100.0 | 103.950 | <0.001 |
| No | 386 | 34.5 | 489 | 43.8 | 55 | 5.2 | 190 | 16.5 | 1120 | 100.0 | |||
| Total | 1122 | 48.1 | 901 | 34.5 | 151 | 6.1 | 306 | 11.3 | 2480 | 100.0 | |||
| Education level | Elementary school | 219 | 24.8 | 440 | 50.5 | 51 | 6.4 | 149 | 18.3 | 859 | 100.0 | 264.604 | <0.001 |
| Junior high school | 124 | 35.3 | 136 | 41.1 | 28 | 9.4 | 57 | 14.3 | 345 | 100.0 | |||
| High school | 379 | 55.7 | 209 | 29.7 | 38 | 4.8 | 75 | 9.8 | 701 | 100.0 | |||
| University | 400 | 70.6 | 115 | 19.5 | 34 | 5.7 | 24 | 4.2 | 573 | 100.0 | |||
| Total | 1122 | 48.1 | 900 | 34.4 | 151 | 6.1 | 305 | 11.3 | 2478 | 100.0 | |||
| Menopause | No | 156 | 62.4 | 72 | 27.5 | 5 | 1.8 | 18 | 8.2 | 251 | 100.0 | 44.015 | <0.001 |
| Yes | 432 | 38.1 | 508 | 43.2 | 56 | 5.4 | 155 | 13.3 | 1151 | 100.0 | |||
| Total | 588 | 43.5 | 580 | 39.8 | 61 | 4.6 | 173 | 12.2 | 1402 | 100.0 | |||
(1) Mean ± SE (2) F-statistics.
Health behavior practice rate between groups.
| Good/PDM | Bad/PDM | Good/DM | Bad/DM | Total | χ 2 | p | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | % (1) | n | % | n | % | n | % | n | % | |||
| Non-smoking | 980 | 84.0 | 756 | 80.1 | 119 | 77.1 | 248 | 80.7 | 2103 | 81.9 | 5.477 | 0.140 |
| Moderate drinking | 896 | 78.7 | 760 | 81.8 | 116 | 76.0 | 255 | 81.5 | 2027 | 79.9 | 3.259 | 0.353 |
| Maintaining a desirable weight | 389 | 33.1 | 268 | 29.0 | 28 | 16.1 | 64 | 21.7 | 749 | 29.4 | 24.155 | <0.001 |
| Adequate sleep | 359 | 32.9 | 263 | 28.7 | 45 | 31.8 | 71 | 20.0 | 738 | 29.9 | 15.694 | 0.001 |
| Eating breakfast | 901 | 77.2 | 722 | 76.9 | 126 | 80.9 | 244 | 77.2 | 1993 | 77.3 | 0.915 | 0.822 |
| Exercising | 520 | 47.4 | 263 | 30.4 | 43 | 30.4 | 80 | 26.5 | 906 | 38.1 | 62.357 | <0.001 |
| Eating out | 713 | 58.2 | 725 | 76.3 | 104 | 64.2 | 241 | 74.3 | 1783 | 66.6 | 58.032 | <0.001 |
(1) The percentage of health behavior practice.
Blood pressure and blood indices between groups.
| Good/PDM | Bad/PDM | Good/DM | Bad/DM | F | p | |
|---|---|---|---|---|---|---|
| SBP | 120.88 ± 0.52 | 124.44 ± 0.62 | 126.4 ± 1.27 | 128.88 ± 1.4 | 15.53 | <0.001 |
| DBP | 77.06 ± 0.34 | 76.21 ± 0.42 | 77.92 ± 1.07 | 75.84 ± 0.83 | 1.73 | 0.160 |
| TC | 202.61 ± 1.28 | 193.08 ± 1.65 | 194.49 ± 4.39 | 181.6 ± 2.89 | 18.62 | <0.001 |
| HDL-C | 50.18 ± 0.4 | 47.88 ± 0.43 | 43.87 ± 1.04 | 44.8 ± 0.86 | 19.14 | <0.001 |
| LDL-C | 122.43 ± 2.80 | 120.39 ± 3.65 | 126.28 ± 5.76 | 105.44 ± 4.95 | 3.43 | 0.017 |
| CVD score | 10.75 ± 0.15 | 12.24 ± 0.16 | 12.59 ± 0.33 | 13.41 ± 0.26 | 35.53 | <0.001 |
| TG | 141.99 ± 3.79 | 155.64 ± 5.82 | 199.56 ± 15.43 | 197.14 ± 15.44 | 9.16 | <0.001 |
| Glucose | 100.55 ± 0.44 | 101.88 ± 0.58 | 147.78 ± 4.17 | 150.54 ± 3.25 | 111.59 | <0.001 |
References
1. L’heveder, R.; Nolan, T. International diabetes federation. Diabetes Res. Clin. Pract.; 2013; 101, pp. 349-351. [DOI: https://dx.doi.org/10.1016/j.diabres.2013.08.003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24119591]
2. Korean Diabetes Association. Diabetes Fact Sheet 2020; Korean Diabetes Association: Seoul, Korea, 2021.
3. Snoek, F.J.; Skinner, T.C.; Steed, L. Diabetes. Chronic Physical Illness: Self-Management and Behavioural Interventions; Newman, S.; Steed, L.; Mulligan, K. McGraw-Hill: New York, NY, USA, 2009; pp. 169-188.
4. Savage, P.J. Cardiovascular complications of diabetes mellitus: What we know and what we need to know about their prevention. Ann. Intern. Med.; 1996; 124, pp. 123-126. [DOI: https://dx.doi.org/10.7326/0003-4819-124-1_Part_2-199601011-00008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/8554203]
5. Booth, G.L.; Kapral, M.K.; Fung, K.; Tu, J.V. Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: A population-based retrospective cohort study. Lancet; 2006; 368, pp. 29-36. [DOI: https://dx.doi.org/10.1016/S0140-6736(06)68967-8]
6. Jylhä, M. What is self-rated health and why does it predict mortality? Towards a unified conceptual model. Soc. Sci. Med.; 2009; 69, pp. 307-316. [DOI: https://dx.doi.org/10.1016/j.socscimed.2009.05.013] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19520474]
7. Shin, K.R.; Kim, J.S. A study on health concern, self-rated health, health status, and health promotion behavior of elderly women in urban area. J. Korean Acad. Nurs.; 2004; 34, pp. 869-880. [DOI: https://dx.doi.org/10.4040/jkan.2004.34.5.869]
8. Shin, K.R.; Kim, J.S.; Kim, J.Y. A comparison on self-rated health, health status, and health promotion behaviors between low income and non-low income elderly women. Korean J. Adult Nurs.; 2005; 17, pp. 732-742.
9. Wu, S.; Wang, R.; Zhao, Y.; Ma, X.; Wu, M.; Yan, X.; He, J. The relationship between self-rated health and objective health status: A population-based study. BMC Public Health; 2013; 13, 320. [DOI: https://dx.doi.org/10.1186/1471-2458-13-320]
10. Badawi, G.; Pagé, V.; Smith, K.J.; Gariépy, G.; Malla, A.; Wang, J.; Boyer, R.; Strychar, I.; Lesage, A.; Schmitz, N. Self-rated health: A predictor for the three year incidence of major depression in individuals with Type II diabetes. J. Affect. Disord.; 2013; 145, pp. 100-105. [DOI: https://dx.doi.org/10.1016/j.jad.2012.07.018]
11. Wennberg, P.; Rolandsson, O.; Jerdén, L.; Boeing, H.; Sluik, D.; Kaaks, R.; Teucher, B.; Spijkerman, A.; de Mesquita, B.B.; Dethlefsen, C. et al. Self-rated health and mortality in individuals with diabetes mellitus: Prospective cohort study. BMJ Open; 2012; 2, e000760. [DOI: https://dx.doi.org/10.1136/bmjopen-2011-000760]
12. Bishwajit, G.; Tang, S.; Yaya, S.; He, Z.; Feng, Z. Lifestyle behaviors, subjective health, and quality of life among Chinese men living with type 2 diabetes. Am. J. Men’s Health; 2017; 11, pp. 357-364. [DOI: https://dx.doi.org/10.1177/1557988316681128]
13. Marte, J.L.C.; Javier, A.; Ruiz-Matuk, C.; Paulino-Ramirez, R. Quality of Life and Nutritional Status in diabetic patients on hemodialysis. Diabetes Metab. Syndr. Clin. Res. Rev.; 2019; 13, pp. 576-580. [DOI: https://dx.doi.org/10.1016/j.dsx.2018.11.020] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30641769]
14. Boehme, S.; Geiser, C.; Renneberg, B. Functional and self-rated health mediate the association between physical indicators of diabetes and depressive symptoms. BMC Fam. Pract.; 2014; 15, 157. [DOI: https://dx.doi.org/10.1186/1471-2296-15-157] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25241039]
15. Kim, K.S. The Factors Affecting Self-Rated Health Status in the Aged with Diabetes. J. Korean Data Anal. Soc.; 2012; 14, pp. 3075-3086.
16. Seo, Y.M.; Choi, W.H. Self efficacy and self care behavior in type 2 diabetes mellitus: The mediating effect of subjective health status. J. Korean Data Anal. Soc.; 2017; 19, pp. 453-556. [DOI: https://dx.doi.org/10.37727/jkdas.2017.19.1.543]
17. Park, J.Y. A Study on Self Management, Hemoglobin A1c(HbA1c), and Perceived Health Status for the Type II Diabetes Patients. J. Korean Biol. Nurs. Sci.; 2010; 12, pp. 106-113.
18. Nam, S.I.; Choi, K.H.; Yi, H.J. Self-perceived health status, Depression, and Suicidal Behavior among People with Diabetes. Korean J. Soc. Welf. Stud.; 2014; 45, pp. 231-254.
19. Jang, U.M.; Kim, G.Y. The Relationship between Self-Rated Health Status and Mental Health among Korean Older Adults: The Moderating Role of Self-Rated Socio-Economic Levels. J. Soc. Sci.; 2021; 10, pp. 121-144. [DOI: https://dx.doi.org/10.16881/jss.2021.10.32.4.121]
20. American Diabetes Association. Standards of medical care in diabetes—2010. Diabetes Care; 2010; 33, (Suppl. S1), pp. S11-S61. [DOI: https://dx.doi.org/10.2337/dc10-S011]
21. WHO. WHO Guidelines on Physical Activity and Sedentary Behaviour; World Health Organization: Geneva, Switzerland, 2020.
22. D’Agostino, R.B., Sr.; Vasan, R.S.; Pencina, M.J.; Wolf, P.A.; Cobain, M.; Massaro, J.M.; Kannel, W.B. General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation; 2008; 117, pp. 743-753. [DOI: https://dx.doi.org/10.1161/CIRCULATIONAHA.107.699579]
23. International Diabetes Federation. IDF Diabetes Atlas; 10th ed. International Diabetes Federation: Brussels, Belgium, 2021.
24. Sarwar, N.; Gao, P.; Kondapally Seshasai, S.R.; Gobin, R.; Kaptoge, S.; Di Angelantonio, E. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: A collaborative meta-analysis of 102 prospective studies. Lancet; 2010; 375, pp. 2215-2222.
25. Testa, M.A.; Simonson, D.C. Health economic benefits and quality of life during improved glycemic control in patients with type 2 diabetes mellitus: A randomized, controlled, double-blind trial. JAMA; 1998; 280, pp. 1490-1496. [DOI: https://dx.doi.org/10.1001/jama.280.17.1490] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/9809729]
26. Vijan, S.; Hofer, T.P.; Hayward, R.A. EStimated benefits of glycemic control in microvascular complications in type 2 diabetes. Ann. Intern. Med.; 1997; 127, pp. 788-795. [DOI: https://dx.doi.org/10.7326/0003-4819-127-9-199711010-00003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/9382399]
27. Skyler, J.S.; Bergenstal, R.; Bonow, R.O.; Buse, J.; Deedwania, P.; Gale, E.A.; Howard, B.V.; Sue Kirkman, M.; Kosiborod, M.; Sherwin, R.S. et al. Intensive glycemic control and the prevention of cardiovascular events: Implications of the ACCORD, ADVANCE, and VA diabetes trials. Circulation; 2009; 119, pp. 351-357. [DOI: https://dx.doi.org/10.1161/CIRCULATIONAHA.108.191305] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19095622]
28. Nam, Y.H.; Nam, J.R. A Study of the Factors Affecting the Subjective Health Status of Elderly People in Korea. Korean J. Fam. Welf.; 2011; 16, pp. 145-162.
29. Kim, J.S.; Cho, B.L. Association between self-Preceived Health Status and Health Related Behavior in Routine Health Examinees. Korean J. Fam. Med.; 2010; 31, pp. 688-696. [DOI: https://dx.doi.org/10.4082/kjfm.2010.31.9.688]
30. Kwon, Y.M.; Yeun, E.J. The self related health status and related factors in the urban elderly. J. Korean Data Anal. Soc.; 2009; 11, pp. 1289-1306.
31. Kang, Y.H.; Kim, M.Y.; Lee, E. The Relationship of Perceived Health Status, Activities of Daily Living and Nutrition Status in the Community-Dwelling Korean Elderly. J. Korean Acad. Nurs.; 2008; 38, pp. 122-130. [DOI: https://dx.doi.org/10.4040/jkan.2008.38.1.122]
32. Bailis, D.S.; Segall, A.; Mahon, M.J.; Chipperfield, J.G.; Dunn, E.M. Perceived control in relation to socioeconomic and behavioral resources for health. Soc. Sci. Med.; 2001; 52, pp. 1661-1676. [DOI: https://dx.doi.org/10.1016/S0277-9536(00)00280-X]
33. Haddock, C.K.; Poston, W.S.; Pyle, S.A.; Klesges, R.C.; Vander Weg, M.W.; Peterson, A.; Debon, M. The validity of self-rated health as a measure of health status among young military personnel: Evidence from a cross-sectional survey. Health Qual. Life Outcomes; 2006; 4, 57. [DOI: https://dx.doi.org/10.1186/1477-7525-4-57]
34. Kim, N.J. The Effect of Regular Exercise on the Subjective Health and Social Activity of the Elderly. Korean J. Phys. Educ.; 2000; 39, pp. 149-158.
35. Kim, N.J. A Study on Correlations among Menopausal Symptoms, Exercise Performance and Subjective Health Conditions of Middle-aged Women. J. Korean Soc. Health Educ. Promot.; 2002; 19, pp. 133-147.
36. Seib, C.; Parkinson, J.; McDonald, N.; Fujihira, H.; Zietek, S.; Anderson, D. Lifestyle interventions for improving health and health behaviours in women with type 2 diabetes: A systematic review of the literature 2011–2017. Maturitas; 2018; 111, pp. 1-14. [DOI: https://dx.doi.org/10.1016/j.maturitas.2018.02.008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29673826]
37. Hong, J.Y.; Park, J.A. Effect of Health status and Health Behavior on the Diabetes Mellitus Prevalence of Korean Adults. J. Korea Contents Assoc.; 2014; 14, pp. 198-209. [DOI: https://dx.doi.org/10.5392/JKCA.2014.14.10.198]
38. Sung, M.J.; Kim, E.J.; Lee, H.J.; Noh, J.W. Analysis of the prevalence of diabetes mellitus and related factors for the local seniors. J. Korea Contents Assoc.; 2014; 14, pp. 244-250. [DOI: https://dx.doi.org/10.5392/JKCA.2014.14.05.244]
39. Kim, S.H. The association between expectation regarding aging and health-promoting behaviors among korean older adults. J. Korean Acad. Nurs.; 2007; 37, pp. 932-940. [DOI: https://dx.doi.org/10.4040/jkan.2007.37.6.932] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17992065]
40. Esteban, C.; Quintana, J.M.; Aburto, M.; Moraza, J.; Egurrola, M.; Pérez-Izquierdo, J.; Aizpiri, S.; Aguirre, U.; Capelastegui, A. Impact of changes in physical activity on health related quality of life among patients with COPD. Capelastegui Eur. Respir. J.; 2010; 36, pp. 292-300. [DOI: https://dx.doi.org/10.1183/09031936.00021409]
41. Song, H.Y. The Airflow Obstruction and Subjective Health Status among Stable Chronic Obstructive Pulmonary Disease Patients Residing in the Community. J. Korean Acad. Nurs.; 2017; 19, pp. 38-47.
42. Kim, H.R. Quality of Life and Its’ Correlates among Older Adults in One Urban Community in Korea: Mediating Effects of Ego Integrity. Korea Gerontol. Soc.; 2012; 32, pp. 223-236.
43. Oh, Y.J.; Kwon, Y.H. Factors Infuencing perceived health status and life satisfaction in rural residents. J. Agric. Med. Community Health; 2012; 37, pp. 145-155. [DOI: https://dx.doi.org/10.5393/JAMCH.2012.37.3.145]
44. Ngetich, E.; Pateekhum, C.; Hashmi, A.; Nadal, I.P.; Pinyopornpanish, K.; English, M.; Quansri, O.; Wichit, N.; Kinra, S.; Angkurawaranon, C. Illness Perceptions, Self-Care Practices, and Glycemic Control among Type 2 Diabetes Patients in Chiang Mai, Thailand. Arch. Public Health; 2022; 80, 134. [DOI: https://dx.doi.org/10.1186/s13690-022-00888-1]
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; Baek, Seunghui 2
; Shin, Jieun 3
1 Robotic Surgery Center, Konyang University Hospital, Daejeon 35365, Korea;
2 Department of Health Exercise Management, Sungshin Women’s University, Seoul 02844, Korea;
3 Department of Biomedical Informatics, College of Medicine, Konyang University, Daejeon 35365, Korea