Purpose
This study aimed to identify socioeconomic clusters of older adults and compare cardiovascular health among the identified clusters. Methods: A secondary analysis was performed using the data from 3,303 older adults (over 65 years of age; 56.5% women) who participated in the Korean National Health and Nutrition Examination Survey (2016~2017). A two-step cluster analysis was used to identify older adults’ socioeconomic clusters based on 11 factors associated with Socioeconomic Status (SES). Differences in the cardiovascular health outcomes among the identified clusters were analyzed using the x² test and one-way ANOVA. Results: A three-cluster solution was selected (p<.001) composed of low (n=715), middle (n=1,425), and high-SES clusters (n=1,163). The three clusters differed significantly in the prevalence of diabetes (p<.010), hypertension (p<.001), and metabolic syndrome (p<.001), with greater prevalence in the lower SES clusters. Similarly, systolic blood pressure (p<.001), body mass index (p<.010), and total cholesterol (p<.010) differed significantly among the clusters in the same pattern. Conclusion: Older adults of lower SES clusters should be a crucial target group for health promotion interventions aimed at the prevention and management of cardiovascular disease risk factors. Tailored interventions can be developed by understanding intersecting SES risk factors in this group.

Citations

• The Contribution of Material, Behavioral, Psychological, and Social-Relational Factors to Income-Related Disparities in Cardiovascular Risk Among Older Adults
  Chiyoung Lee, Qing Yang, Eun-Ok Im, Eleanor Schildwachter McConnell, Sin-Ho Jung, Hyeoneui Kim
  Journal of Cardiovascular Nursing.2021; 36(4): E38.     CrossRef

Purpose
Identify the cluster-type risk factors when disease occurs in patients with coronary artery disease (CAD) and examine the impact of the cluster-type on adverse clinical prognosis in CAD patients.
Methods
Secondary data analysis was utilized with data collected from electronic medical records of patients who underwent percutaneous coronary intervention in a university hospital from 2011 to 2015 and who were on an outpatient follow-up visit as of January 2020. The K-means cluster analysis was performed on seven cardiovascular risk factors. Major adverse cardiac events (MACEs), including hospitalization due to restenosis or cardiac-related death, was required in clinical prognosis. The Cox proportional-hazard regression and Kaplan-Meier survival analyses were used.
Results
Cluster analysis identified three clusters of ‘obesity and family history’ (n=150), ‘smoking and drinking’ (n=178), and ‘chronic disease’ (n=190). The MACEs occurred in 10.4% of study subjects. When the ‘obesity and family history’ cluster (62.94±12.09 years) was used as a reference, the relative risk of MACEs was 2.57 times higher in the ‘smoking and drinking’ cluster (62.63±13.31 years) and 2.41 times higher in the ‘chronic disease’ cluster (70.90±10.30 years).
Conclusion
Cluster-type risk factors are necessary when considering secondary prevention strategies for MACEs in patients with CAD. Patients with smoking, drinking, and chronic diseases are especially required to improve their lifestyles and to regularly monitor their management of underlying diseases during follow-up periods.

Citations

• Effect of risk factor-tailored autonomy enhancement education in the first-time middle-aged patients undergoing percutaneous coronary intervention: a randomized controlled trial
  In Ae Uhm, Seon Young Hwang
  BMC Nursing.2023;[Epub]     CrossRef

PURPOSE
This study was conducted to identify cardiovascular risk factor cluster types in early middle-aged male workers in their 30s and 40s, and to identify differences in awareness of mobile health and preventive health behaviors by cluster type.
METHODS
This study adopted a cross-sectional descriptive design. Male workers aged 30~49 years with cardiovascular risk factors (n=166) at three medical device manufacturers in June, 2019 were recruited. Self-reported questionnaires were administered. K-means cluster analysis was performed using four measurement tools: e-health literacy, behavior of seeking health information on the internet, intent to use mobile health, and preventive health behavior.
RESULTS
Three cluster groups were identified based on 7 risk factors: "unhealthy behavior (51.8%)", "chronic disease (28.9%)", and "dyslipid · family history (19.3%)". In the "unhealthy behavior" group where more than 70% of the participants were smoking and drinking heavily, the awareness of mobile health utilization such as behavior of seeking information on the internet and intent to use mobile health, especially usefulness, was significantly lower than that in the other two groups. The preventive health behavior was also the lowest among the three groups.
CONCLUSION
We suggest that when planning for mobile-use cardiovascular prevention education for early middle-aged male workers, it is necessary to consider a cluster of risk factors. Strategies for raising positive awareness of the use of mobile health should be included prior to cardiovascular health education for workers with unhealthy lifestyles such as smoking and excessive drinking alcohol.

Citations

• The Moderating Effect of Mental Health on the Relationship Between Cardiovascular Disease Awareness and Health Behaviors of Middle-Aged Korean Chinese Workers With Cardiovascular Risk Factors in Korea
  Yu Zhu Zhang, Seon Young Hwang
  Journal of Transcultural Nursing.2023; 34(2): 131.     CrossRef
• Comparison of Factors Related to Health Behavior for Cardiocerebrovascular Disease Prevention in Middle-Aged Women with and without Depression
  Eun Ko, Hyukjoon Kim
  Journal of Korean Academy of Fundamentals of Nursing.2022; 29(4): 543.     CrossRef
• Comparison of Stroke Knowledge, Health Beliefs, and Stroke Prevention Behavior between Early and Middle-Aged Adults
  Eun Ko
  STRESS.2022; 30(2): 98.     CrossRef
• The Impact of Cluster-Type Risk Factors on Adverse Clinical Prognosis in Patients with Coronary Artery Disease: A Secondary Data Analysis
  In Ae Uhm, Seon Young Hwang
  Korean Journal of Adult Nursing.2020; 32(2): 156.     CrossRef