Published online Oct 31, 2016.
https://doi.org/10.7475/kjan.2016.28.5.536
Influencing Effects of Type D Personality on Symptom Experiences and Quality of Life in Patients with Percutaneous Coronary Intervention
Abstract
Purpose
aim of this study was to investigate the influencing effects of type D personality on symptom experiences and quality of life in percutaneous coronary intervention patients.
Methods
descriptive, cross-sectional study design was used. A total of 158 patients with percutaneous coronary intervention participated in this study, between July 1 and November 1, 2015. Data were analyzed by means, standard deviations, t-test, χ2 test, ANCOVA, and stepwise multiple regression analysis using SPSS 22.0 program.
Results
About 53.8% of participants were classified as type D personality. The type D personality group reported statistically significantly higher symptom experience, lower cardiac function, and lower cardiovascular-specific quality of life compared to non-type D personality group. On stepwise multiple regression, the most significant factor of quality of life was symptom experiences (adjusted R2=.25, p<.001), followed by type D personality (adjusted R2=.31, p<.001).
Conclusion
Personality trait assessment is recommended for patients with percutaneous coronary intervention to assess symptom experiences and quality of life. In addition, development of nursing intervention might be beneficial to manage symptom experience and quality of life in percutaneous coronary intervention patients with type D personality.
Table 1
Demographic and Clinical Characteristics between Type D Personality Group and Non-type D Personality Group (N=158)
Table 2
Comparison of Symptom Experience and Quality of Life between Type D Personality Group and Non-type D Personality Group (N=158)
Table 3
Symptom Experience and Quality of Life according to Demographic and Clinical Characteristics (N=158)
Table 4
Correlations between Quality of Life and Related Variables (N=158)
Table 5
Influencing Factors on Quality of Life according to Stepwise Multiple Regression (N=158)
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