<![CDATA[Background: Coronary Heart Disease (CHD) remains one of the leading causes of morbidity and mortality worldwide, including in Indonesia. Modifiable lifestyle factors, particularly physical inactivity and smoking habits, play a substantial role in the development of CHD. Although previous studies have investigated these risk factors separately, evidence examining both physical activity and smoking habits simultaneously among CHD patients in Indonesian healthcare settings remains limited. This study aimed to determine the association between physical activity and smoking habits and the occurrence of Coronary Heart Disease among outpatients at Hospital X. Methods: This quantitative study employed a cross-sectional design. A total of 302 outpatients were recruited using purposive sampling. Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ), while smoking habits were measured using the Glover-Nilsson Smoking Behavioral Questionnaire (GN-SBQ). Coronary Heart Disease status was determined based on physicians’ diagnoses documented in medical records. Data were analyzed using the Chi-square test with a significance level of p < 0.05. Results: The majority of respondents demonstrated high levels of physical activity (46.7%), primarily related to occupational activities rather than structured exercise, and mild nicotine dependence (41.4%). However, the highest proportion of CHD cases was observed among respondents with heavy nicotine dependence (33.1%). Chi-square analysis revealed a significant association between physical activity and CHD occurrence (χ² = 20.30; p = 0.0001) and between smoking habits and CHD occurrence (χ² = 12.15; p = 0.007). Conclusion: Physical activity patterns and smoking habits were significantly associated with Coronary Heart Disease among outpatients at Hospital X. These findings emphasize the importance of promoting regular structured physical activity and smoking cessation programs as key strategies for preventing cardiovascular disease. Future studies using longitudinal designs and multivariable analyses are recommended to further explore causal relationships and control for potential confounding factors]]>
