ABSTRACT
@#Introduction: Acute Coronary Syndrome (ACS) is still a major cause of mortality and morbidity globally. One of the factors that cause a prehospital delay is the delay in early detection and inaccuracy of early treatment of ACS. The Internet of Things, which is supported by the high use of smartphones with the DETAK application, can be an opportunity to facilitate ACS education programs so that ACS can be detected early. Method: This study has used a quantitative research design with a quasi-experimental approach which pretest and posttest, in which both the experimental and control groups participate. The inclusion criteria of this study were age >45 years; obesity; smoker; Respondents with a history of: hypertension/diabetes mellitus/hyperlipidemia/hypercholesterolemia/CVD/families with cardiovascular disease. 252 respondents who met the inclusion criteria were randomly divided into control (n=126) and intervention groups (n=126). The intervention group was given education through the DETAK application and the control group was given leaflet about ACS. Results: The results of the study showed that there was an increased in early treatment ability was only found in the intervention group (p<.001). Mean differences of the ability of early detection (p<.001) and early treatment (p=.019)between intervention and control groups were both significance. Conclusion: There is potential for DETAK applications to improve the early detection and treatment capabilities of ACS.
ABSTRACT
@#Introduction: Stroke is one of the most common neurological diseases, often causing death or gross physical impairment or disability. The associated risk factors such as hypertension, high cholesterol, diabetes, heart disease, and smoking should serve as warnings. However, most people are still not aware of these risks. The main aim of this study is to identify stroke awareness behavior using the construct variable from the Theory of Planned Behavior as the predictor (attitude factor, subjective norm factor, perceived behavioral factor, and intention to perform behavior). Methods: A cross-sectional study was conducted on 256 people who have a high risk of stroke at the Poncokusumo Health Center, Malang, Indonesia. The sampling technique used was purposive sampling. The authors used all the construct variables in the Theory of Planned Behavior. The stroke awareness behavior was measured using a questionnaire developed from the National Stroke Awareness Guide, while the attitude factor, subjective norm factor, perceived behavioral factor, and intention were measured using the instruments developed from standard instruments from the Theory of Planned Behavior. Structural Equation Modeling (SEM-PLS) was used to analyse the data. Result: This study found that 68.4% of respondent with high or low intention of preventing a stroke can be predicted by attitude factors, subjective norm factors, and perceived behavioral factors. While 96.1% of good or bad stroke awareness behavior can be predicted by the model used in this study, the rest (3.9%) is explained by other variables outside this research model. Conclusion: The hypothesis testing results showed that all construct variables in the Theory of Planned Behavior can be strong predictors of stroke awareness behavior. All variables in the Theory of Planned Behavior can be powerful predictors of stroke awareness behavior.