This study’s purpose was to examine the structural relationship of the transtheoretical model (TTM) and the amount of physical activity (PA) among undergraduate students in health and medicine at Universiti Sains Malaysia. A cross-sectional study was carried out among students who took part in the co-curricular program. Co-curricular program includes activities that take place outside of the regular lectures or tutorials in the University. Students recruited through purposive sampling were informed that their participation was entirely voluntarily. Those interested completed the self-administered questionnaire, which consisted of the decisional balance, processes of change, self-efficacy, stages of change scales, and Godin leisure-time exercise questionnaire. Data were analyzed using Mplus version 8 for descriptive statistics and structural equation modeling analysis for inferential statistics. A total of 562 students participated in the study. The majority of the students was female (79.0%) and Malay (73.3%) and average of exercise sessions per week was 2.62, with a mean of 43.37 min per exercise session. The final structural model fit the data well based on several fit indices (SRMR = 0.046, RMSEA (CI: 90%) = 0.061 (0.045, 0.078), RMSEA p = 0.130). The model showed that stages of change significantly affected self-efficacy (p < 0.001), pros (benefits of exercise; p < 0.001), cons (barriers to exercise; p = 0.022), and processes of change (p < 0.001). The model also showed significant inter-relationships among the TTM constructs and supported seven hypotheses. Among all the variables examined, only processes of change significantly affected PA (p < 0.001). However, stages of change (p < 0.001) and pros (p =< 0.001) had significant indirect effects on PA via processes of change. The findings support that individuals’ stages of change affect their self-efficacy level, or the ability to make positive and negative decisions and perform behavior accordingly. The study confirms that making correct decisions and taking action accordingly can increase PA levels.

Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

Background Sex and gender differences in acute coronary syndrome (ACS) have been well studied in the western population. However, limited studies have examined the trends of these differences in a multi-ethnic Asian population. Objectives To study the trends in sex and gender differences in ACS using the Malaysian NCVD-ACS Registry. Methods Data from 24 hospitals involving 35,232 ACS patients (79.44% men and 20.56% women) from 1st. Jan 2012 to 31st. Dec 2016 were analysed. Data were collected on demographic characteristics, coronary risk factors, anthropometrics, treatments and outcomes. Analyses were done for ACS as a whole and separately for ST-segment elevation myocardial infarction (STEMI), Non-STEMI and unstable angina. These were then compared to published data from March 2006 to February 2010 which included 13,591 ACS patients (75.8% men and 24.2% women). Results Women were older and more likely to have diabetes mellitus, hypertension, dyslipidemia, previous heart failure and renal failure than men. Women remained less likely to receive aspirin, beta-blocker, angiotensin-converting enzyme inhibitor (ACE-I) and statin. Women were less likely to undergo angiography and percutaneous coronary intervention (PCI) despite an overall increase. In the STEMI cohort, despite a marked increase in presentation with Killip class IV, women were less likely to received primary PCI or fibrinolysis and had longer median door-to-needle and door-to-balloon time compared to men, although these had improved. Women had higher unadjusted in-hospital, 30-Day and 1-year mortality rates compared to men for the STEMI and NSTEMI cohorts. After multivariate adjustments, 1-year mortality remained significantly higher for women with STEMI (adjusted OR: 1.31 (1.09–1.57), p<0.003) but were no longer significant for NSTEMI cohort. Conclusion Women continued to have longer system delays, receive less aggressive pharmacotherapies and invasive treatments with poorer outcome. There is an urgent need for increased effort from all stakeholders if we are to narrow this gap.

Background Self-efficacy (SE) is a person’s belief in his or her own capability to perform and accomplish a task that could produce a favourable outcome, despite facing obstacles. This study aimed to confirm the validity and reliability of an SE scale among undergraduate students at the Health Campus of the Universiti Sains Malaysia. Methods A cross-sectional study was conducted among the undergraduate students using a self-administered questionnaire. After using a purposive sampling method, 562 students completed the questionnaire. Mplus 8 was employed to conduct the confirmatory factor analysis on the psychometric properties of Bandura’s 18-item SE scale with three factors (internal feeling, competing demands and situational). Then, the composite reliability was calculated for each factor. Results Most of the students were Malay (73.3%) females (79.0%) who exercised 2.62 times a week for an average of 43.37 min per session. The final measurement model was obtained after removing six problematic items, and the model was deemed fit based on several indices [Root Mean Square Error of Approximation (RMSEA) = 0.067, Standardised Root Mean Square Residual (SRMR) = 0.004, Comparative Fit Index (CFI) = 0.924]. The composite reliability values of the three factors were acceptable (0.65 to 0.84). Conclusion The simplified 12-item SE scale with three factors displayed good fit indices with regard to the data, and they were considered to be acceptable for the current sample.

Background: Cardiovascular disease is the leading cause of mortality globally, with most deaths occurring in low- and middle-income countries. The present study aims to provide an overview of the characteristics of the national registries managed by member societies of the Asian Pacific Society of Cardiology (APSC). Methods: The APSC website was searched to identify member countries of the society. Using a combination of keywords, PubMed and Google advanced search were trawled to identify cardiovascular registries from each member country and publications generated from these registries. The number of citations each publication received was identified and correlated with the characteristics of each registry. Results: The search found 12 of the 23 member countries (52.2%) had developed a national cardiovascular registry; seven had acute coronary syndrome (ACS) registries and five had acute myocardial infarction (AMI) registries. The registries were primarily established to assess and improve cardiovascular care, and generated a total of 318 articles, a median of 11 articles per registry. There were variations in numbers of articles produced as well as in citations received, with more publications from high-income countries than middle-income countries. Conclusion: The majority of member countries of the APSC have established national ACS and AMI registries. While there were some inherent differences between countries in terms of output, these registries provide an invaluable resource for benchmarking cardiovascular care and could help contribute to local guidelines development.