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Objectives Motivators and barriers are pivotal factors in the adoption of health behaviors. This study aims to identify patterns of the motivators and barriers influencing heart health behaviors among multi-ethnic Asian adults with behavior-modifiable risk factors for heart disease, namely obesity, physical inactivity and smoking. Methods A population-based survey of 1,000 participants was conducted in Singapore. Participants were assessed for behavior-modifiable risk factors and asked about motivators and barriers to heart health behaviors. Exploratory and confirmatory factor analyses were conducted to identify factors underlying motivator and barrier question items. Logistic regression was conducted to examine the associations of motivator and barrier factors with sociodemographic characteristics. Results The twenty-five motivator and barrier items were classified into three (outcome expectations, external cues and significant others including family and friends) and four (external circumstances, limited self-efficacy and competence, lack of perceived susceptibility, benefits and intentions and perceived lack of physical capability) factors respectively. Among participants with behavior-modifiable risk factors, those with lower education were more likely to be low in motivation factor of “outcome expectations” and “external cues”. The well-educated were more likely to be high in the barrier factor of “lack of perceived susceptibility, benefits and intention” and were less likely to have the motivation factor of “significant others (family or friends)”. Those aged 60–75 years had low motivations and high barriers compared to their younger counterparts. Older age was more likely to be low in motivation factor of “outcome expectations” and “external cues” and high in barrier factor of “limited self-efficacy and competence” and “perceived lack of physical capability”. Conclusions Findings underscore the importance of a targeted intervention and communication strategy addressing specific motivation and barrier factors in different population segments with modifiable risk factors.

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One of the commonest presentations to the Cardiology outpatient clinic is chest pain. Conventional risk scores for predicting coronary artery disease (CAD) depend greatly on chest pain histories which can be subjective and disadvantage individuals who present with less typical symptoms. The coronary calcium score (CACS) has a quick turnabout time and is an objective marker of atherosclerosis which can provide actionable information on presence of coronary artery disease. This study aims to explore a) if CACS can be a surrogate for chest pain history to better manage patients with atypical presentations, and b) determine the feasibility of utilising CACS in a new risk model as a form of triage for chest pain in the outpatient specialist setting. Two cohorts of patients who underwent CT Coronary angiogram (CTCA) were used: Asymptomatic patients with no obstructive coronary artery disease (CAD) and patients with symptomatic chest pain. The readouts of the CTCA include presence or absence of obstructive CAD (epicardial artery stenosis ≥50% on CTCA) and the CACS. In the asymptomatic cohort, we derived the formula for the median predicted CACS using latent class analysis and quantile regression with age and gender. The symptomatic cohort was divided into derivation and validation groups. Multivariate logistic regression was used to select significant risk factors for CAD and develop the prediction model. The presence of a ≥10-point difference between the patient's actual CACS and predicted median CACS was established as a predictive parameter. Performance of the model was assessed and compared with the CAD I consortium score using area under the curve (AUC), net classification index and integrated discriminative index in the validation group. In the asymptomatic cohort of 1911 persons, gender and age were significant factors used to calculate median predicted CACS. In the derivation cohort of 2345 patients, a CACS of 10-point difference between patient's CACS and predicted medium calcium score had a negative predictive value of 96.8%. Performance AUC (Figure 1) of the various models were: new model with chest pain history 0.887 (95% CI 0.858–0.916); without chest pain history 0.884 (95% CI 0.854–0.913); CAD I Consortium score 0.746 (95% CI 0.707–0.784). Both models performed significantly better than calcium score alone, p-value = 0.011. Coronary calcium score is an objective measure of coronary atherosclerosis and appears to be a reliable surrogate for chest pain history. A new risk marker of positive 10-points difference between patient's calcium score and predicted median calcium score can potentially better risk stratify patients presenting with chest pain in the outpatient setting. Funding Acknowledgement Type of funding source: None

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