BACKGROUND:Communicating cardiovascular risk to the general population requires forms of communication that can enhance risk perception and stimulate lifestyle changes associated with reduced cardiovascular risk. OBJECTIVE:The aim of this study was to evaluate the motivational potential of a novel lifestyle risk assessment ("Life Age") based on factors predictive of both premature mortality and psychosocial well-being. METHODS:A feasibility study with a single-arm repeated measures design was conducted to evaluate the potential efficacy of Life Age on motivating lifestyle changes. Participants were recruited via social media, completed a web-based version of the Life Age questionnaire at baseline and at follow-up (8 weeks), and received 23 e-newsletters based on their Life Age results along with a mobile tracker. Participants' estimated Life Age scores were analyzed for evidence of lifestyle changes made. Quantitative feedback of participants was also assessed. RESULTS:In total, 18 of 27 participants completed the two Life Age tests. The median baseline Life Age was 1 year older than chronological age, which was reduced to -1.9 years at follow-up, representing an improvement of 2.9 years (P=.02). There were also accompanying improvements in Mediterranean diet score (P=.001), life satisfaction (P=.003), and sleep (P=.05). Quantitative feedback assessment indicated that the Life Age tool was easy to understand, helpful, and motivating. CONCLUSIONS:This study demonstrated the potential benefit of a novel Life Age tool in generating a broad set of lifestyle changes known to be associated with clinical risk factors, similar to "Heart Age." This was achieved without the recourse to expensive biomarker tests. However, the results from this study suggest that the motivated lifestyle changes improved both healthy lifestyle risks and psychosocial well-being, consistent with the approach of Life Age in merging the importance of a healthy lifestyle and psychosocial well-being. Further evaluation using a larger randomized controlled trial is required to fully evaluate the impact of the Life Age tool on lifestyle changes, cardiovascular disease prevention, and overall psychosocial well-being.

Novel epidemics and pandemics are inherently plagued by scientific uncertainties and a rapidly evolving nature.This can lead to widespread fear and confusion-directly proportional to the level of disease impact and subsequent media coverage, which perpetuates panic. 1 Evolutionary psychologists argue that as response to fear humans tend to distance from those considered a source of danger (infections), guided by self-preservation, desire to find control or unfounded beliefs that those infected bear responsibility.Yet, this leads to misplaced reactions and stigmatisation. 2riginally from ancient Greek, the term 'stigma' was used to 'permanently mark people as criminals, traitors or slaves'.Nowadays, it describes negative associations and discrimination against people with certain attributes (social, physical, behavioural). 3Examples of stigmatisation can be found throughout infectious disease history, affecting communities, caregivers, family and those infected.Among the most stigmatised have been those affected by or associated with HIV/AIDS. 4Many other examples exist, towards Jewish immigrants during the 1892 typhus fever and cholera outbreaks in New York City, 1 towards 'African-ness' during Ebola outbreak(s), towards 'Asian-ness' during SARS 4 and towards those of Mexican/Latin American descent during 2009 H1N1 influenza pandemic (inadequately called 'Mexican flu').These stigmas significantly limited the ability to control diseases and affected mental wellbeing.This is not different for COVID-19.The pandemic provoked worldwide discriminatory behaviours-including violence-and social stigma towards those (perceived) to have or been in contact with the virus or those of specific ethnic backgrounds (especially those of Asian descent and immigrants).This includes deplorable examples such as the use of 'Chinese virus' by government representatives or use of pejorative expressions towards healthcare workers. 5

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

INTRODUCTIONCardiovascular disease is a leading cause of morbidity and mortality in the United States. Heart age (the predicted age of a person's vascular system based on their cardiovascular risk factor profile) and its comparison with chronological age represent a new way to express risk for developing cardiovascular disease. This study estimates heart age and differences between heart age and chronological age (excess heart age) and examines racial, sociodemographic, and regional disparities in heart age among U.S. adults aged 30-74 years.METHODSWeighted 2011 and 2013 Behavioral Risk Factor Surveillance System data were applied to the sex-specific non-laboratory-based Framingham risk score models, stratifying the results by age and race/ethnic group, educational and income level, and state. These results were then translated into age-standardized heart age values, mean excess heart age was calculated, and the findings were compared across groups.RESULTSOverall, average predicted heart age for adult men and women was 7.8 and 5.4 years older than their chronological age, respectively. Statistically significant (p<0.05) racial/ethnic, sociodemographic, and regional differences in heart age were observed: heart age among non-Hispanic black men (58.7 years) and women (58.9 years) was greater than other racial/ethnic groups, including non-Hispanic white men (55.3 years) and women (52.5 years). Excess heart age was lowest for men and women in Utah (5.8 and 2.8 years, respectively) and highest in Mississippi (10.1 and 9.1 years, respectively).CONCLUSIONS AND IMPLICATIONS FOR PUBLIC HEALTH PRACTICEThe predicted heart age among U.S. adults aged 30-74 years was significantly higher than their chronological age. Use of predicted heart age might 1) simplify risk communication and motivate more persons to live heart-healthy lifestyles and better comply with recommended therapeutic interventions, and 2) motivate communities to implement programs and policies that support cardiovascular health.

BACKGROUND:Web-based health applications, such as self-assessment tools, can aid in the early detection and prevention of diseases. However, there are concerns as to whether such tools actually reach users with elevated disease risk (where prevention efforts are still viable), and whether inaccurate or missing information on risk factors may lead to incorrect evaluations.OBJECTIVE:This study aimed to evaluate (1) evaluate whether a Web-based cardiovascular disease (CVD) risk communication tool (Heart Age tool) was reaching users at risk of developing CVD, (2) the impact of awareness of total cholesterol (TC), HDL-cholesterol (HDL-C), and systolic blood pressure (SBP) values on the risk estimates, and (3) the key predictors of awareness and reporting of physiological risk factors.METHODS:Heart Age is a tool available via a free open access website. Data from 2,744,091 first-time users aged 21-80 years with no prior heart disease were collected from 13 countries in 2009-2011. Users self-reported demographic and CVD risk factor information. Based on these data, an individual's 10-year CVD risk was calculated according to Framingham CVD risk models and translated into a Heart Age. This is the age for which the individual's reported CVD risk would be considered "normal". Depending on the availability of known TC, HDL-C, and SBP values, different algorithms were applied. The impact of awareness of TC, HDL-C, and SBP values on Heart Age was determined using a subsample that had complete risk factor information.RESULTS:Heart Age users (N=2,744,091) were mostly in their 20s (22.76%) and 40s (23.99%), female (56.03%), had multiple (mean 2.9, SD 1.4) risk factors, and a Heart Age exceeding their chronological age (mean 4.00, SD 6.43 years). The proportion of users unaware of their TC, HDL-C, or SBP values was high (77.47%, 93.03%, and 46.55% respectively). Lacking awareness of physiological risk factor values led to overestimation of Heart Age by an average 2.1-4.5 years depending on the (combination of) unknown risk factors (P<.001). Overestimation was greater in women than in men, increased with age, and decreased with increasing CVD risk. Awareness of physiological risk factor values was higher among diabetics (OR 1.47, 95% CI 1.46-1.50 and OR 1.74, 95% CI 1.71-1.77), those with family history of CVD (OR 1.22, 95% CI 1.22-1.23 and OR 1.43, 95% CI 1.42-1.44), and increased with age (OR 1.05, 95% CI 1.05-1.05 and OR 1.07, 95% CI 1.07-1.07). It was lower in smokers (OR 0.52, 95% CI 0.52-0.53 and OR 0.71, 95% CI 0.71-0.72) and decreased with increasing Heart Age (OR 0.92, 95% CI 0.92-0.92 and OR 0.97, 95% CI 0.96-0.97) (all P<.001).CONCLUSIONS:The Heart Age tool reached users with low-moderate CVD risk, but with multiple elevated CVD risk factors, and a heart age higher than their real age. This highlights that Web-based self-assessment health tools can be a useful means to interact with people who are at risk of developing disease, but where interventions are still viable. Missing information in the self-assessment health tools was shown to result in inaccurate self-health assessments. Subgroups at risk of not knowing their risk factors are identifiable and should be specifically targeted in health awareness programs.

Purpose. Measuring intentions and other cognitions to perform a behaviour can promote performance of that behaviour (the question-behaviour effect, QBE). It has been suggested that this effect may be amplified for individuals motivated to perform the behaviour. The present research tested the efficacy of combining a motivational intervention (providing personal risk information) with measuring intentions and other cognitions in a fully crossed 2 × 2 design with an objective measure of behaviour in an at-risk population using a randomized controlled trial (RCT). Methods. Participants with elevated serum cholesterol levels were randomized to one of four conditions: a combined group receiving both a motivational intervention (personalized cardiovascular disease risk information) and a QBE manipulation (completing a questionnaire about diet), one group receiving a motivational intervention, one group receiving a QBE intervention, or one group receiving neither. All participants subsequently had the opportunity to obtain a personalized health plan linked to reducing personal risk for coronary heart disease. Results. Neither the motivational nor the QBE manipulations alone significantly increased rates of obtaining the health plan. However, the interaction between conditions was significant. Decomposition of the interaction indicated that the combined condition (motivational plus QBE manipulation) produced significantly higher rates of obtaining the health plan (96.2%) compared to the other three groups combined (80.3%). Conclusions. The findings provide insights into the mechanism underlying the QBE and suggest the importance of motivation to perform the behaviour in observing the effect. What is already known on this subject? Research has indicated that merely asking questions about a behaviour may be sufficient to produce changes in that or related behaviours (referred to as the question-behaviour effect; QBE). Previous studies have suggested that the QBE may be moderated by the individual's motivation to change the behaviour, i.e., the QBE will only produce increases in the behaviour among those with strong motivation to perform the behaviour. However, no study has directly tested this prediction by manipulating motivation and examining impacts on the QBE. What does this study add? The present study tested the individual and combined effects of a motivational and a QBE intervention in a fully crossed design using a randomized controlled trial (RCT) and showed that: a combined intervention significantly increased behaviour. effect partially mediated by cognitions.

OBJECTIVES:To explore the mediating role of measures of persuasion in the relationship between risk perceptions and intentions.METHODS:The first study included 413 obese subjects (mean age = 45.3 years); the second study, 781 overweight subjects (mean age = 46.6 years). All measures were assessed by self-report.RESULTS:Feelings and intervention judgments were mediators in the relationship between risk perceptions and intention to eat healthier, do more physical activity (study 1) and intention to reduce saturated fat (study 2). Feelings was the only mediator in the relationship between risk perceptions and intention to stop smoking (study 1).CONCLUSIONS:Future interventions targeting risk perceptions to increase intentions are likely to be more effective if subjects find the information emotionally impactful, credible, and engaging.

Purpose. Measuring intentions and other cognitions to perform a behaviour can promote performance of that behaviour (the question‐behaviour effect, QBE). It has been suggested that this effect may be amplified for individuals motivated to perform the behaviour. The present research tested the efficacy of combining a motivational intervention (providing personal risk information) with measuring intentions and other cognitions in a fully crossed 2 × 2 design with an objective measure of behaviour in an at‐risk population using a randomized controlled trial (RCT).Methods. Participants with elevated serum cholesterol levels were randomized to one of four conditions: a combined group receiving both a motivational intervention (personalized cardiovascular disease risk information) and a QBE manipulation (completing a questionnaire about diet), one group receiving a motivational intervention, one group receiving a QBE intervention, or one group receiving neither. All participants subsequently had the opportunity to obtain a personalized health plan linked to reducing personal risk for coronary heart disease.Results. Neither the motivational nor the QBE manipulations alone significantly increased rates of obtaining the health plan. However, the interaction between conditions was significant. Decomposition of the interaction indicated that the combined condition (motivational plus QBE manipulation) produced significantly higher rates of obtaining the health plan (96.2%) compared to the other three groups combined (80.3%).Conclusions. The findings provide insights into the mechanism underlying the QBE and suggest the importance of motivation to perform the behaviour in observing the effect.Statement of ContributionWhat is already known on this subject? Research has indicated that merely asking questions about a behaviour may be sufficient to produce changes in that or related behaviours (referred to as the question‐behaviour effect; QBE). Previous studies have suggested that the QBE may be moderated by the individual's motivation to change the behaviour, i.e., the QBE will only produce increases in the behaviour among those with strong motivation to perform the behaviour. However, no study has directly tested this prediction by manipulating motivation and examining impacts on the QBE.What does this study add? The present study tested the individual and combined effects of a motivational and a QBE intervention in a fully crossed design using a randomized controlled trial (RCT) and showed that: a combined intervention significantly increased behaviour. effect partially mediated by cognitions.

OBJECTIVE:The present study aimed to advance our understanding of health-related theory, that is, the alleged intention-behavior gap in an obese population. It examined the mediating effects of planning on the intention-behavior relationship and the moderated mediation effects of age, self-efficacy and intentions within this relationship.METHOD:The study was conducted over a five-week period. Complete data from 571 obese participants were analyzed. The moderated mediation hypothesis was conducted using multiple-regression analysis. To test our theoretical model, intentions (Week 2), action self-efficacy (Week 2), maintenance self-efficacy (Week 5), planning (Week 5), and saturated-fat intake (Weeks 1 and 5) were measured by self-report.RESULTS:As hypothesized, planning mediated the intention-behavior relationship for perceived (two-item scale) and percentage-saturated-fat intake (measured by a food frequency questionnaire). Age, self-efficacy, and intention acted as moderators in the above mediation analysis. In specific, younger individuals, those with stronger intention, and people with higher levels of maintenance self-efficacy at higher levels of planning showed greater reductions in their perceived saturated-fat intake.CONCLUSIONS:For successful behavior change, knowledge of its mediators and moderators is needed. Future interventions targeting planning to change saturated-fat intake should be guided by people's intentions, age, and self-efficacy levels.

Background Forming specific health plans can help translate good intentions into action. Mobile text reminders can further enhance the effects of planning on behavior. Objective Our aim was to explore the combined impact of a Web-based, fully automated planning tool and mobile text reminders on intention to change saturated fat intake, self-reported saturated fat intake, and portion size changes over 4 weeks. Methods Of 1013 men and women recruited online, 858 were randomly allocated to 1 of 3 conditions: a planning tool (PT), combined planning tool and text reminders (PTT), and a control group. All outcome measures were assessed by online self-reports. Analysis of covariance was used to analyze the data. Results Participants allocated to the PT (meansat urated fat 3.6, meancopingplanning 3) and PTT (meansaturatedfat 3.5, meancopingplanning 3.1) reported a lower consumption of high-fat foods (F 2,571 = 4.74, P = .009) and higher levels of coping planning (F 2,571 = 7.22, P < .001) than the control group (meansat urated f at 3.9, meancopingplanning 2.8). Participants in the PTT condition also reported smaller portion sizes of high-fat foods (mean 2.8; F 2, 569 = 4.12, P = .0) than the control group (meanportions 3.1). The reduction in portion size was driven primarily by the male participants in the PTT (P = .003). We found no significant group differences in terms of percentage saturated fat intake, intentions, action planning, self-efficacy, or feedback on the intervention. Conclusions These findings support the use of Web-based tools and mobile technologies to change dietary behavior. The combination of a fully automated Web-based planning tool with mobile text reminders led to lower self-reported consumption of high-fat foods and greater reductions in portion sizes than in a control condition. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN): 61819220; http://www.controlled-trials.com/ISRCTN61819220 (Archived by WebCite at http://www.webcitation.org/63YiSy6R8)

BACKGROUND:A healthy diet, low in saturated fat and high in fiber, is a popular medical recommendation in preventing cardiovascular disease (CVD). One approach to motivating healthier eating is to raise individuals' awareness of their CVD risk and then help them form specific plans to change. OBJECTIVES:The aim was to explore the combined impact of a Web-based CVD risk message and a fully automated planning tool on risk perceptions, intentions, and saturated fat intake changes over 4 weeks. METHODS:Of the 1187 men and women recruited online, 781 were randomly allocated to one of four conditions: a CVD risk message, the same CVD risk message paired with planning, planning on its own, and a control group. All outcome measures were assessed by online self-reports. Generalized linear modeling was used to analyze the data. RESULTS:Self-perceived consumption of low saturated fat foods (odds ratio 11.40, 95% CI 1.86-69.68) and intentions to change diet (odds ratio 21.20, 95% CI 2.6-172.4) increased more in participants allocated to the planning than the control group. No difference was observed between the four conditions with regard to percentage saturated fat intake changes. Contrary to our expectations, there was no difference in perceived and percentage saturated fat intake change between the CVD risk message plus planning group and the control group. Risk perceptions among those receiving the CVD risk message changed to be more in line with their age (change in slope(individual) = 0.075, P = .01; change in slope(comparative) = 0.100, P = .001), whereas there was no change among those who did not receive the CVD risk message. CONCLUSION:There was no evidence that combining a CVD risk message with a planning tool reduces saturated fat intake more than either alone. Further research is required to identify ways in which matching motivational and volitional strategies can lead to greater behavior changes.

Background: “Heart Age” or “Vascular Age” is an alternative expression of age-appropriate cardiovascular risk based on the output of Framingham Risk Scores and shown to promote more accurate risk p...