Biological age calculators to motivate lifestyle change: Environmental scan of online tools and evaluation of behaviour change techniques.

OBJECTIVE: "Biological age" calculators are widely used as a way of communicating health risk. This study evaluated the behaviour change techniques (BCTs) within such tools, underlying algorithm differences and suitability for people with varying health literacy. METHODS: Two authors entered terms into Google (eg, biological/heart age) and recorded the first 50 results. A standard patient profile was entered into eligible biological age calculators. Evaluation was based on Michie et al's BCT taxonomy and a readability calculator. RESULTS: From 4000 search results, 20 calculators were identified: 11 for cardiovascular age, 7 for general biological age and 2 for fitness age. The calculators gave variable results for the same 65-year-old profile: biological age ranged from younger to older (57-87 years), while heart age was always older (69-85+ years). Only 11/20 (55%) provided a reference explaining the underlying algorithm. The average reading level was Grade 10 (range 8.7-12.4; SD 1.44). The most common BCTs were salience of consequences, information about health consequences and credible source. CONCLUSIONS: Biological age tools have highly variable results, BCTs and readability. PRACTICE IMPLICATIONS: Developers are advised to use validated models, explain the result at the average Grade 8 reading level, and incorporate a clear call to action using evidence-based behaviour change techniques.

Experiences of a National Web-Based Heart Age Calculator for Cardiovascular Disease Prevention: User Characteristics, Heart Age Results, and Behavior Change Survey.

BACKGROUND: Heart age calculators are used worldwide to engage the public in cardiovascular disease (CVD) prevention. Experimental studies with small samples have found mixed effects of these tools, and previous reports of population samples that used web-based heart age tools have not evaluated psychological and behavioral outcomes. OBJECTIVE: This study aims to report on national users of the Australian heart age calculator and the follow-up of a sample of users. METHODS: The heart age calculator was launched in 2019 by the National Heart Foundation of Australia. Heart age results were calculated for all users and recorded for those who signed up for a heart age report and an email follow-up over 10 weeks, after which a survey was conducted. CVD risk factors, heart age results, and psychological and behavioral questions were analyzed using descriptive statistics and chi-square tests. Open responses were thematically coded. RESULTS: There were 361,044 anonymous users over 5 months, of which 30,279 signed up to receive a heart age report and 1303 completed the survey. There were more women (19,840/30,279, 65.52%), with an average age of 55.67 (SD 11.43) years, and most users knew blood pressure levels (20,279/30,279, 66.97%) but not cholesterol levels (12,267/30,279, 40.51%). The average heart age result was 4.61 (SD 4.71) years older than the current age, including (23,840/30,279, 78.73%) with an older heart age. For the survey, most users recalled their heart age category (892/1303, 68.46%), and many reported lifestyle improvements (diet 821/1303, 63.01% and physical activity 809/1303, 62.09%). People with an older heart age result were more likely to report a doctor visit (538/1055, 51.00%). Participants indicated strong emotional responses to heart age, both positive and negative. CONCLUSIONS: Most Australian users received an older heart age as per international and UK heart age tools. Heart age reports with follow-up over 10 weeks prompted strong emotional responses, high recall rates, and self-reported lifestyle changes and clinical checks for more than half of the survey respondents. These findings are based on a more engaged user sample than previous research, who were more likely to know blood pressure and cholesterol values. Further research is needed to determine which aspects are most effective in initiating and maintaining lifestyle changes. The results confirm high public interest in heart age tools, but additional support is needed to help users understand the results and take appropriate action.

Should heart age calculators be used alongside absolute cardiovascular disease risk assessment?

BACKGROUND: National estimates of 'heart age' by government health organisations in the US, UK and China show most people have an older heart age than current age. While most heart age calculators are promoted as a communication tool for lifestyle change, they may also be used to justify medication when clinical guidelines advocate their use alongside absolute risk assessment. However, only those at high absolute risk of a heart attack or stroke are likely to benefit from medication, and it is not always clear how heart age relates to absolute risk. This article aims to: 1) explain how heart age calculation methods relate to absolute risk guidelines; 2) summarise research investigating whether heart age improves risk communication; and 3) discuss implications for the use of medication and shared decision making in clinical practice. MAIN BODY: There is a large and growing number of heart age models and online calculators, but the clinical meaning of an older heart age result is highly variable. An older heart age result may indicate low, moderate or high absolute risk of a heart attack or stroke in the next 5-10 years, and the same individual may receive a younger or older heart age result depending on which calculator is used. Heart age may help doctors convey the need to change lifestyle, but it cannot help patients make an informed choice about medication to reduce CVD risk. CONCLUSION: Interactive heart age tools may be helpful as a communication tool to initiate lifestyle change to reduce risk factors. However, absolute risk should be used instead of heart age to enable informed decision making about medication, to avoid unnecessary treatment of low risk people. Evidence-based decision aids that improve patient understanding of absolute risk should be considered as alternatives to heart age calculators for lifestyle and medication decisions.

Racial Differences in Heart Age and Impact on Mortality.

BACKGROUND: Heart age is an estimate of the age of a person's cardiovascular system given their cardiovascular disease (CVD) risk factors. The difference between a person's chronological age and heart age (excess heart age) represents their added CVD risk. OBJECTIVE: To examine racial differences in excess heart age and whether race impacts the association between excess heart age and CVD mortality. METHODS: This analysis included 5110 participants (2449 non-Hispanic white, 1287 non-Hispanic black, and 1374 Mexican-American) from the NHANES III who were free of CVD. Heart age was calculated using the sex-specific non-laboratory-based Framingham risk prediction functions. Multivariable Cox proportional-hazards regression models were used to evaluate the relationship (overall and by race) between excess heart age and CVD mortality. RESULTS: Mean excess heart age was greatest in non-Hispanic blacks (13.0 years), followed by Mexican-Americans (10.5 years), and non-Hispanic whites (8.5 years); p < 0.001 for pairwise differences. Over a mean follow-up of 13.0 years, 394 CVD deaths occurred. Each 10 years of excess heart age was associated with 65% increased risk of CVD mortality (HR, 95% CI: 1.65, 1.53-1.78). This association was stronger in non-Hispanic whites (1.83, 1.63-2.02) compared to non-Hispanic blacks (1.50, 1.29-1.72) and Mexican American (1.60, 1.33-1.87), interaction p = 0.065. CONCLUSIONS: Compared to non-Hispanic whites, non-Hispanic blacks and Mexican Americans have more excess heart age, but the risk of CVD death for the same level of excess heart age appears more pronounced in non-Hispanic whites. Further investigation is needed to show the usefulness of these findings in directing future efforts and resource allocation for reduction of health disparities between ethnic groups.

How old are you, really? Communicating chronic risk through 'effective age' of your body and organs.

In communicating chronic risks, there is increasing use of a metaphor that can be termed 'effective-age': the age of a 'healthy' person who has the same risk profile as the individual in question. Popular measures include 'real-age', 'heart-age', 'lung-age' and so on.Here we formally define this concept, and illustrate its use in a variety of areas. We explore conditions under which the years lost or gained that are associated with exposure to risk factors depends neither on current chronological age, nor the period over which the risk is defined. These conditions generally hold for all-cause adult mortality, which enables a simple and vivid translation from hazard-ratios to years lost or gained off chronological age. Finally we consider the attractiveness and impact of this concept.Under reasonable assumptions, the risks associated with specific behaviours can be expressed in terms of years gained or lost off your effective age. The idea of effective age appears a useful and attractive metaphor to vividly communicate risks to individuals.

Association of cancer treatment with excess heart age among five-year young breast cancer survivors.

PURPOSE: Data evaluating cardiovascular disease (CVD) risk by cancer treatment among young women (≤ 40 years) with breast cancer are limited. METHODS: Among 372 five-year breast cancer survivors aged 30-40 years from the Young Women's Breast Cancer Study, we assessed the association of cancer treatments (anthracyclines, trastuzumab, radiation/laterality, endocrine therapy) and excess heart age (difference between predicted 10-year CVD risk as assessed by adapted Framingham Risk Score and chronological age), prevalent elevated excess heart age (≥ 2 years), and worsening excess heart age (change of ≥ 2 excess heart age years) at breast cancer diagnosis and two- and five-year follow-up using multivariable linear and logistic regressions. RESULTS: Most women had stage I or II (79%), ER + (71%), or PR + (65%) breast cancer. At diagnosis, women had little excess heart age by treatment receipt (range of means = -0.52,0.91 years). Left-sided radiation (ß = 2.49,SE = 0.96,p = 0.01) was associated with higher excess heart age at five-year follow-up. For prevalent elevated excess heart age (two-year = 26%;five-year = 27%), women treated with right-sided radiation had increased risk at two-years (OR = 2.17,95%CI = 1.12-4.19), yet at five-years, associations were observed after any radiation (OR = 1.92,95%CI = 1.09-3.41), especially after left-sided (OR = 2.13,95%CI = 1.09-3.41) radiation. No associations were observed between systemic treatments and prevalent elevated excess heart age or any treatments with worsening excess heart age. CONCLUSIONS: Among young breast cancer survivors, radiation, but not other cancer treatments, was associated with elevated excess heart age. IMPLICATIONS FOR CANCER SURVIVORS: CVD risk tools that incorporate cancer treatment, such as radiation, are needed to identify high risk young breast cancer survivors given the long survivorship and long latency of cardiovascular disease.

Noninvasive Techniques for Tracking Biological Aging of the Cardiovascular System: JACC Family Series.

Population aging is one of the most important demographic transformations of our time. Increasing the "health span"-the proportion of life spent in good health-is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging.

Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival.

Aims: Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age. Methods and results: Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7-6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13-1.34) and adjusted HR 1.11 (1.01-1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01-1.21)], but not in unadjusted analyses [HR 1.00 (0.93-1.08)], making it less easily applicable in clinical practice. Conclusion: A-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods.

Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes.

Background: There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes. Methods: We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p < 0.05)]. Findings: In the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42-1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66-2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75-0.91] for all-cause mortality; HR: 0.78 [0.68-0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased. Conclusion: Biological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes.

Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change (FEEDBACK): study protocol for a cluster randomised controlled trial.

BACKGROUND: The growing burden of type 2 diabetes mellitus (T2DM) and the rising cost of healthcare worldwide make it imperative to identify interventions that can promote sustained self-management behaviour in T2DM populations while minimising costs for healthcare systems. The present FEEDBACK study (Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change) aims to evaluate the effects of a novel behaviour change intervention designed to be easily implemented and scaled across a wide range of primary care settings. METHODS: A cluster randomised controlled trial (RCT) with a 6-month follow-up will be conducted to evaluate the effects of the FEEDBACK intervention. FEEDBACK is a personalised, multi-component intervention intended to be delivered by general practitioners during a routine diabetes consultation. It consists of five steps aimed at enhancing doctor-patient partnership to motivate self-management behaviour: (1) communication of cardiovascular risks using a 'heart age' tool, (2) goal setting, (3) action planning, (4) behavioural contracting, and (5) feedback on behaviour. We aim to recruit 264 adults with T2DM and suboptimal glycaemic control from 20 primary care practices in Japan (cluster units) that will be randomly assigned to either the intervention or control group. The primary outcome measure will be the change in HbA1c levels at 6-month follow-up. Secondary outcome measures include the change in cardiovascular risk score, the probability to achieve the recommended glycaemic target (HbA1c <7.0% [53mmol/mol]) at 6-month follow-up, and a range of behavioural and psychosocial variables. The planned primary analyses will be carried out at the individual level, according to the intention-to-treat principle. Between-group comparisons for the primary outcome will be analysed using mixed-effects models. This study protocol received ethical approval from the research ethics committee of Kashima Hospital, Fukushima, Japan (reference number: 2022002). DISCUSSION: This article describes the design of a cluster RCT that will evaluate the effects of FEEDBACK, a personalised, multicomponent intervention aimed at enhancing doctor-patient partnership to engage adults with T2DM more effectively in self-management behaviour. TRIAL REGISTRATION: The study protocol was prospectively registered in the UMIN Clinical Trials Registry (UMIN-CTR ID UMIN000049643 assigned on 29/11/2022). On submission of this manuscript, recruitment of participants is ongoing.

The Impact and Perception of England's Web-Based Heart Age Test of Cardiovascular Disease Risk: Mixed Methods Study.

BACKGROUND: It is well documented that individuals struggle to understand cardiovascular disease (CVD) percentage risk scores, which led to the development of heart age as a means of communicating risk. Developed for clinical use, its application in raising public awareness of heart health as part of a self-directed digital test has not been considered previously. OBJECTIVE: This study aimed to understand who accesses England's heart age test (HAT) and its effect on user perception, knowledge, and understanding of CVD risk; future behavior intentions; and potential engagement with primary care services. METHODS: There were 3 sources of data: routinely gathered data on all individuals accessing the HAT (February 2015 to June 2020); web-based survey, distributed between January 2021 and March 2021; and interviews with a subsample of survey respondents (February 2021 to March 2021). Data were used to describe the test user population and explore knowledge and understanding of CVD risk, confidence in interpreting and controlling CVD risk, and effect on future behavior intentions and potential engagement with primary care. Interviews were analyzed using reflexive thematic analysis. RESULTS: Between February 2015 and June 2020, the HAT was completed approximately 5 million times, with more completions by men (2,682,544/4,898,532, 54.76%), those aged between 50 to 59 years (1,334,195/4,898,532, 27.24%), those from White ethnic background (3,972,293/4,898,532, 81.09%), and those living in the least deprived 20% of areas (707,747/4,898,532, 14.45%). The study concluded with 819 survey responses and 33 semistructured interviews. Participants stated that they understood the meaning of high estimated heart age and self-reported at least some improvement in the understanding and confidence in understanding and controlling CVD risk. Negative emotional responses were provoked among users when estimated heart age did not equate to their previous risk perceptions. The limited information needed to complete it or the production of a result when physiological risk factor information was missing (ie, blood pressure and cholesterol level) led some users to question the credibility of the test. However, most participants who were interviewed mentioned that they would recommend or had already recommended the test to others, would use it again in the future, and would be more likely to take up the offer of a National Health Service Health Check and self-reported that they had made or intended to make changes to their health behavior or felt encouraged to continue to make changes to their health behavior. CONCLUSIONS: England's web-based HAT has engaged large number of people in their heart health. Improvements to England's HAT, noted in this paper, may enhance user satisfaction and prevent confusion. Future studies to understand the long-term benefit of the test on behavioral outcomes are warranted.

The Impact of Health Literacy-Sensitive Design and Heart Age in a Cardiovascular Disease Prevention Decision Aid: Randomized Controlled Trial and End-User Testing.

BACKGROUND: Shared decision-making is an essential principle for the prevention of cardiovascular disease (CVD), where asymptomatic people consider lifelong medication and lifestyle changes. OBJECTIVE: This study aims to develop and evaluate the first literacy-sensitive CVD prevention decision aid (DA) developed for people with low health literacy, and investigate the impact of literacy-sensitive design and heart age. METHODS: We developed a standard DA based on international standards. The standard DA was based on our existing general practitioner DA. The literacy-sensitive DA included simple language, supporting images, white space, and a lifestyle action plan. The control DA used Heart Foundation materials. A randomized trial included 859 people aged 45-74 years using a 3 (DA: standard, literacy-sensitive, control) ×2 (heart age: heart age + percentage risk, percentage risk only) factorial design, with outcomes including prevention intentions and behaviors, gist and verbatim knowledge of risk, credibility, emotional response, and decisional conflict. We iteratively improved the literacy-sensitive version based on end-user testing interviews with 20 people with varying health literacy levels. RESULTS: Immediately after the intervention (n=859), there were no differences in any outcome among the DA groups. The heart age group was less likely to have a positive emotional response, perceived the message as less credible, and had higher gist and verbatim knowledge of heart age risk but not percentage risk. After 4 weeks (n=596), the DA group had better gist knowledge of percentage risk than the control group. The literacy-sensitive DA group had higher fruit consumption, and the standard DA group had better verbatim knowledge of percentage risk. Verbatim knowledge was higher for heart age than for percentage risk among those who received both. CONCLUSIONS: The literacy-sensitive DA resulted in increased knowledge of CVD risk and increased fruit consumption in participants with varying health literacy levels and CVD risk results. Adding heart age did not increase lifestyle change intentions or behavior but did affect psychological outcomes, consistent with previous findings. This tool will be integrated with additional resources to improve other lifestyle outcomes. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12620000806965; https://tinyurl.com/226yhk8a.

Healthy Eating Index-2015 and Predicted 10-Year Cardiovascular Disease Risk, as Well as Heart Age.

Background and Aims: Dietary factor plays an important role in the prevention of cardiovascular disease (CVD). The healthy eating index-2015 (HEI-2015), an indicator of the overall dietary quality, has been introduced to reflect adherence to the 2015-2020 Dietary Guidelines for Americans (DGA). This study aims to explore the associations of the HEI-2015 with predicted 10-year CVD risk and heart age among United States adults aged 30-74 years old using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. Methods and Results: We conducted a cross-sectional analysis among 6,614 participants aged 30-74 years old. The HEI-2015 scores were calculated from 2-days 24-h dietary recall interviews. The 10-year CVD risk and heart age were derived from the sex-specific Framingham general cardiovascular disease risk score. We defined high cardiovascular disease risk as a predicted 10-year cardiovascular disease risk of > 20%. Multiple linear regression and binary logistic regression models were used to investigate the associations of the HEI-2015 with predicted 10-year CVD risk and heart age. Compared with participants in the lowest HEI-2015 quartile, those in the highest quartile had lower predicted 10-year CVD risk (ß = -2.37, 95% CI: -3.09 to -1.65, P < 0.0001), lower heart age (ß = -2.63, 95% CI: -3.29 to -1.96, P < 0.0001) and lower odds for high risk of CVD (OR = 0.62, 95% CI: 0.49 to 0.80, P-trend < 0.0001) after adjusting for multiple covariates. Conclusion: Higher adherence to the 2015-2020 Dietary Guidelines for Americans is associated with lower predicted 10-year cardiovascular disease risk and lower heart age among United States adults.

The Development and Evaluation of "Life Age"-a Primary Prevention and Population-Focused Risk Communication Tool: Feasibility Study With a Single-Arm Repeated Measures Design.

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.

Interventions Using Heart Age for Cardiovascular Disease Risk Communication: Systematic Review of Psychological, Behavioral, and Clinical Effects.

BACKGROUND: Cardiovascular disease (CVD) risk communication is a challenge for clinical practice, where physicians find it difficult to explain the absolute risk of a CVD event to patients with varying health literacy. Converting the probability to heart age is increasingly used to promote lifestyle change, but a rapid review of biological age interventions found no clear evidence that they motivate behavior change. OBJECTIVE: In this review, we aim to identify the content and effects of heart age interventions. METHODS: We conducted a systematic review of studies presenting heart age interventions to adults for CVD risk communication in April 2020 (later updated in March 2021). The Johanna Briggs risk of bias assessment tool was applied to randomized studies. Behavior change techniques described in the intervention methods were coded. RESULTS: From a total of 7926 results, 16 eligible studies were identified; these included 5 randomized web-based experiments, 5 randomized clinical trials, 2 mixed methods studies with quantitative outcomes, and 4 studies with qualitative analysis. Direct comparisons between heart age and absolute risk in the 5 web-based experiments, comprising 5514 consumers, found that heart age increased positive or negative emotional responses (4/5 studies), increased risk perception (4/5 studies; but not necessarily more accurate) and recall (4/4 studies), reduced credibility (2/3 studies), and generally had no effect on lifestyle intentions (4/5 studies). One study compared heart age and absolute risk to fitness age and found reduced lifestyle intentions for fitness age. Heart age combined with additional strategies (eg, in-person or phone counseling) in applied settings for 9582 patients improved risk control (eg, reduced cholesterol levels and absolute risk) compared with usual care in most trials (4/5 studies) up to 1 year. However, clinical outcomes were no different when directly compared with absolute risk (1/1 study). Mixed methods studies identified consultation time and content as important outcomes in actual consultations using heart age tools. There were differences between people receiving an older heart age result and those receiving a younger or equal to current heart age result. The heart age interventions included a wide range of behavior change techniques, and conclusions were sometimes biased in favor of heart age with insufficient supporting evidence. The risk of bias assessment indicated issues with all randomized clinical trials. CONCLUSIONS: The findings of this review provide little evidence that heart age motivates lifestyle behavior change more than absolute risk, but either format can improve clinical outcomes when combined with other behavior change strategies. The label for the heart age concept can affect outcomes and should be pretested with the intended audience. Future research should consider consultation time and differentiate between results of older and younger heart age. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): NPRR2-10.1101/2020.05.03.20089938.

The effects of communicating cardiovascular disease risk as 'fitness age' on behavioral intentions and psychological outcomes.

OBJECTIVES: There is increasing interest in 'biological age' formats to convey the risk of chronic disease. Fitness Age is a relatively new construct that may be useful for younger people who perceive cardiovascular disease (CVD) risk as less relevant. The current study tested whether Fitness Age increases behavioral intentions and psychosocial outcomes compared to formats commonly used for middle aged adults: Heart Age and percentage risk. METHODS: 180 young adults were randomized to 1 of 3 risk formats: Fitness Age, Heart Age, or lifetime percentage risk of CVD. To make the intervention more personally relevant, participants were assigned to receive a low or high risk result based on self-reported lifestyle factors. Validated measures were used for intentions, worry, perceived risk and credibility. RESULTS: Percentage risk and Heart Age resulted in greater lifestyle change intentions and more accurate numeric risk perception than Fitness Age. High risk results were perceived as less credible but more worrying. CONCLUSIONS: Fitness Age may be detrimental for risk perception and behavior change for young adults. Percentage risk and Heart Age formats were equally effective. PRACTICE IMPLICATIONS: Labels for biological age formats matter when developing risk communication tools, and Fitness Age would not be a recommended format.

Cross-sectional assessment of cardiovascular risk factors in patients with knee osteoarthritis.

Background: Osteoarthritis (OA) and cardiovascular disease (CVD) are prevalent in India. However, there is dearth of literature among Indians studying the relationship between the two. This study was carried out to assess various cardiovascular (CV) risk factors in patients with knee OA with an objective to investigate their association, screening and management.  Methods: In total, 225 patients were included in this cross-sectional study. Participants were diagnosed with knee OA on the basis of the Kellgren and Lawrence (K-L) classification of their radiograph. Participants were also assessed for CV risk factors; age, body mass index, systolic blood pressure, diabetes mellitus, total cholesterol, high-density lipoprotein, smoking. Joint British Society QRisk3 calculator (JBS3) a comprehensive risk score calculator as well as a screening tool, which produces three more variables, namely 10-years risk of developing CVD, physiological heart age and life expectancy, was used. Chi Square, Fishers exact test and one-way ANOVA tests were used to compare the categorical and quantitative variables, respectively.. Multiple regression analysis was done to adjust the multiple con-founders and determine their significance. Results: Patients with severe knee OA had a statistically significantly higher prevalence of CV risk factors (p<0.05). Grade 4 knee OA patients were found to have a mean JBS3 risk of 38%, heart age of 82 years and life expectancy of 77 years as compared to grade 2 patients who had a mean JBS3 risk of 11%, heart age of 63 years and life expectancy of 82 years.  Conclusions: Our study concluded that there is a strong relation between knee OA and CVD, with CV risk score being positively correlated to the severity of OA.

Relationship of healthy vascular aging with lifestyle and metabolic syndrome in the general Spanish population. The EVA study.

INTRODUCTION AND OBJECTIVES: Our objective was to study the relationship of healthy vascular aging (HVA) with lifestyle and the components of metabolic syndrome. We also analyzed the differences between chronological age and heart age (HA) and vascular age (VA) in the Spanish adult population without cardiovascular disease. METHODS: This descriptive cross-sectional study selected 501 individuals without cardiovascular disease (mean age, 55.9 years; 50.3% women) via random sampling stratified by age and sex. HA was estimated with the Framingham equation, whereas VA was estimated with the VaSera VS-1500 device. HVA was defined as a <5-year difference between the chronological age and the HA or VA and the absence of a vascular lesion, hypertension, and diabetes mellitus. RESULTS: Compared with the chronological age, the mean HA and VA were 2.98±10.13 and 3.08±10.15 years lower, respectively. Smoking (OR, 0.23), blood pressure ≥ 130/85mmHg (OR, 0.11), altered baseline blood glucose (OR, 0.45), abdominal obesity (OR, 0.58), triglycerides ≥ 150mg/dL (OR, 0.17), and metabolic syndrome (OR, 0.13) decreased the probability of HVA estimated by HA; an active lifestyle (OR, 1.84) and elevated high-density lipoprotein-cholesterol (OR, 3.26) increased the probability of HVA estimated by HA. Smoking (OR, 0.45), blood pressure ≥ 130/85mmHg (OR, 0.26), altered baseline blood glucose (OR, 0.42), and metabolic syndrome (OR, 0.40) decreased the probability of HVA estimated by VA; abdominal obesity (OR, 1.81) had the opposite effect. CONCLUSIONS: HA and VA were 3 years lower than the chronological age. HA was associated with tobacco consumption, physical activity, and the components of metabolic syndrome. Meanwhile, VA was associated with tobacco consumption, blood pressure, waist circumference, and altered baseline glycemia. CLINICAL TRIAL REGISTRATION: http://www.clinicaltrials.gov. Identifier: NCT02623894.

Disparities in the Prevalence of Excess Heart Age Among Women with a Recent Live Birth.

Background: Understanding and addressing cardiovascular disease (CVD) risk has implications for maternal and child health outcomes. Heart age, the modeled age of an individual's cardiovascular system based on risk level, and excess heart age, the difference between a person's heart age and chronological age, are alternative simplified ways to communicate CVD risk. Among women with a recent live birth, we predicted heart age, calculated prevalence of excess heart age (≥5 years), and examined factors associated with excess heart age. Materials and Methods: Data were analyzed in 2017 from 2009 to 2014 Pregnancy Risk Assessment Monitoring System (PRAMS). To calculate heart age we used maternal age, prepregnancy body mass index, systolic blood pressure, smoking status, and diabetic status. Weighted prevalence and prevalence ratios compared the likelihood of excess heart age across racial/ethnic groups by selected factors. Results: Prevalence of excess heart age was higher in non-Hispanic black women (11.8%) than non-Hispanic white women (7.3%, prevalence ratio [PR], 95% confidence interval [CI]: 1.62, 1.49-1.76) and Hispanic women (4.9%, PR, 95% CI: 2.39, 2.10-2.72). Prevalence of excess heart age was highest among women who were without health insurance, obese or overweight, engaged in physical activity less than thrice per week, or were smokers in the prepregnancy period. Among women with less than high school education, non-Hispanic black women had a higher prevalence of excess heart age than Hispanic women (PR, 95% CI: 4.01, 3.15-5.10). Conclusions: Excess heart age may be an important tool for decreasing disparities and encouraging CVD risk reduction among certain groups of women.

The Impact of Ethnicity on Cardiovascular Risk Reduction and Heart Age After Bariatric Surgery.

BACKGROUND: Risk factors for heart disease include arterial hypertension, high cholesterol, tobacco abuse, and obesity. There is a paucity of data regarding role of ethnicity in bariatric surgery (BS) outcomes. The study's aim is to determine if ethnicity plays a significant role in BS outcomes, heart age, and cardiovascular risk. METHODS: We conducted a retrospective review of data collected concurrently from patients who underwent BS from 2010 to 2015. We analyzed demographics, comorbidities, heart age, and cardiovascular risk-score at surgery and 12-month follow-up. Ethnicities categorized were Caucasian and African American. Heart age was calculated using the Framingham Study Heart Age Calculator and cardiovascular risk-score using the Atherosclerotic Cardiovascular Disease Risk Calculator. RESULTS: A total of 292 patients presented all the variables needed to calculate heart age and cardiovascular risk score. This patient population was composed of 85% Caucasians and 15% African American. Female gender represented 67% (N = 202) of patients with mean age of 52.6 ± 10.7 years. LSG was the most prevalent procedure performed in 73.2% (N = 213) of patients. Mean BMI pre-operatively versus post-operatively by ethnicity was 41.46 ± 4.66 vs 30.08 ± 4.34 Caucasians and 41.90 ± 4.69 vs 32.08 ± 4.68 African Americans. Mean heart age pre-operatively versus post-operatively by ethnicity was 71.35 ± 14.59 vs 62.45 ± 16.12 (p < 0.0001) for Caucasians and 71.38 ± 14.30 vs 65.91 ± 16.61 (p = 0.11) for African Americans. The mean cardiovascular risk scores pre-operatively versus post-operatively by ethnicity were 0.24 ± 0.20 vs 0.15 ± 0.14 (p < 0.0001) for Caucasians and 0.20 ± 0.19 vs 0.16 ± 0.17 (p = 0.23) for African Americans. CONCLUSIONS: Ethnicity does not seem to impact weight loss after BS. However, we found a significant ethnicity-elated difference in reduction of heart age and cardiovascular risk.