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Original research article
Association of Life's Simple 7 and presence of cardiovascular disease in general Australians
  1. Yang Peng and
  2. Zhiqiang Wang
  1. Faculty of Medicine, Centre for Chronic Disease, University of Queensland, Herston, Australia
  1. Correspondence to Dr Yang Peng; y.peng{at}


Objective The American Heart Association developed Life’s Simple 7 to define and monitor cardiovascular health (CVH), but their contributions to cardiovascular disease (CVD) in general Australians are still unclear. Our study aimed to evaluate the separate and combined effects of Life’s Simple 7 on CVD among Australians.

Methods We performed a cross-sectional study based on 7499 adults (≥18 years) who have been tested for total cholesterol and fasting plasma glucose as part of the 2011–2012 Australian Health Survey. Poisson regression analyses were used to estimate the incidence rate ratios and population attributable fractions of those metrics to CVD prevalence. Participants were classified into three CVH status groups based on the number of ideal metrics: inadequate (0–2), average (3–4) and optimal (5–7). Logistic regression analyses were performed to illustrate the relationships between overall CVH and CVD prevalence.

Results 2100 (21.0%) participants were having CVD. Smoking, elevated body mass index, blood pressure, total cholesterol, fasting plasma glucose and physical inactivity were observed as significant indicators of CVD. Compared with the inadequate category, participants in the optimal and average category have a 66% (adjusted OR, 0.34; 95% CI 0.22 to 0.54) and a 33% (adjusted OR, 0.67; 95% CI 0.56 to 0.81) lower CVD risk. One more ideal metric was associated with a 21% reduced CVD risk (adjusted OR, 0.79; 95% CI 0.73 to 0.84).

Conclusions We have identified several modifiable risk factors and contributors of CVD in general Australians. The improvement of overall CVH may also reduce CVD risk.

  • epidemiology
  • coronory artery disease
  • public health

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Key questions

What is already known about this subject?

  • Life’s Simple 7 is associated with the cardiovascular disease (CVD) risk in the USA and some other regions. The magnitudes of individual and/or combined effects of Life’s Simple 7 varied across studies.

What does this study add?

  • Most factors of the Life’s Simple 7 and the overall cardiovascular health were significantly related to CVD risk in general Australians.

How might this impact on clinical practice?

  • Life’s Simple 7 is an easily implementable tool in clinical practice. Hence, doctors and other professionals can provide CVD prevention tips to patients based on their Life’s Simple 7 status.


Cardiovascular disease (CVD) is still a leading cause of mortality, and in 2013 it contributed to over 0.8 million deaths in the USA.1 According to the Global Burden of Disease Study, CVD accounted for more than 30% global overall deaths in 2013.2 In Australia, CVD is also one of the major contributors to mortality. Ischaemic heart disease, the most common type of CVD, ranked as the first cause of mortality among Australians.3

To define and monitor cardiovascular health (CVH) in general Americans, the American Heart Association released seven modifiable cardiovascular behaviours (including smoking status, body mass index (BMI), physical activity and dietary pattern) and factors (including smoking status (also treated as a behaviour), total cholesterol (TC), blood pressure and fasting plasma glucose (FPG)), and they are also called Life’s Simple 7.4 To date, a number of studies have examined the individual and combined effects of Life’s Simple 7 on CVD risk in the USA,5–7 Finland,8 China9 and Korea,10 while the relationship between those factors and CVD risk in general Australian population is still unclear.

The present study used an Australian representative sample collected from the Australian Health Survey (AHS), aiming to clarify the individual and combined associations between Life’s Simple 7 and CVD prevalence.


Study design and subjects

We retrospectively analysed data from the core sample of the 2011–2012 AHS, a nationwide and population-based combined sample of three surveys: National Health Survey (NHS), National Nutrition and Physical Activity Survey (NNPAS), and National Health Measures Survey (NHMS), which included participants from the first two surveys who provided biomedical samples. The Australian Bureau of Statistics (ABS) organised the three surveys. NHS and NNPAS used a stratified multistage random sample from private dwellings usual residents in urban and rural areas of all states in Australia, covering about 97% of the people living in Australia. Overseas visitors staying or intending to stay in Australia for 12 months or more are in scope. Households in very remote areas of Australia and discrete Aboriginal and Torres Strait Islander communities were excluded. Trained interviewers obtained the NHS and NNPAS information through face-to-face, computer-assisted personal interview. For the 24-hour dietary recall collection, computer-assisted telephone interview was used. All participants aged 12 or over and aged 5 or over in both NHS and NNPAS were asked to voluntarily provide fasting blood samples and urine samples, which constituted the NHMS, to the convenient collection centres, and Sonic Healthcare processed the samples. The current NHS was the sixth in a series of regular population surveys, and the previous ones were conducted in 1989–1990, 1995, 2001, 2004–2005 and 2007–2008. NNPAS and NHMS were new surveys. NHS, NNPAS and NHMS were enumerated from 6 March 2011 to 17 March 2012, 29 May 2011 to 9 June 2012 and March 2011 to September 2012, respectively. The core sample consisted of 24 910 adults (≥18 years old) and age was determined by the NHS or NNPAS interview date. We restricted our study to those who voluntarily participated in both TC and FPG tests (n=7499), yielding an overall response rate of 30.1%. All participants provided written informed consent, and our study was approved by the School of Medicine Low Risk Ethical Review Committee in the University of Queensland (approval number 2016-SOMILRE-0161).

Life’s Simple 7

All modifiable metrics were divided into ideal and unideal status. The detailed classification criteria are shown in table 1. Based on the number of ideal metrics, participants were grouped into three CVH categories: inadequate (0–2), average (3–4) and optimal (5–7).11

Table 1

Definition of Life’s Simple 7 ideal and unideal status in our study

Outcome measurement

The self-reported CVD prevalence was based on the 10th version of the International Classification of Diseases, codes I00-I99. To be more specific, our study treated angina, stroke, rheumatic heart disease, heart attack, heart failure, hypertension, hypotension, hardening of the arteries/atherosclerosis/arteriosclerosis, haemorrhoids, varicose veins, rapid or irregular heartbeats/tachycardia/palpitations and any other participant self-entered I00-I99 conditions as CVD. Respondents were asked whether a doctor or a nurse has ever told them that they have those conditions, respectively. They were asked whether they currently have the condition if they have chosen yes to any of them. Those who have been told and currently have at least one of the above conditions were regarded as positive for CVD presence in our study.


The following variables were adjusted as covariates in the present study: age, sex, educational attainment, income status and residence region. Educational attainment was categorised as high (≥12 school years) and low (<12 school years). Income status was evaluated by household income and dichotomised as low (≤50th percentile equivalised weekly household income) and high (>50th percentile equivalised weekly household income). Residence region was classified into major cities, inner regional areas and other areas (outer regional and remote areas).

Statistical analysis

First, we used univariate and multivariate Poisson regression analyses to calculate crude (unadjusted) and adjusted incidence rate ratios (IRRs) and corresponding 95% CIs, and thus to elucidate the association between preventable factors and CVD occurrence.

Second, we calculated adjusted population attributable fractions (PAFs) based on the following equation to measure the effects of each component on CVD reduction.12 Pe is the prevalence of exposure and rate ratios (RRs) were replaced with adjusted IRRs.

Embedded Image

Third, we calculated ORs using logistic regression analyses to explore the relationship between overall CVH and CVD prevalence. Participants with missing values in one or more of Life’s Simple 7 components were not included in the analyses.

To infer results for the total in-scope population, we used biomedical weight and jackknife method in the Life’s Simple 7 and CVD association analyses. We used person weight and jackknife method in characteristics comparisons between participants and non-participants.13 All analyses were conducted within the ABS’s Remote Access Data Laboratory with Stata V.10.0. A two-sided p value <0.05 was used to determine statistical significance.


For the 7499 participants, 2100 are positive for CVD, with a weighted prevalence of 21.0%. Among the seven metrics, FPG has the highest weighted ideal proportion (83.6%), followed by smoking status (55.6%), TC (45.5%), blood pressure (44.2%), BMI (39.2%), physical activity (26.7%) and dietary pattern (4.8%). The metrics and covariates details of participants are summarised in table 2. The comparisons of the characteristics of participants and non-participants are listed in online supplementary table S1.

Supplementary Material

Supplementary Table 1
Table 2

Characteristics of Life’s Simple 7 and covariates in the current study

In the univariate analysis, all of the factors, except dietary pattern, are positively associated with CVD prevalence. The relationships still exist after adjusting for covariates. Raised BMI (adjusted IRR: 1.37; 95% CI 1.21 to 1.55, p<0.01), elevated FPG (adjusted IRR: 1.34; 95% CI 1.21 to 1.49, p<0.01), increased blood pressure (adjusted IRR: 1.27; 95% CI 1.10 to 1.47, p<0.01), physical inactivity (adjusted IRR: 1.23; 95% CI 1.00 to 1.52, p=0.049), unideal TC (adjusted IRR: 1.21; 95% CI 1.06 to 1.38, p=0.01) and smoking (adjusted IRR: 1.18; 95% CI 1.04 to 1.34, p=0.01) are significantly associated with higher CVD risk (table 3). We calculated adjusted PAFs to quantify the contributions of certain factors to CVD prevalence (figure 1). Raised BMI is the largest significant contributor to CVD prevalence, with adjusted PAF of 20%, followed by physical inactivity (15%), increased blood pressure (14%), unideal TC (12%), smoking (8%) and elevated FPG (7%).

Table 3

Individual associations between Life’s Simple 7 and cardiovascular disease presence

Figure 1

Adjusted population attributable fractions of each component of Life’s Simple 7 to cardiovascular disease. BMI, body mass index; FPG, fasting plasma glucose; TC, total cholesterol.

Table 4 displays the relationship between the number of ideal metrics and CVD prevalence. Compared with those in the inadequate category, those in the optimal category had a reduction of 66% in CVD risk (adjusted OR: 0.34; 95% CI 0.22 to 0.54), and those in the average category had a reduction of 33% in CVD risk (adjusted OR: 0.67; 95% CI 0.56 to 0.81). On average, one more ideal metric was associated with a 21% reduced risk of CVD (adjusted OR: 0.79; 95% CI 0.73 to 0.84). Additionally, higher numbers of ideal behaviours and factors are also associated with lower risk of CVD (online supplementary table S2 and supplementary table S3).

Table 4

Number of ideal metrics and CVD presence


To the best of our knowledge, it is the first study that explored the individual and combined effects of Life’s Simple 7 on CVD prevalence among general non-indigenous Australians. We observed that raised BMI, physical inactivity, increased blood pressure, unideal TC, smoking and elevated FPG are independent risk factors and contributors of CVD. Higher number of ideal metrics is associated with reduced CVD risk.

Raised BMI was the largest contributor to CVD risk in our study. Several previous reports also identified overweight/obesity as a significant risk factor of CVD.14–16 Moreover, it has been reported that the prevalence of obese is still increasing over time. In Australia, the age-standardised obesity prevalence has more than doubled from 1980 to 2000.17 Similar increased trends in overweight and/or obesity were also observed in adults from China,18 USA,19 Mongalia20 and Poland.21 Additionally, we noticed that unideal status of physical activity, blood pressure, TC, smoking and FPG are also significant contributors to CVD presence in our study. Their roles in elevating the CVD risks have also been confirmed in a number of studies,9 14 22 23 which suggested that relevant intervention programmes are needed to improve CVH.

Similar to our findings, two US studies also identified BMI as the most crucial contributor to coronary artery calcification24 and venous thromboembolism,25 respectively, while a number of studies from USA,11 26–28 China9 14 16 and Korea10 ranked unideal blood pressure as the most significant contributor of CVD mortality,9–11 26 27 CVD incidence14 and stroke incidence.16 28 The inconsistency in rankings may be suggestive of the various risk factor patterns of different diseases and populations.

Dietary pattern was not independently related to CVD reduction in the current study. Several studies have identified dietary pattern14 29 30 as a CVD risk factor. However, some studies failed to reveal the association.10 28 A possible explanation of the conflicting findings may be due to the various definitions of dietary pattern status in each study. Thus, more studies are warranted to test the relationship more accurately.

We have observed that CVD prevalence showed declining trends with increasing number of ideal CVH metrics, behaviours and factors. Our findings are consistent with several studies that also found the graded inverse relationship between favourable ideal metrics and CVD mortality6 9 11 26 and incidence.5 22 25 28 Our results, along with those of previous studies, suggested that those factors may interrelate with each other and a small improvement of CVH might have a dramatic reduction in CVD burden.

There are some limitations to our study. First, it is a cross-sectional study and we are unable to examine temporality between Life’s Simple 7 and CVD incidence or mortality. Second, we used modified metrics definitions owing to AHS data set structures, and some variables, like dietary evaluation and smoking status, were self-reported and thus may not be completely accurate. Third, the sample excluded those living in very remote areas and indigenous communities, although their population proportion is very small. Additionally, for some characteristics, there are some differences between those with and without TC and FPG samples (online supplementary table S1). Although our findings are representative of the target population by using the biomedical weight, we may still have the risk of selection bias.

In summary, we identified raised BMI, increased blood pressure, physical inactivity, unideal TC, smoking and elevated FPG as significant risk factors and contributors of CVD presence in the general Australians. The higher number of ideal Life’s Simple 7 metrics was associated with reduced risk of CVD.


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  • Contributors YP and ZW: concept/design. YP: data analysis/interpretation, drafting article. ZW: critical revision of article. YP and ZW: approval of article.

  • Funding This study was supported by the University of Queensland International Scholarship (No 44078179) to YP and the National Health and Medical Research Council of Australia (No APP1042343) to ZW. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

  • Competing interests None declared.

  • Patient consent Obtained.

  • Ethics approval The School of Medicine Low Risk Ethical Review Committee in the University of Queensland.

  • Provenance and peer review Not commissioned; internally peer reviewed.

  • Data sharing statement Any requests for data sharing should be made to the corresponding author.