Non-communicable diseases (NCDs) are a major cause of disability and reduced quality of life. The cost of preventable chronic conditions on health care resources and loss of workplace productivity is escalating. Over the last decade there has been a worldwide increased focus on prevention of NCDs [1, 2]. Chronic conditions such as cardiovascular disease, type-2 diabetes, obesity and several cancers are associated to varying degrees with a limited set of common, modifiable health behaviours [3–6]. Globally at least 4.9 million people die each year as a result of tobacco use, 2.6 million as a result of being overweight or obese, 4.4 million as a result of raised total cholesterol levels and 7.1 million as a result of raised blood pressure [7]. The total burden of disease attributed to modifiable risk factors such as high blood pressure (7.6%), tobacco (7.8%), alcohol (2.3%), high cholesterol (6.2%), overweight (7.5%), low fruit and vegetable intake (2.1%) and physical inactivity (6.6%) is high [8]. Adoption of a healthy lifestyle has the potential to significantly reduce morbidity and mortality [9–13].

Most primary health care in Australia is provided within general practice clinics and 86% of the population visit a general practitioner (GP) each year [14]. GPs have substantial knowledge of population health and are in an ideal position to advise individual patients about lifestyle choices. Therefore the general practice setting offers the potential to facilitate healthy changes in lifestyle on a large scale [15, 16]. Behavioural approaches to both single and multiple risk factors have been applied in general practice and the evidence suggests that brief interventions can be effective [17, 18]. Multiple risk factor interventions have been demonstrated to improve health especially in high risk patients. However, there are known barriers to addressing behavioural risk factors in primary care. These include low self-efficacy of GPs for providing health behaviour change information, competing pressures on time, lack of supportive organisational infrastructure or funding to support assessments [19, 20]. Overall, the available evidence indicates the need for lifestyle risk factor interventions that can be incorporated into routine care with minimal disruption to general practice activities and limited demands on GPs' time [21, 22].

The primary aim of this paper is to describe the protocol for the 10SS study, which was designed to evaluate the use of personalised computer-generated health promotion advice to address multiple health behaviours in the general practice setting. The secondary aim is to describe baseline characteristics of the study participants, including the prevalence and patterns of multiple risk behaviours and their socio-demographic correlates.

After data entry, data was manually and statistically checked as a part of the data-cleaning process. Initially, descriptive statistics were calculated for all variables. To identify patterns of health behaviours in the study population, the Prudence Score was stratified by demographic characteristics of gender, age, education, marital and employment status. A bivariate analysis using the Chi2 test was performed to assess differences between meeting health behaviour recomm and the demographic variables of gender, education, work status, marital status, and area of residence. A two-sample t-test was used to assess the differences in the mean Prudence Score for gender, age, education, employment status, marital status and SEIFA.

The Prudence Score was further divided into 3 categories: low (0 to 5), medium (6 & 7) and high (8 to 10) as the primary outcome variable in the multivariate analysis. The multivariate logistic model was used to further assess the impact of each demographic factor on the combined score as well as determine characteristics of individuals at highest risk of having unhealthy lifestyles. Results of this model are reported as relative risk ratios. Records for missing values for any of the prudent variables were deleted from the total Prudence Score calculations. The statistical package STATA version 11 (Stata Corporation, 2008) was used for the analysis.

Response rate

Of the 30 GPs approached, 25 showed initial interest. Four GPs subsequently withdrew because of leave planned during the recruitment period or inadequate computer systems to identify potentially eligible patients. Practice staff identified a total of 8,281 (32% men) potentially eligible patients for the remaining 21 GPs, of whom 4,678 completed and returned the questionnaire. As such, the participation rate at baseline was 59.9%, after notified deaths and returns to sender were omitted (5.2%; n = 412). Figure 1 presents a study flow chart.

This paper describes the study procedures for the 10 Small Steps (10SS) study and provides the baseline characteristics of a sample of the 4678 participants who took part in the study. The 10SS approach differs from other studies in its focus by delivering a health behaviour intervention with minimal interruption to general practice routine care and communicating health behaviour advice using a lifestyle score. The Prudence Score showed that despite a relatively high adherence to at least some health behaviours, there was considerable scope for improvement. Only 2.8% of general practice patients adhered to five important protective behaviours: being a non-smoker, sufficient physical activity, safe alcohol intake, eating 7 or more serves of fruit and vegetables daily, and having a normal BMI. Providing computer-tailored feedback that was endorsed by the patient's GP might have contributed to the participation of a relatively large cohort. This cohort will provide the power to continue with a factorial randomised trial with a good chance of detecting even small changes in behaviour attributable to this minimal intervention.

The dietary and lifestyle items included in the study are based on epidemiological evidence demonstrating their association with morbidity and mortality [11, 29, 33–36]. Behaviours such as smoking and physical activity have direct evidence for prevention of NCDs, whereas other behaviours, such as drinking low fat milk and using spreads other than butter, are indirectly supported due to their role in reducing daily saturated fat intake. A previous study by Spencer and Jamrozik et al., using eight of the items used in this study, suggested that in a large cohort of elderly men the combined score had a linear association with all-cause mortality over the subsequent five years and each health behaviour was individually associated with reduced risk of death due to Myocardial Infarction [13, 37].

While the protocol developed for this study proved feasible in a clear majority of practices that were approached, the uptake of invitations by patients was incomplete and the predominance of female participants reflected the low 'visibility' of men attending health services, especially in the first half of adult life. Australian data report that female patients account for 57.6% of General Practice consultations [14]. The consistently higher Prudence Score among older participants and women is notable, and consistent with previous reports that conclude women to be more health conscious than men [38, 39]. Another explanation could be that females and older participants have more frequent health service contacts, each of which provides an occasion for opportunistic health promotion advice [39] Our data suggest that alternative ways to engage men in health promotion activities need to be sought. For example, the addition of a lifestyle score during a 'Men's Health check' (for Australian men between 45-49 years), might be helpful to increase participation of men in health promotion activities.

The main limitation on this study is the non-response from 40% of invited patients; however, this response fraction is similar to that of other large community surveys [40, 41]. Furthermore, the data collected on diet and lifestyle is comparable with other population-wide data for Australia. For example, 86.1% of participants were non-smokers (never- and ex-smokers combined) compared with a national figure of 83.4% [42]. The mean BMI was 26.7 kg/m2, with 56.1% of participants in the 'overweight' or 'obese' range, which is again consistent with a national estimate of 58.5% [43]. Despite the underrepresentation of males, the study sample may be considered broadly representative of the adult Australian population.

A strength of this protocol is the systematic approach to data collection. The data collection tool is a reliable and valid instrument [23] that can be used in clinical settings to examine and measure multiple risk behaviours associated with chronic conditions. The added value of the Prudence Score is its ease of application without requiring invasive biological measures. It will help to identify patients at higher risk of non-communicable diseases, as well as guide the development of effective strategies for risk-reduction and prevention. The protocol was successfully implemented in order to collect the data and to test the intervention. This study protocol may be useful in general practice for measuring multiple health behaviours simultaneously and communicating the results to patients using the Prudence Score. The next step in the 10SS study is to evaluate the extent to which this minimal computer-tailored intervention leads to positive health behaviour change in the short (three months) and long term (12 months).