A survey to assist in targeting the adults who undertake risky behaviours, know their health behaviours are not optimal and who acknowledge being worried about their health

Understanding what makes some people undertake risky behaviours, and others not to, is somewhat perplexing and the basis of much research. Various social cognition theories and models associated with behaviour change (such as stages of change, risk perception trans-theoretical model, theory of planned behaviour and health belief model) have been formulated to assist in understanding this phenomenon of undertaking risky behaviours [1]. As theorised by many models (see for example [2‐4]), acknowledgement that a risk to one’s health exists is a procurer to behaviour change. This understanding is based on the principle that if one does not believe they are at risk then they are unlikely to perceive a need to change behaviour. This perceived risk can have a positive relationship on seeking health information [2].

Studies have shown that worry is also related to personal action [5] and has an important role in helping people make decisions. Although worry has negative connotations it is an important step in endorsing protection against harm [5] and motivating action towards appropriate health promotion behaviours [6]. Worry has also been shown to be related to the need for an increased amount of information [7] and more positive attitudes towards, and intentions to make, behavioural change [8].

Research has been undertaken on the relationship between perceived risk and worry associated with health effects and the resultant change in risky behaviours [2, 8, 9]. Cameron & Reece [2] argue that perceived risk and worry ‘reflect two parallel systems of information processing’. As argued by many there are two components – a reasoned component (risk) and an emotional component (worry) [9] and that worry can have the strongest predictive nature more than perceived risk [2, 10]. Others have argued that risk perception is but a judgement about worry [9]. Either way, admitting a perceived risk and having a degree of worry about the situation are highlighting a desire for change, a willingness to listen to information provided and a readiness to take action [7, 11, 12].

In most theories and models, especially those highlighting cognitive processes, the more one is ‘under threat’ the more one is likely to accept advice/recommendations [11, 13]. Previous research has shown that higher levels of worry predicts a more positive attitude and intention to change but this was more likely to occur for those with the worst-levels of behaviours [8].

In a unique Australian study [14], questions on four key health-related behavioural risk factors (physical activity, smoking status, alcohol consumption, fruit and vegetable consumption as an indication of good nutrition), and two health status outcomes closely related to behaviour and behaviour modification (body mass index (BMI) as an indication of adiposity, and Kessler 10 (K10) as a measure of psychological distress), were assessed together with perception of whether the respondents believed each of their risk factors was at a desired level. Each respondent who perceived they were not at an optimal level were then asked if they worried that the shortfall was affecting their health. This has allowed analysis to be undertaken to assist in determining appropriate interventions based on people’s perception of risk, their actual behaviour/risk factor and knowledge of how correct the perception was when compared with actual behaviour.

In the endeavour to change inappropriate or risky behaviours of the population, mass media campaigns and interventions based on increased communication and information exchange are often the preferred intervention strategy [15, 16], but improved targeting information is required. If targeted properly, addressing the consumer’s needs, interests and motives, the chance of a successful behaviour change is enhanced [17]. While socio-demographic data provide meaningful evidence of who should be targeted, demographic characteristics are limited in their ultimate changeability [1]. Additional evidence, such as the variables provided in this analysis, are more readily amenable to change hence provide important evidence for health promotion experts.

In this paper we highlight the different profile of risk behaviours and their relationship to perception of risk and actual behaviour. We hypothesise that people who are worried about their health are more likely to change their unhealthy behaviours [13] and as such we provide, for each of the six key risk factors, a multivariate analysis of demographic, socio-economic and other health-related variables.

A ‘Novel approach to Influencing Self Care’ project [14] was funded through the Australian Federal Government Sharing Health Care Initiative. The aim of the mixed methods study was to inform health professionals and policy makers of the best strategies to support targeted groups of people with chronic conditions to more effectively manage their health. Data used in the analysis of this paper were obtained from a national survey - Stage 3 of the ‘Novel approach to Influencing Self Care’ study. The questionnaire was developed from previous stages of the study that included detailed profiles of respondents of the North West Adelaide Health Cohort Study (Stage 1) [18] and semi-structured interviews (Stage 2) [14]. The national survey (Stage 3) – the focus of this stand-alone analysis - was designed to gather information about what was driving decision-making on an everyday basis for people living with and without chronic conditions, as well as what risky behaviours they engage in and if they are aware of this risk. While the survey was used to explore a range of other health-related issues, only selected variables from the national survey were used in these analyses.

Overall 3001 interviews were conducted with 48.7% being with males. The mean age was 44.9 years (SD 15.1). Table 1 contains the demographic characteristics of the complete sample.

Table 2 details, for the whole sample, the overall prevalence estimates associated with each risk factor. In total, 18.2% of respondents had at least four of these six risk factors.

Table 3 highlights, for the whole sample, the actual risk factor category by the perception of risk (healthy or unhealthy) associated with individual behaviour. The proportion in the correct/normal risk category and whose perception matched, ranged from 91.1% for fruit and vegetable consumption to 46.4% for psychological distress. Conversely, those who were actually in the risk category but whose perception was incorrect (believing the risk factor was in the healthy range) varied from 11% for psychological distress to 72.9% for fruit and vegetable consumption meaning that over 70% of respondents believed they were eating a balanced healthy diet when their actual consumption of fruit and vegetables (as an indicator of a healthy diet) was less than the recommended two and five serves of fruit and vegetables per day. Proportions ranged from 27.1% for inadequate serves of fruit and vegetable consumption to 89.0% for psychological distress for those in the risky category, whose perception and actual behaviour matched. Smoking is not included in this table as questions related to smoking were limited to current smoking and worry status, rather than perception of risk, with all current and ex-smokers deemed to be at risk.

Table 4 highlights the worry status and risk factor status only for those respondents who believe their risk profile is not optimal (that is, regardless of their actual behaviour, their perception was that they were undertaking the actual behaviour at an unhealthy level (those in the right hand column of Table 3)). When comparing those who have the risk factor significant differences were apparent for all risk factors, except for short-term alcohol risk. The proportion who are actually in the normal range but who still worry about the risk factor affecting their health ranged from 43.4% for normal BMI to 98.6% of those eating two and five serves of fruits and vegetables per day. The proportion who were at risk and correctly worried about it (our target population) ranged from 48.0% for alcohol to 92.2% for psychological distress.

Table 5 highlights the results of the multivariate analysis for all six risk factors with each column showing the odds associated with the risk factor for respondents who were worried about the individual risk factor. There was a range of conflicting results for different risk factors. For example, being married or living with a partner meant that you were less likely to have high psychological distress (OR 0.65) but more likely to have an increased BMI (OR 1.64). This pattern was also found for example for work status (with increased risk for physical inactivity and alcohol for the unemployed and lower risk for BMI for the unemployed), dwelling type (increased risk for smoking and psychological distress and lower risk for physical inactivity for those renting from the government), household income (higher risk for BMI and psychological distress and lower risk for physical activity for the middle income group), visits to a doctor in the last year (increased risk for BMI and lower risk for smoking), visits to other health professional in the last year (with increased risk for fruit and vegetable consumption and alcohol and lower risk for smoking) and use of trial and error (with increased risk for BMI and fruit and vegetable consumption and lower risk for alcohol). Education level was also different across risk factors with increased odds for those with a degree or higher and who are not eating enough fruit and vegetables (OR 2.06) and for insufficient physical activity (OR 1.53) whilst those with trade, certificate or diploma level of education had a lower risk for smoking (OR 0.68) and short term alcohol risk (OR 0.61).

The results of these analyses highlight firstly, the relationship between actual behaviour and perception of behaviour with large proportions of the population having an incorrect perception of their risk. Secondly, the analysis highlighted the proportions of people who worry about their health as a result of not undertaking the correct behaviour with substantial proportions of this population reporting high levels of worry. The results of the multivariable analyses highlight the similarities and dissimilarities between a wide range of demographic, socioeconomic and other related variables for the six key behavioural indicators. The multivariate analysis concentrated on those whose perception is that they undertake unhealthy behaviours, based on the premise that this perception is required before any behaviour change can be undertaken. If a stage of change model was employed these people would be in the contemplation and preparation stages [1]. What this analysis has shown is that there are clear demographic and health-related variables that are different between the groups who are, and are not, worried about the health effects of their actions.

One of the most striking features of the multivariate analyses was the markedly different profiles for different risk factors. Noticeable in these results is the ‘different’ profile for smokers (less likely to fit with the other risk factors) and the range of positive associations with BMI. This highlights the fact that campaigns need to be targeted differently depending upon the profile of the population who are most likely to act upon the message. As argued by others [26, 27], the tailoring of specific messages to specific groups is an important endeavour to counteract the broad, population-wide, non-specific messages commonly used. There is a need to look past the demographic areas of research so that additional detail on the broader life and health context details are provided.

The most striking commonalities across the behaviours was age with all risk factors associated with at least one age group. The 45 to 54 year olds were most likely to have increased odds for each risk factor. This highlights the middle age groups as key targets for interventions, with those who are in the risk categories and are worried about the effect the risk factor is having on their health, being perfect targets for interventions. Other studies have found that midlife is an important time of life to make positive behavioural changes [28‐30]. Interestingly, a trend was apparent for smoking and psychological distress with each younger age group more likely to have higher odds indicating that the young smokers and the younger persons with high levels of psychological distress are prime targets for interventions.

While research has highlighted the socio-economic differences apparent in risk behaviours with lower income groups more likely to be smokers [31], undertake less exercise [32], and have higher rates of obesity [33], this analysis showed that the relationship is not necessarily as straight forward as it seems. While our only measure of socio-economic status was annual household income, it was the middle household income level ($40,000 to $80,000 per year) who were more likely to be in the final models for BMI and high psychological distress indicting that campaigns targeting middle income levels for this risk factor should be considered. The lower income level (<$40,000) was also statistically significantly more likely to be included in the BMI model indicting that for BMI both lower income groups are also targets for intervention. In contrast, the middle income level was statistically significantly lower for physical inactivity indicting that this income group were less likely to be worried about their inactivity. No such clear message was apparent in our analysis for fruit and vegetable, alcohol and smoking with household income not included in the final models. Again the need for more detailed, topic-specific interventions are warranted.

A visit to a doctor was a variable included in the final model for BMI highlighting the important opportunity the general practitioner has in influencing these adults. Not surprisingly, smokers were significantly less likely to visit a doctor 10 or more times in the past year. This pattern was repeated for visits to other health professionals with smokers statistically significantly less likely to visit other health professionals while those at risk for low fruit and vegetable consumption and alcohol were statistically significantly more likely to visit other health professionals at least one to four times per year. Previous research has highlighted the important role that general practitioners and other medical specialists have in encouraging and influencing positive behavioural change of their patients [34, 35], although concerns have been expressed on how successful the uptake of guidelines in this area have been [36].

Interestingly the overall health status variable was included in only three of the models (physical inactivity, smoking and high psychological distress) with higher odds for those respondents reporting fair/poor health. The variable that assessed anger with current health status was also included in these three models in addition to the fruit and vegetable model. While it is acknowledged that anger is associated with many chronic diseases including heart disease [37], depression and other mental health problems [38], diabetes [39], and arthritis [40] the relationship with risk factors has not been explored and highlights an area for further research.

One of the major strengths of this study is the use of a large randomly selected sample of the Australian population. The large sample size allows for greater generalisation of results. The weaknesses of this study include the cross-sectional nature of the data collection with the consequent inability to determine direction of effect. The reliance on self-report for some of the assessed variables is vulnerable to social desirability or other biased responses and is also a weakness of this study. In addition, sampling by telephone directory is likely to under sample some groups in the community. The study only involves community living adults and as such people living in supported accommodation such as aged care facilities would be missed from the sample. The response rate of nearly 44% is acceptable for this type of survey but the potential for survey non-response bias is acknowledged. Response rates are declining in surveys based on all forms of interviewing [41, 42] as people have become more active in protecting their privacy. The growth of telemarketing has disillusioned the community and diminished the success of legitimate social science research by means of telephone-based surveys. In addition, the increased use of mobile telephones and decreased use of land-lines could result in an under-representation of younger respondents (with younger persons more likely to have mobile telephones only and hence be excluded from sampling frames based on listed telephone numbers). Up to 5% of telephone calls made were on mobile telephones (those that are listed in the EWP or those that are obtained when contact is made with the household).

Other weaknesses of the study are the lack of validation of some of the variables and the fact that these data elements were collected with a range of other variables that were not included in the analysis. This exclusion of these other variables did not allow for consideration of potential confounders. Only using questions pertaining to fruit and vegetable consumption to represent a balanced diet could also be seen as a weakness of the study. Notwithstanding these weaknesses, the overall prevalence estimates obtained from this survey are in line with state and national estimates indicating a non-biased sample.

Research is needed on the relationship between worry, perceived risk and actual behaviours rather than behavioural intentions [8] and this study has assisted in this development. Further research could develop this relationship between perceived risk, worry and actual behavioural by assessing intentions to change.

While there is a known cluster of risk factors [43] the characteristics associated with those who worry about the risk factor, as shown in this study, vary remarkably. As argued by Baron et al. [5] some people over-worry or worry about the wrong aspects and this study has shown that much of the population are worrying about the health affect of a behaviour they are actually undertaking adequately. This research has determined a unique way of providing evidence for health promotion campaigns centred on reducing inappropriate health behaviours.