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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

BMC Public Health201313:120

DOI: 10.1186/1471-2458-13-120

Received: 30 April 2012

Accepted: 5 February 2013

Published: 8 February 2013

Abstract

Background

Research indicates that those who are worried about their health are more likely to change their in-appropriate behavioural-related risk factors. A national survey was undertaken to determine adults who correctly perceive and actually undertake in-appropriate behavioural-related risk factors (smoking, physical activity, alcohol intake, fruit and vegetable consumption, weight and psychological distress) and are worried about their health.

Methods

Australian 2010 CATI survey of 3003 randomly selected adults. Perception and self-reported levels of each risk factor, and whether they worried that the level was affecting their health were assessed using univariate and multivariate analyses.

Results

The comparisons between perception of healthy behaviour and actual behaviour varied for each risk factor with 44.1% of people in the un-healthy weight range and 72.9% of those eating less than sufficient fruit and vegetables having the perception that their behaviour was healthy. The demographic and other related variables in the multivariate analyse for each risk factor varied considerably. For example the variables in the final multivariate model for smokers who were worried about their risk factor were markedly different to the other risk factor models and 45 to 54 year olds were more likely to be included in the final models for nearly all of the risk factor analyses.

Conclusion

By limiting this analyses to those who are acknowledging (correctly or otherwise) that their perception of behaviour is making their health worse, this study has shown that the profile for each risk factor varies considerably. As such, evidence suggests specific targeted programs are required rather than a broad brush approach.

Keywords

Worry Risk factors Perception Australia Health promotion

Background

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 [24]), 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.

Method

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.

Data collection

All households in Australia with a connected telephone and the telephone number listed in the Australian Electronic White Pages (EWP) were eligible for selection. Within each household contacted, a random person (the person, aged 18 years or over, who was last to have a birthday) was selected. There was no replacement for non-contactable persons. On average, interviews took 15 minutes to complete. In an endeavour to increase response rates, a letter outlining the purpose of the study was sent to selected households. Data collection was undertaken by a contracted agency using trained interviewers in April and May, 2010. Interviews were conducted using Computer Assisted Telephone Interview (CATI) methodology.

A minimum of 10 call-backs were made to telephone numbers selected for interview. Different times of the day or evening were scheduled for each call-back. If the person could not be interviewed immediately they were re-scheduled for interview at a time suitable to them. Replacement interviews for persons who could not be contacted or interviewed were not permitted. Ten percent of each interviewer’s work was randomly selected for validation by the supervisor.

An initial sample of 10,000 telephone numbers was drawn. Sample loss of 3,138 occurred due to non-connected numbers (n = 2,641), non-residential number (n = 276), fax/modem connections (n = 206) and ineligible households (n = 15). The overall sample response rate was 43.8%.

Questionnaire

Respondents were initially asked their perception of their risk factor (do you think you exercise enough; do you eat a balance diet; do you drink more alcohol than is good for you or than you should; do you think you are overweight, underweight, OK weight; and do you think you worry or stress more than is good for you). If they answered negatively for exercise or diet, positively for alcohol or stress, or responded underweight or overweight regarding weight, they were then asked whether they worried about it (eg does it worry you that not exercising enough may affect your health). Current smokers and ex-smokers were also asked if they worry that their previous/current smoking could affect their health.

Risk factor questions were asked towards the end of the questionnaire and were: how many times a week physical activity of at least 30 minutes was undertaken (insufficient activity defined as less than 150 minutes of physical activity per week), how many serves of vegetables and fruit per day consumed (with respondents deemed to not be eating the required number if they reported less than five serves of vegetables or two serves of fruits per day) [19]; how often and on how many days alcohol was consumed (with risky levels for men, defined as consuming seven or more drinks on any one occasion or alcohol consumption four or more times per week; for women, defined as consuming five or more drinks on any one occasion or alcohol consumption four or more times per week) [20]; BMI (self-reported height and weight) [21]; psychological distress using the Kessler 10 (by receiving a score of 22 or higher on the Kessler 10 instrument) [22]; and smoking status. These questions have all been tested for validity and reliability in the Australian CATI setting [23].

The value of this study is the wide range of ancillary health-related questions asked in the survey. These other questions included in the analysis were based on issues brought up in the focus group discussions and these covered three key concepts - ‘perception of health’ and ‘health service use’ and ‘health action’. Perceptions of health questions were overall health status, whether life was affected by health conditions, how often pain stopped activity, how often the respondent had enough energy, how often they felt angry about their health, and whether they cared about their health. Health service use questions were use of complementary and alternative medicines, doctor visits in the previous year, and other health professional visits in the previous year. Health action related questions were how often they had to adjust pace because of health, whether they did things to reduce their stress, whether they tried to stay connected with people, and if they had ever used trial and error. ‘Trial and error’ is a decision-making strategy that is personal and purposefully implemented to assist an individual to make sense of what is/is not possible for them to do in everyday circumstances. Decisions people make are not necessarily made being mindful of how their decisions will impact on their future health status [24].

Demographic questions asked included age, sex, marital status, work status, country of birth, highest education level obtained, housing status, and annual household income.

Statistical methods

Raw data from the CATI system were analysed using SPSS Version 18.0 and Microsoft Excel. The data were weighted by age and sex to reflect the structure of the Australian population 18 years and over using the Australian Bureau of Statistics 2006 Estimated Residential Population. The weights reflect unequal sample inclusion probabilities and compensate for differential non-response.

Initially, a prevalence estimate for each risk factor was produced for the whole sample. In addition, a comparions between actual risk factor status and perceived risk (healthy, unhealthy) was undertaken using data from all participants. As the focus of this paper was on perceived unhealthy behaviours, no further analyses were undertaken for those whose perception was that they were undertaking the behaviour at a healthy level. Actual risk factor status was then assessed against worry status (worried, not worried). To determine the population most likely to change their behaviour (based on the fact that they believe they are at risk, they actually are in the risky category plus they are worried about the affect of the risk factor on their health – our target population), univariable analyses using chi-square tests were employed to compare differences for each of the six behaviours/risk factors. Six separate multivariable logistic regression models were subsequently developed. As recommended by Hosmer and Lemeshaw [25], all variables with a p-value <0.25 at the univariable level, were included in the initial multivarialbe model in order to ascertain independently associated factors. Final models were obtained using backward stepwise elimination of non-significant variables based on the log likelihood ratio tests. A p-value less than 0.05 was regarded as statistically significant.

The research was carried out following approval from the University of South Australia Human Research Ethics Committee, which are guided by the Australian code for the responsible conduct of research & the National Statement on Ethical Conduct in Human Research 2007.

Results

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 1

Socio-economic and demographic characteristics of respondents, Australia, 2010

Demographic and socioeconomic profile of respondents

 

n

%

(95% CI)

Sex

   

Male

1463

48.7

(46.9–50.5)

Female

1540

51.3

(49.5–53.1)

Age group

   

65+ years

528

17.6

(16.3–19.0)

55 to 64 years

432

14.4

(13.2–15.7)

45 to 54 years

558

18.6

(17.2–20.0)

35 to 44 years

589

19.6

(18.2–21.1)

18 to 34 years

897

29.9

(28.3–31.5)

Number of adults in household aged 18+ years

   

One

332

11.1

(10.0–12.2)

Two

1688

56.2

(54.4–58.0)

Three or more

983

32.7

(31.1–34.4)

Country of birth*

   

Australia

2349

78.2

(76.7–79.6)

UK / Ireland

184

6.1

(5.3–7.0)

Other

469

15.6

(14.4–16.9)

Aboriginal or Torres Strait Islander

   

No

2287

97.4

(74.6–77.7)

Aboriginal / Torres Strait Islander

58

2.5

(1.5–2.5)

Family structure

   

Family and children

1574

52.4

(50.6–54.2)

Adult living alone

284

9.5

(8.5–10.6)

Adult living with partner - no children

769

25.6

(24.1–27.2)

Adults living together - related / unrelated

343

11.4

(10.3–12.6)

Other

30

1.0

(0.7–1.4)

Marital status

   

Never Married

651

21.8

(20.3–23.3)

Married/living with partner

1999

67.0

(65.2–68.6)

Separated/divorced

189

6.3

(5.5–7.2)

Widowed

147

4.9

(4.2–5.8)

Employment status

   

Full time employed

1287

42.9

(41.1–44.6)

Part time employed

564

18.8

(17.4–20.2)

Unemployed

105

3.5

(2.9–4.2)

Economically inactive (Home duties, student, retired, unable to work, other)

1046

34.8

(33.2–36.6)

Highest education level obtained

   

No schooling to secondary

1383

46.1

(44.3–47.9)

Trade, certificate, diploma

758

25.2

(23.7–26.8)

Degree or higher

813

27.1

(25.5–28.7)

Undertake volunteer activities

   

Yes

1018

33.9

(32.2–35.6)

No

1985

66.1

(64.4–67.8)

Provide long term care

   

Yes

771

25.7

(24.2–27.3)

No

2232

74.3

(72.7–75.8)

Gross annual household income

   

$80,000 or more

1136

37.8

(36.1–39.6)

$40,001-$80,000

698

23.2

(21.8–24.8)

Up to $40,000

575

19.1

(17.8–20.6)

Not stated

594

19.8

(18.4–21.3)

Household money situation

   

Spending more money than receive

125

4.2

(3.5–4.9)

Just enough money to get through to next pay day

549

18.3

(16.9–19.7)

Some money left over each week but spend it

184

6.1

(5.3–7.0)

Save a bit every now and then

1441

48.0

(46.2–49.8)

Save a lot

590

19.6

(18.3–21.1)

Don’t know

87

2.9

(2.4–3.6)

Refused

27

0.9

(0.6–1.3)

Dwelling type*

   

Owned or being purchased

2458

82.1

(80.7–83.4)

Rented from government housing

86

2.9

(2.3–3.5)

Rented privately

398

13.3

(12.1–14.6)

Community housing / Retirement village/other

52

1.7

(1.3–2.3)

Total

3003

100.0

 

Note: The weighting of the data can result in rounding discrepancies or totals not adding. *Don’t know category not included.

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 2

Prevalence estimates associated with each risk factor, Australia, 2010

 

n

%

(95% CI)

BMI

   

Underweight

79

3.0

(2.4–3.8)

Normal

1059

40.5

(38.6–42.4)

Overweight

924

35.3

(33.5–37.2)

Obese

553

21.1

(19.6–22.7)

Fruit and vegetable consumption

   

At least 2 & 5 serves per day

266

8.9

(7.9–9.9)

Less than 2 & 5 serves per day

2737

91.1

(90.1–92.1)

Physical activity

   

Sufficient activity

1592

53.1

(51.3–54.8)

No activity/activity but not sufficient/don’t know

1409

46.9

(45.2–48.7)

Smoking

   

Non-smoker

1390

46.3

(44.5–48.1)

Ex-smoker

1084

36.1

(34.4–37.8)

Current smoker

529

17.6

(16.3–19.0)

Short term alcohol risk

   

Non-drinker

685

22.8

(21.3–24.4)

Low risk

1463

48.8

(47.0–50.5)

Risky

725

24.2

(22.7–25.7)

High risk

128

4.3

(3.6–5.1)

Psychological distress

   

Low

1872

62.6

(60.8–64.3)

Moderate

762

25.5

(23.9–27.1)

High

256

8.6

(7.6–9.6)

Very high

101

3.4

(2.8–4.1)

Number of risk factors

   

None

53

2.0

(1.6–2.7)

One

382

14.7

(13.3–16.1)

Two

810

31.1

(29.3–32.9)

Three

884

33.9

(32.1–35.8)

Four

386

14.8

(13.5–16.3)

Five

76

2.9

(2.3–3.6)

Six

14

0.5

(0.3–0.9)

Total

3003

100.0

 
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 3

Risk factor perception versus actual behaviour, Australia, 2010

 

Perception - healthy

Perception - unhealthy

Actual measure

n

%

(95% CI)

n

%

(95% CI)

BMI

      

Normal

958

90.5

(88.6–92.1) ↑

101

9.5

(7.9–11.4) ↓

Underweight /Overweight / Obese

688

44.1

(41.7–46.6) ↓

870

55.9

(53.4–58.3) ↑

Fruit and vegetable consumption

      

Recommended amount

243

91.1

(87.1–94.0) ↑

24

8.9

(6.0–12.9) ↓

Less than recommended amount

1995

72.9

(71.2–74.5) ↓

742

27.1

(25.5–28.8) ↑

Physical activity

      

Sufficient activity

1003

63.0

(60.6–65.3) ↑

589

37.0

(34.7–39.4) ↓

No activity/activity but not sufficient/don’t know

364

25.9

(23.6–28.2) ↓

1044

74.1

(71.8–76.4) ↑

Short term alcohol risk

      

Non-drinker/no risk

1694

78.9

(77.1–80.5) ↑

454

21.1

(19.5–22.9) ↓

Risky/high risk

285

33.4

(30.3–36.6) ↓

568

66.6

(63.4–69.7) ↑

Psychological distress

      

Low/ Moderate

1223

46.4

(44.5–48.4) ↑

1411

53.6

(51.6–55.5) ↓

High /Very high

39

11.0

(8.1–14.6) ↓

318

89.0

(85.4–91.9) ↑

↑↓ Statistically significantly higher or lower than other group (p < 0.05).

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 4

Risk factors status (for those whose perception is of unhealthy behaviour), by worry status, Australia, 2010

 

Worried

Not worried

Actual measure

n

%

(95% CI)

n

%

(95% CI)

BMI

      

Normal

44

43.4

(34.2–53.2) ↓

57

56.6

(46.8–65.8) ↑

Underweight /Overweight / Obese

612

70.3

(67.2–73.3) ↑

258

29.7

(26.7–32.8) ↓

Fruit and vegetable consumption

      

Correct amount

23

98.6

(83.7–99.9)

1

1.4

-

Less than correct amount

564

76.1

(72.9–79.0) ↓

178

23.9

(21.0–27.1) ↑

Physical activity

      

Sufficient activity

439

74.5

(70.9–77.9) ↓

150

25.5

(22.1–29.1) ↑

No activity/activity but not sufficient/don’t know

849

81.3

(78.8–83.5) ↑

196

18.7

(16.5–21.2) ↓

Smoking

      

Non/ex-smoker

525

67.5

(64.2–70.7) ↓

252

32.5

(29.3–35.8) ↑

Current smoker

421

79.6

(75.9–82.8) ↑

108

20.4

(17.2–24.1) ↓

Short term alcohol risk

      

Non-drinker/no risk

230

50.6

(46.0–55.2)

224

49.4

(44.8–54.0)

Risky/high risk

272

48.0

(43.9–52.1)

296

52.0

(47.9–56.1)

Psychological distress

      

Low/ Moderate

960

68.0

(65.5–70.4) ↓

451

32.0

(29.6–34.5) ↑

High /Very high

295

92.9

(89.5–95.2) ↑

23

7.1

(4.8–10.5) ↓

↑↓ Statistically significantly higher or lower than other group (p < 0.05).

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).
Table 5

Multivariate analysis of factors associated with participants who worry about their behaviour and who thought their behaviour unhealthy, by risk factor, Australia, 2010

 

BMI

F & V

PA

Smoking

Short term alcohol risk

High Psychological distress

Number of people in each behaviour/worry group

612/971

564/765

849/1633

421/1306

272/1022

295/1728

% in each behaviour/worry group (95% CI)

63.0 (60.0–66.0)

73.7 (70.5–76.7)

51.9 (49.5–54.4)

32.2 (29.7–34.8)

26.7 (24.0–29.5)

17.1 (15.4–18.9)

Sex

      

Male

    

1.00

 

Female

    

1.41 (0.04)

 

Age

      

65+ years

1.00

1.00

1.00

1.00

1.00

1.00

55 to 64 years

1.82 (0.02)

2.04 (0.13)

1.28 (0.29)

2.80 (<0.01)

1.35 (0.39)

1.61 (0.17)

45 to 54 years

2.21 (<0.01)

3.30 (0.01)

1.30 (0.25)

3.94 (<0.01)

2.44 (0.01)

2.15 (0.03)

35 to 44 years

1.48 (0.12)

2.23 (0.07)

1.74 (0.02)

6.18 (<0.01)

1.71 (0.13)

2.17 (0.03)

18 to 34 years

1.47 (0.13)

1.88 (0.15)

1.35 (0.19)

8.60 (<0.01)

1.80 (0.06)

3.91 (<0.01)

Marital status

      

Never Married

1.00

1.00

1.00

  

1.00

Married/living with a partner

1.64 (0.01)↑

0.89 (0.63)

0.66 (0.01)

  

0.65 (0.04)

Separated/divorced

1.02 (0.95)

0.43 (0.02)

0.80 (0.41)

  

0.98 (0.96)

Widowed

1.37 (0.40)

1.10 (0.88)

0.89 (0.7)

  

0.61 (0.29)

Education

      

No schooling to secondary

 

1.00

1.00

1.00

1.00

 

Trade, certificate, diploma

 

1.09 (0.69)

1.23 (0.12)

0.68 (0.02)

0.61 (0.01)

 

Degree or higher

 

2.06 (0.01)

1.53 (<0.01)

0.73 (0.10)

0.93 (0.70)

 

Work status

      

Full time employed

1.00

 

1.00

1.00

1.00

1.00

Part time employed

0.87 (0.38)

 

1.15 (0.33)

0.84 (0.32)

0.78 (0.30)

1.18 (0.46)

Unemployed

0.32 (0.01)

 

2.86 (<0.01)

0.83 (0.62)

3.33 (0.01)

1.12 (0.74)

Economically inactive

1.06 (0.71)

 

1.24 (0.15)

0.60 (0.01)

1.48 (0.05)

1.80 (<0.01)

Dwelling

      

Owned or being purchased

  

1.00

1.00

 

1.00

Rented from Government Housing

  

0.36 (0.01)

2.32 (0.01)

 

3.08 (<0.01)

Rented Privately

  

1.19 (0.28)

2.29 (<0.01)

 

1.02 (0.93)

Community/Retirement Village/ Other

  

1.54 (0.31)

0.78 (0.72)

 

5.80 (<0.01)

Country of Birth

      

Australia

 

1.00

    

UK/Ireland

 

0.55 (0.19)

    

Other

 

0.59 (0.03)

    

Household annual income

      

>$80,000

1.00

 

1.00

  

1.00

$40,001-$80,000

1.81 (<0.01)

 

0.54 (<0.01)

  

1.60 (0.02)

<$40,000

1.66 (0.01)

 

0.65 (0.02)

  

1.07 (0.79)

Not stated

1.85 (<0.01)

 

0.55 (<0.01)

  

0.83 (0.45)

Overall quality of life

      

Excellent/very good

  

1.00

1.00

 

1.00

Good

  

1.23 (0.09)

1.15 (0.37)

 

1.44 (0.05)

Fair/poor

  

1.55 (0.01)

1.97 (<0.01)

 

2.24 (<0.01)

Had complementary and alternative medicine

      

No/don’t know

 

1.00

1.00

  

1.00

Yes

 

1.85 (<0.01)

1.56 (<0.01)

  

1.49 (0.02)

Life affected by health

      

Activities limited/bedridden most of the time

1.00

     

No problems /Can work & live normally day to day

1.55 (0.02)

     

How often pain stops you doing what you want

      

Always

 

1.00

   

1.00

Sometimes

 

1.90 (0.11)

   

0.53 (0.04)

Not/hardly at all

 

2.84 (0.01)

   

0.46 (0.02)

Doctor visits in past year

      

None

1.00

  

1.00

  

One to four times

1.65 (0.04)

  

0.78 (0.31)

  

Five to ten times

1.61 (0.07)

  

0.96 (0.87)

  

More than 10 times

1.99 (0.02)

  

0.34 (<0.01)

  

Other health professional visits in past year

      

None

 

1.00

 

1.00

1.00

 

One to four times

 

1.94 (<0.01)

 

0.63 (<0.01)

1.72 (<0.01)

 

Five to ten times

 

0.88 (0.68)

 

0.39 (<0.01)

0.92 (0.75)

 

More than 10 times

 

1.28 (0.52)

 

0.41 (<0.01)

0.98 (0.93)

 

How often have enough energy

      

All/most of the time

   

1.00

 

1.00

Some of the time

   

1.47 (0.04)

 

2.10 (<0.01)

A little/none of the time

   

0.99 (0.97)

 

3.39 (<0.01)

How often have to adjust pace because of health

      

A little/none of the time

  

1.00

 

1.00

1.00

Some of the time

  

1.35 (0.03)

 

1.54 (0.03)

1.56 (0.02)

All/most of the time

  

0.75 (0.11)

 

2.10 (0.01)

0.49 (0.01)

Do things to reduce stress

      

Yes/sometimes

 

1.00

   

1.00

No

 

1.83 (0.01)

   

1.49 (0.04)

Try and stay connected to people

      

Yes/sometimes

     

1.00

No

     

2.02 (0.01)

Ever used trial and error

      

No/ don’t know/refused

1.00

1.00

  

1.00

 

Yes/sometimes

1.71 (<0.01)

2.23 (0.05)

  

0.67 (0.03)

 

How often do you feel angry about your health

      

A little/none of the time

 

1.00

1.00

1.00

 

1.00

Some of the time

 

2.23 (<0.01)

1.28 (0.09)

1.55 (0.02)

 

2.41 (<0.01)

All/most of the time

 

1.26 (0.53)

2.12 (<0.01)

2.37 (0.01)

 

7.12 (<0.01)

Do you care about your health

      

A little/none of the time

1.00

  

1.00

  

Some of the time

1.43 (0.21)

  

2.09 (0.02)

  

All/most of the time

1.89 (0.01)

  

1.11 (0.69)

  

Discussion

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 [2830]. 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.

Conclusion

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.

Abbreviations

BMI: 

Body Mass Index

CATI: 

Computer Assisted Telephone Interviewing

EWP: 

Electronic White Pages

K10: 

Kessler 10

OR: 

Odds ratio

SPSS: 

Statistical Package for Social Sciences.

Declarations

Acknowledgement

This work was supported by a Department of Health and Ageing Sharing Care Health Initiative Grant

Authors’ Affiliations

(1)
Population Research and Outcome Studies, University of Adelaide
(2)
Associate Professor Kay Price, School of Nursing and Midwifery, University of South Australia-City East Campus

References

  1. Armitage CJ, Connor M: Social cognition models and health behaviour: a structured review. Psychol Health. 2000, 15: 173-189. 10.1080/08870440008400299.View ArticleGoogle Scholar
  2. Cameron LD, Reeve J: Risk perceptions, worry, and attitudes about genetic testing for breast cancer susceptibility. Psychol Health. 2006, 21 (2): 211-230. 10.1080/14768320500230318.View ArticlePubMedGoogle Scholar
  3. Rose JP: Are direct or indirect measures of comparative risk better predictors of concern and behavioural intentions?. Psychol Health. 2010, 25 (2): 149-165. 10.1080/08870440802340164.View ArticlePubMedGoogle Scholar
  4. Purdie N, McCrindle A: Self-regulation, self-efficacy and health behavior change in older adults. Educ Gerontol. 2002, 28: 379-400. 10.1080/03601270290081353.View ArticleGoogle Scholar
  5. Baron J, Hershey JC, Kunreuther H: Determinants of priority for risk reduction: the role of worry. Risk Anal. 2000, 20 (4): 413-427. 10.1111/0272-4332.204041.View ArticlePubMedGoogle Scholar
  6. Kaptein AA, van Korlaar IM, Camerson LD, Vossen CY, van der Meer FLM, Rosendaal FR: Using the common-sense model to predict risk perception and disease-related worry in individuals at increased risk for venous thrombosis. Health Psychol. 2007, 26 (6): 807-812.View ArticlePubMedGoogle Scholar
  7. Myers JR, Henderson-King DH, Henderson EI: Facing technological risks: the importance of individual differences. J Res Pers. 1997, 31: 1-20. 10.1006/jrpe.1997.2174.View ArticleGoogle Scholar
  8. Schmiege SJ, Bryan A, Klein WMP: Distinctions between worry and perceived risk in the context of the theory of planned behaviour. J Appl Soc Psychol. 2009, 39 (1): 95-119. 10.1111/j.1559-1816.2008.00431.x.View ArticleGoogle Scholar
  9. Sjoberg L: Worry and risk perception. Risk Anal. 1998, 18 (1): 85-93. 10.1111/j.1539-6924.1998.tb00918.x.View ArticlePubMedGoogle Scholar
  10. Cameron LD, Diefenbach MA: Responses to information about psychosocial consequences of genetic testing for breast cancer susceptibility: Influences of cancer worry and risk perceptions. J Health Psychol. 2001, 6 (1): 47-59. 10.1177/135910530100600104.View ArticlePubMedGoogle Scholar
  11. de Hoog N, Strobe W, de Wit JBF: Pers Soc Psychol B. 2005, 31 (24): 24-33.View ArticleGoogle Scholar
  12. Smerecnik CMR, Mesters I, de Vries NK, de Vries H: Alerting the general population to genetic risks: the value of health messages communication the existence of genetic risk factors for public health promotion. Health Psychol. 2009, 28 (6): 734-745.View ArticlePubMedGoogle Scholar
  13. Das EHHJ, de Wit JBF, Stroebe W: Fear appeals motivate acceptance of action recommendations: evidence for a positive bias in the processing of persuasive messages. Pers Soc Psychol B. 2003, 29 (5): 650-664. 10.1177/0146167203029005009.View ArticleGoogle Scholar
  14. University of South Australia: People with chronic disease and the influence of trial and error practices as a self-care strategy: a novel approach Summary of qualitative data. 2010, Downloaded from http://www.unisa.edu.au/nur/research/projects/chronic_disease/default.asp (accessed 27/10/2011)Google Scholar
  15. Donaldson C: Marketing Health, influencing behaviour. J R Soc Promot Health. 2008, 128 (4): 152-153. 10.1177/14664240081280041102.View ArticlePubMedGoogle Scholar
  16. Wakefield MA, Loken B, Hornik RC: Use of mass media campaigns to change health behaviour. Lancet. 2010, 376: 1261-1271. 10.1016/S0140-6736(10)60809-4.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Verbeke W: Impact of communication on consumers’ food choices. Proc Nutr Soc. 2008, 67: 281-288. 10.1017/S0029665108007179.View ArticlePubMedGoogle Scholar
  18. Grant JF, Chittleborough CR, Taylor AW, Dal Grande E, Wilson DH, Phillips PJ, Ruffin R, North West Adelaide Health Study team: The North West Adelaide Health Study: methodology and baseline self-reported and biomedical results of a cohort along a chronic disease and risk factor continuum. Epidemiol Perspect Innovat. 2006, 3: 4-10.1186/1742-5573-3-4. 12 April 2006View ArticleGoogle Scholar
  19. Health N, Council MR: Dietary guidelines for Australian Adults. 2003, Canberra: Commonwealth of AustraliaGoogle Scholar
  20. Health N, Council MR: Australian alcohol guidelines: Health risks and Benefits. 2001, Canberra: NHMRCGoogle Scholar
  21. World Health Organization: Obesity: Preventing and managing the global epidemic. 2000, Geneva: WHOGoogle Scholar
  22. Kessler R, Mroczek D: Final versions of our non-specific psychological distress scale. 1994, Michigan: Institute for Social Research, University of MichiganGoogle Scholar
  23. Daly A, Taylor A: Population Health Monitoring and Surveillance, Question development field testing – Field testing 1 report (Asthma, Demographic Characteristics and Diabetes). 2003, http://www.nphp.gov.au/catitrg/documents/fieldtest01rpt.pdf,Google Scholar
  24. Price K, Van Loon A, Taylor A, Kralik D: People with chronic disease and the influence of trial and error practices as a self- care strategy: a novel approach: summary of qualitative data. 2010, Adelaide: University of South Australia, ISBN 978-0-9807778-0-2Google Scholar
  25. Hosmer DW, Lemeshow S: Applied Logistic Regression. 1989, New York, NY: J Wiley and SonsGoogle Scholar
  26. Resnicow K, Davis R, Zhang N, Tolsma D, Calvi J, et al: Tailoring a fruit and vegetable intervention on ethcnic identity: results of a randomised study. Health Psychol. 2009, 28: 394-403.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Hagger MS: Personality, individual differences, stress and health. Stress and Health. 2009, 25: 381-386. 10.1002/smi.1294.View ArticleGoogle Scholar
  28. Anderson R, Anderson D, Hurst C: Modeling factors that influence exercise and dietary change among midlife Australian women: results form the Helathy Aging of Women Study. Maturitas. 2010, 67: 151-158. 10.1016/j.maturitas.2010.06.007.View ArticlePubMedGoogle Scholar
  29. Shi HJ, Nakamura K, Takano T: Health values and health-information-seeking in relation to positive change of health practice among middle-aged urban men. Prev Med. 2004, 39: 1164-1171. 10.1016/j.ypmed.2004.04.030.View ArticlePubMedGoogle Scholar
  30. Hooker S, Harmon B, Burroughs EL, Rheaume CE, Wilcox S: Exploring the feasibility of a physical activity intervention for midlife African American men. Health Educ Res. 2011, 26 (4): 732-738. 10.1093/her/cyr034.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Laaksonen M, Rahkonen O, Karvonen S, Lahelma E: Socioeconomic status and smoking. Analysing inequalities with multiple indicators. Eur J Public Health. 2005, 15 (3): 262-269. 10.1093/eurpub/cki115.View ArticlePubMedGoogle Scholar
  32. Bauman A, Ma G, Cuevas F, Omar Z, Waqanivalu T, Phongsavan P, Keke K, Bhushan A: for the Equity and Non-communicable Disease Risk Factors Project Collaborative Group. Cross-national comparisons of socioeconomic differences in the prevalence of leisure-time and occupational physical activity, and active commuting in six Asia-Pacific countries. J Epidemiol Community Health. 2011, 65 (1): 35-43. 10.1136/jech.2008.086710.View ArticlePubMedGoogle Scholar
  33. Paeratakul S, Lovejoy JC, Ryan DH, Bray GA: The relation of gender, race and socioeconomic status to obesity and obesity comorbidities in a sample of US adults. Int J Obes. 2002, 26 (9): 1205-1210. 10.1038/sj.ijo.0802026.View ArticleGoogle Scholar
  34. Flocke SA, Antognoli E, Step MM, Marsh S, Parran T, Mason MJ: A teachable moment communication process for smoking cessation talk: description of a group randomized clinician-focused intervention. BMC Health Serv Res. 2012, 12: 109-10.1186/1472-6963-12-109.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Goldstein MG, Whitlock EP, DePue J: Multiple behavioural risk factor interventions in primary care. Summary of research evidence. Am J Prev Med. 2004, 27: 61-79. 10.1016/j.amepre.2004.04.023.View ArticlePubMedGoogle Scholar
  36. Green LW, Ottoson JM, Garcia C, Hiatt RA: Diffusion theory, and knowledge dissemination, uitilisation and intregation in public health. Annu Rev Public Health. 2009, 30: 151-174. 10.1146/annurev.publhealth.031308.100049.View ArticlePubMedGoogle Scholar
  37. Laszlo K, Janszky I, Ahnve S: Anger expression and prognosis after a coronary event in women. Int J Cardiol. 2010, 140: 60-65. 10.1016/j.ijcard.2008.10.028.View ArticlePubMedGoogle Scholar
  38. Ahmed AO, Green BA, McCloskey MS, Berman ME: Latent structure of intermittent explosive disorder in an epidemiological sample. J Psychiatr Res. 2010, 44 (10): 663-72. 10.1016/j.jpsychires.2009.12.004.View ArticlePubMedGoogle Scholar
  39. Golden SH, Williams JE, Ford DE: Anger temperament is modestly associated with the risk of type 2 diabetes mellitus. The atherosclerosis risk in communities study. Psychoneuroendocrinology. 2006, 31 (3): 325-332. 10.1016/j.psyneuen.2005.08.008.View ArticlePubMedGoogle Scholar
  40. McCracken J, Lindner H, Sciacchitano L: The mediating role of secondary beliefs: enhancing the understanding of emotional responses and illness perceptions in arthritis. J Allied Health. 2008, 37 (1): 30-7.PubMedGoogle Scholar
  41. Groves RM: Non-response rates and non-response bias in household surveys. Public Opin Q. 2006, 70: 646-675. 10.1093/poq/nfl033.View ArticleGoogle Scholar
  42. Curtin R, Presser S, Singer E: Changes in telephone survey non-repsonse over the past quater century. Public Opin Q. 2005, 69: 87-88. 10.1093/poq/nfi002.View ArticleGoogle Scholar
  43. Schuit AJ, van Loon JM, Tijhuis M, Ocke MC: Clustering of lifestyle risk factors in a general adult population. Prev Med. 2002, 35: 219-224. 10.1006/pmed.2002.1064.View ArticlePubMedGoogle Scholar
  44. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/13/120/prepub

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© Taylor et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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