Effectiveness of Health Promotion Interventions upon High Risk Lifestyle

Behaviours of Adult Clients of Health Benefits Organisations

Haralds (Jack) Dzenis

This thesis is part of the requirements for a Ph.D. degree at

Queensland University of Technology - 2004.

KEY WORDS

Health promotion. health self-care. medical self-care. health self-efficacy.

I

Abstract

Over the past 100 years the average life span of humans has increased in developed

countries. Mortality rates have changed because of the virtual eradication of infectious

diseases, such as polio and smallpox, and the increase in chronic diseases. Chronic

diseases, such as coronary heart disease, are related to lifestyle behaviour, a factor over

which the individual has some control.

Matarazazo (1984) believes that “behavioural pathogens” are the key to understanding

health behaviours of the individual and subsequently designing more effective methods

of dealing with chronic disease and illness. Fries (1980) suggests another approach to

dealing with chronic disease, through the strategy of “compressed morbidity”. This

refers to the postponement of chronic infirmity relative to average life duration. By

achieving compressed morbidity, it is expected that health costs will decrease and

improvement of quality of life will occur. This may be possible in at least two ways:

firstly, by self-empowerment of the individual and secondly by the development of health

self-efficacy. Thus giving the individual the power to act upon certain health-damaging

behaviours as well as the confidence to influence behavioural change and persistence to

cope with difficulties whilst the process of change is occurring. Thirdly, as a result of

this, behaviour changes will occur and this would lead to a reduction in health cost which

would be of overall benefit to the community.

ii

One method of reducing these health care costs is through health promotion and health

education. Improvements in health knowledge and skills through health education and

health promotion has been shown to facilitate changes in lifestyle and so reduce the

incidence of various diseases.

This study examined the effectiveness of two types of self-care models, health self-care

and medical self-care. Health self-care refers to individuals assuming more responsibility

for prevention, detection and the treatment of health problems using self-care

information. Medical self-care involves the use of General Practitioners (GP) offering

advice to their patients and subsequently patients making informed decisions about their

health. The health self-care model Healthtrac, attempts to provide an effective use of the

Australian health care system. Healthtrac is an information and skills based mail delivery

program designed to assist individuals in elevating their perceptions of health self-

efficacy and improve their lifestyle behaviours. Better Health is the medical self-care

model which is designed with the perspective that GP’s are the best suited as the

initiators of change in individual health self-care.

Participants (N = 864) are adult males and females. The methodology for this study

involved 864 high risk of chronic disease participants who have been identified using the

Healthtrac Health Risk Assessment (HRA) instrument. There were (n = 343) participants

in the health self-care group, (n =66) in the medical self-care group and (n = 455) in the

control group. This instrument was designed to identify individuals who have or are at

high risk of developing chronic disease. These participants were part of the Better Health

iii

promotion program of a Health Insurance company. All the participants received a letter

of advice detailing the presence of certain risk factors as determined by their health risk

appraisal. They were requested to visit their local GP who recommended the necessary

behavioural changes and medical support required for medically satisfactory outcomes.

They were encouraged to follow the advice of the GP and received a second HRA after 6

months and again12 months after the start of the project. The Healthtrac component of

the study involved 343 subjects who completed the HRA instrument. Participants in this

group were matched with the Better Health subjects for variables such as age, gender,

employment, disease or lifestyle and educational level. Baseline impact variables were

calculated and compared with the same variables at 6 monthly intervals during the 12

month period of the study. Process variables such as user satisfaction were determined

by a questionnaire. Investigation of the Health Benefits Organisation records were used

to gather data on the number of claims for hospitalisation and other medical costs. A

control group of 455 participants were matched with the same variables as those

participants in the health self-care model and medical self-care groups.

The analysis of results indicate that variables such as number of doctor’s visits, days

spent in hospital and total risks scores for the health self-care model were lower than the

Medical model scores. The variable, cost of disease findings indicate that there were no

significant differences between the two experimental groups, from the baseline data (Q1)

to the 12 month period (Q3). The cost of diseases for heart disease was able to be

lowered more by participants in the health self-care than the medical self-care model.

The opposite occurred for the blood pressure condition.

The health self-efficacy questionnaire results indicate that the health self-care group

participants reported higher self-efficacy scores, therefore they were more confident

about the self-management of their health behaviours than the members of the medical

self-care group. No significant differences occurred among the experimental and control

groups on such variables as achievement of outcomes and management of disease on

self-efficacy scores.

Both experimental groups, health self-care and the medical self-care model philosophies

have strengths and weaknesses. Health self-care provides health information and support

through printed materials whereas the medical self-care model provides health

information through GP’s. Both health promotion programs are important in making the

individual aware of methods needed to improve health and in developing the knowledge

necessary to modify clients health behaviours. This in turn is an important factor in the

reduction of medical costs and the prevention of some diseases.

iv

CERTIFICATION

The work contained in this thesis has not been previously submitted for a degree or

diploma at any other higher education institution. To the best of my knowledge and

V

belief, the thesis contains no material previously published or written by another person

except where due reference is made.

Signed:________________________________

Date: ___________________________________

Acknowledgement

I wish to thank a number of people who generously gave their time so that I was able to

produce this thesis. Firstly I would like to thank Dr. Tom Cuddihy who has been my

principal supervisor for this time and his patience. My associate supervisor Associate

Professor Peter Davies for this wisdom and this humour. Professor Tony Parker as head

of Human Movement Studies for this advice and encouragement. Secondly, I would like

to acknowledge all the staff at Healthtrac for the assistance they have offered me over the

years.

I would like to thanks both of my parents for the inspiration to do things over the years and least of all pursue a higher degree and the value of knowledge.

VI

Contents

Key words -------------------------------------------------------------------------------- i Abstract -------------------------------------------------------------------------------- ii Certification ----------------------------------------------------------------------------- iii Acknowledgement --------------------------------------------------------------------- iv Tables ------------------------------------------------------------------------------------ v Figures ----------------------------------------------------------------------------------- vi Introduction--------------------------------------------------------------------------------- 1

1.0- REVIEW OF LITERATURE ---------------------------------------------- 8

1.1 Health promotion --------------------------------------------------- 8

1.2 Behavioral model ------------------------------------------------- 12

1.3 Health belief model ------------------------------------------------ 13

1.4 Transtheoretical model -------------------------------------------- 14

1.5 Self-efficacy theory ----------------------------------------------- 17

1.6 Health self-care and medical self-care ------------------------- 26

1.7 Gender issues ------------------------------------------------------- 31

1.8 Healthtrac and Better health models --------------------------- 35

vii

2 .0 - Health in Australia ----------------------------------------------------------- 37

2.1 Major issues --------------------------------------------------------- 37

2.2 Migrant health ------------------------------------------------------ 40

2.3 Aboriginal and Torres Strait Islander health ------------------ 42

3.0 - Health promotion in Australia ------------------------------------------------- 44

3.1 History of health promotion and Commonwealth govt ----- 44

3.2 State government agencies and health promotion ----------- 53

3.2.1 Victoria -------------------------------------------------- 53

3.2.2 South Australia --------------------------------------- 56

3.2.3 Western Australia ----------------------------------- 59

3.2.4 Australian Capital Territory ---------------------- 62

3.2.5 Other states ------------------------------------------ 63

3.3.1 Non government agencies ------------------------------------ 69

3.3.2 Cancer Funds, Councils and Societies --------- 72

3.3.3 Australian Drug Foundation --------------------- 74

4.0 - Health Insurance Industry ------------------------------------------------ 80

4.1 Background ---------------------------------------------------- 80

4.2 History ---------------------------------------------------------- 81

4.3 Private health insurance ------------------------------------- 84

4.4 Current issues ------------------------------------------------- 92

4.5 Health care costs and the future -------------------------- 93

Methodology ----------------------------------------------------------------------- 99

1.1 Data gathering ----------------------------------------------- 101

viii

1.2 The Medical self-care Model ------------------------------- 105

1.3 Control group ----------------------------------------------- 105

1.4 Questionnaires ---------------------------------------------- 106

1.4.1 Self-efficacy ------------------------------------ 106

1.4.2 Healthtrac Health Risk Assessment (HRA) - 112

Results ------------------------------------------------------------------------ 115

6.1 - Healthrac (experimental group) ------------------------- 115

6.2 – Medical self-care (experimental group)----------------- 136

6.3 – Control group ---------------------------------------------- 146

6.4 – Health self-care, medical self-care and control group-- 157

6.5 – Health self-efficacy ----------------------------------------- 166

6.5.1 – Health self-care ----------------------------------- 166

6.5.2 – Medical self-care --------------------------------- 178

6.5.3 – Control group ------------------------------------- 187

3.6 – Comparisons across all groups ---------------------------- 194

Discussion --------------------------------------------------------------------------- 199

4.1 – High Risk Assessment Questionnaire (HRA)--------- 201

4.2 – Health self-efficacy questionnaire --------------------- 206

Recommendation for future research ------------------------ 216

Appendix ------------------------------------------------------------------------------------- 218

1 - HHRA questionnaire and letters

2 - Health self-efficacy questionnaire

3 - Health promotion information (books)

ix

4 - Flow diagram of health self-care, medical self-care

and control groups – HRA questionnaire

Abbreviations --------------------------------------------------------------------- 219

References ------------------------------------------------------------------------- 220

Tables

Table 1 – Healthtrac –age groups and types of disease/risk factors – baseline data- 116

Table 2 – Cost of health disease ($)… ---------------------------------------------------- 118

Table 3 – Cost of disease ($) and types of disease in (Q1,Q2,Q3) ----------- 119

Table 4 – ANOVA -Cost of disease ($) and age (Q1,Q2,Q3) ------ 120

Table 5 – Cost of disease ($) and age groups --------------------------- 120

Table 6 – Age and doctors visits for (Q1,Q2,Q3) ----------------------------- 121

Table 7 – Doctors visits for (Q1,Q2,Q3). ------------------------------------- 122

Table 8 – Number of days spent in hospital and age groups----------------- 123

Table 9 – ANOVA – days spent in hospital for (Q1,Q2,Q3) --------------------- 123

Table 10 – Repeated measures for days in hospital and age-------------------- 124

Table 11 – Heart risk scores, category and gender ---------------------- 127

Table 12 – Gender and heart disease risk scores ----------------------- 127

Table 13 – Risk of heart disease and age (Q1,Q2,Q3) ---------------------- 128

Table 14 – Cancer risk category and gender -------------------------------- 128

Table 15 – Repeated measures cancer risk scores and age---------------------- 129

x

Table 16 – Total risk scores and age groups ----------------------------------- 130

Table 17 – ANOVA – total risk scores and age ----------------------------- 132

Table 18 – Correlation matrix- ideal weight ------------------------------- 133

Table 19 – Correlation matrix – age, gender, cost of disease ------------------- 134

Table 20 – Correlation matrix – age, risk of cancer ------------------------------ 135

Table 21 – Cost of various diseases ------------------------------------------ 137

Table 22 – Total risk scores for gender ------------------------------------- 137

Table 23 – Gender, doctors visits (Q1,Q2,Q3) -------------------------------------- 138

Table 24 – Gender and days in hospital (Q1,Q2,Q3-------------------------------- 139

Table 25 – Mean risk of heart disease scores for gender ------------------------ 140

Table 26 - Cost of disease and age groups (Q1,Q2,Q3)--------------------------- 141

Table 27 – Cost of various diseases ------------------------------------------------- 142

Table 28 – Repeated measures- cost of disease and age ----------------- 143

Table 29 – Correlation matrix – age and doctors visits–---------------------- 144

Table 30 – Correlation matrix – total risk scores --------------------------------- 145

Table 31 – Gender and number of participants ---------------------------------- 147

Table 32 – Cost of disease and age groups ------------------------------------ 148

Table 33 – Gender and cost of disease---------------------------------------- 149

Table 34 – ANOVA – cost of disease and age ------------------------------ 149

Table 35 – Gender and total risk scores (Q1,Q2,Q3) ----------------------- 150

Table 36 – Mean and SD for gender and doctors visits (Q1,Q2,Q3) ------------ 151

xi

Table 37 – Age groups and hospital visits (Q1,Q2,Q3) --------------------- 152

Table 38 – Correlation matrix – age and cost of disease (Q1,Q2,Q3) --------- 153

Table 39 – Correlation matrix – total risk scores, doctors visits ------------------- 154

Table 40 – Correlation matrix – total risk scores, gender, risk of cancer ---- 156

Table 41 – Total risk scores ------------------------------------------------------- 159

Table 42 – Percentage scores for total risk scores for all groups ------- 159

Table 43 – Cost of disease for all groups ------------------------------------ 161

Table 44 – Percent difference between all groups in cost of disease ------------- 161

Table 45 – Mean cost of various diseases for all groups (Q1,Q2,Q3) ---- 162

Table 46 – Precent differences in disease costs (Q1,Q2,Q3) ----------- 162

Table 47 – Risk of heart disease scores for all groups (Q1,Q2,Q3) –------- 164

Table 48 – Mean percentage risk of heart disease scores for all groups -------- 164

Table 49 – Risk of cancer scores for all groups (Q1,Q2,Q3) ---------------- 165

Table 50 – Percentage differences between all groups –----------------- 165

Table 51 – Gender and percent -------------------------------------------------- 166

Table 52 – Martial status frequency and percent ----------------------- 167

Table 53 – Participant numbers and percentage for health status --- 168

Table 54 – Perceptions of how illness interferes with normal daily living –- 170

Table 55 – Perceptions of self-management of health behaviour ---------- 171

Table 56 – Perceptions of the management of disease ------------------------ 172

Table 57 – Perceptions for the achievement of outcomes variables -------- 173

Table 58 – Perceptions for the health self-efficacy variables --------------- 174

Table 59 – Correlation matrix – self-management exercise variable and age -- 175

xii

Table 60 – Correlation matrix – GP variables and management of disease---- 176

Table 61 – ANOVA – gender and sets goals to improve health --------------- 177

Table 62 – ANOVA – GP questions within self-management and age groups- 178

Table 63 – Frequency and percentages for gender --------------------------------- 178

Table 64 – Current state of health ------------------------------------------------------- 179

Table 65 – Health compared to 12 months earlier --------------------------------- 180

Table 66 – Perception of how illness interferes with activities of daily life -- 180

Table 67 – Perceptions of self-management for behaviour variables -------- 181

Table 68 – Perceptions related to disease management ------------------------ 182

Table 69 – Issues of achievement of outcomes ---------------------------------- 183

Table 70 – Perceptions of health self-efficacy issues --------------------------- 184

Table 71 – Correlation matrix- health self-efficacy issues, age and gender -- 185

Table 72 – Correlation matrix –health self-efficacy and management of disease 186

Table 73 – Age and management of disease GP questions --------------- 187

Table 74 – Perceptions of how illness interferes with daily living ------- 188

Table 75 – Perceptions of self-management variables ---------------------- 189

Table 76 – Perceptions of the management of disease ---------------------- 190

Table 77 – Perceptions of the achievement of outcomes -------------------- 191

Table 78 – Perceptions of health self-efficacy -------------------------------- 192

Table 79 – Correlation – GP questions in self-management ---------------- 193

Table 80 – Groups types and health self-efficacy scores ------------------- 194

Table 81 – Group type and total self-management scores ------------------ 195

Table 82 – Group type and total achievement of outcome scores --------- 195

xiii

Table 83 – Type of group and management of disease scores ------------- 196

Table 84 – Group type and GP questions within self-management --------- 196

Table 85 – Type of group and management of disease GP questions ---------- 197

Table 86 – Illness Intrusive scale for type of group -------------------------------- 198

Figures

Figure 1 – The increasing rectangular survival curve – Fries 1980.------------ 10

Figure 2 – Mean number of minutes of exercise for different types of exercise- 125

Figure 3 – Mean number of minutes of exercise for different types of exercise

by age groups --------------------------------------- 126

Figure 4 – Total mean risk scores, age groups (Q1,Q2,Q3) ---------- 131

Figure 5 – Participants in different age groups ---------------------------------- 136

Figure 6 – Cost of disease and age groups ---------------------------------- 141

Figure 7 – Number of participants and age groups --------------------- 148

Figure 8 – Comparison of mean doctors visits for all groups ---------------- 158

Figure 9 – Mean total risk scores for health self-care --------------------------- 160

Figure 10 – Participants within age groups ---------------------------------------- 168

Figure 11 – Current rating of health status when compared 12 months ago -- 169

Figure 12 – Participants within age groups ------------------------------------- 180

xiv

INTRODUCTION

Over the past 100 years the average life span of humans has increased in developed and

Western countries. In 1907 there were 12 deaths per 1,000 persons per year, by the

middle of this century the crude death rate had fallen to 10 per 1,000 persons, and in 1992

it was down to 7.1 per 1,000 (Australian Institute of Health and Welfare, (AIHW, 1994a).

In 1998 this figure had fallen to 6.8 per 1,000 persons (AIHW, 2000). During the same

period the life expectancy for males has risen from 47 to 74 years, while for females it

has increased from 30 years to just over 80, (Australian Life Tables, 1995). In Australia

since 1901, life expectancy at birth has increased by 38 percent (from 55.2 years) for

males and by 39 percent (from 58.8 years) for females (AIHW, 2000). There are many

factors which have contributed to this increased life expectancy.

At the turn of the century mortality patterns were dominated by acute, usually infectious

diseases (Fries, 1980). Infectious diseases such as Polio and Smallpox, which caused

high morbidity and death, have almost been eradicated. Chronic diseases now form the

major part of our health problems. Garrett (1994) suggests that the elimination of

Smallpox as the most important factor in the decreasing death rates. The controlling of

bacterial infections, which were common before 1944 when the first antibiotic drugs

became available, is another major contributing factor (Garrett, 1994). In 1921,

infectious and parasitic diseases were the second major cause of death at a death rate of

1

1.8 deaths per 1,000 persons. This accounted for 12 percent of the population, but by the

1950s this had fallen to 0.4 per 1,000. In the 40 years since there has been a further

decline to 0.05 deaths per 1,000 (AIHW, 1994b). Cardiovascular diseases (CVD),

which include diseases of the heart and the circulatory system, accounted for 44.4 per

cent of deaths from all causes among Australians in 1992 ( AIHW, 1994b). Diseases

such as Coronary Heart Disease (CHD) are related to lifestyle behaviours over which the

individual has some degree of control. Consequently, there has been a ‘health transition’

from infectious diseases such as Tuberculosis to chronic diseases such as CVD (AIHW,

2000). Other factors contributing to the increased life expectancy of Australians include

modern medicine and public awareness of preventative practices, especially those related

to lifestyle, nutrition and exercise (Telford et al. 1993).

Preventable factors relating to morbidity and mortality have been termed, “behavioural

pathogens” (Matarazazo, 1984). Behavioural pathogens are the key to understanding

health behaviours of the individual and therefore to the subsequent design of more

effective methods of dealing with chronic diseases and illness. Fries (1980) suggests

that chronic diseases should be approached with the strategy of “compressed morbidity”

rather than cure. The compressed morbidity for some of he chronic diseases can be

achieved by altering the behavioural pathogens of the individual. For example the

decline in death rates from CVD is due to many factors, but certainly the lifestyle

behavioural changes have exerted considerable influence (AIHW, 2000). Diseases such

as CVD can have behavioural strategies applied to postpone the onset of this type of

disease. Behavioural strategies such as involvement in exercise programs and low

2

saturated fat diets can be applied to prevent the onset of CVD (Egger, Spark, & Lawson,

1990). Evidence from Australian studies suggests that there has been a marked decline

in mortality from CVD over recent decades (Waters & Bennett, 1995).

A behavioural approach towards a healthy lifestyle stems from the individual’s

perception of what is appropriate to maintain health. In this regard, the concept of self-

empowerment has been described as an important issue to the individual (Breslow, 1996).

Self-empowerment deals with the ability of the individual to act on health-related

decisions and therefore decrease the susceptibility to engage in health-damaging

behaviours (Colquhoun, Goltz, & Sheehan, 1997). Such behaviours, however, can be

affected by a number of factors. Firstly the individual needs to acknowledge his or her

behaviour to be health-damaging. Secondly, and more importantly they have to persist in

various coping strategies and skills to disengage from this detrimental behaviour.

Bandura's (1977) concept of self-efficacy theory, which was developed within the

framework of social-learning theory, has important implications in predicting how

individuals engage and disengage in certain types of health behaviours. Self-efficacy is

one of the most important factors influencing judgments of health behaviour change

(Love et al. 1996). Self-judgment of efficacy determines choice behaviour, that is which

activities will be attempted and which will be avoided. Self-efficacy also affects the

amount of effort devoted to a task and the duration of persistence when difficulties are

encountered (O'Leary, 1985). The confidence that one can control a health threat seems

3

to require not only a belief that the coping response is effective but also that the coping

response can be successfully performed (Beck & Lund, 1981).

Other factors have been implicated as important in the health behavioural change context.

From an empowerment point of view individuals should take responsibility for their own

health because ultimately they are the ones who pay the cost of medical care. The

individual should be given appropriate health information in order that judgments about

poor health behaviours can be altered. This information may be gained either from a

general practitioner or from some other health promotional medium for example print

media. Health educators can also offer health information and provide some other health

service. It is only through the provision of such appropriate health promotion material

that a reduction in the cost of health care may occur (Fries et al. 1993).

The financial cost of health care to the community is substantial. In 1989-1990

Australian health care costs exceeded $3,300 million per year (AIHW, 1994). By 1997-

98 total health services expenditure by both government and non-government sectors was

$47,030 million; by 1998-99 the preliminary estimate for that financial year was $50,335

million (AIHW, 2000). These costs are both direct, indirect and intangible. Direct costs

include money spent on treating, caring and diagnosing individuals, whereas indirect

costs are those related to lost work output, rehabilitation and premature death. Intangible

costs related to the individual’s (and their family’s) income, in reduction of quality of life

through issues such as pain, disability, bereavement, anxiety and suffering (CDHAC,

2000). In Australia, the direct costs are estimated to be $2,200 million while indirect

costs are in the region of $1100 million (AIHW, 1994). Intangible costs are difficult to

4

measure in terms of dollars because of the effects upon both the community and the

individual’s family. Thus, individuals who make positive changes to their health are in

effect reducing some of the direct and indirect costs to the community. If a 20 per cent

reduction in the incidence of CHD, a potential annual 'saving' of $95 million in health

care cost is possible (Commonwealth Department of Human Services and Health,

(CDHH), 1995). If individuals involved themselves in some level of physical activity it

is estimated that there would a potential saving in health care costs of $2.6 million

(CDHAC, 2000). Health expenditure has more than doubled between 1960-61 and 1997-

98 jumping from $7,313 million to $47,030 million. This represents a real average annual

increase of 5 percent (AIHW, 2000). Due to ever increasing costs, health expenditure has

therefore become a major issue in Australia.

Health promotion is defined as a dynamic process that emphasizes the shift of power for

personal health from professionals to individuals. It is an action-oriented concept

providing direction for specific activities related to improvements in health ( DuGas,

1993). Improvement in health knowledge and skills through health education and health

promotion is one method which has been shown to promote changes in lifestyle and thus

reducing the incidence of various diseases. Another flow-on effect of such initiatives

has been the reduction in both direct and indirect medical costs (Vickery, et al. 1983).

For example, alcoholism in Australian industry costs employers around $2 billion per

annum, while employer-based programs have been successful in reducing this cost

(Egger et al. 1990). The central goal of health promotion programs is the improvement

in health habits and ultimately, the postponement and prevention of major chronic

5

illnesses (Fries, et al., 1993). The health insurance health promotion program has an

important role to play in changing personal health behaviours as well as changing the

status of population health in Australia.

A number of models have been used to link health promotion with self-care. Health self-

care and medical self-care are two models which will be examined more closely.

Medical self-care is concerned with the General Practitioner (GP) providing lifestyle

advice to their patients such as losing weight. As a result patients may make better

informed decisions about their health. Health self-care refers to the situation where the

individual assumes more responsibility for prevention, detection and treatment of health

problems through the use of self-care information (Moore, LoGerfo, & Inui, 1980). It is

suggested that individuals involved in self-care programs can significantly contribute to a

reduction in outpatient visits (Lorig, Kraines, Brown, & Richardson, 1985).

This research will involve examining the differences in health variables such as total risk

scores between the two different models, namely health self-care and medical self-care.

These two health care model s are currently used by health insurance companies. Those

models underlie the health promotion models of two current Australian Health Insurance

companies, Healthtrac and Better Health. The "Healthtrac" model is a health information

and self-management skills based program designed to assist individuals in elevating

their perceptions of health self-efficacy, improving their lifestyle behaviours and using

the Australian healthcare system more effectively. The "Better Health" model makes the

assumption that GPs are best suited as the initiators of change in individual health self-

6

care. The fundamental belief is that the health self-care model will be more effective in

changing high risk health behaviours than the medical self-care model. Consquently, the

aim of this project will be to evaluate the process, impact and outcome effectiveness of

the two different health promotion models. This will be done by examining the

differences in scores in such variables as total risk scores, risk of heart disease, number of

doctors visits, days spent in hospital and blood pressure scores. The self-efficacy research

questions will examine the differences between the two health promotion models in

sections such as achievement of outcomes and management of disease.

Currently little research has been conducted in this area within the Australian context.

The limitation of existing research is that most of the studies especially in the health self-

care have been conducted in the United States but little research has been examining the

two health self care models in both countries. Thus this research will help understand not

only the differences and similarities of the two models but relate it to the Australian

situation.

7

REVIEW OF LITERATURE This review of literature will examine some the health promotion and behaviour models

associated with changing health behaviours such as the health self-care and medical self-

care. This chapter will also review some of the health issues and health promotion in

Australia as well as the role of health insurance in the promotion of health.

1.0 - Health Promotion/Education Theory

Health promotion is a relatively new area of research and professional activity. The

World Health Organization (WHO) has been instrumental in conceptualising,

popularising and framing the international development of this field (Colquhoun et al.

1997). In 1986, WHO developed the Ottawa Charter for Health Promotion an initiative

whereby governments and organizations are able to create through policy change

conditions conducive to health and healthy choices (Colquhoun et al. 1997).

In 1993 Australia developed national health goals and targets with a view to encouraging

health promotion practitioners to work towards goals of reduction of high risk factors as

well as preventive programs. These targets and goals had a strong emphasis on health

promotion programs as a method of developing a healthier society (Commonwealth

Department of Human Services and Health, (CDHSH), 1994).

Howat, Maycock, Cross, Collins, Jackson et al., (2003) view health promotion and health

education are interchangeable concepts. These authors suggest that health education uses

educational strategies to bring about health related changes where as health promotion is

8

a combination of strategies which included health education as well as political changes

that improve public health. Some of the changes are related to concerns about the genetic

predisposition to different types of disease … understanding and implications for health

promotion (Giles-Corti et al., 2004). Also about the increasing concern about

globalisation which is fuelling an epidemic of diet related diseases world wide through

the promotion of energy-dense foodstuffs and diets (Giles-Corti et al., 2004).

1.1 - Health Promotion

In Australia policies for national goals and targets were developed using a number of

health promotion theories, several of which have been developed over time. Some have

been utilized to examine behavioural change, while others have developed from

principles of communication theory. Health promotion, as an applied science, has

developed its theoretical foundation by borrowing from the fields of Social Psychology,

Behavioural Psychology, Sociology, Social Marketing, Anthropology, Communication,

and Community Organizational practice (Love, Davoli, & Thurman, 1996). The WHO

considers health promotion as a combination of educational, organization, economic, and

political actions designed with consumer participation . . . improve health through

attitudinal, behavioural, social and environmental changes (WHO, 1997). However, all

of the theories of health promotion are centred around the determinants of health. These

determinants reveal five factors which interact to influence the health of an individual or

population:

1. Biological factors such as aging and genetics

9

2. Lifestyle, including behaviour

3. Environment, which includes communicable diseases

4. Social and economic factors

5. The use of and access to health services.

(Downie, Fyfe & Tannahill,1990).

Health promotion as it relates to ageing is considered to be a factor over which there is

little control. Its aim rather is to "delay the entry into the disability zone" (Evans &

Rosenberg, 1992). In terms of the morbidity graph alterations in the survival curve; i.e.

percentage living beyond 70 years, will slowly progress towards 'rectangularization'.

This zone may be represented graphically in the shape of a rectangle (Figure 1).

Rectangularization is concerned with the average age at first infirmity being raised,

thereby making the morbidity curve more rectangular. Working on and developing

health promotion strategies to 'rectangularize' the survival curve is therefore a key basic

tenent in health promotion.

.

Figure 1. Fries 1980. p.130

10

This 'rectangularization' tends to lead to a 'compression of morbidity. The data equally

indicates a slowing of increases in life expectancy and a delay towards the onset of major

chronic diseases (Fries,1989).

One suggested approach to the promotion of health is through a model of ecology. This

theory takes into account the influence of the environment on health and health related

behaviours. It integrates an individual’s efforts to modify health behaviour within the

environment. The ecological approach focuses on efforts of individuals and

environmental interventions to enhance physical and social surroundings (Stokols,1996).

It presents health as a product of the interdependence between the individual and the

ecosystem (Green, Richards & Potvin, 1996).

Environmental changes have traditionally been one of the cornerstones of public health -

the provision of potable water, garbage disposal and sanitation. However such living

standard improvements have also inflicted a degree of environmental degradation. (Egger

et al. 1990). Others believe that this model suggests there are factors beyond the control

of the individual. Factors such as social, physical, economic, housing, unsafe work

environments, inequalities in gender, socioeconomic status and ethnicity, inform the

argument underpinning this model (Colquhoun et al. 1997). Much of ill-health lies in

structural and socio-political causes (Egger et al., 1990), thus governments play an

important role in the health of a nation. They have a duty to encourage positive health as

a means of preventing ill-health (Downie, Fyfe, & Tannahill, 1990). Individuals too

11

have a certain degree of control over their health; a control beyond the parameters of the

ecological approach. This control may be due to attitudes and behaviours developed by

the individual.

Health behaviour, like other behaviour, is motivated by salient stimuli apparent in an

individuals environment (Egger et al.1990). Components of individual behaviour, such

as attitudes, values, motives and intentions have been the focus of a number of models of

behavioural change (Bunton, Murphy & Bennett, 1991). The likelihood of individuals

being motivated to adopt health-enhancing behaviours as opposed to health-

compromising ones, is dependent upon their level of knowledge, their attitudes and their

skill in relation to the health risk (Egger et al. 1990). A number of behavioural models

such as the Social Cognitive model have been developed to investigate the influence of

behaviour on health and how these models can be used in health education and

promotion.

1.2 - Behavioural Models

A number of theories attempt to predict or explain why people behave as they do in

relationship to their health. These theories such as the Health Belief Model, Health

Locus of Control, Attribution Theory, the Theory of Reasoned Action and the

12

Transtheoretical Model of Behaviour Change, focus primarily on psychological factors

(Clark, & McLeroy, 1995). Other theories also in this domain incorporate the fields of

Psychology and Sociology, such as Social Cognitive Theory, Self-regulation and Freire’s

Psychosocial Model.

One behavioural theory examines the role of Planned Behaviour (Fishbein & Ajzen,

1977), in which behaviour is believed to be a predictor of intentions. This model

proposes that there is an element of perceived behavioural control in the things that we

do. It also proposes perceived behavioural control can influence intention as well as

attitudinal and normative components thus having a direct influence in situations where

behaviour is not under the total control of the individual (Godin et al, 1991). The

changing of attitudes and the dimensions is important behaviour according to this model.

Ajzen and Fishbein (1977) identified four specific elements of attitude:

1. The action element (i.e. what behaviour is to be performed)

2. The target element (i.e. at what target the behavior is to be directed)

3. The context element ( i.e. in what context the behaviour is to performed)

4. The time element ( i.e. when the behaviour is to be performed).

( Downie et al. 1990).

One of the key objectives in any health education or health promotion program is to

affect an attitudinal change. This particular model can be used by examining the four

different elements of attitude and how they relate to behaviour change. Attitudes can be

changed by challenging the knowledge or value base, or by altering people’s behaviour

(Downie et al. 1990).

13

1.3 - Health Belief Model

The Health Belief Model, developed by Rosenstock (1970), is another of the behavioural

models. Its principal tenet is concerned with the way in which an individual perceives

the world and how these perceptions motivate his or her behaviour (Egger et al. 1990).

The Health Belief Model is based upon the following assumptions about behavioural

change:

1. The person must believe that his or her health is in jeopardy.

2. The person must perceive the potential seriousness of the condition in terms

of pain or discomfort, time lost from work, economic difficulties, and so

forth.

3. On assessing the circumstances, the person must believe that benefits

stemming from the recommended behaviour outweigh the costs and

inconvenience and are indeed possible and within his or her grasp

4. There must be a “cue to action” or a precipitating force that makes the person

feel the need to take action

(Green & Kreuter, 1991).

If a person believes that he or she is susceptible to an illness and that illness is serious ,

this belief alone will not ensure action (Fiest & Brannon, 1988). Two of the dimensions

of this model - belief in susceptibility and belief in severity of consequence, could be

interpreted as fear of a disease or condition or behaviour which in itself is a powerful

motivational force (Green et al. 1991).

14

1.4 - Transtheoretical Model

A behavioural model of particular interest is the Transtheoretical Model (TM) which

focuses on the elimination of negative behaviours such as smoking (Prochaska &

DiClemente, 1983). Understanding and examining the process of change involved in the

cessation of such habits as smoking is the central focus of the TM framework

(DiClemente, Prochaska, Fairhurst, Velicer, Velusque, & Rossi, 1991). This model is

important in understanding health behaviour changes because it reflects the temporal

dimension in which changes unfold (Marcus & Simkin, 1993). The TM is basically

formulated from a number of theories related to behavioural change. Prochaska &

Marcus (1995) have proposed that the Self-efficacy and Decisional Balance theories can

be integrated within the Transtheoretical approach. This model has a number of

important dimensions to it. The most prominent centers around the notion of stages of

change. These stages may represent an appropriate level of abstraction from

understanding chronic behavioural risk factors such as smoking, obesity, high fat diets

and sedentary lifestyles (Prochaska et al. 1993). These stages have been divided into a

number of sub-stages;

precontemplation

contemplation

preparation

action

maintenance and

15

termination

The other dimensions of this model are concerned with the process of change. As

individuals increasingly experience difficulties in a variety of areas of life functions e.g.

they often contemplate changing their own patterns and initiating a process of self-

change (Marlatt & Gordon, 1980). This process focuses on activities and events that

create successful modifications of a problem behaviour. The procedural change in turn

would account for how people change on their own as well as how they change with

therapy (Prochaska & DiClemente, 1983).

In the TM changes in behaviour occur at each stage of the model. These changes do not

always occur in a linear manner, but may be cyclical as many individuals make several

attempts at behavioural change before they achieve their goal (Marcus, Banspach,

Lefebvre, Rossi, Careleton & Abrams, 1992). DiClemente et al. (1991) proposes that the

stages of change allow us to examine the process microanalytically. The process is

undertaken with relevance for outcome and process considerations and thus provide a

substantial challenge for intervention development. The TM is a dynamic model of

intentional behavioural change in which change is viewed as a process, rather than a

dichotomous state of exhibiting or not exhibiting the behaviour of interest (Armstrong,

Sallis, Hovell & Hofstter, 1993).

Other health promotion and health education theories have been advanced to describe

methods of intervention. Precede/Proceed (CAPS) is a planning model which examines

the use of resources and how they can be delivered to the community (Green et al. 1991).

16

The theory of Empowerment is based on the belief that equality and equity of

participation are related, not only to access needed health services and physical health

status, but to emotional health as well (Clark et al. 1995). Health education seeks to

empower by providing the necessary information and helping people to develop skills

and a healthy level of self-esteem. Individuals come to feel that significant control

resides within themselves, rather than feeling buffeted by external forces outside their

sphere of influence (Downie et al. 1990). This theory forms the basis for the Ottawa

Charter for Health Promotion (1986). Other theories that have influenced health

education practice including the Diffusion of Innovation Theory, Social Exchange

Theory, and various communications theories (Freudenberg et al. 1995). Above all else

these Health Education /Health Promotion theories should be used as planning

frameworks for action against specific diseases or risk factors (Downie et al. 1990).

Health promotion theory gives direction to intervene at levels beyond the individual and

family (Egger, 2002).

1.5 - Self-efficacy theory Self-efficacy may be defined as a cognitive function which relates to the individual's

belief that he/she can successfully perform a behaviour necessary to produce a desired

outcome (Bandura, 1977a). It is a central to the concept of the social learning theory

(Bandura, 1977b, 1982). Social learning theory emphasizes the importance of self-

control and self-efficacy in the development of human behaviour (Sallis et al. 1998).

17

Self-efficacy is a subjective perception, that is, it reflects what a person believes, rather

than accurately representing the true state of affairs. Such an understanding is critical to

the role self-efficacy as a conceptualization of self-confidence (Sallis et al. 1998). Self

efficacy may be defined as a belief in one's ability to successfully perform a behaviour;

or possessing the judgment of his or her own ability to cope effectively in a situation

(Clarke, Abrams, Niaura, Eaton & Rossi, 1991). Desharnais, Bouillion & Godin (1986)

concur with this opinion: “self-efficacy can effect behaviour in a number of ways;

whether or not one attempts to perform a given task, how persistent one is when

difficulties are encountered, and ultimately, how successful one is in performing the

task”. If one's capabilities are successful in a course of action this is sufficient to satisfy

the situational demands (Clarke et al. 1991). This further supports the premise that

situational demands play a role in self-efficacy.

The belief in self-efficacy is learned in various ways, including personal experiences

(good or bad) and the provision of examples by others (modeling). Therefore, self-

efficacy can play a significant role in health behaviour modification.

The role of self-efficacy in health behaviour modification is an important issue. It

provides one common mechanism through which people exercise influence over their

own motivation and behaviour (O'Leary, 1985). Perceived self-efficacy can affect

health behaviour in a number of ways (O'Leary, 1985). For instance people are unlikely

to attempt change if they do not think they will succeed (Bunton et al. 1991). People

tend to avoid tasks and situations that they believe will exceed their capabilities, however

18

will readily undertake activities they judge themselves capable of performing (Lorig,

Stewart, Ritter, Gonzalez, Laurent, & Lynch, 1996). If the individual regards a health

problem with concern and wishes to change a behaviour they must have the perceived

ability to do so. This ability is termed personal efficacy (Beck et al. 1981). A scale to

illustrate levels of personal efficacy may be drawn. At the top would be situated the

smoker who uses the treat of contracting lung cancer as motivation to quit. His or her

action would be deemed ‘high response efficacy’. Conversely a person who is convinced

they are incapable of quitting, or even reducing their smoking would be situated at the

lower end of the scale and hence lack of personal efficacy (Beck et al. 1981).

Instances of direct manipulation of self-efficacy may have a positive effect on habitual or

addictive responses such as smoking and obesity (Weinberg et al. 1984). In fact,

manipulation of self-efficacy by researchers in high risk health areas such as smoking and

obesity has had some success. It is believed that self- efficacy has some applicability

and utility in the study of change in habitual behaviours (DiClemente, 1981). In

addictive health behaviour, self-efficacy has been correlated to the ability of self-

changers to achieve and maintain smoking cessation and this increases over time in the

maintenance cycle (DiClemente, 1986). Self-efficacy is an important and relevant aspect

of self-change (DiClemente, Prochaska & Gibertini, 1985). Nicki, Remington &

MacDonald (1984), used smoking to test their self efficacy manipulation methods. Their

findings revealed a parallel between in increase in self efficacy and decrease in smoking

rates/nicotine intake.

19

Similarly, obesity can be considered as habitual behaviour when examined from the point

of view of eating behaviour. Individual eating behaviour must be undertaken in

moderation, both in terms of intake and type. Obesity is the most common eating

disorder where food become an irresistible force, and the gaining of weight is a process

that cannot be prevented (O'Leary, 1985). Eating behaviours take place in social

contexts and reflect established norms, values, and practice (Slater, 1989). Eating also

co-occurs with a simultaneous variety of internal states (e.g., hunger, anxiety, pleasure)

and external circumstances (e.g., availability of appealing food, time of day) (Glynn, &

Ruderman, 1986). These different variables are important when integrating self-efficacy

with the behaviour modification methods for the treatment of obesity. Sallis et al. (1998)

developed a Self-efficacy Eating Behaviour Scale in order to examine dietary behaviour

change. Their research proposes that, self-efficacy for eating behaviours is strongly

related to attempts to alter dietary habits, and that these self-efficacy scales show promise

as tools for increasing understanding of important health-related behaviours. Thus self-

efficacy has been used to manage eating disorders such as obesity and who will succeed

in overcoming these eating disorders (O'Leary, 1985).

Self-efficacy has been applied to other unhealthy behaviours which have a detrimental

effect on one's health. The lack of exercise undertaken within Australian society for

instance has caused major health problems (AIHW, 1994). Physical activity is important

in preventing such medical conditions such as Coronary Heart Disease, Hypertension,

Non Insulin Dependent Diabetes Mellitus, Osteoporosis, Obesity, and some mental

20

problems: specifically depression and issues relating to self esteem (Abraham,

d'Espaignet & Stevenson, 1995).

It has been suggested that individuals attempting to increase exercise behaviour via the

use of self-efficacy methods could be influenced by self-judgment of the expected

benefits of regular exercise and the perceived ability to undertake that exercise on a

regularly basis (Godin et al. 1991). Exercise challenges might be especially salient or

intimidating for those who are sedentary, aged or obese (McAuley, 1992). In the

exercise domain, efficacy cognition will influence how long, hard or often one exercises.

Consequently these latter parameters serve as sources of information for future self-

efficacy expectations (McAuley, 1992). McAuley, et al. (1994) has postulated that self-

efficacy may be influenced by various strategies, which is the active ingredient

responsible for any exercise behaviour change. Strategies that actively improve the

individual’s concept of self-efficacy within a program have to then overcome the difficult

variables of time spent and effort expended on that program. An individual's belief

about a given type of behaviour, in this case involvement in an exercise program, will

yield an outcome. The outcomes include weight loss, improved health and feelings of

well-being, which will ultimately lead to a greater perception of their self-efficacy

(Wadden et al. 1992). Individual's evaluate performance using some yardstick and thus

become either satisfied or dissatisfied with their results (Dzewaltowski, 1989). Bandura

(1986) argues that dissatisfaction motivates the individual to attain a goal and thereby

become satisfied.

21

Exercise is a unique behaviour because individuals differ in the outcomes they expect to

receive (Dzewaltowski, 1989). An individual could believe exercise outcomes to be

within their control, however at times may perceive themselves as having an inadequate

ability to maintain an activity routine (Dishman, 1988).

Self-efficacy expectations appear related to exercise behaviour, especially in the early

stages of participation (McAuley et al. 1994). Increasing self-efficacy through the

application of positive incentives is critical at this stage. It is not as important in the latter

stages. Self-efficacy is also an important factor in both exercise adoption and adherence.

It plays a greater role in the initial stages of exercise adoption, however than it does

towards the end. Expectation, self-confidence, incentives and task difficulty all have

been shown to effect the individual’s self-efficacy to exercise and maintain an exercise

program (McAuley et al. 1994).

Self-efficacy is measured using three criteria:

* level of self-efficacy refers to the person’s expected performance attainments

* strength expresses the confidence people have that they can attain each

expected level.

* generality refers to the number of domains of functioning in which people

judge themselves to be efficacious (O’Leary, 1985).

By using these variables an investigation regarding their relationship to the various areas

of health behaviour may be undertaken. Self-efficacy has been used in the examination

22

of a number of health problems. Areas such as relapse from recovery from illness and

trauma, dealing with pain, adherence to medical regimens and substance abuse.

Relapse is an event that terminates the action or maintenance phase of a behavioural

change (DiClemente et al. 1991); a situation where failure and hopelessness occur

(Brownell & Wadden, 1991). Areas such as smoking have been examined to determine

the causes of relapse and the role which self-efficacy can play in its prevention.

DiClemente et al. (1985) explored the effect of self-efficacy and quitting smoking. Their

results showed long-term quitters had the highest self-efficacy scores while those with

low self-efficacy scores were more likely to relapse. Determining the relapse situation is

an important part in the treatment of various health problems. The predictive power of

self-efficacy regarding smoking outcomes has potential and it also suggests the potential

utility of examining individuals’ self-efficacy in order to tailor treatments to the specific

needs (O’Leary, 1985). Generality is a situation where the individual copes with the

various situations and how strong the efficacious behaviour of that individual is so that

relapse will be less likely to occur. Other areas in which relapse plays an important role

are weight loss and exercise. Individuals tend to lose weight but after a period of time

discontinue either dieting or activity and as a result revert to their previous weight.

Having the belief of one’s own self-efficacy is significant for modification of behaviour

to take place more, so than the skill needed to regulate one’s own behaviour. Bandura,

(1989) proposes that training of cognitive skills can produce more generalized and lasting

effects if it raises self-belief in efficacy as well as imparting skills. Thus raising the self-

23

belief of an individual may actually decrease the possibility of, or allow to cope more

effectively with a relapse.

Mastery is one of the cornerstones to the concept of self-efficacy. Bandura (1989)

argues that through the raising of beliefs in their capabilities, individuals’ structure

mastery tasks in ways that bring success and will avoid placing them prematurely in

situations where they are likely to fail. This relationship of mastery to health behaviour

is significant. Programs that provide mastery experiences in particular situations will

enhance expectations for success in similar situations on future occasions (Kaplan &

Atkins, 1984). Cognitive mastery enhances strength as well as the level of perceived

efficacy (Bandura 1982). Programs that provide mastery experiences also provide the

individual with self-motivation to continue to change their behaviour and improve their

health, or to adhere to a particular health program. Those who have come to believe in

the futility of any effort to change need a guided self-enablement program that provides

graduated mastery experience in the exercise of personal control (Bandura, 1997b).

Self-efficacy affects the thinking process, either as events of interest in their own right or

as an intervening influence of other aspects of psychosocial functioning. It can also

enhance or impair the level of cognitive functioning (Bandura, 1989). Self-efficacy from

a health behaviour perspective has many positive aspects to it when integrated into a

behavioural treatment program. When dealing with self-efficacy and chronic disease it

is not simply a matter of knowing what to do, rather it reflects a capacity to organize and

integrate cognitive, social and behavioural skills to meet a variety of purposes (Lorig al

24

et. 1996). Self-efficacy can be used as a treatment method as well as a method of

assessing health behaviour. Bandura’s model represents both a central mechanism of

change for traditional therapies and a basis for devising new therapeutic treatments based

on a direct manipulation of self-efficacy (Weinberg et al. 1984). Coping with challenges

posed by chronic disease requires knowledge and skill. However an individual also

needs to believe in their ability to use those skills in a realistic context, and believe that

the use of those skills will produce the desired outcomes (Bandura, 1986). The belief

that individual’s can motivate themselves and regulate their own behaviour plays a

crucial role in the consideration of changing detrimental health habits or pursing

rehabilitation activities (Bandura, 1997a).

In Australian health self-efficacy plays an important role. The growth of various multi-

ethnic groups, displaced individuals from their native countries and some disadvantaged

populations, such as Aboriginals, place strain on the Local, State and Federal health

systems. The hardships of migration, unemployment, and poverty foster risky health

habits (Schwarzer & Fuchs, 1995). Perceived self-efficacy plays a unique role within

this population group. Migration is generally a stressful life transition which causes a

shift in perceived self-efficacy. These shifts can be due to social factors - such as social

change, involuntary unemployment, anxiety, and social support (Jerusalem & Mittag,

1995). Perceived self-efficacy is a powerful personal resource when examining the

impact of migration stress on cognitive appraisals as well as on psychological and

physical well-being (Jerusalem & Mittag, 1995).

25

Perceived self-efficacy amongst recent immigrants seems to play a critical role in health

status. Those immigrants who were satisfied with their job and their life in Australia, or

who intended to remain in Australia, had substantially better self-rated health, (mental

health for males, but not for females, a lower prevalence of long term conditions), than

did unsatisfied immigrants or those not sure of staying in Australia or were planning to

return home (Kliewer & Jones, 1997). This view is supported by Jerusalem and Mittag

(1995) when they state that rapid reemployment after migration might be a consequence

of high perceived self-efficacy and respective coping effectiveness . . . migrants who had

a high sense of self-efficacy reported less anxiety and better health than those of low self-

efficacy. For this particular population in Australian perceived self-efficacy can have a

significant bearing on health outcome and health behaviours. Migrants undertake

tremendous personal change in a new society thus perceived self-efficacy can effect

every phase of personal change (Bandura, 1991). Having a job and satisfaction with a

new life tends to build a sense of self-efficacy, while failure will undermine it (Oettingen,

1995). A strong sense of personal efficacy seems to reduce the likelihood of negative

appraisals of stressful life demands, and, as a consequence, it provides protection against

emotional distress and health impairments (Jerusalem et al., 1995). Perceived self-

efficacy may overcome some formidable barriers such as language, cultural patterns,

ethnic differences, and hostility as intruders (Schwarzer et al. 1995).

26

1.6 - Health Self-care and Medical Self-Care

The main trust of this study centres around the concept of health self-care and medical

self-care. Self-care may be defined as all actions that individuals take with respect to

health and medical care (Vickery & Iverson, 1994). The term self-care has a wide range

of implications in the interpretation of this definition. One of these interpretations is

self-care which can be a process of self-determination and self-reliance. Self-care can be

categorized in a number of ways. It may include learning how to care for and support

others and how to take action to change the factors that may limit the capacity for self-

care (Murphy, 1993). Self-care can be divided into two categories that of health self-care

and medical self-care. Health self-care is defined as those actions aimed at maintaining

and improving health (Vickery et al. 1994 ). While medical self-care is taking action

concerning medical problems with the initial help from a GP. The distinction between

these two concepts is important in a number of ways. Health self-care is mainly

concerned with dealing, maintaining or improving health with health information

provided by a health care organization. Where as medical self-care is seen as an

alternative for professional care. Perhaps the most critical medical care occurs when

individuals enter into shared decision making with medical professionals (usually

physicians) concerning major medical interventions such as long-term medications,

surgery, and hospitalisation (Vickery et al. 1994 ). With these two concepts in mind it is

apparent that a duality of self-care exists. Both medical self-care and health self-care are

seen as managing one’s health, whether it be from a personal behaviour modification

strategy or in combination with a medical professional. In terms of chronic diseases there

is a strong case to be made for participation of patients in management decisions,

27

treatment practices, and for physicians to build upon that participation (Holman,

Mazonson & Lorig, 1989). The long - term participation of individuals with chronic

conditions can be improved by prevention, self-management and professional care

guidelines (Fries et al. 1997a). Above all else the individual has to take primary

responsibility for decisions because in the final analysis only the individual can place

value on the benefits of participation (Vickery et al. 1994). Thus the duality of self-care

is an important one to understand from a health self-care and medical self-care

perspective.

Prevention of illness is the philosophy of health self-care. It has to be the major avenue

where by spiralling costs of medical care may be decreased in this country. Only a small

proportion of the total health care budget is allocated for prevention. In the United

States the national investment in prevention is estimated to be a very low 5% of the total

annual health care cost (McGinnis & Foege, 1993). In Australia the health portfolio is

highly concentrated on medical and hospital services. In 1991-92 the total expenditure

for health care access was $10,181 million, compared with $196 million allocated to the

Health Advancement program, $21.2 million allocated to health promotion, and $49

million spent on HIV prevention (AIHW, 1994).

Health self-care and prevention is rooted in lifestyle choices. These lifestyle choices

affect our health in some way, whether it be in a positive or negative manner. The

negative choices we make affect our health status because the leading causes of death are

factors such as tobacco, diet, activity patterns and alcohol. These are all are rooted in

28

behavioural choices ( McGinnis & Foege, 1993). These behavioural choices are mostly

lifestyle choices. Thus, health self-care places emphasis on lifestyle and environmental

decisions since these are both powerful and largely within the control of the individual

(Vickery et al. 1994).

Medical self-care is the stage where a medical problem arises which needs the attention

of medical professional. A decision has to be made by an individual to seek the

appropriate medical services. Factors that determine the probability of choosing one

option over another include the individual’s perception of the severity of the problem, the

availability of professional medical care, personal medical self-care skills, and the

individual’s belief in his or her capacity to deal with the problem (self-efficacy) (Vickery

et al.1994). These are very important factors when it comes to choosing the type of

medical care. However, there are also many underlying factors which influence the

individual seeking medical treatment. Some of these factors include poverty, access to

health care, education and decent housing, cultural values and inequalities in wealth and

income (Peterson, 1994). Other factors such as gender, play a role in choosing an

appropriate medical service. In general females seek medical care more than males do

(Mann, 1996). This view is supported by AIHW (1995) when they state that females

consistently account for more medical services than males. The biggest discrepancies

occur in the 20 to 24 and 25 to 34 ages ranges - where females account for about twice as

many services as their male counterparts. The sick role is more compatible with the

traditional female role. The female may feel less constrained than males in defining and

reporting mild symptoms as illness. The male could also feel threatened by disclosing

29

intimate feelings, giving over control to a medical professional or depending on others for

his care (Mann, 1996). Age is another variable which will influence the type of medical

care the individual chooses. Physical issues that affect adults are often accentuated with

age, complaints such as cardiovascular illness, mental illness, mobility, and disease

(Reagan & Brookins-Fisher, 1997). Thus medical care tends to increase with age as

does the cost per person. The last years of life tend to be costly in terms of medical care.

It is estimated that there are substantial increases in health care costs per year above the

age of 65 and that 18 percent of lifetime medical costs occur in the final last years (Fries,

1989).

The cost factor in medical self-care is an important issue. One of the main purposes of

self-care is to reduce health care costs through a variety of self-care and self-management

programs. The target populations are those individuals who are classified as being in a

high risk health category. High risk factors suggest that a particular condition is

somehow related to the occurrence of a disease; they do not prove that the condition

causes the disease (Feist & Brannon, 1988). Many long term diseases and long term

patient outcomes are affected by such factors as exercise and lifestyle (Mann et al, 1996).

Cigarette use, lack of exercise, excessive alcohol intake, lack of fibre, and excess fat in

the diet have been linked to many major chronic diseases (Leigh & Fries, 1992b).

Therefore, these factors can be considered to be high risk in the subsequent development

of certain diseases. The decreasing of these risk factors through regular exercise,

avoiding tobacco and heavy drinking are known to reduce the risk of heart disease,

cancer, chronic obstructive pulmonary disease and diabetes (Leigh et al. 1992b). The

30

primary objective of health self-care is to develop strategies that will reduce the numbers

of people who are in the high-risk category thus in the long term reducing medical costs

for the nation.

The economic cost to individuals and the health care system can be substantial. Smoking

can be used as an example. It has been estimated that the cumulative impact of excess

medical care required by smokers at all ages outweighs shorter life expectancy and

smokers incur higher expenditures for medical care over their lifetime than do never-

smokers (Hodgson, 1992). Analysis of the Hodgson research findings indicate that the

expected life-time medical expenditure of the average smoker exceeds those of the

average never-smoker by 28 percent for males and 21 percent for females. This view is

supported by Leigh et al. (1992b) when they point out that a typical one-pack-a-day

smoker experiences .52 more hospital days, .13 more doctor visits, and 10.9 more sick

days every six months than the typical non-smoker. As these figures suggest, the total

expenditure for medical care for a smoker can be substantial over a lifetime, and this is

not only for medical care but includes indirect costs such as lost work days. To reduce

these costs, the demand and need for medical services has to also be reduced through

strategies, which individual “need” such as encouragement into healthy behaviours.

“Need” in this context refers to the illness burden of a defined population, the integrated

sum of all heart attacks, strokes, lung cancers, arthritis and all other forms of human

illness in all members of a population (Fries, 1997a). Excessive need is generated by the

occurrence of preventable illness, resulting from cigarette, drug or alcohol abuse, lack of

31

exercise, poor dietary habits, excess obesity, and other factors (Fries et al. 1998).

“Demand” is concerned with requests for medical services. Excess demand refers to

requests for medical services that are unlikely to improve health (Fries et al. 1998).

Preventing chronic illness would offer hope of a reduction in demand eg. if a Coronary -

Artery Bypass Graft Procedure could be avoided that would amount to a saving of $50,

000 per operation (Fries et al. 1998). There seems to be a positive correlation between

health preventive behaviours and medical claims. Those individuals more than 30

percent above desirable weight had an 11 percent higher medical claim, 45 percent more

hospital days, and 48 percent more major claims (Fries et al. 1989).

1.7 Healthtrac and Better Health Models

After a review of definitions of health promotion, health education and behaviour models

and how these theories contribute to the understanding of the underlying philosophy

behind health self-care and medical self-care.

Two health promotion models will be used in this study to examine health outcomes,

process and impact following a health education intervention. Healthtrac is a model

used to assist individuals increase their perception of self-sufficiency, to improve their

lifestyles, and use the health care system appropriately (Vickery et al. 1994). This model

also proposes that by providing information and skills development it will assist the

participants in elevating their perceptions of health self-efficacy. Individuals will act as

32

their own agents for change in health self-care. Fries et al. (1992) believes that effective

programs require four processes. These are: identification of particular health problems

in an individual requiring change, motivation of the individual to begin change,

continued re-evaluation of progress, and continued reinforcement of positive

accomplishments. Healthtrac is based on these principles. Healthtrac’s program will

identify individuals who are in the high - risk group and send them information that

specifically relates to the disease they are susceptible to. Evaluation occurs every 6

months which is a form of reinforcement.

The Better Health model premise is that General Practitioners (GPs) are best suited as

initiating agents for change in individual health self-care. There is support for this idea in

research conducted in Australia. Research on active and inactive Australians revealed

that individuals who wish to obtain health knowledge seek medical advice more so than

from books or video. Males tend to seek this type of medical advice more so than

females (CDHAC, 1995). This advice can also be sought from either a GP or health

education professional. However, it is anticipated that GP’s will be either, too busy to

spend the time required to act as an appropriate counsellor, or will be untrained in

appropriate health education methodology. Thus the Better Health Model depends on

the GP to be the agent for self-care, while the Healthtrac Model is specifically designed

to increase the individual’s ability to act as their own agent for self-care.

33

2.0 - Health in Australia

The purpose of this chapter is to discuss some of the health issues in Australia and the

major health priorities which are of concern in this country.

It has been suggested that Australia is one of the healthiest countries in the world. There

are, however, some areas where improvements are both necessary and important (AIHW,

1998). The single underlying factor and the greatest cause of ill health is poverty

(Commonwealth Department of Community Services and Health, (CDHSH) 1989).

Identifying those in such a state, therefore must be of assistance in highlighting those

most at risk of circuming to health problems (Peterson, 1994). Whether measured by

income, educational level, occupation or socio-economic disadvantage, there is a distinct

relationship between socio-economic status and health (CDHSH, 1994). For instance

smoking, physical inactivity, obesity and harmful levels of alcohol consumption are

generally more prevalent amongst people of lower socio-economic status (CDHSH,

1994). Thus economic prosperity generally contributes to the well-being of the

population, and this, in itself, reduces illness (AIHW,1994).

2.1 - Major issues

One of the major health problems facing Australia is that of obesity. During the 1980’s

the proportion of overweight or obese adults increased steadily. On the average, women

were 3 kg heavier in 1989 than they were in 1980. Similarly the male average increased

1.7kg in the same period (National Health and Medical Research Council (NHMRC),

34

1999). Obesity is an illness in itself and increases the risk of contracting several other

conditions (AIHW, 1995). It has been cited as a risk factor in many chronic diseases,

including heart disease (Reagan & Brookins-Fisher, 1997). High levels of obesity have

been observed to be associated with increased mortality, heart disease, adult-onset

diabetes, and digestive diseases (Feist & Brannon, 1988).

Obesity is one of the risk factors for Cardiovascular Disease (CVD). As Australia’s

greatest health problem it accounts for 43.8 percent of deaths from all causes (Water &

Bennett, 1995). Ischaemic Heart Disease (more commonly known as Coronary Heart

Disease (CHD), accounted for 25.5 percent of death from all causes while

cerebrovascular disease (stroke) accounted for 9.7 percent of all deaths (AIHW, 1994).

Australia has experienced a strong decline in deaths from CVD. The current annual

decrease is estimated to be 3.2 percent in males and 2.1 percent in females. The

declining annual death rate from heart attacks is 4 percent in men and 2.7 percent in

women and for strokes the figure is currently declining at around 4.5 percent per year in

both sexes (AIHW, 2000). The decline in mortality from CVD over the past decades is

regarded as a positive aspect of health promotion. A reduction in smoking and blood

pressure levels and improvements in medical care have also contributed to the decline in

mortality from CHD (Abraham et al. 1995). However Australia does not compare

favourably with other developed countries. Australia’s CVD death rate is 41 percent in

males which is 57 percent higher than in France. In addition, Ischaemic Heart Disease

death rates are nearly 5 times greater for males and over 4 times greater for females than

similar rates in Japan (AIHW, 1994).

35

Corbin et al. (2002) proposed that lifestyle changes, more than any other factor are the

best way of preventing illness and early death. Smoking is one such lifestyle habit. The

number of individuals who smoke cigarettes is of major concern. In terms of death,

smoking is overwhelmingly the largest preventive health hazard in Australia. It is

associated with both years of use and amount smoked. There is no identified safe level of

tobacco consumption (CDHSH, 1994). Tobacco is a major cause of preventable drug-

related mortality in Australia. In 1992 72 percent of all drug-related deaths were

attributable to tobacco use (CDHSH, 1994). Smoking rates in women have been

declining at a slower rate than men, but death from lung cancer for men is still three times

greater than that reported for women (AIHW, 1994). This may be attributed to the

increased take-up rate of young women (CDHSH, 1993).

Another drug-related problem is the inappropriate use of alcohol. Alcohol is second

only to tobacco as the major cause of drug-related mortality in Australia (CDHSH, 1993).

It has also been linked to all kinds of personal and social ills such as homelessness, road

crashes and ‘alcoholism’ (Peterson, 1994). Excess alcohol intake is associated with

many chronic diseases and conditions, such as heart disease, stroke, high blood pressure

and certain types of cancer (AIWH, 1994). There has been a decline in the proportion of

men and women drinking alcohol which is hazardous to their health. However, it has

been reported that women are more likely than men to overestimate the number of drinks

they could have consumed which could be of risk to the health of someone of their own

sex (AIWH, 1994). Other problems associated with long-term consumption of alcohol

36

includes cirrhosis of the liver, brain damage, foetal alcohol syndrome, osteoporosis,

malnutrition, emotional disturbances and suicide (Lester, 1994).

Physical activity has been shown to be important in managing a number of chronic

conditions such as CHD, hypertension, non insulin dependent diabetes mellitus,

osteoporosis, and some mental health problems, specifically depression and self-esteem

(Abraham et al. 1995). It has been suggested that the lack of physical activity by a

growing number of Australians has become an epidemic of ‘sedentary behaviour’ or

‘incidental inactivity’ (NHMRC, 1997). Between 1983 and 1995 the proportion of 25-

64 year olds engaging in any exercise had not changed substantially (AIHW, 1994).

There is also concern about Australian children and their lack of activity. A recent

NHMRC (1997) obesity paper points out that young Australian people tend to engage in

substantial sedentary behaviour such as reading, sitting in class, surfing the Internet,

playing video games, and probably the most serious sedentary behaviour of all, that of

watching television for long periods of time. These may be some of the factors which

are contributing to the prevalence of obesity. An increase in the amount of physical

activity at all age levels within the community is an essential element in the health of all

Australians. Participation rates are affected by many variables such as education level,

time availability, lack of company in which to exercise, lack of motivation, belief about

being too old, rural living, married or single, migrant or just having no motivation to

exercise (CDHHCS, 1993).

37

2.2 - Migrant health

A high proportion of the Australian population are migrants. The 1986 Census showed

that 21 percent of all Australians were born elsewhere and that 11 percent were born in

non-English speaking countries (AIHW, 1998). Many overseas-born Australians come

from culturally distinct regions, often with specific traditions, religious and language

differences, and a range of beliefs and values about which there is limited awareness in

Australia (Lester, 1994). These different variables within the Australian community have

led to different changes in health status. In has been shown that between the ages of 15

and 74 most migrant groups have lower, and in many groups significantly lower death

rates than equivalent sections of the Australian population (AIHW, 1998). Men and

women aged 25 years and over in 1985-87, who were born overseas had significantly

lower CVD death rates than their Australian-born counterparts (Water et al. 1995).

Obesity in some immigrant groups is more prevalent than in Australian born

counterparts. Immigrants from Southern Europe who are in the 20 to 69 year old age

group tended to be two to three times more overweight (or obese) than their Australian-

born counterparts (NHMRC, 1997). Men from Eastern Europe tend to have a

significantly higher body mass index (BMI) than Australian men (NHMRC, 1997).

Overall, there tends to be a relatively low death rate from CVD in some of the major

migrant groups, particularly Greeks, Italians, Central and South Americans, Vietnamese

and Yugoslavs (AIHW, 1998).

Research conducted by Kliewer and Jones (1997) examined recent immigrants to this

country. After a period of six months had elapsed subjects were examined in relation to

38

the following variables: self-reported physical and mental health status and utilization of

health services. Findings were arranged on the basis of country of birth, non-English

speaking background and English language proficiency and by examining the

relationship between migration and settlement factors. The study found that individuals

who had poor English skills tended to have worse self-rated health and a greater

prevalence of mental illness and long-term conditions than those who spoke English well.

This study also found that there were large differences in the health status according to

region and country of birth. Some of the reasons suggested are connected with social,

cultural, and economic factors. Mental health status differed among immigrants who had

a university degree - they recorded better scores than those who had less than 10 years of

education. Female immigrants utilised medical services more than men, with 58.4 per

cent of females reporting visits to a health centre, doctor or other medical practitioner

since their arrival in Australia; this is compared to 45.2 per cent of males (p.23). This

type of information is essential for the development of health promotional material and

for targeting of specific subgroups of immigrants who are at risk of experiencing poor

health (p.54).

2.3 - Aboriginal and Torres Strait Islander health

Another population group within Australian society requiring special attention in regards

to health is the Aboriginal and Torres Strait Islanders group. Their health issues are of

major concern to all Australian society. In 1990-92, the average life expectancy of a

newborn Aboriginal boy was, depending on where he lived, up to 18.2 years less than

39

that of their non-Aboriginal counterparts; the gap was 19.8 years for an Aboriginal girl

(AIHW, 1994). Diseases of the circulatory system, particularly CHD and

Cerebrovascular Disease, injury and poisoning are still the major causes of Aboriginal

deaths (AIHW, 1998).

Australia’s health is a complex issue with a number of variables playing key roles. Diet,

immigrant health, education, age, gender, martial status, socio-economic status, place of

residence i.e. (rural, city or country), special population groups, employment, social and

cultural factors all need to be examined when the overall health status of Australia is put

under the microscope. Individuals are not entirely free to choose particular lifestyles but

rather must adapt their behaviour to their life situations, consequently this advice must be

kept in mind when programs are evaluated (AIHW, 1998).

2.4 - Gender Health Issues

The role of gender as a health variant is significant in both the types of diseases suffered

and the treatment of those diseases. The term “gender differences” refers to the

differential behaviours that are learned as appropriate for either males or females (Mann,

1996). Gender is a dynamic construct that interacts with the psychological, social,

physical, and behavioural factors in influencing disease risk, expression, and prognosis

(Chesney & Nealey, 1996). Sex differences by contrast refer to the biological

distinctions that exist between males and females. Sometimes it is difficult to tell

whether the differences are based on biology (sex differences) or culture (gender

40

differences) (Mann, 1996). These are important differences, however when it comes to

examining health outcomes these differences are important. The distinctions between

male and female health outcomes fall into three categories: differences in which illnesses

occur, differences in how often illnesses occur, and differences in the relationships

amongst risk factors and illness. Mather (1996) believes that there are five reasons that

account for sex differences in health:

1. biological risks - intrinsic differences between men and women based on their

genes, physiology, hormones,

2. acquired risks - including lifestyle and health habits, work and leisure related

injuries,

3. illness behaviour - including perceptions and awareness of illness and

propensity to seek treatment,

4. health reporting behaviour - how people talk about their health, including to

interviewers and

5. prior health care - how treatment provided influences the course of current

diseases and the incidence of new diseases.

A greater variation of occurrence exists in certain illnesses such as osteoporosis, which

has a predilection toward the female gender. A similar pattern may be evidence with

regards to the reproductive organs (Prostate organs and ovaries). Causes specially related

to biological variation may also account for health differences. For example the amounts

and types of hormones may induce different responses to the same disease or to different

41

diseases. Women appear to be less affected by high blood cholesterol. This may be due

to their higher estrogen levels. These levels may also serve to some degree as a

protectorate against CVD (Bush, Conner, Criqui, Wallace, Suchindran, Tyroler &

Rifkind, 1987).

The proliferation of illnesses or diseases between and within the different genders is due

to a number of factors. Variety in behavioural patterns can cause differences in the types

of diseases that occur. It has been suggested that men suffer in disproportionate numbers

from some mental and physical health disorders (Christoper et al, 2000). A number of

theories have been proposed to account for these differences. One such idea is that men

are socialized to engage in high risk-behaviours, high-risk employment, and high-risk

leisure activities to validate their masculinity (Copenhaver & Eisler, 1996). As a result

they have learned to rely on coping behaviours that may increase their risk of injury, ill

health, and early mortality. Many men place tremendous emphasis on being able to

prevail in situations that require physical strength and physical fitness (Copenhaver et al.

1996). Also, men, in contrast to women, are more prone to antisocial personality

disorders, drug and alcohol abuse (Fletcher, 1995). This view is supported by Raphael &

Martinek (1995) who suggest that men, more so than women, are involved in substance

abuse. Patterns of alcohol use may strongly reflect male cultural prescriptions. The risk

factors associated with substance abuse for males include an increase in the break-up of

important relationships, suicide, violence, and a greater risk of suffering from Antisocial

Personality Disorders, psychoses and depression. Antisocial behaviour which results in

imprisonment is higher in men than women. Jorn (1995) states that men make up 95% of

42

the prison population in Australia; and a comparable percentage is represented across all

crime types. Thus overall men experience more alcohol and drug abuse and anti-social

behaviour, while women experience more anxiety, depression and eating disorders (Jorn,

1995).

The role culture plays with regards to gender in health is substantial. Each gender is

taught from an early age to behave differently to various situations. Males are taught to

behave in a particular way that is acceptable to the society in which they live

(Buchbinder, 1995). This leads to different types of health problems during the course of

a lifetime. The culture of masculinity being influential from childhood, through to

adolescence and adult years (Raphael et al. 1995). One prevalent health problem involves

the area of mental health. Masculine stress may arise from the belief that one is not

living up to culturally sanctioned masculine role behaviour. Men may experience stress

if they have acted in an unmanly fashion (Copenhaver et al. 1996). Traditionally male

stress has been related to the work place while stress in women has emanated from their

role as unpaid carers to the immediate family and relatives (Peterson, 1994). Stress for

the male is mainly due to conditions of work, or lack of work and therefore may account

in part for the different rates of some diseases experienced in middle aged men in the late

1980’s (AIHW, 1994). Men appraise, experience, and deal with stressors differently to

women and they manage stress-related health problems differently (Coperhaver et al.

1996).

43

There are a number of gender differences associated with diseases caused by lifestyle.

Some of these diseases are linked to lifestyle habits such as smoking, drinking, diet and

lack of exercise. Smoking in the male population is proportionally higher (32.1%) than

in the female population (24.7%) (AIHW, 1998). Due to smoking, diseases such as

hypertension, heart disease, asthma, high cholesterol, and neoplasms are more prevalent

(AIWH, 1998).

44

3.0 - Health promotion in Australia

This chapter will deal with some of the major historical, federal government, state

government and non-government organizations that promote health in Australia.

3.1 - History of health promotion and the Commonwealth government

Health promotion in Australia is a relatively new concept but a very complex issue. The

Australian health-care system has operated for more than 100 years without defined goals

and targets (Wise & Nutbean, 1994). In Australia since the early 1970’s there has been

an increasing emphasis on the self-responsibility perspective, which appeals for changes

in an individual’s behaviour and lifestyle. As a result of this growing influence of the

public health movement and health promotion philosophy there has been a challenge to

the medical approach, which focuses on cure rather than prevention (Peterson, 1994).

This view is supported by Wise & Nutbean, (1994) who argued that the health system

before 1985 - which included the structures, legislative and policy frameworks and

resources was focused overwhelming on diagnosing and treating those who were ill.

Health promotion efforts were limited in scope, they had access to limited resources and

priorities and were often set without fully understanding the health needs of the

community (either from the communities’ perspective or from an examination of the

limited epidemiological data available at the time) (Wise at al. 1994).

45

In 1973 the Community Health Program was launched by the Whitlam government.

Within its framework were such programs as the Area Improvement Plan, Australia

Assistance Program, and the Disadvantaged Schools Program whose main aim was to;

(a) improve health services to those living in areas where a significant need for

health services was unmet;

(b) promote aspects of health care, prevention, health maintenance and

rehabilitation

(c) provide an alternative to costly institutional care (Peterson, 1994).

Also under the Labor Government of Gough Whitlam Medicare was introduced. This

was the first attempt at a State and Federal level to set goals and specific targets for

health promotion and to define priorities for intervention programs (Oldenburg, Wise,

Nutbeam, Leeder & Watson, 1994).

By the mid 1980’s the Commonwealth government under the auspices of Better Health

Commission started to play a major role in health care. The Commission worked to

enhance the credibility and to influence health promotion and disease prevention in the

Australian health care system (Owen & Lowe, 1994). The specific terms of reference

required the Better Health Commission to focus on illness prevention, health promotion

and community involvement (Oldenburg et al. 1994). Much of the resulting data is

published in three volumes related to health. These were “ Looking forward to Better

Health” in 1986, “Health for all Australians” in 1988, and the final publication in the

series in 1993 was “Goals and Targets for all Australians Health in the Year 2000 and

beyond”. The “Health for all Australians” report was endorsed by the Australian Health

46

Ministers conference in 1988. In this report 20 goals and 65 targets were grouped into

three major categories: population groups, major causes of sickness and death, and risk

factors (Nutbeam, Wise, Bauman & Leeder, 1993). “Health for all Australians”

represented a landmark in the history of health promotion in Australia. For the first time

the potential health gains to be made from promoting health and preventing illness or

injury were given political prominence. The goals and targets represented priorities for

action and the targets provided a sense of direction and magnitude of change that would

be required in order to achieve health gains (Wise et al. 1994).

The 1993 publication ‘Goals and Targets for Australian health in the Year 2000 and

beyond,’ focused on developing a new set of health goals and targets. These revised

goals attempted to address the shortcomings of ‘Health for all Australians’. In the

process of this realignment , however it also became apparent that the goals and targets

differed in a number of ways. Health outcomes were no longer confined to changes in

mortality or morbidity but changes occurred to make the physical, social and economic

environment more health supporting. They now were considered to be outcomes, as were

improvements in community and individual knowledge and skills (Wise et al. 1994).

This report also challenged the current patterns of resources investment stating that both

health care services and health promotion activities should be judged on their

contribution to improved health status (Oldenburg et al. 1994). The important health

target issue of health literacy and health skills were fundamental to individuals to

improve their personal health, optimising available health services and to act collectively

to seek change where appropriate (Nutbean et al. 1993). This represented a significant

47

shift in health policy and practice in Australia, - the previous being focused on health care

services and towards improvements in population health (Wise et al. 1994). The political

aspects of the implementation of the goals and strategies rested with the Australian

Health Ministers Advisory Council (AHMAC), which consisted of all the state and

territory’s health ministers plus the Commonwealth Health Minister. A conference in

1988 endorsed the goals and targets which were developed in ‘The Health for all

Australians’ report. The AHMAC provided the political impetus for reform and the

subsequent ‘Goals and Targets for Australian Health in the Year 2000 and beyond’ to be

endorsed and expanded in 1993. Using the new Medicare agreement in January 1993 the

AHMAC agreed to specific actions to achieve goals and targets which would focus

initially on the four priority areas of Cardiovascular Disease, Cancer, injury and mental

health (Oldenburg et al. 1994). A month later the AHMAC met and discussed the

commitment to these targets and goals. The results being a common program in relation

to the measurement and use of health outcomes (Nutbeam et al., 1993). The revised

goals and targets reflected the growth in knowledge and understanding of the relationship

between poor health and the limited access to resources and the amount required by

individuals and populations to achieve and maintain good health (Wise et al. 1994).

A meeting in October 1995 by the AHMAC agreed that a key part of the implementation

process was to address the broader social justice issue from the health goals and targets

process. The social justice principles developed by the AHMAC were:

1. All Australians should have access to a comprehensive range of health care

services regardless of financial status and place of residence.

48

2. Health services should be of a consistently high quality across Australia.

3. There should be continuity of care across the health system, with appropriate

higher level services.

4. Major causes of ill health and premature death, including environment and

lifestyle factors, should be identified, addressed and cooperative

strategies to reduce them developed and implemented.

(Pickering, Bennett & Ashpole, 1994)

Organisations such as the National Health and Medical Research Council (NHMRC) and

the Australian Institute of Health and Welfare (AIHW) played a key role in the

implementation, reporting and monitoring of the goals and targets at the national level.

The NHMRC was first established in 1936. The present day structure of the NHMRC

was established under the auspices of the National Health and Medical Research Council

Act of 1992. Thus it became a statutory body within the portfolio of the Commonwealth

Minister for Health and Family Services department. Its main role as a body, is to be

responsible for leading health and medical research in Australia. Within the framework

of the NHMRC are four principal committees responsible for different aspects of health.

These committees are the National Health Advisory Committee (NHAC), Australian

Health Ethics Committee (AHEC), The Medical Research Committee (MRC) and the

Strategic Research Development Committee (SRDC). The NHAC deals with the

management and development of advice on all health issues. Within this advisory

program resides portfolios concerned with epidemiology, prevention and control of

communicable diseases and illness prevention and health promotion (NHMRC, 1999).

49

The terms of reference also require this committee to inquire and advise Council on

matters which include health promotion, illness and injury prevention (NHMRC, 1999).

Not only is the NHMRC involved in health promotion but also in health research. In 1998

Australia spent $650 million on medical research (Wooldridge, 1989). Also in the same

year NHMRC announced another method of providing funding for research and

development. This came in the form of commercial funding provided by Australian

owned companies. The proposal requires Australian companies to invest in health and

medical research and in return they acquire a share of the intellectual property generated

by the research. County Investment Management Ltd - a subsidiary of National Australia

Bank - attempts to secure multi-million dollar commitments from superannuation and

investment funds to go towards a proposed fund offering investors an investment

alternative (NHMRC,1998). The aim of this approach is to develop research into

commercially viable products that will both remain in Australia and would present

considerable benefits to future research and development.

Another important organisation within the Commonwealth Government is the Australian

Institute of Health and Welfare (AIHW). The main role of the AIHW is to inform the

community and to support public policy making on health and welfare issues. This is

achieved by coordinating, developing, analysing and disseminating national statistics on

the health of Australians, on their health and welfare services and by undertaking a

supporting role related to research and analysis (d’Espaignet, Steveson & Mather, 1994).

In recent years the role of AIHW has increased to take on a number of new roles as set

50

out by the National Health Information Agreement (1993). One objective of this

agreement is to provide cooperative national structures and mechanisms to improve the

collection, quality and dissemination of national health information (AIHW, 1998).

Important developments specific to this agreement include a National Health Information

Work Program, the National Health Information Development Plan, the National Health

Information Knowledge Base, the National Health Information Model, the National

Health Data Dictionary and the National Aboriginal and Torres Strait Islander Health

Information Plan. With these mechanisms in place it is possible to create or use, or have

the capacity to link records of different health information collections agencies. This in

turn will greatly increase the usefulness and cost effectiveness of information that has

already been collected or will be collected in the future (AIHW, 1998).

In the mid to late 1990’s the AIHW began developing various initiatives such as a

National Centre for Monitoring Cardiovascular Disease. This was established in 1996.

A national register was also established to monitor and report on insulin-treated diabetes

mellitus (ITDM) - National Insulin-Treated Diabetes Mellitus Register. The register was

developed to provide population statistics, determine the incidence, assess the feasibility

and cost of estimating complete ITDM prevalence, provide information to health service

providers and planners at Commonwealth, State and local levels, and to assist in

monitoring national diabetes indicators (AIHW,1998).

Another project undertaken by the AIHW concerns the development of health

information which conforms to international classifications. In 1990 the World Health

51

Organisation (WHO) developed an International Classification of Diseases and Related

Health Problems (ICD-10) (10 refers to the 10th version). This particular coding system

is now used in Australia for the coding of morbidity. The ICD-10 is a disease

classification based upon the model proposed by WHO with modifications to ensure a

current and appropriate classification for Australian clinical practice (AIHW, 1998).

Not only is the AIHW developing various classification systems but it is also undertaking

reviews of measuring instruments to evaluate population health. One of these

instruments is the DALY (Disability-Adjusted Life Year) which originated from the

World Bank in 1993. It was designed to measure the loss of health associated with a

specific disease/s, injury and risk factors and allows for disease specific measures of

population health. It also allows measurement of the potential for population health gains

(outcomes) in relation to a particular health problem, and monitoring the actual health

gains in the population (AIHW, 1998). As a result of this evaluation instrument and data

the AIHW hopes to develop national estimates of disease burden.

The AIHW have also collaborated with universities such as the University of Sydney to

do surveys of general practitioners in which 1000 are sampled on a yearly basis. The data

collected is of a demographic nature and includes such variables as characteristics of

patients, payment types, the patient’s reason for encounter, up to four diagnoses,

information on patient’s smoking and alcohol consumption and other characteristics

(AIHW, 1998). This program is called BEACH (Bettering the Evaluation and Care of

Health). The information from this survey will be used to develop the areas of alcohol

52

and drug treatment services, mental health services and palliative care that include

outpatient and community care (AIHW,1998).

A recent initiative by the Commonwealth government on the health promotion front has

been the formation of a National Public Health Partnership. Established by a

Memorandum of Understanding (MOU) between Commonwealth, State and Territory

Health Ministers in 1996, the main aim is to have a national effort in public health and to

improve the health status of Australians in particular population groups at risk. This

partnership proposed an improved collaboration, co-ordination and strengthening of

public health infrastructures and capacity (Commonwealth Department of Health and

Family Services, (CDHFS) 1996). Initially to work for five years. This MOU involved

the setting out of joint priorities and how they would progress and be monitored. In

addition its aim is to respond to public health issues of particular relevance and add valve

to the work of each jurisdiction (CDHFS, 1996).

Although the partnership is an alliance between governments, its success, and the success

of the wider public health effort in Australia will depend on consultation and involvement

by local government, health professionals, key public health organisations and consumer

representatives (Petersen, 1984). Rationalisation of funding and program arrangements

would support the move to a focus on accounting for outcomes, provide greater flexibility

for State/Territories to allocate resources to meet local population needs and thus reduce

Commonwealth involvement in service provisions (CDHFS, 1996).

53

3.2 - State government agencies and health promotion

The Commonwealth government has played a significant role in health promotion in

terms of the collection of data to be used to explain in the explanation of health trends of

Australians. However, State and Territory governments have played and continue to play

a significant role in the implementation of various health promotion programs. Each

State and Territory within Australia has it own health promotion unit whether it is within

a health department or as an independent statutory body.

3.2.1 Victoria

Victoria was one of the first states to develop the concept of establishing a Health

Promotion Foundation. This came about in 1987 when the Victorian government

introduced legislation to ban tobacco advertising and sponsorship. They increased the

tobacco tax as a means of replacing tobacco money and to fund other health oriented

activities (Daube, 1993). The Tobacco Act (1987) was supported by all political parties.

Section 17 of the Act specially identified key objectives as a means of promoting health

“to increase awareness of the programs for promoting good health in the community

through sponsorship of sport, the arts, and popular culture (Betts, 1993). This increase

tax on tobacco was designed to overcome the sponsorship offered by the tobacco

companies and also provided funds for the sponsorship of the arts, sport and health. As

an independent statutory body the Victorian Health Promotion Foundation (VHPF) is

allocated a wholesale tax levied on tobacco products which raised approximately $28

54

million each year to promote good health and to prevent disease, accidents, or disability

in the Victorian community (Galbally, 1993). The idea of sponsorship by the VHPF was

to support valve-for-money health promotion opportunities which would increase

participation levels in sporting activities or arts events, particularly by those people who

where disadvantaged by gender, age, disability, race, geographic location or a non-

English speaking background (Betts, 1993). The idea of sponsorship and health

promotion is one that is becoming increasely relevant in this day and age. A sponsorship

agreement between health agencies such as VHPF has enabled sponsors to use the health

agencies to promote their activities. This can be seen when the Victorian Football

League used the sponsorship of the QUIT program to sponsor one of their football teams,

Footscray. The substantial funds generated from the tobacco tax sponsorship was not

only confined to sport but also to other programs where participation occurred. The

VHPF is also only involved in the sponsorship of the arts, sport and health, but in a health

research programs.

The Research Committee of VHPF allocated 20 per cent of the VHPF’s budget to the

Research Program, which in 1992-93 was $7.2 million. Since 1988 around 150 research

grants have been made and approximately $35.6 million has been allocated through the

Research program (Cassell,1993). Not only were research grants given out but post-

graduate scholarships offered in the area of public health. Some of the research funding

went to centres of excellence such as The Centre for Adolescent Health at the Royal

Children’s Hospital in Melbourne. These centres have a public health/health promotion

research focus (Cassell, 1993). Each year the Research Committee’s focus on changes in

55

the area of research. In 1989 the priority area of research was the basic causes of disease.

In 1990, cardiovascular disease, cancers, nutrition, sexually transmitted diseases and the

health of adolescent and women was examined. In 1992 the focus concerned funding for

primary prevention (risk reduction) and secondary prevention (early intervention)

(Cassell, 1993).

The data gained from each research area has lead to the development of health promotion

programs. One such program is the Dental Health Promotion Project which was

designed as a preventive dental program for South East Asian adolescents. The program

was based on a number of studies conducted in dental health (1992 National Health

Strategy). It had been suggested that people from ethnic communities were less likely to

visit a dentist than their Australian counterparts. The project received funding for three

years from the VHPF. It focus therefore was to demonstrate a way of reaching

vulnerable groups in the community and inform them of dental health and dental services

through the involvement of community decision makers and community leaders

(Wilkins, 1994). In 1996 the focus shifted to health promotion programs for child health.

The program was specifically aimed at child health, a prevention strategy which focused

on increasing immunisation rates, healthy lifestyles, injury and youth suicide

(Noticeboard, 1996). Other programs to be instigated have been related to non-English

speaking individuals and health issues. The major focus of health promotion interventions

in Victoria has been on projects and programs which have lead to an improvement in the

health status of individuals and groups within the Victorian community. These have been

56

developed through integrated campaigns, social marketing, community organisations and

targeted health information and education (Galbally, 1993).

3.2.2 South Australia

The South Australian Health Promotion Foundation - Foundation S.A. was formally

established by the South Australian government in July 1988 to replace tobacco

companies as a major sponsor of sport and art activities and generally to promote health

(Court, 1993). The establishment of Foundation S.A. was made easier by what had

occurred in Victoria in 1987. Daube (1993) believes that the success of the Victorian

legislation made it easier to introduce similar measures in other jurisdictions and South

Australia, Western Australia and the Australian Capital Territory all had tobacco control

legislation by 1991. The source of income for Foundation S.A. comes from a state

tobacco licensing fee. This is different from that of Victoria which came in the form of a

tobacco tax, but generally Foundation S.A. is closely modelled on Victorian legislation.

The State tobacco licensing fee is divided in the following way; 60 percent for sport and

recreation, 20 percent for cultural sponsorship and 20 percent to support health promotion

(Court, 1993). Of the 20 percent received by Foundation S.A. from the tobacco fee, 80

percent of that has to be used for sponsorship. This promotional funding focuses on

maximising the value of health sponsorship through the use of health awareness

campaigns that are closely linked to national health promotion priorities.

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In the area of health awareness campaigns, Foundation S.A. works closely with the South

Australian Health Commission, The Anti-Cancer Foundation, the National Heart

Foundation, other major health organisations and its five expert reference groups (Court,

1993). As a result of this liaison between all of these health promotion groups a

Community Grant program was developed by Foundation S.A. This Community Grants

program is mainly involved in media campaigns which promote the health message.

Sponsored events such as racing fixtures, football matches and art exhibitions are among

the events which provide an opportunity for local health professional to access different

sections of their population (Wylie, 1993).

A number of campaigns were untaken by Foundation S.A. One of those was a recycling

health promotion program, which outlined the advantages and economic benefits. In

1992 a media-based campaign was used to promote bread and cereals. This campaign

was designed to have a two-stage implementation. The first stage being a public

awareness campaign to promote the use of breads and cereals and the second stage giving

the public more detailed information about recommended daily allowance of these foods.

Health promotion has also been taken into the workplace by Foundation S.A. An

investigation was undertaken into small-to-medium sized companies in South Australia.

The survey wanted to find out which companies had health promotion programs and what

type of health issues the companies were examining. The results showed that among the

companies that did offer workplace health promotion activities for their employees, the

majority tackled only a limited number of health issues, namely back care, smoking and

accident prevention (Williams, Noblet & Owen, 1997).

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Another survey was conducted to examine how physically active South Australians were.

This particular survey collected information from 3000 adults from across the State. The

results will be used to assist older people to remain physically active and to examine the

reasons which prevent them from becoming physical active (Noticeboard, 1998b).

Foundation S.A. has also been working in partnership between the arts, health and the

disability sector. A major seminar was conducted to examine the mental health of

women. It explored creative ways of working to improve their mental health and well-

being (Noticeboard, 1997). The theme of the seminar was to examine how links occurred

between cultural and social values and the women’s place in society.

Not only does South Australia have a Foundation S.A. as a health promotion unit but also

a health promotion unit within the Department of Human Services. The Health

Promotion Unit (HPU) is part of the Public and Environment Health Service which in

turn is contained in the Department of Human Services. The goal of this unit is to

provide strategic leadership for improved health promotion outcomes in South Australia

(Department of Human Services, 1998). Some of the key programs involved are the

mental health promotion program, tobacco control program, workplace health promotion

program, health promoting schools program and health for older persons program.

Overall the on going research, development, implementation and promotion of health in

South Australia is extremely active. The continuous funding from tobacco fees is likely

59

to play an active role in health promotion, producing programs which are relevant from

their research and relevant to the people who are living in that state.

3.2.3 Western Australia

Following the establishment of the VHPF in 1987 and Foundation S.A. in 1988, the

Western Australian government introduced a Tobacco Control Act in 1990. As a result

of this legislation the Western Australia Health Promotion Foundation, Healthway was

formed. Again this was a statutory body governed by an 11 member board representing

arts, sporting, health, youth and country interests (Carroll, 1993). The idea of reducing

the effect of tobacco sponsorship in the arts and sport was the main theme behind the

legislation. The method in which Healthway receives funding compared to the VHPF and

Foundation S.A. is different. Healthway is provided with approximately $11 million per

annum or 10 percent of the wholesale tax of tobacco products. Not less than 30 percent of

this amount is allocated to sporting organisations and not less than 15 percent to arts

(Carroll, 1993). One of the major differences between the VHPF and Foundation S.A.

bodies and Healthway it does not conduct health promotion programs itself but provides

funds to enable a range of government and non-government agencies to do so (Carroll,

1993). Some of its other roles are similar however Healthway has developed areas of

priority such as determinants of healthy behaviour, effective health communication,

prevention of injury, cancer, cardiovascular disease, mental health promotion, physical

activity promotion, good nutrition education, musculoskeletal disorders, tobacco smoking

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control, alcohol abuse, HIV infection prevention, sexually transmitted disease prevention,

sex and fertility education and education in human relationships (Carroll, 1993).

The formation of Healthway supposedly provided extra funds for health promotion, but

this may not be the case. McGuiness, Corti, Holman & Donovan (1995) suggest that

there has only been a marginal increase in funds for health promotion. State government

commitment to health promotion increased in real terms from $4.9 million 1984-85 to

$12 million in 1991-92 but his was offset by a reduction in activities of the Health

Department of Western Australia health promotion budget of $2 million. Overall there

has been an increase of 17 percent for health promotion in Western Australia. But the

funding was meant to be proportionally shared between the arts, sports and health

promotion. In 1991-92 State government expenditure on sport was 1.8 times higher than

health promotion and expenditure on the arts was 4.3 times higher. Total commitment by

the sate government to sport was approximately 10 times higher, and to the arts

approximately 6 times higher than was commitment to health promotion (McGuiness et

al. 1995).

The effectiveness of using the arts or sport as a method of promoting the health message

has had mixed results. Is the money being spent on sport from Healthway funds being

effective in promoting the health message and changing some high-risk behaviour?

These were some of the questions poised in a study by Dovovan, Corti, Holman, West &

Petter (1993) where they examined the effects of the QUIT campaign as used by the West

Australian Football League (WAFL). This study attempted to examine how health

61

sponsorship affected attitudes towards promoted brands such as cars and health activity.

The promoted brands being Nissan and Town and Country, a credit union bank. The

results suggested that the QUIT sponsorship had more impact than the other two

commercial sponsors did. This football study showed that a health message sponsorship

may increase positive attitudes towards sponsored health behaviour but to what extent the

increase is maintained after exposure at the end of the event is open to debate (Dovovan

et al. 1993).

The other area in which Healthway utilized the tobacco tax funds was for research.

Healthway research funds effectively doubled the value of health promotion research

undertaken in Western Australia (McGuiness et al. 1995). The research funds however

are subject to particular distributed guidelines which preclude applications for funding

where the programs are already the responsibility of the State or Commonwealth

government. Areas of research in recent times where Healthway has given the most

attention are health behaviour, health communication, mental health promotion, physical

activity promotion, health of young people and disadvantaged groups (McGuiness et al.

1995). In a recent Healthway Board meeting grants and sponsorship were approved to

the value of $560,000, making a total of $9.3 million for the 1998/99 financial year

(Healthway, 1999).

One of the health promotion campaigns launched by Healthway, or in partnership with

other health promotion organisation have included a Children’s Fruit ‘n’ Veg Campaign.

This was designed to encourage children to ask parents and school canteens for different

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types of fruits and vegetables (Noticeboard, 1996). Campaigns which have focused on

young people such as the ‘Young People and Smoking Project’ and ‘Drug Aware’, have

targeted 10 to 14-year olds. Most of these campaigns were implemented in stages over a

three-year period. Not only focusing on youth, but also the aged. Campaigns such as

‘Stay on Your Feet ‘ have been designed to reduce the risk of falls in our older population

(Noticeboard,1998a).

3.2.4 Australian Capital Territory (ACT)

The ACT also established a Health Promotion Fund as a result of replacing tobacco

sponsorship of sport, the arts and other cultural activities. Again, like the states of

Western Australia, Victoria and South Australia they have used a wholesale increase in

tobacco tax as a means of funding the Health Promotion Fund. Unlike similar bodies in

other states, the ACT Health Promotion Fund is not an autonomous body but part of the

ACT Government Service, administered by the ACT Department of Health under the

direction of the ACT Minister for Health (Thompson, 1993). These Health Promotion

Funds gain revenue by a tobacco tax, but only 3 percent of the taxes raised go to the

Health Promotion Fund. Of this 3 percent, 40 percent of this revenue must be paid to

organisations for the purpose of health promotion. Another 15 percent of the funds must

be spent on the Arts and cultural activities that offer opportunities for health promotion

(Thompson, 1993). Again the fund targets young people and members of the community

63

disadvantaged in terms of access to health services just as the other health promotion

funds do.

The ACT Health Promotion Fund is actively involved with organisations such as the

National Heart Foundation and the Cancer Society to promote healthy lifestyles for the

young, the aged, the disadvantaged and different ethnic groups.

3.2.5 - Other States

Most of the other States and Territories within Australia have their own health promotion

units within departments of health. In Tasmania for example their health promotion unit

is situated also in the Department of Health and Human Services. Tasmania is one of the

states within Australia where the population is much older than other states and the socio-

economic status is at the lower end of the spectrum in the country. This is due to

Tasmania’s small, highly dispersed rural population a group with generally poorer health

than urban dwellers (Department of Health and Human Services, 1998). There does not

seem to be a clear Health Promotion Unit within the Department of Health and Human

Services. They have a small health promotion unit called the Hobart District Health

Promotion Group, which promotes health in the Hobart District and collaborates with the

Department of Health and Human Services. This group has small amounts of money for

projects and these are one-off seeding grants where groups can apply for up to $2000.

Not only does the group collaborate with government but works with private health

organisations such as the National Heart Foundation (NHF).

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Within the Department of Health and Human Services one of the mission statements is to

prevent poor health and to improve the overall health of the community. This has a high

priority with plans to shift the balance from over-emphasis on dependency to providing

increased services which would promote good health and illness prevention (Department

of Health and Human Services, 1998).

New South Wales (NSW) has a Centre for Disease Prevention and Health Promotion

(CDPHP) within the NSW Department of Health. Within this department lies the Public

Health Division and this is where the CDPHP is situated. The Public Health division

believes it is an essential process to acquire knowledge about health of a population,

about the factors that influence health and about effective ways to promote health or

prevent ill health (NSW Health Department, 1999). The CDPHP hosts a number of units

within that department such as risk analysis, environmental health, sun exposure and

physical activity, drug treatment services, tobacco health, illicit drugs and health, food

and nutrition, dental health, AIDS and infectious disease, alcohol and health and injury

prevention (NSW Department of Public Health Division, 1999). The CDPHP is involved

in a number of health promotion projects. NSW Health works closely with other groups

including the food producing sector, Commonwealth government, consumer and

professional associations to run programs such as the ’Food Safety’ campaign in 1997.

They are actively involved in programs such as childhood and influenza immunisation.

The focus of this program being diseases such as Diphtheria, Tetanus, Whooping cough,

Poliomyelitis, Measles, Mumps, Rubella and Hepatitis B. In the influenza immunisation

program they have targeted two groups, one being all persons over the age of 65 years

65

and older Aboriginal and Torres Strait Islanders over the age of 50 years and health

professionals. The objective of the program is to increase the awareness of high-risk

groups and to dispel the myths and misconceptions about influenza and the vaccine.

The CDPHP has been active in producing resource materials in a number of high-risk

health areas. Another has been used in both primary and secondary schools to develop

awareness and education of some of the high-risk problems. One of the resources kits

focuses on body image and eating disorders. After a summit on this issue the NSW

Department of Health developed a strategy to educate primary, secondary and health

professionals about the problems of body image and eating disorders. “Nobody is

perfect” material for teaching and learning about body image and gender was distributed

to all government primary and secondary schools by the Department of Education and

Training in late 1977 (NSW Health, 1999).

Another program and information kit was developed based on the theme ‘Live the

Future’ which relates to drug and alcohol use. With the assistance of the AMA

Charitable Foundation, the State Library of NSW and the NSW Health Department

multi-media kits included videos, comics, books and reference material were placed in

10 libraries throughout NSW. This resource kit was intended to be used by 10-19 year

olds and was considered a success from the evaluation of the project (NSW Health

Department, 1999).

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The NSW CDPHP also participates in a national program called ‘Active Australia’ which

focuses on physical activity. It is a 4-year strategy aimed at making individuals aware of

the benefits of regular moderate physical activity. The campaign has four strategies each

targeting different groups within the population. An important component of “Active

Australia” is the multilingual campaign, which is managed by the NSW Multicultural

Health Communications Service (NSW Health, 1999). The NSW Multicultural Health

Communications Service used the ethnic press, handouts, ethnic radio and SBS to

encourage individuals of ethnic background as to the benefits of regular, moderate

physical activity.

On the theme of multiculturalism the same service - NSW Multicultural Health

Communication Service was recently awarded the Australian Hospital Association,

National Outreach Award for its program “Health is Gold” an anti-smoking project

targeting the Vietnamese community, especially Vietnamese-speaking General

Practitioners. A number of these multicultural health promotion programs and projects

have been developed by NSW because of their higher proportion of people of ethnic

origin (NSW Health, 1999).

Queensland Health has a Public Health Unit whose main role is to promote and protect

Queenslanders health. The Public Health Service in cooperation with key partners has an

integrated, specialised capacity for community and population wide responses for the

protection of health; prevention of disease, illness and injury; and the promotion of health

and well-being (Queensland Health, 1999).

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It has 13 units incorporated within its body, ranging from planning health services to the

delivery of these services. This unit is also responsible for developing and promoting

various health promotion programs. One of these units is the Queensland Health

Alcohol, Tobacco and other Drug Services (ATODS). This particular unit’s mission is to

support Queenslanders to make informed choices about alcohol, tobacco and other drugs

through the provision of quality public health and clinical interventions which are

evidence based and reflect contemporary best practice (Queensland Health, 1999).

Within this unit they run a number of health promotion programs such as ‘100 percent in

control’, ‘Youth Campaign’, ‘Young Adults’ and ‘Drug Project’, ‘Adult and Drug

Project’, ‘Adult Alcohol’, ‘Safety Action Project’ and ‘Quit smoking’. One of these

campaigns, ‘100 percent in control’ targets young people as a means of positively

influencing life long attitudes and behaviours associated with alcohol and other drugs’

use (Queensland Health, 1999). The objectives of the campaign were to reduce the

incidence and consequences of binge drinking amongst young people 12-17 years and to

provide them with innovative health promotion activities, to demonstrate that alcohol and

other drugs are not needed to have a good time, to reinforce healthy alternatives to

alcohol and drugs use and to install awareness and positive behaviour of the target group

regarding alcohol and other drugs (Queensland Health, 1999).

Another health promotion service that the ATODS provides is a 24 hour-a-day Alcohol

and Drug Information Service (ADIS). Individuals are able to ring a hotline and ask

questions about drugs from trained counsellors. Services such as rehabilitation programs,

methadone programs, needle exchanges or other forms of assistance are also provided.

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The Safety Action Project is a combined community and government campaign to reduce

the levels of intoxication, alcohol related violence and to prevent injury in a licensed

environment. Community groups such as police, liquor licensing, venue managers and

local government are all involved in this project (Queensland Health, 1999).

In Cairns, Queensland Health has a Tropical Public Health Unit which examines and

implements various health promotion programs with Indigenous communities. Two

projects, one an Indigenous injury surveillance system and the other an Indigenous

smoking project focus on the health and well being of the Indigenous people of

Queensland. One of the reasons for developing these projects is based upon evidence

suggesting that smoking rates among Indigenous populations are two or three times

higher than those of non-Indigenous people (Queensland Health, 1998). With the injury

surveillance system project, it is believed that there is inadequate incidence of injury and

prevention opportunity in Indigenous communities (Queensland Health, 1998).

Queensland Health has also worked closely with the Queensland Department of

Education to introduce 100 school-based youth health nurses into state high schools and

other state schools. By the introduction of health schools nurses it is hoped they will

provide health promotion for adolescents through the school setting on such issues as

drugs and alcohol abuse, eating disorders, depression, self-harm behaviours, suicide and

sexual health (Queensland Health, 1998).

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Queensland, unlike Western Australia or Victoria, has no health promotion foundations.

It’s main focus of health promotion is through the Department of Health. The health

promotion programs are scattered throughout the various regional units of the

Department and programs are especially designed on health issues for that particular

regions; for example the Tropical Health Unit in Cairns.

3.3.1 Non government health promotion organisations

There are a number of non-government health promotion organisations within the

Australian community. One of these is the National Heart Foundation (NHF). Founded

over 40 years ago in 1961 by a group of doctors and other individuals within the

community, its role was to fight heart disease. It has now become one of the leading

health organisations dealing with the prevention, research and education of heart disease.

The National Heart Foundation’s health messages are based on four decades of medical

and scientific research supported by extensive health promotion (National Heart

Foundation, 1999a). The NHF is an independent Australia-wide, non-profit health

organisation funded almost entirely by donations from Australians (National Heart

Foundation, 1999a)

The NHF has run a number of health promotion programs that focus on the heart health

message. A number have been aimed at the primary and lower secondary school level.

Programs such as ‘Jump Rope for Heart’ focuses on children being involved in physical

activity for at least 30 minutes a day. ‘Food Smart for School Canteens’ and ‘Breakfast

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at School’ promote healthy lifestyles at an early age thus eliminating some of the

potential risks associated with heart disease.

Within the general adult community in Australia the NHF has developed a program

called ‘Pick and Tick Food Approval Program’. It has been designed to offer individuals,

who buy products from supermarkets, make healthy choices with their food selection.

Products with the ‘Tick’ have been independently tested and meet the NHF’s strict

nutritional criteria for fat, salt, sugar, and fibre content (NHF, 1999). Within 5 years of

its launch in 1989 this program supported more then 120 companies and the ‘Tick’

appears on more then 600 products (NHF, 1999a).

The NHF has also produced a number of educational resource materials on the prevention

and treatment of heart and blood vessel disease. It provides advice and resource

materials to health professionals, teachers, employers, community groups, general

practitioners, journalists and the general public (NHF, 1999a). Amongst these

publications are, Bypass, Living with angina, Your blood pressure, All about coronary

angiography, Exercise your heart and How to have a health heart.

The NHF has committed to heart disease research. Since 1959 the NHF has invested

more than $100 million into the research of the causes, treatment and prevention of heart

disease and stroke (NHF, 1999a). Over the years it has committed funds to such projects

as an initial survey of cardiac surgery in Australia which was a first in the world. This

occurred in 1964. A year later, it funded a project on care of sufferers of heart attack

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which lead to the concept of intensive coronary care. In 1997, the Heart Foundation

funded a research program called LIPID (Long term Intervention with Pravastatin in

Ischeamic Disease). This is a 7-year trail into a drug Pravastatin that may lower

cholesterol, thus reducing the risk of coronary disease and strokes. The research is being

conducted in conjunction with Sydney University and a drug manufacturer. Results have

suggested that the LIPID study demonstrates that patients with cholesterol in the average

range, who have suffered a heart attack and stroke, may reduce their need for heart

surgery or angioplasty by taking a cholesterol lowering drug - Pravastation (Tonkin,

1998) (In NHF, 1999). Further research is being conducted. These are only some of the

preliminary results.

The NHF has also developed a number of positional statements on issues related to heart

disease. One of which concerns the health benefits of physical activity and the

prevention of heart disease. In 1997 the NHF developed this position on prevention of

heart disease and physical activity based on recent research evidence. The 11 point

position statement pointed out a number of benefits of physical activity and heart disease,

and these ranged from the risks of inactivity to the types of physical activity that should

be performed to gain cardiovascular benefits. They also suggested strategies and

programs, which should be aimed at specific populations who are more likely to be

sedentary or minimally active, to increase physical activity (NHF, 1999b).

Funding for most projects that the Heart Foundation undertakes comes from bequests and

regular donors. Other funding comes from “Jump Rope for Heart”, special events,

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corporate support and doorknock appeals (NHF, 1999a). The largest proportion of

funding is spent on research followed by health promotion and education.

3.3.2 Cancer Funds, Councils and Societies

All of Australia’s States and Territories either have a cancer society, fund or council. All

of these organisations are closely linked to one another and are members of the

Australian Cancer Society (ACS), which is a national organisation for the control of

cancer. The purpose of the various cancer organisations is to control the disease through

prevention, and by saving lives and enhancing the quality of life of people diagnosed

with cancer (NSW Cancer Council, 1998).

The Cancer Societies throughout Australia are involved in a number of health promotion

projects. The ACS runs a number of cancer prevention programs such as the National

Skin Cancer Action Week and Australia’s Breast Cancer Awareness Day. In Western

Australia the Cancer Foundation has programs such as ‘Me No Fry’ adolescent campaign

and ‘Cover Up Schools’ Project which is designed to promote effective methods of

preventing skin cancer in the younger individuals of the population. This foundation also

runs a series of activities to promote early detection of testicular and prostate cancer in

men, which includes videos, resources kits and public education sessions.

In Queensland the Queensland Cancer Fund has produced a series of educational

information packages which give information on smoking, sun cancer, cervical cancer,

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breast cancer, prostate, testicular and bowel cancer in males and females. In the schools

educational information package, smoking and sun cancer are the two main areas targeted

by the organisation.

A large proportion of the funds received by the various cancer organisations is devoted to

research. In NSW the Cancer Council awards funds for external research, which is based

on peer review principles, to areas such as molecular biology, epidemiology, behavioural

research and supportive care. The NSW Cancer Council has in-house research programs

such as a Cancer Education Research Program, which investigates the behavioural aspect

of primary, secondary and tertiary prevention to reduce the risk of cancer in the

community and to use appropriate screening tests to detect cancer. The other in-house

research program is a Cancer Epidemiology Research Unit which uses data collected by

the NSW Central Cancer Registry to increase the understanding of the causes, incidence

and treatment outcomes of cancer (NSW Cancer Council, 1998).

Funds are also devoted to research institutions for the Ph.D award for research into

cancer. Professional education and training also uses some of the funds from the various

cancer organisations. The professional training is done in the area of postgraduate and

undergraduate, especially in the area of medicine. Most of the training is done through

the Royal Australian College of Radiologists, the Royal College of Physicians, the Royal

College of General Practitioners, the Royal Australasian College of Surgeons and the

College of Nursing (NSW Cancer Council, 1998). Professional updates for specialists

74

and GP’s occur in the form of newsletters. Also grants are awarded to health

professionals to study overseas.

Other activities where funds are allocated are rehabilitation and continuing care for

cancer patients, a bone tumour registry, day hospice facility, cancer Helpline for

counselling and information, melanoma screening program and accommodation facilities

for country patients (NSW Cancer Council, 1998).

Funding for Australia’s cancer societies comes mainly from community sources.

Donations and bequests make up part of the sources of funds but other methods of

include the Daffodil Day, auctions, fun runs and Gala Balls. Community fundraising

takes the form of fetes, raffles, fashion parades, golf days and cake stalls. As a result of

this fund raising and donations the NSW Cancer Council has been able to spend about $4

million each year on understanding the causes of cancer and to find more effective

treatment for cancer patients (NSW Cancer Council, 1998).

3.3.3 Australian Drug Foundation

The Australian Drug Foundation (ADF) was founded in 1959 with a main emphasis on

the treatment of people dependent on alcohol. Over the years this emphasis has shifted to

focus on drug problems and alcohol related problems. The philosophy of the ADF is

concerned with the consequences of drug use rather than drugs per se. “We do not view

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drug use from a moral stance but from the perspective of the harm it causes” (ADF,

1998). The mission of the ADF is to prevent and reduce alcohol and other drug problems

in the Australian community through the provision of quality information and practical

assistance in a professional manner (ADF, 1998). This is being attempted through a

number of strategies. The first being the development of a Center for Youth Drug

Studies (CYDS). This initiative’s aim is to develop strategies to reduce drug use in

young people; for teachers to use materials that the ADF has produced that will

effectively educate their students about drug issues, and for parents to be able to

effectively communicate about drug issues with their children and act as positive role

models (ADF, 1998). The CYDS provides a central point at which youth workers,

teachers, police and other workers with young people may seek professional advice and

training (ADF, 1998).

CYDS is also providing drug and alcohol education for culturally and linguistically

diverse communities in Australia. They offer educational information in a number of

languages and these educational kits have been designed to take into consideration the

cultural values of each community. Communities which are comprised of second and

third generation youth from migrant families have very little information which is

culturally relevant. They still live in culturally diverse families for which mainstream

English may not be appropriate (ADF, 1998). Thus the ADF is attempting to introduce

drug and alcohol education across the broad spectrum of Australian society.

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The ADF is able to offer health promotion materials to schools through their education

unit. This unit publishes curriculum material on reducing the risk of alcohol related

problems, exploring gender relationships and alcohol. ‘Next Step’, deals with illegal

drugs and secondary school students while ‘Primary Steps’ gives information and

activities to inform children in the 5-14 year old range about drugs.

‘The Sporting Clubs Alcohol Project’ is an interesting project which has been developed

by the ADF in conjunction with, and funded by the Victorian Health Promotion

Foundation, the Department of Justice, and the Department of Human Services. The

main thrust of this project has been the development of strategies to combat within some

of the sporting clubs of Victoria. Over the years some clubs have been havens for the

excessive alcohol consumption of their players. This has resulted in players’ losing

drivers licenses, inability to attend training or death. Not only have government

departments supported this project but so have some major sporting organisations such as

the Victorian Cricket Association. On the other hand alcohol companies such as Carlton

and United Breweries have been conspicuously absent from this project. The ADF has

helped sporting clubs with the development of policies related to alcohol use and offered

alternative ways of developing revenue - not only from alcohol.

The ADF produces a wide variety of drug educational kits, videos, journals, books and

pamphlets. A newsletter is produced every quarter providing updated information on

educational methods and resources and events which are occurring or about to occur.

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This is available to all the community as a resource for any type of health promotion

program.

ADF funding comes from Commonwealth and State governments of approximately

$1million. The remainder of the funding comes from the community they serve, through

donations, fee-for-service work and sales of education and information materials (ADF,

1998).

Throughout Australia there are numerous organisations who are involved in the

promotion of health within the community. These are at the Commonwealth and State

and Territory level. But other non-governmental organisations also play a vital role in

the promotion of health. Some of these organisations specialise in different types of

health problems and diseases such as the National Heart Foundation. The role of the

various government bodies in providing funds for research and education into health has

been extremely important. With the shift occurring from somebody else looking after

your health problems to the individual being more responsible for their own health over

the last 20 years it has been an important step in health promotion in this country. The

long term benefits of this approach will slowly bear fruit in areas such as cardiovascular

disease where there has been a decline in the death rate over the past 20 years. The MOU

agreement between the Commonwealth, State and Territory health ministers have given

previously neglected areas of health a more important role. The gathering of health

information by Commonwealth, State and Territory bodies will help us understand some

health issues better but also to provide improved programs which take into consideration

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culture, language, gender and ethnic background. This in turn may offer an all round

improved health system.

Not only are governments providing better health promotion programs but health

insurance organisations are now also developing health promotion programs for their

clients. One of the reasons why health insurance organisations are developing these

programs is to lower their claims costs.

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4.0 - Health insurance Industry

The purpose of this chapter is to examine the role of the health insurance industry within

the Australian health care system. Also this chapter helps to give a basic understanding of

the health insurance industry as well as the role in plays in this study.

4.1 - Background

Over the years health insurance has been a football in the political arena. Over the past

few years Australian governments both national and state have attempted to reduce the

health budget. As a result of this there has been a simultaneous push by the

Commonwealth Government to encourage individuals to take out private health

insurance. In 1992-93, of the $44.3 billion expended on health, $23.2 billion was

provided by governments, the Commonwealth government providing $15.1 billion and

the States and local governments $8.1 billion, with a further $8.1 coming from the private

sector (AIHW, 1994). This health expenditure increased in 1995-96 to $38.9 billion, of

which private health insurance accounted for 11.4 percent or $4.4 billion. By 1996-97

the spending on private health insurance by individuals had increased by $4.7 million

(AIHW, 1998).

As a result of the increasing cost of health to the Commonwealth, the Federal government

decided that individuals should bear the cost of further increases in health expenditure

(AIHW, 2000). However, the situation now exists whereby individuals within Australian

society are already paying for health cover through a Medicare levy. This is a levy paid

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by all tax payers on all earnings to help offset any additional costs to government of

Medicare (AIHW, 2000). As a result of this situation, individuals and families can be

paying for the universal medical benefit as well as private health insurance, thus doubling

up on medical cover.

4.2 - History

The Universal medical benefit has its origins within the formation of the Australian

Constitution, when authorisation was given to the new Commonwealth Government to

legislate for medical benefits with respect to age and invalid pensions. The notion of a

medical levy from taxable income was floated in 1938 when parliament passed a

National Health and Pension Insurance Bill, but this legislation was never implemented

due to the outbreak of World War II. At that time a national health insurance scheme was

planned which would levy a two percent tax on wage earnings, thus giving the nation its

first national health scheme. The Commonwealth had little jurisdiction over health and

social policies until 1946 when an amendment to the Constitution conferred wide-ranging

powers with respect to health (Mooney & Scotton, 1999). Thus the Commonwealth

government had powers to develop legislation to provide, sickness, medical and dental

among others, to the Australian population. In 1953 a Medical Benefits Scheme was

passed that remained in effect for more than two decades and which provided hospital

and medical benefits, pharmaceutical benefits and the Pensioners’ Medical Service

(Health Insurance Commission, 1997a). This scheme was based on individuals attending

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a doctor of their choice and then claiming a refund from one of the government registered

privately managed health insurance funds.

By the 1960’s, pressure was being applied for this system to change because of an

increase in services usage resulting in increasing costs. In 1968, a proposal by the then

government was introduced for compulsory health insurance called ‘ a share of universal

insurance’. This proposal was for medical and hospital coverage. It was designed to

look after low-income earners and high individual users, plus a provision for medical

services to in-patients of public hospitals. In 1969, the Liberal Party introduced the

National Health Act and a list of ‘common fees’.

A Labor government came to power in 1972 and introduced a single universal health

insurance scheme that would cover all Australian residents for medical, optometry and

hospital costs (Health Insurance Commission, 1997a). After the general election in 1975,

which was won by the Coalition, National/Liberals it was decided to scrap Medibank and

introduce a new health insurance scheme. A 2.5 percent levy was introduced for those

individuals and families who wished to remain in Medibank. Private health insurance

companies were now able to offer full private health insurance with basic medical and

hospital insurance plans. As a result, private health insurance became part of the

Australian health scene. The government of the time also decided to introduce their own

private health insurance fund called Medibank Private. In 1984 this private fund became

the only scheme to be administered by the Health Insurance Commission and thus

Medibank exited from the health insurance scene. As a result of the Labor policy, the

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Health Insurance Commission was formed in 1984, to administer a new health insurance

fund called Medibank. This had some interesting features, including automatic universal

cover, a single government operated fund to pay all medical benefits at 85 percent of

scheduled fees with a maximum gap of $5.00, or alternatively doctors could bill the

Commission directly. Hospital treatment in standard wards of public hospitals was

available to all free of charge without a means test. Hospital fees were set nationally and

recognised hospital operating costs were shared on a 50/50 basis between federal and

state governments (Health Insurance Commission, 1997b). As a result of the introduction

of Medibank in 1984, the Commonwealth government allowed private health funds to

offer private and semi-private accommodation in certain recognised public hospitals or

private hospitals. Private health funds were limited to marketing the ‘gap’ insurance,

which is the difference between the scheduled fee and the Medibank rebate.

One of the reasons for the Commonwealth government’s involvement in private health

insurance was to provide greater competition within the health insurance industry sector,

thus, enabling the government to gain some control over issues such as health insurance

premiums. This became clearer in later years, when the Howard government attempted

to manipulate some aspects of private health insurance, for example premiums. Over the

years, each government elected to office has tampered either with the health care levy or

some other part of the health system.

In 1984, the first full year of operation of Medicare, the levy raised $1.223 billion or 2.3

percent of the total taxation revenue. By 1996-97 the total revenue collected amounted to

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$4.130 billion, or 2.9 percent of total taxation revenue ( AIHW, 1998). The Medicare

levy at the present time (1999) is set at 1.5 percent of taxable income but there is also an

additional surcharge of 1.0 percent of taxable income in respect to high income earners

who do not have private health insurance cover (AIHW, 1998). This particular measure

was introduced in 1997.

4.3 - Private Health Insurance

Private health insurance in Australia holds a significant place within the Australian health

care system. It is a voluntary organisation for the funding of hospital care and ancillaries

which sits alongside a compulsory tax-financed public system (Medicare) and is available

to all citizens (Productivity Commission, 1997). In Australia the government intervenes

in health care financing both through the provision of universal social insurance under

Medicare which covers both medical and public hospital services. The Australian

government intervenes in health care funding in two ways. Firstly it does so by the

provision of universal social insurance, covering both medical and public hospital

services, through Medicare. Secondly, it regulates and subsides private health insurance,

covering private inpatient care in hospitals and some other privately provided services

(Mooney et al. 1999). The introduction of Medicare led to the private health insurance

fund’s share of health expenditure falling from 21.4 percent to 9.5 percent, although it

had recovered to 12.9 percent in 1992-93 (AIHW, 1994). By 1997-98 this had again

declined to 9.6 percent of health expenditure (AIHW, 2000).

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Health insurance funds are mainly operated by Registered Health Benefits Organisations,

which are organisations registered under the National Health Act, (1953) for the purpose

of conducting a health benefit. These health benefit funds were not permitted to offer

health cover or any part of a service provided by medical practitioners outside of

hospitals before 1984 (AIHW, 1998). The Health Insurance Act, (1973) was designed to

limit competitive behaviour in order to promote anti-discrimination social objectives

involving the setting of premiums and paying benefits regardless of health status, age,

race, sex, or use of services (Mooney et al. 1999). As a result of this legislation, the

private health insurance funds had to restructure and reorient their activities significantly.

This led to the private funds starting to focus on the area of provisional hospital benefits.

They also began to offer ancillary services such as dentistry and physiotherapy which

represented 22 percent of health expenditure in 1992-93 (AIHW, 1994). In 1994/95

private health insurance funded 11.5 percent of Australia’s current health expenditure

(Mooney et al., 1999). In 1996-97 the private health insurance funds paid benefits

totalling $2.437 billion in respect of private hospital care, and $360 million in benefits

paid for insured patients in public hospitals (AIHW, 1998).

In recent years the Coalition government has developed a number of ideas about health

insurance and who should be paying for health. Debate has raged over the years between

the private and public health sectors. The debate has included health funding options,

decline in private health insurance membership, and the increasing pressure on the public

health sector. This debate has substance, because these issues are the ones that confront

every individual within Australian society. The Royal Australian College of

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Obstetricians and Gynaecologists believes that there is little doubt that the long-term

decline in private health insurance membership has greatly increased the pressure on the

public health sector (The Age, Jan 8, 1999).

In 1996 the Coalition government attempted to encourage people back to private health

insurance because of the rapid decline of membership within private funds and the strain

on public hospitals. Over the years there has been a slow decline in the proportion of the

population with private health insurance. This proportion has continued to fall each year

since the introduction of Medicare in 1984. At the end of June 1984, about 50 percent of

the Australian resident population was covered by private health insurance. By the end

of 1992 this had fallen to 40.2 percent, and by December 1997 it was at an all - time low

of 31.6 percent (AIHW, 1998). This decline was not uniform across all the States and

Territories - in South Australia coverage fell from 56 percent to 33 percent between 1984

and December 1997, whereas in Queensland the fall was from 36 percent to 30 percent

over the same period (AIHW, 1998).

One reason suggested for this decline was that individuals were not renewing their

membership of private health funds due to membership costs increasing. It has been

suggested that annual family costs for 100 percent hospital and ancillary cover is now

well over $2,000 in many funds and it is no wonder that many low risk members are

turning away from private health insurance (Australian Private Hospital Association,

1996). As a result of individuals not renewing or taking up private health insurance, the

risk profile of the health insurance funds increased, resulting in a rise in health premiums

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which in turn led people to abandon health insurance funds altogether. Young healthy

individuals who belonged to health insurance funds were also opting out, because they

felt that they were subsidising individuals in the high-risk health group. These were

some of the issues that faced the government when they attempted to develop policies

that were fair and just to all sectors of the Australian community.

In an effort to understand this trend, the Commonwealth government had to consider data

that had been collected over a number of years and develop policies in accordance with

that information. Private health fund membership has changed considerably over the

years. Not only has there been a decline in membership, for example, single membership

dropped from 1,303,733 in 1984-85 to 1,287,000 in 1996-97, and family membership

dropped from 1,989,206 in 1984-85 to 1,547,500 in 1996-97 (AIHW, 1998). Another

variable to consider was the ageing of the population. There has been a gradual increase

over time in the number of widowed aged people and couples who no longer need family

health insurance cover due to one partner having been admitted to a nursing home

(AIHW, 1998). This ageing factor represents a decline in membership for couples from

64 percent in 1986 to 47 percent in 1995 (AIHW, 1998).

In 1996 the Coalition government attempted to address some of these issues by

introducing legislation which provided for a means test rebate being introduced to

encourage more individuals and families to join private health insurance. This failed due

to the large premium hikes by private health insurance funds, which effectively wiped out

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the value of the $450 incentive payment encouraging low-income families to sign up

(The Sydney Morning Herald, Dec 5, 1998).

Why did these increases in health insurance premiums occur? There are a number of

reasons, depending upon whose viewpoint you take. The Australian Health Insurance

Association believes that costs should rise because of reduced funding for the public

health system with the private health insurance system having to bear the burden of

cutbacks to health. The other argument the Australian Health Insurance Association

made was that that there is an uncoordinated proliferation of doctor’s bills. A substantial

increase in hospital fees and the introduction of new technology has also led to increases

in health insurance premiums (AIHW, 1994). New health care technology has significant

cost implications for the allocation of health care resources. Expenditure on new

technologies in Australia is not definitively known, although it is certainly substantial

(AIHW, 1994).

Another point to consider is the amount of monetary reserves held by health insurance

organisations. The Health Insurance Act requires that health insurance funds hold a

reserve the equivalent of two months benefits to meet unexpected demand and ensure

solvency. During the 1995/96 year a substantial operating loss was incurred by most

health insurance funds and this provoked a rise in health insurance premiums (Mooney et

al. 1999). In 1997 there were 48 health benefit organisations, but the largest six had

nearly 80 percent of the total private health fund membership and the top two funds had

at least half of the market share between them (Industry Commission, 1997). The price

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of private health insurance has been rising inexorable at a rate averaging three and half

times CPI inflation since 1990 - an average of 9.8 percent per year (CPI is 2.9 percent per

year) (AIHW, 1998). Finally, the complexity of the product has meant that many

consumers are unaware of the exact nature of the benefits to which they are entitled until

they need to claim and then they are unpleasantly surprised (Industry Commission, 1997).

As a result of the continual increase in health insurance premiums, the Commonwealth

government in 1996 decided to establish an inquiry into the private health insurance

industry. This inquiry was undertaken by the Industry Commission. The inquiry focused

upon why a decline had occurred in private health insurance membership and why

increases had occurred in health insurance premiums. A number of recommendations

resulted from the inquiry.

These were some of the major recommendations of this inquiry;

1. 65 year olds who had entered private health insurance at the age of 35 would

pay a much lower premium than individuals who entered at age 60. They

would pay the same premiums as somebody entering today at age 35.

2. There is a need to revise reinsurance arrangements so that those funds which

effectively contain unit costs or utilisation do not subsidise those which do

not.

3. There should be more scope for funds to target products to attract lower-risk

members, for example non-smokers.

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4. Most of Australia’s private health insurers are “mutual’ and lack strong

accountability to members. The recommendation is to have a mechanism in

place to facilitate takeovers.

5. Inherent in the health system is a tendency for overuse, where patients receive

services that they perceive as of less value than the cost of provision. This

tendency is compounded, as technology makes feasible an ever increasing

range of procedures, which are of high cost, but sometimes of questionable

additional clinical worth.

6. Health funds should be free to choose with which private hospitals they wish to

contract and for which services.

7. Governments should neither control nor screen price changes of health

insurance products.

8. There is a need phase provisions in the rebate and levy to reduce the current

extreme marginal tax peaks at ceiling/threshold income levels.

9. Money should be set aside for rebates on ancillary cover and additional

encouragement given for members to take out hospital cover.

(Industry Commission, 1997)

As a result of these findings the Coalition government in 1998 introduced new legislation

into parliament to implement some of these recommendations. A Private Health

Insurance Incentive Bill (1998), Private Health Insurance Incentives Amendment Bill

(1998) and Taxation Laws Amendment (Private Health Insurance) Bill (1998) were

introduced to the lower house of parliament and passed in the Senate in December 1998.

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The Private Health Insurance Incentive Bill 1998 provided for a non-income tested

financial incentive for people who took out or maintained private health insurance (PHI).

The incentive was in the form of a direct payment, reduced premium or tax offset and

was equal to 30 percent of the cost of PHI cover (Parliament of Australia, 1998). The

rebate was universal by intention - not only was it intended to make health insurance tax

effective for middle and higher income earners but to encourage more individuals into the

private health system (Commonwealth Department of Health and Aged Care, 1997).

These measures were introduced as a way of arresting the decline in the numbers of

individuals and families who were dropping out of PHI.

This Commonwealth legislation caused serious debate amongst different parties who

were stakeholders in private health insurance. Some factions within the health debate

suggested that the private health insurance rebate would cost the government an

additional $1.09 billion in 1999-2000 which would go to those already privately insured

anyway, as well as those who subsequently took out private health insurance (The

Sydney Morning Herald, Dec 5, 1998). The ultimate intention of the legislation was of

course, to increase membership of private health insurance funds. Sections of the private

insurance industry suggested that this legislation would increase membership from 30.3

percent of the population to 45.6 percent, an increase of 15.3 percent (Australian Private

Hospital Association, 1998b). Some other groups within the health sector suggested this

was over-estimated and a much lower figure was more realistic, and that the rebate would

only arrest the decline of membership. Even the Coalition government was forced to

admit that its proposed $1.5 billion health insurance rebate would lift membership in

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private health funds by only 2.7 percent - to 33 percent of the population (The Sydney

Morning Herald, Dec 5, 1998). Other sections within the political arena suggested that

the $1.3 billion would be better spent on public hospitals to reduce the waiting lists for

surgery and on other areas of health needs such as rural health services and indigenous

health services, rather than on the proposed rebate (Commonwealth Government,

Parliament of Australia debate, 1998).

Approximately 30 percent of the population are likely to have private health insurance

leaving the other 70 percent of the population to face ever-increasing waiting lists, pot-

luck with their doctors, overcrowded emergency departments and an ever-ready supply of

patients waiting to fill dwindling hospital beds (The Sydney Morning Herald, Dec 5,

1998). The increase in waiting lists for elective surgery and other types of medical

treatment has caused considerable political conflict between the States and the

Commonwealth. The States have argued that the Commonwealth should provide more

funds to reduce waiting lists for public hospitals. An agreement between the States and

the Commonwealth called the Medicare Agreement, gave the States the responsibility for

hospital services with the Commonwealth providing funds for hospitals. The

Commonwealth Government is the major provider of funds for nursing homes, medical

services, pharmaceuticals and public acute care hospitals (AIHW, 1998). Expenditure on

recognised public hospitals fell from 32.8 percent of recurrent expenditure in 1984/85 to

28.9 percent in 1995/96 (AIHW, 1998). Bearing the brunt of this has caused the States to

reduce services in public hospitals. These include such items as elective surgery, where

waiting lists continue to grow as a result of individuals spending more time on those lists.

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Between 1989-90 and 1995-96, there was an 11 percent reduction in available public

acute care hospital beds (AIHW, 1998).

4.4 - Current Issues

There are a multitude of different problems facing our health system. Whether these

problems are related to government policy or whether they are created by some external

factors over which the individual has little control is sometimes difficult to answer. Both

play their part to a certain degree. For the individual the sheer understanding of such

problems as choosing the right policy for the family or understanding how the rebate

works can be difficult. The health insurance industry has to give clients more value for

their premiums and provide other programs which may reduce costs to clients. A report

by the Private Health Insurance Administration Council showed that funds coffers were

boosted by more than $300 million in1997, but the benefits paid to members increased by

only $26 million (The Australian, Nov 27, 1998). This is a significant difference

between the funds received and the benefits paid to members. With this extra capital the

private health insurance industry should follow one of the recommendations of the

Industry Commission that private health funds should provide a wider range of products

for their members. One of these products could be a prevention program, so that those

members who are classified as being in the high-risk health area are given programs to

reduce their risk status. This could lead to a reduction or a plateau effect in health

premium costs.

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4.5 - Health Care Costs and the Future

The health system in Australia will be under considerable pressure in the years to come

because of our ageing population. In 1901 the male population aged over 85 years was

2,038 and the female population of 85 years and over was 2,207. In 1991 the male

population over 85 years had increased to 44,200 and the female population to 110,027

(AIHW, 1995). The difference in the male and female populations in 1901 was not that

great but by 1991 the proportion of male to female was approximately 1:3. Increases in

the older population have added extra strain to the health budget because there tends to be

more use of medical services by this age group.

Between 31 December 1991 and 31 December 1996 the population of Australia increased

by 6 percent to 18.4 million, a slow down on the 7.7 percent growth rate achieved

between 1986-1991. However the population aged 70 years and over, the highest

consumers of health services, increased by 18.1 percent (AIHW, 1998). This increase in

the use of health services by individuals over the age of 70 years has caused some

concern within health circles. This age group’s use of health services has increased, but

there has been a decline in this group taking out health insurance. In 1983 the proportion

of the population who were 75 years old and over and who had private health insurance

was 36 percent. This had declined to 29 percent by 1995 (AIHW, 1998). The average

health system costs for females over the age of 75 was $7,500 in 1993-94 and for males

about $6,800 for the same years (AIHW, 1998). The average cost for private health

insurance for a year for individuals who have ‘top cover’ averages out at $1,230 and for

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families with the same type of cover it is $2,460, the equivalent of 8.5 percent of average

weekly earning after tax (Industry Commission, 1997). Individuals who are 75 years and

over find that it is a considerable financial burden to have private health insurance. As a

result of this financial burden many aged individuals opt out of private health insurance

and become part of the 70 percent of the population who are in the universal health

scheme.

With our aged population increasing it is expected that the total health expenditure will

double between 1995-2015. It will be driven mainly by an increase in the demand for

and use of health services. Increased average age and projected population growth are

expected to contribute 28 percent of the increase in expenditure up to 2015 (Australian

Government Budget, 1999-2000, 1999). Older individuals tend to have higher rates of

admissions to hospitals and they tend to stay longer - longer stays on average are 7.3 days

as compared to 4.5 days for all age groups (AIHW, 1998). There were also differences

between very old males and females (85 and over) in length of stay in hospitals. Males

for this age group stayed in hospital on average for 11 days as compared to 14 days for

females (AIHW, 1998). Evidence suggests that between 1988 and 1993 the proportion of

people aged 80 and over, with a severe or profound handicap and living in the

community, increased from 50 percent to 59 percent (AIHW, 1998).

To keep health expenditure at a realistic level, health promotion programs and incentives

by health insurance funds for individuals to remain healthy are recommended. Not only

would health promotion programs play a significant role in controlling health expenditure

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but the government should encourage individuals through incentive programs to remain

in health insurance funds. The economic value of health promotion is of economic benefit

to the community at large. The main aim of a prevention program is the production of

good health (Cohen and Henderson, 1988). This means that a prevention program could

well be cost effective in terms of maximum benefits. A good example of this is the

National Breast Screening Program whose main goal is to detect early breast cancers. A

result of this program has been an increase in the number women participating in the

screening program thus enabling more early detection of breast cancer. To prevent the

onset of breast cancer through the use of mammography screening at an early stage of

cancer can help reduce the morbidity and mortality of this disease (AIHW, 1998). How

are the cost benefits determined by the implementation of a breast screening program?

The cost benefits could be seen in terms of preventing premature death and a

rehabilitation program that improves the quality of life. Implementation of early detection

programs (such as mammography and colonoscopy) has shown to improve health since

their cost effectiveness (rather than cost savings) is usually relatively high compared with

other medical interventions (Fries et al. 1997a).

Some private health insurance funds do provide some form of preventive program to their

clients. An example of this is the Queensland Teachers’ Union Health Fund. Within the

health fund is Healthtrac, whose major function is to oversee a health promotion

program. Healthtrac is an organisation which had its beginnings in the United States of

America under the guidance of James Fries, a Professor of Medicine at Stanford

University School of Medicine. The concept of Healthtrac is to provide health

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information to those individuals who have scored highly on a health assessment

questionnaire. The higher the score, the greater the risk of chronic disease. As a result of

this questionnaire, Healthtrac focuses on particular high-risk health problems and

provides educational material to the individual. Educational material is mailed out as a

method of changing health-related knowledge, attitudes and behaviour in participants.

The use of printed material is regarded as a very effective way of changing attitudes and

behaviours in relation to a wide range of health related issues (Paul & Redman, 1997). It

tends to be a cost-effective way of providing health intervention. The intervention

materials provided are in the form of books and booklets. Another type of intervention

used by Healthtrac is the summary of a questionnaire whose results classify individuals

into various health status groupings. Information is also provided which results in an

action plan which will hopefully improve their health status. Within this information is a

personal vitality report offering advice on the individual’s current risk status and

discussing goals to improve health status (Appendix 1). Overall, the program is designed

to improve participants’ lifestyles as well as to increase feelings of personal self-efficacy

and give a sense of appropriate health care ultilisation (Fries et al. 1992). As a result of

this type of intervention program, the industry provider anticipates that health costs in the

form of lower health premiums could be seen in the near future.

The particular health promotion programs that this study will examine are Healthtrac and

Better Health. Health self-efficacy is the basis of the Healthtrac program (health self-

care) which uses health promotion printed materials in an attempt to enhance health self-

efficacy so that costs may be lowered and promote health behaviours that will reduce the

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need for medical services and take individuals out of the high-risk category. Better Health

(medical self-care) uses GPs as a method of health promotion. As a result of the

differences between the two models a number of research questions have been developed.

The research hypotheses to be tested are the following;

The hypothesis is that there will be no differences in variables such as total risk

scores, doctor’s visits, risk of heart disease, risk of cancer and total minutes of

exercise between the two health promotion models and the control group.

This hypothesis will be extended to other variables within the HRA questionnaire.

The hypothesis for the self-efficacy questionnaire is that there will be differences in

self-efficacy scores in variables such as self-management, achievement of outcomes,

management of disease and health self-efficacy scores between health self-care and

medical self-care.

The health self-care model will have lowered their overall health risk scores during

the time of this study more than the medical self-care model.

Differences will occur in health self-efficacy scores among the control and two

experimental groups.

The health self-care model participants will increase their self-efficacy scores more

than the medical model over the duration of the study.

The control group will have lower self-efficacy scores than the two experimental

groups during the time of this study.

There will be a difference in health care costs between all the groups within the study.

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5.0 - Methodology

The aim of this project was to evaluate the process, impact, and outcome effectiveness of

two different health promotion models. All participants in this study were members of a

health benefits organisations and, as such, have paid money for medical insurance.

Consequently, as the participants have been organisationally grouped by the health

benefits organisation and were not randomly appointed to either condition of the study,

the design employed is a parallel quasi-experimental structure. The design also has

elements of a time series because it collects data a number of times within the

experimental period of 12 months. This study was a collaborative effort between

university and industry (health benefits organisation). The health benefits organisation

applied limitations to the study such as momentary especially for the Better Health

experimental group. Other limitations consisted of access to a number of data bases and

the amount of time Healthtrac staff could spent on assisting the author in this study.

The project compared two different philosophies which underpin the models of health

promotion following the use of a common Health Risk Appraisal (HRA) instrument and

was intended to be conducted over 12 months (see appendix 4). It was anticipated that

200 high-risk participants were identified within the pool of 8,000 current Better Health

Model members (medical self-care). Out of those 200 high risk participants, 62

eventually were selected in the medical self-care group. The reason for the small group

was due to cost factors imposed by the health benefits organization. The remaining 138

subjects out of the 200 high risk participants were advised by the health benefits

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organization that they were at high risk and given appropriate health promotion

information for their particular type of condition. Subsequently, 455 Healthtrac Model

members (health self-care) were randomly selected, but matched in disease type to the

specific high risk factor participants identified from the medical self-care group. The

justification for the selection of 455 participants was the sole domain of the health

benefits organization. Finally, a further 344 clients of the health benefits organisation,

who had completed the HRA but were not participants in either the medical self-care or

the health self-care groups, comprised a quasi control group. This group was matched by

disease type and specific high-risk factors. Disease types such as arthritis, diabetes and

high blood pressure were used in this study. Specific risk factors were alcohol, smoking,

lack of exercise, fat intake, salt, sun cancer, fibre intake and stress were used as variables

within this study (appendix 1). Age, gender and educational level were used as matching

variables for all the groups.

The medical self-care group observed the following process. After administration of the

initial HRA, the identification of individuals with a high risk of specific chronic disease

were subsequently referred to the local GP. These GP’s had prior knowledge of the

potential arrival of such referred clients. This had been done by the health benefits

organisation through a series of letters and phone calls. From the GP’s, these patients

gained specialist knowledge concerning the disease of which they are at risk, methods of

treatment and lifestyle counseling for recommended behavioural change. Visits to the

GP for this group was based on need. The cost of these visits was borne by the health

benefits organisation.

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The health self-care group observed the following process. After administration of the

initial HRA, streaming of the participants into normal or specific high-risk categories and

disease types occurred. This group was matched to the medical care group on the same

disease type and specific risk factors (appendix 1). Except that this group was larger (n =

455) than the medical care group. A delivery of specific health information targeted to

the age, gender, and educational level of the individual participant occurred. Information

concerning recommended additions to their lifestyle (e.g. adoption of a light physical

activity program, wearing of sunscreen materials) or the best practice methods of

reducing selected detrimental behaviours, (e.g. cigarette smoking or overuse of saturated

fats in the diet) was delivered. The health promotion materials consisted of booklets and

pamphlets that had been designed to suit the particular high-risk category. These printed

materials were part of the health benefits organization and were not designed by the

author. These printed materials were also given to the GP’s of the medical self-care

group. Some of the health education materials are included in appendix 3. Others

cannot be include due to size of some of these materials and some materials are not for

publication due to company policy. The health self-care materials also provided advice

in appropriate decision making, as to whether a visit to the local GP was advisable.

5.1 - Data gathering

All participants received the HRA questionnaire at the beginning of the project, after 6

months and 12 months later. The HRA is a well validated and a reliable instrument,

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which has been employed more than two million times (Fries et al. 1992). The gathering

of the data was co-ordinated by the health benefits organization and was collated in a

printed form by this organisation. The researcher’s input consisted of collating the printed

data and the organisation of the data into various groups for future analysis as well as

sending out reminders to those participants who had not returned their questionnaires.

Reminder letters were sent out during all facets of the study i.e. at 6 and 12 months. All

the mailing and the administrative work occurred at the health benefits organization. This

was due to the sensitive and confidential nature of the data and cost factors. The health

benefits organisation wanted to have access to their own data at all times.

Once the data had been collated by the researcher at the health benefits organisation it

was subsequently taken out of the organisation and analyzed using the statistical package

SPSS. This data was transformed by the researcher into a workable statistical form using

SPSS. All analysis of the data was performed by the author and not the health benefits

organisation. The health benefits organisation was only responsible for the printing of

the data from the questionnaires.

The study design was a combination of input between the researcher and the health

benefits organisation. The HRA questionnaire had been designed by the health benefits

organisation and was a standard instrument used by that organisation. The health self-

efficacy questionnaire design was solely developed by the author based on Lorig et al.

(1996) model of self-management of arthritis. Input for this questionnaire was also

sought from the author’s principle advisor and Healthtrac staff.

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Process outcomes such as user satisfaction with the program and issues of program

delivery were evaluated via questions within the HRA as well as by components of a

separate questionnaire. This questionnaire was concerned with the satisfaction of the

participants to such items as the type of health promotion information and support offered

by the health benefits organization i.e. follow up phone calls about the information sent to

them. Baseline variables include the health risk score as well as measures of chronic

disease risk factors such as smoking, exercise, dietary fat, alcohol consumption, fiber

intake, perceptions of stress levels and health self-efficacy. Baseline data is where data

are collected in the initial questionnaire. Outcomes were evaluated and measured by

calculating changes from baseline data in variables such as health risk scores as well as

by changes in individual risk factors against the subsequent questionnaires at 6 and 12

months. Long-term economic benefits in terms of number of doctor visits, and number

and dollar amounts of health care claims were determined via questionnaires and through

investigation of the financial records of the health benefits organisation. Health self-

efficacy outcomes were evaluated via a separate questionnaire to participants. In

addition, differences between the health self-care and medical self-care models in change

of scores for all variables were calculated following the final administration of the HRA.

All completed HRA’s from participants of both health promotion models as well as the

control group were mailed (freepost) to the Brisbane Healthtrac office, which was staffed

with personnel to code and input all of the responses.

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In addition to the data gathered by the HRA, focus group discussions were conducted to

provide qualitative support for quantitative results. These focus groups were conducted

by a GP and health educators. These discussion groups would provide feedback and

allow for interactive conversation to occur, thus providing positive reinforcement for

changes in health behaviour. These focus groups were not part of study but acted as a

supportive role for the participants within the health benefits organization.

Changes from the baseline for dependent variables such as health risk score, self report of

medical utilisation (captured as number of doctor visits and hospital days) and indirect

costs as represented by sick days or confined-to-home days were determined by repeated

measures ANOVA. The health risk score was computed from individual health risk data

(such as smoking, saturated fat intake and level of physical activity) and calculated from

a set of algorithms which are based on the Framingham Study and other established risk

factor models (Fries & McShane, 1998). These dependent variables were compared via

dependent t-tests and non-parametric tests such as Freidman’s for differences between the

two models as well as the control group. Basic descriptive data analysis was used to

measure means(M), standard deviations(SD) and range.

Other statistical methods used were correlations and partial correlations, which examined

the relationships between a number of different variables as well as considering the

influence of other variables within that correlation. In addition to the use of ANOVA to

compare the means of the three groups, a post hoc Tukey HSD test was used to find

where the significance lies within the three groups at the .05 level. An effect size (ES)

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was calculated to determine the meaningfulness of the means, which is the difference in

means between questionnaires in the two experimental and control groups. Effect size

(ES) is considered one of the major forms of statistical analysis within inferential

statistics. Effect size statistics provides a quantification of the magnitude of the

association between data and its influence on the significant value obtained (Mullineaux,

Bartlett, & Bennett, 2001). The use of the effect size as a statistical tool is very useful

when groups sizes are small – which is the case for the medical model group. Mullineaux

et al. (2001) believes that reporting (ES) provides readers with the means to interpret the

importance of findings. The interpretation of these findings can be classified according to

Cohen’s (1977) threshold of effect size which suggests that the ES between < -.02 – 0.02

is a trivial effect, > 0.02 - < .50 a small effect size, > .05 - < .80 medium effect and > .80

large effect, which is the same for negative standardized means differences. Not all

findings may be positive such as in total risk scores. Some findings may suggest a

deterioration in scores and the effect size is able to account for changes that occur in both

directions (Middel, Stewart, Bourma, Van Sondera, & Heuval, 2001). Effect size is

performance measure as well as a method of analysis of data over a period of time

(Cohen, 1977). Thus effect size is important in determining not only the mean differences

among the groups but means differences within the group i.e. mean differences between

(Q1) and (Q3).

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5.2 - The medical self-care model

Following the completion of the Health Risk Assessment (HRA) instrument, 62 male

and female adults who were at high risk of developing chronic diseases and participated

in the Better Health model of Health Promotion (initial n = 8,000) were randomly

appointed to participate in this research project. All of the participants received a letter

of advice concerning the presence of certain risk factors as determined by their HRA and

were requested to take the letter to their local GP. General Practitioners in the Division

agreed to participate in the study and had received basic training in health promotion

materials for the different high risk chronic diseases which was provided by a health

benefits organisation. The GP’s agreed to prescribe the necessary behavioural changes

and medical support required for medically satisfactory outcomes. That is provide health

promotion information related to their chronic disease and provide other support advice

such as medications. The participants were requested to follow these action plans to the

best of their ability.

5.3 - Control Group

The intention of this component was to follow over the 12 months a comparison group of

455 adults, who acted as controls for this study. These participants were matched with

those in the medical self-care and the health self-care group on disease type, specific high

risk factors, age, gender, employment and marital status, disease or lifestyle behaviour

and educational level. These participants completed the HRA instrument before, during

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and after the 12-month intervention period as experienced by the medical self-care and

health self-care participants. Subsequent to the initial HRA, the participants received a

letter from the standard Healthtrac program, which identified their health risks and

supplied a nominal level of informed commentary regarding their health status. No

further educational or awareness-raising material was delivered. To overcome the ethical

question of participants being at high risk of disease and not in one of the experimental

groups a section within their HRA questionnaire related to seeking the appropriate advice

from medical professionals was included.

5.4 - Questionnaires

5.4.1 - Self-efficacy questionnaire (Appendix 2)

A health self-efficacy questionnaire was developed using the model of Lorig et al.

(1996). It is a paper and pencil self-assessment instrument. The questions within this

self-efficacy questionnaire were of a closed variety. This questionnaire’s central theme

was principally based on the model of the social learning theory of Albert Bandura

(1977). Self-efficacy is one of the key concepts associated with this model. This

particular questionnaire was divided into a number of sections. Lorig et al., (1996)

describes this construction of measures as a conceptual framework which includes;

one subcategory of behaviour (self-management and three sub-categories

of self-efficacy beliefs; 1. concerning the performance of specific

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behaviours, 2. the management of disease in general, and 3. the

achievement of outcomes)

two sub-categories of outcomes (health status and health care utilization).

The researcher chose this particular instrument because Lorig’s (1996) work was related

to self management of disease. In this particular case arthritis. The questions within her

work had particular emphasis on arthritis therefore similar but different questions were

developed as part of the health self-efficacy questionnaire. The health self-efficacy

questions were designed to examine self management of disease as a broad topic and not

the narrow questions related to self management of arthritis. The conceptual framework

of Lorig et al. (1996) was used as a broad concept to understand the role of self-efficacy

in the self-management of disease.

The questionnaire followed a particular format that began with a general background of

the participants. The general background section consisted of basic questions such as

age, gender, and marital status. This form of question is classified as an attribute

question because it examines the characteristics of the individual. Included in this section

were questions regarding ethnic background, which is very important when it comes to

the use of and types of medical service utilisation. Different ethnic groups utilise medical

services in different ways. Foreign-born individuals tend to be healthier and utilise

medical services less frequently although there are marked variations between immigrant

groups. Virtually nothing is known about the factors that affect their health status and

utilisation of health services (Kliewer & Jones, 1997). Another question considered, how

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many generations ago their forefathers came to this country. There seems to be little

empirical data available on the health status of different generations after immigration to

Australia.

The questions in the next section relate to how individuals would rate their health

currently as compared to 12 months ago. Devins et al. (1983) called these types of

questions the Illness Intrusive Rating Scale (IIRS). The IIRS has five sub-scales:

physical well-being and diet; work and finances; marital, sexual, and family relations;

recreation and social relations and other aspects of life. There are a number of methods

recommended to evaluate the participants responses. One method is to sum the scores of

the individual and then generate a total Perceived Intrusiveness score or to average each

of the sub-scales for the items in that scale. The results were rated on a 5-point Likert

scale, 1 being much better than 12 months ago and 5 much worse than 12 months ago.

The questions that followed this section were concerned with how much illness or

treatment interferes with health, diet, work, active recreation (walking), passive

recreation (playing cards), financial situation, relationship with spouse, sex life, family

relationships, other social relationships, self-expression/self-improvement, religious

expression and community involvement. Therefore, the lower the score the better the

personal health of the person. These questions were also scored on a 1-to-5 Likert scale, 1

being not very much and 5 being very much. Participants were asked to choose an

appropriate number between 1 and 5 and circle the response of how they felt at the

present time about these variables.

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The next four parts of this questionnaire refer to self-efficacy. Section 1 of the self-

efficacy questions focused on self-management of health behaviours. Participants were

asked how confident they were about doing certain activities regularly at the present time.

They were asked to use a 1-to-5 Likert self-efficacy semantically anchored strength scale,

1 being not confident at all and 5 being extremely confident. The reason for the adoption

of a 5-point Likert self-efficacy semantically anchored strength scale instead of a 10 point

Likert self-efficacy semantically anchored strength scale, as suggested by Lorig (1996),

was that a 5 point scale would have the benefit of ease of administration in a community

setting and with individuals with lower literacy skills (Mailbach, & Murphy, 1995). This

is very important because not all migrants have high literacy skills. This may be due to

English being their second language. Kliewer et al. (1997) believe that because some

migrants lack English skills they face barriers in accessing the health care system, which

in turn affects their health status. Another benefit of using a 5-point self-efficacy

semantically anchored scale is the decreased length of time of administration. It is

important to remember that self-efficacy scales must be tailored to specific domains of

functioning, and there are no standard set of domains specific to self-efficacy items

applicable to all people in all situations (Maibach et al. 1995). Questions in this section

concerned activities such as exercise, visits to general practitioners and family support.

The next section of the questionnaire dealt with management of disease(s) in general.

The same 5-point Likert self-efficacy semantically anchored scale was used in this

section as in the previous section. Questions concerning managing health problems in

conjunction with GP visits were the main thrust of this section. Other questions

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concerned making behavioural changes to the individuals health. This set of questions

were behavioural in nature and related to managing health behaviour.

The achievement of outcomes section used the 5-point Likert self-efficacy semantically

anchored scale as used in the previous sections. Here the questions focused on situations

that occur in everyday life and how confident the individual was in performing those

tasks. The last three questions dealt with cognitive functions relating to sadness, feeling

discouraged and feeling lonely.

The final section of the questionnaire was concerned with the capacity to change

unhealthy behaviour and habits. Items 1 and 2 asked whether the participant could set

and achieve goals to improve health and decrease the risk of disease. Items 3, 4 and 5

related to motivation, personal control and adherence. The last set of items dealt with

general aspects of changing health behaviour, such as use of health knowledge, being

financially able to afford to improve health and access to health services.

Overall this questionnaire has 73 items. The original questionnaire of Lorig et al. (1996)

had 90 items but some of these items were discarded due to ambiguity, double negatives

and length of questions. This process was achieved through a pilot study and an

examination of the questions by individuals in this area of study.

Another method was used to examine the internal consistency of the test items. This

method used the coefficient alpha which is an index of internal consistency. It examines

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the inter-relatedness of the individual items within the questionnaire. The alpha

coefficient for this questionnaire ranged from .70 to .80. It has been suggested that tests

designed to be administered to individuals more than once, it would be reasonable to

expect that the test demonstrate reliability across time – in this case test-retest reliability

is appropriate (Gregory, 2000). In this particular questionnaire the test-retest reliability

was .78 and this is considered to be at the low end of reliability.

The self-efficacy component of this questionnaire has been extensively used in research

in areas such as community-based education programs for people with arthritis (Lorig &

Gonzalez, 1992) and The Chronic Disease Self-management Program (Lorig, Laurent &

Gonzalez, 1994). Lorig et al. (1996) used a multi-trait scaling analysis to test and

evaluate each self-efficacy scale. The test for reliability used internal-consistency and

test-retest methods and the results suggest reliability coefficients above .70. Validity

measures used in this case were convergent and discriminate tests, which were part of

multitrait scaling analysis. Construct validity of all resulting scales were examined by

evaluating correlations to determine whether these correlations were low enough to

indicate that the measures were independent. All items in the final self-efficacy scale met

the criteria of item convergence (Lorig et al. 1996). The same method was used to

determine the validity of this questionnaire. The results indicate that the correlations were

low which suggests that the measures were independent.

A number of variables within each of the sections were collapsed, so that an overall

insight could be gained from the questions within that section. An example can be seen

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in section one of the self-efficacy questionnaire, where a number of questions were

related to exercise. By combining these questions (collapsing these variables) a clearer

understanding was gained about exercise and self-efficacy and how it relates to the two

different health promotion models. This method was also done in other sections, where

there were questions on a common theme e.g. doctor’s visits (management of disease).

5.4.2 - Health Risk Assessment Questionnaire (HRA)

Research papers utilizing this instrument have been published in a number of peer

reviewed journals, by Vickery et al, 1988, Fries et al, 1994, Fries 1993 et al, Montgomery

et al, 1994. This was a self-reported health risk assessment paper and pencil

questionnaire developed by James Fries, medical director of Healthtrac programs. It is

based on a predictive model that has been proven to be highly accurate for those

individuals, who may need accelerated support, can be identified by the results of the

questionnaire. The primary endpoint of this questionnaire is to measure health risk

scores. These are computed from individual health habits using algorithms which

approximate the Framingham Multiple Risk Logistic for cardiovascular disease; employs

literature data of other associations between health habits and disease consequences; and

accounts for the relative frequencies of different major medical conditions and causes of

death (Fries, Fries, Parcell & Harrington, 1992; Gazmararian, Foxman, Tze-Ching,

Morgenstern, & Edington, 1991). Because of commercially in confidence issues the

exact nature of these algorithms cannot be published here. The health risk score variable

represents a weighted average of individual health risk behaviours, with the greatest

113

weight given to smoking behaviours, exercise, fat intake, cholesterol, blood pressure and

obesity (Fries, Bloch, Harrington, Richardson, & Beck, 1993). This method of weighted

scores is the result of the Centers for Disease Control (1984) research and Clark et al,

1995.

The health risk scores represent this weighed average of the individual lifestyle

behaviours but they do not include measures that cannot be changed such as age and sex.

Health risk scores have been tested for reliability and validity using a six-month test-

retest questionnaire on participants who were not receiving an intervention. This yielded

an r - score of .79 with a p - value of less than .0001 (Fries et al. 1992). To assess

internal validity, the health risk scores were correlated with smoking behaviour (packs

per day) (r = .65, p < 0.0001) and exercise (minutes per week) (r = .33, p <0.0001) (Fries

et al. 1993).

This part of the questionnaire was mainly the work of Healthtrac with reference to other

studies such as Centres of Disease Control (1984). They used their own calculations to

measures such variables as cost of the various disease such as blood pressure. These were

calculated from claims made by the participants to the health insurance organisation

during the course of the study. The other calculations made with the raw data from the

health benefits organisations were done by the researcher. As was stated in the previous

paragraph how the calculations were determined and what method was used to produce

some of data are the commercial property of the health benefits organization.

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6.0 - Results

The aim of this section is to examine the results from the point of view of outcome

effectiveness, impact and process. The outcome effectiveness variables consist of such

items as changes in health status over the three questionnaires. These items are changes

in exercise patterns, number of doctor’s visits, total risk scores, heart disease risk scores,

cost of disease, days spent in hospital, minutes of exercise per week and cancer risks.

The impact variables are those which will effect health status and the role they play on

the effectiveness variables. The process variables relate to changes in health self-efficacy

and the strength and direction of those changes.

6.1 Health self-care (experimental group)

The total number of subjects (N = 455) in this group comprised 51.4% males (n = 234)

and 48.6% females (n = 221). These subjects contributed the baseline data for

questionnaire 1 (Q1). This group was divided into four age categories (two 20-yearly

interval and two 10 yearly intervals) so that more accurate analysis could occur among

the age groups.

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Table 1 Healthtrac: age groups and types of disease ⎯ baseline data (Q1)

age groups 20-40 40-50 50-60 60-80 over Totalarthritis 9 27 32 28 96back pain 23 8 12 6 49blood pressure 6 18 29 25 78cancer 1 1combined disease 46 25 10 8 89diabetes 2 7 4 10 23heart 4 4 8 16smoking 13 11 15 2 41weight total 17 17 21 7 62Total 116 117 127 95 455

The rank order of health diseases/risk factors within the Healthtrac group is arthritis (ar)

(n = 96), combined risk factors (cr) (n = 89), blood pressure (bp) (n = 78), overweight

(wl) (n = 62), back pain (ba) (n = 49), smoking (sm) (n = 41), diabetes (db) (n = 23) and

heart (ht) (n = 16) (see Table 1). Arthritis accounted for the highest proportion at 21.1%.

This was followed by combined risk of a number of diseases/risk factors such as smoking

and overweight (19%), blood pressure (17%), overweight (13%), back pain (10%),

smoking (9%), diabetes (5%) and heart disease (3%).

The results for the variable ‘cost of disease’ can be seen in Table 2. This is the baseline

data from (Q1). This variable refers to the cost of various diseases to the health insurance

company over one year. The cost is calculated from the number of claims made to the

health insurance company over that period. These results indicate that heart disease (ht)

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(n = 16) generated the highest mean of $1238 and smoking (sm) (n = 40) the lowest mean

of $895. The average mean cost of disease was $971. Table 2 indicates only the mean

cost in dollars of the various health conditions in (Q1). In a separate part of the results

section these costs will be compared with the other data from the other questionnaires.

Table 2. summarises the cost of a number of different diseases across the three

questionnaires. The results for arthritis indicate a difference in the means between (Q1)

(M = $1037) and (Q3) (M = $943). This was not significant. However, there was a

significant difference between (Q1) and (Q3) in total mean costs of disease t(174) = 5.89,

p =.001. The statistical power of this can in doubt due to the low ‘n’ in (Q3); a

consequence of not all subjects having claims during this period.

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Table 2. Cost of disease ($) for the disease types in (Q1- baseline, Q2- 6months and Q3-12 months).

1037 1109 943

94 24 39

284 210 387

925 1013 899

49 8 14

258 315 320

954 895 913

78 24 27

243 184 353

1317

1

.

947 941 942

88 36 34

339 407 542

977 1022 972

23 5 7

233 155 138

1238 1298 1075

16 6 11

437 497 497

895 706 699

40 7 11

210 277 186

994 1044 976

60 28 34

400 417 356

979 994 935

449 138 177

308 349 399

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

type of disease arthritis

back pain

blood pressure

cancer

combined risk

diabetes

heart disease

smoking

weight loss

Total

cost of disease#1

cost of disease#2

cost of disease#3

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There is a significant difference between cost of disease ($) and type of disease in (Q1)

F(8, 440) = 2.83, p = .005. This was also the case in (Q2) F(8, 129) = 2.04, p = .04 (see

Table 3). Tukey’s HSD post hoc analysis indicated that a significant difference occurred

between blood pressure and heart disease in the cost of disease. This indicates that the

cost of heart disease is much greater then the cost of blood pressure.

Table 3. Cost of disease ($) and type of disease in (Q1, Q2, Q3)

2080883.61 8 260110.45 2.83 .005

40465900.31 440 91967.96

42546783.93 448

1867984.81 8 233498.10 2.04 .047

14774098.15 129 114527.89

16642082.96 137

930326.68 8 116290.84 .72 .672

27080128.04 168 161191.24

28010454.72 176

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

cost of disease #1by type of disease

cost of disease #2 by type of disease

cost of disease #3by type of disease

Sum of Squares df Mean Square F Sig.

A number of significant differences occurred between age and the cost of disease; (Q1) F

(3, 445) = 13.63, p = .001 (see Table 4). Tukey’s post hoc analysis indicates that the 60-

80-and-over age group was significantly different from the following age groups: 20−40

(mean difference of $259), 40-50 (mean difference of $171) and 50-60 (mean difference

of $181) (Q1)(see appendix 3). These differences in means indicate that there was a cost

difference between 60-80-and-over and these age groups; with costs being greater for the

60-80-and-over age group (see Table 5).

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Table 4. ANOVA – cost of disease ($) and age (Q1,Q2,Q3).

3592590.95 3 1197530 13.68 .001

38954192.98 445 87537.51

42546783.93 448

1734552.46 3 578184.2 5.20 .002

14907530.50 134 111250.2

16642082.96 137

2777171.69 3 925723.9 6.35 .001

25233283.03 173 145857.1

28010454.72 176

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

cost ofdisease #1 byage

cost of disease#2 by age

cost of disease#3 by age

Sum ofSquares df

MeanSquare F Sig.

Table 5. Mean and SD for cost of disease ($) and age groups.

881 872 853

114 33 35

256 327 349

969 906 856

115 34 39

261 283 395

959 1033 874

127 38 59

322 378 410

1141 1162 1153

93 33 44

341 333 355

979 994 935

449 138 177

308 349 399

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

age20-40

40-50

50-60

60-80 over

Total

cost of disease#1

cost of disease#2

cost of disease#3

220

The impact variable of age was used to examine other factors within this study. Age is

considered to be an important variable, influencing such factors as number of doctors

visits. The results indicate that age played a significant role in the number of doctors

visits per participant. Descriptive data indicates that the mean for the 20−40 age group

for doctors visits in the past six months was 3.70 visits. For other age groups such as

60−80-and-over, the mean was 5.14 doctors visits in the past six months; both of these

are baseline data (see Table 6). The results of the Freidman test indicates that there was a

significant difference between (Q1) and (Q3) in the total mean scores Χ2 (162) = 109.70.

p <.001 (ES = .48). The ES indicates that there is a small but significant result between

the two questionnaires. There is a significant decline in the number of doctors visits

between (Q1) (M = 4.25, SD = 4.84) and (Q3)(M = 2.23, SD = 2.22) irrespective of age

(see Table 6).

Table 6. Mean and SD for age and doctors visits for (Q1, Q2, Q3)

3.70 2.20 1.84

116 44 51

3.16 2.61 1.68

4.65 2.04 2.23

117 50 44

5.94 2.26 2.48

3.71 1.31 1.96

127 71 67

4.69 1.83 1.89

5.14 1.98 3.00

95 47 51

5.11 2.79 2.70

4.25 1.82 2.23

455 212 213

4.84 2.35 2.22

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

AGE1 20-40

2 40-50

3 50-60

4 60-80 over

Total

doctors visits#1

doctors visits#2

doctors visits#3

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Table 7. Doctors visits and (Q1,Q2,Q3)

1106.12 10 110.61 4.50 .001

4965.19 202 24.58

6071.31 212

211.20 10 21.12 4.23 .001

753.44 151 4.99

964.64 161

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

doctors visits #1* doctors visits #3

doctors visits #2 *doctors visits #3

Sum of Squares dfMeanSquare F Sig.

The findings above indicate a similar pattern of increases in doctors visits when age and

number of days in hospital are examined. The mean for the 20−40 age group (Q1) is 1.26

days spent in the hospital with .45 days spent in hospital for (Q2) and .67 days in (Q3). A

lower mean for this variable occurred in the 40−50 (M = .79) but increased in the 50−60

(M = 1.54) age groups (Q1) (see Table 8). Decreases in total means occurred between

(Q1) (M =1.32, SD = 4.72) and (Q3) (M =.62, SD = 1.75) These means were

significantly different in (Q2) F(6, 205) = 2.36, p < .03. For most of the results in Table

6, the SD is larger than the mean. This is due to the small size of ‘n’ within each of the

age groups, as not all subjects spent time in hospital and, in some cases subjects spent

many days in hospital while others spent only one. The same may be said about the

number of doctors visits.

There was a significant difference between the means for days in hospital across the three

questionnaires. These differences occurred between (Q1) and (Q3) F(8, 124) = 6.10, p =

.01 (ES = .67) as well as between (Q2) and (Q3) F(8, 127) = 12.11, p = .01 (ES = .76)

(see Table 9). The effect size (ES) in both of these cases was considered to be moderate.

The results indicate that between (Q1) and (Q3) there was significant difference; this

122

difference being a higher (Q1) total mean (M = 1.32) than (Q3) (M = .62). This would

indicate that there has been a significant reduction in days spent in hospital from (Q1) to

(Q3) probably as a result of the Healthtrac program. Using a repeated measures analysis,

the results indicate that age was a significant factor in the number of days spent in

hospital across the three questionnaires F(3, 121) = 3.48, p = .018 (see Table 10).

Table 8. Number of days spent in hospital by age group (Q1, Q2, Q3).

1.26 .45 .63

74 31 30

2.55 1.75 1.83

.79 .00 .29

78 40 31

2.81 .00 .69

1.54 .37 .38

87 57 47

6.72 1.41 1.11

1.77 .26 1.19

60 42 36

5.24 .91 2.68

1.32 .27 .62

299 170 144

4.72 1.20 1.75

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

AGE1 20-40

2 40-50

3 50-60

4 60-80 over

Total

hospital days #1 hosptial days #2 hosptial days #3

Table 9. ANOVA -days spent in hospital (Q1, Q2, Q3)

533.80 8 66.73 6.10 .01

1356.59 124 10.94

1890.39 132

56.44 8 7.06 12.11 .01

73.96 127 .58

130.40 135

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

hospital days #1 * hosptial days #3

hosptial days #2 * hosptial days #3

Sum of Squares df Mean Square F Sig.

Table 10. Repeated measure for days in hospital and age

123

Tests of Between-Subjects Effects

Measure: MEASURE_1

Transformed Variable: Average

103.58 1 103.58 13.47 .001

80.32 3 26.77 3.48 .018

930.42 121 7.69

SourceIntercept

age

Error

Type III Sumof Squares df Mean Square F Sig.

Within the exercise part of the questionnaire, the results indicate that the category ‘other’

produced the highest mean of 132 minutes per week. ‘Other’ in this case refers to

physical activity not of a structured nature such as gardening. This was followed by

walking with a mean of 122 minutes per week. One of the lowest means reported was for

swimming with a mean of 16 minutes per week (see Figure 2).

Examination of the variable ‘minutes of exercise per week’ in relation to age groups,

indicated that the 60−80-and -over age group had the highest mean for ‘other’ types of

exercise (169 minutes per week), but the results indicate aerobic exercise produced the

lowest mean (7 minutes per week) of all the age groups. The same age group results

indicated that biking had one of the lowest mean (12 minutes per week). For activities

such as biking, the 40−50 age group had the highest mean (52 minutes per week). Also

this age group recorded one of the lowest means for swimming (15 minutes per week)

(see Figure 3).

124

0 1 0 0 2 0 0 3 0 0 4 0 0

w a lk in g

o th e r

s w im m in g

b ik in g

jo g

a e r o b icty

pes

of a

ctiv

ity

n u m b e r o f m in u t e s

NM e a n

Figure 2. Mean number of minutes of exercise per week for different types of exercise

In the 20−40 age group, ‘other’ was the activity most participated with a mean of 102

minutes per week. This compared to activities such as swimming (19 minutes per week)

or biking (10 minutes per week).

‘Other’ was the preferred exercise type for the 40−50 age group (156 minutes), followed

by walking (104 minutes) (see Figure 3). Swimming (15 minutes) and aerobics (16

minutes) were the activities with the lowest rates of participation compared to other

exercise such as biking (52 minutes per week).

125

020406080

100120140160180

20-40 40-50 50-60 60-80over

age groups

num

ber o

f min

s ex

erci

se

per w

eek

w alking

other

sw im

bike

jog

aerobic

Figure 3. Mean number of minutes per week of different types of exercise by age group

A number of risk scores were determined from combinations of variables within the

questionnaire. Individuals were classified into the risk categories: mild, moderate and

severe (Fries et al. 1992) using the HHRA questionnaire. The heart disease scores were

calculated from participants’ responses to questions on diet, exercise, smoking and stress,

and if available, blood pressure and cholesterol measurements. A score of <= 21 was

considered to be mild, 22 < = 51 was moderate and 52 < = 76 was severe. This

classification of scores was determined from Framingham Multiple Risk Logistic for

cardiovascular disease (Centres for Disease Control, 1984). Analysis of this variable,

heart disease scores, indicated that 50 participants were ranked in the mild category, 217

participants in the moderate category and 77 participants in the severe category. When

the gender was analysed, the mild category comprised 32 males and 28 females, the

moderate category 156 males and 161 females, and the severe category 46 males and 31

females (see Table 11). This is baseline data from (Q1).

126

Table 11. Heart disease risk scores, category and gender

GENDER Mild >=21 moderate >=51 severe >=76male 32 156 46female 28 161 31

Gender risk of heart disease risk scores indicate that both male and female (Q1) scores

(M =36.56, SD = 14.23) (M = 36.48, SD = 13.10) were very similar. Males by (Q3) had

lowered their scores (M = 21.60, SD = 19.13) more than females (M = 23.13, SD =

17.22), however this gender difference was not significant (see Table 12). There were

significant differences between the means for total heart disease risk scores for age for

(Q1) F(3, 451) = 4.16, p = .006 and for (Q2) F(3, 166) = 3.55, p = .01 (see Table 13).

These significant differences between the means occurred for the 60−80-and-over and

20−40 as well as the 40-50 age groups.

Table 12. Gender and heart disease risk scores for (Q1, Q2, Q3).

36.56 31.36 21.60

234 81 65

14.23 14.39 19.13

36.48 33.16 23.13

221 89 71

13.10 13.23 17.22

36.52 32.30 22.40

455 170 136

13.68 13.78 18.11

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

gender male

female

Total

risk of heartdisease #1

risk of heartdisease #2

risk of heartdisease #3

127

Table 13. Risk of heart disease scores and age for questionnaires (Q1, Q2, Q3)

2287.69 3 762.56 4.16 .006

82657.82 451 183.28

84945.51 454

1937.03 3 645.68 3.55 .016

30152.67 166 181.64

32089.70 169

55.32 3 18.44 .06 .983

44201.24 132 334.86

44256.56 135

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

risk of heartdisease #1 andage

risk of heartdisease #2 andage

risk of heartattack #3 andage

Sum ofSquares df

MeanSquare F Sig.

The cancer risk scores were calculated from the participant’s responses to questions on

smoking, alcohol, weight and fat. The same three scoring indexes were used as for the

heart risk scores. The three categories and their sample sizes were mild (n = 389),

moderate (n = 47) and severe (n = 16). This was further broken down using the age–

gender variable. In the mild category there were 188 females and 201 males, in the

moderate category 28 females and 19 males and in the severe category 4 female and 12

males (see Table 14). A repeated ANOVA results indicate that age was significant in

cancer risk scores F(1, 3) = 4.39, p = .005 (see Table 15).

128

Table 14. Cancer risk category and gender.

mild moderate severemale 201 19 12female 188 28 4

Total 389 47 16

Table 15. Repeated measures for risk of cancer scores and age

Tests of Between-Subjects Effects

Measure: MEASURE_1

Transformed Variable: Average

113453.38 1 113453.38 169.06 .001

8839.07 3 2946.36 4.39 .005

104014.90 155 671.06

SourceIntercept

age

Error

Type III Sumof Squares df Mean Square F Sig.

The outcome variable results for the total risk scores indicate that scores

decrease with subject’s age. Total risk scores are calculated on algorithms based on age,

sex, and known effects of major risk factors such as smoking and exercise on health.

These decreases are noticeable after the 20-40 age group. The 20−40 age group had the

highest mean of (M = 24.56, SD = 9.77)(Q1). The lowest mean occurred in the 60−80-

and-over age group (M =19. 22, SD = 9.77)(Q1) (see Figure 4 and Table 16). Decreases

in total risk scores occurred for all age groups from (Q1) to (Q3). The largest decreased

occurred in the 20-40 age group (Q1) (M = 24.26, SD = 9.77) - (Q3) (M = 12.41, SD =

10.94) (see Table 16).

Table 16. Total risk scores and age groups (Q1,Q2,Q3).

129

24.56 23.87 12.41

116 31 29

9.77 10.74 10.94

24.18 20.70 15.62

117 40 29

10.19 9.41 16.03

22.85 18.77 13.53

127 57 43

10.60 9.11 10.51

19.72 16.76 13.43

95 42 35

9.55 6.73 8.55

22.97 19.66 13.71

455 170 136

10.20 9.24 11.49

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

AGE1 20-40

2 40-50

3 50-60

4 60-80 over

Total

total risk score#1

total risk scores#2

total risk scores#3

35432929 35432929 35432929N =

age groups

60-80 over50-6040-5020-40

Mea

n to

tal r

isk

scor

es

40

30

20

10

0

total risk score #1

total risk scores #2

total risk scores #3

Figure 4. Total mean risk scores and age groups (Q1, Q2, Q3).

130

These results indicate that there is a significant difference between age groups and total

risk scores (Q1) F(3, 451) = 4.84, p = .003 (see Table 17). Mean total risk scores were

also statistically significant between (Q1) and (Q3) t (135) = 9.34, p = .001 (ES = .88)

and between (Q2) and (Q3) t(135) = 6.49, p = .001(ES = .82). The ES in both of these

cases was large and this indicates that the treatment played a significant role in the

reduction of total risk scores.

Table 17. Total risk scores, age groups for questionnaires (Q1, Q2, Q3)

1472.39 3 490.80 4.84 .003

45735.29 451 101.41

47207.68 454

990.67 3 330.22 4.08 .008

13423.54 166 80.86

14414.21 169

158.69 3 52.90 .40 .757

17663.13 132 133.81

17821.82 135

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

total risk score #1 *age

total risk scores #2* age

total risk scores #3* age

Sum of Squares dfMean

Square F Sig.

131

This part of the analysis used the results from all three questionnaires and correlations

were between the variables related to total risk scores which included the number of

cigarettes smoked per day, kilograms over ideal body weight, number of minutes walking

and obesity score. Total risk scores (Q1) positively correlated with variables such as

packs of cigarettes per day (r = .67, p < .01) and obesity scores (r = .18, p < .01) are

reported in Table 18). This means that as scores for variables (such as obesity) increase or

decrease, so does total risk score. Table 18 shows a negative correlation between number

of minutes walking and total risk scores (r = −.25, p < .01). This indicates that as

individuals increase the total number of minutes walking per week, their total risk score

decrease.

Table 18. Correlation matrix ⎯ ideal weight in kilograms, obesity score, number of

cigarettes smoked per day, number of minutes walking per week and total risk score

132

1.00 .28** -.06 .04 .11*

. .00 .28 .48 .03

393.00 393.00 392.00 262.00 393.00

.28** 1.00 -.08 -.02 .18**

.00 . .09 .74 .00

393.00 455.00 453.00 300.00 455.00

-.06 -.08 1.00 .00 .67**

.28 .09 . .93 .00

392.00 453.00 453.00 298.00 453.00

.04 -.02 .00 1.00 -.25**

.48 .74 .93 . .00

262.00 300.00 298.00 300.00 300.00

.11* .18** .67** -.25** 1.00

.03 .00 .00 .00 .

393.00 455.00 453.00 300.00 455.00

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

ideal weight inkilograms

obesity score #1

packs ofcigarettes per day

number ofminutes walkingper week #1

total risk score#1

ideal weight inkilograms

obesity score#1

packs ofcigarettesper day

number ofminutes walking

per week #1

totalrisk

score #1

Correlation is significant at the 0.01 level (2-tailed).**.

Correlation is significant at the 0.05 level (2-tailed).*.

The cost of disease was positively correlated to a number of variables one being age

within the three questionnaires, (Q1) (r = .26, p < .01), (Q2) (r = .31, p < .01) and (Q3) (r

= .25, p < .01). This indicates that there is a strong positive relationship between the cost

of disease and age (see Table 19).

Table 19. Correlation matrix for age, gender and cost of disease ($) (Q1, Q2, Q3)

133

1.00 .02 -.10 -.04 -.06

. .70 .25 .60 .24

455 449 138 177 455

.02 1.00 .81** .68** .26**

.70 . .01 .01 .01

449 449 138 174 449

-.10 .81** 1.00 .85** .31**

.25 .01 . .01 .01

138 138 138 111 138

-.04 .68** .85** 1.00 .25**

.60 .01 .01 . .01

177 174 111 177 177

-.06 .26** .31** .25** 1.00

.24 .01 .01 .01 .

455 449 138 177 455

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

gender

cost of illness#1

cost of illness#2

cost of illness#3

age

gender cost of

illness #1 cost of

illness #2 cost of

illness #3 age

Correlation is significant at the 0.01 level (2-tailed).**.

The influence of the age variable on the risk of various diseases such as cancer, heart

disease and total risk scores was considered. The results indicate that a number of

negative correlations occurred between these variables and age. For the risk of cancer

scores, a negative correlation was found for (Q1) r = −.16, p < .01 as well as for (Q1)

total risk scores (r = −.16, p < .01). The results between total risk scores (Q1) and risk of

heart disease (Q1) indicates that there is a strong relationship between these two variables

134

r = .88, p < .01 (see Table 20). Higher total risk scores are correlated with higher risk of

heart disease scores.

Table 20. Correlation matrix ⎯ age, risk of cancer, risk of heart disease, total risk scores

and alcohol consumption for (Q1)

1.00 .00 -.14** -.16** -.16**

. .99 .01 .01 .01

455 450 455 455 455

.00 1.00 .16** .04 .22**

.99 . .01 .39 .01

450 450 450 450 450

-.14** .16** 1.00 .44** .80**

.01 .01 . .01 .01

455 450 455 455 455

-.16** .04 .44** 1.00 .88**

.01 .39 .01 . .01

455 450 455 455 455

-.16** .22** .80** .88** 1.00

.01 .01 .01 .01 .

455 450 455 455 455

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

age

alcoholconsumption

risk of cancer#1

risk of heartattack #1

total riskscore #1

age alcohol

consumption risk of

cancer #1

risk ofheart

disease #1

totalrisk score

#1

Correlation is significant at the 0.01 level (2-tailed).**.

135

6.2 Medical self-care (experimental group)

The total number of subjects (N = 66) comprised 53% males (n = 35) and 47% females (n

= 31). The subjects were divided into four age categories, two covered a 10-year interval

and the other two a 20-year interval (see Figure 5). This group was smaller due to cost

factors (refer to Methods section).

age

60-80 over50-6040-5020-40

Freq

uenc

y

40

30

20

10

0

Figure 5. Frequency and age groups

The cost of the various diseases is outlined in Table 22. Heart disease incurred a mean

cost of $1404 (SD = $251) which was on of the highest, and one of the lowest mean costs

of $757 (SD = $204) was associated with smoking . Some of the results are not valid due

to the low number of subjects within each disease group. Results may not be significant

when applied to the broader population. These results are related to the medical self-care

model group and are different to the health self-care group.

1367

Table 21. Cost of various diseases ($)

PRECOST1 pre cost of illness #1

903.33 9 232.64

1076.71 14 435.45

929.60 10 197.42

1472.50 2 550.84

869.64 14 232.66

1266.33 3 327.29

1404.00 3 251.73

757.67 3 204.67

835.11 9 277.84

969.94 66 329.00

Type of diseasear arthritis

ba back pain

bp blood pressure

cc cancer

cr combined risk

db diabetes

ht heart disease

sm smoking

wl weight loss

Total

Mean N Std. Deviation

Analysis of the total risk scores for both males and females indicate that there is a decline

in those scores. For instance, the male mean total risk scores decreased from (Q1) (M =

19.94, SD = 10.95) to (Q3) (M = 17.53, SD = 7.38). This is the same for the females:

(Q1) (M = 19.55, SD = 8.05) to (Q3) (M = 15.44, SD =7.68) (see Table 22). A significant

difference could not be determined because both male and female variables did not reach

the specified .05 significance level.

Table 22. Total risk scores for gender

19.94 17.66 17.53

35 35 19

10.95 7.82 7.38

19.55 15.29 15.44

31 31 16

8.05 4.19 7.68

Mean

N

Std. Deviation

Mean

N

Std. Deviation

gendermale

female

total riskscore #1

total riskscores #2

total riskscores #3

137

For the outcome variable number of doctors visits the results indicate that there was a

steady increase in the mean for male subjects: (Q1) (M = 3.14, SD = 2.87), (Q2) (M =

3.31, SD = 3.95) and (Q3) (M = 4.79, SD = 5.97). The results for the female subjects

indicated a different trend. A decrease in the number of doctors visits occurred between

(Q1) (M = 4.26, SD = 2.98) and (Q2) (M = 3.06, SD = 2.45) with an increase in the

number of doctors visits in (Q3) (M = 3.50, SD = 2.48) (see Table 23). There were no

significant differences between the gender groups.

Table 23. Gender, doctors visits and questionnaires (Q1, Q2, Q3)

3.14 3.31 4.79

35 35 19

2.87 3.95 5.97

4.26 3.06 3.50

31 31 16

2.98 2.45 2.48

Mean

N

Std. Deviation

Mean

N

Std. Deviation

gendermale

female

doctors visits #1 doctors visits #2 doctors visits #3

The number of days spent in hospital results indicate that a different pattern occurred for

males and females. Males spent more days in hospitals then females: (Q1) (M = 1.83, SD

= 7.34) for males compared to (Q1) (M = .65, SD = 1.52) for the female. Generally, these

results indicate that females spent less time in hospitals than males across all of the

questionnaires (see Table 24). The SDs for this variable were much greater than the

means and this could be due to the small ‘n’ in both the female and male groups. Again

no significant differences were found between the gender groups.

138

Table 24. Mean and SD for gender, days in hospital (Q1, Q2, Q3)

1.83 1.14 2.47

35 35 19

7.34 3.72 8.66

.65 1.13 .75

31 31 16

1.52 5.38 3.00

1.27 1.14 1.69

66 66 35

5.44 4.54 6.66

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

gender male

female

Total

hospital days #1 hospital days #2 hospital days #3

The results for risk of heart disease scores indicate that the overall mean for all the

questionnaire decreased: (Q1) (M = 32.79, SD = 13.30), (Q2) (M = 27.85, SD = 10.44)

and (Q3) (M = 28.51, SD = 12.18). This could not be said for the male subjects within

this study. The results for males indicated that risk of heart disease scores remained the

same across all of the questionnaires: (Q1) M = 31.11, (Q2) M = 29.31 and (Q3) M =

30.89. Whereas the female subjects’ risk of heart disease scores decreased from the

baseline questionnaire: (Q1) M = 34.68, (Q2) M = 26.19 and (Q3) M = 25.69 (see Table

25). The results for the outcome variable risk of heart disease score and gender showed

that a significant difference occurred between (Q1) and (Q2) for females: F(1, 30) = 3.36,

p < .05 (ES = .80). No significant differences occurred in the male group between the

questionnaires.

139

Table 25. Mean risk of heart disease scores by gender

31.11 34.68 34.68

35 31 31

13.69 12.82 12.82

29.31 26.19 26.19

35 31 31

12.41 7.50 7.50

30.89 25.69 25.69

19 16 16

13.44 10.19 10.19

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

risk of heartdisease #1

risk of heartdisease #2

risk of heartdisease #3

male female Total

GENDER

The cost of disease increased with age. The results indicate that as age increases, the cost

of the treatment of disease increases: 20−40 age group (M = $787, SD = $297), 40−50

(M = $892, SD = $264), 50−60 (M = $993, SD = $401) and 60−80-and-over (M = $1041,

SD = $307) (see Table 26). There was a decrease in the overall mean from (Q1) (M =

$969, SD = $329) to (Q2) (M = $897, SD = $352). This trend did not continue to (Q3).

The results indicate that there was an increase in the overall mean in (Q3) (M = $947, SD

= $331) compared to (Q2).

140

Table 26. Cost of diseases and age groups and (Q1, Q2, Q3)

787.50 667.25 658.67

8 8 6

297.69 170.73 156.77

892.77 790.15 816.50

13 13 10

264.44 330.67 236.58

993.75 797.50 919.43

16 16 14

401.95 281.62 411.27

1041.72 1065.00 1074.85

29 29 27

307.68 369.09 291.85

969.94 897.80 947.54

66 66 57

329.00 352.30 331.69

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

age 20-40

40-50

50-60

60-80over

Total

cost ofdisease #1

cost ofdisease #2

cost ofdisease #3

The cost of the various diseases also followed the same pattern as the age groups.

Arthritis mean costs increased between (Q1) (M = $903, SD = $232) and (Q3) (M = $973,

SD = $287). The same pattern was also found for blood pressure (Q1) (M = $929, SD =$

197) and (Q3) (M = $984, SD = $414) (see Table 27).

141

Table 27. Cost of various diseases ($) (Q1, Q2, Q3)

903.33 1077.11 973.00

9 9 8

232.64 415.52 287.86

1076.71 787.57 979.58

14 14 12

435.45 230.63 423.11

929.60 958.50 984.00

10 10 10

197.42 276.77 414.54

1472.50 1307.00 1219.50

2 2 2

550.84 134.35 153.44

869.64 736.79 790.92

14 14 13

232.66 226.92 202.72

1266.33 1426.00 1355.50

3 3 2

327.29 791.00 .71

1404.00 1134.50 1098.50

2 2 2

251.73 51.62 27.58

757.67 622.33 598.00

3 3 2

204.67 92.50 79.20

835.11 845.22 967.67

9 9 6

277.84 344.82 289.38

969.94 897.80 947.54

66 66 57

329.00 352.30 331.69

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Type of diseasear arthritis

ba back pain

bp blood pressure

cc cancer

cr combined risk

db diabetes

ht heart disease

sm smoking

wl weight loss

Total

cost ofdisease #1

cost ofdisease #2

cost ofdisease #3

A repeated measures analysis indicated that there was a significant difference between

cost of disease and age: F(1, 3) = 5.04, p = .004 (see Table 28). A post hoc Tukey’s HSD

analysis indicated that a significant difference occurred between the 60−80-and-over and

142

20−40 and the 40−50 age groups, p < .05. The results show that the cost of disease

increases as age increases (see Figure 6).

Table 28. Repeated measures ⎯ cost of disease and age

Measure: MEASURE_1

Transformed Variable: Average

100658231.64 1 100658232 567.46 .000

2683378.48 3 894459.49 5.04 .004

9401326.51 53 177383.52

SourceIntercept

age

Error

Type III Sumof Squares df Mean Square F Sig.

age groups

60-80 over50-6040-5020-40

Cos

t of d

isea

se ($

)

1200

1100

1000

900

800

700

600

500

Q1

Q2

Q3

Figure 6. Cost of disease ($) and age groups (Q1,Q2,Q3).

A number of correlations were determined from the Medical model data. The results for

some outcome variables such as number of doctors visits and age indicate significant

positive correlations: (Q2) (r = .32, p < .01). The results also indicate that other

significant positive correlations occurred between doctors visits (Q1) and (Q2) (r = .49, p

143

< .01) (see Table 29). This suggests that individuals who visited the doctor in (Q1) were

also likely to visit the doctor in (Q2).

Table 29. Correlation matrix of age and doctors visits

1.00 .49** .39* .19

. .00 .02 .13

66 66 35 66

.49** 1.000 .22 .33**

.00 . .20 .01

66 66 35 66

.39* .22 1.00 .20

.02 .20 . .25

35 35 35 35

.19 .33** .20 1.00

.13 .01 .25 .

66 66 35 66

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

doctors visits #1

doctors visits #2

doctors visits #3

age

doctorsvisits #1

doctorsvisits #2

doctorsvisits #3 age

Correlation is significant at the 0.01 level (2-tailed).**.

Correlation is significant at the 0.05 level (2-tailed).*.

Total risk scores and risk of heart disease results indicate that a strong positive correlation

occurred between these two variables (r = .87, p < .01). Also a strong positive

correlation occurred between risk of cancer and total risk scores (r = .77, p < .01) (see

Table 30). The increased risk of cancer and heart disease produces an increase in total

risk scores.

144

Table 30. Correlation of total risk scores, cost of disease, risk of heart disease and risk of

cancer.

1.00 .40** .77** -.19

. .01 .01 .12

66 66 66 66

.40** 1.00 .87** -.09

.01 . .01 .49

66 66 66 66

.77** .87** 1.00 -.18

.01 .01 . .16

66 66 66 66

-.19 -.09 -.18 1.00

.12 .49 .16 .

66 66 66 66

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

risk of cancer #1

risk of heartdisease #1

total risk score #1

cost of disease #1

risk ofcancer #1

risk ofheart

disease #1total riskscore #1

cost ofdisease #1

Correlation is significant at the 0.01 level (2-tailed).**.

145

7.4 Control group The control group consisted of a total of 344 subjects (N =344) which comprised males (n

=205) and females (n = 139). Males accounted for 59% of the subjects and females 40%

(see Table 31).

Table 31. Gender and frequency of participants

205 59.6

139 40.4

344 100.0

male

female

Total

ValidFrequency Percent

The frequency for the different age group can be seen in Figure 7. The pattern of

distribution for these ages groups is similar, that is that all the groups have ‘n’s that are

about equal.

146

7678

8082

8486

8890

92

agegroups

20-40 40-50 50-60 60-80over

age groups

freq

uenc

y

Figure 7. Frequency and age groups The cost of disease in each age group can be seen in Table 32. The results indicate that

costs are lower in the 20−40 age group and remain constant in the 40−50, 50−60 and

increases in 60−80-and-over age groups. As can be seen in Table 32, costs increase

slowly as the population ages. In some age groups, cost of disease increased between

(Q1) and (Q3) such as the 50−60 group: (M = $972, SD = $308) and (M = $1011, SD =

$270). In other age groups, the cost of disease decreased such as in the 20-40 group: (Q1)

(M =$ 928, SD = $ 246) to (Q3) (M = $834, SD = $269). The mean overall costs of

disease remained constant between the questionnaires: (Q1) (M = $976, SD = $273), (Q2)

(M = $948, SD = $296) and (Q3) (M = $964, SD = $310) (see Table 32). The overall

mean cost of disease did not change significantly over the period of the questionnaires,

but different age groups show some variation over the same period of time.

147

There were some gender differences in the cost of disease over the period of the three

questionnaires. In (Q1) the females had a greater mean cost (M = $1002, SD = $290) than

the males (M = $957, SD = $260). This changed over the period of the questionnaires

with (Q3) having a greater mean cost of disease for males: (M = $978, SD = $308) (see

Table 33). These results were not significantly different.

Table 32. Cost of disease and age groups (Q1, Q2, Q3)

928.66 868.90 834.97

88 20 36

246.27 196.47 269.53

945.72 868.50 925.53

81 18 30

245.39 321.48 292.66

972.48 917.43 1011.92

91 28 49

308.75 238.35 270.85

1060.67 1042.67 1024.89

82 42 57

271.94 340.25 353.08

976.01 948.99 964.12

342 108 172

273.59 296.58 310.60

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

age 20-40

40-50

50-60

60-80over

Total

cost of disease#1

cost of disease#2

cost of disease#3

148

Table 33. Gender and cost of disease for (Q1, Q2, Q3)

957.91 989.16 978.72

204 37 82

260.80 315.70 308.83

1002.77 928.06 950.81

138 71 90

290.36 286.18 313.33

976.01 948.99 964.12

342 108 172

273.59 296.58 310.60

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

gender male

female

Total

cost of disease#1

cost ofdisease #2

cost ofdisease #3

ANOVA results for cost of disease and age indicate there is a significant difference

between these two variables: (Q1) F(3, 338) = 3.93, p < .05, (Q2) and (Q3) F(3, 168) =

3.49, p = .019 (see Table 34). Tukey’s post hoc analysis indicated a significant difference

between the 60-80 and over and the 20-40 age groups, p< .05.

Table 34. ANOVA cost of disease and age for (Q1, Q2, Q3)

860502.88 3 286834.29 3.93 .009

24663307.08 338 72968.36

25523809.95 341

641358.50 3 213786.17 2.54 .061

8770578.49 104 84332.49

9411936.99 107

967600.19 3 322533.40 3.49 .017

15529135.48 168 92435.33

16496735.67 171

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

cost of disease#1 * age

cost of disease#2 * age

cost of disease#3 * age

Sum of Squares df Mean Square F Sig.

149

Mean total risk scores and gender results indicate that male and female scores decreased

between the questionnaires. The baseline mean for males (Q1) (M = 23.51, SD = 11.60)

was similar to the female baseline score (Q1) (M = 23.41, SD = 11.16). Male mean

scores decreased between all of the questionnaires, whereas female mean scores

decreased between (Q1) (M = 23.41, SD = 11.16) and (Q2) (M = 20.57, SD = 10.39) but

increased in (Q3) (M = 21.11, SD = 11.16). There was no significant difference between

males and females on mean total risk scores. Overall total risk mean scores decreased

over the period of the study but were not significant (see Table 35).

Table 35. Gender and total risk scores for (Q1, Q2, Q3)

23.51 19.65 18.73

205 91 74

11.60 10.05 9.81

23.41 20.57 21.11

138 96 71

11.16 10.39 11.16

23.47 20.12 19.90

343 187 145

11.41 10.21 10.52

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

gendermale

female

Total

total riskscore #1

total riskscores #2

total riskscores #3

Gender and doctors visits results indicate that female visits decreased over a period of

time, from (Q1) (M = 4.54, SD = 4.43) to (Q3) (M = 2.97, SD = 3.56). This result was

statistically significant: t(175) = 2.58, p = .01(2-tailed). Male visits to the doctor

remained constant over this period of time. Overall the mean results indicate a decline in

the number of doctors visits: (Q1) (M = 4.07, SD = 4.17) (Q3) (M = 3.22, SD = 2.90) (see

Table 36). Females reduced their visits to the doctor over the period of the study,

150

whereas male visits remained constant, but these results were not significant. The SDs in

both gender groups were higher than the mean in some cases and this could be the results

of number of cases in each group.

Table 36. Mean and SD for gender and number of doctors visits for (Q1, Q2, Q3)

3.75 2.09 3.37

205 103 107

3.97 2.64 2.40

4.54 1.46 2.97

138 71 68

4.43 2.37 3.56

4.07 1.83 3.22

343 174 175

4.17 2.54 2.90

Mean

N

Std. Deviation

Mean

N

Std. Deviation

Mean

N

Std. Deviation

gendermale

female

Total

doctorsvisits #1

doctorsvisits #2

doctorsvisits #3

The results of days spent in hospital by different age groups indicate that there was an

overall increase in the number of days spent in hospital from (Q1) (M = .92, SD = 2.91)

to (Q3) (M = 1.89, SD = 2.62). This result was statistically significant: t(127) = 4.20, p =

.01 (2-tailed). The 20−40 age group mean for this variable was (Q1) (M = .81 SD = 2.22)

and (Q3) (M = 1.89, SD = 2.98) which means they spent more time in hospital. In

contrast, the 50−60 age group spent less time in hospital (Q1) (M = 1.43, SD = 4.43) and

(Q2) (M = .75, SD = 1.64). There were increases in days spent in hospital for most age

groups (see Table 37). No significant differences were found between the age groups, p

> .05. The SD was again greater than the mean.

151

Table 37. Mean and SD for different age groups and hospital visits (Q1, Q2, Q3)

.81 .48 1.89

88 31 19

2.22 1.12 2.98

.54 1.12 2.04

81 34 27

1.39 2.00 2.23

1.43 .75 1.95

91 44 41

4.43 1.64 2.44

.87 1.04 1.73

83 52 40

2.50 3.42 2.93

.92 .87 1.89

343 161 127

2.91 2.36 2.62

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

Mean

N

Std.Deviation

age 20-40

40-50

50-60

60-80over

Total

hospital days#1

hospital days#2

hospital days#3

There was a positive significant relationship between age and cost of disease for (Q1) and

age (r = .17, p < .01) and (Q3) and age (r = .27, p < .01). There were also other

significant positive relationships between the questionnaires such as (Q1) and (Q2) (r =

.70, p < .01 (see Table 38). These results indicate that age plays a significant role in the

cost of disease. This result is not unexpected because this is the control group for which

there was not a treatment effect.

152

Table 38. Correlation matrix ⎯ age and cost of diseases for (Q1, Q2 ,Q3)

1.00 .17** .24* .23**

. .01 .01 .01

344 342 108 172

.17** 1.00 .75** .55**

.01 . .01 .01

342 342 108 171

.24* .75** 1.00 .89**

.01 .01 . .01

108 108 108 107

.23** .55** .89** 1.00

.01 .01 .01 .

172 171 107 172

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

age

cost ofdisease #1

cost ofdisease #2

cost ofdisease #3

age cost of

disease #1 cost of

disease #2 cost of

disease #3

Correlation is significant at the 0.01 level (2-tailed).**.

Correlation is significant at the 0.05 level (2-tailed).*.

Table 39 shows a number of correlations which relate to (Q1) and indicate that there are a

number of strong positive relationships between variables such as cost of disease and

doctors visits (r = .73, p < .01). Cost of disease also had a positive significant relationship

with days spent in hospital (r = .48, p < .01) and days missed work (r = .32, p < .01) (see

Table 39).

153

Table 39. Correlation matrix ⎯ total risk scores, doctors visits, days in hospital, days

missed work, risk of heart disease and cost of illness for (Q1) only

1.00 .88** .02 .09 -.01 .04

. .01 .75 .10 .91 .47

343 343 342 343 343 343

.88** 1.00 .02 .07 .04 .03

.01 . .77 .19 .46 .58

343 343 342 343 343 343

.02 .02 1.00 .33** .48** .73**

.75 .77 . .01 .01 .01

342 342 342 342 342 342

.09 .07 .33** 1.00 .32** .44**

.10 .19 .01 . .01 .01

343 343 342 343 343 343

-.01 .04 .48** .32** 1.00 .30**

.91 .46 .01 .01 . .01

343 343 342 343 343 343

.04 .03 .73** .44** .30** 1.00

.47 .58 .01 .01 .01 .

343 343 342 343 343 343

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

risk of heartdisease #1

total riskscore #1

cost ofdisease #1

days missedwork #1

hospitaldays #1

doctorsvisits #1

risk ofheart

disease #1 total riskscore #1

cost ofdisease #1

daysmissed

work #1 hospitaldays #1

doctorsvisits #1

Correlation is significant at the 0.01 level (2-tailed).**.

154

The results for risk of cancer scores indicate that some positive significant correlations

exist between a number of variables such as total risk scores (r = .80, p < .01), risk of

heart disease scores (r = .50, p < .01) and number of cigarettes per day (r = .93, p < .01).

Total risk scores results also indicate a positive significant relationship with variables

such as packs of cigarettes per day (r = .71, p < .01) and weight (r = .21, p < .01) (see

Table 40). The act of smoking more cigarettes per day, or even smoking at all, or

increasing in weight will result in higher total risk scores for an individual.

155

Table 40. Correlation matrix ⎯ total risk scores, gender, risk of cancer, risk of heart

disease, weight in kilograms, packs of cigarettes smoked per day for (Q1) only

1.00 .88** .00 .26** .42** .50**

. .01 .95 .01 .01 .01

343 343 343 343 340 343

.88** 1.00 .00 .21** .71** .80**

.01 . .94 .01 .01 .01

343 343 343 343 340 343

.00 .00 1.00 -.33** .07 .04

.95 .94 . .01 .18 .41

343 343 344 343 340 343

.26** .21** -.33** 1.00 -.07 .10

.01 .01 .01 . .18 .05

343 343 343 343 340 343

.42** .71** .07 -.07 1.00 .93**

.01 .01 .18 .18 . .01

340 340 340 340 340 340

.50** .80** .04 .10 .93** 1.00

.50 .80 .04 .10 .93 1.00

343 343 343 343 340 343

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

risk of heartdisease #1

total risk score#1

gender

weight inkilograms #1

packs ofcigarettes perday #1

risk of cancer#1

risk ofheart

disease #1

totalrisk

score #1 gender

weight inkilogram

s #1

cigarettes per

day

risk ofcancer

#1

Correlation is significant at the 0.01 level (2-tailed).**.

7.3 Health self-care, Medical self-care and control group

156

Results for doctors visits between the three groups in (Q1,Q2,Q3) indicate that there were

no significant differences between the means for the health self-care group and the

control group, p > .05. The only significant difference for doctors visits occurred between

Healthtrac (Q3) and Medical model (Q1) F(10, 24) = 2.28, p = .04 (ES = .48). Results

for days spent in hospital indicate that no significant differences occurred between

Healthtrac and the Medical model. The only significant differences occurred between the

Medical model (Q2) and the control (Q2) F(5, 29) = 2.84, p = .03 (ES = .37).

242424242424242424N =

Groups

healthtrac (Q3)healthtrac (Q2)

healthtrac (Q1)medical (Q3)

medical (Q2)medical (Q1)

control (Q3)control (Q2)

control (Q1)

Mea

n -

95%

CI

10

8

6

4

2

0

-2

Figure 8. Mean doctors visits for all groups: control: health self-care, medical self-care

and control for all questionnaires (Q1,Q2,Q3).

157

The results for the outcome variable total mean risk indicated that scores decreased for

Healthtrac (Q1) (M = 22.97, SD = 10.20) (Q3) (M = 13.71, SD = 11.49), control group

(Q1) (M = 23.47, SD = 11.41) (Q3) (M = 19.90, SD = 10.52) and the Medical model

group (Q1) (M = 19.76, SD = 9.63) (Q3) (M = 16.57, SD = 7.48) (see Figure 8). A

significant difference occurred between the health self-care and medical self-care model

groups: t(60) = 4.40, p = .01 (2-tailed).

Percentages were used to determine whether an increase or decrease in the treatment

effect (health promotion program) had occurred in the two experimental groups and

control group. These results indicate that there was a decrease in total risk scores of 40%

between (Q1) and (Q3) for the health self-care group. In the medical self-care model,

total risk scores decreased by 16% from (Q1) to (Q3). In contrast, the control group

findings were similar to the medical self-care model where the total risk scores decreased

by 15% from (Q1) to (Q3) (see Table 41). These findings confirm the results of the

previous section of this study ⎯ that a significant difference exists between health self-

care and the medical self-care model in mean total risk scores between (Q1) and (Q3). All

the groups were able to reduce their mean total risk scores but the health self-care group

was able reduce the scores more than the other groups (see Figure 9).

158

Table 41. Total risk scores for all groups and (Q1, Q2, Q3)

Questionnaire Healthrac (n = 343) Medical model (n =66) Control (n = 455)M SD M SD M SD

Total risk score #1 22.97 10.2 19.76 9.63 23.47 11.41Total risk score #2 19.66 9.24 16.55 6.44 20.12 10.21Total risk score #3 13.71 11.49 16.57 7.48 19.9 10.52

0

5

10

15

20

25

Total risk score #1 Total risk score #2 Total risk score #3

Questionnaire number

Mea

n Healthrac (n = 343)

Medical model (n =66)

Control (n = 455)

Figure 9. Mean total risk scores and all groups (Q1,Q2,Q3)

Table 42. Percentage scores for total risk scores across all groups and (Q1, Q2, Q3)

Healthtrac (n = 343 ) Medical model (n =66) Control (n = 455)Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3

Q1 -14.41 -40.31 -16.24 -16.14 -14.59 -15.21Q2 -30.65 0.12 1.09Q3

Note. These scores are calculated from mean total risk scores for each of the questionnaires within each group.

Negative scores indicate a decrease in total risk as a percentage. Positive scores indicate an increase in total risk scores

as a percentage.

159

The cost of disease across the different groups can be seen in Table 43. These are the

mean and SD figures for all the questionnaires within the groups. In the health self-care

group there was a drop in the mean from (Q1) (M = $979, SD = $308) to (Q3) (M = $935,

SD $398), representing a significant difference between the two questionnaires: t(174) =

5.89, p = .01(2-tailed). The control group means remained constant from (Q1) (M =

$976, SD = $273) to (Q3) (M = $964, SD = $310). This result was not significant. The

Medical model results were similar to those of the control group. There were small

differences in the mean and SD between (Q1) (M = $969, SD = $329) and (Q3) (M =

$947, SD = $331) (see Table 43). This result was also not significant. No significant

differences occurred between the health self-care, medical self-care and control groups

for cost of disease.

Examination of the percentage differences between the questionnaires indicated a

percentage difference of −4.4% between (Q1) and (Q3) in the health self-care group.

This indicates a saving of 4.4% in costs for this group. The medical self-care model

recorded -2.2% indicating a saving in of 2.2%. The control group saving of 1.2% was

similar to that of the Medical model (see Table 44). Health self-care group produced the

largest saving followed by the medical self-care group. All the groups were able to

decrease their costs but one group’s results were not significant over the others.

160

Table 43. Cost of disease ($) for all groups in (Q1, Q2, Q3)

Healthtrac (n = 343) Medical model (n = 66) Control (n = 455)M SD M SD M SD

Q1 979 308 969 329 976 273Q2 994 348 897 353 948 296Q3 935 398 947 331 964 310

Table 44. Percent differences between all groups in (Q1, Q2, Q3) in cost of disease

Healthtrac(n =343) Medical model (n = 66) Control (n = 455)Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3

Q1 1.5 -4.4 -7.4 -2.2 -2.8 -1.2Q2 -5.9 5.5 1.6Q3

Note. Negative scores indicate a reduction in cost of disease. Positive scores indicate an increase in cost of disease.

These have been calculated from the means in Table 43.

The results of the cost of various diseases within each of the questionnaires and within

the groups are presented in Table 45. The results for arthritis indicate that the health self-

care group achieved a lower mean cost (Q3) ($892) than the medical self-care group (Q3)

($1065) or the control group (Q3) ($1101). The same lower mean cost was achieved by

health self-care for smoking (Q3) ($793) as compared to (Q3) ($1022) in the medical

self-care group and (Q3) ($859) in the control group. For some of the diseases mean

costs increased over the 12 month period while others decreased. For example, in the

medical self-care group heart disease increased from (Q1) ($814) to (Q3) ($1165) (see

Table 45).

161

Table 45. Mean cost ($) of various diseases for all groups and (Q1, Q2, Q3)

Healthtrac (n = 343) Medical model (n =66) Control (n = 342)

Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3arthritis 948 910 892 795 631 1065 953 1053 1101back pain 925 891 687 1151 932 1015 946 849 855blood pressure 1013 1016 1018 890 983 758 1005 957 994cancer 839 789 1127 1724combined risk 967 1014 980 896 900 915 930 884 804diabetes 1016 1059 868 903 850 776 998 971 1040heart disease 978 828 714 814 1027 1165 1194 1014 1093smoking 948 999 793 1047 973 1022 947 929 859weight loss 1034 1227 1216 918 720 937 965 911 1003

Table 46. Percent differences in diseases ⎯ combined risk, diabetes, heart disease and

blood pressure for all groups and all questionnaires (Q1, Q2, Q3)

Health selfare (n=343) Medical model (n = 66) Control (n =345)Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3

combined Q1 4.8 1.3 0.4 2.1 -4.9 -13.54Q2 -3.5 1.6 -9.1Q3

diabetes Q1 4.2 -14.5 -5.8 -14.6 -2.7 4.2Q2 -18.1 -8.7 7.1Q3

heart Q1 -15.3 -26.9 26.8 43.1 -15.1 -8.4Q2 -13.7 13.4 7.7Q3

BP Q1 0.2 0.4 10.4 -14.8 -4.7 -1.1Q2 0.1 -22.8 3.8Q3

The data in Table 46 represents the percentage differences between the questionnaires for

various diseases. The results indicate that “factors’ such as combined risk factors

(combined risk factors are where a participant can suffer from high blood pressure as well

as smoking). The results indicate that diseases such as combined risk all groups

decreased for the control group (−13.5%) from questionnaire (Q1) to (Q3). This was not

the case for the health self-care (1.3%) or medical self-care (2.1%) where increases in

162

costs occurred. The opposite occurred for diabetes. The health self-care group and the

medical self-care reduce the cost of diabetes by 14%. Heart disease costs were reduced

by health self-care (26%) and the control group (8%), but the medical self-care group

cost increased by 43%. There was a significant difference between health self-care and

the medical self-care in heart disease costs: t(32)= 3.07, p = .05 (2-tailed).

Risk of heart disease scores means and SDs from the various groups can be seen in Table

51. All of the groups were able to reduce the mean risk of heart disease scores between

(Q1) and (Q3). For health self-care this reduction was 38% (see Table 47). There was a

significant difference between the two questionnaires in risk of heart disease scores:

t(136) = 9.79, p = .01 (2-tailed) (ES = .85). The medical self-care group showed a

reduction in its scores but by a smaller margin of 13%. Reduction in these scores also

occurred in the control group, by 20% (see Table 48). A significant difference occurred

between health self-care and the medical self-care in risk of heart disease scores t(42)

=2.12, p = .04 (2-tailed). No significant differences occurred between the health self-care

and control groups for this variable. Reduction in risk of heart disease scores occurred

across all the groups and these findings suggest that irrespective of which groups the

participants were in, reduction in the risk of heart disease scores still occurred.

163

Table 47. Mean and SD for risk of heart disease scores for all groups for (Q1, Q2, Q3).

Healthtrac (n = 343) Medical model (n = 66) Control (n = 455)M SD M SD M SD

Q1 36.52 13.68 32.79 13.66 36.39 15.06Q2 32.3 13.78 27.85 14.56 31.08 13.7Q3 22.4 18.11 28.51 15.21 28.95 13.36

Table 48. Mean percentage risk of heart disease scores for all scores for (Q1, Q2, Q3)

Healthtrac (n = 343) Medical model (n =66) Control (n = 455)Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3

Q1 -11.55 -38.66 -15.06 -13.05 -14.59 -20.44Q2 -30.65 2.36 -6.8Q3

Note. These scores are calculated from mean risk of heart disease scores (Table 47). Negative scores indicate a

decrease in heart disease scores. Positive scores indicate an increase in heart disease scores.

The outcome variable risk of cancer scores indicated that mean scores for Healthtrac and

the control group decreased between the questionnaires: Healthtrac (Q1) (M = 14.62, SD

= 9.62) (Q3) (M = 12.12, SD = 9.93), Medical model (Q1) (M = 11.30, SD = 9.92) (Q3)

(M = 8.37, SD = 6.26) (see Table 49). No significant difference occurred between the

groups.

Healthtrac clients reduced the risk of cancer by 17% (Q1−Q3), whereas the Medical

model clients reduced their risk of cancer scores by 25% (Q1−Q3) (see Table 50). Both

experimental groups were able to reduce the risk of cancer, whereas the control group

risk of cancer scores did not change. There were no significant differences between the

two experimental groups.

164

Table 49. Risk of cancer scores for all groups and (Q1, Q2, Q3)

Healthtrac (n =343) Medical model (n =66) Control (n = 455)M SD M SD M SD

Q1 14.62 9.62 11.3 9.92 15.36 12.08Q2 13.74 10.74 8.12 6.73 14.31 11.31Q3 12.12 9.93 8.37 6.26 15.5 12.55

Table 50. Percentage difference between all groups and (Q1, Q2, Q3)

Healthtrac (n = 343) Medical model (n = 66) Control (n = 455)Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3

Q1 -6.01 -17.09 -28.14 -25.92 -6.8 0.91Q2 -11.79 3.07 8.3Q3

Note: These scores were calculated from the means in Table 49. Negative scores indicate a reduction in the mean from (Q1) to (Q3),(Q2) to (Q3) and (Q1) to (Q2) ⎯ this is a cost saving. A positive score is one that indicates an increase in costs.

165

7. 5 Health self-efficacy questionnaire

This section of results deals with three main areas of self-efficacy. These are:

management of disease, self-management of disease, achievement of outcomes and

health self-efficacy (see Methods).

7. 5. 1. – Health self-care

For this questionnaire, 53.2% of the total number of subjects (N = 62) were male (n =

33), and 46.8% were female (n = 29) (see Table 51). The frequency distribution of the

various age group is as follows: 20−40 (n = 12), 40−50 (n = 13), 50−60 (n = 11), 60-80-

and-over (n = 26) (see Figure 10).

Table 51. Gender and percent

33 53.2

29 46.8

62 100.0

male

female

Total

Count Percent

166

age groups

60-80 over50-6040-5020-40

coun

t

30

20

10

0

Figure 10. Participants within age groups

Most participants of this group were married (n = 45), 72% of the total. The group

forming the next highest percentage (9%) was the widowed participants (n = 6) (see

Table 52). Marital status may influence other variables such as participation in exercise.

Table 52. Marital status frequency and percent

4 6.5

45 72.6

6 9.7

4 6.5

2 3.2

1 1.6

62 100.0

single

married

widowed

divorced

seperated

de facto

Total

Count Percent

167

At the time of answering this questionnaire, subjects were asked to compare their health status with that of

12 months earlier. Most subjects (58%) considered their health status to be about the same. The next

highest percentage (19%) considered it to be somewhat better (see Table 53). These results allow subjects

to be grouped according to whether they consider their health to be excellent (1 point), very good (2

points), fair (4 points) or poor (5 points). Most subjects consider their health status to be good (n = 30). A

smaller number (n = 18) considered their health status to be very good (see Figure 11).

Table 53. Participants numbers and percentage for health status now compared to 12 months earlier

8 12.9

12 19.4

36 58.1

4 6.5

2 3.2

62 100.0

1 much better

2 somewhat better

3 about the same

4 somewhat worse

5 much worse

Total

ValidFrequency Percent

05

101520253035

excellent verygood

good fair poor

health self rating

Cou

nt

Figure 11. Current ratings of health status when compared to 12 months earlier.

Table 54 presents the means and SDs for those variables classified as having the potential

to interfere with normal daily living (Illness Intrusive Rating Scale ⎯ IIRS). This self-

168

rating scale of measurement was a Likert scale, ranging from 1 ‘not very much’ to 5

‘very much’. The results indicate that a variable such as diet in the 20−40 age group (M

= 1.80, SD = 1.48) was not as important issue as compared to 60−80- and-over age group,

for which the mean was much higher (M = 2.27, SD = 1.40). This indicates that older

participants believe that diet influences illness. Passive recreation (such activities as

gardening)(see Methods) interfered with health the most in the 20−40 age group (M =

2.20, SD = .92) compared to the 60−80-and-over age group (M = 1.58, SD = 1.06) (see

Table 54).

In this context ‘interfere’ refers to variables such as passive recreation or diet that need to

be modified, or where illness has prevented the participants from participating in some

activity.

Table 54. Perceptions of how illness interferes with normal daily living ⎯ and age (Illness Intrusive Rating Scale)

169

2.20 1.03 2.31 1.03 1.55 .69 2.19 1.39 2.10 1.16

1.80 1.48 1.85 1.07 2.00 1.34 2.27 1.40 2.05 1.32

2.80 1.48 1.69 .85 1.82 1.08 2.26 1.66 2.14 1.39

3.30 1.49 2.08 1.38 2.09 1.38 2.50 1.56 2.47 1.50

2.20 .92 1.62 .96 1.73 1.01 1.58 1.06 1.72 1.01

2.40 1.43 1.31 .63 2.09 1.38 1.58 .78 1.76 1.06

2.20 1.23 1.38 .65 1.14 .38 1.70 1.08 1.64 .98

2.20 1.62 1.31 .63 2.11 1.76 2.11 1.41 1.92 1.38

1.70 1.06 1.54 .88 1.73 1.42 1.73 1.19 1.68 1.13

2.00 1.49 1.54 .97 1.73 1.10 1.85 1.05 1.78 1.11

2.60 1.43 1.85 1.14 2.09 1.38 2.00 1.39 2.08 1.33

1.20 .63 1.23 .83 1.45 1.21 1.61 1.27 1.42 1.07

1.90 1.37 1.62 1.04 2.27 1.56 1.81 1.27 1.87 1.28

interfere health

interfere -diet

interfere -work

interfere -activerecreation

interfere -passiverecreation

interfere -finanicalsituation

interfere -spouserelationship

interfere sex life

interfere -family relations

interfere- social

interfere - self

interfere-religion

interfere -communityinvolvement

M SD M SD M SD M SD M SD

20-40 40-50 50-60 60-80 over Total

age

In the ‘self-management’ section of the questionnaire (see Table 55), the results indicate

that participants were more confident for the variable ‘manage health problems after

visiting a GP’, than any other (M = 4.05, SD = 1.07). These findings suggest that after

participants visit a GP and with their knowledge of self-management of the disease, they

were very confident about dealing with their health problems. The participants were less

confident at receiving ‘help with daily tasks from resources other than friends or family’

(M = 2.78, SD = 1.32). The results indicate that participants were moderately confident

at ‘performing exercise 3 to 4 times per week’ (M = 3.13, SD =1.36) and ‘using exercise

170

to improve health’ (M = 3.35, SD = 1.39). Furthermore, the results indicate that these

participants would ‘continue an exercise program for the next three months’ (M = 3.02,

SD = 1.40). Participants were moderately confident when it came to working out

differences in ‘treatment with their GPs’ (M = 3.92, SD = 1.18) and ‘obtaining answers to

health problems’ (M = 3.66, SD = 1.18).

Table 55. Perceptions of self-management of health behaviours

62 3.13 1.36

62 3.02 1.40

62 3.35 1.39

61 3.20 1.29

60 3.35 1.39

62 3.39 1.33

61 3.92 1.29

60 3.82 1.10

61 4.05 1.07

59 3.92 1.18

59 3.66 1.18

59 3.39 1.31

59 3.71 1.27

60 3.55 1.31

60 2.78 1.32

exercise 3-4 times per week

continue exercise for next 3months

exercise to improve health

flexibility exercises 3-4 timesper week

aerobic exercise, walking 3-4times per week

exercise makes symptomsworse

matters concerning health askGP

follow instructions from GP

manage health after visitingGP

work out differences intreatment with GP

obtain answers from GP

get help from family andfriends

emotional support for healthproblems

emotional support to improvehealth problems

family help with daily tasks

N Mean Std. Deviation

In the ‘management of disease’ section of the questionnaire, the health self-care

participants recorded the highest mean for ‘understanding changes in illness’ (M = 4.05,

171

SD = .98) and ‘understanding health problems’ (M = 3.97, SD = 1.07). In ‘making

behaviour changes that requires taking less medication’ these subjects were moderately

confident (M = 3.13, SD = 1.27) (see Table 56).

Table 56. Perceptions of the management of disease.

62 3.65 1.07

62 3.61 1.03

61 3.36 1.10

60 3.13 1.27

60 4.05 .98

59 3.41 1.18

59 3.42 1.00

60 3.70 1.05

59 3.97 1.07

59 3.42 1.09

manage disease problems

manage disease regularbasis

manage disease withoutvisiting GP

behavioural changes thatrequires less medication

changes in illness then visitGP

behavioural changes thatreduce need to visit GP

reduce emotional distressto improve health

use health information toimprove health

understand healthproblems

make behaviour changethat wiil positive managehealth

N Mean Std. Deviation

In the ‘achievement of outcome’ section of the questionnaire, the most confidence was

reported for ‘do errands despite health problems’(going to the bank) (M = 3.84, SD =

1.19) and the lowest confidence was reported for ‘keep yourself from feeling sad or down

due to disease’ (M = 3.05, SD = 1.13). Participants were moderately confident at

‘keeping physical discomfort and pain from interfering with daily living’ (M = 3.28, SD =

1.20) (see Table 57).

172

Table 57. Perception for the achievement of outcomes variables

62 3.65 1.07

62 3.61 1.03

61 3.36 1.10

60 3.13 1.27

60 4.05 .98

59 3.41 1.18

59 3.42 1.00

60 3.70 1.05

59 3.97 1.07

59 3.42 1.09

manage disease problems

manage disease regularbasis

manage disease withoutvisiting GP

behavioural changes thatrequires less medication

changes in illness then visitGP

behavioural changes thatreduce need to visit GP

reduce emotional distressto improve health

use health information toimprove health

understand healthproblems

make behaviour changethat wiil positive managehealth

N Mean Std. Deviation

In the ‘health self-efficacy’ section of this questionnaire, the health self-care participants

believed they were moderate to very confident in ‘accessing necessary health services’

(M = 3.72, SD = 1.12) as well as ‘putting into action the advice of health professionals’

(M = 3.64, SD = 1.03). These participants were moderately confident at ‘continuing their

health program under conditions of excessive demands’ (M = 3.18, SD = .93) (see Table

58).

Table 58. Perceptions for the health self-efficacy variables.

173

60 3.45 .96

60 3.52 1.02

60 3.37 1.06

60 3.33 .95

60 3.38 1.14

60 3.52 1.02

59 3.64 1.03

60 3.18 .93

59 3.61 .91

60 3.32 1.10

60 3.72 1.12

achieve goals to decrease riskof disease

set goals to improve health

spend time to improve health

adhere to preventive program

personal control over healthproblems

motivation to improve health

put into action advice ofhealth professionals

continue health programunder excessive demands

modify behaviour to improvehealth

financially afford to improvehealth

access necessary healthservices

N Mean Std. Deviation

Correlations within the self-management section of the health self-efficacy questionnaire

indicate no significant correlation with age for variables such as ‘exercising 3 to 4 times

per week’ (r = −.21, p > .05) or ‘using exercise to improve your health’ (r = −.14, p >

.05). There were other variables within this part of the questionnaire that indicated a

significant relationship between other options such as ‘exercise 3 to 4 times per week’

and ‘use exercise to improve your health’ (r = .78, p < .01). Other significant

relationships occurred between ‘aerobic exercise 3 to 4 times per week’ and ‘continuing

with an exercise program for the next 3 months’ (r = .81, p < .01) (see Table 59).

Table 59. Correlation matrix ⎯ self-management exercise variables and age.

174

1.00 -.16 -.09 -.11 -.05 -.16

. .22 .50 .39 .69 .22

62 62 62 62 61 60

-.16 1.00 .81** .78** .69** .79**

.22 . .01 .01 .01 .01

62.00 62.00 62.00 62.00 61.00 60.00

-.09 .81** 1.00 .83** .85** .72**

.50 .01 . .01 .01 .01

62 62 62 62 61 60

-.11 .78** .83** 1.00 .75** .73**

.39 .01 .01 . .01 .01

62 62 62 62 61 60

-.05 .69** .85** .75** 1.00 .66**

.69 .01 .01 .01 . .01

61 61 61 61 61 60

-.16 .79** .72** .73** .66** 1.00

.22 .01 .01 .01 .01 .

60 60 60 60 60 60

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

PearsonCorrelation

Sig.(2-tailed)

N

age

self-managementexercise 3-4 timesper week

self-managementcontinue exercisefor next 3months

self-managementexercise toimprove health

self-managementdo flexibilityexercises

self-managementaerobic such aswalking 3-4 timesweek

age

self-management

exercise 3-4times per

week

self-management

continueexercise for

next 3months

selfmanagementexercise toimprovehealth

self-management

flexibilityexercises

self-management

aerobicsuch aswalking3-4 week

Correlation is significant at the 0.01 level (2-tailed).**.

175

A number of significant correlations were observed in the management of disease

questions that related to GPs. Some of these correlations occurred between management

of ‘health problems without visiting a GP’ and ‘making behavioural changes that will

reduce the need to visit a GPs’ (r = .50, p < .01). The variable ‘make behavioural

changes that will require less medication’ and ‘management of health problems without

visiting a GP’ also indicates a significant correlation (r = .43, p < .01) (see Table 60).

Table 60. Correlation matrix ⎯ GP variables and management of disease.

1.00 .43** .34** .50**

. .01 .01 .01

61 60 59 58

.43** 1.00 .40** .64**

.01 . .01 .01

60 60 59 58

.34** .40** 1.00 .53**

.01 .01 . .01

59 59 60 59

.50** .64** .53** 1.00

.01 .01 .01 .

58 58 59 59

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

manage diseasewithout visitingGP

manage diseasemake behaviouralchanges lessmedication

manage diseasechanges in illnessthen visit a GP

manage diseasebehavioualchanges reduceneed to visit a GP

managediseasewithout

visiting GP

manage diseasebehaviouralchanges lessmedication

manage diseasechanges inillness then

visit GP

manage diseasebehavioural

changes reduceneed to visit

GP

Correlation is significant at the 0.01 level (2-tailed).**.

176

ANOVA and other non-parametric methods (Friedman Test) were used to examine for

significant differences between some groups of variables such as total health self-efficacy

scores or individual variables such as exercise. The total health self-efficacy scores and the total management of disease score questions

indicated that there was a significant difference between the means: t(54) = 3.64, p = .01.

Total self-management scores were significantly different from the total management of

disease scores: t(54) = 14.14, p = .001. Gender results indicate that there is a significant

difference between the means when it comes to ‘setting goals to improve health’: F(1,

58) = 4.39, p = .04 (see Table 61).

Table 61. ANOVA ⎯ variables ‘gender’ and ‘set goals to improve health’

HHSE2 health self-efficacy -set goals to improve health

4.29 1 4.29 4.39 .041

56.69 58 .98

60.98 59

Between Groups

Within Groups

Total

Sum of Squares dfMean

Square F Sig.

Females are more confident than males at setting goals to improve health: male (M =

3.26, SD = .93), female (M = 3.66, SD = .97). No significant differences were found

between the genders’.

Age was a significant factor within a number of variables, one of those being ‘answer to

health problems from GP’: F (3, 55) = 4.60, p = .006 (see Table 62). A Tukey’s post hoc

analysis determined that differences occurred between the 60-80-and-over and the 40-50

177

age groups. This would indicate that older participants within this experimental group

where more confident with the answers to questions about health problems from the GP.

Table 62. ANOVA ⎯ GP questions within the self-management and age groups

16.30 3 5.43 4.60 .006

64.92 55 1.18

81.22 58

Between Groups

Within Groups

Total

obtain answers tohealth problemsby age

Sum ofSquares df

MeanSquare F Sig.

7.5.2. Medical self-care

The medical self-care group (N = 54) consisted of 54% males (n = 29) and 46% females

(n = 25) and 12.9% (see Table 62). The frequency for the age consisted of 20−40 (n =

16), 40−50 (n = 9), 50−60 (n = 11) and 60−80- and-over (n = 18) (see Figure 12).

Table 63. Frequency and percentages for gender

29 53.7

25 46.3

54 100.0

male

female

Total

Frequency Percent

178

0

5

10

15

20

age 20-40 40-50 50-60 60-80over

age groups

cou

nt

Figure 12. Participants within age groups

At the time the questionnaire was given, most of the participants considered their health

to be good or very good. The next largest group considered their health fair (see Table

64). Compared to 12 months earlier, 50% of participants considered their health to be

about the same and 24% considered themselves to be in better health (see Table 65).

Table 64. State of health now

1 1.9

16 29.6

27 50.0

9 16.7

1 1.9

54 100.0

excellent

very good

good

fair

poor

Total

ValidFrequency Valid Percent

.

179

Table 65. Health compared to 12 months earlier

13 24.1

11 20.4

27 50.0

3 5.6

54 100.0

much better

somewhat better

about the same

somewhat worse

Total

ValidFrequency Valid Percent

The Illness Intrusive Rating Scale (IIRS) for the medical self-care results indicate that the

participants believe that passive recreation interfered most with illness (M = 2.49, SD =

1.35) followed by work (M = 2.18, SD = 1.27). The variable to least interfere with the

participant’s illness was religion (M = 1.49, SD = 1.00) (see Table 66).

Table 66. Perception of how illness interferes with activities of daily life.

52 2.06 1.0652 1.90 1.2249 2.18 1.2751 2.49 1.35

51 1.82 1.03

51 1.90 1.27

46 1.87 1.29

48 2.10 1.45

51 1.59 1.08

51 1.71 1.14

52 1.90 1.19

49 1.49 1.00

52 1.94 1.16

42

interfere health

interfere -diet

interfere -work

interfere -active recreation

interfere - passiverecreation

interfere - finanical situation

interfere - spouserelationship

interfere sex life

interfere - family relations

interfere- social

interfere - self

interfere- religion

interfere - communityinvolvement

Valid N (listwise)

N MeanStd.

Deviation

180

In the results section for the self management of health behaviours, participants were very

confident about the following variables, ‘asking a GP about matters that concern them’

(M = 4.02, SD = 1.18), ‘carrying out instructions the GP has recommended’ (M = 4.04,

SD = 1.10) and ‘managing health problems after visiting a GP’ (M = 4.06, SD = 1.03).

The participants were moderately confident about ‘exercising 3 to 4 times per week’ (M =

3.55, SD = 1.17) and ‘continuing with an exercise program for the next three months’ (M

= 3.46, SD = 1.25) (see Table 67).

Table 67. Perceptions of self-management for behaviour variables

53 3.55 1.17

54 3.46 1.25

53 3.64 1.06

53 3.49 1.25

53 3.62 1.24

54 3.67 1.29

53 4.02 1.18

54 4.04 1.10

53 4.06 1.03

53 3.79 1.21

53 3.64 1.33

53 3.74 1.23

52 3.77 1.21

52 3.40 1.21

exercise 3-4 times per week

continue exercise for next 3months

exercise to improve health

flexibility exercise 3-4 timesper week

aerobic- walking 3-4 times perweek

exercise without makingsymptoms worse

ask GP health problems thatare of concern

instructions from GP

manage health problems aftervisiting GP

work out differences in healthproblems with visit to GP

answers from GP

emotional support from familyand friends

emotional support to improvehealth

help with daily tasks

N Mean Std. Deviation

181

Management of disease results for this group indicate that participants are very confident

(M = 4.21, SD = 1.04) in ‘understanding health problems’, as well as ‘judging when the

changes in illness occur and this requires a visit to the GP’ (M = 4.02, SD =1.01). They

were moderately confident at ‘making behavioural changes that require less medication

to be used’ (M = 3.57, SD =1.29). This was also the case for ‘make behavioural changes

that will reduce the need to visit a GP’ (M = 3.75, SD = 1.20). Other variables such as

‘make behavioural changes that will positively manage health problems’ were considered

by these participants as being moderately confident at achieving these changes (M = 3.85,

SD =1.03) (see Table 68).

Table 68. Perceptions related to disease management

53 3.94 .99

53 3.92 1.00

53 3.85 .95

53 3.57 1.29

53 4.02 1.01

52 3.75 1.20

52 3.81 1.03

52 3.94 .98

53 4.21 1.04

53 3.85 1.03

manage disease problems

manage disease on regularbasis

manage disease withoutvisiting GP

behavioural changes thatrequire less medication

judge changes in illness -visit a GP

reduce need to visit GP

emotional distress causedby health condition

use health information toimprove health

understand health problem

positive behaviour changeto improve health

N Mean Std. Deviation

Data from the achievement of outcomes section of the questionnaire indicates that the

participants were very confident that about being ‘able to do activities with friends and

182

family’ (M = 4.14, SD = .94) as well as doing ‘errands despite having health problems’

(M = 4.17, SD = .96). However, they were moderately confident at ‘reducing their

physical discomfort and pain’ (M = 3.63, SD = .96) and ‘controlling any symptoms or

health problems so that they do not interfere with daily life’ (M = 3.61, SD = .90) (see

Table 69).

Table 69. Issues of achievement of outcomes

51 3.63 .96

51 3.63 .98

51 3.61 .90

51 3.63 .87

51 3.98 1.07

51 4.14 .94

54 3.76 1.40

52 4.17 .96

51 4.02 1.10

52 3.75 1.15

52 3.69 1.21

52 3.98 1.02

52 3.96 1.05

reduce pain /physicaldiscomfort

fatigue caused by disease

control symptoms-interferewith daily life

pain from disease interfere -with daily life

continue with recreation andhobbies

do activities with family andfriends

do household chores despitehealth problems

do errands despite healthproblems

shortness of breath interfere-what you can do

discouraged when nothingmakes a difference

feeling sad

make yourself feel better

keep yourself from feelinglonely

N Mean Std. Deviation

183

For all health self-efficacy issues (e.g. ‘to have the motivation to improve health’), the

mean responses indicate that they were moderately confident (M = 3.89, SD = 1.07) (see

Table 70).

Table 70. Perceptions of health self-efficacy issues.

53 3.81 .90

53 3.87 .96

53 3.75 1.00

53 3.66 1.04

52 3.90 .98

53 3.89 1.07

53 3.96 1.00

52 3.56 1.07

52 3.77 .96

53 3.49 1.25

53 3.96 1.07

achieve goals to decrease riskof disease

set goals to improve health

spend time to improve health

adhere to preventive program

personal control over healthproblems

motivation to improve health

put into action advice of healthprofessionals

excessive demands to continuewith health program

use knowledge to modifybehaviour

financially afford to improvehealth

access to health services

N Mean Std. Deviation

The correlation matrix (see Table 71), displays the results from testing for significant

relationships between gender, age and some health self-efficacy questions . No

significant relationship was detected for age and gender with ‘achievement of goals to

decrease the risk of disease’ or ‘setting goals to improve health’ (p > .05). The results

indicated that there were some strong significant relationships between ‘achieving goals

to decrease the risk of disease’ and ‘setting goals to improve health’ (r = .84, p < .01).

Another strong positive significant relationship exists between ‘spending time to improve

health’ and ‘setting goals to improve health’ (r = .93, p < .01) (see Table 71).

184

Table 70. Correlation matrix ⎯ health self-efficacy issues, age and gender

1.00 -.22 .02 .05 -.04

. .11 .87 .74 .76

54 54 53 53 53

-.22 1.00 .06 .11 .10

.11 . .66 .43 .48

54 54 53 53 53

.02 .06 1.00 .84** .85**

.87 .66 . .01 .01

53 53 53 53 53

.05 .11 .84** 1.00 .93**

.74 .43 .01 . .01

53 53 53 53 53

-.04 .10 .85** .93** 1.00

.76 .48 .01 .01 .

53 53 53 53 53

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

gender

age

health self-efficacybeliefs - achievegoals

health self-efficacy -set goals toimprove health

health self-efficacy -spend time toimprove health

gender age

health self-efficacybeliefs -achievegoals

healthself-efficacy -set goals to

improvehealth

healthself-efficacy -spend time to

improvehealth

Correlation is significant at the 0.01 level (2-tailed).**.

Motivation had a number of significant relationships with variables such as ‘having

personal control over health problems’ (r = .70, p < .01) and ‘making behavioural

changes that will positively manage health problems’ (r = .64, p < .01). Other positive

significant relationships occurred between using ‘health information to improve health’

and ‘making behavioural changes that will positively manage health problems’ (r = .77, p

183

< .01). A significant relationship was found between ‘taking personal control over health

problems’ and ‘using health information to improve health’ (r = .71, p < .01) (see Table

72).

Table 72. Correlation matrix for health self-efficacy (motivation, personal control) and

management of disease (health information, behaviour change).

1.00 .70** .71** .79**

. .01 .01 .01

52 52 51 51

.70** 1.00 .58** .64**

.01 . .01 .01

52 53 51 52

.71** .58** 1.00 .77**

.01 .01 . .01

51 51 52 52

.79** .64** .77** 1.00

.01 .01 .01 .

51 52 52 53

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

health self-efficacy -personal control

health self-efficacy -motivation toimprove health

manage disease -health information

manage disease -behaviour change

healthself-efficacy -

personalcontrol

healthself-efficacy -motivation to

improvehealth

managedisease -

healthinformatio

n

managedisease -

behaviourchange

Correlation is significant at the 0.01 level (2-tailed).**.

There was a significant difference between age and some variables including ‘making

behavioural changes that will require less medication to be used’ F(3, 49) = 2.79, p = .05

(ES = .48) ( see Table 73). A post hoc analysis could not determine in which age groups

these significant differences occurred.

186

Table 72 . Age and management of disease GP questions

2.88 3 .96 1.07 .370

43.92 49 .90

46.79 52

12.68 3 4.23 2.79 .050

74.33 49 1.52

87.02 52

4.91 3 1.64 1.67 .186

48.07 49 .98

52.98 52

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

Between Groups

Within Groups

Total

manage disease withoutvisiting GP * age

manage diseasebehavioural changes *age

manage disease changesin illness * age

Sum ofSquares df

MeanSquare F Sig.

7.3.2 – Control group

The sample size for this group (N = 21) was small. Therefore most of the

analysis consists of determining the differences between means and

examining the relationship between variables through correlations.

187

The results for the IIRS indicated that participants believe that diet

interfered a little with their illness or treatment (M = 2.35, SD = 1.27),

whereas religion did not at all (M = 1.22, SD = .55) (see Table 80).

Table 80. Perceptions of how illness interferes with daily living (IIRS)

20 2.10 1.17

20 2.35 1.27

18 1.94 1.26

20 2.15 1.27

19 1.74 .93

20 1.75 1.16

17 1.76 1.35

17 1.71 1.36

19 1.42 .69

19 1.53 .90

19 1.63 .83

18 1.22 .55

19 1.53 .84

15

interfere health

interfere -diet

interfere -work

interfere -active recreation

interfere - passive recreation

interfere - finanical situation

interfere - spouserelationship

interfere sex life

interfere - family relations

interfere- social

interfere - self

interfere- religion

interfere - communityinvolvement

Valid N (listwise)

N Mean Std. Deviation

The results for the self-management questions indicate that participants

were very confident in areas such as, ‘asking the GP about matters that

concern health’ (M = 4.38, SD = 1.20) and ‘doing aerobic exercise such as

walking 3 to 4 times per week’ (M = 4.20, SD = 1.15). These participants

were moderately confident with questions that related to ‘receiving

188

emotional support from family and friends to improve health’ (M = 3.86,

SD = 1.35) as well as ‘support from family and friends regarding health

problems (M = 3.81, SD = 1.44) (see Table 81).

Table 81. Perceptions of self-management variables

21 3.81 1.25

21 3.57 1.36

20 3.70 1.17

20 3.70 1.22

20 4.20 1.15

21 3.81 1.36

21 4.38 1.20

21 4.33 1.20

21 4.57 .81

21 4.05 1.16

21 3.95 1.32

21 3.81 1.44

21 3.86 1.35

21 3.86 1.20

exercise 3-4 times per week

continue exercise program fornext 3 months

exercise to improve health

flexibility exercises 3-4 timesper week

aerobic- walking 3-4 times perweek

worse when exercise

concern about health - visit GP

instructions by GP

manage health problems aftervisiting GP

differences worked out with GP

answers to health problems -GP

family and friends help

emotional support regardinghealth problems

emotional to improve toimprove health

N Mean Std. Deviation

Participants were very confident at ‘managing health problems’ (M = 4.24,

SD = .89) as well as ‘judging when changes in health occurred and then

visit a GP’ (M = 4.14, SD = .79). They were only moderately confident at

189

‘managing health problems without visiting a GP’ (M = 3.33, SD = 1.39)

(see Table 82).

Table 82. Perceptions of the management of disease.

21 4.24 .89

21 4.10 1.14

21 3.33 1.39

21 3.24 1.41

21 4.14 .79

20 3.75 1.16

20 3.80 .77

21 3.90 1.00

21 4.10 .83

21 3.67 1.15

manage health problems

manage disease regularbasis

manage disease withoutvisiting GP

make behavioural changesrequires less medication

changes in illness the visitGP

reduce need to visit GP

reduce emotional distress

health information toimprove health

understand health problem

make behaviour changes tomanage health

N Mean Std. Deviation

In the achievement of outcome questions, participants were very confident

regarding ‘doing errands despite health problems’ (M = 4.32, SD = .58)

and ‘doing activities with friends and family’ (M = 4.24, SD = .62). The

participants were moderately confident for questions relating to ‘reducing

physical discomfort and pain’ (M = 3.81, SD = .93) (see Table 83).

Table 83. Perceptions of the achievement of outcomes.

190

21 3.81 .93

21 3.71 1.06

21 3.71 .96

21 3.76 .83

21 4.19 .68

21 4.24 .62

21 4.14 .73

19 4.32 .58

19 3.84 1.07

21 3.81 1.08

21 3.67 .86

21 3.95 .92

21 3.95 .86

reduce pain /physicaldiscomfort

fatigue interfere with thing todo

symptoms interfere with dailylife

control symptoms

continue with recreation andhobbies

do activities with friends andfamily

household chores despitehealth problems

errands despite healthproblems

shortness of breath interferewith tasks

discouraged when nothingmakes a difference

feeling sad

feel better

feeling lonely

N Mean Std. Deviation

191

In the health self-efficacy section, the particpants considered themselves to be moderately

confident for all questions (see Table 84).

Table 84. Perceptions of health self-efficacy

21 3.76 .89

21 3.62 .97

21 3.62 .80

21 3.67 .91

21 3.90 .83

21 3.76 1.04

20 3.90 .91

21 3.19 1.03

21 3.57 .93

20 3.30 1.17

21 3.67 1.15

achieve goals to decrease riskof disease

set goals to improve health

spend time to improve health

adhere to preventive program

personal control over healthproblems

motivation to improve health

put into action advice of healthprofessionals

excessive demands- continuewith health program

modify behaviour to improvehealth

financially afford to improvehealth

access to health services

N Mean Std. Deviation

The correlations for a number of GP questions from the self-management and

management of disease sections of the questionnaire can be found in Table 85. There

were a number of significant positive correlations between such variables as ‘working out

differences with a GP regarding treatment’ and ‘obtaining answers to health problems

from the GP (r = .21, p < .01). No significant positive or negative correlations occurred

between the GP questions for management of disease and self-management (see Table

85).

192

Table 85. Correlation ⎯ GP questions in self-management and management of disease

1.00 .61** .68** .04

. .01 .01 .85

21 21 21 21

.61** 1.00 .82** .05

.01 . .01 .82

21 21 21 21

.68** .82** 1.00 -.05

.01 .01 . .85

21 21 21 21

.04 .05 -.05 1.00

.85 .82 .85 .

21 21 21 21

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

PearsonCorrelation

Sig. (2-tailed)

N

self managementhealth after visitingGP

self management workout differences withGP

self managementanswers to healthproblems from GP

manage disease withoutvisiting GP

selfmanagementhealth aftervisiting GP

selfmanagement

work outdifferences with

GP

selfmanagementanswers to

healthproblems from

GP

manage diseasewithout visiting

GP

Correlation is significant at the 0.01 level (2-tailed).**.

193

7.6 Health self-efficacy questionnaire ⎯ comparison across all groups

The results for the self-management total scores across the three groups indicate that the

control group had the highest mean score (M = 55.40, SD = 12.64), followed by medical

self-care(M = 55.02, SD = 13.33) and then the medical self-care (M = 52.81, SD = 13.19)

(see Table 86). There were no significant differences among the type of group on these

scores.

Table 84 Type of group and management of disease Group type N Mean SD Health self care 57 35.53 7.90 Medical self care 53 42.23 10.10 Control 19 38.53 7.46 Mean total health self-efficacy scores differed little across the three groups: medical self-

care (M = 41.09, SD = 9.77), control (M = 39.14, SD = 9.13) and health self-care (M =

38.26, SD = 8.63). No significant differences occurred between the three groups (see

Table 86).

194

Table 86. Group type and total self-management scores

59 52.81 13.19

52 55.02 13.33

20 55.40 12.64

total self management

total self management

total self management

group type health self-care

medical self-care

control

N Mean Std. Deviation

Mean total health self-efficacy scores differed little across the three groups: medical self-

care (M = 41.52, SD = 9.38), control (M = 39.14, SD = 9.13) and health self-care (M =

38.03, SD = 8.66). No significant differences occurred among the three groups (see

Table 87).

Table 87. Group types and health self-efficacy scores

59 38.03 8.66

54 41.52 9.38

21 39.14 9.13

total health selfefficacy

total health selfefficacy

total health selfefficacy

group typehealthself-care

medicalself-care

control

N MeanStd.

Deviation

195

This was also the case with the achievement of outcomes questions, no

significant difference in the means among medical self-care (M = 47.39,

SD = 12.38), control (M = 45.90, SD = 9.48) and health self-care (M =

42.97, SD = 9.16) (see Table 88).

Table 88. Group type and total achievement of outcomes scores.

60 42.97 9.16

54 47.39 12.38

21 45.90 9.48

total achievement ofoutcomes scores

total achievement ofoutcomes scores

total achievement ofoutcomes scores

group typehealthself-care

medicalself-care

control

N MeanStd.

Deviation

The results for management of disease indicate that there were no

significant differences among the means for the medical self-care (M =

42.23, SD = 10.10), control (M = 38.58, SD = 7.70) and health self-care

(M = 35.53, SD = 7.90) (see Table 89).

Table 89. Type of group and management of disease scores.

196

57 35.53 7.90

53 42.23 10.10

19 38.58 7.46

total managementof disease scores

total managementof disease scores

total managementof disease scores

group typehealthself-care

medicalself-care

control

N MeanStd.

Deviation

GP questions within the self-management section of the questionnaire

indicate there were no significant differences between the means of

medical self-care (M = 19.83, SD = 5.13), control (M = 20.90, SD = 5.11)

and health self-care (M = 19.59, SD = 4.78) (see Table 90).

Table 90. Group type and GP questions within self-management.

58 19.59 4.78

52 19.83 5.13

21 20.90 5.11

sum of GPquestions

sum of GPquestions

sum of GPquestions

grouptypehealthself-care

medicalself-care

control

N MeanStd.

Deviation

The GP questions in the management of disease section also indicates no significant

differences occurred among the groups, medical self-care (M = 11.60, SD = 2.87), control

(M = 11.15, SD = 2.60) and health self-care (M = 10.78, SD = 2.60) (see Table 91).

These three GP questions within the management of disease part of the questionnaire

were combined to form one question. The results for the GP questions in both sections of

the questionnaire, indicate that the highest means came from the medical self-care group

but these means were not significant.

197

Table 91. Type of group and management of disease GP questions.

58 10.78 2.60

52 11.60 2.87

20 11.15 2.60

GP questions withinmanagement of disease

GP questions withinmanagement of disease

GP questions withinmanagement of disease

group typehealthself-care

medicalself-care

control

N MeanStd.

Deviation

The results from the exercise component within the self-management

section of the questionnaire indicate that there was a significant difference

between health self-care and the medical self-care: t(54) = 5.13, p = .01

The health self-care group were more confident at performing exercise

than the medical model group.

In the health self-efficacy section of the questionnaire for the

variable ‘spend time to improve health’ there was a significant different

between health self-care and the control group: t(52) 2.41, p = .01. The

health self-care group was more confident at setting goals to improve their

health than the control group.

198

The IIRS questions were totalled to produce a total score for this section.

The result indicate that there was a significant difference in the means

between the health self-care group (M = 20.26, SD = 6.67) and medical

self-care (M = 27.33, SD = 12.15): t(54) 5.42, p = .01.(see Table 92).

These results indicate that in the health care group ‘illness’ interfered less

with daily activities than in the medical self-care.

Table 92. Illness Intrusive Scale for type of group

62 20.26 6.67

54 27.33 12.15

17 22.88 8.00

Illness InstrusiveRating Scale

Illness InstrusiveRating Scale

Illness InstrusiveRating Scale

group type healthself-care

medicalself-care

control

N MeanStd.

Deviation

199

Discussion This chapter will discuss the results of this study in reference to other research findings. These discussions will include some suggested reasons for some of the findings and recommendations for future research in this area.

Reducing the need and demand for medical services is a positive health strategy, one that

will bring better health for the individual and one that will lower the medical costs that

now utilize a dangerously high proportion of our nation’s productivity (Fries et al. 1997).

One of the positive health strategies is the use of self-care and self-management.

Providing health information for individuals about the self-management of disease has

had positive benefits in the form of reduced medical costs, better use of medical services

and increased self - confidence (self-efficacy) in making healthy choices (Bandura, 1997

b; Sterns et al. 2000; Fries et al. 1997a; Lorig et al. 1993a).

7.1 High Risk Assessment Questionnaire (HRA) The role of this questionnaire was very important in this study. This questionnaire is a

simple and user friendly one and designed to cater for lower reading levels which is one

of its advantages (Fries et al. 1992). Another advantage of this questionnaire is that it is

able to be administered over a short period of time due to its length of one page. One of

the disadvantages this questionnaire poses is that it is self-administered and may not

produce accurate results (see methods section). Its strength lies in the validation of this

questionnaire. Self-reporting bias is not likely to be present because of the validation of

200

this instrument and from previous research (Fries et al, 1992). The use of this

questionnaire in longitudinal studies may also give it some validation.

Self-assessed health status is strongly related to age. Age is an impact variable that has a

profound effect on individual’s health. At ages over 44 years, the proportion of

individuals reporting excellent or very good health declines with increasing age (AIHW,

2000). The results from all of the groups suggest that this is the case in this particular

study. Take the example of the health self-care group. Serious health problems were

reported in the age group 40-50 and this percentage increased in the 50-60 age group.

These results are skewed because of the nature of the participants in the study. The

participants within the health self-care group were all in the high health risk category as

determined by an HRA questionnaire and as a result of this bias could occur. Therefore, it

could be suggested that this could skew the results. This skewness in results across all

the groups is because of the category of the subjects in this study, that is they are all high

health risk. Self-assessed health status is strongly related to age, with the greater

proportion of the population reporting fair or poor health as increases in age occur

(AIHW,1998). A skewness in results from the HRA questionnaire could also be the result

of the ‘Hawthorn effect’. Behavioural changes could occur as the result of just

participating in this study. This could be seen more in the 6 month period (Q2) after the

administration the initial HRA questionnaire (Q1). Changes in health behaviour that

have occurred in the first six months may have lost their positive effect (decay effect) by

12 months (Q3). The educational influence of the printed materials could also be

considered as a ‘Hawthorn effect’. Use of these materials can increase illness related

201

knowledge, change attitudes towards personal susceptibility to disease and alter social

expectations for any medical care (Vickery et al. 1989). Thus as a result of the HRA

questionnaire and the educational support material changes did occur in self-efficacy.

This questionnaire can also act as a self-evaluation tool and result in positive

reinforcement. Participants see changes in their scores in the 6 month (Q2) after the

initial questionnaire (Q1) and believe that the behavioural changes they have made can be

continued on into the 12 month period (Q3).

The Null hypothesis for the outcome variable total risk scores was rejected. The total risk

scores over time for all the groups, experimental and control suggested a positive trend,

which is a decrease in the mean from the initial questionnaire (Q#1) to the time of the

second collection (Q#2). At (Q#2) and (Q#3) the means were similar to the initial mean.

This can be seen particularly in the medical self-care group (Q#1) M = 19.76, SD = 9.63)

(Q#2) M = 16.55, SD = 6.44) and (Q#3) M = 19.57, SD = 7.48). In the health self-care

group there was a decrease in the mean scores from (Q#1) M = 22.97, SD = 10.20) to

(Q#3) M = 13.71, SD = 11.49). The decrease in total risk scores was greatest for the

health self-care model. Decreases of 40 percent occurred between the initial and final

questionnaire. These results are much higher than Leigh et al. (1992) who reported

decrease of total risk scores of 7 percent over a 12 month period. The effect size (ES) for

the health self-care group of .48 is considered to be moderate - this is considered as a

meaningful treatment effect (Cohen 1977).

202

Thus, it can be argued that the health self-care group method of health promotion was

moderately more successful at reducing total risk scores than the medical self-care and

control groups. It suggests that the health self-care model of health promotion will

significantly lower total risk scores by a moderate amount across a period of six months

to one year, but how long this can be maintained is another research question. This may

be dependent on the extent of the support offered to maintain the changed behaviour.

Self-efficacy plays a significant role in this program and is considered to be one of the

main components in the changing unhealthy behaviours (Sallis et al. 1988, Kingsley et

al., Perri et al. 1986, Di Clemente 1981, Beck et al. 1982). Reduction of total risk scores

also occurred in the other groups but these were not significant.

Analysis of the outcome variable number of doctors visits suggests that there is a

significant difference between the two experimental groups therefore the Null hypothesis

was rejected. The examination of (Q#3) between the health self-care and medical self-

care results indicate a significant difference between the means (M = 2.23, SD = 2.22. M

= 4.14, SD = 4.22). The health self-care group had significantly fewer doctor’s visits

than the medical self-care group. These findings are supported by Vickery et al. (1983).

This could be due to the health self-care group having access to health information

material and being able to deal with their particular health problem without visiting a GP.

This would reduce the number of claims made to the health benefits organisation and thus

reducing costs. Thus, it may be suggested that the medical self-care group are more likely

to visit a GP to attend to their health problem or that a GP is the preferred model for

dealing with a health problem. Consulting a doctor (GP) is the second most common

203

health-related action taken by Australians, after the use of medication (AIHW, 2000).

These results reflect a similar finding by Stearns et al. (2000), when they state that the use

of self-care (health self-care) practices may also be associated with subsequent reductions

in the use or cost of health services. A factor to consider within these results is that of the

type of disease the individual suffers.

Another outcome variable associated with the use of medical services is that of days

spent in hospital . There were no significant differences in the means between the two

experimental groups. These data indicate that days spent in hospital by the health self-

care and medical self-care group participants were the same. A point to consider here is

that the questionnaire did not take into account the type of hospital care. There is an

increasing tendency towards day surgery and procedures and treatments that previously

required admission overnight and are now frequently being provided by out patient

clinics and day care facilities or community health services (AIHW, 2000). Some of the

advances in medical care have required individuals to spend less time in hospitals which

still makes this result important. Overall the health self-care participants were not

significantly different from the medical self-care as well as from the control group.

Vickery et al. (1988, 1989) believes that participants have a better understanding of how

to use and take advantage of the system therefore results of this kind for this outcome

variable are not uncommon. This outcome variable days spent in hospital within the

questionnaire has to be defined more clearly, such as outpatient in hospitals compared to

overnight or extended stays in hospitals.

204

Analysis of the outcome variable cost of disease suggests that there were some

differences among the groups, but these differences were not significant. The results

indicate that the health self-care group had a lower mean than the medical self-care and

control groups. This indicates that the health self-care program was able to lower the cost

of disease over a 12 month period more than the medical self-care and control groups.

Over the period of the questionnaire there were no significant differences between the

means of the two experimental groups. This results indicates that both experimental and

control groups were able to lower disease costs over the 12 month period (health self-care

– 4.4%, medical self-care – 2.2 % and control – 1.2%). Fries et al. (1992) was able to

achieve lowering the cost of disease by 5 percent over a 12 month period with the health

self-care program which is similar to this study. It would probably be more important in

this particular variable to examine changes over time especially longer than a one year

period. Other researchers report that, self-care activities have the potential for cost saving

and may be significant and attainable over the long term (Stearns et al. 2000). Some self-

care programs have been able to reduce cost by as much as 18 percent over an 18 month

period (Fries et al. 1992).

The examination of individual diseases within the cost of disease variable indicates that

diseases such as arthritis in the health self-care group achieved a lower mean cost than

the medical self-care group, but these were not significant. For diseases such as blood

pressure the opposite occurred. This could be due to the medical self-care group being

able to reduce blood pressure more quickly through medication than the health self-care

group. In this case it would be more cost effective to reduce blood pressure as quickly as

205

possible, therefore the medical self-care model would be an advantage. The cost of heart

disease increased in the medical self-care group where as the cost of this disease was

lowered in both the control and health self-care groups. This could be due to more visits

to the GP by the medical self-care group and this being reflected in the number and cost

of claims.

7.2 - Health self-efficacy questionnaire

Self-efficacy is a person’s judgement of his or her ability to cope effectively in a situation

(Clark et al. 1991). In this specific situation it is the ability to cope with the effects of an

individuals high-risk health behaviour. Individuals with high self-efficacy will be able to

confront a high risk situation and cope successfully (Clark et al. 1991a). The results for

this section of the study suggest that health self-efficacy plays a major role in the

changing of behaviour. The examination of self-efficacy as it relates to the three groups

suggests the following results.

The Null hypothesis was rejected and the alternative accepted for the process variable

self-management. Within the questionnaire there are sub sections which relate to various

aspects of self-efficacy. Self-management is the day to day tasks an individual must

undertake to control or reduce the impact of disease on physical health status (Clark et al.

1991b). The results indicate that the health self-care group participants were more

confident about self- managing their high risk health behaviours than the other two

206

groups. These results suggest that individuals who are in the health self-care program

have a greater belief that they are able to change their behaviour and to reduce their high

health risk status to one of a lower risk status in terms of self-management.

Individual questions within this section of the questionnaire indicate that significant

differences occurred in questions such as exercise between the health self-care and

medical self-care groups. The health self-care group were more confident at participating

in exercise 3–4 times per week and exercising for the next three months. The role of self-

efficacy is important not only in exercise participation but continuing on with a program

(Marcus et al. 1992, 2000, McAuley, 1992). Health self-care provides the support

gained by constant evaluation by questionnaire and materials sent to participants seems to

add to the participants feeling of self-efficacy. Continuing feedback about how one is

doing is essential in sustaining the process of change (Bandura, 1997b).

One aspect of self-management is the ability of the individual to use the knowledge that

is provided to improve their health status. People achieve self-directed change when they

understand how personal habits threaten their well-being and are taught how to modify

them, as well as the belief in their capabilities to marshal the effort and resources needed

to exercise control (Bandura, 1997a).

Health self-care provides individual information about each of the conditions the

individual suffers and how to improve personal health based on this formation. The

medical self-care model provides interventions based on GP’s which results in only small

207

changes in health outcomes (CDHA, 2001). Enhancement of the self-efficacy belief,

leads to increases in motivation and success with behavioural efforts (Maibach &

Murphy, 1995). Adherence to the feedback provided by health self-care could also be a

factor in the differences between the two experimental groups. Bandura (1982), suggests

that ‘enactive’ information which is feedback from performance in this case feedback

from health self-care, may be an effective source of strengthening the individuals belief

in change. An important aspect which was not examined in this study was how age of the

participants effects self-efficacy in relationship to self-management. This needs further

investigation. There seems to be some evidence to suggest that age does have an effect

on health self-efficacy (Clark et al. 1991b).

The second sub group within the health self-efficacy questionnaire was related to

management of disease. Management of disease is meant to provide information about

how to use different methods to manage disease e.g. doctor’s visits. The results for this

process variable indicates that there were no significant differences between health self-

care, medical self-care and control groups therefore the Null hypothesis was accepted.

These results may not be accurate because of the number of subjects within the control

group, (n =20). There may not be the statistical power to produce a meaningful result

from the control group. These findings suggest that self-efficacy was the same between

the health self-care and medical self-care regarding management of disease. It may be

indicate that both experimental groups were effective in the management of disease.

Self-efficacy scores for the both groups suggest this. The participants have confidence in

the management of disease whether they visit a GP or receive printed health

208

information. It has been suggested that GPs need to be up to date and skilled at selecting

and using specific preventive interventions that have been shown to be effective

(CDHAC, 2001).

The section of the questionnaire relating to the variable achievement of outcomes,

results indicated that there were no significant differences between the groups indicating

that the Hull hypothesis was accepted. Participants in each of the groups within the study

believed that their self-efficacy was strong enough to achieve an outcome regarding their

health behaviour or believed that self-efficacy would help them achieve an outcome. By

visiting a GP or receiving health information the participants felt that they had the

confidence to change their health status. It is important to remember that both

experimental groups received some form of health information and felt confident with

that information, that is, they could change their high-risk health behaviour.

The final section of the questionnaire related to health self-efficacy and the belief the

participants could change unhealthy behaviours. The results from this section indicate

that there were no significant differences between the three groups. It did not matter

which group the participants belonged to their belief was that they could change their

unhealthy behaviour. This could be due to a belief that all the subjects were in a high risk

health grouping and they needed to change their behaviour to improve their health. It did

not matter whether they visited a doctor or had health information material send to them,

they believed that they were confident about changing their unhealthy behaviours. Self-

efficacy beliefs are dynamic and subject to influence; they are the product of on-going

209

cognitive, behavioural and communication processes (Maibach et al. 1995). What people

need is knowledge about how to regulate their behaviour and to possess a firm belief in

their personal efficacy to turn concern into effective action (Bandura, 1997). This is what

the health self-care and medical self-care models were able to do, provide knowledge for

the individual to regulate their behaviour either from a GP or printed health information.

The control group knew that they were classified as high risk and decided that

improvements to their health needed to take place using the resources that were available

to them.

One group of process variables that of GP questions within the management of disease

was collapsed and the results suggest there were no significant differences between the

groups. The results propose that self-efficacy was the same for the health self-care and

medical self-care on this particular variable. It could be argued that participants within

these two groups were still able to manage disease whether they were under the care of a

GP or receiving health information. Long term studies need to be carried out to answer

this question in more detail. It has been argued that many GP’s do not know how to

change high risk behaviour because they are not able to spend the time or make much

money doing it (Bandura, 1997). Sidel (1998) suggests that patients must be educated

about the nature of illness and the treatment choices so they may participate fully in their

care . . . the GP has the responsibility to provide those supports when possible.

In the sub section, self-management of disease a number of questions about GP’s and

self-management were collapsed. The results here indicate no difference between the

210

groups. Health self-care can be seen as a home-based health promotion program. Where

as medical self-care requires a visit to a health professional to promote health. Research

into older population groups suggests that self-management programs have the ability to

improve the individuals ‘desire for information’ which in turn has the long term effect of

motivating the individual to self-manage their own needs through empowerment

(McWilliams et al. 1999).

The overall results for the different sections of the questionnaire produced mixed results.

The Null hypothesis was accepted in sections such as management of disease, health self-

efficacy and achievement of outcomes and rejected in the sections such as self-

management. No differences occurred between the three groups in some sections but

some significant differences occurred in individual questions such as exercise. The

health self-care group participants received the printed health information sent to them

which provides a guide on how to deal with their particular health problem, where as the

participants in the medical self-care group were advised to seek advice from their GP.

This appears to have the effect, that the participants within the medical self-care group

perhaps were not as confident at self-management of their health problem and

consequently tended to seek verbal advice from their GP. This view is supported by

Bandura (1997b) when he suggests that, self-management programs (health self-care)

based on a self-efficacy model improve the quality of health and reduce the need for

medical services. This can be seen in the results such as number of doctor’s visits.

Decreases occurred more in the health self-care group than the medical self-care group.

211

These programs equip participants with the skills and personal efficacy needed to

exercise self-directed change (Bandura, 1997b).

These results confirm that the level of education whether it primary, secondary or

tertiary has a bearing on behaviour change. The health self-care group results indicate

that the level of education played a significant role in the differences in total health self-

efficacy scores and self-management scores. The higher the level of education the more

confident participants were at understanding their health problems and in using the

resources to improve health. Health inequalities are caused by a complex play of a

number of factors one of which is levels of education (AIHW, 2000). In this study a

confounding variable to consider is that the participants in the health self-care groups all

belonged to a health benefits organisation - namely that of a professional teachers

organisation. Thus, it can be assumed that most of the members who belong to this

organisation are likely to have university or college qualifications which could bias the

results. Some of the participants live in rural areas where health services are relatively

poor compared to the facilities and services in cities and metropolitan area. Another

variable to consider is that of geographical location. Personal health risk factors tend to

be worse in remote areas than in metropolitan areas (AIHW, 2000).

The results of this study indicate a number of significant differences occurred between

the two experimental groups that of health self-care and the medical self-care. Analysis

of the health self-care group revealed that variables such as, number of doctors visits,

risk of heart disease and total risk scores were significantly different from the medical

212

self-care group. The ES (0.4) of these outcome variables was considered to be moderate.

The moderate meaningfulness of these results suggests that the health self-care

participants visited the doctor on less occasions, able to decrease heart risk more and

produced lower total risk scores than the medical self-care group. The health self-care

model of health promotion may provide the individual with a better understanding of

health issues and support services than the medical self-care model. On variables such

as, risk of heart disease positive differences occurred within the health self-care group

over the study period (38% decrease) In contrast, the medical self-care group reduced its

scores by a smaller margin of 13%. The health self-care approach seems to be an

effective health promotion model in terms of reducing risk of heart disease. Risk of

cancer scores were reduced by both group but these findings were not significant. The

cost of disease findings indicate that no differences occurred between the two

experimental groups. However, there were differences in some type of disease such as

blood pressure where the medical self-care reduced more than the other two groups.

In the health self-efficacy questionnaire the findings suggest that enhanced self-efficacy

has a positive influence on the ability of individuals to manage and change their health

behaviour. Individuals achieve self-directed change when they understand how personal

habits threaten their well-being, given information how to modify them, and believe in

their capabilities to marshal the effort and resources needed to exercise control (Bandura,

1997b). With regards to management of disease the findings suggest that both models

developed similar self-efficacy levels in the participants.

213

This is also true for individuals in the variable, achievement of outcomes - no

differences occurred among any of the groups. It may be concluded that no differences

occurred among the groups, in the belief that you can change your unhealthy behaviour to

achieve an outcome. Self-efficacy is a strong indicator of behavioural change as

suggested by a number of authors (Bandura 1997a: Maibach et al. 1995: Sallis et al.

1998: Di Clemente, 1981, 1986, 1991: Ewart et al, 1986: Freldman et al, 2000).

There are strengths and weaknesses in the two experimental group philosophies when it

comes to self-efficacy. The health self-care approach provides health information,

support and feedback which encourages individuals to change their behaviour where as

the medical self-care approach provides information and feedback to participants in the

form of individual consultations by a GP. GP’s are ideally situated to foster preventive

habits but believe and their efforts e.g. encouraging quit smoking, will really produce

results (Bandura 1997b). This view is supported by Mullins et al. (1999) who found that

visiting GP’s with the view of quitting smoking that half the smokers reported getting no

advice or inappropriate advice from their GP. This may occur in the short term but they

believe that in the long term individuals go back to their old habits. Not only is health

knowledge provided to the individual by the health self-care concept but a vast number of

participants are helped concurrently at low cost and this lends itself readily to preventive

purposes (Bandura 1997a). Health self-care offers the individual the chance of personal

empowerment where they are able to influence their own health through appropriate

behaviour. The value of health promotion programs are important in medical costs

reduction and in making individuals aware of health risk and finally the identification of

214

those risks (Musich et al. 2000). Thus, the provision of health knowledge to the

individual is that they may improve their own health. Also developing the capacity to act

on that knowledge to modify their health behaviour is an important aspect in medical cost

reduction.

This study’s findings are mainly related to health benefits organisations in the Australian

setting. The author feels that that these results may be portable to other situations such as

lower socioeconomic status groups as well as migrants. The printed materials as used by

Healthtrac can be used effectively within these groups except that it would have to be

modified to suit the educational and cultural needs of different groups within Australian

society. These materials can be used by other health professional such as community

nurses to improve and make aware of the different methods to improve the health status

of their communities. In some areas of Australia there are not always GP’s available for

consultation for issues that another health professional could deal with such as changing a

high-risk behaviour. From the findings of this study the results indicate that this

approach would be appropriate to change high-risk behaviours. A shared care approach

to the prevention and management of disease would be the ideal situation for the

reduction of chronic diseases. Sidel (1998) believes that the age or gender or skin colour

or the language or the ethnic origin or the educational level or social class of the patient,

the absence of insurance coverage or the patient’s inability to pay for care can all alter the

care given by a doctor.

215

7.3 Recommendations for future research

The concept of self-efficacy needs to be researched to examine why differences occur in

health behavioural scores over a period of time. There is an initial score which is the

base line data, and at the first measure after 6 months there is a significant difference

between the means. Over a period of time the scores regress to the baseline score - why

does this happen? (decay effect). What behavioural and social factors result in the

increase in risk scores from the improved scores to the originals ?

There is a need to examine the role of self-efficacy in health behaviour in regards to

motivation to change high-risk health behaviours in all populations especially teenagers

and young adults. This is to understand and develop programs that will help teenagers

and young adults to overcome and deal with high health risk behaviours that will improve

health status of the individual. This would decrease medical costs and use of medical

facilities in the long term.

There is a need to design health behavioural programs that are particularly related to

certain types of the high risk areas using self-efficacy. This has been achieved in some

areas such as arthritis (Lorig et al. 1989) but needs to be extended to other high risk

behaviour areas. The role of bibliotherapy (medical intervention from a book) such as

how different combination of printed materials may assist in reducing the high risk

behaviours of individuals.

216

There is a need to examine the health issues of our multi-cultural society in terms of

cultural self-efficacy and situational self-efficacy. We need to understand why there are

differences in health patterns within Australia’s multi-cultural groups and how these

relate to generational changes in these populations.

The role of self-efficacy in this study referred to individuals who belonged to health

benefits organisations. There could be differences in health self-efficacy between

individuals who do not belong to these organisations and the need to examine these

differences in terms of variables such as socio-economic status and ethnicity within the

Australian community.

This study examined participants who were part of health benefits organisations however,

do individuals outside such organisations react in the same way as individuals who are

not in a health benefit organisation? Components of these variables could include

geographical location, socio-economic status, levels of disease and age. Individuals may

have different literacy skills which would not allow them to comprehend health

information, therefore, they are less likely to improve their health status. If we are to

examine the concept of empowerment this is a vital issue.

Research needs to be conducted on different methods of delivery of health information to

participants such as the electronic media and how self-efficacy relates to the different

types of delivery systems.

217

APPENDIX 1. Healthtrac - Healthtrac’s HRA questionnaire and risk profile. 2. Health self-efficacy questionnaire. 3. Health promotion/education materials 4. Flow Chart – HRA questionnaire (Q1, Q2, Q3).

218

Abbreviations

ACS Australian Cancer Society ACT Australian Capital Territory ADF Australian Drug Foundation AHEC Australian Health Ethic Committee AHMAC Australian Health Ministers Advisory Council AIHW Australian Institute of Health and Welfare BMI Body Mass Index CDPHP Centre for Disease Prevention and Health Promotion CHD Coronary Heart Disease CVD Cardio vascular disease CDHA C Commonwealth Department of Health and Aged Care CDHSH Commonwealth Department of Human Services and Health CYS Centre for Youth Drug Studies DHFS Department of Health and Family Services ES Effect size HRA Health Risk Assessment GP General Practitioner ITDM Insulin-treated Diabetes Mellitus MOU Memorandum of Understanding MRC Medical Research Council NHAC National Health Advisory Council NHF Nation Heart Foundation NHMRC National Health and Medical Research Council NSW New South Wales Q1 HRA questionnaire #1 – baseline data (initial) Q2 HRA questionnaire #2 – 6 month data collection Q3 HRA questionnaire #3 – 12 month data collection (final) TM Transtheoretical Model VHPF Victorian Health Promotion Foundation WHO World Health Organization

219

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Appendix 2

HEALTH PROGRAM SURVEY This questionnaire focuses on the health promotion program in which you have been involved. The information will help us provide a better health service to you. Please fill out this questionnaire to the best of your ability. This information will remain confidential at all times. Follow the instructions as set out for each question. If you have any questions please call Jack Dzenis at Queensland University of Technology on (07) 3864 3360. The first few questions are for background information. Please circle the appropriate response Gender male 1 female 2 Age ____ Martial status single 1 married 2 widowed 3 divorced 4 separated 5 de facto 6 Are you an Australian citizen? yes 1 no 2 Is English your first language? yes 1 no 2 If English is not your first language, what language do you speak as your first language? _________ Are you a 1st generation Australian 1 2nd generation Australian 2 3rd generation Australian 3 greater than 3rd generation 4 Do you live in a city 1 town 2 rural or 3 remote area 4 What level of education have you achieved? primary 1 secondary 2

tertiary 3 In general would you describe your state of health as . . . excellent 1 very good 2 good 3 fair 4 poor 5 Compared to 12 months ago, how would you rate your health in general now? Please circle the appropriate number much better than 12 months ago 1 somewhat better now than 12 months ago 2 about the same as 12 months ago 3 somewhat worse than 12 months ago 4 much worse than 12 months ago 5 How much does your illness or treatment interfere with your . . . Please circle the appropriate number from 1 to 5 not very much a little very much 1 2 3 4 5 health 1 2 3 4 5 diet 1 2 3 4 5 work 1 2 3 4 5 active recreation 1 2 3 4 5 passive recreation 1 2 3 4 5 financial situation 1 2 3 4 5 relationship with spouse 1 2 3 4 5 sex life 1 2 3 4 5 family relations 1 2 3 4 5 other social relationships 1 2 3 4 5 self-expression/self-improvement 1 2 3 4 5 religious expression 1 2 3 4 5 community involvement 1 2 3 4 5 Self-management of your health behaviours We would like to know how confident you are in doing certain activities. For each of the following questions, please circle a number between 1 and 5 that corresponds to your confidence that you can do the tasks regularly at the present time. How confident are you that you can . . . Not at all moderately confident extremely confident

1 2 3 4 5 1. Exercise 3-4 times per week? 1 2 3 4 5 2. Continue with an exercise program for the next 3 months? 1 2 3 4 5 3. Use exercise to improve your health? 1 2 3 4 5 4. Gently do flexibility/ strengthening exercises 3-4 times per week? 1 2 3 4 5 5. Do aerobic exercise such as walking 3-4 times per week 1 2 3 4 5 6. Exercise without making your symptoms worse? 1 2 3 4 5 7. Ask your GP about matters that concern you? 1 2 3 4 5 8. Carry out the instructions the GP has recommended? 1 2 3 4 5 9. Manage your health problems after visiting your GP? 1 2 3 4 5 10. Work out differences with your GP regarding your treatment? 1 2 3 4 5 11. Obtain all your answers to your health problems from your GP? 1 2 3 4 5 12. Get family and friends to help you with the things you need? 1 2 3 4 5 13. Receive emotional support from family and friends regarding your health problems 1 2 3 4 5 14. Receive emotional support from family and friends to improve your health 1 2 3 4 5 15. Receive help with your daily tasks from resources other than friends or family if needed? 1 2 3 4 5 Manage your Disease(s) in General We would like to know how confident you are in doing certain activities. For each of the following questions please circle a number between 1 and 5 that corresponds to your confidence that you can do the tasks regularly at the present time. How confident are you that you can . . . Not at all moderately confident extremely confident 1 2 3 4 5 1. Manage your health problems? 1 2 3 4 5 2. Manage your health problems on a regular basis? 1 2 3 4 5 3. Manage some of your health problems without visiting a GP? 1 2 3 4 5 4. Make behavioural changes that will require less medications to be used? 1 2 3 4 5

5. Judge when the changes in your illness occur and when you should visit a GP? 1 2 3 4 5 6. Make behavioural changes that will reduce the need to visit a GP? 1 2 3 4 5 7. Reduce the emotional distress caused by your health condition so that it does not affect daily life? 1 2 3 4 5 8. Use health information to improve your health? 1 2 3 4 5 9. Understand your health problem? 1 2 3 4 5 10. Make behavioural changes that will positively manage your health problem? 1 2 3 4 5 Achieve Outcomes We would like to know how confident you are in doing certain activities. For each of the following questions, please circle a number between 1 and 5 that corresponds to your confidence that you can do the tasks regularly at the present time. How confident are you that you can . . . Not at all moderately confident extremely confident 1 2 3 4 5 1. Reduce your physical discomfort or pain? 1 2 3 4 5 2. Keep the fatigue caused by your disease from interfering with the things you want to do? 1 2 3 4 5 3. Control any symptoms or health problems so that they don’t interfere with daily life? 1 2 3 4 5 4. Keep physical discomfort or pain from your disease interfering with daily life? 1 2 3 4 5 5. Continue to do hobbies and recreation? 1 2 3 4 5 6. Continue to do activities with friends and family? 1 2 3 4 5 7. Complete your household chores, despite your health problems? 1 2 3 4 5 8. Do your errands despite your health problems? 1 2 3 4 5 9. Keep your shortness of breath from interfering with what you can do? 1 2 3 4 5 10. Keep from getting discouraged when nothing you do seems to make a difference? 1 2 3 4 5 11. Keep yourself from feeling sad or down in the dumps? 1 2 3 4 5 12. Do something to make yourself feel better when you are feeling discouraged? 1 2 3 4 5 13. Keep yourself from feeling lonely? 1 2 3 4 5 Health self-efficacy - your belief in your capacity to change unhealthy behaviours and habits

We would like to know how confident you are in doing certain activities. For each of the following questions, please circle a number between 1 and 5 that corresponds to your confidence that you can do the tasks regularly at the present time. How confident are you that you can . . . Not at all moderately confident extremely confident 1 2 3 4 5 1. Achieve your goals to decrease the risk of disease?1 2 3 4 5 2. Set goals to improve your health? 1 2 3 4 5 3. Spend the time to improve your health? 1 2 3 4 5 4. Adhere to a preventive health program after returning to old habits? 1 2 3 4 5 5. Have personal control over your health problems?1 2 3 4 5 6. Have motivation to improve your health? 1 2 3 4 5 7. Put into action the advice of health professionals? 1 2 3 4 5 8. Continue with your health program under conditions of excessive demands? 1 2 3 4 5 9. Use health knowledge to modify your behaviour so that it can improvement in health? 1 2 3 4 5 10. Financially afford to improve your health? 1 2 3 4 5 11. Access necessary health services? 1 2 3 4 5 Thank you for participating in this questionnaire and helping to improve the program for future participants

Appendix 3. Books and phamplets

Ferguson, J. Habits not diets. Bull Publishing Co: Palo Alto California. The essentials for health and fitness – high blood pressure. Baker Research Institute The essentials for health and fitness – depression and anxiety. Mental Health Foundation of Australia Matthews, A. Being happy – a handbook to greater confidence and security. Media Masters: Singapore Better Health. Nutrition. Healthtrac health education and research centre. Lowe, E & Arsham, G. Diabetes – A guide to living well – A program of individualized self-care. Chronimed Publishing: Minneapolis Nash J. Now that you’ve lost it. Bull Publishing: Palo Alto. California. Lorig, K et al. Living a healthy life with chronic conditions – self- management of heart disease, arthritis, stroke, diabetes, asthma. Bronchitis, emphysema and others. Bull Pulishing. Palo Alto California. Fortman S. & Breitrose, P. The blood pressure book – how to get it down and keep in down. Bull Publishing. Palo Alto California. Swezey, R & Swezey, A. Good news for bad backs. Cequal Publishing Co. Santa Monica California. How to get cool – quitting guide. Program based on “Cool turkey” by Stanford Heart Disease Prevention Program, Stanford University. Fries, J. Aging well – a guide for successful seniors Addison- Wesley Publishing Co. Sydney.

Exercise for arthritis. Healthtrac exercise program. Healthtrac health education and research center. Jovanovic, L. Living with diabetes type II- a guide for people with non-insulin dependent diabetes. Bookman Press. Melbourne Understanding back trouble – practical advice on how to prevent, treat and cope with back problems. A Choice Book. Good food and good health for life is your choice – Healthtrac centre for research and health education.

Appendix 4

Flow diagram of health self-care, medical self-care and control groups – HRA

questionnaire

Healthtrac Better Health (health self-care) (medical self-care) n = 799 n =8,000 HRA questionnaire (initial) #1 HRA questionnaire (initial) #1 N = 455 high risk Control group n = 200 high risk N = 344 HRA questionnaire #2 HRA questionnaire # 2 HRA questionnaire # 2 (6 months) (6 months) (6 months) HRA questionnaire # 3 HRA questionnaire # 3 HRA questionnaire # 3 (12 months) final (12 months) final (12 months) final