Five-factor personality traits and trajectories of glycaemic ...

Sweet Disposition: Five-factor personality

traits and trajectories of glycaemic control,

self-care, negative affect and coping in

Australian youth with type I diabetes

Daniel Waller

B.Psychology (Hons)

A thesis submitted in fulfilment of the requirements for

the degree of Doctor of Philosophy

School of Education

University of Western Sydney

January 2013

Acknowledgements

Completing my doctoral thesis has been one of the most challenging

experiences of my life. Achieving this goal would have been impossible

without the following people who have my heartfelt thanks.

I’d like to thank my supervisors Associate Professor Christine Johnston, Dr

Lorraine Smith and Associate Professor Jane Overland for their guidance

and unerring support. Thanks Chris for your encouragement, good humour

and countless free coffees. Thanks Lorraine for your calm reassurance,

organisation and willingness to drop everything for your students. Thanks

Jane for your dedication, constructive feedback and astute

recommendations.

I’d also like to thank the rest of the team from the Diabetes Research into

Adolescent Transitions (DRAT) project. Thanks to Lin Singh who worked

tirelessly to organise us and keep the project moving forward. Thanks to my

fellow PhD student Kristy Hatherly for her welcoming manner and support.

Special thanks both to Lynda Molyneaux and Dr Alex Yeung (the statistics

gurus) for their help with data analysis. Thanks to the administrative staff at

the University of Western Sydney who provided fantastic advice. Thanks to

the Australian Research Council, Novo Nordisk and the Royal Prince Alfred

Diabetes Centre for funding the DRAT project and thanks to the participating

pharmacies and hospitals for help with recruitment of participants. My

gratitude is also to Dr Steve Provost and the psychology staff at Southern

Cross University who provided much support in my final year of research. A

great big thank you to the unbelievably hospitable participants of the DRAT

study without whom this research would be impossible.

Most of all, I would like to thank my family and my gorgeous girlfriend Amy

Armour (I love you!) for their patience and support over the years. The faith

you have in me has been inspiring and I could not have finished this thesis

without you.

Declaration Certificate

I hereby certify that the work embodied in this thesis is the result of original

research and has not been submitted for a higher degree at any other

University or Institution.

Signed……………………………………..…..

Dated……………………………………..……

Publications and Presentations

The research presented in this thesis has been published and presented as

follows:

Peer-reviewed journal articles

Waller, D., Johnston, C., Molyneaux, L., Brown-Singh, L., Hatherly, K.,

Smith, L., & Overland, J. (2013). “Glycaemic control and blood glucose

monitoring over time in a sample of young Australians with type 1

diabetes: the role of personality.” Diabetes Care, In Press.

Hatherly, K., Smith, L., Overland, J., Johnston, C., Brown-Singh, L., Waller,

D., and Taylor, S. (2011). “Glycaemic control and type 1 diabetes:

the differential impact of model of care and income”. Diabetes. 12(2)

115-9. doi: 10.1111/j.1399-5448.2010.00670.x.

Conference presentations


D. and Taylor, S. (2010). “Implementation of Australian Paediatric

Endocrine Group (APEG) clinical management guidelines: A major

shortfall identified”. Australian Diabetes Society and Australian

Diabetes Educators Association Annual Scientific Meeting. (Sydney,

September 2010).

Waller, D., Johnston, C., Smith, L. & Overland, J. (2009) “Big Five

Personality Traits and Self-Management of Type 1 Diabetes in

Adolescence”. 8th Australasian Conference on Personality & Individual

Differences. (University of Sydney, Australia, 25th November, 2009).

Waller, D. (2009) “Personality and Type I Diabetes”. College of Arts

Conference. (University of Western Sydney, 11 October, 2009).

Waller, D. (2009) “Metabolic Control in Adolescents with Type I Diabetes: An

Argument for the Role of Personality”. 23rd Annual Congress of the

European Health Psychologists Society. (Palazzo Dei Congressi, Pisa,

Italy, 25th September, 2009).


D., and Taylor, S. (2009). “Exploring the role of healthcare in the lives

children and adolescents with Type 1 diabetes”. Faculty of Pharmacy

seminar series. (University of Sydney, September 2009).

Waller, D. (2008). “Personality and Type 1 Diabetes”. College of Arts and

Education. (University of Western Sydney, October 2008).


D., and Taylor, S. (2008). “The role of diabetes teams in the care of

children and adolescents with Type 1 diabetes”. Australian Disease

Management Association 4th Annual National Disease Management

Conference. (Sydney, September 2008).


D., and Taylor, S. (2008). “Children and Adolescents with Type 1

diabetes: an Australian experience”. Postgraduate Seminar Series,

Faculty of Pharmacy. (University of Sydney, June 2008).

i

Table of Contents

List of Tables ................................................................................................ v

List of Figures ........................................................................................... viii

List of Abbreviations ................................................................................... x

Abstract ....................................................................................................... xi

Chapter 1 Introduction ................................................................................ 1

Type I diabetes .................................................................................................. 2

The Diabetes Control and Complications Trial ............................................... 4

Self-management of type I diabetes ................................................................ 6

Determining quality of diabetes self-management ......................................... 6

Self-management of type I diabetes in youth .................................................. 8

Factors influencing self-management of type I diabetes in youth ................. 9

Personality and self-management of type I diabetes .................................... 11

Chapter 2 Why Study Personality? .......................................................... 13

The five-factor model: The unique strengths of structure

and permanence .............................................................................................. 15

The five-factor model of personality .............................................................. 16

Employing the five-factor model as an organisational framework .................. 20

Employing the five-factor model as a longitudinal predictor........................... 25

Theoretical and research perspectives on personality-health interactions 29

The Health–Behaviour Model ....................................................................... 30

The Stress Model ......................................................................................... 33

The Adaptation to Illness Model .................................................................... 34

The value of personality research: Identification and intervention ............. 37

Summary .......................................................................................................... 42

Chapter 3 Five-factor Personality Traits and Management of Diabetes 43

Personality traits and self-management of type I diabetes .......................... 45

Conscientiousness........................................................................................ 45

Emotional regulation ..................................................................................... 49

Agreeableness .............................................................................................. 55

Extraversion.................................................................................................. 59

Openness to experience ............................................................................... 60

ii

New directions for research ........................................................................... 62

Proposing methodological and theoretical explanations for research findings .......................................................................................... 62

Promoting a comprehensive view of diabetes self-management ................... 65

Examining the role of personality in self-management outcomes over time .. 66

Aim, research questions and hypotheses ..................................................... 67

Research Question 1: ................................................................................... 67



Hypotheses .................................................................................................. 68

Chapter 4 Design and Methodology ......................................................... 70

The DRAT project design................................................................................ 71

Recruitment of participants ............................................................................ 72

The personality study design ......................................................................... 74

Measures ......................................................................................................... 75

Demographic information .............................................................................. 75

Diabetes treatment and level of responsibility for care .................................. 76

Personality .................................................................................................... 76

Self-care behaviours ..................................................................................... 78

Glycaemic control ......................................................................................... 79

Incidence of hyperglycaemic emergency and hypoglycaemia ....................... 79

Depression, anxiety and stress ..................................................................... 79

Coping .......................................................................................................... 83

Procedures ...................................................................................................... 84

Annual visits ................................................................................................. 85

Quarterly phone calls .................................................................................... 86

Collection of personality (FFPI-C) data ......................................................... 87

Procedures to maximise retention ................................................................. 88

Ethical approval and conduct ........................................................................ 88

Chapter 5 Statistical Procedures .............................................................. 90

Data screening ................................................................................................ 91

Identification of missing data ......................................................................... 91

Reliability analysis ........................................................................................ 92

Exploratory factor analysis of CODI avoidance scale .................................... 94

Univariate analyses ...................................................................................... 94

Data transformation attempts ........................................................................ 96

iii

Description of the sample and preliminary analyses ................................... 97

Inclusion and exclusion of variables from personality analyses ..................... 99

Answering research questions .................................................................... 100

Bivariate analyses ...................................................................................... 101

Longitudinal analyses of personality traits and self-management trajectories ...................................................................... 104

Chapter 6 Sample Characteristics and Preliminary Analyses ............ 106

Description of the sample ............................................................................. 107

Age and duration of diabetes ...................................................................... 107

Treatment Modality ..................................................................................... 110

Ethnicity ...................................................................................................... 111

Geographical location ................................................................................. 111

Household annual income .......................................................................... 112

Responsibility for diabetes care .................................................................. 112

Personality ..................................................................................................... 115

Self-management outcomes ......................................................................... 119

Glycaemic control ....................................................................................... 120

Blood glucose monitoring............................................................................ 121

Adherence to prescribed insulin .................................................................. 123

Utilisation of health-care ............................................................................. 124

Hospitalisations and hypoglycaemia ........................................................... 125

Coping ........................................................................................................ 126

Depression ................................................................................................. 128

Anxiety........................................................................................................ 130

Stress ......................................................................................................... 133

Summary of preliminary results ................................................................... 136

Inclusion of variables in further analyses .................................................... 139

Chapter 7 Results .................................................................................... 141

Research Question 1 ..................................................................................... 141

Five-factor model traits and HbA1c values ................................................... 141

Five-factor model traits and frequency of blood glucose testing .................. 145

Five-factor model traits and hypoglycaemia ................................................ 146

Five-factor model traits and depression ...................................................... 146

Five-factor model traits and anxiety ............................................................ 148

Five-factor model traits and stress .............................................................. 149

Five-factor model traits and acceptance coping .......................................... 150

iv

Five-factor model traits and avoidance coping ............................................ 150

Summary of bivariate analyses ................................................................... 151


Determining independent trait predictors of HbA1c scores ........................... 152

Determining independent trait predictors of blood glucose testing .............. 155

Determining independent trait predictors of hypoglycaemia ........................ 157

Determining independent trait predictors of depression scores ................... 158

Determining independent trait predictors of anxiety scores ......................... 160

Determining independent trait predictors of stress scores ........................... 162

Determining independent trait predictors of acceptance coping .................. 163

Determining independent trait predictors of avoidance coping .................... 165

Summary of regression analyses ................................................................ 166


Conscientiousness and HbA1c trajectories .................................................. 168

Agreeableness and HbA1c trajectories ........................................................ 170

Emotional regulation and HbA1c trajectories ................................................ 172

Conscientiousness and blood glucose testing trajectories .......................... 174

Emotional regulation and depression trajectories ........................................ 176

Emotional regulation and anxiety trajectories .............................................. 177

Emotional regulation and stress trajectories ............................................... 179

Emotional regulation and avoidance coping trajectories ............................. 180

Agreeableness and avoidance coping trajectories ...................................... 182

Openness to experience and acceptance coping trajectories ..................... 183

Summary of longitudinal analyses .............................................................. 185

Chapter 8 Conclusions and Future Directions ...................................... 187

Study strengths ............................................................................................. 187

Summary of key findings .............................................................................. 189

Limitations ..................................................................................................... 199

Avenues for intervention .............................................................................. 202

References................................................................................................ 205

List of Appendices ................................................................................... 231

v

List of Tables

Table 4.1: Raw scores for DASS and related Z scores and percentile ranks ............................................................................. 82

Table 4.2: Psychometric properties of CODI subscales ............................... 84

Table 5.1: Cronbach’s alphas for measurement scales ............................... 93

Table 5.2: Descriptives for first year variables ............................................. 96

Table 5.3: Control and outcome measures included in personality analyses ...................................................................... 100

Table: 6.1: Baseline characteristics for personality study and DRAT project .............................................................................. 107

Table 6.2: Depression scores for newly diagnosed participants ................ 109

Table 6.3: Acceptance coping stratified by gender .................................... 110

Table 6.4: Mean-levels of responsibility over three years .......................... 113

Table 6.5: Relationships between demographics and self-management ... 115

Table 6.6: Test-retest correlations for FFPI-C traits over three years ........ 116

Table 6.7: Gender differences in openness to experience ......................... 117

Table 6.8: Gender differences in emotional regulation ............................... 118

Table 6.9: Relationships between demographics and personality ............. 119

Table 6.10: Diabetes outcomes at baseline ............................................... 119

Table 6.11: Pearson’s test-retest correlations for HbA1c over three years ................................................................... 120

Table 6.12: Spearman’s test-retest correlations for BGL tests over three years ................................................................................................. 122

Table 6.13: Adherence to prescribed insulin over three years ................... 123

Table 6.14: Diabetes appointments over three years ................................. 125

Table 6.15: Hospitalisations and hypoglycaemia over three year .............. 125

Table 6.16: Spearman’s test-retest correlations for hypoglycaemia frequency .................................................................. 126

Table 6.17: Mean-levels of coping over three years .................................. 127

vi

Table 6.18: Spearman’s test-retest correlations for coping over three years ..................................................................... 127

Table 6.19: Mean-levels of depression over three years ........................... 128

Table 6.20: Mean-levels of depression for participants outside normal range at baseline....................................................... 130

Table 6.21: Mean-levels of anxiety over three years .................................. 130

Table 6.22: Mean anxiety scores for individuals with anxiety outside of the normal range ..................................................................................... 132

Table 6.23: Mean-levels of anxiety for participants outside normal range at baseline ............................................................................................. 133

Table 6.24: Mean-levels of stress over three years ................................... 134

Table 6.25: Mean-levels of stress for participants outside normal range at baseline ............................................................................................. 135

Table 6.26: Control, predictor and outcome measures .............................. 139

Table 7.1: Correlations between five-factor model traits and HbA1c ............... 142

Table 7.2: Correlations between five-factor model traits and blood glucose testing ................................................................................................ 145

Table 7.3: Correlations between five-factor model traits and hypoglycaemia ............................................................................ 146

Table 7.4: Correlations between five-factor model traits and depression .................................................................................. 147

Table 7.5: Correlations between five-factor model traits and anxiety ........ 148

Table 7.6: Correlations between five-factor model traits and stress........... 149

Table 7.7: Correlations between five-factor model traits and acceptance .. 150

Table 7.8: Correlations between five-factor model traits and avoidance .... 151

Table 7.9: Forced-entry multiple regressions of HbA1c scores ................... 154

Table 7.10: Forced-entry multiple regressions of blood glucose testing .... 156

Table 7.11: Forced-entry multiple regressions of hypoglycaemia .............. 158

Table 7.12: Forced-entry multiple regressions of depression scores ......... 159

Table 7.13: Forced-entry multiple regressions of anxiety scores ............... 161

vii

Table 7.14: Forced-entry multiple regressions of stress scores ................. 162

Table 7.15: Forced-entry multiple regressions of acceptance coping ........ 164

Table 7.16: Forced-entry multiple regressions of avoidance coping .......... 166

Table 7.17: Summary of regression analyses ............................................ 167

Table 7.18: Longitudinal analyses of personality and self-management .... 167

Table 7.19: Mean HbA1c levels for baseline conscientiousness groups ..... 168

Table 7.20: Mean HbA1c levels for baseline agreeableness groups ........... 171

Table 7.21: Mean HbA1c levels for baseline emotional regulation groups .. 173

Table 7.22: Mean number of blood glucose tests per fortnight for baseline conscientiousness groups .............................................. 175

Table 7.23: Mean depression scores for upper and lower baseline tertiles of emotional regulation ...................................................................... 176

Table 7.24: Mean anxiety scores for upper and lower baseline tertiles of emotional regulation ...................................................................... 178

Table 7.25: Mean stress scores for upper and lower baseline tertiles of emotional regulation ...................................................................... 179

Table 7.26: Mean avoidance coping scores for upper and lower baseline tertiles of emotional regulation ........................................................... 181

Table 7.27: Mean stress scores for upper and lower baseline tertiles of agreeableness ................................................................................... 182

Table 7.28: Mean acceptance coping for upper and lower baseline tertiles of openness .................................................... 184

viii

List of Figures

Figure 4.1: Geographical distribution of DRAT study participants at baseline ..................................................................................... 73

Figure 4.2: Procedures for the current personality study ............................ 85

Figure 5.1: Statistical procedures ................................................................ 90

Figure 5.2: Procedures for preliminary analyses ......................................... 98

Figure 5.3: Procedure for statistical analysis of personality data .............. 101

Figure 5.4: Procedure for regression analyses ......................................... 104

Figure 5.5: Procedure for longitudinal analyses ........................................ 105

Figure 6.1: Rates of depression outside of the normal range ................... 129

Figure 6.2: Rates of anxiety outside the normal range .............................. 131

Figure 6.3: Rates of stress outside the normal range ............................... 134

Figures 7.1 to 7.3: Scatterplots of emotional regulation and HbA1c ................ 143

Figure 7.4: HbA1c values for baseline tertile groups of conscientiousness over three years ............................................. 170

Figure 7.5: HbA1c values for baseline tertile groups of agreeableness over three years .................................................. 172

Figure 7.6: HbA1c values for baseline quintile groups of emotional regulation over three years .......................................... 174

Figure 7.7: Mean number of blood glucose tests per fortnight for baseline tertile groups of conscientiousness over three years ................... 175

Figure 7.8: Mean depression scores for baseline tertile groups of emotional regulation over three years ...................................... 177

Figure 7.9: Mean anxiety scores for baseline tertile groups of emotional regulation over three years ...................................... 178

Figure 7.10: Mean stress scores for baseline tertile groups of emotional regulation over three years ...................................... 180

Figure 7.11: Mean avoidance coping for baseline tertile groups of emotional regulation over three years ...................................... 181

Figure 7.12: Mean avoidance coping scores for baseline tertile groups of agreeableness over three years ............................................... 183

ix

Figure 7.13: Mean acceptance coping scores for baseline tertile groups of openness to experience over three years ................................ 184

x

List of Abbreviations

ACT Australian Capital Territory

BGLT Blood Glucose Level Testing

CODI Coping with a Disease Inventory

DASS Depression, Anxiety, Stress Scales

DCCT Diabetes Control and Complications Trial

DRAT Diabetes Research into Adolescent Transitions

FFPI-C Five Factor Personality Inventory – Children

HbA1c Glycosylated haemoglobin

IQR Inter-quartile range

NSW New South Wales

SD Standard deviation

Abstract

Type I diabetes is a chronic endocrine disease which can significantly impact

upon the life of a young person. Poor glycaemic control can lead to grave

and irreversible complications and the prevalence of depression, anxiety and

stress in this cohort is high. Despite therapeutic advances, there is much

variability in youths’ responses to treatment programs and many struggle to

cope effectively with their condition.

Whilst prior research suggests that five-factor model personality traits

influence a young person’s self-management of diabetes, longitudinal

findings have not been reported and the role of personality in determining

psychological wellbeing in this population is unclear. This is unfortunate as

personality research could help health-care professionals to identify

individuals at risk for poor outcomes and allow them to intervene early.

The aim of the current investigation was to elucidate the role of five-factor

model personality traits in longitudinal trajectories of glycaemic control, self-

care and psychological wellbeing in youth with type I diabetes. A total of 104

Australian children and adolescents (aged 8-19 years at baseline) from the

Diabetes Research into Adolescent Transitions (DRAT) project participated in

this study.

Participants completed the Five-Factor Personality Inventory for Children

(FFPI-C), the Depression, Anxiety, Stress Scales (DASS 21) and the Coping

with a Disease questionnaire (CODI) annually over three years. Participants

also reported annual demographic, self-care and treatment information and

provided a yearly capillary blood sample to determine quality of glycaemic

control.

Bivariate analyses and multiple regressions were employed to identify five-

factor model traits that predicted self-management outcomes independent of

Abstract

xii

controls. Next, groups were created using baseline scores of personality

traits and mixed-design and repeated-measures analyses of variance were

employed to assess the role of personality in trajectories of self-management

outcomes.

Results demonstrated that five-factor model personality traits are associated

with several critical aspects of self-management. Furthermore, these

relationships often persist or have a cumulative effect over time. Low levels

of conscientiousness were associated with less frequent blood glucose

testing and deterioration of glycaemic control over the course of the three-

year study. Similarly, low agreeableness was associated with worsening

glycaemic control and was also associated with greater avoidance coping.

Low levels of emotional regulation were related to higher ratings of

depression, anxiety and stress and greater avoidance coping. Interestingly,

emotional regulation showed a curvilinear relationship with glycaemic control.

Those moderate in emotional regulation had superior glycaemic control over

time. Finally, high levels of openness to experience were related to increases

in acceptance of diabetes over the time.

Overall, these findings suggest that personality plays a crucial role in a young

person’s self-management of type I diabetes over time. This underlines the

need for clinicians to consider patient personality when formulating treatment

plans. Further research is needed to uncover variables that mediate

associations between the personality and self-management outcomes

measured here.

Chapter 1

Introduction

Type I diabetes is a chronic endocrine disease that requires steadfast

dedication to self-management practices on a constant and continual basis.

Despite therapeutic advances, there is still variability in people’s responses to

treatment programs and many individuals struggle to maintain optimal control

of their disease, particularly children and adolescents (Dabadghao, Vidmar, &

Cameron, 2001; Holl et al., 2003; Svensson, Eriksson, & Dahlquist, 2004;

Vollrath, Landolt, Gnehm, Laimbacher, & Sennhauser, 2007).

Whilst much research has focused on the role of physiological factors in the

management of young people’s diabetes (Buczkowska, Jarosz-Chobot, &

Machnica, 2009; Mortensen et al., 2010; Skowera et al., 2008), there is a

growing body of evidence that demonstrates psychosocial investigations can

also provide significant insights (Grey, Davidson, Boland, & Tamborlane,

2001; E. Harkness et al., 2010; La Greca, Swales, Klemp, Madigan, & Skyler,

1995; Peyrot & Rubin, 2007). Such knowledge is essential as it can be

utilised to identify personal factors that may predict critical outcomes for

those with this disease.

One personal factor shown to be important to a young person’s management

of their diabetes is their personality (Skinner, Hampson, & Fife-Schaw, 2002;

Vollrath et al., 2007). This suggests that clinicians need to consider patient

personality when making treatment recommendations and formulating

management plans involving youth. Therefore, it is critical that we have a

broad and accurate body of personality research to guide these clinical

decisions.

Chapter 1: Introduction

2

Unfortunately, there has been little research investigating relationships

between personality and youths’ self-management of diabetes (Vollrath et al.,

2007). Whilst existing studies are laudable (Skinner et al., 2002; Vollrath et

al., 2007), longitudinal findings have not been reported and this makes it

difficult to predict how a young person’s personality may influence their

management of diabetes over time. Furthermore, there has been no research

investigating whether personality is associated with important psychological

outcomes such as negative affect or coping in youth with diabetes.

Consequently, a comprehensive understanding of the role of personality in

young people’s management of diabetes is yet to be achieved.

The current research thesis presents an investigation into the role of

personality in trajectories of type I diabetes self-management in Australian

youth. Accordingly, this chapter introduces the main issues surrounding

management of diabetes within this cohort. The purpose of this discussion is

to articulate the nature of diabetes self-management and its implications and

to provide a platform for discussing the role of personality in health. Particular

focus is given to the devastating complications caused by poor glycaemic

control and the high levels of psychological morbidity in young people with

diabetes.

Type I diabetes

Type I diabetes is caused by an autoimmune response which occurs when a

person with a genetic predisposition for this disease is exposed to some

environmental trigger, such as infection (Hanas, 1998). This leads to the

cellular mediated destruction of pancreatic islet beta cells, which produce

insulin; a hormone used for proper cell function. Insulin transports glucose

from the blood stream into the cells of the body, and without this hormone,

blood glucose levels continually rise (leading to hyperglycaemia) whilst the

cells of the body become starved of energy (Hanas, 1998). Medical

intervention becomes essential with individuals requiring lifelong injections of

exogenous insulin for their survival.


3

Whilst prevalence data for type I diabetes are still unavailable in many

countries (Soltesz, Patterson, & Dahlquist, 2007), epidemiological research

shows that it is one of the most common non-communicable diseases in

North America, the United Kingdom and Australia (S. Wild, Roglic, Green,

Sicree, & King, 2004). In Australia, around 91,000 people (almost 0.5% of the

population) have been diagnosed with this disease, and an estimated 13,700

of these individuals are under the age of 25 (Australian Bureau of Statistics,

2006a; Australian Institute of Health and Welfare, 2008).

Incidence rates for childhood-onset type I diabetes have increased in

Australia over the past fifteen years at a rate of about 3% per year

(Catanzariti, Faulks, & Waters, 2007; Craig, Howard, Silink, & Chan, 2000;

Haynes, Bower, Bulsara, Jones, & Davis, 2004; Taplin et al., 2005) and this

appears to be a worldwide trend, with international registries reporting

significant increases in the incidence of type I diabetes; especially in young

people (Soltesz et al., 2007). In fact, the Australian Institute of Health and

Welfare (2002) argues that type I diabetes is one of the fastest growing

chronic conditions in youth today.

Research into the rising incidence of type I diabetes suggests that many

individuals who develop this disease may not have acquired it (or at least as

early) in prior generations (Fourlanos et al., 2008; Soltesz et al., 2007).

Evidence suggests that increased incidence rates are explained by a growing

preponderance of environmental risk factors, with individuals with lower

genetic risk now more likely to develop the disease early (Fourlanos et al.,

2008). This research warns health professionals to expect more cases of

type I diabetes in the future: a concerning prospect due to the significant

personal and economic costs of this disease.

Type I diabetes can cause devastating long-term microvascular,

macrovascular and neurological complications (The Diabetes Control and

Complications Trial Research Group, 1993) leading to significant personal

disability and reductions in quality of life (Overland, 2004; Rose, Hildebrandt,


4

Fliege, Klapp, & Schirop, 2002). Since the introduction of synthesised insulin,

diabetic complications have been the chief cause of mortalities attributed to

type I diabetes (The Diabetes Control and Complications Trial Research

Group, 1993). In comparison to their non-diabetic counterparts, individuals

with diabetes are twice as likely to develop cardiovascular disease, are three

times more likely to have a stroke and are fifteen times more likely to require

a lower-limb amputation (Australian Institute of Health and Welfare, 2008).

Diabetes is the main cause of new cases of blindness among adults 20 to 74

years old (Aylward, 2005; Fong, Aiello, Ferris, & Klein, 2004) and is one of

the leading causes of kidney failure (McDonald, Chang, & Excell, 2007).

Diabetic complications also create a substantial economic toll. The DiabCost

report (Australian Diabetes Society, 2002) listed the average annual hospital

cost of an individual with diabetes without complications as $4,025 (AUD),

whilst the average annual hospital cost of a patient with diabetes with both

macrovascular and microvascular complications is recorded as $9,645 (AUD)

(Australian Diabetes Society, 2002). These figures highlight the increasing

incidence of type I diabetes as a significant issue for health policy makers

and reinforce the importance of preventing complications.

Research suggests that complications may be prevented by avoiding

persistent hyperglycaemia (The Diabetes Control and Complications Trial

Research Group, 1993). High blood sugar levels damage both large and

small blood vessels in the body, giving rise to a host of related medical

problems. Fortunately, the landmark Diabetes Control and Complications

Trial (DCCT) (1993) has demonstrated that an individual with diabetes can

avoid these complications by effectively self-managing their blood glucose

levels.

The Diabetes Control and Complications Trial

The Diabetes Control and Complications Trial (1993) investigated the impact

of intensive insulin therapy on the development and progression of diabetes


5

complications in a sample of people with type I diabetes aged 13 to 39 at

baseline (baseline n = 1,441). In the DCCT, intensive therapy was designed

to keep blood glucose values as close to the normal range as possible and

was achieved through three or more daily injections of insulin or treatment

with an insulin pump. Daily adjustments in insulin dosages were made

according to self-monitoring of blood glucose (at least four times a day),

dietary intake and anticipated exercise. Patients in intensive therapy had

frequent contact with health professionals to review and adjust treatment

regimens and were given a three monthly goal for blood glucose

concentrations of glycosylated haemoglobin (HbA1c) levels less than 6.05%

(within the normal range). HbA1c serves as a broad index of blood-sugar

levels over the preceding 6-8 weeks and is generally accepted as the best

available measure of glycaemic control (Hanas, 1998; Kilpatrick, 2000). In

contrast to intensive therapy, conventional therapy in the DCCT consisted of

one or two daily injections of insulin, daily monitoring of blood glucose and

education about diet and exercise. Daily adjustments of insulin use were not

usually made for the conventional therapy cohort.

Although participants in the intensive therapy cohort struggled to maintain

HbA1c values within the goal range of 6.05% or less, results demonstrated

that keeping blood glucose averages (HbA1c values) as close to the normal

range as possible reduced the threat of developing long-term complications.

The DCCT (1993) demonstrated that intensive blood glucose control reduced

the risk for eye disease by 76%, kidney disease by 50% and nerve disease

by 60%.

These findings were revolutionary in that for the first time they clearly

established that the complications associated with diabetes could be

prevented or at least delayed by the patient’s engaging in good self-care

behaviour. Based on these results, researchers from the DCCT (1993) called

for a redefinition of therapeutic goals in type I diabetes, with an intensified

focus on self-management of glycaemic levels. In Australia today, youth are

advised to maintain a general target HbA1c range of < 7.5% to avoid


6

complications (National Health and Medical Research Council, 2005; Rewers

et al., 2009).

Self-management of type I diabetes

In response to the Diabetes Control and Complications Trial, modern

treatment approaches have shifted the emphasis of management from the

clinician to the patient. Individuals with diabetes are now expected to take

personal responsibility for the management of their condition (Funnell &

Anderson, 2004). Whilst this move towards self-management represents a

positive step forward in the treatment of type I diabetes, it means that the

personal traits of the patient are now more important than ever, with the

success of treatment chiefly depending on the patient’s maintenance of

adherence and self-care behaviours.

To maintain good glycaemic control people with type I diabetes must adjust

their behaviour on a daily basis, balancing the increases in blood glucose

caused by carbohydrate intake or hormones against the decreases in blood

glucose caused by insulin dose or physical activity (Schneider et al., 2007).

They are required to repeatedly check their blood glucose levels and use this

information to make judicious management decisions (Schneider et al.,

2007). To accomplish this, individuals with diabetes need to actively cope

with the daily demands of their disease and must curb the impact of their

condition on their emotional and social life (Flinders Human Behavioural &

Health Science Unit, 2006). This shows that self-management of type I

diabetes is quite the balancing act involving complex processes of day-to-day

self-regulation. Critically, the extent to which optimal self-management is

achieved is affected by a range of personal characteristics, some

physiological, some behavioural and some psychosocial.

Determining quality of diabetes self-management

Despite the importance of optimal diabetes self-management, there has been

no consensus on a universal definition of this concept within the literature (R.


7

M. Anderson, 1995; Glasgow, 1995; Glasgow & Anderson, 1999; Hearnshaw

& Lindenmeyer, 2006; McNabb, 1997; Schilling, Grey, & Knafl, 2002a,

2002b; Schneider et al., 2007). Researchers have employed a multitude of

different measures to operationalise self-management and this makes it

difficult to identify outcome variables that should be included in future studies.

Historically, many researchers have relied solely on measures of behavioural

adherence to reflect the quality of an individual’s self-management of

diabetes (Ikeda & Tsuruoka, 1994; Rhee et al., 2005; The Diabetes Control

and Complications Trial Research Group, 1993; Zisser, Bailey, & Jovanovic,

2006). This approach to research typically employs popular measures such

as insulin adherence, frequency of blood glucose testing, frequency of

hypoglycaemia and HbA1c to gauge self-management success. Whilst these

outcomes are important and necessary components of self-management, this

approach may disregard the significance of relevant psychological outcomes.

Consequently, many researchers now apply an extended bio-psychosocial

model to conceptualise diabetes self-management (Amer, 1999; Flinders

Human Behavioural & Health Science Unit, 2006; Graue, Wentzel-Larsen,

Bru, Hanestad, & Savik, 2004; Petersen, Schmidt, & Bullinger, 2006; Taylor,

Frier, Gold, & Deary, 2003). This approach emphasises the importance of

psychological and social outcomes in the management of type I diabetes,

arguing that optimal self-management is more than just a function of sound

behaviours relating to blood glucose testing and insulin regimes. Diabetes

researchers are now considering psychological outcomes such as

depression, anxiety, stress and coping alongside measures of behavioural

adherence (Amer, 1999; Ciechanowski, Katon, & Russo, 2000; Coelho,

Amorim, & Prata, 2003; Dantzer, Swendsen, Maurice-Tison, & Salamon,

2003; Delameter, 1992; Grey et al., 2001; Herzer & Hood, 2010; Kramer,

Ledolter, Manos, & Bayless, 2000; Luyckx, Seiffe-Krenke, & Hampson, 2010;

Taylor et al., 2003). This is a constructive development within self-

management research as this approach not only gives a picture of

behavioural management of diabetes but also represents how well an


8

individual has adapted to living with this disease. For the current research

thesis, this model of type I diabetes self-management is employed.

Self-management of type I diabetes in youth

Disturbingly, research demonstrates that youth with type I diabetes struggle

to effectively self-manage their condition (Dabadghao et al., 2001; Holl et al.,

2003; Svensson et al., 2004). HbA1c levels rise steadily throughout

adolescence and research suggests that as many as 50% of young people

with type I diabetes will develop at least one complication by the time they

reach adulthood (Svensson et al., 2004). Despite treatment advances a

significant proportion of youth with diabetes still show unsatisfactory

glycaemic control and the prevalence of microvascular complications in this

group remains high (Holl et al., 2003; Olsen et al., 1999; Svensson et al.,

2004). Indeed, even in the Diabetes Control and Complications Trial (1993),

youths demonstrated significantly worse glycaemic control in the intensive

self-management condition than their adult counterparts suggesting that they

struggled with aspects of this treatment approach.

Furthermore, many youths with type I diabetes report difficulties coping with

the demands of their disease and there is a greater incidence of psychiatric

disorder in this cohort (Blanz, Rensch-Reimann, Fritz-Sigmund, & Schmidt,

1993; Dantzer et al., 2003; Grey, Cameron, Lipman, & Thurber, 1995;

Hanson et al., 1989; Kovacs, Goldston, Obrosky, & Bonar, 1997; Wysocki,

1993). Adolescents with diabetes are at higher risk of having elevated levels

of depression and anxiety in contrast to their non-diabetic peers (Dantzer et

al., 2003; Kanner, Hamrin, & Grey, 2003; Massengale, 2005), and

maladaptive coping styles, stress, anxiety and depression have all been

linked to poor self-care behaviour and glycaemic control (Dantzer et al.,

2003; De Groot, Anderson, Freedland, Clouse, & Lustman, 2001; Graue et

al., 2004; Herzer & Hood, 2010; P. J Lustman et al., 2000). These findings

further highlight the poor prognosis for young people with type I diabetes and

underline the importance of uncovering predictors of negative affect and poor


9

coping in this cohort. These findings further highlight the poor prognosis for

young people with type I diabetes and underline the importance of

uncovering predictors of negative affect and poor coping in this population.

But why do youths struggle? Whilst research suggests that hormonal

fluctuations may play an influential role in glycaemic control (Hanas, 1998)

and even mood states (Buchanan, Eccles, & Becker, 1992) during

adolescence, there is much evidence to suggest that the majority of the

variability in these factors can be attributed to psychosocial factors and the

young person’s management of their disease (Brendgen, Lamrache, Wanner,

& Vitaro, 2010; Holl et al., 2003; Levine et al., 2001; Schneiders, Nicolson, &

Berkhof, 2007; Silverstein et al., 2005; Weissberg-Benchell et al., 1995).

Adolescence is a transitional period for patients with diabetes where they are

expected to take more responsibility for the management of their condition

and begin to gain independence from their parents. Unfortunately, self-

management often falters during this time and this can lead to poor

glycaemic control (Dashiff, McCaleb, & Cull, 2006; Holl et al., 2003;

Jacobson et al., 1990). By distinguishing personal characteristics that predict

self-care behaviours, glycaemic control, negative affect and coping, we may

be able to detect individuals at risk of developing complications and identify

variables on which we could focus to improve self-management.

Factors influencing self-management of type I diabetes in

youth

A number of research studies have shown that demographic, social and

treatment factors impact on diabetes outcomes (Amer, 1999; Anderson,

Auslander, Jung, Miller, & Santiago, 1990; Anderson et al., 2002; Chisholm

et al., 2007; Grey, Whitmore, & Tamborlane, 2002; Hassan, Loar, Anderson,

& Heptulla, 2006; Hepburn, Langan, Deary, Macleod, & Frier, 1994;

Kavanagh, Gooley, & Wilson, 1993; Levine et al., 2001; Pickup, Mattock, &

Kerry, 2002). Often cited predictor variables include age, gender, method of

treatment, time lived with diabetes and the level of responsibility a patient


10

takes for their care (Anderson et al., 1990; Chisholm et al., 2007; Hepburn et

al., 1994). Other predictors of interest have included ethnic background,

household income and geographical location (Ambler, Fairchild, Craig, &

Cameron, 2006).

Research suggests that glycaemic control and self-care behaviours are

typically worst in older adolescents (15 - 20) and those who have had

diabetes for longer periods of time (Dabadghao et al., 2001; Holl et al., 2003;

Svensson et al., 2004). In addition, gender interacts with age so that teenage

girls and boys display increases in average HbA1c at different periods

(Hanas, 1998). These differences are partly due to hormonal fluctuations that

occur during puberty (Hanas, 1998).

Older adolescents (15 - 20) also report higher rates of depression, anxiety

and stress and these reports are most prevalent in females (Amer, 1999;

Dantzer et al., 2003; Eiser, Riazi, Eiser, Hammersley, & Tooke, 2001; Herzer

& Hood, 2010; Kanner et al., 2003; La Greca et al., 1995; Massengale,

2005). Unsurprisingly, anxiety and depression are also prominent in

individuals newly diagnosed with diabetes (Grey et al., 1995; Grey & Kanner,

2000).

Finally, individuals who hail from low-income families, non-European

backgrounds, and/or rural areas are likely to engage in poorer self-

management of type I diabetes (Ambler et al., 2006; Australian Institute of

Health and Welfare, 2005). Sub-optimal glycaemic control and increased

rates of depression and anxiety are common within these groups and this

could be somewhat mediated by access to health-care (Ambler et al., 2006;

Ciechanowski et al., 2000; Hassan et al., 2006; Massengale, 2005; Zgibor et

al., 2000). Youth who use insulin pumps and share the responsibility of

diabetes care with parents are also likely to have better control of their

diabetes (Anderson et al., 2002; Pickup et al., 2002).


11

The reliability of these predictors has differed widely across studies and a

substantial proportion of the variance in specific self-management outcomes

remains unexplained when relying solely on these measures. This indicates

that other predictive variables need to be identified and tested. Any further

factors need to be tested against these variables to determine whether they

explain additional variance in outcomes. One individual factor that has

received relatively little attention is personality.

Personality and self-management of type I diabetes

A young person’s personality may be particularly important in the long-term

self-management of type I diabetes. Indeed, in many respects, the type of

person the patient is determines their self-management of diabetes. An

individual with type I diabetes requires psychological resources and self-

regulatory skills in order to negotiate the complex daily demands of living with

type I diabetes and the success of treatment is significantly dependent on the

patient having the necessary resources to maintain adherence and self-care

behaviours.

Whilst psychosocial variables such as emotions or self-efficacy may fluctuate

over time (Röcke, Li, & Smith, 2009; Thompson, Dorsey, Miller, & Parrot,

2003), personality traits are enduring psychological constructs (Pervin, 2003)

and these may impact upon the behavioural management of diabetes on a

daily basis. In addition, personality is known to influence more narrow

psychological constructs such as coping and affect (Carver & Connor-Smith,

2010; Digman, 1989; Goldberg 1990; McCrae & Costa, 1999) and could thus

provide a framework from which to better understand these phenomena

within the ambit of type I diabetes.

If personality does play a role in the long-term management of young

people’s diabetes it is critical to understand how it does this. By discovering

traits that influence trajectories of self-management, it may be possible to

identify individuals at risk and intervene early. This information could also


12

help to guide the design and implementation of programs aimed at improving

adolescents’ psychological adaptation to diabetes and their glycaemic

control. Given these potential opportunities, further investigation into the role

of personality in the management of youths’ type I diabetes is required.

Hence, the study with which this thesis is concerned arises from a

longitudinal study following a sample of 158 Australian children and

adolescents (aged 8-19 years) with type I diabetes. Of this sample, 104

completed personality data over the three waves of the study. The aim of this

research was to investigate the role of five-factor model personality

dimensions in trajectories of self-management in youth with type I diabetes.

To this aim, a bio-psychosocial approach to health was taken with measures

of psychological well-being (depression, anxiety, stress and coping) and

diabetic management (blood glucose testing and glycaemic control) included

in analyses. This study incorporated a cross-sectional design with annual

quantitative data on glycaemic control, psychosocial, demographic and

treatment variables gathered over three years.

Chapter 2

Why Study Personality?

The study of personality has a firm foothold in the growing fields of

behavioural medicine and health psychology (Smith, 2006) and involves

systematic efforts to understand relationships between an individual’s

consistent patterns of thought, emotion and behaviour and their health.

The increased interest in the role of personality in health is likely due to three

reasons. Firstly, perspectives in health-related fields have moved towards a

bio-psychosocial approach which emphasises the importance of biological,

psychological and social factors in conceptualisations of health (Caltabiano,

Sarafino, Byrne, & Martin, 2002). Secondly, scientific and medical

advancements mean that many of the leading causes of mortality are now

due to modifiable factors such as an individual’s behaviour or lifestyle

(Bermudez, 1999). Finally, a general acceptance of the five-factor model as

the dominant structural paradigm in personality research has re-energised

the field creating an upsurge in personality research in recent years

(Bermudez, 1999; Booth-Kewley & Vickers Jr, 1994; Chua & Job, 2000; De

Raad & Perugini, 2002; Goodwin & Friedman, 2006; Hampson, Goldberg,

Vogt, & Dubanoski, 2006; Raynor & Levine, 2009).

Together, these developments appear to have moved researchers and

theorists to consider personality as a factor that may determine health

outcomes (Contrada, Leventhal, & O'Leary, 1990; Friedman et al., 1993;

Marshall, Wortman, Vickers, Kusulas, & Hervig, 1994; Smith & Williams,

1992; Wiebe & Smith, 1997). Such research is laudable as it may help to

identify individuals at risk for poor health and allow clinicians to intervene

early thus preventing the development of more serious health consequences

Chapter 2: Why Study Personality?

14

(Conrod, Castellanos, & Mackie, 2007; Smith & MacKenzie, 2006). In fact,

the Australian Institute of Health and Welfare (2010) lists health promotion

and disease prevention as national health priorities underlining the

importance and practical utility of predicting medical outcomes from personal

characteristics.

Unfortunately, the burgeoning interest in relationships between personality

traits and health factors that appears evident in today’s psychological

literature (Bermudez, 1999; Booth-Kewley & Vickers Jr, 1994; Bryan &

Stallings, 2002; Byrdon et al., 2010; Chua & Job, 2000) is not reflected in

research involving youth with type I diabetes (Vollrath et al., 2007).

Consequently, there is still much to know about the role of personality in

young people’s self-management of this disease.

But why focus on the influence of personality on self-management

outcomes? What is the rationale for studying personality over the plethora of

competing psychosocial constructs of interest? What theoretical support is

there for a relationship between personality and health? And most

importantly, what do we stand to gain from such investigations? The

following chapter endeavours to provide the reader with answers to these

questions and aims to develop a scientific and logical argument for the need

for personality investigations in research involving youth with type I diabetes.

The first section of this chapter introduces the reader to the five-factor model

of personality and focuses on two distinctive strengths of this framework.

These strengths are the structural organisation and enduring nature of the

five-factor model personality dimensions. Using the literature, it is asserted

that the five-factor model provides a broad and hierarchically structured

reference point from which to understand and integrate health research

involving a diverse range of psychosocial factors. Furthermore, it is argued

that the enduring nature of these personality dimensions allows researchers

to explore longitudinal relationships between personality and significant

health outcomes.


15

In the second section, relevant theoretical models supporting the role of

personality in health are evaluated. These models are the health-behaviour

model, the stress-health model, and the adaptation to chronic illness model.

Evidence to support these theoretical frameworks is presented and the

pertinence of these models to type I diabetes is underlined. On the basis of

these models, it is asserted that a bio-psychosocial approach is needed

when conceptualising indicators of diabetes self-management. It is

maintained that researchers assessing youth with type I diabetes should

utilise measures of self-care and glycaemic control in conjunction with

measures of psychological wellbeing such as depression, anxiety, stress and

coping.

Finally, in the closing section of this chapter, the success of personality-

based health interventions is reviewed and the applicability of such

interventions to youth with type I diabetes considered. It is contended that

personality research could lead to the development and implementation of

targeted interventions for youth who struggle to self-manage their diabetes.

Based on the reviewed literature, it is concluded that personality

investigations employing the five-factor model may provide enhanced insight

into relationships between psychological variables and health, particularly in

the case of youth with type I diabetes. In undertaking such research, we may

clarify the role of five-factor personality traits in trajectories of self-

management in youth with type I diabetes and this could lead to interventions

aimed at improving long-term psychological wellbeing and glycaemic control

in this cohort.

The five-factor model: The unique strengths of structure and

permanence

Over the past two decades, researchers and theorists have encouraged us

to pay greater attention to the role of personality in health (Contrada et al.,

1990; Friedman et al., 1993; Lahey, 2009; Marshall et al., 1994; Smith &


16

Williams, 1992; Wiebe & Smith, 1997). Notably, much of the interest in this

area appears to have been motivated by a growing acceptance of the five-

factor model as the preeminent framework for understanding and

researching personality (De Raad & Perugini, 2002; Goldberg, 1993).

Indeed, the five-factor model possesses certain unique strengths not offered

by other personality frameworks or psychosocial variables (Marshall et al.,

1994).

Two distinctive strengths of the five-factor model are the hierarchically

organised structure and enduring nature of the five-factor model dimensions

(Bermudez, 2006; Costa & McCrae, 1992a; Friedman et al., 1993; Marshall

et al., 1994). Research suggests that the five-factor model of personality can

be used as a lasting structural reference for organising and understanding

research and predicting long-term health outcomes (Friedman, Hawley, &

Tucker, 1994; Hampson et al., 2006; Marshall et al., 1994). Hence, the five-

factor model may help investigators to synthesise psychosocial findings in

type I diabetes research and forecast trajectories of self-management. For

these reasons, the five-factor model of personality was employed in the

current study. Accordingly, a description of the model and its unique

strengths is detailed below.

The five-factor model of personality

The five-factor model of personality is a trait model that suggests that

personality can be parsimoniously assessed using five broad and

independent dimensions of personality which are made up of a collection of

more specific traits that correlate (McCrae & Costa, 1999). There has been

increasing consensus on the reliability and validity of these five dimensions

of personality (De Raad & Perugini, 2002; Goldberg, 1993) and this has led

proponents of the model to hail it as the foremost structural paradigm for

conducting personality research (Costa & McCrae, 1992a; Goldberg, 1992;

John & Srivastava, 1999; McCrae & Costa, 1999; Saucier, 2002).


17

A central tenet of the five-factor model is that personality is somehow

universally structured across individuals and that individual differences result

from dimensional rather than categorical variations in personality (Pervin &

John, 2001). This suggests that differences in personality are due to the

degree to which individual traits are expressed rather than any differences in

general structure. In this way, personality can be conceptualised as

consisting of a finite number of lasting and universally distributed personality

traits. Although trait levels may change over time (Roberts, Walton, &

Viechtbauer, 2006), the structure of personality should remain constant.

Based on this premise, researchers have employed psycho-lexical and factor

analytic approaches to try to extrapolate the underlying structure of

personality (Allport & Odbert, 1936; Cattell, 1943; Fiske, 1949; Tupes &

Christal, 1961). Much of this research has been based on the lexical

hypothesis which posits that the most significant and socially important

personality constructs are encoded in our general lexicon and thus can be

uncovered by examining words we use to describe ourselves and others

(Pervin & John, 2001). If this hypothesis is correct, we should be able to

derive the basic structure of human personality by sampling language for

descriptors of individual differences.

Over the years, many researchers have conducted factor analyses of large

sets of trait adjectives in an attempt to derive a reliable structure for

personality (Cattell, 1947, 1956; Digman & Takemoto-Chock, 1981; Eysenck,

1946; Fiske, 1949; Guilford, 1975; Norman, 1963; Peabody, 1967; Tupes &

Christal, 1961). The majority of these investigators have tended to agree

upon the existence and validity of five orthogonal dimensions within their

data (Digman & Takemoto-Chock, 1981; Fiske, 1949; Norman, 1963; Tupes

& Christal, 1961). The case for a five-factor model of personality has been

further bolstered by research that has found the same five-factor dimensions

in independently gathered item pools (Digman & Inouye, 1986; Goldberg,

1990, 1992) and separately constructed personality tests (Costa & McCrae,

1988; John, 1990; Ostendorf & Angleitner, 1992).


18

Notably, several five-factor models of personality have been proposed

(Goldberg, 1993) with the most prominent of these being McCrae and

Costa’s five-factor model (McCrae & Costa, 1999) and the big-five model of

personality (Digman, 1989; Goldberg, 1990). Although these frameworks

were developed from independent approaches to studying the underlying

structure of personality, they differ only slightly in regard to the labels and

definitions of the five emergent personality factors. Subsequently, the

personality measures developed from these models show high correlations

and are dimensionally aligned (Goldberg, 1993). Given the equivalence of

these models, the term five-factor model will be exclusively used throughout

this thesis in order to aid presentation.

The five bipolar factors encapsulated by the five-factor model are termed

conscientiousness, emotional regulation (or neuroticism), extraversion,

agreeableness, and openness to experience (Costa & McCrae, 1992c;

McGhee, Ehrler, & Buckhalt, 2007). There is much research attesting to the

replicablility of these personality dimensions in English speaking cultures

(Costa & McCrae, 1992a; Digman, 1989; McCrae & John, 1992; Ostendorf &

Angleitner, 1994; Rolland, 1993). These five factors are described below:

The conscientiousness dimension of the five-factor model refers to an

individual’s tendency to be reliable, perseverant and self-disciplined.

Individuals high in conscientiousness are more likely to delay gratification, be

goal directed, and follow norms and rules whilst individuals low in

conscientiousness are characterised by an inability to control impulses or

stick to plans (Bogg & Roberts, 2004; Costa & McCrae, 1992c).

The emotional regulation dimension of the five-factor model concerns an

individual’s capability to control their emotional responses to their

environment and others. People scoring low on this factor are prone to be

emotionally overactive and may be more vulnerable to stress and negative

emotional states such as anger, sadness, fear or anxiety (Digman, 1990).

Those scoring high on emotional regulation tend to be calm, rational and


19

resistant to worry or stress (Digman, 1990). The emotional regulation

dimension was originally termed ‘neuroticism’ to reflect an individual’s

proneness to being emotionally overactive, but today the terms ‘emotional

regulation’ or ‘emotional stability’ are preferred to reflect the adaptive nature

of affective control (McGhee et al., 2007). Thus, within this thesis the term

‘emotional regulation’ is used exclusively to avoid any confusion.

The extraversion factor concerns an individual’s propensity towards activity

and social engagement. Individuals high in extraversion are usually

physically active, friendly, assertive and talkative (McGhee et al., 2007).

Those low on this factor are typically quiet, reflective, soft spoken and prefer

solitude.

The agreeableness factor relates to a person’s tendency to cooperate and

get along with others (McGhee et al., 2007). Individuals high on this factor

are compassionate, trusting and altruistic in their interpersonal relationships

and are seen as approachable, friendly and courteous. Those low in

agreeableness may be stubborn, self-centred, opinionated, cynical and rude.

The openness to experience factor refers to one’s tendency to be

intellectually curious and crave new experiences. Individuals high in

openness are creative, curious, insightful and willing to try new things

(McGhee et al., 2007). Individuals who are low in openness are practical,

routine-oriented and may be close-minded when confronted with new

experiences or situations.

There is a large amount of research supporting the existence of the

abovementioned personality dimensions (Costa & McCrae, 1992; Digman,

1989; McCrae & John, 1992; Ostendorf & Angleitner, 1994; Rolland, 1993).

In fact, research demonstrates that the five-factor model of personality is

replicable across age, gender, and to some extent, cultural backgrounds

(Costa & McCrae, 1992a, 1992b; Saucier & Goldberg, 2001). Furthermore,

the five-factor dimensions show good convergent, discriminant and criterion-

related validity (Costa & McCrae, 1992a, 1992b; McGhee et al., 2007;


20

Saucier & Goldberg, 2001) and can be used to predict significant outcomes

in applied settings (Bogg & Roberts, 2004; Friedman et al., 1993; Hong &

Paunonen, 2009; John & Srivastava, 1999; McCrae & Costa, 1999).

However, this does not mean that the five-factor model is a perfect

representation of personality. Thoughtful critiques of the five-factor model

(Block, 1995, 2001; Eysenck, 1991, 1993; Tellegen, 1993) have highlighted

several issues that investigators need to be mindful of when employing this

personality framework. These are typically related to the development,

theoretical underpinnings, universality and comprehensiveness of the five-

factor model dimensions (Poropat, 2004). Nevertheless, the five-factor

structure appears to be the most reliable and parsimonious structural

representation of personality available (Costa & McCrae, 1992a; Goldberg,

1992; John & Srivastava, 1999; McCrae & Costa, 1999; Poropat, 2004;

Saucier, 2002). Indeed, structural models of personality involving more or

less than five independent factors have not been widely supported by

research results (Saucier, 2002).

Together, these findings (Costa & McCrae, 1992a; Goldberg, 1992; John &

Srivastava, 1999; McCrae & Costa, 1999; Poropat, 2004; Saucier, 2002)

underline the validity, reliability and robustness of the five-factor model of

personality and provide support for the utilisation of the five-factor model in

personality-health research.

Employing the five-factor model as an organisational framework

The beauty of personality research is that it recognises individuals in their

entirety (Pervin & John, 2001). Necessarily, the five-factor model of

personality is a broad and hierarchically-structured psychological framework

which has been argued to encompass a vast number of smaller psychosocial

traits (Costa & McCrae, 1992a).


21

Given the breadth and hierarchical structure of the five-factor model (Costa &

McCrae, 1992a), it seems reasonable to suppose that this framework may

provide a useful reference from which to base psychological investigations in

health research (Marshall et al., 1994). Indeed, when talking about the role of

psychological factors in the management of diabetes, parents in the current

study appeared more likely to discuss their child’s global personality traits

(such as conscientiousness) rather than specific psychosocial variables as

contributing factors.

This is likely due to the fact that we naturally organise narrow psychosocial

variables into broader traits in order to understand others’ behaviour (Pervin

& John, 2001). In this way, the hierarchical structure provided by the five-

factor model may serve as an organisational framework to better compare,

and synthesise findings from research studies focusing on the role of

psychosocial and personality factors in health. By taking this approach, we

may be able to find common dimensions that influence general health and

the management of type I diabetes.

Unfortunately, there appears to have been little work focused on integrating

health research in this way. Past investigations have studied a plethora of

psychosocial constructs, yet many of these focus on a single or small

number of narrowly defined variables which have unknown relationships to

one another (Marshall et al., 1994; Walsylkiw & Fekken, 2002). Furthermore,

the array of competing psychosocial measures available creates problems

for a researcher trying to gauge convergent and divergent validity hampering

confidence in the generalisability of obtained results (Marshall et al., 1994;

Smith & Williams, 1992). Consequently, researchers are left with isolated

fragments of knowledge, which are difficult to synthesise and interpret.

Regrettably, this then creates difficulties for health practitioners trying to

make informed clinical decisions.

This quandary appears evident in psychosocial research involving youth with

type I diabetes. For instance, investigators have studied the role of a diverse


22

range of narrow psychosocial constructs in the management of youths’

diabetes including but not limited to; impulsiveness, self-efficacy, optimism

and locus of control (Holmes et al., 2006; Ryden, Nevander, Johnsson,

Westbom, & Sjoblad, 1990; Wright, 1997). However, there appears to have

been little attempt to synthesise this research meaningfully. Consequently,

these results are difficult to interpret meaningfully in any broad sense and

this could have significant implications for the quality of health-care and

interventions in this area.

Whilst research on isolated variables provides valuable and rich information

about the complexity of the experience of chronic disease, the value of this

information is lessened if it cannot be meaningfully integrated into a pool of

knowledge that can be utilised for improving treatment services. Without

some conceptual framework to synthesise the plethora of psychosocial

research in the area of type I diabetes, we cannot bring adequate coherence

to the field.

The utilisation of the five-factor model of personality in health research may

serve to ameliorate this problem (Bermudez, 1999; Marshall et al., 1994).

The five-factor model hails from an empirical approach to the study of

personality and hence there is an abundance of research on the universality,

reliability, convergent validity and lower order structure of these factors

(Costa & McCrae, 1992a, 1992c; Digman, 1989; Digman & Inouye, 1986;

Digman & Takemoto-Chock, 1981). Additionally, many narrow psychological

variables (such as hostility, dutifulness and sociability) can be subsumed and

meaningfully interpreted through this framework (Bermudez, 1999; Costa &

McCrae, 1992a). These findings suggest that the five-factor model may be

used to bring order to prior psychosocial research involving people with type

I diabetes. Undeniably, utilisation of this structural framework affords several

significant advantages for health researchers.

Firstly, the universal structure of the five-factor model allows researchers to

compare discrete traits across individuals, groups and testing occasions


23

(Costa & McCrae, 1992a; Marshall et al., 1994; McCrae & John, 1992). For

instance, a researcher may compare five-factor model traits of individuals

with good control of type I diabetes against those who have poor control of

diabetes. Alternatively, a researcher could test for differences in the

expression of these traits between individuals with diabetes and the general

population. A researcher could also follow a cohort of individuals with

diabetes over an extended period noting any changes in five-factor model

dimensions that occur over time. These are very different research questions

but the universal structure provided by the five-factor model allows

researchers to test all of them.

Secondly, measures of five-factor model dimensions show good convergent

validity (Costa & McCrae, 1992a; De Raad & Perugini, 2002; Marshall et al.,

1994), which means that researchers can be assured that they are

measuring the same construct across different studies. This means that we

can easily replicate health research employing different five-factor measures

to test the authenticity and reliability of previously uncovered associations.

Replication is an important scientific process as it reduces the likelihood of

type I error (Meltzoff, 2004) and by doing this we can achieve greater

accuracy in personality-health research and reduce the likelihood of invalid

conclusions.

Thirdly, the large amount of research on relationships between five-factor

traits and health variables allows researchers to extrapolate from studies

involving different populations to other populations where little research has

been conducted (Marshall et al., 1994; Van Heck, 1997). For example, we

may be able to understand more about the role of young people’s personality

in type I diabetes by examining the role of five-factor model personality

dimensions in young people’s management of other chronic diseases or the

role of five-factor model dimensions in the management of diabetes in older

populations. In this way, the five-factor model serves as an especially useful

tool for investigators working in fields, such as adolescent type I diabetes

research, where little personality research is available.


24

Finally, as previously stated, the hierarchical structure of the five-factor

model provides a framework with which to integrate research on narrow

psychological constructs known to play a role in health (Bermudez, 1999;

Costa & McCrae, 1992a; Marshall et al., 1994). The accumulation of

research on the lower order structure of the five-factor model suggests that

many narrow traits can be meaningfully understood through this framework

(Costa & McCrae, 1992a) enabling researchers to organise and streamline

the vast amount of psychosocial research conducted in health domains.

To illustrate, past research has shown that the construct of attention span is

related to quality of type I diabetes management in youth (Rovert & Ehrlich,

1988). Research also suggests that the construct of attention span can be

subsumed by the five-factor dimension of conscientiousness (Costa &

McCrae, 1992c; Pervin, 2003). Together, these findings provide speculative

support for the role of conscientiousness in diabetes outcomes. By using this

information we may begin to build hypotheses about the function of this trait

in the management of this disease. Admittedly, not all psychological

constructs will fit under the umbrella of the five-factor model, yet many do

(Costa & McCrae, 1992a), and this highlights the usefulness of continued

research in this area.

In all, the five-factor model of personality provides researchers with a broad

structural framework from which to base health investigations. The

framework allows researchers to replicate, compare, integrate and

extrapolate from studies focusing on the role of psychosocial factors in

health. To date, there appears to have been little work focused on integrating

and extending past health research in this way and this suggests that further

research in this area should be conducted. Further research could lead to a

greater understanding of the role of psychosocial factors in the management

of young people’s diabetes and perhaps herald significant improvements to

health care services and interventions for young people with type I diabetes.


25

Employing the five-factor model as a longitudinal predictor

The other great advantage of personality research lies in the enduring nature

of this construct. By its very definition, personality should have a constant

and continual influence on the manner in which we live (Pervin, 2003). If this

is true, personality could be a useful predictor of long-term health.

Health issues and disease often develop and progress over a course of

decades (Caltabiano et al., 2002). In the case of type I diabetes,

complications arise from extended periods of persistently elevated blood

glucose levels due to routine poor self-management (The Diabetes Control

and Complications Trial Research Group, 1993). This demonstrates that

poor health behaviours typically need to be maintained over long periods to

have significant consequences for the individual. Thus for personal factors to

influence longitudinal health outcomes, the individual must display a

characteristic style of responding to everyday situations (or stressors) that is

unhealthy or maladaptive.

This suggests that psychosocial factors need to demonstrate two properties

if they are to reliably predict long-term health. First, they need to be present

most (or all) of the time, and second, they need to be relatively stable across

occasions. Of all the psychosocial phenomena, the construct of personality

appears to best fit this description (Allik, Laidra, Realo, & Pullmann, 2004;

Asendorpf & van Aken, 2003; Costa & McCrae, 1992a, 1992c; Donnellan,

Conger, & Burzette, 2007; Hampson & Goldberg, 2006; Roberts, Caspi, &

Moffitt, 2001; Roberts & DelVecchio, 2000; Shiner & Caspi, 2003; Sneed,

Gullone, & Moore, 2002).

Research and theory suggests that personality is one of the only

psychosocial constructs that is a continual factor in our lives (Costa &

McCrae, 1992a; McCrae & Costa, 1999; Pervin, 2003). Indeed, the five-

factor model personality traits have been evidenced in child, adolescent,

adult and elderly populations and this speaks to the permanence of these

personality structures (Allik et al., 2004; Asendorpf & van Aken, 2003; Costa


26

& McCrae, 1992a, 1992c; Shiner & Caspi, 2003; Sneed et al., 2002). In

contrast, other psychological variables such as mood states can fluctuate

widely over time or may not be expressed (Röcke et al., 2009). Therefore,

personality may be a superior predictor of long-term health as it is more likely

to influence our thoughts, affect and behaviour on a daily basis. Certainly, at

any given point in time, not all people will be depressed yet all will have a

personality (however dull).

It should be noted that a stable pattern of response is needed for personality

to have predictive value for long-term health outcomes. If an individual’s

personality changed from day-to-day it would be difficult to untangle any

genuine relationships between long-term health and this construct.

Consequently, it is important to examine the stability of personality across

the lifespan. Only if personality traits are stable across time can we use them

to predict long-term management of diabetes or the development of

complications.

There has been ongoing debate about the stability of personality over the life

course and there appears to be evidence supporting both consistency and

change in personality traits over time (Donnellan et al., 2007; Hampson &

Goldberg, 2006; Roberts et al., 2001; Roberts & DelVecchio, 2000). Whilst

mean level changes are evidenced across the lifespan, research suggests

that relative differences within an age cohort are often preserved (Roberts &

DelVecchio, 2000). Thus, people’s comparative position to other individuals

in their age group remains relatively unchanged despite normative changes

in their personalities. This is referred to as rank-order consistency and is

measured using correlations to test if individuals have changed ranks within

their cohort on a particular trait over time.

Within adults, personality traits show substantial rank-order consistency over

time with test-retest correlations improving from 0.51 in early adulthood to

0.75 around age 50 (Roberts & DelVecchio, 2000). This suggests that adult

personality is a stable construct that continues to develop across the


27

lifespan. In regards to the five-factor model traits, extraversion and

agreeableness (r = 0.54) appear to be the most stable traits across

adulthood whilst emotional regulation (r = 0.50) is the least (Roberts &

DelVecchio, 2000). This may be due to the fact that emotional regulation is

the factor most associated with transient emotional states (Hampson &

Goldberg, 2006). Nevertheless, according to Cohen’s rule of thumb,

correlations between 0.30 and 0.70 reflect medium to large effects (Field,

2009) and such findings demonstrate that the five-factor traits, including

emotional regulation, are stable psychological constructs in adulthood.

The stability of personality in younger populations appears to be less

assured (Donnellan et al., 2007; Hampson & Goldberg, 2006; Roberts et al.,

2001; Roberts & DelVecchio, 2000). Roberts and DelVecchio (2000)

demonstrated that the rank-order correlation for personality in children aged

6 to 12 years is 0.45, which improves to 0.47 in youth aged 12 to 18.

Similarly, Hampson and Goldberg (2006) demonstrated that short-term test-

retest reliabilities for the five-factor model traits range from 0.22 to 0.55 in

childhood compared to a range of 0.70 to 0.79 in adulthood. As in adults, the

trait of emotional regulation showed the lowest rank-order correlation (r =

0.22) (Hampson & Goldberg, 2006). Together these findings suggest that

personality is less stable in youth but that increases in stability occur with

maturity (B. W. Roberts & DelVecchio, 2000). This implies that predicting

adult personality from childhood traits may be a difficult task.

However, Hampson and Goldberg’s study (2006) demonstrated that certain

five-factor traits in childhood (as rated by teachers) are related to self-rated

personality traits 40 years later. In particular, extraversion (r = 0.29),

conscientiousness (r = 0.23), openness to experience (r = 0.16), and to a

lesser extent, agreeableness (r = 0.09) showed statistically significant

consistency over the 40-year period. Further, Roberts, Caspi and Moffit

(2001) followed a sample of teenagers and found test-retest correlations for

personality traits over an eight year test interval that ranged from 0.43 to 0.67

with an average of 0.55 suggesting that there is relative stability from


28

adolescence to young adulthood. Whilst many of these correlations would

not be considered as representing large effects (Field, 2009) they do show

that there is at least some continuity in personality from childhood to

adulthood, especially over shorter periods (Roberts et al., 2001).

Furthermore, longitudinal research demonstrates that childhood personality

factors can influence self-reported health, health behaviours and even

survival rates over periods of 40 to 60 years (Friedman et al., 1995;

Friedman et al., 1993; Hampson et al., 2006; Hampson, Goldberg, Vogt, &

Dubanoski, 2007). These are remarkable findings and they underline the

importance of identifying five-factor personality model traits that may have a

significant impact upon long-term health.

The five-factor trait of conscientiousness appears to be of most importance in

long-term health (Brickman, Yount, Blaney, & Rothberg, 1996; Friedman et

al., 1995; Friedman et al., 1993; Hampson et al., 2006, 2007). Conscientious

children appear to be more likely to avoid accidents and to grow up to have

better health habits (Friedman et al., 1995). They appear to be less likely to

be heavy drinkers or smokers and often have lower body mass indexes

(Friedman et al., 1993; Hampson et al., 2006). The association between

conscientiousness and self-reported health appears to be mediated through

educational attainment, eating habits and smoking, yet, several direct effects

of childhood personality on health status have also been observed

(Hampson et al., 2007). Whilst conscientiousness predicts longevity it does

not differentially predict cause of death (Friedman et al., 1995) and, in this

way, conscientiousness appears to serve as a general protective factor

rather than predicting specific causes of mortality.

Other five-factor model traits have also been implicated in long-term health

(Hampson & Goldberg, 2006; Hampson et al., 2007). For example, low

agreeableness and high extraversion in childhood appear to be related to

smoking behaviour in later life (Hampson et al., 2007). Furthermore, high

levels of extraversion and low levels of emotional regulation in childhood


29

have been associated with alcohol use (Hampson et al., 2006). Also, lower

levels of childhood agreeableness are related to higher body mass index

(Hampson et al., 2006) and extraversion appears to be related to physical

activity (Hampson et al., 2007).

Although this is only a brief summary of the multitude of relationships found

between childhood five-factor traits and long-term health, this information

provides researchers with much knowledge from which to base further

investigations into health. Whilst the majority of the relationships found

between childhood personality and long-term health showed small effects

(Friedman et al., 1995; Friedman et al., 1993; Hampson et al., 2006, 2007),

even associations of a small magnitude can have great implications for

public health, especially if it means the difference between life and death

(Smith & Spiro, 2002).

Overall, the research presented shows that personality is a constant and

continual factor in our lives, which can have a significant influence on long-

term health outcomes and even mortality (Friedman et al., 1993; Hampson et

al., 2006). Whilst the long-term stability of child personality over the lifespan

is muted (Hampson et al., 2006), personality is relatively stable during the

transition from adolescence to young adulthood (Roberts et al., 2001) and

this period would be long enough for an individual with type I diabetes to

develop complications (The Diabetes Control and Complications Trial

Research Group, 1993). Together, these findings speak to the potential role

of personality in determining outcomes in type I diabetes and underline the

importance of conducting longitudinal personality research in young people

with this disease.

Theoretical and research perspectives on personality-health

interactions

Another reason for studying the role of five-factor model traits in the

management of young peoples’ diabetes comes from the strong theoretical


30

ties between personality and health outcomes (Wiebe & Smith, 1997). A

theoretical foundation is critical in exploratory research (such as the current

study) as it can provide researchers with a framework within which to work

and can guide the formulation and testing of specific research hypotheses

(Meltzoff, 2004). With respect to the role of personality in diabetes, there are

several theoretical models that can be used to direct and inform research

(Wiebe & Smith, 1997) and these provide an excellent starting point from

which to devise research hypotheses and make measurement decisions.

Three models that may be particularly relevant to the management of type I

diabetes are health-behaviour models, stress models and adaptation to

illness models and these paradigms appear to be supported by empirical

research (Bogg & Roberts, 2004; Chida & Steptoe, 2009; Wiebe & Smith,

1997). Given the empirical support for these models, research utilising these

frameworks in type I diabetes appears warranted. Accordingly, the

applicability of each of these models to the management of young people’s

diabetes is discussed in detail below.

The Health–Behaviour Model

The health-behaviour model concentrates on the influence of personality on

specific behaviours which may increase or decrease the risk for disease or

mortality (Suls & Rittenhouse, 1987; Wiebe & Smith, 1997). Theorists

contend that individuals create and seek out situations that suit their

personality, and hence, some people may be exposed to riskier

circumstances than others (Suls & Rittenhouse, 1987). For example, some

individuals may be more likely to engage in unhealthy habits or risky

practices (such as unsafe sex, dangerous driving, drug and alcohol use,

sedentary lifestyle or poor diet), whilst others may be more likely to engage

in health protective behaviours (such as healthy diet, exercise or medical

screening) (Bermudez, 1999).


31

Unsurprisingly, research supports the health-behaviour model with five-factor

variables found to be related to a multitude of behaviours in several large-

scale and meta-analytic studies (Bogg & Roberts, 2004; Hong & Paunonen,

2009; Munafo, Zetteler, & Clark, 2006; Raynor & Levine, 2009; Rhodes &

Smith, 2006). In adolescent cohorts, five-factor dimensions have been

implicated in health-relevant behaviours such as smoking (Brook, Whiteman,

Czeisler, Shapiro, & Cohen, 1997), sun protective behaviours (Brayne, Do,

Green, & Green, 1998), exercise adherence (Douthitt, 1994) and drug and

alcohol abuse (Brook et al., 2001; Loukas, Krull, Chassin, & Carle, 2000).

Such findings have led researchers to argue that behaviour may play a

greater role in mediating personality-health relationships than currently

acknowledged (Contrada, Coups, Cameron, & Leventhal, 2003). Undeniably,

many of the main causes of mortality today are related to an individual’s

engagement in unhealthy behaviours (Bermudez, 1999).

Given the associations between five-factor model personality traits and

health behaviour (Hong & Paunonen, 2009; Munafo et al., 2006; Nicholson,

Soane, Fenton-O'Creevy, & Willman, 2005; Rhodes & Smith, 2006), and the

behavioural nature of type I diabetes self-management (Glasgow et al.,

1999), research into the personality determinants of diabetes self-

management and glycaemic control is warranted.

Youth with type I diabetes need to regulate their behaviour on a daily basis in

order to maintain near-normal blood glucose levels (Schneider et al., 2007)

and an individual who is dedicated in their self-care is more likely to avoid

long-term complications (The Diabetes Control and Complications Trial

Research Group, 1993). Furthermore, an unhealthy diet and poor self-care

behaviours can impact blood glucose levels (Hanas, 1998) and increase the

risk for long-term complications (The Diabetes Control and Complications

Trial Research Group, 1993), drug use can lead to hyperglycaemia (and in

some cases hyperglycaemic coma) (Brenner, 2006) and excessive alcohol

use increases the risk for hypoglycaemic attack which can in some cases

lead to unconsciousness and hospitalisation (Kerr, Cheyne, Thomas, &


32

Sherwin, 2007). Thus, if five-factor model personality traits truly predispose

individuals to engage in either protective or risky health behaviours in

diabetes, it is critical for researchers to understand these relationships.

It is important to note that observed personality-health relationships may in

fact be related to self-reports of health behaviour or illness reports rather

than actual health or behaviour (Wiebe & Smith, 1997). Self-report measures

of health and health behaviour are not guaranteed to be objective or reliable

and this underlines the importance of employing unbiased measures in

personality research. An objective measure of diabetic control is required.

Glycosylated haemoglobin (HbA1c) tests provide such a measure. As

aforementioned, HbA1c tests are the gold standard for assessing a patient’s

blood glucose control over the previous 6-8 weeks and regular testing is

employed by clinicians to guide insulin prescription and self-care

recommendations (Hanas, 1998; Kilpatrick, 2000). By using HbA1c as an

objective measure of behavioural management, researchers can assess the

validity of any proposed relationships between personality and diabetic

management and investigate any associations between this measure and

self-reported health. This may promote health and prevent adverse

outcomes by allowing us to predict poor behavioural management of type I

diabetes.

Finally, researchers and clinicians need to be aware that the level of

responsibility one takes for their care may influence the role of personality in

health management. If an individual is not responsible for engaging in self-

care behaviours, then the predictive value of personality may be attenuated.

This may be of particular relevance to younger individuals with type I

diabetes who may not yet take charge of many diabetes tasks. In such

cases, parents of the person with diabetes may take more responsibility for

behaviours such as insulin administration, blood glucose testing or diet.

Therefore, it is essential to consider how levels of responsibility for self-care

may impact upon the results of personality-health studies.


33

The Stress Model

It has been hypothesised that personality characteristics may also influence

physiological stress response, which can play a role in the development and

progression of disease (Suls & Rittenhouse, 1987; Wiebe & Smith, 1997).

Theorists argue that some individuals respond with greater physiological

reactivity when confronted with stressful situations (Suls & Rittenhouse,

1987) and this can influence health through several physiological pathways

(Wiebe & Smith, 1997). A stress response causes hyper-arousal of the

sympathetic nervous system, reduced immune function and altered

production of hormones (Contrada et al., 1990; Van Heck, 1997; Wiebe &

Smith, 1997) and it is surmised that if this physiological hyper-arousal is

continued or intense, this may strain bodily organs and increase risk of

disease (Suls & Rittenhouse, 1987). Furthermore, stress levels should be

seen as an independent outcome in type I diabetes as they are a good

general indicator of psychological wellbeing and are associated with quality

of life (Ames, Jones, Howe, & Brantley, 2001; Ward & Tannner, 2010).

Theory suggests that individuals’ stress levels are influenced by their

appraisal of challenging events whilst the behavioural and cognitive

strategies they employ may influence the intensity or the duration of the

event (Contrada et al., 1990; Wiebe & Smith, 1997). Interestingly, it has been

suggested that certain people may be more likely to create stressful

situations through their choices and behaviours (Wiebe & Smith, 1997), and

taken together, this information suggests that personality may predict stress

levels and subsequent health.

Prominent support for the role of personality in stress-health interactions

came from research applications that attempted to elucidate relationships

between Type A behaviour and coronary heart disease (Cooper, Detre, &

Weiss, 1981). Individuals displaying Type A behaviour are generally

characterised as hostile, competitive and impatient, and early research

demonstrated that these people displayed increased physiological response

to stressors, accentuated appraisals of general stress, and greater


34

susceptibility for coronary heart disease (Caltabiano et al., 2002). Whilst this

research was challenged on methodological grounds (Mann & Brennan,

1987), research improvements have revitalised this area of investigation,

focusing on the traits of anger and hostility which have been shown to better

predict heightened physiological reactivity to stress (Byrdon et al., 2010) and

development of coronary heart disease (Chida & Steptoe, 2009; Jackson,

Kubzansky, Cohen, Jacobs Jr, & Wright, 2007; Myrtek, 2001). Above all,

research has demonstrated that interventions aimed at reducing hostility and

stress are successful in reducing coronary risk (Daubenmier et al., 2007;

Gidrpn, Davidson, & Bata, 1999) underscoring the practical utility of

personality-health research and stress-health models.

The stress-health model may be particularly relevant to type I diabetes, in

that stress responses lead to changes in adrenaline and neuroendocrine

function (Wiebe & Smith, 1997) which are known to play a role in the

regulation of blood glucose (Hanas, 1998). Research shows that adolescents

with type I diabetes who report higher life stress display poorer glycaemic

control (Helgeson, Escobar, Siminerio & Becker, 2007). Likewise, individuals

with type I diabetes who report reduced ability to deal with stress display

poorer HbA1c results (Skocic, Rudan, Brajkovic, & Marcinko, 2010).

Moreover, there may be a reciprocal relationship between stress levels and

diabetic outcomes; higher stress levels may increase HbA1c scores, whilst

receiving news of a higher HbA1c score may potentially influence stress

levels. If five-factor model traits do predict stress levels in type I diabetes, it is

crucial to understand these associations as it may help to identify those at

risk for poor psychological wellbeing and self-management outcomes.

The Adaptation to Illness Model

A further model of personality-health interaction focuses on the role that

personality plays in coping with acute medical crises and adaptation to

chronic disease (Wiebe & Smith, 1997). This model underlines the

importance of personality in an individual’s psychological response to


35

disease and treatment and aims to identify personality characteristics that

may make people more or less vulnerable to psychological morbidity,

negative affect and poor coping when living with illness (Smith & Williams,

1992). A real asset of this approach is that it underlines the importance of

coping and the absence of psychological morbidity in conceptualisations of

health; they can be seen as predictors of health outcomes or as outcomes in

their own right.

This model of personality-health association is closely aligned with the bio-

psychosocial model of health (Engel, 1977) and represents a positive move

from reductionist biomedical models of disease that assume a simple

dichotomy between health and physiological illness (Harris et al., 2000). This

approach offers a comprehensive conceptualisation of wellbeing which

emphasises levels of health and vitality as much as levels of impairment or

disability (Bandura, 2005).

Whilst measures of self-care or physiological outcomes may serve to indicate

how a person’s behaviour corresponds with medical regimens or advice from

health professionals, it does not capture the complexity of chronic condition

self-management or the need for individuals to control the disease within the

context of their lives (Schilling et al., 2002a). Certainly, no clinician could

argue that an adolescent with perfect glycaemic control has optimal

management of their condition if they also display clinical levels of

depression due to the demands of their disease. By focusing on factors such

as general coping style, depression and anxiety we can get a picture of how

a young person has adapted to living with a chronic condition (Livneh &

Antonak, 2005) and may better understand how this disease affects and

impacts upon their life.

Youths with type I diabetes often report difficulties adjusting to their disease

and there are higher rates of depression, anxiety and maladaptive coping in

this cohort (Dantzer et al., 2003; Grey et al., 1995; Hanson et al., 1989;

Kanner et al., 2003; Wysocki, 1993). Poor coping, anxiety and depression


36

have also been associated with poor glycaemic control (Dantzer et al., 2003;

De Groot et al., 2001; Graue et al., 2004; Herzer & Hood, 2010; P. J

Lustman et al., 2000) and this highlights the importance of finding five-factor

personality traits that may predict such outcomes.

In summary, the above theoretical models of personality-health interactions

make a strong case for the potential role of personality in the management of

type I diabetes and underline the importance of taking a bio-psychosocial

approach towards conceptualisations of health. Whilst it is possible that any

relationship between personality and health could simply reflect the work of a

third latent variable or constitutional predisposition (Wiebe & Smith, 1997),

the links found between personality and health related behaviours (Bogg &

Roberts, 2004; Hong & Paunonen, 2009) suggest that the proposed models

do hold validity.

The aforementioned models are not thought to be mutually exclusive (Suls &

Rittenhouse, 1987; Van Heck, 1997; Wiebe & Smith, 1997) and it is

reasonable to expect bi-directional and reciprocal effects amongst these

processes (Peyrot & Rubin, 2007; Van Heck, 1997). In this way, there may

be multiple pathways between five-factor model personality traits and self-

management outcomes in type I diabetes.

By using these frameworks, researchers may better understand the complex

nature of personality-health relationships in type I diabetes and propose

testable hypotheses regarding the role of personality in this disease. This

suggests that investigators need to consider markers of psychological

wellbeing (such as stress, anxiety, depression and coping) and markers of

diabetic control (such as HbA1c, hypoglycaemia and self-care behaviours)

when examining self-management outcomes in type I diabetes research.


37

The value of personality research: Identification and

intervention

A final reason for studying the role of personality in health comes from what

can be achieved with this information. The ultimate goal of personality-health

research should be to guide the development and implementation of

interventions for the prevention and management of disease (Smith &

MacKenzie, 2006). Certainly, a comprehensive approach towards the

creation of health interventions requires consideration of a multitude of

determinants of health; including personality (Smith, Jenkins, & Orleans,

2004).

If as suggested, personality influences health in predictable ways, personality

research could help clinicians to detect individuals vulnerable for illness and

identify variables on which we could focus to improve health (Bermudez,

1999). An increased awareness of the role of personality factors in type I

diabetes may help to identify people at risk of poor psychological wellbeing

or glycaemic control and allow for the development of tailored interventions

aimed at improving these outcomes.

Researchers have argued that standard care is now insufficient to achieve

optimal outcomes in type I diabetes (Murphy, Rayman, & Skinner, 2006) and

that advances in diabetes treatments (such as intensive insulin regimes and

insulin pumps) have necessitated increases in patient support and education

(Glasgow et al., 1999). There has been a call for ongoing assessment of

people’s psychological and social situation in the medical management of

diabetes (Gage et al., 2004; Glasgow et al., 1999; Murphy et al., 2006; van

der Ven et al., 2005) and this has led to the conceptualisation and

development of a variety of educational, behavioural and psychosocial

interventions aimed at improving outcomes for individuals with type I

diabetes (Gage et al., 2004; Hampson, Skinner, et al., 2000; Hampson et al.,

2001; E. Harkness et al., 2010; Murphy et al., 2006). These developments

signal a positive step forward in the treatment of this disease, particularly for

adolescents for whom intervention may be especially relevant.


38

Adolescence is a pivotal time for implementing diabetes-focused

interventions as responsibility for self-management typically increases during

this period whilst metabolic control generally declines (Dashiff et al., 2006;

Holl et al., 2003; Jacobson et al., 1990). The detection of adolescents who

are at risk for poor metabolic control is critical because, without intercession,

these people are likely to have poor control throughout their adult lives

(Dabadghao et al., 2001) increasing the risk for developing complications

(The Diabetes Control and Complications Trial Research Group, 1993).

Furthermore, the fact that Australian youth have greater access to diabetes

clinics than adults (National Health and Medical Research Council, 2005),

and the fact that clinicians can effectively incorporate structured interventions

into patient sessions (Peyrot & Rubin, 2007) suggest that this is a good time

for implementing interventions aimed at improving glycaemic control.

Meta-analyses and systematic reviews of the literature support this

demonstrating that behavioural, psychosocial and educational interventions

have a beneficial effect on diabetes management practices and HbA1c levels

in adolescents (Gage et al., 2004; Hampson, Skinner, et al., 2000; Hampson

et al., 2001; Murphy et al., 2006). Whilst the benefits of such interventions

are not always sustained (Gage et al., 2004), some studies have reported

significant reductions in HbA1c over periods of up to 2 years (Rubin, Peyrot, &

Saudek, 1989) underscoring the importance of accurately conceptualising

and developing such programs.

Regrettably, many interventions for adolescents with poorly controlled type I

diabetes are not theoretically guided (Gage et al., 2004; Hampson, Skinner,

et al., 2000) and there is large inter-individual variability in the effectiveness

of specific interventions (Hains, Davies, Parton, & Silverman, 2001). Such

findings imply that intervention programmes that work well in one setting (or

with a certain cohort) may not work as well in others. Research demonstrates

that theoretically-based interventions produce larger effect sizes (Hampson,

Skinner, et al., 2000) underlining the need for an expanded theoretical base

to guide the selection of appropriate intervention techniques and assessable


39

outcomes. It may be that the greatest therapeutic benefits for individuals with

type I diabetes are achieved by ‘matching’ patients with specific treatments

or interventions.

There is research to suggest that targeted approaches to intervention in type

I diabetes may be effective (Christensen, 2000; Franks, Chapman,

Duberstein, & Jerant, 2009; van der Ven et al., 2005). Firstly, five-factor

model personality traits are known to moderate the effectiveness of

interventions in chronic disease (Franks et al., 2009). In a study by Franks et

al (2009), a self-efficacy enhancing intervention proved successful only for

individuals who were low in the personality traits of emotional regulation

(termed neuroticism by study author), agreeableness, conscientiousness and

extraversion.

Secondly, personal characteristics and disease-related contextual variables

appear to interact to determine the success of health interventions or

treatment. For example, Christensen (2000) found that an active coping style

was associated with better adherence when individuals were undergoing

home-based dialysis that is highly patient-directed whereas a passive coping

style was associated with better adherence in individuals receiving hospital

based, clinician-controlled treatment.

Together, these findings suggest that specific health interventions need to

work in congruence with the patient receiving treatment. Specific

interventions may produce superior outcomes for a particular subset of the

patient population whilst this relationship may not occur or may even be

reversed in other subgroups (Christensen, 2000). This shows that we need

to identify individuals displaying high-risk characteristics and develop

interventions for cohorts in which therapeutic effects are likely to be

strongest.

Personality research provides promise for tailored health interventions and

this appears to be a growing area of interest (Castellanos & Conrod, 2006;


40

Conrod et al., 2007; Conrod, Stewart, Comeau, & Maclean, 2006; Conrod et

al., 2000). Personality-based health interventions generally try to match

individuals to treatments and are designed to help the patient manage

maladaptive behaviours and coping strategies that are influenced by their

personality (Conrod et al., 2006).

Research studies (Conrod et al., 2007; 2006; 2000) have highlighted the

efficacy of targeted personality-interventions in reducing alcohol and drug

abuse behaviour. In these studies, participants who showed elevated scores

on one of the personality risk factors related to alcohol and drug misuse

(negative thinking, sensation seeking, anxiety sensitivity, impulsivity or

introverted-hopelessness) were assigned to a personality-matched

intervention that focused on the individual’s thoughts, behaviours and

emotions in a personality-specific fashion. The goal of these programs was

to intervene at the level of personality risk focusing on the maladaptive

coping strategies associated with personality and problem behaviours.

Individuals in these studies were randomly assigned to one of three brief

interventions where they either received a motivation-matched intervention

which involved personality-specific motivational and coping skills training, a

control group which involved viewing a motivational film and a supportive

discussion with a therapist, or a motivational mismatched intervention which

targeted a theoretically different personality profile (Castellanos & Conrod,

2006; Conrod et al., 2007; 2006; 2000). The findings from this research are

encouraging and have several implications.

Firstly, this research suggests that ‘personality matched’ interventions are

effective in preventing or reducing alcohol and drug abuse whereas

‘personality mismatched’ interventions have no effect (Conrod et al., 2000).

These findings demonstrate the importance of personality variables in

response to treatment interventions and may explain the inter-individual

variability observed in the effectiveness of specific interventions (Hains et al.,


41

2001). Such results suggest that an intervention that does not ‘match’ the

individual’s personality is likely to be ineffective.

Secondly, the effect sizes obtained for ‘personality matched’ interventions

were comparable to, or greater than, those reported by high-quality

interventions for youth alcohol or drug prevention programs (Conrod et al.,

2007; 2006). In one study, the effect size for a specific ‘personality matched’

intervention was double any reported by youth alcohol prevention programs

(Conrod et al., 2007). Furthermore, this lasted for over 12 months whereas

the effects of other brief interventions often disappeared after a few weeks

(Conrod et al., 2007). These findings suggest that personality-targeted

interventions may produce robust changes in maladaptive behaviour and

may have greater long-term benefits than standard interventions.

Thirdly, findings demonstrate that personality-targeted interventions not only

improve health behaviour but also improve psychological wellbeing

(Castellanos & Conrod, 2006). ‘Personality matched’ interventions produced

reductions in personality-related psychological symptoms. A negative

thinking intervention significantly reduced depression scores, whilst an

anxiety intervention reduced panic attacks and truancy. These findings

demonstrate that personality-targeted interventions not only improve health

behaviour but also improve psychological wellbeing; a remarkable finding as

there are scant interventions that improve both physical and mental health in

type I diabetes (E. Harkness et al., 2010).

Together, these studies suggest that personality-targeted interventions can

engender significant and long-lasting health behaviour change and underline

the importance of continued research into personality-health interactions.

Furthermore, the techniques employed in these intervention studies

(Castellanos & Conrod, 2006; Conrod et al., 2007; 2006; 2000) may be

successful in the treatment of individuals with type I diabetes. In the future,

by identifying individuals who display personality traits that predict poor self-

management, we may be able to intervene early providing focused care that


42

concentrates on specific personality traits that may determine outcomes.

Results of this research revealed that the interventions did not influence

scores on personality dimensions implying that the interventions help youths

to better manage their personality rather than change it (Conrod et al., 2006).

Summary

In summary, personality investigations could greatly contribute to our

knowledge of type I diabetes self-management in younger populations.

Personality research is a growing area and the dominance of the five-factor

model provides researchers with unique opportunities to create a

comprehensive body of information, which we could potentially use to predict

long-term outcomes. In addition, theoretical perspectives show that

personality could influence a range of outcomes in type I diabetes such as

depression, anxiety, stress and coping along with traditionally employed

outcomes such as measures of self-care and glycaemic control. By using the

five-factor model to look at the role of personality in longitudinal trajectories

of such outcomes we may be able to recognise individuals at risk and identify

variables on which we could focus to improve self-management of type I

diabetes. Therefore, the following chapter examines research relevant to the

five-factor model of personality, self-care, psychological wellbeing and

glycaemic control in young people with type I diabetes.

Chapter 3

Five-factor Personality Traits and

Management of Diabetes

Given the popularity of the five-factor personality model (Costa & McCrae,

1992a; Digman, 1989; McCrae & John, 1992; Ostendorf & Angleitner, 1994;

Rolland, 1993), and the importance of five-factor traits in health (Bogg &

Roberts, 2004; Brickman et al., 1996; Friedman et al., 1995; Hampson et al.,

2006; Hong & Paunonen, 2009), it is surprising that little personality research

has been conducted in the area of adolescent type I diabetes using this

framework.

To date, there have only been two studies that have examined relationships

between five-factor model traits and type I diabetes outcomes in younger

cohorts (Skinner et al., 2002; Vollrath et al., 2007). Unfortunately, this lack of

research hampers any in-depth understanding of the role of personality in

young people’s management of this disease. Although the aforementioned

investigations have provided valuable information, it is inappropriate to draw

broad conclusions based on the findings of so few studies.

Consequently, further investigation is required to validate previous findings

and bring more certainty to the field (Skinner et al., 2002; Vollrath et al.,

2007). Additionally, this research could help to uncover important new

knowledge, resolve incongruent findings, identify particular issues that

require further attention and assist clinicians in helping young people at risk

for poor self-management of diabetes.

Chapter 3: Five-factor Personality Traits and Diabetes

44

In order to embark on such a task, it is imperative to objectively review the

literature relevant to personality and young people’s experience of diabetes.

Familiarity with this research will help investigators to formulate hypotheses

regarding associations between five-factor model traits and important self-

management outcomes. Researchers can also determine new areas of

inquiry by acknowledging any limitations of this work.

Due to the small amount of research involving five-factor model measures of

personality and youth with type I diabetes (Skinner et al., 2002; Vollrath et

al., 2007), it is important to broaden the literature review to include research

involving similar populations (such as adults with type I diabetes and people

with type II diabetes) and variables conceptually and empirically related to

five-factor model traits (such as temperament or narrow personality

constructs). By using the five-factor model to compare, integrate and

extrapolate from these studies, a clearer picture of the role of personality in

the management of type I diabetes may be achieved.

Therefore, the current chapter presents a broad review of research relevant

to the five-factor model of personality and self-management of type I

diabetes in youth. A bio-psychosocial approach to health is taken and the

role of personality in diabetic control and psychological wellbeing is

considered. In addition, the limitations of the available research are

highlighted and areas that deserve further attention are discussed. To aid

presentation, this review is divided into three sections.

The first section examines research relevant to the role of five-factor model

traits in diabetic control and psychological wellbeing. The second section

discusses the limitations of current research and the implications for future

studies. The third section presents the aim, research questions and

hypotheses of the current study.


45

Personality traits and self-management of type I diabetes

A systematic examination of the research was performed to determine

current knowledge regarding the role of personality traits in determining self-

management outcomes in type I diabetes. Exploration of the literature

demonstrated that the existing studies have typically focused on outcomes

such as blood glucose testing behaviours, glycaemic control and insulin

adherence (Edwards, 1999; Lane et al., 2000; Marks, 2000; Skinner et al.,

2002; Vollrath et al., 2007). The role of personality in an individual’s

psychological adaptation to diabetes has received little attention.

The discussion therefore focuses on relationships between personality

factors and measures of self-care and glycaemic control in diabetic

populations. In addition, potential associations between personality and

psychological adaptation to diabetes are postulated by examining links

between personality and psychological wellbeing in non-diabetic populations.

To bring order and coherence to an exploration of these findings, the review

is organised in reference to the traits of the five-factor model of personality.

Conscientiousness

The five-factor model trait of conscientiousness has been linked to a large

variety of health outcomes across the life course (Bogg & Roberts, 2004;

Friedman, 2000; Friedman et al., 1994; Hampson et al., 2006; B. W. Roberts,

Walton, & Bogg, 2005). Such findings have led some researchers to contend

that this trait may be the most important and reliable personality predictor of

long-term health (Bogg & Roberts, 2004; B. W. Roberts et al., 2005).

Research suggests that associations between conscientiousness and health

are primarily mediated by health behaviours (Bogg & Roberts, 2004; B. W.

Roberts et al., 2005). Individuals high in conscientiousness typically engage

in health protective behaviours and avoid unhealthy practices whereas those

low in conscientiousness exhibit poorer self-care and partake in more risky

activities (Bogg & Roberts, 2004; Friedman et al., 1995; Hampson, Andrews,


46

Barckley, Lichtenstein, & Lee, 2000; Hampson et al., 2006; Hong &

Paunonen, 2009; Munafo et al., 2006; Raynor & Levine, 2009; Rhodes &

Smith, 2006). If conscientiousness similarly predicts engagement in health

behaviours in youth with type I diabetes, this could have weighty

consequences for individuals low on this trait.

The trait of conscientiousness is related to the characteristics of

perseverance and discipline and is associated with an individual’s ability to

plan, monitor and control their behaviour (Costa & McCrae, 1992c; Gailliot,

Mead, & Baumeister, 2008; Hoyle, 2006). Type I diabetes requires continual

and proactive self-management (Glasgow et al., 1999), and people high in

conscientiousness may better achieve this as they are more adept at self-

regulating their actions on a daily basis (Costa & McCrae, 1992c; Gailliot et

al., 2008; Hoyle, 2006). We might therefore expect conscientious youth with

type I diabetes to display better engagement in self-care and superior

glycaemic control. Unsurprisingly, research involving both youth and adults

with diabetes appears to support this proposition (Brickman et al., 1996;

Christensen & Smith, 1995; Edwards, 1999; Garrison, Biggs, & Williams,

1990; Liakopoulou, Korvessi, & Dacou-Voutetakis, 1992; Marks, 2000;

Rovert & Ehrlich, 1988; Ryden et al., 1990; Skinner et al., 2002; Vollrath et

al., 2007).

For example, Vollrath, Landolt, Gnehm, Laimbacher and Sennhauser (2007)

followed a sample of newly diagnosed youth (aged 6-16) and found that

average conscientiousness scores (calculated from measures at 6 weeks, 6

months and 12 months after diagnosis) were negatively correlated with

average HbA1c values (calculated from measures at 6 months, 12 months

and 24 months after diagnosis). Significant correlations were also found for

the conscientiousness subscales of concentration, perseverance and order.

A stepwise multiple regression of five-factor model personality traits on

HbA1c further highlighted the importance of conscientiousness (Vollrath et al.,

2007). Conscientiousness was the only significant predictor of glycaemic


47

control explaining 11% of the variability in HbA1c scores (Vollrath et al.,

2007). These results suggest that conscientiousness may be the most

important personality factor in young people’s glycaemic management.

Therefore, clinicians can expect youth with diabetes who are focused, orderly

and perseverant to have good glycaemic control.

There is also evidence to suggest that a relationship between

conscientiousness and glycaemic control in young people may be due to an

association between this personality factor and specific diabetes self-care

behaviours. In a mediational study, Skinner, Hampson & Fife-Schaw (2002)

found that high levels of conscientiousness in young people with type I

diabetes were associated with reports of better diet, increased exercise

engagement, greater number of blood glucose tests and adherence to

insulin. Structural equation modelling of these data suggested self-care

behaviours were influenced by health beliefs about self-efficacy to control

their diabetes and prevent complications, which were in turn, influenced by

conscientiousness.

Finally, further (albeit speculative) support for a potential role for

conscientiousness in young people’s management of diabetes comes from

research involving adults with diabetes and research investigating the role of

narrow personality traits and temperamental factors. High conscientiousness

in adults with diabetes has been related to health beliefs, adherence, self-

care behaviours and glycaemic control (Christensen & Smith, 1995;

Edwards, 1999; Marks, 2000; Szymborska-Kajanek, Wrobel, Cichocka,

Grzeszczak, & Strojek, 2006) whilst superior adherence and glycaemic

control in youth with type I diabetes has been associated with

conscientiousness-related traits such as longer attention span (Garrison et

al., 1990; Rovert & Ehrlich, 1988), greater regularity in routine (Rovert &

Ehrlich, 1988), greater effort at school (Liakopoulou et al., 1992) and low

impulsivity (Ryden et al., 1990). Collectively, these findings make a strong

case for the importance of conscientiousness in the behavioural

management of type I diabetes.


48

It is also important to note that conscientiousness may influence

psychological adaptation to type I diabetes. Conscientious people tend to

accept difficult or unchangeable situations and work proactively within the

constraints of their circumstances (Carver & Connor-Smith, 2010; Connor-

Smith & Flachsbart, 2007; Kidachi, Kikuchi, Nishizawa, Hiruma, & Kaneko,

2007). They are also unlikely to use disengagement strategies such as

avoidance to cope with demands (Carver & Connor-Smith, 2010; Connor-

Smith & Flachsbart, 2007). Based on these findings, we might expect highly

conscientious youth with diabetes to come to terms with a diagnosis of a

chronic disease, learn to live with the personal limitations imposed by this

situation and develop some sense of understanding. Additionally, we can

expect these people to actively engage in the management of their diabetes

and not evade the daily demands placed on them by intensive insulin

regimens. This may have consequences for psychological wellbeing in youth

with this disease.

Research suggests that individuals high in conscientiousness report lower

rates of depression, anxiety and stress and that this association remains

when objective measures of psychological wellbeing are employed (K. W.

Anderson & McLean, 1997; Bartley & Roesch, 2011; Carver & Connor-Smith,

2010; Ebstrup, Eplov, Pisinger, & Jorgensen, 2011; Kotov, Gamez, Schmidt,

& Watson, 2010; Scher & Osterman, 2002; Vollrath, 2000, 2001; Weiss,

Sutin, Duberstein, Friedman, Bagby, & Costa Jr, 2009). This shows that

conscientiousness may serve as a protective factor against psychological

morbidity and may even reduce the risk of exposure to stressful or

distressing events. High conscientiousness may promote acceptance coping,

suppress avoidance coping and shield youth with type I diabetes from

experiencing maladaptive levels of negative affect.

The above research suggests superior outcomes for individuals high in

conscientiousness. Individuals high in conscientiousness are likely to display

better self-care behaviours, superior glycaemic control and greater

psychological adaptation to type I diabetes. It is likely that people high in


49

conscientiousness will accept their diabetes and utilise less avoidance

coping strategies to adapt to their disease. It is also likely that rates of

depression, anxiety and stress would be lower in young people high on this

trait.

Emotional regulation

A second five-factor model trait that may play an indispensable role in a

young person’s self-management of diabetes is emotional regulation.

Emotional regulation has been related to a variety of mental and physical

outcomes in non-diabetic populations and has recently been highlighted as a

factor that has a significant impact on public health (Carver & Connor-Smith,

2010; Charles, Gatz, Kato, & Pedersen, 2008; Clark, Watson, & Mineka,

1994; Connor-Smith & Flachsbart, 2007; Goodwin, Cox, & Clara, 2006;

Kendler, Gatz, Gardner, & Pedersen, 2006; Khan, Jacobson, Gardner,

Prescott, & Kendler, 2005; Lahey, 2009; Shipley, Weiss, Taylor, & Deary,

2007; Smith & MacKenzie, 2006; Watson, Clark, & Harkness, 1994). Given

the importance of emotional regulation in these other health domains, it is

crucial to understand the role of this trait within the context of type I diabetes.

Emotional regulation is strongly linked to psychological adaptation and could

thus have significant implications for coping and mental wellbeing in youth

with diabetes (Carver & Connor-Smith, 2010; Clark et al., 1994; Connor-

Smith & Flachsbart, 2007; Kendler et al., 2006; Khan et al., 2005; Watson et

al., 1994). For instance, individuals low in emotional regulation appraise

themselves as having poor coping resources, are less likely to accept difficult

circumstances and are more likely to engage in avoidance coping as a way

of dealing with stress (Bolger, 1990; Carver & Connor-Smith, 2010; Cimbolic-

Gunthert, Cohen, & Armeli, 1999; Connor-Smith & Flachsbart, 2007). They

also report more negative life events and poorer quality of life (Arrindell,

Heesink, & Feij, 1999; Taylor et al., 2003; Vollrath, 2001).


50

Furthermore, low levels of this trait appear to be a general risk factor for

lifetime psychopathology (Carver & Connor-Smith, 2010; Clark et al., 1994;

Kendler et al., 2006; Khan et al., 2005; Watson et al., 1994). Low scores on

emotional regulation have been consistently associated with increased rates

of depression, anxiety and stress (Bienvenu et al., 2004; Bolger &

Zuckerman, 1995; Kendler et al., 2006; Khan et al., 2005; Kotov et al., 2010;

Lahey, 2009; Roelofs, Huibers, Peeters, & Arntz, 2008) and these

relationships remain even when overlapping measurement items are taken

into account (Lahey, 2009). This has led some researchers to argue that the

trait of emotional regulation may actually index a genetic risk for negative

affect and psychopathology (Kendler et al., 2006; Lahey, 2009). Undeniably,

research looking at the physiological mechanisms underlying these

psychological constructs supports this position (Hauner et al., 2008).

Emotional regulation can then be expected to play a central role in

determining levels of negative affect and coping in youth with a chronic

disease. In fact from a stress-diathesis perspective, being able to regulate

one’s emotions may be more important for individuals with a chronic

condition as they are exposed to additional stressors not experienced by the

general population (Glasgow et al., 1999). The higher rates of depression

and anxiety evidenced in diabetic cohorts (Dantzer et al., 2003; Kanner et al.,

2003; Massengale, 2005) support this idea and suggest that greater

emotional regulation may be needed for individuals to adapt successfully to

living with this disease. If this is so, we might expect the association between

emotional regulation and measures of negative affect and coping to be

particularly strong in this group. Furthermore, if emotional regulation does

influence coping, depression anxiety and stress in youth with diabetes, we

can also expect this trait to influence self-care behaviours and physical

health.

Low emotional regulation is a significant risk factor for inadequate self-care

and poorer long-term health in the general population (Charles et al., 2008;

Goodwin et al., 2006; Lahey, 2009; Shipley et al., 2007; Smith & MacKenzie,


51

2006). Whilst often linked to over-reporting of symptoms or illness, poorly

regulated emotion has also been associated with a diverse range of objective

health outcomes such as arthritis, type II diabetes, kidney disease, ulcer,

cardiovascular disease, coronary heart disease, gastrointestinal conditions

and mortality in older adults (Charles et al., 2008; Costa Jr & McCrae, 1987;

Feldman, Cohen, Doyle, Skoner, & Gwaltney Jr, 1999; Goodwin et al., 2006;

Lahey, 2009; Lindberg, 2002; Shipley et al., 2007). Furthermore, low levels

of this trait have been associated with negative health behaviours such as

increased smoking, greater alcohol intake, and abuse of illicit substances

(Goodwin & Friedman, 2006; Goodwin & Hamilton, 2002; Hong & Paunonen,

2009; Loukas et al., 2000; Terracciano & Costa Jr., 2004). In all, these

associations suggest that low emotional regulation may be a risk factor for

poorer self-care and glycaemic control in people with diabetes. Indeed, low

emotional regulation may predict the co-morbidity of mental and physical

health problems potentially multiplying the risk for poorer long-term outcomes

(Lahey, 2009).

There is research that suggests poor emotional regulation may predict

substandard self-care and glycaemic control (Chatzialexiadi et al., 2005;

Gilbert, 1992; Vollrath et al., 2007). For instance, Vollrath and colleagues

(2007) discovered that youth scoring low on emotional regulation had worse

glycaemic control as measured by HbA1c scores. Similar results were

uncovered by Gilbert (2004) who found a positive association between

emotional regulation and HbA1c using Eysenck’s three-factor personality

questionnaire (EPQ). In addition, Chatzialexiaidi, Kounenoy, Kompoti et al.,

(2005) demonstrated that adults with type I diabetes with good glycaemic

control (HbA1c < 7.0%) had significantly higher scores on emotional

regulation than those with poor glycaemic control (HbA1c > 8.0%). Together,

these studies imply that low emotional regulation is disadvantageous and is

related to negative outcomes in type I diabetes.

However, other research conflicts with these findings (Lane et al., 2000;

Taylor et al., 2003; Weissberg-Benchell & Glasgow, 1997). Using Eysenck’s


52

personality questionnaire, Taylor, Frier, Gold, and Deary (2004) found a

correlation between low emotional regulation and good glycaemic control in a

sample of adults recently diagnosed with type I diabetes. Participants with

poorly regulated emotion demonstrated better HbA1c results 12 months after

diagnosis. Similarly, Lane and colleagues (2000) found lower blood glucose

levels to be associated with lower scores on emotional regulation in a sample

of adults with Type II diabetes. Finally, Weissberg-Benchell and Glasgow

(1997) found that a tendency towards negative moods was also related to

better HbA1c values. Collectively, these studies suggest that low emotional

regulation is associated with better glycaemic control in individuals with

diabetes; a finding that does not sit well with the aforementioned research.

These intriguing results demonstrate an inconsistency within the literature

and suggest that the relationship between emotional regulation and

glycaemic control may be less straightforward than originally conceived.

Whilst it is important to note that the differing methods, measures and

populations employed in these studies could influence results (Chatzialexiadi

et al., 2005; Gilbert, 1992; Lane et al., 2000; Taylor et al., 2003; Vollrath et

al., 2007; Weissberg-Benchell & Glasgow, 1997), these differences may be

better explained by taking a rigorous theoretical approach to the research

findings.

One simple explanation for the differences across studies is that the results

may have been confounded by participants’ experiences of hypoglycaemia.

Experimental research shows that low blood glucose levels can induce

negative mood states (Heller, 2002; McCrimmon, Frier, & Dreary, 1999;

Merbis, Snoek, Kanc, & Heine, 1996) and this tendency towards negative

affect could itself influence scores on measures of emotional regulation.

Hence, researchers need to examine the relationships between negative

affect and hypoglycaemia to ensure that low blood glucose levels do not

unduly prejudice scores on emotional regulation.


53

A second explanation for the differing results across studies is that the

relationship between emotional regulation and glycaemic control may be

non-linear in nature. In particular, this relationship may reflect a curvilinear

function, which is commonly found in psychological literature (Anderson,

1994; Wiebe & Christensen, 1996). Probably the best known example of a

curvilinear relationship is the Yerkes-Dodson law (1908) which states that

task performance increases with emotional arousal up to an ‘optimum point’;

beyond which reductions in performance occur. Notably, these kinds of

relationships are masked when using traditional linear statistical approaches.

If the relationship between emotional regulation and glycaemic control is

curvilinear, the results obtained from empirical studies would depend on

where along the dimension of emotional regulation the participants tested lie.

Consequently, differences in levels of emotional regulation between samples

could significantly influence research results. Additionally, the strength of any

associations found between these variables would be underestimated using

linear statistics such as correlation and regression (Goodwin & Leech, 2006;

Wiebe & Christensen, 1996). The importance of considering non-linear

relationships when investigating associations between personality and

glycaemic control is clear.

Indeed, there is some evidence to support the existence of a curvilinear

relationship between emotional regulation and glycaemic control. In a study

involving diabetes patients on dialysis treatment, Brickman, Yount, Blaney et

al (1996) demonstrated that extreme scores on emotional regulation (either

high or low) were related to shorter renal deterioration times. These

researchers suggested that those moderate in emotional regulation took

longer to develop the renal complications associated with type I diabetes due

to better long-term glycaemic control. Whilst this study is limited by the

absence of more objective or short-term measures of glycaemic control, the

results do suggest that further consideration needs to be given to the

possibility of a non-linear relationship. These findings suggest that there may

be several pathways linking emotional regulation and glycaemic control.


54

Levels of negative affect may mediate one such pathway. As mentioned, low

levels of emotional regulation are strongly linked to depression, anxiety and

stress (Bienvenu et al., 2004; Bolger & Zuckerman, 1995; Kendler et al.,

2006; Khan et al., 2005; Lahey, 2009; Roelofs et al., 2008) and these high

levels of psychological distress may impel a person to primarily focus on their

emotions to the detriment of their physical health. Importantly, poor

regulation of emotions could predict reductions in positive health behaviours

as well as increases in negative health behaviours used to help cope with

psychological distress (Vollrath, Knoch, & Cassano, 1999).

Unsurprisingly, research confirms that negative affect influences health

behaviours in type I diabetes. Depression, anxiety and stress are related to

poorer self-care and glycaemic control and low emotional regulation is

related to greater fear of hypoglycaemia, which is known to influence

hypoglycaemia avoidance behaviours such as overeating or deliberate

omission of prescribed insulin dose (Dantzer et al., 2003; De Groot et al.,

2001; Helgeson et al.; Herzer & Hood, 2010; Lustman et al., 2000; Wild et

al., 2007). This suggests that individuals with very low levels of emotional

regulation may be too distressed about their illness (or other aspects of their

life) to adequately self-manage their diabetes.

In contrast, a high level of emotional regulation may be problematic as this

could influence risk judgements and subsequent motivation for self-care.

Individuals high in emotional regulation are more externally focused, discount

health risks and tend to worry less about developing diabetes complications

(Vollrath et al., 1999; Wiebe, Alderfer, Palmer, Lindsay, & Jarrett, 1994).

Importantly, these health perceptions translate into poorer engagement in

self-care behaviours (Skinner, Hampson & Fife-Schaw, 2002).

It may be, then, that a moderate level of psychological distress is needed for

individuals with diabetes to maintain appropriate self-care behaviours over

long periods. Indeed, lower levels of emotional regulation have been

associated with greater attention to symptoms of ill-health, greater utilisation


55

of health care and overestimation of blood glucose levels (Cox, Borger,

Asmundson, & Taylor, 2000; Howren, Bryant, & Suls, 2011; Rosmalen,

Neeleman, Gans, & de Jonge, 2007; Williams et al., 2010). Such findings

seem to support the Yerkes-Dodson law (1908); peak performance (in this

instance motivation to engage in long-term self-care) is most likely to occur

at optimal levels of emotional arousal.

Whilst it is likely that emotional regulation plays a crucial role in individuals’

long-term management of type I diabetes, further research is needed to

investigate differences in glycaemic control between individuals moderate,

high and low in emotional regulation. By testing for non-linear relationships,

researchers may reduce divergence in the field and achieve more clinically

meaningful results.

Agreeableness

A third personality trait that may influence a young person’s self-

management of type I diabetes is agreeableness. Individuals high in

agreeableness report positive health attitudes, better self-rated health,

superior health maintenance behaviours and less engagement in risky

practices (Bermudez, 2006; Booth-Kewley & Vickers Jr, 1994; Hampson et

al., 2007; Lemos-Giraldez & Fidalgo-Aliste, 1997; Loukas et al., 2000;

Nicholson et al., 2005; Vollrath et al., 1999). They are also more likely to

seek out complementary medicines and are more inclined to adhere to

medical treatments (Ediger et al., 2007; Sirois & Purc-Stephenson, 2008;

Telles-Correia, Barbosa, Mega, & Monteiro, 2009; Zugelj et al., 2010). This

trait is therefore likely to have considerable implications for those working

with young people with type I diabetes.

Indeed, Vollrath and colleagues (2007) have shown that children high in

agreeableness demonstrate better average HbA1c results. For youth in this

study, the agreeableness trait showed the second strongest correlation with

glycaemic control. Similar results have been observed in adults with both


56

type I and type II diabetes (Auerbach et al., 2002; Szymborska-Kajanek et

al., 2006). In fact, Auerbach et al (2002) showed that a combination of

agreeableness and a desire for behavioural control accounted for as much

as 21% of the variance in patients’ HbA1c scores. Finally, Giles et al (1992)

demonstrated that a socially desirable method of responding to others is

related to better glycaemic control (Giles et al., 1992). Again, this speaks to

the importance of higher levels of agreeableness in determining better self-

care and control of blood glucose levels.

Furthermore, agreeableness has been consistently associated with higher

levels of social support and may actually influence therapeutic relationships

between doctors and patients (Bermudez, 2006; Carver & Connor-Smith,

2010; Stone & McCrea, 2007). Thus, researchers suggest that the negative

correlations between agreeableness and HbA1c may be mediated by trust

and adherence to treatment (Auerbach et al., 2002). Higher agreeableness is

related to greater trust in others and a less confrontational attitude which

may influence a person’s willingness to engage in proper self-care (Costa &

McCrae, 1992a; McGhee et al., 2007; Mooradian, Renzl, & Matzler, 2006;

Sanz, Garcia-Vera, & Magan, 2010).

Conversely, individuals low in agreeableness have been found to have

poorer relationships with health professionals and often report being

unhappy with medical treatments (Carver & Connor-Smith, 2010; Roter &

Hall, 2006; Serber, Cronan, & Walen, 2003; Stone & McCrea, 2007).

Importantly, these relationships with physicians appear to influence

adherence and glycaemic control in people with diabetes (Lee & Lin, 2009;

Mancuso, 2009, 2010). Here, too, there are strong implications for those

working with those with type I diabetes.

However, there is some research to suggest that agreeableness may not

always be a protective factor (Helgeson et al., 2007; Hyphantis et al., 2005;

Lane et al., 2000). For example, Lane and colleagues (2000) found that the

agreeableness facet of altruism was positively correlated with average


57

weekly blood glucose values and HbA1c scores at baseline, 6 months and 12

months. Intriguingly, this suggests that individuals high in agreeableness

may have spent more time looking after the needs of others than caring for

their own health. Related results were uncovered by Hegelson, Siminerio,

Escobar and Becker (2007) who found that unmitigated communion, a focus

on others at the expense of oneself, was a marginal predictor of poor

glycaemic control in youth with diabetes. Furthermore, individuals with type II

diabetes who adopt a self-sacrificing defence style tend to delay the initiation

of treatment which also has implications for glycaemic management

(Hyphantis et al., 2005). Together, these studies imply that focusing on the

needs of others could prove to be a risk factor for poor glycaemic control.

This research brings the role of agreeableness in type I diabetes into

question (Helgeson et al., 2007; Hyphantis et al., 2005; Lane et al., 2000).

Whilst high levels of this trait may prompt adherence to treatment, they may

also promote a tendency to neglect self-care for the sake of others. Further

research is needed to verify the role of agreeableness in youths’ self-care

and glycaemic management.

It may also be important to investigate the role of agreeableness in people’s

psychological adaptation to type I diabetes. Research suggests that

agreeableness may play a role in determining adaptive coping processes

with this trait having been related to marginal increases in acceptance coping

and some modest decreases in avoidance strategies (Burgess, Irvine &

Wallymahmed, 2010; Carver & Connor-Smith, 2010; Connor-Smith &

Flachsbart, 2007; Hambrick & McCord, 2010; Kidachi et al., 2007; Lawson,

Bundy, Belcher, & Harvey, 2010; Ratsep, Kallasmaa, Pulver, & Gross-Paju,

2000). Whilst the strength of associations uncovered between coping and

agreeableness are negligible when compared to those found for

conscientiousness and emotional regulation (Carver & Connor-Smith, 2010;

Connor-Smith & Flachsbart, 2007), agreeableness has been linked to greater

active coping in youth with type I diabetes (Lawson et al., 2010). If

agreeableness influences levels of acceptance or avoidance coping, this


58

could have implications for general psychological wellbeing pointing to the

need for further study.

Finally, research suggests that low rates of agreeableness may not only

confer a slight risk for increased depressive symptoms (Kendler & Myers,

2010; Weiss, Sutin, Duberstein, Friedman, Bagby, & Costa, 2009) but also

may make individuals more vulnerable to relapse and recurrence of

depression (Harkness, Bagby, Joffe, & Levitt, 2002; Hoth, Christensen,

Ehlers, Raichle, & Lawton, 2007; Ode & Robinson, 2009; Weiss, Sutin,

Duberstein, Friedman, Bagby, & Costa, 2009). Indeed, high agreeableness is

related to less negative social exchanges and greater social support and this

may influence a person’s recovery from depression (Finch & Graziano, 2001;

Hoth et al., 2007). Furthermore, it would appear that agreeableness interacts

with social support. Individuals high in agreeableness show decreases in

depressive symptomology with increased social support whereas increased

social support has no effect for those with low levels of agreeableness (Hoth

et al., 2007). In all, this research suggests that low levels of agreeableness

may play a more important role in determining recovery from depression than

initial depressive symptoms.

In summary, agreeableness may play a role in determining quality of

diabetes self-management. Whilst the relationship between agreeableness,

self-care and glycaemic control may be complex, prior research involving

youth with diabetes suggests that higher levels of this trait should be

advantageous (Vollrath et al., 2007). Additionally, higher levels of

agreeableness may predict more adaptive coping responses and improved

trajectories of depressive symptoms. Further research is needed to clarify

the role of this trait in trajectories of self-care, glycaemic control and

psychological wellbeing.


59

Extraversion

Whilst there is relatively strong evidence to suggest that the traits of

conscientiousness, emotional regulation and agreeableness are important to

the self-management of type I diabetes, support for a central role for

extraversion in a person’s management of this disease is less compelling

(Edwards, 1999; Lane et al., 1988; Lustman, Frank, & McGill, 1991; Marks,

2000; Vollrath et al., 2007). Although a significant positive relationship

between extraversion and blood glucose levels has been reported in the past

(Lane et al., 1988), this association has not been widely replicated (Edwards,

1999; Lane et al., 2000; Marks, 2000; Taylor et al., 2003; Vollrath et al.,

2007). Hence, there is little evidence to support a definitive relationship

between extraversion, self-care and glycaemic control.

This may be due to the fact that higher levels of this trait are associated with

both positive and negative health behaviours (Raynor & Levine, 2009;

Rhodes & Smith, 2006). Individuals high in extraversion are more likely to

engage in risky behaviours, smoke cigarettes, drink large quantities of

alcohol, use illicit substances, lose sleep, engage in unsafe sex and have

multiple sexual partners (Munafo et al., 2006; Nicholson et al., 2005; Raynor

& Levine, 2009; Vollrath & Torgensen, 2008). On the other hand, higher

levels of this trait are also associated with positive outcome expectancies

and engagement in health maintenance behaviours (such as physical

exercise) which help to prevent disease (Rhodes & Smith, 2006; Williams,

O'Brien, & Colder, 2004). These findings imply that any relationship between

extraversion and health outcomes may be so complex and affected by other

factors that comprehensive study in individuals with diabetes would prove

difficult to achieve.

However, extraversion may be a somewhat clearer predictor of psychological

wellbeing. Studies suggest modest (r = 0.15) associations between

extraversion and engagement coping with higher levels of extraversion

related to greater acceptance (Carver & Connor-Smith, 2010; Connor-Smith

& Flachsbart, 2007). Further, high extraversion appears to confer a slightly


60

decreased risk for depression, anxiety and stress although whether low

extraversion is a cause or consequence of psychological distress is debated

(Bienvenu, 2006; Burgess et al., 2010; del Barrio, Moreno-Rosset, Lopez-

Martinez, & Olmedo, 1997; Jylha & Isometsa, 2006; Kendler et al., 2006;

Kendler & Myers, 2010; Kotov et al., 2010).

Altogether, the above research suggests that extraversion may play a limited

role in determining self-management outcomes in youth with type I diabetes.

Whilst to some extent higher extraversion may predict lower rates of negative

affect and better coping, whether these relationships are significant or

influence glycaemic control is yet to be seen.

Openness to experience

Definitive links between openness to experience and management of type I

diabetes are also yet to be established (Edwards, 1999; Lane et al., 1988; P.

J. Lustman et al., 1991; Marks, 2000; Vollrath et al., 2007). Indeed, studies

investigating associations between openness, health behaviours and

psychological wellbeing have often reported mixed results perhaps indicating

that straightforward relations between this trait and diabetes outcomes may

be difficult to uncover (Bermudez, 1999; Booth-Kewley & Vickers Jr, 1994;

Carrillo, Rojo, Sanchez-Bernardos, & Avia, 2001; Kendler & Myers, 2010;

Kotov et al., 2010; Lemos-Giraldez & Fidalgo-Aliste, 1997; Vollrath et al.,

1999; Wolfenstein & Trull, 1997).

For instance, high levels of openness have been related to negative health

behaviours such as substance abuse and risky sexual practices but have

also been related to positive health maintenance behaviours and less driving

accidents (Bermudez, 1999; Booth-Kewley & Vickers Jr, 1994; Lemos-

Giraldez & Fidalgo-Aliste, 1997; Vollrath et al., 1999). Similarly, whilst meta-

analyses and reviews of the literature suggest no link between openness to

experience and disordered levels of negative affect (Kendler & Myers, 2010;

Kotov et al., 2010), higher scores on the aesthetics, feelings and fantasy


61

facets of openness to experience have been associated with increased

depression whereas higher scores on the actions facet of this trait have been

associated with decreases in depression (Carrillo et al., 2001; Wolfenstein &

Trull, 1997). Hence, the role of openness in type I diabetes management is

likely to be very complex or unpredictable at best.

Indeed, to date, no replicable relationships between openness to experience

and management of diabetes have been reported (Edwards, 1999; Marks,

2000; Vollrath et al., 2007). Furthermore, the existing research involving

conceptually related behavioural traits is inconclusive (Lane et al., 1988;

Lustman et al., 1991; McCrae & Costa, 1999; McCrae & John, 1992). For

example, Lane and colleagues (1988) found that low curiosity was

associated with poorer glycaemic control whereas Lustman, Frank and

McGill (1991) found that low novelty seeking was associated with better

blood glucose control. These results reinforce the notion that the role of

openness to experience may be difficult to predict. Importantly, these

conflicting findings may indicate no relationship between openness and

diabetic management or could indicate the existence of a non-linear

association between these variables. Alternatively, the personality

assessments utilised in prior research may not be comparable or suitably

fine-grained to uncover reliable trends.

Perhaps the only straightforward or predictable relationship between

openness to experience and diabetes outcomes occurs with acceptance

coping. Individuals high in openness are more likely to employ adaptive

coping strategies such as active planning and reframing and typically accept

and deal with stressful experiences (Carver & Connor-Smith, 2010; Connor-

Smith & Flachsbart, 2007; Lawson et al., 2010). Accordingly, clinicians can

expect individuals high in openness to accept a diagnosis of diabetes and

integrate this into their everyday life.

Overall, prior research suggests that personality may play an important role

in determining a youth’s management of type I diabetes. In particular, the


62

traits of conscientiousness, emotional regulation and agreeableness may

have a significant influence on self-care, glycaemic control, coping, and

levels of negative affect. Based on this research, it appears that individuals

high in conscientiousness and agreeableness and moderate in emotional

regulation may display superior self-care and glycaemic control whereas

individuals high in conscientiousness, agreeableness and emotional

regulation may display greater psychological wellbeing. Although the

influence of extraversion and openness upon diabetes outcomes may be

restricted, these traits may play a small role in determining levels of

acceptance coping and depression. Given the small amount of research

looking at self-care, glycaemic control and psychological wellbeing in youth

with type I diabetes, further research is needed to confirm or clarify these

relationships.

New directions for research

Whilst the abovementioned personality studies have provided a wealth of

information to help clinicians individualise treatment approaches for youth

with type I diabetes, there is room for methodological development and new

avenues of inquiry within this field. In order to improve and extend upon the

existing studies, researchers can propose and test methodological and

theoretical explanations for research findings, promote a more

comprehensive view of diabetes self-management, and examine the role of

personality in self-management outcomes over time. By enhancing research

in these ways, it may be possible to achieve a greater understanding of the

relationship between a young person’s personality and how they manage

their diabetes. Accordingly, specific approaches to achieving these goals are

discussed below.

Proposing methodological and theoretical explanations for

research findings

As stated earlier, personality research involving individuals with diabetes has

at times provided mixed results (Chatzialexiadi et al., 2005; Gilbert, 1992;


63

Lane et al., 2000; Taylor et al., 2003; Vollrath et al., 2007; Weissberg-

Benchell & Glasgow, 1997). Consequently, it is difficult to translate this

research into resolute practical recommendations or guidelines that could

promote optimal self-management in youth with type I diabetes. Theorists

have argued that a lack of consistent and reliable findings is one of the

biggest impediments towards developing a comprehensive model of diabetes

self-management (Lorig, 1993). Therefore, it is key that researchers find

methodological or theoretical explanations for incongruent or unexpected

research results.

The simplest way of explaining unexpected results is to look in detail at the

research methodologies of conflicting studies. By taking study design and

measurement methodology into account, it is possible to determine whether

unanticipated results represent a genuine effect or whether these findings

are a product of a particular research approach. Although the ability of the

five-factor model to provide reliable and valid personality data has already

been highlighted (Bermudez, 1999; Marshall et al., 1994), there are other

methodological considerations that may also be important.

One explanation for the differing results across prior studies is that some

investigations have employed samples that have amalgamated distinct

groups such as populations with type I and type II diabetes (Edwards, 1999;

Lustman et al., 1991) or children and adults (Skinner et al., 2002). Another

explanation for differences in study results is that researchers have at times

employed outcome measures which cannot be readily compared which

makes evaluating results across investigations difficult. For instance, some

researchers have measured self-care behaviours in youth with type I

diabetes whilst others have employed HbA1c as an index of behavioural

management (i.e. Skinner et al., 2002; Vollrath et al., 2007).

These difficulties indicate that researchers need to emphasise research

design and methodology when comparing and contrasting their results with

the work of others. Future studies should focus on well-defined populations


64

and employ standardised measures such as HbA1c or scientifically validated

scales to ensure some equivalency of research outcomes. By using this

approach, researchers can conduct replicable and valid psychological

research, which will help to determine reliable associations between

personality and diabetes management.

A second method of explaining unexpected research results is to explore

extraneous variables that may be influencing outcomes. Demographic and

treatment variables could alter the strength and direction of relationships

uncovered between personality and diabetes self-management, suggesting

that researchers may need to control for the influence of these factors. In

particular, variables such as the participant’s age, gender, duration of

diabetes and responsibility for diabetes tasks may have a significant impact

on relationships between personality and self-management.

The influence of these extraneous variables is especially important to

consider when studying younger individuals. Indeed many of these people

may not be old enough or have had diabetes for long enough to take

complete responsibility for carrying out many diabetes tasks. By

understanding the influence of demographic and treatment variables upon

self-management outcomes researchers can control for the influence of

these variables and propose testable hypotheses to explain conflicting

results.

A final method of explaining incongruent results is to interrogate theoretical

accounts for research outcomes. To date, personality research in the area of

type I diabetes has not been extensively tied to theoretical models and this is

unfortunate as theory can help to clarify results and provide further testable

hypotheses. By appropriating theoretical models from other relevant areas of

psychology researchers may find solutions to research quandaries and

promote a more comprehensive understanding of the role of personality in

type I diabetes. To do this, researchers can focus on established personality-

health models and hypothesise mediating pathways worthy of further


65

attention. As mentioned, it may also be important to examine the potentiality

of non-linear relationships. This analytical approach to explaining research

results was employed in the current study.

Promoting a comprehensive view of diabetes self-management

A second way of improving upon prior research is to endorse a more

comprehensive view of diabetes self-management. Whilst theorists argue

that chronic condition self-management involves a complex interplay of

psychosocial, demographic and service factors (Flinders Human Behavioural

& Health Science Unit, 2006), this paradigm has not been consistently

studied by personality researchers. Thus far, personality studies involving

participants with diabetes have typically focused on a single or small number

of measures (such as HbA1c) to define a person’s quality of diabetes self-

management (Lane et al., 2000; Vollrath et al., 2007). Unfortunately, this

approach does not fully explain the complexity of the experience of chronic

disease. Indeed, the role of personality in determining psychological

wellbeing, tendency towards hypoglycaemia, or engagement in specific self-

care behaviours in individuals with type I diabetes has received little

attention.

There is thus still much to know about how an individual’s personality may

influence their day-to-day management of type I diabetes. To do so the

scope of current personality research needs to be broadened by including

measures of psychological wellbeing such as depression, anxiety, stress,

and coping alongside indices of HbA1c, hypoglycaemia and self-care

behaviour. This could help us to obtain a more comprehensive picture of the

role of five-factor model traits in the management of young people’s

diabetes.


66

Examining the role of personality in self-management outcomes

over time

A final way of improving upon prior personality research involving individuals

with diabetes is to investigate the role of personality factors in trajectories of

diabetes self-management. To date, personality studies involving individuals

with diabetes have largely employed cross-sectional or retrospective designs

making it difficult to examine the role of personality in diabetic management

over time (Brickman et al., 1996; Marks, 2000; Skinner et al., 2002; Vollrath

et al., 2007 etc).

Importantly, retrospective personality research is problematic as it may be

subject to recall bias. Further, it is difficult to infer causality using this

research design; personality may influence health status or health status

may influence personality (Smith & Spiro, 2002). Cross-sectional research is

also problematic as this only provides a brief snapshot of the relationship

between personality and health. Using cross-sectional methods it is

impossible for researchers to test the reliability of uncovered associations or

elucidate the role of personality in trajectories of important outcomes (Smith

& Spiro, 2002). Therefore, it is critical to conduct longitudinal research to

examine whether five-factor model traits predict improvement, stability or

deterioration in diabetes self-management. By engaging in this research it

may be possible to identify personality risk factors that have a cumulative

impact over time.

Overall, the limitations of prior research demonstrate that there is still much

to know about the role of personality in diabetes self-management. Future

research can expand on this groundwork by proposing and testing

methodological and theoretical explanations for research findings, promoting

a comprehensive view of diabetes self-management, and examining the role

of personality in trajectories of management outcomes. This approach could

lead to a more in-depth understanding of how a person’s personality

influences their management of diabetes and could help to identify

individuals who need extra support.


67

Aim, research questions and hypotheses

The current research study was designed to build upon the previously

conducted personality investigations in the area of adolescent type I diabetes

(Skinner et al., 2002; Vollrath et al., 2007). The study aims to elucidate the

role of five-factor model traits in trajectories of diabetes self-management in

youth aged 8 to 18 years at baseline. By utilising the five-factor model in

conjunction with outcome measures of HbA1c, frequency of hypoglycaemia,

blood glucose testing behaviour, depression, anxiety, stress and coping, it

was hoped that a comprehensive picture of the role of personality in the

management of this disease would emerge. Accordingly, the research

questions for this study are listed below.

Research Question 1:

What relationships exist between five-factor model traits and HbA1c,

hypoglycaemia, frequency of blood glucose testing, depression, anxiety,

stress, avoidance coping and acceptance coping in youth with type I

diabetes? What are the directions of these relationships and are they linear?


Are there particular five-factor model traits that predict HbA1c,


stress, avoidance coping and acceptance coping independent of age,

duration of diabetes, responsibility and gender? Which five-factor model trait

is the best personality predictor of these specific self-management

outcomes?


Do five-factor model personality traits predict trajectories of HbA1c,


stress, avoidance coping and acceptance coping over time?


68

Hypotheses

Based on the reviewed literature, hypotheses for the role of each five-factor

personality trait were proposed:

Conscientiousness should be a protective factor independent of

control variables. Individuals high in conscientiousness should display

greater self-care and superior glycaemic control as indexed by more

frequent blood glucose testing, less frequent hypoglycaemia and

lower annual HbA1c scores. Furthermore individuals high in

conscientiousness should display better psychological wellbeing as

measured by lower levels of depression, anxiety, stress and

avoidance coping and higher levels of acceptance coping. Differences

between those high and low in conscientiousness at baseline should

remain stable or increase over time.

Emotional regulation should have a clear relationship with

psychological wellbeing. Low levels of emotional regulation should

predict greater depression, anxiety, stress and avoidance coping and

lower levels of acceptance independent of control variables. Moderate

levels of emotional regulation may predict greater blood glucose

testing, less frequent hypoglycaemia and lower annual HbA1c results.

Differences between emotional regulation groups at baseline should

remain stable or increase over time.

Agreeableness should also be a protective factor independent of

control variables. Individuals high in agreeableness should display

greater self-care and superior glycaemic control and greater overall

psychological wellbeing. Differences between those high and low in

agreeableness at baseline should remain stable or increase over time.

Extraversion may play a limited role in determining self-management

outcomes. Individuals high in extraversion may display greater overall

psychological wellbeing as indicated by lower levels of depression,


69

anxiety, stress and avoidance coping and higher levels of acceptance

coping. These relationships may not reach statistical significance.

There may be a weak relationship between high extraversion and

worse glycaemic control as measured by HbA1c. Differences between

those high and low in extraversion may be variable over time.

Openness to experience may also play a limited role determining self-

management outcomes. Individuals high in openness may display

greater acceptance coping. Differences between those high and low in

openness to experience may be variable over time.

To date this study is the only prospective longitudinal study of five-factor

model traits in youth with type I diabetes to employ a battery of self-

management outcomes measures. By examining changes in specific

outcomes over time it may be possible to identify five-factor model traits that

predict improvement or deterioration of diabetes management throughout

adolescence.

Chapter 4

Design and Methodology

The current personality investigation was an extension of the Diabetes

Research into Adolescent Transitions (DRAT) project, a comprehensive

study exploring the complex interplay among a host of psychosocial,

demographic, family and service factors and glycaemic control in 158

Australian children and adolescents (8-18 years) with type I diabetes. The

DRAT project ran for three years and aimed to map the range of pathways

leading to optimal self-management of type I diabetes by determining the

relative contribution and stability over time of these factors.

The decision to investigate the role of personality within the scope of the

DRAT project was made after the first wave of data collection was initiated.

The rationale for including a personality scale to the battery of existing

measures came from theoretical and research considerations (Wiebe &

Smith, 1997) and on the basis of anecdotal reports from parents of

participants in the DRAT study who saw their child’s personality as important

in managing diabetes. A number of parents referred to the importance of

stable traits such as dependability and conscientiousness when discussing

their child’s self-management and this suggested that studying personality

might be a worthy avenue of inquiry.

In order to investigate the role of personality in young people’s management

of type I diabetes, the five-factor personality inventory for children (FFPI-C)

was added to the battery of DRAT measures. For the current investigation,

child personality data and related consent forms were gathered from 142

Chapter 4: Design and Methodology

71

families in the DRAT project with 104 of these families (65.8% of the total

sample) returning data for all 3 years of the study

The DRAT project design

The DRAT project incorporated a longitudinal, cross sectional design with

annual quantitative data on metabolic control, psychosocial, demographic

family and service variables gathered over three years. Participants were

also contacted quarterly via the telephone to collect self-reported data on

diabetes control.

To maximise variability in the demographic variables of interest, the decision

was made to recruit DRAT participants across the state of New South Wales

and the Australian Capital Territory. This approach differentiates this project

from many others in the area of type I diabetes research in that we engaged

participants independent of source of care and thus did not simply rely on a

single sample drawn from one hospital population. In addition, the decision

was made to hold annual interviews at the participants’ homes in the interest

of maximising retention rates for the DRAT project. Annual interviews

provided the opportunity to foster positive and genuine relationships between

the DRAT researchers and participating families and this appeared to be

successful with the DRAT project recording relatively small dropout rates

(143 retained from 158 over three years). This is especially noteworthy for

research involving children and adolescents.

The broad and comprehensive approach to data collection taken in the

DRAT project prohibited the recruitment of a very large sample due to the

financial and time constraints associated with travel for annual interviews and

quarterly phone calls. Nevertheless, given the amount and richness of

information gathered from participating families, the DRAT project represents

a comprehensive and in-depth study of a varied sample of youth with type I

diabetes.


72

Recruitment of participants

Research documents the difficulties of recruiting young participants for

research studies, particularly those over 15 years (Croft, Webber, Parker, &

Berenson, 1984; Gattuso, Hinds, Tong, & Srivastava, 2006; Liese et al.,

2008; Roberts, 2000). For example, in a large study looking at participation

rates in youngsters with diabetes (Liese et al., 2008), individuals between 15

and 17 years were about half as likely as children younger than 10 to

participate in research. Individuals aged 18 years of age at recruitment had

an even smaller likelihood of participating.

Research shows that employing a variety of approaches to recruitment tends

to lead to optimal participation (Drews et al., 2009). Hence, the DRAT project

utilised a variety of recruitment methods which are outlined below.

Recruitment efforts were focused in New South Wales (NSW) and the

Australian Capital Territory (ACT) and the broad and mixed-method

approach employed ensured wide variation in potential demographic factors

of interest. Figure 4.1 shows the geographical locations across NSW and the

ACT where participants lived at baseline.

One of our first and most successful recruitment approaches involved

advertising the DRAT project. Paid advertisements were included in the New

South Wales Sunday Herald newspaper and diabetes relevant magazines

(Appendix 1). Posters and advertisements were also displayed in pertinent

areas around hospitals, pharmacies, schools, and in school letters (Appendix

2). The DRAT project also arranged for an information sheet and consent

form to be sent to families who were registered members of the Juvenile

Diabetes Research Fund (JDRF) notifying them of the study (Appendix 3).

This gave the project exposure to a broad demographic of individuals who

met the criteria for inclusion. These advertisements provided information

about the project and contact details of project organisers for interested

individuals to contact.


73

Figure 4.1: Geographical distribution of DRAT study participants at baseline

Next, researchers visited participating diabetes clinics and gave individuals

aged between 8-19 years and their parents information about the project

together with a reply paid envelope to mail the consent form if they wished to

participate (see Appendix 3). Contact details of the researchers were

provided in case the young person or their family wanted more information

before making a decision. Participants were also recruited from the hospital

clinic setting by clinic staff, using the information sheet regarding the project

(see Appendix 3). Local nursing staff were asked to speak to potential

participants about the study at their appointment and provide them with the

information statement, consent form and a stamped self-addressed

envelope.

A final method of recruitment used was a snowball recruitment approach

(Appendix 4). Participating families who knew someone else who was

eligible to participate were asked to pass on the study information to third


74

parties and leave it to them to initiate contact with the researcher if they were

interested.

The DRAT recruitment period ran from late 2006 to mid 2007 and resulted in

a final sample size of n =158. We had initially hoped to recruit 200

participants for the DRAT project but stopped recruitment at just over 6

months to enable the study to be completed within the proposed timeframe.

Given the difficulty of recruiting young people for research studies (Liese et

al., 2008), it is unsurprising that we were unable to reach a sample of 200

participants in the allocated timeframe. Indeed, longer recruitment periods

are to be expected when recruiting children and adolescents (Savage &

McCarron, 2009).

The personality study design

As an extension of the DRAT project, the current study incorporated the

same recruits, design and measures as the parent study. The aim of the

personality study was to determine the role of personality in the self-

management of young people’s diabetes by examining the associations

between five-factor model traits and the psychosocial, physiological and self-

care variables measured in the DRAT project.

However, because the personality inventory was included after the first wave

of DRAT data collection had commenced, a different approach was needed

to gather these data. Participants received an additional informed consent

form (Appendix 5), and an instructional form (Appendix 6), so that personality

data could be gathered either through postal or interview methods (see

procedures). Due to this different method of data collection, the personality

study received less returned data from the participating families than the

DRAT project. Thus, to allow for longitudinal comparisons, statistical

analyses for this research thesis were conducted using only the information

from participants returning all three years of personality data (n = 104).


75

This provided three waves of personality data that could be matched to the

three waves of annual data collected through the DRAT study. Whilst the

association between the first wave of personality and DRAT data was

retrospective in nature, the amount of time between data collection points

was minimal (1-5 months). Furthermore, it was important to include this

information to have three waves of data to test longitudinal stability and

change.

Measures

Due to the reduced number of participants returning personality data for all

waves, and the large number of measures involved in the DRAT project, the

decision was made to analyse a subset of the battery of completed DRAT

measures for this research thesis. This decision was made in the interest of

reducing the likelihood of type I error and for the purpose of focus for the

thesis. The Self Description Questionnaires (Marsh, 1988, 1990a, 1990b),

the Michigan Diabetes Research Centre’s Brief Diabetes Knowledge Test

(Michigan Diabetes Research Training Center) and the Self Efficacy for

Diabetes Scale (Grossman, Brink, & Hauser, 1987) were excluded from

analyses. Accordingly, a short synopsis of these excluded measures and the

reason for their exclusion is detailed in the appendices (Appendix 7).

The measures included in the personality study assessed demographic

characteristics, diabetes treatment modality, level of responsibility for

diabetes care, personality traits, quality of diabetes control and levels of

psychological wellbeing. These included measures are detailed below.

Demographic information

Demographic information was collected using an annual personal information

form (Appendix 8). Information was gathered on the participant’s contact

details, age, gender, age of diagnosis with diabetes and geographical

location. Furthermore, parents of participants were asked to report annual

household income on a four-point scale aligned with corresponding


76

Australian Census data (less than $31,199, $31,200 to $51,999, $52,000 to

$83,199 and more than $83,200) (Australian Bureau of Statistics, 2006b).

Parents were also asked to provide information about their ethnic origin.

Further data such as the child’s birth order, family history of diabetes, and

work/study arrangements were gathered, although, these variables were not

included in analyses.

Diabetes treatment and level of responsibility for care

Self-report information on participants’ treatment for diabetes was gathered

at the three annual visits using an annual data collection form (Appendix 9).

Quarterly updates were made over the phone using a shortened version of

the annual form (Appendix 10). To determine method of diabetes treatment,

individuals were asked whether they were primarily using injections or an

insulin pump to deliver insulin.

In addition, the Diabetes Family Responsibility Questionnaire (DFRQ)

(Anderson et al., 1990) was used at each annual interview to measure parent

involvement in their child’s diabetes care (Appendix 11). The DFRQ is a 17-

item scale designed to assess sharing of diabetes responsibilities between

children with type I diabetes and their parents. The measure was piloted on

121 American youth (aged 6-21) with type I diabetes and their mothers and

measures perceptions of who takes responsibility for specific diabetes tasks.

Personality

Quantitative personality data for this study were gathered using the Five-

Factor Personality Inventory for Children (FFPI-C). The FFPI-C is an un-

timed pen and paper questionnaire designed to tap the big-five personality

dimensions of openness to experience, conscientiousness, extraversion,

agreeableness and emotional regulation in children and adolescents aged

from 9 years, 0 months to 18 years, 11 months (McGhee et al., 2007). The

FFPI-C was normed on an age-stratified sample of 1,284 North American

youth and has demonstrated high test-retest reliability (r for different factors


77

ranging from 0.84 to 0.88 over two week period), internal consistency (alpha

for different factors ranging from 0.74 to 0.86) and criterion-related validity

(McGhee et al., 2007). It is an efficacious measure for this study as it is

representative of our cohort, takes only a short period of time to complete

and can be self-administered, individually administered or group

administered (McGhee et al., 2007). Due to copyright issues, the Five-Factor

Personality Inventory for Children is not available in the appendices of this

thesis.

The FFPI-C consists of 75 items (15 items per factor) with each item having

two opposing anchor statements aimed to assess trait variability on the

dimensions of the five-factor model. There are five circles between the

anchor statements which allow the test taker to choose the statement that

best reflects their opinion and make a qualitative decision on the degree of

support for their choice (McGhee et al., 2007). If a person agrees with a

sentence they are instructed to colour in the circle closest to that sentence. If

they somewhat agree they are asked to colour in the second closest circle to

that sentence. If unable to decide between sentences, test takers are

instructed to colour in the middle circle. Test takers are instructed that there

are no right or wrong answers but are encouraged to make a decision

between statements and use the middle circle as little as possible. In the

practice example below the individual has indicated that they somewhat

agree with the statement ‘I think dogs are nice’.

Example:

I think dogs are nice. ○ ● ○ ○ ○ I think dogs are scary.

(McGhee, et al., 2007, p. 7)

To score the FFPI-C all questions need to be answered. Raw scores are

then summed (some questions are reversed) for each dimension (15 items

for each factor) and converted to T-scores using the manual provided (see

FFPI-C manual for T-score conversion tables). T-scores are computed from

a distribution that has a mean of one and a standard deviation of ten and


78

provide a more accurate and understandable indicator of variation in a

person’s personality profile (McGhee et al., 2007).

Whilst the DRAT project included participants out of the age range of the

FFPI-C norms (thirteen participants under 9 years old and two participants

over 18 and 11 months at baseline), the benefits of employing a single and

reliable measure of personality were considered to outweigh any potential

disadvantages. The FFPI-C has a low reading level (year three) (McGhee et

al., 2007) and was judged as the best available measure for indexing five-

factor model dimensions within our cohort. To further ensure the validity of

FFPI-C responses, parents were instructed to help younger children with any

difficult or ambiguous items if needed.

Self-care behaviours

Information relating to the participants’ adherence to insulin regimen, health-

care utilisation and frequency of blood glucose testing were gathered using

the annual data collection form at the three annual visits (Appendix 9).

Quarterly updates were collected over the phone using the abridged form

(Appendix 10). To measure adherence to insulin, participants were asked

whether they had taken their insulin as prescribed and were provided with a

four-point ordinal scale to answer (none of the time, some of the time, most

of the time or all of the time). Frequency of blood glucose testing was

measured by asking the participant to recall the number of blood glucose

tests carried out over the preceding two weeks. Self-reports of glucose

testing were matched against actual test results recorded in the memory

meters of blood glucose monitors and were closely correlated (r = 0.86 in

year one, r =0.82 in year two, r = 0.81 in year three, all p’s < 0.01). Finally,

self-report information on health-care utilisation was gathered by noting the

number of planned and actual attendances at appointments with diabetes

health care professionals over the preceding three-month period.


79

Glycaemic control

Glycosylated haemoglobin (HbA1c) levels were employed as an objective,

physiological index of glycaemic control. Researchers collected a capillary

blood sample from participants at annual visits and sent the sample to a

central laboratory to undergo an HLPC assay. The same laboratory and

HLPC assay were used for all three years of the study. As aforementioned,

HbA1c serves as a broad index of blood-sugar levels over the preceding 6-8

weeks and is commonly accepted as the best available measure of

glycaemic control (Hanas, 1998; Kilpatrick, 2000).

To avoid the development and progression of diabetes complications, the

International Society for Pediatric and Adolescent Diabetes (ISPAD) and the

Australian National Health and Medical Research Council (NHMRC) suggest

a general target HbA1c range of < 7.5% for all youth (National Health and

Medical Research Council, 2005; Rewers et al., 2009). Thus for the current

study, participants with HbA1c values above 7.5% were considered to have

‘poor’ glycaemic control.

Incidence of hyperglycaemic emergency and hypoglycaemia

The frequency and severity of hypoglycaemia (low blood glucose) and

diagnosis of hyperglycaemic emergency (very high blood glucose) were

recorded on a quarterly basis. At the three annual visits and on a quarterly

basis for the first two years of the study participants were asked how many

times they had obtained a blood glucose reading below 3 mmol/L in the last

two weeks. They were also asked to count the number of hypoglycaemic

attacks that led to unconsciousness and the number of times they had been

admitted to hospital with elevated blood glucose levels over the preceding 3

months (see Appendices 9 and 10).

Depression, anxiety and stress

Data on stress and emotional states in our sample were gathered annually

using the Australian developed, short form of the Depression, Anxiety, Stress


80

Scales (DASS21) (Lovibond & Lovibond, 1995a), a set of brief self-report

measures designed to index changes in the core emotional symptoms of

depression, anxiety and stress over time (Appendix 12).

The Depression, Anxiety, Stress Scales (DASS) were originally developed

with the aim of creating a measure of depression and anxiety that provided

maximum discrimination between these conceptually distinct constructs.

Depression and anxiety inventories had historically shown high correlations,

and by removing any item overlap between scales, Lovibond and Lovibond

(1995a) were able to minimise construct relatedness. Through the

development process of the DASS, multiple group factor analysis revealed a

third factor of stress that appeared to deal with non-specific arousal (such as

restlessness). This factor was considered sufficiently differentiated from

anxiety to warrant the addition of further items to define the stress construct.

In an iterative procedure, the researchers added stress-related items and

removed items that did not load substantially on any of the scales. This

process resulted in the final 42-item Depression, Anxiety, and Stress Scales

(DASS) that employed 14 items for measuring each construct. The DASS21

is a shortened version of the complete Depression, Anxiety, and Stress

Scales that was created for testing situations where a brief measure is

required and includes 7 items for measuring each construct (norms for these

constructs are provided in Appendix 13).

One of the major strengths of the DASS is that they are based on a

dimensional rather than categorical conceptualisation of psychological

disorder. On the basis of clinical and pilot research for the DASS, Lovibond

and Lovibond (1995) concluded that the experience of depression and

anxiety is not qualitatively different between clinical and non-clinical

populations. This approach suggests that anxiety and depression run along a

continuum and it is the degree of severity that differentiates these groups.

Consequently, the DASS was normed on a non-clinical population and is

valid for use in both clinical and non-clinical populations


81

Specifically, the DASS was normed on 2,914 Australian individuals aged 17-

69 years who showed no clinical signs of depression or anxiety (Lovibond &

Lovibond, 1995a). Both the DASS and the DASS21 have high internal

consistency, show good criterion-related validity, and can measure current

state or change in depression, anxiety and stress over time (Lovibond &

Lovibond, 1995a, 1995b). Notably, the scales can be administered and

scored by non-psychologists.

Both the DASS and the DASS 21 are designed to measure emotional states

rather than traits. Respondents are asked to rate the degree to which they

have experienced each state over the past week on a four point scale

ranging from 0 (did not apply to me at all) to 3 (applied to me very much or

most of the time). The three distinct scale scores are determined by

summing the scores for each relevant item. Importantly, the scales of the

DASS21 were created by selecting items that were representative of the

subscales in the full version DASS (7 subscales for depression, 4 for anxiety,

5 for stress) and can hence be converted to full DASS scores by multiplying

by 2.

Z scores can be computed from the full-scale scores so that comparisons

can be made across the depression, anxiety and stress scales and against

normative values (Z score conversions for the DASS 21 are listed in

Appendix 14). The researchers also provide recommended cut offs for

conventional severity levels, which can be seen in Table 4.1.

Due to the positive skew of the normative data, a Z score of 0 corresponds to

the 60th percentile, which indicates that 60% of the subjects in the sample

scored at or below the mean. These individuals are assigned to the ‘normal’

category indicating that there is no significant emotional disturbance within

this group. The category ‘mild’ is given to depression, anxiety or stress

scores that fall between the 78th and 87th percentile, ‘moderate’ refers to

scores between the 87th and 95th percentile ‘severe’ refers to scores within


82

the 95th and 98th percentile and ‘extremely severe’ refers to scores between

the 98th to 100th percentile.

Table 4.1: Raw scores for DASS and related Z scores and percentile ranks

Raw Scores

Z Score Percentile Depression Anxiety Stress

Normal

< 0.5 0-78 0-9 0 -7 0-14

Mild

0.5 – 1.0 78-87 10-13 8-9 15-18

Moderate

1.0 – 2.0 87-95 14-20 10-14 19-25

Severe

2.0 – 3.0 95-98 21-27 15-19 26-33

Extremely Severe > 3.0 98-100 28+ 20+ 34+

* Conversion of Z scores to percentiles is not exact because frequency distributions for the DASS are un-identical

(Lovibond & Lovibond, 1995a, p. 27)

Whilst the lower age limit of the development sample for the DASS was 17

years (Lovibond & Lovibond, 1995a), the researchers who developed these

scales argue that there should be no problem using them with children as

young as 12-years. This is still a concern for our sample though, as we

included participants as young as 8 years old. Unfortunately, there are few

measures of depression, anxiety and stress that were developed for younger

populations that can be scored easily by non-psychologists.

To help ensure that the DASS was scored correctly for those of a younger

age, researchers in the DRAT project were careful to ensure that the

participants understood the meaning of each item during testing. Whilst this

cannot assure the validity of participants’ responses, there appeared to be no

comparative measures available for the age group in our sample and we

were hesitant to use a multitude of differing measures of depression, anxiety

and stress within the same study.


83

Coping

Coping was measured at baseline and annually using a modified version of

the Coping with a Disease (CODI) questionnaire (Petersen, Schmidt,

Bullinger, & the DISABKIDS Group, 2004). The CODI is a 29-item measure

designed to assess how well a young person handles living with a chronic

disease and how upsetting tasks or thoughts related to their illness are

(Appendix 15). The scale was piloted on a sample of 188 young people

(aged 8-18 years) with a range of chronic diseases hailing from several

countries and has been shown to be a valid and reliable measure for

estimating patients’ general coping with chronic disease (Petersen et al.,

2004). The CODI can be administered and interpreted quickly and easily,

making it useful for research studies such as this one involving a battery of

tests.

There are six hypothetically orthogonal subscales of the CODI, which are

indexed on a 5-point likert scale ranging from ‘never’ to ‘always’. These

factors are acceptance, avoidance, cognitive-palliative coping, distance,

emotional reaction, and wishful thinking. There is also one question focused

on overall coping which asks the individual to rate how well they cope with

their disease on a 5-point likert scale ranging from ‘not very well’ to ‘very well’

(Petersen et al., 2004). To make the CODI more suitable for the population

under investigation, we replaced the word ‘illness’ with ‘diabetes’ in 15 of the

original 29 items. These modifications were deemed innocuous, as they did

not change the actual content of the questions.

As the CODI subscales are unable to be meaningfully aggregated, the a

priori decision was made to employ only two subscales from this measure in

statistical analyses. The avoidance and acceptance subscales were chosen

for this purpose as they have been more consistently studied than the other

factors (Connor-Smith & Flachsbart, 2007), have been linked to management

of type I diabetes in prior studies (Coelho et al., 2003; Duangdao & Roesch,

2008; Hanson et al., 1989) and were most closely aligned with structural and

hierarchical models of general coping (Connor-Smith & Flachsbart, 2007).


84

Furthermore, the items within these scales were considered to best gauge

how well an individual with type I diabetes has adapted to living with their

disease. The test-retest reliability scores, range, means and standard

deviation for the included CODI subscales are detailed below in Table 4.2.

Table 4.2: Psychometric properties of CODI subscales

Scale Range Mean Std. Dev

Acceptance 0.83 6-30 22.66 5.15

Avoidance 0.72 4-20 12.74 4.01

(Petersen et al., 2004, p. 639)

A limitation of the CODI is that it has not been comprehensively normed

(Petersen et al., 2004) which makes it difficult to compare the results

obtained from the current study to the CODI development sample. The

original CODI data showed signs of skew (Petersen 2010, personal

communication, September, 2010) yet details regarding medians and inter-

quartile ranges for the developed sub-scales are not provided with the

measure (Petersen et al., 2004). Unfortunately CODI researchers were

unable to access this information at the time of data analysis (Petersen 2010,

personal communication, September, 2010).

This means that it is difficult to compare scores on this measure across

populations. Nevertheless, the CODI appears to be the only measure

available that is designed for gauging levels of coping in children with a

chronic disease (Petersen et al., 2004) and is a useful measure for the

purpose of understanding the role of personality in coping with type I

diabetes in the current sample.

Procedures

The procedures for the personality study (with which this thesis is concerned)

largely followed those of the DRAT parent study (see Figure 4.2). For clarity,

the procedures relevant to the collection of data and retention of participants

for the personality study are covered below.


85

Figure 4.2: Procedures for the current personality study

Annual visits

Data collection interviews were held at participants’ homes and the average

length of time between these visits was 12.60 months (S.D. = 1.81). After

receiving informed consent, participating families were contacted by phone to

organise a time for a data collection interview at their home. Participating

families were reminded that the interview would take about one hour and that

the young person with diabetes and the parent most responsible for diabetes

care would both need to be present. Parents were not strictly required to be

at interviews for participants older than eighteen years. During the interview,

participants and their parents were asked to complete the battery of

psychosocial, demographic, family and service measures and the young

person with diabetes was asked to provide a sample of blood to undergo

HbA1c analysis. To improve validity of responses, participants were

instructed not to spend too much time on any particular items in the battery

of questionnaires and, researchers were available to provide support with

clarifying any difficult or ambiguous items.

As touched upon earlier, the decision to conduct interviews at the

participants’ homes was made in order to improve retention rates for the

Recruitment

Home visit – year 1



Telephone reviews (3 2

nd personality

questionnaire posted

3rd

personality questionnaire posted

1st personality

questionnaire taken to interview or posted


86

study. Young people are notoriously difficult to retain in longitudinal research

studies (Bruzzese, Gallagher, McCann-Doyle, Reiss, & Wijetunga, 2009;

Jones & Broome, 2001; Kealey et al., 2007; Seed, Juarez, & Alantour, 2009;

Villarruel, Jemmot, Jemmot, & Eakin, 2006) and retention rates can be

improved by researchers developing a relationship with participants and

providing empathy and respect (Good & Schuler, 1997; Jones & Broome,

2001). Annual visits provided the opportunity for researchers to build genuine

relationships with participating families and this appeared to influence

retention rates in both the DRAT sample and the specific personality study.

Over the three years, a small number of families were unable to arrange a

suitable time for interviews. In this case, participants were sent a postal pack

that included a letter of thanks (Appendix 16), a list of questionnaires to be

completed (Appendix 17), and instructions on how to collect and send a

blood sample for HbA1c analysis (Appendix 18).

Quarterly phone calls

Participating families were called every three months via the telephone to

collect self-reported data on self-management and diabetes control. This

information provided researchers with a rich and detailed view of the

individual’s self-management of diabetes over time and reduced the potential

for recall bias. By examining the quarterly data, it was possible to examine

stability and change in the quality of self-management over time.

Quarterly phone calls also provided frequent contact between participants

and researchers which can help maximise retention rates (Bruzzese et al.,

2009). Quarterly phone calls were non-invasive and took no more than ten

minutes to complete. If unable to contact the participating family within 8

weeks of the scheduled quarterly phone call, these data were coded as

missing.


87

Collection of personality (FFPI-C) data

Collection of baseline personality data was undertaken through two methods.

For individuals who had not received their first annual visit in the DRAT

project, a consent form (see Appendix 5) and a copy of the FFPI-C were

taken along to the annual interview. If individuals consented, they were

asked to complete the FFPI-C along with the battery of other tests. If the

family had already participated in annual data collection, the FFPI-C and the

informed consent form were posted to them shortly (1-5 months) after their

baseline interview. A phone call was also made to the family detailing the

measure and why it had been included in the study.

For the second and third waves it was decided that all FFPI-C data collection

would be carried out through postal methods. This decision was influenced

by the high response rate to post-outs in the initial wave and concerns over

the large number of measures that participants were required to complete in

annual visits. It was concluded that avoiding invalid response styles related

to fatigue and boredom effects would improve the authority of obtained

results. Accordingly, a copy of the FFPI-C and instructions were posted to

participants one month preceding the allocated date of their second and third

annual DRAT visits.

For self-administered tests, instructions (see Appendix 6) were provided with

the measure and parents were asked to assist their child as needed.

Participants were instructed to complete the measure in one sitting. Self-

administration of the FFPI-C was not deemed a problem as all of our

participants had a reasonable command of the English language and this

measure was designed for this purpose (McGhee et al., 2007). Participants

were explicitly told that if they had any concerns or queries about the

questionnaire they could contact the DRAT team. On receipt of completed

questionnaires, researchers ensured all items were answered and scored

accurately.


88

For missing items or items with multiple ratings, researchers contacted

participants by phone and asked them to re-evaluate their answer and

decide on one choice. These calls were made because all questions need to

be completed to correctly score the FFPI-C (McGhee et al., 2007). If the

participant could not be contacted, the missing or multiple-rated response

was given a middle value. Only 17 missing or multiply rated items were

recorded and replaced over the 3 waves of data collection.

Procedures to maximise retention

To improve retention, participants were sent thank you cards (Appendix 19)

and a special biannual newsletter (Appendix 20) which detailed the progress

of the DRAT and personality studies and provided helpful information about

diabetes relevant services.

Ethical approval and conduct

Ethical approval for the DRAT project was obtained through the University of

Western Sydney’s Human Research Ethics Committee (Appendix 21).

Additionally, in order to approach and recruit individuals from diabetes

clinics, ethical approval was obtained from the Hunter New England Health

ethics committee (Appendix 22). No further ethics submissions were made to

area health providers as the major diabetes services within NSW and the

ACT are based within Sydney and the Hunter region.

Informed consent for participation in the study was obtained from parents of

the individuals with type I diabetes. If the person with diabetes was over the

age of 18, they also signed a consent form. Researchers ensured that

children under the age of consent assented to the research and were under

no undue pressure to participate. Participants were given the contact details

of the Human Research Ethics Committee at the University of Western

Sydney to voice any concern or reservations about the ethical conduct of this

research.


89

Furthermore, individuals reporting elevated depression, anxiety or stress

within the ambit of the research study were provided with resources and

contacts for psychological care if they wished to utilise them.

Chapter 5

Statistical Procedures

Once the current research study had concluded, the data were entered and

analysed using predictive analytics software (PASW) version 17.0. All of the

reported statistics and graphs included in the results sections of this thesis

were produced using this program. These results were regarded as

significant at the p < 0.05 (two-tailed) level.

Statistical procedures for the analysis of the study data progressed in four

main steps.

Figure 5.1: Statistical procedures

The first step involved screening the data. The second step involved

describing the sample and determining the quality of diabetes self-

management in this cohort (see Chapter 6 for results). The third step

Data screening

Data screened to identify

missing information,

ensure reliability of

included measures and

examine distributions of

variables.

Description of sample

Preliminary analyses

used to describe sample,

examine quality of self-

management and

highlight relationships

between demographics

and diabetes outcomes.

Addressing research

questions

Multivariate and mixed-

design analyses used to

address research

questions and clarify the

role of personality in

specific diabetes

outcomes.

Exclusion of variables

Variables that did not meet inclusion criteria for further analyses

removed to provide focus and reduce likelihood of error.

Chapter 5: Statistical Procedures

91

involved excluding variables from further analyses (see Chapter 6 for

results). The final step involved answering the specific research questions of

the current study (see Chapter 7 for results). These procedures are

discussed in detail below and are outlined in Figure 5.1.

Data screening

Data screening is a critical process in research involving statistics (Field,

2009). By thoroughly examining study data, researchers can ensure that

reported results are not biased by poorly performing measures or violations

of the underlying assumptions of statistical tests (Field, 2009; Meltzoff,

2004). Therefore, the current study data were subjected to a rigorous

inspection process to assure the validity of findings. This involved excluding

measures with unacceptable rates of missing data, assessing the reliability of

the psychosocial measurement scales and examining the distributions of

each of the included study variables.

Identification of missing data

Missing data causes problems for researchers employing statistics (Field,

2009; Singer & Willett, 2003). Specifically, a smaller number of responses on

a particular measure reduces statistical power thus decreasing the likelihood

of achieving significant results (Field, 2009). More importantly, if there is a

consistent reason for missing data (i.e. data are not missing completely at

random), then the results of any statistical analyses are likely to be biased

(Singer & Willett, 2003). It was thus vital to identify missing data and assess

whether this missing information could influence results.

To identify missing data, frequencies were calculated for each of the

measured variables (Appendix 23). Results demonstrated that there were

little missing data from the three annual collection periods. However, there

was missing information from the quarterly collections of self-management

data. Furthermore, these data did not appear to be missing at random. In

particular, frequencies for the second and third quarterly collections of the


92

second year showed that a number of participants did not provide

information at these times suggesting that some level of participant fatigue

may have occurred.

Whilst painstaking attempts were made to acquire these data (including

leaving messages on phone voicemail), researchers were also wary of

‘hounding’ participants for information. Thus, if quarterly data were not

received after several phone calls, these data were coded as missing.

Despite best efforts, the amount of missing data from quarterly self-

management measures was deemed to exceed acceptable limits. Hence, the

decision was made to drop this information from statistical analyses.

Consequently, quarterly values for self-management variables are not

described in this thesis, only the annual data are reported. Although this

means less data were analysed, the high completion rates for the annual

collections of data suggest that this information provides unbiased and valid

estimates from which to examine relationships between variables (beyond

that which could be achieved by analysing quarterly information).

Reliability analysis

It is important for researchers to assess the internal reliability of the

psychosocial measures they employ (Meltzoff, 2004). Thus, the next step in

data screening involved assessing the internal consistency of the Five Factor

Personality Inventory – Children (FFPI-C), the Depression, Anxiety, Stress

Scales (DASS), the Diabetes Family Responsibility Questionnaire (DFRQ)

and the Coping with a Disease questionnaire (CODI). By examining the

internal consistency of these questionnaires it was possible to ensure that

any obtained results reflected genuine relationships rather than variations

caused by measurement error.

Internal consistency was assessed using Cronbach’s alpha enabling

judgement of how well items within each scale performed and how reliable


93

the scales were as a whole. A Cronbach’s alpha below 0.70 for an entire

scale is problematic as it suggests that items within the measure are poorly

related (Steiner & Norman, 2003). Therefore, any measurement scale with a

Cronbach’s alpha below 0.70 was earmarked for further scrutiny.

In general, the included scales showed good internal consistency (see table

5.1). The only problematic measure was the avoidance scale of the CODI,

which had a Cronbach’s alpha of 0.67. Examination of the inter-item

correlations for this scale (Appendix 24) demonstrated that the item ‘I think

about my diabetes’ (which was reverse-scored) was only weakly related to

the other items. Furthermore, item-total statistics suggested that the

Cronbach’s alpha for this scale would be considerably improved (from 0.67

to 0.79) if this item were deleted (Appendix 25).

Table 5.1: Cronbach’s alphas for measurement scales

Measure α Measure α

Depression 0.91 Openness 0.75

Anxiety 0.83 Conscientiousness 0.88

Stress 0.87 Extraversion 0.81

Acceptance 0.77 Agreeableness 0.79

Avoidance 0.67* Emotional regulation 0.81

Responsibility 0.85 * Cronbach’s alpha below 0.70

Due to the findings of the reliability analysis, the decision was made to

remove the reverse-coded item from the CODI avoidance scale. This course

was taken because the CODI has not been as comprehensively developed

as the other included measures (Anderson et al., 1990; Lovibond &

Lovibond, 1995a; McGhee et al., 2007; Petersen et al., 2004) and research

suggests that reverse-coded items can be harmful to the reliability and

validity of measurement scales (Barnette, 2000; Herche & Engelland, 1996;


94

Marsh, 1996). No items were dropped from the other included measures

because of the high internal consistency of these scales.

Exploratory factor analysis of CODI avoidance scale

To test whether removal of the reverse-coded item from the CODI avoidance

scale would affect construct validity, an exploratory factor analysis (EFA) was

conducted. Using the guidelines recommended by Field (2009), a factor

loading below 0.40 was taken as suggesting that the item may tap a different

psychosocial construct (and hence may be removed).

The four items from the CODI avoidance scale were included in the

exploratory factor analysis and a one-factor solution was forced. Results

(Appendix 26) showed that the reverse-coded item loaded poorly on the

avoidance scale (factor loading of 0.15). All of the other avoidance items

loaded well on this factor (all factor loadings above 0.60). Together, these

results suggest that the reverse-coded item does not sufficiently tap the

construct of avoidance coping thus supporting the decision to drop the

problematic item from the CODI scale. Consequently, any reported data for

the CODI avoidance scale within this thesis excludes this item.

Univariate analyses

The next stage in data screening involved running univariate analyses to

examine the distributions of the baseline variables. First, descriptive statistics

were used to identify continuous variables with skew, kurtosis or restricted

variance. These analyses showed that some of the study variables had

limited variance whilst others were skewed or kurtotic (Appendix 27).

Specifically, the number of hospitalisations for diabetes, number of planned

diabetes appointments and number of missed diabetes appointments

showed limited range and variance. Hence, these variables were dropped

from statistical analyses because limited variance restricts the likelihood of

achieving statistically significant results (Field, 2009). Nevertheless,


95

descriptives for these variables were employed to describe the sample and

are reported in results (see chapter 6).

Descriptives also showed that age, duration of diabetes, acceptance coping,

depression, anxiety, number of blood glucose tests, number of

hypoglycaemic incidents, number of days off, number of diabetes

appointments, number of missed appointments, number of hospitalisations

for diabetes and adherence to insulin showed signs of skew and kurtosis.

Thus, Kolmogorov-Smirnov statistics (Appendix 27), frequency distributions

(Appendix 28) and P-P plots (Appendix 29) were employed to gauge whether

variable distributions significantly deviated from normality. These

demonstrated that HbA1c, responsibility and the five-factor model traits were

normally distributed. In contrast, the other demographic and self-care

variables, depression, anxiety, stress, and coping significantly deviated from

normality. Table 5.2 shows the descriptive statistics for the main variables of

interest in this study (see Appendix 27 for further detail).

Importantly, the variables of gender, household income, experience of

unconscious hypoglycaemia, geographical location, treatment modality and

insulin adherence were either nominal or ordinal data. Therefore, the non-

continuous nature of these variables was taken into account when

conducting later statistical analyses


96

Table 5.2: Descriptives for first year variables

Variable Mean Std. Dev Variance Skew Kurtosis

Age in years 12.15 2.77 7.67 0.30 -0.81

Years with diabetes 4.05 3.21 10.33 1.21 1.13

HbA1c 8.50 1.25 1.59 0.21 0.97

Openness 50.76 7.74 59.85 0.21 0.09

Conscientiousness 49.84 7.44 55.38 -0.18 0.57

Extraversion 47.89 9.31 86.74 -0.01 0.18

Agreeableness 54.46 6.24 38.89 -0.27 0.16

Emotional regulation 52.35 7.66 58.60 -0.50 0.13

Avoidance Coping 7.17 3.44 11.84 0.59 -0.65

Acceptance Coping 24.30 4.30 18.52 -0.76 0.48

Depression 7.06 9.08 82.40 1.80 2.79

Anxiety 7.29 8.41 70.75 1.67 2.49

Stress 10.02 9.09 82.60 0.88 -0.12

Responsibility 32.31 5.56 30.95 0.22 0.08

No. BGL tests 73.63 34.15 1165.88 3.15 17.59

No. Hypos 2.45 2.91 8.48 2.65 9.27

Data transformation attempts

Given that many statistical tests work on the assumption of normally

distributed variables (Field, 2009), square root and logarithmic

transformations were attempted to normalise the data. These

transformations can help to correct skewed or kurtotic data (Field, 2009) but

were unsuccessful in this instance. Nevertheless, non-parametric statistics

can be successfully employed to analyse non-normal data and some

parametric statistics such as analysis of variance (ANOVA) and regression

can be robust to deviations in normality (Field, 2009).

Furthermore, some theorists argue that transforming data is not always

advisable as it does not necessarily improve the validity of probability

statements (Field, 2009; Glass, Peckham, & Sanders, 1972; Norris & Aroian,

2004) and also changes the nature of the proposed hypotheses and


97

measured constructs (Grayson, 2004). Together, this demonstrates that the

non-normal nature of the study data is unproblematic as long as care is given

when performing statistical analyses.

Description of the sample and preliminary analyses

The second step in statistical procedures was to describe the sample and

examine relationships between demographic variables and self-management

outcomes (see Chapter 6 for results). This was achieved through in-depth

exploration of the three annual waves of collected data. In doing this, it was

possible to achieve a greater understanding of the cohort, and how well they

managed their diabetes. This also served to identify specific control variables

that should be included in later analyses of personality and diabetes

management.

Descriptives including means (with standard deviation), medians (with inter-

quartile range) and percentages were employed to describe the baseline

sample and their quality of self-management. Average HbA1c levels for the

cohort were compared to the target range recommended by the International

Society for Pediatric and Adolescent Diabetes (ISPAD) and the Australian

National Health and Medical Research Council (NHMRC). Rates of

depression, anxiety and stress were established by using the DASS-21 cut-

offs for conventional severity levels (see table 4.1 in chapter 4) (Lovibond &

Lovibond, 1995a). Finally, repeated measures statistics were employed to

determine the longitudinal stability of self-management variables and

bivariate analyses were conducted to highlight any statistically significant

relationships between demographic characteristics and self-management.

Because transformations were unsuccessful in normalising the study data,

the decision was made to employ non-parametric statistics when analysing

continuous variables that were non-normally distributed (see chapters 6 and

7). Therefore, the demographic and self-care variables, depression, anxiety,

stress, and coping were all analysed using Spearman’s correlations, Mann-


98

Whitney tests and Wilcoxon signed-rank tests. These results were reported

using medians with inter-quartile range. Conversely, HbA1c and the five-

factor personality model traits (which were normally distributed) were

analysed using Pearson’s correlations, independent t-tests and repeated

measures t-tests. These results were expressed as mean with standard

deviation. If statistical analyses involved both normally distributed and non-

normally distributed variables, non-parametric statistics were favoured.

Importantly, parametric analyses of variance (ANOVAs) were employed with

both normal and non-normal data. ANOVA is known to be robust to

deviations in normality (P.F. Lovibond 2010, personal communication,

September, 2010; Field, 2009; Keith, 2006). The decision to employ this

statistical test with non-parametric data was made in the interest of

maintaining a consistent approach to analyses since in later statistical

analyses, mixed-design ANOVAs were employed which have no non-

parametric equivalent. Thus, parametric analyses of variance (ANOVAs)

were used for all continuous data to provide comparable results. When

employing ANOVA, results were reported using the mean and standard

deviations of included variables. Figure 5.2 shows the procedures for these

preliminary analyses.

Figure 5.2: Procedures for preliminary analyses

Describe sample

Means, medians, and

percentages used to

describe sample.

Conventional cut-offs

used to determine levels

of glycaemic control and

psychological morbidity.

Assess stability of

self-management

Repeated-measures

ANOVAs, Wilcoxon tests,

dependent t-tests and

retest correlations

employed to assess

stability of self-

management over time.

Bivariate analyses

Mann-Whitney tests, t-

tests, correlations and

one-way ANOVAs used

to examine relationships

between demographics

and diabetes outcomes.


99

Inclusion and exclusion of variables from personality analyses

The current study took a comprehensive approach to measuring

demographic and self-management variables that may play a role in

determining important outcomes in type I diabetes. However, the sample size

(n =104) precluded complex statistical analysis and the large number of

variables measured increased the risk of making type I errors (Field, 2009;

Meltzoff, 2004; Singer & Willett, 2003). It was thus critical to reduce the

number of variables included in analyses aimed at answering the research

questions. This approach increases confidence in the validity of the results

and minimises the risk of reporting chance findings (Meltzoff, 2004).

Therefore, in order for variables to be retained in further analyses, they

needed to meet two basic criteria. Firstly, there had to be substantive

theoretical or clinical reasons for retaining these data. Theoretical and clinical

research (Dabadghao et al., 2001; Dantzer et al., 2003; Dashiff et al., 2006;

Eiser et al., 2001; Hanson et al., 1989; Hochhauser, Rapaport, Shemesh,

Schmeidler, & Chemtob, 2008; Kramer et al., 2000; Naar-King et al., 2006;

Turan, Osar, Turan, Damci, & Ilkova, 2002 etc) helped to identify important

outcome measures and distinguish control variables that should be included

in statistical analyses. Secondly, the retained variable needed to

demonstrate acceptable data properties. The majority of conventional

statistical tests rely on continuous data that have suitable levels of variance

(Field, 2009). Thus, data that were non-continuous and data that had low

variance were scrutinised further. The results of preliminary analyses

(Chapter 6) were then employed to guide the exclusion of some of these

variables.

This approach ensured that the most imperative self-management outcomes

were examined and that any reported relationships between personality and

these outcomes were not better explained by demographic variables. The

specific control and outcome variables to be included in further analyses are

detailed below in table 5.3. However, reasons for inclusion and exclusion of

specific variables are provided in more detail at the end of chapter 6. As can


100

be seen, the included outcome variables provide a good representation of

the complexity of diabetes self-management.

Table 5.3: Control and outcome measures included in personality analyses

Control variables Outcome variables

Age HbA1c

Gender Frequency of blood glucose testing

Duration of diabetes Depression

Responsibility for diabetes care Anxiety

Stress

Acceptance coping

Avoidance coping

Frequency of hypoglycaemia

Answering research questions

The final stage of statistical analyses involved answering the specific

research questions of the current study. Thus, statistical tests were

employed to determine significant relationships between five-factor model

personality traits and the specific diabetes outcomes listed above. In

addition, statistics were utilised to examine the role of baseline personality

traits in trajectories of these self-management outcomes.

To achieve this, the first, second and third waves of personality data were

‘matched’ to the first, second and third waves of self-management outcomes.

Bivariate analyses were then carried out to test for linear and non-linear

associations between the five-factor model personality traits and outcomes.

Next, multiple regressions were employed to identify five-factor model traits

that predicted self-management outcomes (independent of controls) for each

’matched’ year. Non-significant trait predictors of outcomes were excluded

from any further analyses based on the results of these regressions. Finally,

mixed-design and repeated-measures analyses of variance (ANOVAs) were


101

used to assess the role of baseline personality traits in trajectories of self-

management.

By employing this procedure it was possible to identify five-factor model traits

important to glycaemic control and psychological wellbeing. Furthermore, this

approach helped to achieve a greater understanding of the role of these

traits in the management of diabetes over time. Figure 5.3 depicts the

procedure for these statistical analyses.

Figure 5.3: Procedure for statistical analysis of personality data

Bivariate analyses

Correlations and scatterplots were employed to test for simple relationships

between five-factor model personality traits and self-management outcomes.

Pearson’s correlations were employed to identify significant linear

relationships between the FFPI-C traits and parametric variables (HbA1c)

whilst Spearman’s correlations were used to identify significant linear

associations between FFPI-C traits and non-parametric variables

(depression, anxiety, stress, blood glucose tests, hypoglycaemia,

acceptance coping and avoidance coping).

Bivariate Analyses

Scatterplots,

correlations and

bivariate regressions

employed to test for

linear and non-linear

associations between

personality and self-

management

outcomes.

Multiple Regressions

Regressions

including control

variables employed

to identify five-factor

model traits that are

significant predictors

of self-management

outcomes.

Longitudinal analyses

Mixed-design ANOVAs and repeated-measure ANOVAs employed to determine the role of baseline personality traits in trajectories of self-management outcomes.


Non-significant trait predictors of self-management

outcomes excluded from further analyses.


102

Scatterplots were then inspected to determine the possibility of non-linear

relationships between self-management outcomes and personality traits. To

confirm non-linear associations between personality traits and specific

outcomes, bivariate regressions were conducted following the procedure

outlined by Keith (2006) and Mallery (2009). Based on scatterplots, these

analyses tested for significant curvilinear relationships between specific

personality and outcome variables (see chapter 7).

Multiple regressions of five-factor model traits on self-management

Next, the five-factor model personality traits were regressed onto the self-

management outcomes for each ‘matched’ year. This helped to identify

which five-factor model traits were the best predictors of these outcomes. For

each of the regressions, the control variables of gender, age, duration of

diabetes and level of responsibility for diabetes tasks were included to

ensure that any trait predictors of self-management outcomes were not

confounded or better explained by these variables.

Initially, hierarchical multiple regressions were employed. These were carried

out using prior research findings to guide block-wise entry of predictor

variables for all analyses. Block-wise entry is a preferable method of

regression analysis as it relies more on past research and theory than on

arbitrary statistical computations (Field, 2009). Based on prior research and

theory (Anderson et al., 1990; Dabadghao et al., 2001; Hochhauser et al.,

2008; Naar-King et al., 2006), the control variables (gender, age,

responsibility and duration of diabetes) were entered into the first regression

block. Research findings (Deary et al., 1995; Kendler et al., 2006; Roelofs et

al., 2008; Skinner et al., 2002; Vollrath et al., 2007 etc) were then employed

to guide block-wise entry of the five-factor model personality traits.

After this round of analyses, significant predictors of specific self-

management outcomes were retained and put into a final regression model

for each year following the procedure outlined by Field (2009). Field

recommends dropping non-significant variables from hierarchical regressions


103

and re-running multiple regressions using significant predictors only. This

can increase the reliability of regression analyses and can improve

confidence in the obtained statistical models (Field, 2009). The rationale for

this method was based on concerns over the study sample size (n =104) and

the small size of anticipated effects. Adoption of this procedure allowed the

number of predictors to be reduced thereby producing more reliable

regression models (Field, 2009; Keith, 2006).

Accordingly, multiple regressions for each year were re-run using only

significant independent predictors from the three ‘matched’ years. To

minimise the risk of type II error, any variable identified as a significant

predictor in any year of the study was included in these regressions. As

suggested by Field (2009), these variables were inputted into regression

models at the same time using forced-entry methods.

This resulted in six regressions being run for each self-management

outcome (3 x hierarchical regressions and 3 x forced-entry regressions).

Thus, to maintain a coherent structure for reporting results, regression tables

for the forced-entry regressions are presented in the results section whereas

regression tables for hierarchical regressions are included in the appendices

only (see chapter 6). The figure below (figure 5.4) depicts the procedure for

these regression analyses.


104

Figure 5.4: Procedure for regression analyses

Longitudinal analyses of personality traits and self-management

trajectories

Once significant trait predictors of outcomes had been identified, longitudinal

analyses were conducted to test the role of these personality factors in self-

management trajectories. To do this, groups were created using baseline

scores of personality traits. By doing this, it was possible to assess how

baseline personality traits influenced changes in self-management outcomes

over time. The decision to group the participants was made based on the

ease of analysis and because groups could represent logical and distinct

end-points for personality constructs.

Groups were created using the upper and lower tertiles of the five-factor

model traits. The upper and lower tertile groups were employed as they

represented discrete populations yet retained a large enough sample within

each group to retain statistical power. A different approach was employed to

test the role of variables displaying curvilinear relationship with outcome

variables. For these analyses, quintiles were computed and the high, low and

middle quintiles were compared. Again, this procedure was based on

Hierarchical regressions

3 x hierarchical regressions employed for each self-management outcome. All FFM traits and control variables included to determine independent trait predictors.

Forced-entry regressions

3 x forced-entry regressions employed for each self-management outcome. Only significant predictors included to improve model reliability.


Non-significant trait predictors of self-management outcomes excluded from further regressions.


105

maximising differences between groups whilst retaining an adequate sample

size to maintain statistical power.

Using these newly formed groups, mixed design analyses of variance

(ANOVAs) were performed to examine overall differences in glycaemic

control between groups and to test for any significant interaction between

personality and self-management outcomes over time. If a significant

interaction was discovered, repeated measures analyses of variance

(ANOVAs) with bonferonni adjustment were conducted to examine whether

glycaemic control changed significantly over time within each group. Figure

5.5 demonstrates the procedure used for these longitudinal analyses.

Figure 5.5: Procedure for longitudinal analyses

In summary, a systematic approach to data analysis was employed for this

research. Data were meticulously screened and the sample was

comprehensively scrutinised to improve the relibility and validity of results.

The multivariate and longitudinal methods of analyses helped to achieve a

greater understanding of the complex role of personality in longitudinal

trajectories of self-management in the current sample.

Create baseline

groups

Tertile scores used to

create high and low trait

groups to examine

linear relationships.

Quintile scores used to

create high, moderate

and low trait groups to

examine curvilinear

relationships.

Repeated measures ANOVAs

Repeated-measures

ANOVAs employed to

test for significant

within-group changes in

self-management

outcomes.

Mixed-design

ANOVAs

Mixed-design ANOVAs

employed to test for

between-group effects

and interactions

between personality

and self-management

over time.

Chapter 6

Sample Characteristics and Preliminary Analyses

The current study presents researchers with a detailed picture of Australian

youth with type I diabetes. The breadth of data gathered over multiple time-

points provides rich information about the course of self-management and

may afford enhanced insight into the experience of type I diabetes during

childhood and adolescence. Therefore, before evaluating the role of

personality in the self-management of diabetes, it is important to describe the

sample and determine how well these young people have managed their

disease.

This chapter therefore presents information regarding the characteristics of

the sample and the quality of participants’ self-management of diabetes.

Firstly, a description of the group is provided including demographics,

treatment modality and degree of responsibility for diabetes care. Attention is

given to relationships between these variables and self-management

outcomes. Next, the longitudinal stability of the five-factor model personality

traits is assessed and relationships between personality and demographic

variables are highlighted. The quality of glycaemic control, levels of

engagement in self-care behaviour and rates of psychological wellbeing are

then reported. The stability of these outcomes over time is also examined.

Finally, a brief summary of preliminary results is provided followed by a

rationale for the exclusion of particular variables from further analyses.

Chapter 6: Sample Characteristics and Preliminary Analyses

107

Description of the sample

Table 6.1 provides a summary of the demographic characteristics of

participants in the personality study and the parent DRAT project. As can be

seen, there were few differences between those who chose to participate in

the personality study and those who did not. Independent t-tests and Mann-

Whitney tests confirmed this with no significant differences found between

those who returned personality data and those who did not on any of the

demographic or self-management variables of interest (all p values > 0.05).

This suggests that there was no selection bias occurring in the personality

study sample.

Table: 6.1: Baseline characteristics for personality study and DRAT project

Personality Study DRAT project

Baseline sample size (n) 104 158

Baseline HbA1c (%) 1 8.50 (1.26) 8.65 (1.42)

Age in years 2 12.60 (10.40 - 14.80) 12.40 (10.00 - 14.00)

Years with diabetes 2 3.40 (1.52 - 6.02) 3.70 (1.82 - 6.90)

Male 3 49.03% 50.00%

Injections as main treatment 3 75.00% 72.20%

Urban dwelling 3 72.12% 68.40%

Mother of Anglo-Celtic origin 3 68.27% 65.20%

Father of Anglo-Celtic origin 3 64.42% 65.80%

Income over $83,200 3 50.00% 48.10%

Results expressed as: 1 mean and standard deviation,

2 median and inter-quartile range,

3

percentage

Age and duration of diabetes

The baseline age of the participants in the personality study ranged from 8 to

19 years (all were under 18 at time of recruitment). The median age of the

sample was 12.60 years (inter-quartile range = 10.40 - 14.80 years) and the


108

median amount of time since diagnosis was 3.40 years (inter-quartile range =

1.52 - 6.02 years). Examination of scatterplots (Appendix 30) confirmed that

there were no non-linear (or age-group specific) relationships between self-

management outcomes and age or duration of diabetes. Thus, Spearman’s

correlations were employed to uncover linear associations.

Unexpectedly, HbA1c was not associated with age or duration of diabetes at

any point in the three-year study (all p values > 0.05). Such findings are at

odds with prior investigations that report positive correlations between these

variables in similarly-aged cohorts (Dabadghao et al., 2001; Helgeson,

Siminerio, Escobar, & Becker, 2007; Ingerski, Anderson, Dolan, & Hood,

2010; Springer et al., 2006).

However, age and duration of diabetes were associated with measures of

self-care. Duration of diabetes was negatively correlated with frequency of

blood glucose testing in the first year (ρ = -0.23) and age was negatively

associated with frequency of blood glucose testing in the first (ρ = -0.22),

second (ρ = -0.26) and third years (ρ = -0.23) (all p values < 0.05). These

results support prior studies that have demonstrated poorer engagement in

self-care behaviour amongst older adolescents with diabetes (Dashiff et al.,

2006; Ingerski et al., 2010; Weissberg-Benchell et al., 1995).

Results also showed that age and duration of diabetes were related to

psychological wellbeing. For example, duration of diabetes was significantly

related to depression scores in the first year (ρ = -0.22) of the study. The

direction of this association suggests that those with a more recent diagnosis

of diabetes were likely to report elevated levels of depression. This finding is

consistent with research that shows high levels of depression in youths

newly diagnosed with type I diabetes (Grey et al., 1995).

Remarkably, duration of diabetes was not significantly related to depression

levels in the second or third year (all p values > 0.05). This suggests that

individuals with a shorter duration of diabetes may have psychologically


109

adapted to living with diabetes over time. A repeated-measures analysis of

variance supported this assumption showing that depression scores

significantly improved over the three years for individuals who had a duration

of diabetes under 12 months at baseline, F (2, 30) = 3.51, p = 0.04. Table 6.2

shows the depression scores for this group over three years.

Table 6.2: Depression scores for newly diagnosed participants

Depression score

Year 1 11.13 (11.29)

Year 2 5.25 (5.05)

Year 3 6.50 (8.63)

Results expressed as: mean and standard deviation

Noteworthy associations were also found between age and anxiety levels.

Spearman’s correlations demonstrated that the age of the participant was

negatively associated with anxiety levels in the second (ρ = -0.21) and third

(ρ = -0.24) waves of data collection (all p values < 0.05). Thus, older

participants were less anxious at these occasions. These results are

interesting as they conflict with findings from prior research that shows higher

rates of anxiety in older adolescents (Dantzer et al., 2003; de Matos, Barrett,

Dadds, & Shortt, 2003). Importantly, the association between anxiety and

age was not explained by duration of diabetes (all p values > 0.05).

Finally, neither age nor duration of diabetes was significantly related to

stress, acceptance coping, avoidance coping or frequency of hypoglycaemia

(all p values > 0.05). This suggests that age and duration of diabetes did not

play a strong role in determining these self-management outcomes within the

current sample.


110

Gender

The gender split within the study sample was almost 50:50 with 51 males

and 53 females returning three years of personality data. No significant

gender differences were found for glycaemic control, depression, anxiety,

stress, avoidance coping, frequency of blood glucose testing or frequency of

hypoglycaemia over the three years (all p values > 0.05).

However, Mann-Whitney tests demonstrated that there were significant

gender differences in levels of acceptance coping in the second and third

years. In the second year, males reported significantly higher levels of

acceptance than females, U = 976.00, z = -2.18, p = 0.03. Males also

reported higher acceptance of their diabetes in the third year compared to

females, U = 813.00, z = -3.15, p = 0.00. These results suggest that males in

the current cohort accepted their diabetes more than females. Table 6.3

shows the results for acceptance coping stratified by gender.

Table 6.3: Acceptance coping stratified by gender

Male Female

Year 1 25.00 (22.00 - 29.00) 24.00 (21.00 - 27.00)

Year 2 25.00 (22.75 - 29.00)* 24.00 (22.00 - 25.00)*

Year 3 26.50 (23.50 - 28.25)** 23.00 (22.00 -26.00)**

Results expressed as: median and inter-quartile range

* Between-groups difference significant at p <0.05

** Between-groups difference significant at p <0.01

Treatment Modality

In the first year of the study, 78 (75.00%) participants were receiving

injections as their main source of treatment whilst 26 (25.00%) were using an

insulin pump. Pump use increased over the three-year period with 47

(45.19%) of the participants using pumps in the second year and 52

(50.00%) using pumps in the third year. These findings likely reflect an


111

increased drive from health-care professionals to put youths on insulin

pumps.

No significant differences in HbA1c scores were found between individuals

receiving injections or pump therapy over the three years of the study (all p

values > 0.05). This is a noteworthy finding as it demonstrates that use of an

insulin pump does not necessarily improve glycaemic control as measured

by HbA1c. This finding runs counter to that from a prior systematic review

looking at these differences between these treatment methods (Pickup et al.,

2002).

Similarly, no significant between-group differences in acceptance coping,

avoidance coping, depression, anxiety, stress, blood glucose testing or

frequency of hypoglycaemia occurred over the three-year period (all p values

> 0.05). Together, these findings suggest that treatment modality did not

have a direct impact upon self-management outcomes within this cohort.

Ethnicity

The sample included a range of ethnic backgrounds. However, the majority

of participants reported an Anglo-Celtic heritage which is to be expected in

Australia (Australian Bureau of Statistics, 2006b) and people with type I

diabetes (Hanas, 1998). Independent t-tests and Mann-Whitney statistics

comparing those of Anglo-Celtic heritage to the rest of the sample suggested

no between-groups differences on any of the self-management outcomes of

interest (all p values > 0.05).

Geographical location

Participants came from a broad range of locations within New South Wales

(NSW) and the Australian Capital Territory (ACT) (see figure 4.1 for a map

displaying geographical dispersion). To analyse these data, the Australian

Standard Geographical Classification system (Australian Bureau of Statistics,

2007) was employed. Using these guidelines, 75 (72.12%) individuals from


112

the study were identified as living in an urban area whilst 29 (27.88%) were

identified as living in a rural or remote area. Independent t-tests and Mann-

Whitney tests suggested that there were no differences in self-management

outcomes between these groups over the course of the study (all p values >

0.05).

Household annual income

Of those families who reported their annual income (n = 97), 52 earned over

$83,200 a year, 24 earned between $52,000 and $83,199, 13 earned

$31,200 to $51,999 and 8 reported an annual household income of less than

$31,199. When compared to Australian Bureau of Statistics data (2009), our

study sample appears to have a greater percentage of families within the

mid-to-high brackets of annual income and a lower percentage of low income

families. This suggests that those with lower incomes may be less willing or

able to participate in research studies. Alternatively, participants may have

been less likely to report low household income.

To examine whether annual income was related to self-management

outcomes, the sample was split into two groups. Those reporting an annual

income above $83,200 were labelled the high-income group whilst those

earning below this amount were labelled the low to mid-range income group.

These categories were formed to create larger sample sizes between the

groups thus minimising the likelihood of making statistical errors (Field,

2009). Independent t-tests and Mann-Whitney tests demonstrated no

significant differences between these income groups on any of the measured

self-management outcomes (all p values > 0.05).

Responsibility for diabetes care

Results of a one-way repeated-measures analysis of variance (ANOVA)

showed that mean-levels of responsibility increased for the sample over the

course of the three year study, F (1.81, 168.67) = 26.73, p = 0.001. Table 6.4


113

shows the means and standard deviations for responsibility scores over the

three years.

Unsurprisingly, responsibility for diabetes tasks was related to age.

Spearman’s correlations demonstrated a strong positive association between

age and responsibility in the first (ρ = 0.68), second (ρ = 0.69) and third year

(ρ = 0.74). These results highlight the fact that adolescence is a transitional

period where youths begin to take greater responsibility for self-management

tasks as they get older (Anderson et al., 1990; Ingerski et al., 2010). Notably,

duration of diabetes, gender and HbA1c values were unrelated to levels of

responsibility over the three years (all p values > 0.05).

Table 6.4: Mean-levels of responsibility over three years

Responsibility score

Year 1 32.51 (5.61)

Year 2 34.21 (6.14)

Year 3 35.73 (6.82)


Interestingly, responsibility was related to avoidance coping. Baseline levels

of responsibility were negatively associated with avoidance coping in the

second (ρ = -0.27) and third years (ρ = -0.27). Furthermore, second year

responsibility was associated with second year avoidance coping (ρ = -0.24)

and third year avoidance coping (ρ = -0.24). Together, this suggests that

those who took greater responsibility for their care relied less on avoidance

as a way of coping with their disease. No significant associations were found

between third year responsibility and avoidance coping (all p values > 0.05),

which could be a result of a slight floor effect for avoidance in this year.

Finally, responsibility was associated with levels of anxiety and stress in the

sample. Responsibility in the second year was negatively associated with


114

anxiety in the second (ρ = -0.29) and third years (ρ = -0.34) and stress in the

second year (ρ = -0.22). Similarly, responsibility in the third year was related

to anxiety in the second year (ρ = -0.25) and third year (ρ = -0.23). These

results show that individuals who took less responsibility for their care had

greater levels of anxiety and stress.

At first glance, these findings appear counterintuitive. One would typically

expect individuals who take greater responsibility for their diabetes to be

more anxious and stressed about their condition. However, these results

may be explained by task familiarity. For example, individuals who exhibit

lower levels of responsibility are likely to be at the initial stages of transition

to self-managed care (Anderson et al., 1990; Anderson et al., 2002; Hanas,

1998). At this stage, the individual is confronted with many new and

unfamiliar tasks. In contrast, individuals who take greater responsibility are

likely to be accustomed to carrying out these duties (Hanas, 1998). Research

demonstrates that familiarity with tasks leads to reductions in anxiety and

stress (Fink, 2000; MacIntyre & Gardner, 1994; Pagano, 1973). Thus, higher

rates of anxiety and stress in individuals with lower levels of responsibility

could reflect the complex process of transitioning to self-managed care.

In all, demographic characteristics appeared to have a role in self-

management of type I diabetes within the study cohort. Variables of

particular note include the participant’s age, duration of diabetes, gender and

level of responsibility for diabetes tasks. The statistically significant

relationships (all p values < 0.05) between these variables and self-

management outcomes demonstrate that these particular measures should

be retained in later analyses aimed at answering the research questions.

Table 6.5 summarises the significant relationships uncovered between

sample characteristics and self-management outcomes.


115

Table 6.5: Relationships between demographics and self-management

Outcome

Responsibility

Neg

correlation

Avoidance

Neg

correlation

Anxiety

Neg

correlation

Stress

Age

Neg

correlation

BGL tests

Neg

correlation

Anxiety

Duration of

diabetes

Neg

correlation

BGL tests

Neg

correlation

Depression

Gender

Males

have greater

Acceptance

Personality

Test-retest correlations (Table 6.6) were employed to examine the

longitudinal stability of the five-factor model traits. These demonstrated good

rank-order consistency for each personality factor over the three years of

data collection. Any correlation above 0.50 represents a large effect (Field,

2009) and this suggests that personality was stable in the study cohort.

Importantly, the high stability of these traits means that baseline measures of

personality may be employed to predict trajectories of self-management

outcomes within this sample.

Statistical analysis revealed that demographic variables were associated with

personality traits. Age was positively associated with levels of openness to

experience in the first (ρ = 0.27), second (ρ = 0.29) and third (ρ = 0.20)

‘matched’ years. These results demonstrate that older children in the sample

were more open to experience than their younger peers.


116

Table 6.6: Test-retest correlations for FFPI-C traits over three years

In addition, Pearson’s correlations demonstrated that responsibility for

diabetes was associated with openness in the second (r = 0.26) and third (r =

0.32) ‘matched’ years. These findings show that individuals with higher

scores on openness took greater responsibility for their self-care. However,

the significance of this relationship disappeared when controlling for age (all

p values > 0.05).

Agreeableness was also related to responsibility in the first (r = 0.25) and

third years (r = 0.22). This suggests that youth who are more agreeable take

on greater responsibility for diabetes tasks.

Notably, conscientiousness was only associated with responsibility for

diabetes care in the final year of data collection (r = 0.29). Here, individuals

higher in conscientiousness reported greater responsibility for diabetes

management.

Finally, there appeared to be some gender differences in personality traits.

For example, males were significantly higher in openness to experience in

the first year, t (102) = 2.50, p = 0.01. Whilst research findings regarding

gender differences in openness to experience are mixed (Schmitt, Voracek,

Realo, & Allik, 2008), this finding seems likely to be explained by young

r 1st and 2nd year r 2nd and 3rd year r 1st and 3rd year

Conscientiousness 0.67** 0.78** 0.65**

Extraversion 0.73** 0.84** 0.72**

Openness 0.68** 0.85** 0.76**

Agreeableness 0.56** 0.78** 0.58**

Emotional regulation 0.59** 0.77** 0.64**

* = significant at p <0.05

** =significant at p <0.01


117

males’ propensity to endorse new adventures and novel experiences

(Zupancic, Slobodskaya, & Knyazev, 2008). Indeed, perusal of the five-factor

personality inventory for children (FFPI-C) demonstrates that the items for

the openness to experience scale tend to reflect openness to ideas rather

than openness to feelings or aesthetics (McGhee et al., 2007).

Males were also significantly higher in emotional regulation than females in

the third year of the study, t (102) = 2.63, p = 0.01. This result echoes prior

research findings that show adolescent and adult males report greater

emotional regulation than their female counterparts (Costa Jr, Terracciano, &

McCrea, 2001; Schmitt et al., 2008; Zupancic et al., 2008). No other

demographic variables showed significant relationships with personality (all p

values > 0.05). Table 6.7 and table 6.8 show the annual means and standard

deviations for openness to experience and emotional regulation stratified by

gender.

Table 6.7: Gender differences in openness to experience

Male Female

Year 1 52.65 (6.91)* 48.94 (8.11)*

Year 2 50.89 (8.05) 48.89 (8.33)

Year 3 51.71 (8.48) 48.32 (9.30)


* = between-groups difference significant at p <0.05

** = between-groups difference significant at p <0.01


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Table 6.8: Gender differences in emotional regulation

Male Female

Year 1 52.67 (7.57) 52.04 (7.79)

Year 2 52.71 (8.46) 50.32 (8.24)

Year 3 54.04 (7.62)* 49.81 (8.73)*


* = between-groups difference significant at p <0.05

** = between-groups difference significant at p <0.01

In summary, the five-factor model personality traits were stable within the

cohort over the course of the three-year study. The test-retest correlations for

this sample were higher than reported in prior studies involving youth and

this appears to be due to the shorter time-frame involved and the fact that

the measure employed was specifically designed for this cohort (Donnellan

et al., 2007; Hampson & Goldberg, 2006; McGhee et al., 2007; B. W.

Roberts et al., 2001; B. W. Roberts & DelVecchio, 2000). Importantly, the

temporal stability of the five-factor model dimensions within this study

indicates that baseline personality factors may be used as predictor variables

when examining longitudinal trajectories of self-management outcomes.

In addition, the five-factor model traits were linked to age, gender and

responsibility for diabetes care. Whilst the causal directions of some of these

associations are unclear, the statistical significance of these relationships (all

p values < 0.05) underlines the importance of retaining these variables in any

further analyses involving personality traits. By retaining these variables, it

will be possible to assess whether longitudinal relationships between

personality and self-management are better explained by these demographic

factors. Thus to assist the reader, table 6.9 summarises the significant

relationships found between sample characteristics and the five-factor model

personality traits.


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Table 6.9: Relationships between demographics and personality

Outcome

Responsibility

Pos

correlation

Openness

Pos

correlation

Agreeableness

Pos

correlation

Conscientiousness

Age

Pos

correlation

Openness

Gender

Males

have greater

Openness

Males

have greater Emotional

regulation

Self-management outcomes

In order to look at how well the youths in the study sample self-managed

their diabetes, it was important to take a bio-psychosocial approach.

Therefore, the participants were assessed not only on levels of glycaemic

control and self-care, but also on measures of psychological well being such

as depression, anxiety, stress and coping. The information gleaned from this

sample provides a valuable snapshot into the lives of people with type I

diabetes. Accordingly, table 6.10 provides a summary of the diabetes

outcomes for the study sample at baseline.

Table 6.10: Diabetes outcomes at baseline

HbA1c (%)1 8.50 (1.26)

Number of hypoglycaemic incidents in two weeks 2 2.00 (1.00 - 4.00)

Number of blood glucose tests in two weeks 2 70 (56.00 - 84.00)

Always taken insulin as prescribed 3 61.17%

Attended one or more appointments in 3 months 3 96.15%

Missed one or more booked appointments in 3 months 3 1.92%

Depression score out of normal range 3 28.85%

Anxiety score out of normal range 3 34.62%

Stress score out of normal range 3 27.88%

Unconscious hypoglycaemic incident in three months3 3.85%

Hospitalised for diabetes in three months3 2.88%

HbA1c over 7.5% 3 77.88%

Results expressed as: 1 mean and standard deviation, 2 median and inter-quartile

range, 3 percentage


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Glycaemic control

HbA1c values in the first year of data collection ranged from 5.70% to 12.90%

with a mean of 8.50% (standard deviation = 1.26). Alarmingly, the majority of

the sample (77%) had HbA1c values greater than the benchmark of 7.5% set

by the International Society for Pediatric and Adolescent Diabetes (ISPAD)

and the Australian National Health and Medical Research Council (NHMRC)

(National Health and Medical Research Council, 2005; Rewers et al., 2009).

No statistically significant changes in glycaemic control were observed for

the cohort in the following years, F (2, 198) =1.25, p= 0.29. In the second year

of data collection the mean HbA1c score was 8.66% (standard deviation =

1.25) and 87.50% of the sample had sub-optimal glycaemic control (HbA1c >

7.5%). In the third year, the mean HbA1c score was 8.61% (standard

deviation = 1.26) and 84.62% of the sample had sub-optimal glycaemic

management (HbA1c > 7.5%).

These findings support the large number of studies that show that youths

struggle to manage their blood glucose levels effectively (Dabadghao et al.,

2001; Holl et al., 2003; Svensson et al., 2004). For each year, a substantial

proportion of the sample displayed sub-optimal glycaemic control (National

Health and Medical Research Council, 2005; Rewers et al., 2009).

Furthermore, test-retest correlations (table 6.11) showed high temporal

stability in HbA1c values, which suggests that a subsample of this cohort had

chronic poor management of blood glucose levels.

Table 6.11: Pearson’s test-retest correlations for HbA1c over three years

r 1st and 2nd year r 2nd and 3rd year r 1st and 3rd year

HbA1c 0.63** 0.60** 0.52**

* = significant at p <0.05 ** =significant at p <0.01

These results demonstrate that many of the individuals in the study cohort

are at increased risk for developing diabetic complications (National Health


121

and Medical Research Council, 2005; Rewers et al., 2009; The Diabetes

Control and Complications Trial Research Group, 1993) underlining the

importance of finding predictors of poor glycaemic control. Importantly, if

personality traits predict long-term management of blood glucose levels, we

could use this information to identify those young people at risk and

intervene early in targeted ways.

Blood glucose monitoring

The median number of blood glucose level (BGL) tests carried out in the two

weeks prior to baseline interview was 70 (Inter-quartile range = 56.00 -

84.00). This suggests that the majority of participants were conducting

around five tests per day which is above the minimum benchmark of four

daily tests proposed by most diabetes experts (Benjamin, 2002; National

Health and Medical Research Council, 2005; The Diabetes Control and

Complications Trial Research Group, 1993).

However, the number of BGL tests carried out over the two-week period was

highly variable across individuals. Reports ranged from 6 tests per fortnight

to 295 tests per fortnight. This means that at the lowest end of the scale, an

individual was testing their blood glucose once every second or third day,

whilst at the other end of the scale, a participant was testing their blood

glucose around 21 times a day.

Similar results were found for the second and third years of the study. In the

second year, the median number of blood glucose tests was 70 (Inter-

quartile range = 56.00 - 84.00) but reports of testing ranged from 3 tests per

fortnight to 169 tests per fortnight. The median number of tests in the third

year was also 70 (Inter-quartile range = 50.00 - 84.00) but the reported

number of tests ranged from 0 to 160. Engagement in blood glucose testing

was thus highly variable across participants.


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To examine mean-level changes in blood glucose testing, a repeated-

measures analysis of variance (ANOVA) was employed. One influential case

(the individual who reported testing 295 times) was removed from analysis.

This approach was taken because large outliers can significantly impact

upon ANOVA statistics (Field, 2009). Results demonstrated that the number

of blood glucose tests performed within the sample did not significantly

change over the course of the three year study, F (1.83, 177.11) = 2.29, p = 0.10.

Furthermore, Spearman’s correlations showed that the frequency of blood

glucose testing was relatively stable within individuals over the three years

(see Table 6.12). This indicates that individuals with poor self-monitoring of

blood glucose at baseline were likely to have poor self-monitoring throughout

the course of the study. It should be noted that removal of the outlier case

did not considerably change these correlations (Appendix 31).

Table 6.12: Spearman’s test-retest correlations for BGL tests over three years

ρ 1st and 2nd year ρ 2nd and 3rd year ρ 1st and 3rd year

Blood glucose testing 0.54** 0.67** 0.43**


Finally, Spearman’s correlations were employed to assess whether blood

glucose testing influenced quality of glycaemic control (see Appendices 32

and 33 for correlations including and excluding outlier case). Results

demonstrated that blood glucose testing was significantly associated with

HbA1c values in the third year only (ρ = -0.32). This finding highlights the fact

that self-monitoring of blood glucose is but one aspect of glycaemic control

(Flinders Human Behavioural & Health Science Unit, 2006; Schneider et al.,

2007). Nevertheless, blood glucose testing is a critical factor in diabetes

management (Holmes et al., 2006; Ikeda & Tsuruoka, 1994; Ingerski et al.,

2010; Laffel, Antisdel, Brackett, Dietrich, & Anderson, 1998) and it is

important to uncover predictors of this behaviour.


123

In all, results demonstrated that blood glucose level testing was highly

variable across participants. Furthermore, the reported frequency of testing

was relatively stable within participants across the three-year period. There

are therefore large individual differences in engagement in specific diabetes

self-care behaviours and these differences are maintained over time. These

are important findings, especially given the observed relationship between

blood glucose testing and glycaemic control in the third year. Given the vast

individual differences evidenced in blood glucose monitoring, interest in the

role of personality appears warranted.

Adherence to prescribed insulin

Examination of participant responses (table 6.13) demonstrated that the

majority of the sample reported being adherent to their prescribed insulin

regimen over the three years.

The average HbA1c score for individuals who reported adhering to treatment

‘all the time’ was compared to that of the rest of the study sample.

Independent t-tests demonstrated that there were no significant between-

groups differences in HbA1c for the first, second or third year (all p values >

0.05). This finding is noteworthy as it disagrees with the results of a number

of studies that have identified insulin adherence as a critical factor

determining glycaemic control in type I diabetes (Krapek et al., 2004;

Stewart, Emslie, Klein, Haus, & White, 2005; Toljamo & Hentinen, 2001).

Table 6.13: Adherence to prescribed insulin over three years

Adhered all of

the time

Adhered most

of the time

Adhered some

of the time

Adhered none

of the time

Year 1 61.17% 33.98% 3.88% 0.97%

Year 2 54.90% 40.20% 4.90% 0%

Year 3 61.00% 35.00% 4.00% 0%

Results expressed as: percentage of respondents


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This result may be due to the fact that the scale used did not enable

sufficient differentiation. The majority of participants reported adhering to

prescribed insulin ‘all’ or ‘most’ of the time. In order for statistically significant

differences in HbA1c to be observed, there may need to be a greater

difference in rates of adherence between the compared groups.

Nonetheless, for the purpose of the current study, reported adherence did

not appear to be related to glycaemic control.

Utilisation of health-care

The National Health and Medical Research Council (2005) recommends that

young people with type I diabetes visit a health-care professional once every

three-to-four months in order to assess glycaemic management. Self-reports

from participating families suggested that the majority of youth in the study

did this. However, the number of participants who missed booked

appointments increased over the three years. In other words, health care

attendance become poorer as our participants got older.

Notably, the participants who reported not attending a diabetes appointment

were different for each year, which shows that all participants had access to

some level of care over the course of the study. These findings suggest that

the poor glycaemic control observed in the majority of the study sample was

caused by factors other than deprived access to diabetes services. Table

6.14 shows the percentage of respondents who reported attending or

missing a diabetes appointment in the three months prior to each annual

interview.


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Table 6.14: Diabetes appointments over three years

Attended one

appointment in prior

three months

Attended more than

one appointment in

prior three months

Missed booked

appointment in prior

three months

Year 1 96.15% 25.96% 1.92%

Year 2 89.22% 19.61% 5.88%

Year 3 93.00% 20.41% 7.14%


Hospitalisations and hypoglycaemia

As can be seen in table 6.15, few people were hospitalised for diabetes over

the course of the study. This shows that only a small group of individuals in

the study experienced life-threatening acute symptoms of diabetes such as

extreme hypoglycaemia or extreme hyperglycaemia (ketoacidosis).

Table 6.15: Hospitalisations and hypoglycaemia over three years

Hypoglycaemia in

two weeks

Unconscious

hypoglycaemia in

three months

Hospitalised for

diabetes in three

months

Year 1 76.92% 3.85% 2.88%

Year 2 83.33% 1.96% 5.88%

Year 3 79.59% 3.00% 3.00%


Nonetheless, the prevalence of mild hypoglycaemia in the sample was high.

For each year, the majority of respondents reported obtaining a blood

glucose reading below 3.00 mmol/L. in the preceding two weeks. Of these

people, a small percentage lost consciousness. Overall, these findings

demonstrate that mild levels of hypoglycaemia were common amongst the

participants of this study.


126

For each year, the median number of hypoglycaemic events experienced by

individuals over a period of two weeks was 2. The number of hypoglycaemic

incidents ranged from 0 to 16 (inter-quartile range = 1.00 - 4.00) in the first

year, 0 to 15 (inter-quartile range = 1.00 - 4.00) in the second year and 0 to

16 (inter-quartile range = 0.00 - 4.00) in the third year. Unsurprisingly a

repeated-measures analysis of variance showed no significant changes in

the mean-level of hypoglycaemic incidents over the three years, F (2, 188) =

0.63, p = 0.54.

Spearman’s test-retest correlations (table 6.16) showed that frequency of

hypoglycaemia had relatively low temporal stability. In fact, the number of

hypoglycaemic incidents in the first year was uncorrelated with the number of

hypoglycaemic incidents in the third year. This shows that an individual who

reported a high number of hypoglycaemic incidents at baseline did not

necessarily have a high number of hypoglycaemic incidents in the final year

of the study. Such findings suggest that experience of hypoglycaemia may

be more difficult to predict than glycaemic control as measure by HbA1c.

Table 6.16: Spearman’s test-retest correlations for hypoglycaemia frequency


Number of hypos 0.32** 0.34** 0.07

* = Significant at p <0.05 ** =Significant at p <0.01

Coping

Repeated measures analyses of variance were employed to examine mean-

level changes in coping over time. Results demonstrated no significant

changes in acceptance coping, F (1.78, 174.14) = 0.12, p = 0.86, or avoidance

coping, F (1.85, 174.00) = 0.95, p = 0.38 over the three years (see table 6.17 for

means).


127

Table 6.17: Mean-levels of coping over three years

Acceptance coping Avoidance coping*

Year 1 24.26 (4.40) 7.65 (3.35)

Year 2 24.16 (3.94) 7.12 (3.50)

Year 3 24.39 (4.03) 7.29 (3.42)

Results expressed as: mean and standard deviation * Mean for avoidance coping does not include reverse-coded item

Given the gender differences found in acceptance coping, a second repeated

measures analyses of variance was conducted to look for temporal changes

in acceptance coping within each sex. Results showed that acceptance

coping did not change over time in either males, F (1.73, 83.16) = 0.87, p = 0.41,

or females, F (1.65, 80.63) = 0.51, p = 0.60. Mean-levels of coping remained

unchanged throughout the course of the study.

Notably, Spearman’s correlations did suggest some instability in the rank-

order of coping scores over time. Test-retest correlations for coping (table

6.18) were relatively strong for the second and third year. However,

correlations between the first and second, and first and third year only

revealed small to medium sized relationships (Field, 2009). This shows that

intra-individual changes in coping occurred over time.

Table 6.18: Spearman’s test-retest correlations for coping over three years


Acceptance coping 0.41** 0.52** 0.22**

Avoidance coping 0.29** 0.56** 0.26**

* = Significant at p <0.05 ** =Significant at p <0.01

Whilst coping was found to be changeable over time, no consistent pattern in

changes was discernable for this cohort. Coping may thus be a highly

personal experience again speaking to the role of personality in determining


128

outcomes. Surprisingly, Spearman’s correlations showed no significant

relationships between glycaemic levels as measured by HbA1c and

acceptance or avoidance coping (all p values > 0.05).

Depression

There were no significant mean-level differences in depression scores for the

sample over the three years, F (1.79, 173.21) = 2.02, p = 0.14. This suggests

that overall depression scores did not change for the group throughout the

course of the study (for means see table 6.19). However, when examining

severity levels of depression (Lovibond & Lovibond, 1995a), a different story

emerged.

Table 6.19: Mean-levels of depression over three years

Depression score

Year 1 7.37 (9.22)

Year 2 5.88 (6.90)

Year 3 5.59 (7.73)


The DASS-21 cut-offs for conventional severity levels (Lovibond & Lovibond,

1995a) were employed to distinguish rates of depression that were outside of

the normal range (see Table 4.1, chapter 4). At baseline, 28.85% of

respondents (30 people) reported elevated levels of depression compared to

22.00% of the DASS normative sample (Lovibond & Lovibond, 1995a).

Notably, when participants reported depression, these ratings were often of a

greater severity than that reported by norms (see Appendix 34).

Elevated severity of depression appeared to drop in the second and third

years of the study. 24.51% of respondents (25 people) reported depression

out of the normal range in the second year whilst 20.00% of respondents (20

people) reported levels of depression outside of the normal range in the third


129

year. Figure 6.1 shows the percentage of participants who showed higher

than normal levels of depression over the three years compared to the DASS

norms (Lovibond & Lovibond, 1995a).

Figure 6.1: Rates of depression outside of the normal range

These findings are intriguing. The baseline results support prior research that

has observed higher depression rates in young people with type I diabetes

(Dantzer et al., 2003; Kanner et al., 2003; Massengale, 2005), yet, the

reductions in depression severity across the years show that participants in

the current study may have adapted to the demands of living with diabetes.

Indeed, by the third year, the prevalence of depression outside of the normal

range (20.00%) was lower than that found in the DASS normative sample

(22.00%). Thus, by the final year of data collection, rates of depression in the

sample appear to be lower than what is expected in the general population.

A repeated measures analysis of variance using the DASS-21 conventional

severity cut-offs (see table 6.20 for means) supported the argument for

adaptation, showing that depression scores significantly improved over the

three years for individuals who reported some level of depressive

symptomology at baseline, F (2, 56) = 24.67, p = 0.01.


130

Table 6.20: Mean-levels of depression for participants

outside normal range at baseline

Depression score

Year 1 18.97 (9.10)

Year 2 10.00 (8.18)

Year 3 7.03 (7.24)


These are exciting results, which appear to be unique to this study. Whilst

prior research involving youth with type I diabetes has demonstrated

improvements in depression over time, these gains are often short-lived and

at no point did the individuals with diabetes report lower levels of depression

than the general population (Dantzer et al., 2003; Grey et al., 1995;

Massengale, 2005). If replicable, these results should provide hope for

health-care professionals as they suggest that the majority of youth adapt to

the challenge of living with type I diabetes and eventually display ‘normal’

rates of depression.

Anxiety

A repeated-measures analysis of variance demonstrated a significant main

effect of time on mean-levels of anxiety, F (1.77, 171.68) = 3.44, p = 0.04.

Perusal of the means (table 6.21) showed that overall anxiety scores

decreased over the three years. Pair-wise comparisons (with Bonferroni

adjustment) demonstrated no statistically significant differences between

these individual time-points (all p values > 0.05) suggesting gradual

improvements in mean-levels of anxiety over the course of the study.

Table 6.21: Mean-levels of anxiety over three years

Anxiety score

Year 1 7.59 (8.52)

Year 2 6.00 (5.51)

Year 3 5.76 (6.22)



131

However, looking at rates of anxiety that exceeded the DASS norms

provided a contrasting picture (see Appendix 34 for severity levels). At

baseline, 34.62% of respondents (36 people) reported anxiety symptoms

outside the normal range compared to 22.00% in the DASS sample

(Lovibond & Lovibond, 1995a). These rates rose in the second year with

41.18% of respondents (42 people) reporting some form of anxiety. In the

third year, prevalence rates for anxiety symptoms outside the normal range

dropped to their lowest level of 27.00% (27 people). Figure 6.2 shows the

percentage of individuals experiencing greater than normal levels of anxiety

over the three years and compares these to the levels observed in the DASS

normative sample (Lovibond & Lovibond, 1995a).

Figure 6.2: Rates of anxiety outside the normal range

These findings are interesting because the temporal pattern for mean-levels

of anxiety did not correspond with the number of participants reporting

anxiety symptoms that were outside of the normal range. Whilst mean-levels

of anxiety declined over each testing occasion, a larger proportion of the

study sample reported above average symptoms of anxiety in the second

year (rather than the first).


132

This disparity appears to be explained by a reduction in the severity of

anxiety for many participants in the second year. In the first year, 9.62% of

respondents reported ‘extremely severe’ levels of anxiety as defined by

(Lovibond & Lovibond, 1995a) compared to 0.96% of respondents in the

second year (see Appendix 34 for details). Thus, whilst the number of

individuals reporting above-average anxiety increased in the second year,

the severity of this anxiety was lower than that reported at baseline. This

reduced the grand mean for the entire sample in the second year.

Observation of the means for those reporting anxiety outside of the normal

range in each year substantiates this deduction. These findings suggest that

even though the prevalence of anxiety increased in the second year, the

overall severity of anxiety improved each year within the cohort. Table 6.22

shows the mean anxiety scores for those reporting anxiety outside the

normal range at the three separate time-points.

Table 6.22: Mean anxiety scores for individuals with anxiety outside of the normal range

n Anxiety score

Year 1 36 18.00 (8.24)

Year 2 42 14.24 (7.34)

Year 3 27 13.04 (8.05)


A repeated measures ANOVA was employed to examine whether the

improvements observed in mean-levels of anxiety could be due to

participants with anxiety at baseline reporting less severe anxiety over time.

Results demonstrated that mean-levels of anxiety significantly decreased for

this group over the course of the study, F (2.00, 68.00) = 35.28, p = 0.00. It may

be that these individuals may have begun to adapt to living with type I

diabetes. Pair-wise comparisons showed a highly significant difference

between anxiety scores in the first and second years in this cohort (p = 0.00)


133

indicating that much of the improvement in anxiety levels occurred between

these times. Table 6.23 shows the anxiety scores for those individuals who

reported elevated anxiety at baseline.

Table 6.23: Mean-levels of anxiety for participants outside normal range at baseline

Anxiety score

Year 1 16.40 (8.50)

Year 2 8.80 (5.78)

Year 3 7.14 (6.85)


It is important to note that whilst some improvements in the severity of

anxiety were observed over the course of the study, the prevalence of

anxiety in the sample was still higher than the established DASS norms

(22.00%) in each of these years (Lovibond & Lovibond, 1995a). This finding

supports research that shows a greater prevalence of anxiety and anxiety

disorders in youth with type I diabetes (Dantzer et al., 2003) and

demonstrates that researchers need to identify factors that may influence the

experience of anxiety within this cohort.

Crucially, reports of anxiety in this study do not seem to be a product of

biased reporting due to hypoglycaemic symptoms mirroring those of anxiety.

Spearman’s correlations demonstrated that anxiety was unrelated to

frequency of hypoglycaemia over the three years (all p’s > 0.05), which

suggests that the DASS anxiety scores reflect valid experience of anxiety.

Stress

A repeated-measures analysis of variance demonstrated a significant main

effect of time on mean-levels of stress, F (1.87, 181.42) = 3.35, p = 0.04. As can

be seen in table 6.24, mean scores for stress declined over the course of the

three-year study. Pair-wise comparisons (with Bonferroni adjustment)


134

showed a significant difference between the first and third year (p = 0.04)

which shows that mean-levels of stress had substantially improved by the

end of this investigation.

Table 6.24: Mean-levels of stress over three years

Stress score

Year 1 10.20 (9.22)

Year 2 8.71 (7.45)

Year 3 8.00 (6.84)


Comparable results were found when looking at the DASS-21 cut-offs for

stress scores outside of the normal range (Lovibond & Lovibond, 1995a). In

the first year 27.88% of respondents (29 people) reported symptoms of

stress but this dropped to 23.53% of respondents (24 people) in the second

year (for severity levels see Appendix 34).

Figure 6.3: Rates of stress outside the normal range

Rates dropped again in the third year, and by this time, the study group

reported lower rates of non-trivial stress (16.00%) than the DASS normative


135

sample (22.00%) (Lovibond & Lovibond, 1995a). Figure 6.3 shows the

percentage of participants who reported stress outside of the normal range

over the three years compared to the DASS cohort (Lovibond & Lovibond,

1995a).

A repeated measures analysis of variance suggested that much of the

improvement in mean-levels of stress occurred in participants reporting

elevated levels of stress at baseline F (2.00,54.00) = 39.60, p = 0.00. Table 6.25

shows the mean levels of stress for each year within this group.

Table 6.25: Mean-levels of stress for participants outside normal range at baseline

Stress score

Year 1 22.57 (5.68)

Year 2 11.00 (8.07)

Year 3 11.71 (7.88)


Again, these findings support the notion of youth with type I diabetes

adapting to the demands of their disease. Whilst the baseline results agree

with research that has demonstrated increased stress levels in individuals

with type I diabetes (Amer, 1999; Delameter, 1992), third year figures show

that the number of individuals reporting greater-than-average stress was

lower in the study cohort than in the general population (Lovibond &

Lovibond, 1995a). Furthermore, those reporting elevated stress at baseline

improved significantly across the three years. Thankfully, this finding implies

that health-care workers can expect to see improvements in stress levels in

the majority youth with type I diabetes over time.

It is vital to note that the above-reported findings for the DASS-21 scales

were not a product of biased reporting from children outside the specified

age range of the Depression Anxiety Stress Scales (Lovibond & Lovibond,

1995a). Participants under the age of 12 did not significantly differ from the


136

rest of the sample on measures of depression, anxiety and stress in any year

of data collection (all p values > 0.05). This authenticates the validity of

results and suggests that the responses of children as young as 8 years old

can be considered as legitimate and unbiased when using the DASS-21

measure. Whilst the DASS-21 is just one of a suite of indices required to

diagnose clinical levels of depression, anxiety and stress, the reported

results give a good indication of levels of negative affect and general

psychological wellbeing within the study population.

Summary of preliminary results

In summary, preliminary analyses provided much information about the study

sample and their management of diabetes over time. This knowledge allows

researchers to assess the generalisability of obtained results and identify

specific self-management outcomes that require further attention.

Accordingly, the most noteworthy findings are detailed below.

First, the sample appears to provide a valid cross-section of Australian

youth with type I diabetes. Participants came from a diverse range of

cultural backgrounds and lived in a variety of geographical locations in

New South Wales and the Australian Capital Territory. The group

included almost equal numbers of males and females and there was a

satisfactory distribution of ages within this cohort. Similarly, there was a

good distribution of income groups (albeit with a slight ceiling effect) and

treatment modes. Crucially, all participants had satisfactory access to

health-care indicating that personal factors must have contributed to

results.

Second, demographic variables were related to diabetes self-

management and personality. Age, gender, duration of diabetes, and

responsibility for diabetes care were related to specific self-management

outcomes, whereas age, gender and responsibility were related to

personality traits. This demonstrates that these demographic factors


137

should be retained as control variables in further analyses investigating

the role of personality in type I diabetes self-management.

Third, results demonstrated that a sizeable proportion of the sample

displayed poor glycaemic control in each year of the study (National

Health and Medical Research Council, 2005; Rewers et al., 2009).

Critically, individual glycaemic control showed little temporal variance.

This suggests that many participants had chronic poor control which

increases the risk for developing serious diabetic complications (National

Health and Medical Research Council, 2005; Rewers et al., 2009; The

Diabetes Control and Complications Trial Research Group, 1993). Such

findings highlight the need to find predictors of long-term glycaemic

control in this population.

Fourth, self-care behaviour appeared to be variable across participants.

There was large inter-individual variability in the number of blood glucose

tests conducted within the sample. Furthermore, self-monitoring of blood

glucose was stable over time which suggests that some participants

were engaging in what has been determined as sub-standard self-care

(Benjamin, 2002; National Health and Medical Research Council, 2005;

The Diabetes Control and Complications Trial Research Group, 1993) at

every time-point in the study. The high individual variability in blood

glucose testing suggests that factors such as personality may be

important.

Fifth, hypoglycaemia was a common factor in participants’ lives. The

majority of the sample reported obtaining a blood glucose reading below

3.00 in the two weeks preceding each annual interview. Remarkably,

hypoglycaemia showed some rank-order instability. This means that a

person who experienced a low number of hypoglycaemic incidents at

baseline did not necessarily experience a low number of hypoglycaemic

incidents throughout the rest of the study. Thus, it may be interesting to


138

explore whether personality influences these temporal changes in

individuals’ experiences of hypoglycaemia.

Sixth, the extent to which individuals coped with diabetes was variable

over time. Mean-levels of coping did not change throughout the study but

test-retest correlations demonstrated rank-order instability in acceptance

and avoidance coping. This suggests that coping was a highly

personalised experience and speaks to the role of personal factors in

coping with type I diabetes.

Seventh, participants appeared to improve on measures of depression,

anxiety and stress over time. Whilst there were high rates of negative

affect at baseline, the prevalence of depression and stress scores that lie

outside the normal range were below population norms by the third year.

Prevalence of anxiety symptomology also began to approach ‘normal’

levels by this time. These findings suggest that some individuals may

have adapted to living with diabetes over the course of the study. By

finding predictors of trajectories of negative affect it may be possible to

identify those at risk for chronic poor psychological wellbeing.

Finally, personality was stable over the three-year investigation. Test-

retest correlations across the three years were strong and this high

temporal stability suggests that any relationships uncovered between

personality and self-management could have long-term implications.

In all, preliminary results demonstrate that the current study sample provides

researchers with an excellent opportunity to examine the role of personality

in the self-management of type I diabetes over time. Furthermore, these

results clearly identify control variables and self-management outcomes

worthy of further interest. These are briefly discussed below.


139

Inclusion of variables in further analyses

Table 6.26 shows the control, predictor and outcome variables included

further analyses aimed at answering the research questions.

Table 6.26: Control, predictor and outcome measures

Control variables Predictors Outcome variables

Age Openness to experience HbA1c

Gender Conscientiousness Depression

Duration of diabetes Extraversion Anxiety

Responsibility for care Agreeableness Stress

Emotional regulation Acceptance coping

Avoidance coping

Frequency of BGL testing

Frequency of hypoglycaemia

Control variables

The variables of age, gender, duration of diabetes and responsibility for

diabetes care were retained for further analyses based on their theoretical

importance (Anderson et al., 2002; Dabadghao et al., 2001; Helgeson et al.,

2007; Ingerski et al., 2010; Olsen et al., 1999; Springer et al., 2006) and their

relationships with personality and/or self-management outcomes in the

current study. Retaining these variables ensured that any relationships

between personality and self-management outcomes were not better

explained by these phenomena. No other control variables were retained

based on their non-significant relationships with outcomes.

Predictors

The five-factor model personality traits of openness to experience,

conscientiousness, extraversion, agreeableness and emotional regulation


140

were all retained for further analyses based on their longitudinal stability.

This permitted longitudinal associations between these personality and self-

management outcomes to be examined.

Outcomes

The variables of HbA1c, blood glucose testing, depression, anxiety, stress,

coping and frequency of hypoglycaemia were retained as self-management

outcomes because they have theoretical and clinical importance (Dantzer et

al., 2003; Holmes et al., 2006; Ikeda & Tsuruoka, 1994; Ingerski et al., 2010;

Laffel et al., 1998; Massengale, 2005; National Health and Medical Research

Council, 2005; Rewers et al., 2009; The Diabetes Control and Complications

Trial Research Group, 1993; Wiebe & Smith, 1997), showed good data

properties and provided a good overview of diabetes self-management. The

other outcome measures from this study were excluded from further

analyses because they were categorical, ordinal or had low variance.

Together, the included variables should provide researchers with a wealth of

information about the role of personality in young people’s diabetes. By

engaging in this research, a clearer understanding of the long-term influence

of personality may be achieved.

Chapter 7

Results

Chapter 7 details the results of statistical analyses used to answer the

research questions of the current study. To lend order to these findings, the

chapter is divided into three sections, one section per research question.

Results for specific self-management outcomes are discussed separately

within these sections. Significant correlations, results of forced-entry

regressions and results of mixed-design and repeated measures analyses of

variance (ANOVAs) are presented within the text. For any further information

the reader is directed to the appendices. There, a complete table of

correlations is presented in Appendix 35 and a complete list of hierarchical

regressions is presented in Appendix 36. Prior research and the clinical

implications of current findings are considered when discussing the obtained

results. The potentially prospective nature of associations between

personality and self-management outcomes are also highlighted.

Research Question 1

What relationships exist between five-factor model traits and HbA1c,


stress, avoidance coping and/or acceptance coping in youth with type I

diabetes?

Five-factor model traits and HbA1c values

Pearson’s product-moment correlations (Table 7.1) demonstrated that

agreeableness and conscientiousness were the only five-factor model

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142

personality traits to show repeated linear associations with HbA1c values. As

hypothesised, these traits were generally negatively correlated with HbA1c

scores meaning that individuals high in conscientiousness and

agreeableness tended to have better glycaemic control over the course of

the study. These results echo the findings of previous studies involving youth

with diabetes (Vollrath et al., 2007). However, there was an unexpected

finding. HbA1c values from the first year of data collection demonstrated few

significant linear associations with five-factor model personality traits.

Table 7.1: Correlations between five-factor model traits and HbA1c

1st year HbA1c 2nd year HbA1c

3rd year HbA1c

1st year Conscientiousness - -0.25* -0.35**

2nd year Conscientiousness - -0.30** -0.30**

3rd year Conscientiousness - -0.26** -0.32**

1st year Agreeableness - -0.36** -0.28**

2nd year Agreeableness - -0.31** -

3rd year Agreeableness -0.22** -0.34** -.31**

2nd year Openness -0.20* -0.29** -0.28**

*Significant at p<0.05 (2-tailed)

** Significant at p<0.01 (2-tailed)

Computed using Pearson’s product-moment correlations

The absence of significant associations in the first year is difficult to explain

and may be an artefact of the study sample. Nevertheless, first year scores

for agreeableness and conscientiousness were related with HbA1c levels in

the second and third years and this illustrates that personality traits may

influence future glycaemic control. The nature of such prospective

relationships warrants further investigation.

Whilst second year openness to experience was significantly correlated with

HbA1c values across the three years, the first and third year measures of this

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143

trait were unrelated to glycaemic control. Furthermore, no consistent

relationships between HbA1c and the five-factor trait of extraversion were

uncovered. As postulated, it appears that these traits have a less reliable or

important role in the regulation of blood glucose values in youth with type I

diabetes.

Examination of the scatterplots (Figures 7.1 to 7.3) for emotional regulation

and HbA1c uncovered a non-linear relationship between these two variables.

In accordance with hypotheses, there appeared to be a curvilinear

association between these phenomena with individuals high or low on

emotional regulation having poorer HbA1c results.

Figures 7.1 to 7.3: Scatterplots of emotional regulation and HbA1c

To establish the validity of a curvilinear relationship between emotional

regulation and HbA1c, bivariate regressions using emotional regulation as an

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144

independent variable and HbA1c as a dependent variable were conducted for

each year of data collection following the procedure outlined by Keith (2006)

and Mallery (2009). A quadratic term for emotional regulation was included in

these regression analyses, which allowed linear and quadratic regression

models to be compared.

If the addition of a quadratic term for an independent variable significantly

improves the overall fit of a bivariate regression model, this means that the

relationship between the independent and dependent variables is of a

curvilinear nature (Keeley, Zayac, & Correia, 2008; Keith, 2006; Mallery,

2009). Thus, to support the existence of a curvilinear association between

emotional regulation and HbA1c, a regression model that includes both a

linear and quadratic term for emotional regulation (model 2) would need to

provide a better fit than a model that includes a linear term only (model 1).

For the first year, a linear model did not significantly predict HbA1c values

(R2= 0.01, F (1,102) = 0.55, p = 0.46). However, model 2 which included a

quadratic term produced a significant change in R2 (R2 change = 0.05 F (1,

101) = 5.69, p = 0.02) and a significant model (R2= 0.06, F (2,101), p = 0.05).

For the second year, a linear model did not predict HbA1c (R2= 0.01, F (1,102)

= 1.29, p = 0.26), and although inclusion of a quadratic term significantly

improved fit (R2 change = 0.04F (1, 101) = 4.09, p = 0.05), this did not produce

a significant model (R2= 0.05, F (2,101) = 2.71, p = 0.07). Similarly, a linear

model did not achieve significance in the final year (R2= 0.03, F (1, 98) = 2.76,

p = 0.10), but addition of a quadratic term produced a significant change in

R2 (R2 change = 0.05 F (1, 97) = 5.21, p = 0.03) and a significant model (R2=

0.08, F (2, 97) = 4.05, p = 0.02).

In all, the fact that inclusion of a quadratic term for emotional regulation

significantly improved the fit of regression models of HbA1c for each year of

the research study provides evidence for a U-shaped relationship between

these variables (Keeley, Zayac, &Correia, 2008; Keith, 2006; Mallery, 2009).

Examination of the scatterplots confirmed that the direction of this

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145

association remained constant over the three years of the study with

individuals low or high in emotional regulation having poorer glycaemic

control than individuals who were moderate on this trait. These results are

intriguing and have not been previously reported in studies involving youth

with type I diabetes (Skinner et al., 2002; Vollrath et al., 2007). Given these

findings, all further analyses of the role of emotional regulation in glycaemic

control were conducted with the curvilinear nature of this association in mind.

Five-factor model traits and frequency of blood glucose testing

Spearman’s correlations established that the traits of conscientiousness and

agreeableness were related to frequency of blood glucose testing in the

second and third years (Table 7.2). Like HbA1c, there was no association

between blood glucose testing and personality traits in the first year, which

again indicate that personality, may have a prospective relationship with

diabetes self-management. The results of correlations showed that

individuals higher in agreeableness and conscientiousness tested their blood

glucose levels on a more regular basis. These findings are consistent with

prior research that relates conscientiousness and agreeableness to

adherence with medical treatments and self-care behaviours (Auerbach et

al., 2002; Bogg & Roberts, 2004; Lee & Lin, 2009; Mancuso, 2009, 2010; B.

W. Roberts et al., 2005; Skinner et al., 2002).

Table 7.2: Correlations between five-factor model traits and blood glucose testing

1st year

BGT 2nd year

BGT

3rd year

BGT

1st year Conscientiousness - 0.28** 0.23*

2nd year Conscientiousness - 0.32** 0.28**

3rd year Conscientiousness - 0.31** 0.26**

1st year Agreeableness - 0.22* 0.21*

2nd year Agreeableness - - -

3rd year Agreeableness - 0.24* -

*Significant at p<0.05 (2-tailed) ** Significant at p<0.01 (2-tailed)

Computed using Spearman’s Rho

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146

Five-factor model traits and hypoglycaemia

Spearman’s correlations demonstrated few significant relationships between

frequency of hypoglycaemia and personality traits (Table 7.3). The only five-

factor model trait significantly associated with hypoglycaemia was

agreeableness and this was only so for the number of hypoglycaemic

incidents experienced in the first year. This reveals that the relationship

between agreeableness and hypoglycaemia may be unreliable or a product

of type I error. Further research may be needed to look at this relationship in

more depth.

Table 7.3: Correlations between five-factor model traits and hypoglycaemia

1st year Hypo 2nd year Hypo 3rd year Hypo

1st year Agreeableness -0.21* - -

2nd year Agreeableness -0.30** - -

3rd year Agreeableness -0.22* - -

* Significant at p<0.05 (2 tailed)



Five-factor model traits and depression

As expected, Spearman’s correlations demonstrated significant and reliable

negative associations between emotional regulation and depression scores

(Table 7.4). Furthermore, the traits of conscientiousness, extraversion and

openness also showed some negative correlations with depression.

These findings are in line with a large body of research that argues emotional

regulation is a critical predictor of negative affect (Bienvenu et al., 2004;

Bolger & Zuckerman, 1995; Kendler et al., 2006; Khan et al., 2005; Kotov et

al., 2010; Lahey, 2009; Roelofs et al., 2008). However, the relationship

between emotional regulation and depression was not as strong as

expected. The strength of correlations between emotional regulation and

depression ranged from -0.24 to -0.39 in the current study whereas

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147

correlations stronger than -0.50 are often reported in the general population

(Bienvenu et al., 2004; Bolger & Zuckerman, 1995; Kendler et al., 2006;

Khan et al., 2005; Kotov et al., 2010; Lahey, 2009; Roelofs et al., 2008).

Table 7.4: Correlations between five-factor model traits and depression

1st year Dep 2nd year Dep 3rd year Dep

1st year Conscientiousness - - -0.20*

3rd year Conscientiousness -0.19* - -

1st year Extraversion - - -

2nd year Extraversion - - -0.24*

3rd year Extraversion - - -0.24*

1st year Emotional regulation -0.24* -0.38** -0.32**

2nd year Emotional regulation -0.31** -0.38** -0.39**

3rd year Emotional regulation -0.34** -0.41** -0.34**

1st year Openness - - -

2nd year Openness -.020* - -

3rd year Openness -0.22* - -




These findings reveal that the strength of the association between emotional

regulation and depression may be weaker in youth with type I diabetes than

in the general population. Importantly, this could be due to extraneous

variables such as diabetes control and contact with health professionals

(including psychological services) influencing depression scores.

Alternatively, a restricted sample size and slight floor effects for depression

scores may have influenced these results. Whilst these results are not

definitive, they do point to the need for further research in this area.

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148

Five-factor model traits and anxiety

Spearman’s correlations also demonstrated reliable associations between

emotional regulation and anxiety scores (Table 7.5). These correlations

indicate that higher levels of emotional regulation protect a young person

with diabetes from experiencing high levels of anxiety. Similar to depression,

the size of observed correlations for emotional regulation and anxiety were

smaller than reported in studies involving non-diabetics (Bienvenu,

2006;Khan, et al., 2005; Kotov, et al., 2010; Roelofs, et al., 2008). Again, this

highlights that other latent factors may play a role in determining negative

affect within this cohort.

Table 7.5: Correlations between five-factor model traits and anxiety

1st year Anx 2nd year Anx 3rd year Anx

1st year Conscientiousness - - -0.23*

3rd year Agreeableness -0.20* - -

1st year Extraversion - - -0.28**

2nd year Extraversion - - -

3rd year Extraversion - - -0.23*


2nd year Emotional regulation -0.24* -0.30** -0.29**


1st year Openness - - -0.22*

2nd year Openness - - -0.21*

3rd year Openness - -0.21* -




Notably, significant negative associations between anxiety and openness,

extraversion and conscientiousness were also observed. This indicates that

these traits may have also influenced anxiety levels. However, these

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149

relationships were less consistent and it is difficult to infer causality. For

example, low extraversion may be a cause or consequence of anxiety.

Five-factor model traits and stress

Emotional regulation was the only personality trait to demonstrate a reliable

relationship with stress levels in the study cohort (Table 7.6). Emotional

regulation was negatively associated with stress across the three years

indicating that individuals with type I diabetes who are able to regulate their

emotions feel less stress. Such findings mirror the results of prior research

that posit a principal link between this personality trait and stress (Bolger &

Zuckerman, 1995; Cimbolic-Gunthert, Cohen, & Armeli, 1999; Vollrath,

2001).

Remarkably, there were no significant associations between the five-factor

model trait of conscientiousness and stress. This finding runs counter to the

study hypotheses and illustrates that a planful and responsible disposition

does not necessarily reduce stress levels in youth with diabetes. Finally,

there was a significant negative association observed between third year

stress and first year extraversion. However, the lack of a reliable temporal

relationship between these variables signifies that this association may be

due to type I error. Together, these results suggest that emotional regulation

is the key personality trait that determines levels of stress in youth with

diabetes.

Table 7.6: Correlations between five-factor model traits and stress

1st year Str 2nd year Str 3rd year Str

1st year Extraversion - - -0.20*


2nd year Emotional regulation -0.32** -0.31** -0.42**





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150

Five-factor model traits and acceptance coping

As hypothesised, individuals high in openness to experience relied on

acceptance as a way of coping with their diabetes. Spearman’s correlations

(Table 7.7) demonstrated that openness to experience was related to

acceptance in both the second and third ‘matched’ years. Furthermore, third

year acceptance showed significant associations with levels of openness in

the first, second and third year. Such results hint at the possibility of a

prospective relationship between these two variables.

Similarly, high levels of emotional regulation appeared to be related to

greater acceptance of diabetes. However, these relationships appeared to be

less consistent. Once more, these relationships appeared to be prospective

underlining the importance of further longitudinal analyses.

Table 7.7: Correlations between five-factor model traits and acceptance

1st year Acc 2nd year Acc 3rd year Acc

1st year Emotional regulation - - -

2nd year Emotional regulation - - 0.22*

3rd year Emotional regulation - 0.27** 0.21*

1st year Openness - - 030**

2nd year Openness - 0.25* 0.23*

3rd year Openness 0.20* 0.27** 0.28**




Five-factor model traits and avoidance coping

Associations between five-factor traits and avoidance coping also followed

the directions hypothesised (Table 7.8). Emotional regulation had clear and

relatively reliable negative correlations with avoidance. Conscientiousness

followed a similar pattern with individuals high on this trait reporting lower

avoidance coping in the second and third years. Finally, agreeableness

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151

displayed some significant (albeit less reliable) relationships with avoidance

coping. Together these results imply that a personality pattern of high

conscientiousness, high emotional regulation and high agreeableness may

reduce reliance on avoidance as a method of coping in youth with diabetes.

Table 7.8: Correlations between five-factor model traits and avoidance

1st year Avo 2nd year Avo 3rd year Avo

1st year Conscientiousness - -0.27** -0.22*

2nd year Conscientiousness - -0.23* -0.25*

3rd year Conscientiousness - - -0.21*

1st year Agreeableness -0.21* - -

3rd year Agreeableness - - -0.29**


2nd year Emotional regulation - -0.23* -0.30**

3rd year Emotional regulation -0.24* -0.24* -0.27**




Summary of bivariate analyses

Overall, the results of bivariate analyses reveal that personality does play a

role in youths’ self-management of type I diabetes. In particular, the traits of

conscientiousness, agreeableness and emotional regulation seem important

to maintenance of glycaemic control whilst the traits of emotional regulation,

conscientiousness and openness to experience seem to play a role in coping

and negative affect.

Remarkably, many of the associations between first year personality and

outcomes measures did not reach statistical significance. This could imply

that the influence of personality is cumulative over time and hence has a

greater impact on future outcomes. At-risk individuals (either low or high on a

particular personality trait) may show progressive declines in self-

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152

management and this could explain the greater number of significant

associations for the second and third years of data collection. Repeated-

measures ANOVAs were later employed to test this presumption.

Also interesting is the fact that often several personality traits were related to

a specific self-management outcome. This indicates that a number of

personality factors may influence self-management at the same time and this

may have an additive effect in some instances (i.e. individuals low in

emotional regulation, agreeableness and conscientiousness may have worse

glycaemic control than individuals low in emotional regulation alone).

With these findings in mind, multiple regression analyses were conducted to

determine whether personality traits served as independent predictors of

glycaemic control. These multiple regressions were conducted with two

goals. The first goal was to determine whether personality traits predict an

outcome independently of other demographic or disease-related contextual

variables. The second goal was to determine whether a personality trait

predicts an outcome independently of other personality factors.

Research Question 2

Are there particular five-factor model traits that predict HbA1c,


stress, avoidance coping and/or acceptance coping independent of age,

duration of diabetes, responsibility and gender? Which five-factor model trait

is the best personality predictor of these self-management outcomes?

Determining independent trait predictors of HbA1c scores

Hierarchical multiple regression analyses including five-factor model traits

and control variables as predictors of HbA1c scores indicated that the traits of

emotional regulation (plus a quadratic term for this variable),

conscientiousness and agreeableness should be retained for further analysis

(Appendix 36). In contrast, the traits of extraversion and openness to

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153

experience and the control variables of age, gender, duration of diabetes and

level of responsibility for diabetes tasks did not significantly predict HbA1c

scores over the three year period.

These findings demonstrate that personality had a greater influence on

HbA1c levels over the course of the study than control variables previously

identified as important to glycaemic control (Anderson et al., 1990;

Dabadghao et al., 2001; Hochhauser et al., 2008; Naar-King et al., 2006).

Furthermore, the results of these regressions denote that any associations

between personality and glycaemic control are not strongly mediated or

moderated by responsibility, age, duration of diabetes or gender as the

inclusion of these variables did not substantially weaken associations

between five-factor model traits and HbA1c.

In all, results indicate that researchers and clinicians should concentrate

further on the role of emotional regulation, agreeableness and

conscientiousness in the daily regulation of blood glucose. Indeed, the

importance of these traits has been highlighted in prior research studies

involving young people with diabetes (Skinner et al., 2002; Vollrath et al.,

2007). Consequently, these traits were entered into three forced-entry

regressions that examined the impact of these predictors on glycaemic

control for each ‘matched’ year (Table 7.9).

For the first year, the traits of conscientiousness, agreeableness and

emotional regulation (plus quadratic term) did not produce a significant

model to predict HbA1c scores, R2 = 0.08, F (4, 99) = 2.04, p = 0.09. However,

both the linear and quadratic terms for emotional regulation were significant

independent predictors (both p’s = 0.02) indicating that this personality trait

best predicted glycaemic control on this measurement occasion.

For the second year, the traits of conscientiousness, agreeableness and

emotional regulation (plus quadratic term) produced a significant model that

explained around 16% of the variance in HbA1c scores, R2 = 0.16, F (4, 99) =

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154

4.73, p = 0.00. This demonstrates that a sizeable proportion of the variance

in glycaemic control was accounted for by these personality traits in this

year. Of these factors, agreeableness was the only significant independent

predictor of HbA1c scores (p = 0.03).

Table 7.9: Forced-entry multiple regressions of HbA1c scores

Year 1 B

Std. Error B

Beta

t Sig.

Constant 19.23 4.02 4.78 0.00

Emotional regulation -0.39 0.16 -2.36 -2.47 0.02*


(quadratic)

0.00 0.00 2.30 2.41 0.02*

Agreeableness -0.03 0.02 -0.13 -1.30 0.20

Conscientiousness 0.01 0.02 0.08 0.70 0.48

R2 = 0.08, F (4, 99) = 2.04, p = 0.09

Year 2 B

Std. Error B

Beta

t Sig.

Constant 17.19 2.68 6.41 0.00

Emotional regulation -0.19 0.11 -1.26 -1.76 0.08


(quadratic)

0.00 0.00 1.25 1.77 0.08

Agreeableness -0.04 0.02 -0.22 -2.19 0.03*

Conscientiousness -0.04 0.02 -0.20 -1.96 0.05

R2 = 0.16, F (4,99) = 4.73, p = 0.00**

Year 3 B

Std. Error B

Beta

t

Sig.

Constant 19.40 3.16 6.14 0.000



(quadratic)

0.00 0.00 1.68 2.04 0.04*

Agreeableness -0.04 0.02 -0.21 -2.10 0.04*

Conscientiousness -0.04 0.02 -0.22 -2.05 0.04*

R2 = 0.18, F (4, 95) = 5.43, p = 0.00**

*Significant at the 0.05 level (2 tailed) **Significant at the 0.01 level (2 tailed)

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155

For the third year of data collection, a significant model was also achieved,

R2 = 0.18, F (4, 95) =5.43, p = 0.00. This time, the traits of conscientiousness,

agreeableness and emotional regulation were all independent predictors of

glycaemic control. This suggests that these traits had a cumulative effect in

this year. Thus, individuals with low scores on all three of these personality

traits were likely to demonstrate the poorest glycaemic control.

Overall, these results give strong support to a role of personality in

determining glycaemic control. However, it was impossible to make a

definitive judgement regarding the most important personality predictor of

HbA1c scores. Furthermore, the lack of a significant overall model for the first

year is puzzling and signifies that some other unmeasured factors may have

better explained differences in the regulation of blood glucose levels during

this period. Such results could also indicate that personality may better

predict future rather than current glycaemic levels, particularly given the

correlations evidenced between first year personality measures and

subsequent annual measures of glycaemic control.

Determining independent trait predictors of blood glucose testing

Hierarchical multiple regressions indicated that conscientiousness, gender

and age played a significant role in determining the number of blood glucose

tests that a participant carried out over a two week period (Appendix 36). The

results of these analyses indicate that conscientiousness was the only

significant personality predictor of blood testing behaviour, which highlights

the importance of further interest in this trait.

Thus, to test whether conscientiousness independently predicted blood

glucose testing, the predictors of conscientiousness, age and gender were

entered into three forced-entry regressions that examined the impact of

these predictors on blood testing for each ‘matched’ year (Table 7.10).

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156

Results from the first year regression demonstrate that the variables of

gender, age and conscientiousness did not produce a significant model for

blood glucose testing, R2 = 0.05, F (3, 100) = 1.67, p = 0.18. This means that

other unmeasured factors must have influenced this outcome at this time.

Nevertheless, conscientiousness was listed as a significant independent

predictor within this model (p = 0.05).

Table 7.10: Forced-entry multiple regressions of blood glucose testing

Year 1

B

Std. Error

B

Beta

t Sig.

Constant 40.67 30.46 1.34 0.19

Gender 1.47 6.68 0.02 0.22 0.83

Age -1.10 1.19 -0.09 -0.92 0.36

Conscientiousness 0.89 0.45 0.20 1.99 0.05*

R2 = 0.05, F (3, 100) = 1.67, p = 0.18

Year 2

B

Std. Error

B

Beta

t Sig.

Constant 33.26 24.76 1.34 0.18

Gender 3.31 4.99 0.06 0.66 0.51

Age -2.66 0.90 -0.27 -2.97 0.00**

Conscientiousness 1.34 0.37 0.33 3.57 0.00**

R2 = 0.19, F (3, 96) = 7.64, p = 0.00**

Year 3

B

Std. Error

B

Beta

t

Sig.

Constant 36.63 24.36 1.50 0.14

Gender -1.44 5.10 -0.03 -0.28 0.78

Age -2.43 0.90 -0.25 -2.71 0.01**

Conscientiousness 1.37 0.35 0.36 3.90 0.00**

R2 = 0.19, F (3, 99) = 7.52, p = 0.00**


A different result was found for the second year regression. In the second

year, the included predictor variables produced a significant model that

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157

predicted around 19% of the variance in blood glucose testing, R2 = 0.19, F

(3, 96) = 7.64, p = 0.00.For this model, conscientiousness was a highly

significant independent predictor of glucose testing behaviour (p = 0.00).

Similarly, the third year ‘matched’ data produced a significant regression

model, R2 = 0.19, F (3, 99) = 7.52, p = 0.00. Again, this model predicted 19%

of the variance in blood glucose testing, and again, conscientiousness was a

strong independent predictor (p = 0.00).

In total, the results of these regression analyses confirm that

conscientiousness is a strong independent predictor of blood glucose testing

behaviour. Furthermore, this trait appears to be the sole personality variable

determining the frequency of these tests. Whilst prior research studies have

often related blood glucose testing to age and gender (Anderson et al., 1990;

Dabadghao et al., 2001; Hochhauser et al., 2008; Naar-King et al., 2006), the

results of these regressions verify that conscientiousness also plays a key

role. Indeed, the fact that a combination of age, gender and

conscientiousness predicted 19% of the variance in blood glucose testing at

two different time points suggests that conscientiousness may be one of the

most critical psychological factors determining blood glucose testing

behaviour. It is thus imperative to examine whether conscientiousness may

predict increases or decreases in blood glucose testing over time.

Determining independent trait predictors of hypoglycaemia

Whilst hierarchical analyses confirmed that responsibility and extraversion

play a role in hypoglycaemia (Appendix 36), subsequent forced entry

regressions did not produce significant models over the course of the three-

year study (all p’s > 0.05). These results indicate that the predictor variables

measured in this study did not reliably predict experience of hypoglycaemic

events (Table 7.11).

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158

Given the nature of the collected data, these findings are unsurprising. Whilst

the majority of participants experienced a hypoglycaemic event, the number

of events experienced by each person was generally low. This caused a

slight floor effect and a restriction of range for these data and this most likely

influenced the results of regressions. Based on the absence of significant

results and the restrictions of these data, no further analyses of interactions

between personality and hypoglycaemia were undertaken.

Table 7.11: Forced-entry multiple regressions of hypoglycaemia

Year 1 B

Std. Error

B Beta

t Sig.

Constant -2.06 2.37 -0.87 0.39

Responsibility 0.05 0.05 0.10 1.02 0.31

Extraversion 0.06 0.03 0.18 1.823 0.07

R2 = 0.04, F (2, 101) = 2.14, p = 0.12

Year 2 B

Std. Error

B Beta

t Sig.

Constant -1.15 2.35 -0.49 0.63

Responsibility 0.03 0.05 0.06 0.57 0.57

Extraversion 0.07 0.04 0.18 1.79 0.08

R2 = 0.04, F (2, 97) = 1.91, p = 0.15

Year 3 B

Std. Error

B Beta

t Sig.

Constant 4.34 1.99 2.17 0.03

Responsibility -0.07 0.04 -0.17 -1.64 0.10

Extraversion 0.01 s0.03 0.04 0.42 0.67

R2 = 0.03, F (2, 96) = 1.35, p = 0.26


Determining independent trait predictors of depression scores

Initial hierarchical regressions including both personality and control

variables (Appendix 36) indicated that emotional regulation and duration of

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159

diabetes played a role in determining depression scores. Accordingly, these

variables were retained in forced-entry regressions to clarify the nature of

these relationships (Table 7.12).

Table 7.12: Forced-entry multiple regressions of depression scores

Year 1

B

Std. Error

B Beta

t Sig.

Constant 38.03 5.64 6.70 0.00

Duration of diabetes -0.71 0.25 -0.25 -2.90 0.01**

Emotional regulation -0.54 0.10 -0.45 -5.20 0.00**

R2 = 0.24, F (2,101) = 16.36, p = 0.00**

Year 2 B

Std. Error

B Beta

t Sig.

Constant 22.82 3.94 5.79 0.00

Duration of diabetes 0.12 0.19 0.06 0.65 0.52


R2 = 0.19, F (2, 99) = 11.33, p = 0.00**

Year 3 B

Std. Error

B

Beta

t

Sig.

Constant 20.31 4.81 4.22 0.000



R2 = 0.32, F (2, 96) = 5.33, p = 0.01**


For the first year, duration of diabetes and emotional regulation produced a

significant model that predicted 24% of the variance in depression scores, R2

= 0.24, F (2,101) = 16.36, p = 0.00. Both of the included variables were

significant independent predictors of depression (p = 0.01 and 0.00

respectively). These results establish that youth with a shorter duration of

diabetes and low levels of emotional regulation were more susceptible to

depression in the first ‘matched’ year of data collection.

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160

Similar results were found for the second year. Here, the variables of

duration of diabetes and emotional regulation produced a significant model

that predicted 19% of the variance in depression scores R2 = 0.19, F (2, 99) =

11.33, p = 0.00. However, in this year, emotional regulation was the only

significant independent predictor of depression (p = 0.00).

Results from the third year followed a comparable pattern. For this year,

duration of diabetes and emotional regulation predicted 32% of the variance

in depression, R2 = 0.32, F (2, 96) = 5.33, p = 0.01. However, as in the second

year, emotional regulation was the only independent predictor of outcomes

(p = 0.00).

Together, these results support research that emphasises that importance of

emotional regulation in determining levels of negative affect (Bienvenu et al.,

2004; Bolger & Zuckerman, 1995; Kendler et al., 2006; Khan et al., 2005;

Kotov et al., 2010; Lahey, 2009; Roelofs et al., 2008). Furthermore, when

combined with duration of diabetes, this personality trait appears to be a

particularly effective predictor of depression scores.

Determining independent trait predictors of anxiety scores

Hierarchical multiple regression analyses including personality traits and

control variables as predictors of anxiety scores demonstrated that emotional

regulation and responsibility for diabetes tasks deserved further scrutiny

(Appendix 36). Forced-entry regressions of these traits on ‘matched’ anxiety

scores supported the hypothesis that emotional regulation plays a central

role in determining anxiety levels in youth with diabetes (Table 7.13).

A forced-entry regression of first year responsibility and emotional regulation

on anxiety produced a significant model that predicted 14% of the variance in

anxiety scores, R2 = 0.14, F (2, 101) = 8.16, p = 0.00. For this year, emotional

regulation was a significant independent predictor of anxiety (p = 0.00) whilst

responsibility was not (p = 0.37).

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161

Table 7.13: Forced-entry multiple regressions of anxiety scores

Year 1 B

Std. Error

B Beta

t Sig.

Constant 23.43 7.51 3.12 0.00

Responsibility 0.13 0.14 0.08 0.91 0.37


R2 = 0.14, F (2, 101) = 8.16, p = 0.00**

Year 2 B

Std. Error

B Beta

t Sig.

Constant 24.73 4.02 6.15 0.00

Responsibility -

0.260

0.08 -0.29 -3.14 0.00**


R2 = 0.18, F (2, 99) = 10.84, p = 0.00**

Year 3 B

Std. Error

B Beta

t Sig.

Constant 22.97 4.46 5.15 0.00

Responsibility -0.15 0.09 -0.16 -1.69 0.09


R2 = 0.14, F (2, 97) = 7.96, p = 0.00**


In the second year, a combination of these variables produced a significant

model that explained 18% of the variance in anxiety scores, R2 = 0.18, F (2,

99) = 10.84, p = 0.00. This time, both responsibility and emotional regulation

independently predicted anxiety levels (both p’s = 0.00). A final forced-entry

regression of third year data produced a significant model that explained

14% of variance in anxiety scores, R2 = 0.14, F (2, 97) = 7.96, p = 0.00. Once

again, responsibility did not predict anxiety levels (p = 0.09) whereas

emotional regulation was a strong independent predictor (p = 0.00).

Overall, the results of these regressions reveal that emotional regulation is a

key predictor of anxiety in young people with diabetes. Furthermore, this trait

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162

predicted anxiety independently of responsibility. This indicates that the daily

demands of diabetes are not a wholly sufficient explanation for the increased

anxiety levels commonly reported in youth with type I diabetes. Such findings

demonstrate that personality researchers should focus on the trait of

emotional regulation when trying to implement interventions aimed at

improving anxiety in youth with diabetes. Future research could look at

interactions between these variables to clarify relationships.

Determining independent trait predictors of stress scores

Hierarchical regression confirmed that emotional regulation, extraversion and

openness to experience deserved further attention (Appendix 36).

Accordingly, table 7.14 presents the results of forced-entry regressions of

these variables over the three years.

Table 7.14: Forced-entry multiple regressions of stress scores

Year 1 B

Std. Error B Beta

t Sig.

Constant 30.07 7.64 3.94 0.00


Extraversion 0.14 0.10 0.14 1.33 0.19

Openness -0.12 0.12 -0.11 -1.05 0.30

R2 = 0.11, F (3, 100) = 4.22, p = 0.01**

Year 2 B

Std. Error

B Beta

t Sig.

Constant 11.38 5.98 1.90 0.06


Extraversion 0.18 0.09 0.20 1.96 0.05

Openness 0.09 0.09 0.10 1.04 0.30

R2 = 0.12, F (3, 98) = 4.42, p = 0.01**

Year 3 B

Std. Error

B Beta

t Sig.

Constant 16.34 4.96 3.30 0.00


Extraversion 0.01 0.08 0.01 0.11 0.91

Openness 0.19 0.07 0.25 2.56 0.01*

R2 = 0.19, F (3, 96) = 7.62, p = 0.00**

*Significant at the 0.05 level (2 tailed)

**Significant at the 0.01 level (2 tailed)

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163

The first year regression produced a significant model that explained 11% of

the variance in stress, R2 = 0.11, F (3, 100) = 4.22, p = 0.01. Only emotional

regulation independently predicted stress levels at this time (p = 0.00). For

the second ‘matched’ year, regression analyses produced a significant model

that predicted 12% of the variance in stress scores, R2 = 0.12, F (3, 98) = 4.42,

p = 0.01. Again, the only independent predictor of outcomes was emotional

regulation (p = 0.00). Comparable results were found for the third year of

data collection with the included predictors producing a significant model that

explained 19% of the variance in stress levels, R2 = 0.19, F (3, 96) = 7.62, p =

0.00. This time, the traits of emotional regulation and openness to

experience both independently predicted stress scores (p = 0.00 and 0.01

respectively).

These results illustrate that the trait of emotional regulation is a critical factor

determining stress levels in youth with type I diabetes. Clinicians therefore

need to be mindful of a young person’s ability to manage their emotions

when recommending difficult-to-achieve self-management goals that may

contribute to stress levels. Further research may help to clarify the role of

openness to experience in stress responses.

Determining independent trait predictors of acceptance coping

Initial hierarchical regression analyses (Appendix 36) established that

responsibility, age, gender and openness to experience were important to

acceptance coping in youth with diabetes. Therefore, these variables entered

into forced-entry regression for each matched year (Table 7.15)

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164

Table 7.15: Forced-entry multiple regressions of acceptance coping

Year 1 B

Std. Error

B Beta

t Sig.

Constant 24.67 6.46 3.82 0.00

Responsibility -0.07 0.09 -0.09 -0.72 0.47

Age 0.10 0.18 0.07 0.56 0.58

Gender -0.66 0.88 -0.08 -0.75 0.46

Openness 0.03 0.06 0.06 0.59 0.56

R2 = 0.03, F (4, 99) = 0.86, p = 0.49

Year 2 B

Std. Error

B Beta

t Sig.

Constant 19.08 3.20 5.96 0.00

Responsibility 0.04 0.09 0.06 0.41 0.68

Age 0.09 0.19 0.07 0.48 0.63

Gender -1.30 0.76 -0.17 -1.72 0.09

Openness 0.09 0.05 0.20 1.94 0.06

R2 = 0.10, F (4, 97) = 2.80, p = 0.03*

Year 3 B

Std. Error

B Beta

t Sig.

Constant 23.20 3.05 7.61 0.00

Responsibility 0.17 0.09 0.28 1.81 0.07

Age -0.41 0.21 -0.29 -1.96 0.05

Gender -2.45 0.78 -0.30 -3.16 0.00**

Openness 0.10 0.04 0.22 2.22 0.03*

R2 = 0.19, F (4, 96) = 5.62., p = 0.00**


The first year regression produced a non-significant model, R2 = 0.03, F (4, 99)

= 0.86, p = 0.49. However, different results were obtained in the second year

of data collection where a significant model was achieved that predicted 10%

of the variance in acceptance coping scores, R2 = 0.10, F (4, 97) = 2.80, p =

0.03. None of the variables included in this model served as a significant

independent predictor of acceptance coping (all p’s >0.05).

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165

A final regression of third year values also produced a significant model, R2 =

0.19, F (4, 96) = 5.62., p = 0.00. This time, the model predicted 19% of the

variance in coping scores and the variables of gender and openness to

experience served as independent predictors of acceptance (p = 0.00 and

0.03 respectively). This analysis verifies that the relationship between

acceptance and openness was not solely mediated through gender. This

also indicates that individuals who crave new experiences may be more

likely to accept unchangeable events and try to integrate these into their life.

Consequently, this relationship was flagged for further longitudinal analysis.

Determining independent trait predictors of avoidance coping

The results of hierarchical regressions indicated that responsibility, emotional

regulation and agreeableness help to predict scores on avoidance coping

(Appendix 36).

Forced-entry regression analysis (Table 7.16) of the first round of ‘matched’

data demonstrated that responsibility for diabetes tasks and the personality

traits of emotional regulation and agreeableness significantly predicted 11%

of the variance in scores for avoidance coping, R2 = 0.11, F (3, 100) = 4.27, p =

0.01. Emotional regulation (p = 0.03) was a significant independent predictor

in this model.

The second year regression also produced a significant model in which

responsibility and emotional regulation were independent predictors of

outcomes, R2 = 0.12, F (3, 96) = 4.49, p = 0.01. Similarly, the third year

regression produced a significant model R2 = 0.18, F (3, 94) = 6.85, p = 0.00

with emotional regulation and agreeableness independently predicting

coping scores. These results suggest further attention be given to the traits

of agreeableness and emotional regulation in determining trajectories of

avoidance coping in youth with type I diabetes.

Chapter 7: Results

166

Table 7.16: Forced-entry multiple regressions of avoidance coping

Year 1 B

Std. Error

B Beta

t Sig.

Constant 11.49 4.12 2.79 0.01

Responsibility 0.11 0.06 0.18 1.87 0.06


Agreeableness -0.05 0.05 -0.10 -1.04 0.30

R2 = 0.11, F (3, 100) = 4.27, p = 0.01*

Year 2 B

Std. Error

B Beta

t Sig.

Constant 18.77 3.53 5.32 0.00

Responsibility -0.14 0.06 -0.24 -2.51 0.01*


Agreeableness -0.04 0.05 -0.08 -0.85 0.40

R2 = 0.12, F (3, 96) = 4.49, p = 0.01**

Year 3 B

Std. Error

B Beta

t Sig.

Constant 22.32 3.46 6.45 0.00

Responsibility -0.05 0.05 -0.10 -1.01 0.31

Emotional regulation -.012 0.04 -0.29 -3.03 0.00**

Agreeableness -0.14 0.05 -0.25 -2.56 0.01*

R2 = 0.18, F (3, 94) = 6.85, p = 0.00**


Summary of regression analyses

In summary, personality traits appear to influence several self-management

outcomes in youth with type I diabetes, independent of other psychosocial

predictors. Table 7.17 displays the five-factor model personality traits and

highlights those that that were significant independent predictors of

outcomes.

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167

Table 7.17: Summary of regression analyses

HbA1c BGL

tests Hypo Dep Anx Stress Accept Avoid

Conscientiousness

Agreeableness

Emotional

regulation

Openness to

experience

Extraversion

Specifically, conscientiousness predicted HbA1c scores and blood glucose

testing, emotional regulation predicted HbA1c, depression, anxiety, stress

and avoidance coping, agreeableness predicted HbA1c and avoidance

coping whilst openness to experience somewhat predicted stress and levels

of acceptance coping.

On the basis of these results, longitudinal analyses were undertaken to

examine whether baseline levels of these personality traits were associated

with longitudinal trajectories of these associated self-management outcomes.

Table 7.18 summarises the longitudinal analyses undertaken.

Table 7.18: Longitudinal analyses of personality and self-management

Personality trait Self-management trajectories examined

Conscientiousness HbA1c, Blood glucose testing

Agreeableness HbA1c, Avoidance coping

Emotional regulation HbA1c, Depression, Anxiety, Stress, Avoidance coping

Openness to experience Stress, Acceptance coping

Extraversion No associations tested

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168

Research Question 3

Do five-factor model personality traits predict trajectories of HbA1c,


stress, avoidance coping and acceptance coping over time?

Conscientiousness and HbA1c trajectories

A mixed design ANOVA using baseline tertile groups for conscientiousness

demonstrated no significant main effect of time on HbA1c values, F (2, 134)

=1.02, p= 0.36, but a significant between-groups effect of conscientiousness

on HbA1c values F (1, 67) = 4.74, p = 0.03. This denotes that, for the included

sample, glycaemic control did not change but that there were differences in

control between those in high and low conscientiousness groups.

There was a significant interaction between conscientiousness group and

time, F (2, 134) = 5.00, p = 0.01, which verifies that conscientiousness levels

were associated with improvement or deterioration of glycaemic control over

the three year period. Repeated tests of within-subjects contrasts

demonstrated that the interaction effect was significant between the first and

second years F (1, 67) = 6.78, p = 0.01. Between-group differences in

glycaemic control therefore significantly increased over this period.

Examination of the means for HbA1c (Table 7.19) demonstrated that

individuals in the lowest tertile of conscientiousness had worsening control

whilst individuals in the highest tertile group demonstrated improvements in

glycaemic control over time.

Table 7.19: Mean HbA1c levels for baseline conscientiousness groups


3rd year HbA1c

Low Conscientiousness

n = 39

8.6 (1.2) 9.1 (1.4) 9.2 (1.5)

High Conscientiousness

n = 30

8.5 (1.1) 8.4 (1.0) 8.3 (1.0)

Quartile points differ across longitudinal analyses due to FFPI-C T-Score conversions and small missing data

Results presented as mean and standard deviation

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169

To clarify the nature of changes in glycaemic control within each

conscientiousness group, separate repeated-measures ANOVAs were

conducted. This was undertaken to test if the changes in HbA1c within each

group were statistically significant. Results demonstrated a significant main

effect of time on HbA1c scores for the low conscientiousness group F (2, 76) =

5.35, p = 0.01. Pair-wise comparisons with Bonferroni adjustment

demonstrated that first year HbA1c values were significantly different from

second (p = 0.01) and third year (p = 0.03) HbA1c values in this cohort. This

indicates that individuals low in conscientiousness experienced significant

deterioration of glycaemic control over time.

In contrast, no significant changes in HbA1c values were found for the high

conscientiousness group F (2, 58) = 0.87, p = 0.42 which demonstrates that

high conscientiousness may protect against the worsening control that often

occurs during adolescence (Dabadghao et al., 2001).

These findings reveal that low levels of baseline conscientiousness may

predict statistically significant deterioration of glycaemic control over time. In

this way, low conscientiousness at time of diagnosis may present a risk for

poor long-term management of glycaemic levels. The figure below (Figure

7.4) depicts trajectories of glycaemic control for these groups over the course

of the three-year study.

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170

Figure 7.4: HbA1c values for baseline tertile groups of conscientiousness over three years

Agreeableness and HbA1c trajectories

A mixed design ANOVA comparing the upper and lower baseline tertiles of

agreeableness established no significant main effect of time on HbA1c

values, F (2, 140) = 0.69, p= 0.50, but a significant between-groups effect of

agreeableness on HbA1c values F (1, 70) = 11.02, p = 0.00. There was also a

significant interaction between agreeableness group and time, F (2, 140)

=3.44, p = 0.04. This verifies that differences in HbA1c values occurred

between groups over time. Tests of within-subjects contrasts substantiated

that the interaction effect was significant between the first and second years

F (1, 70) = 8.65, p= 0.00. Examination of the means (Table 7.20) indicated that

individuals in the lowest tertile of agreeableness had worsening glycaemic

control whilst individuals high in agreeableness showed improvement in

HbA1c from baseline.

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171

Table 7.20: Mean HbA1c levels for baseline agreeableness groups


3rd year HbA1c

Low Agreeableness

n = 38

8.9 (1.3) 9.3 (1.5) 9.2 (1.3)

High Agreeableness

n = 34

8.4 (1.4) 8.2 (0.9) 8.3 (1.1)



To test the statistical significance of changes in HbA1c within groups,

separate repeated-measures ANOVAs were employed. These demonstrated

no significant main effect of time on HbA1c scores for the low agreeableness

group, F (1.54, 56.93) = 3.10, p = 0.07. However, pair-wise comparisons with

Bonferroni adjustment showed that second year HbA1c values in this group

were significantly higher than first year values (p = 0.00). This provides

evidence that low agreeableness is a risk factor for deteriorating glycaemic

control over time. No significant main effect of time on HbA1c values was

found for the high agreeableness group F (2, 66) = 0.83, p = 0.44.This signifies

that glycaemic control was stable within this group over time. Figure 7.5

displays the mean HbA1c values for both the high and low agreeableness

groups over the course of the three-year study.

Together, these findings support the importance of agreeableness in

management of blood glucose levels during adolescence. Like individuals

low in conscientiousness, those low in agreeableness in our sample had

worsening glycaemic control over time.

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172

Figure 7.5: HbA1c values for baseline tertile groups of agreeableness over three years

Emotional regulation and HbA1c trajectories

The upper, middle and lower quintile groups for baseline emotional

regulation were compared to examine the role of this trait in long-term

glycaemic control. These groups were compared based on the curvilinear

relationship between emotional regulation and HbA1c scores.

A mixed design ANOVA revealed no significant main effect of time on HbA1c

values, F (2, 130)= 0.76, p= 0.47 and no significant interaction between

emotional regulation group and time, F (4, 130) =1.11, p = 0.36. However,

examination of the means (Table 7.21) indicated that there were differences

in glycaemic control between groups.

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173

Table 7.21: Mean HbA1c levels for baseline emotional regulation groups


3rd year HbA1c

Low Emotional regulation

n = 20

9.1 (1.4) 9.2 (1.6) 9.3 (1.4)

Moderate Emotional regulation

n = 28

8.2 (0.9) 8.4 (0.9) 8.4 (1.2)

High Emotional regulation

n = 21

9.0 (1.4) 9.1 (1.4) 8.6 (1.2)



Statistical analysis supported the existence of a significant between-groups

effect, F (2,65)= 4.02, p = 0.02. Pair-wise comparisons confirmed that the

HbA1c scores for the low emotional regulation group were significantly

different to the moderate emotional regulation group (p = 0.03) over the

course of the study. As can be seen in table 7.20, the individuals in the low

emotional regulation group had consistently worse glycaemic control than

those in the moderate emotional regulation group over the three years.

There were no other significant differences between emotional regulation

groups. This implies that those low in emotional regulation have persistently

elevated blood glucose levels which increase the risk of developing diabetic

complications (The Diabetes Control and Complications Trial Research

Group, 1993). The figure below (Figure 7.6) shows the mean HbA1c values

for the high, low and moderate emotional regulation groups over the course

of the research study.

In all, these findings confirm that the five-factor personality model traits of

conscientiousness, agreeableness and emotional regulation play a role in the

regulation of glycaemic levels over time. Specifically, low conscientiousness,

low agreeableness and low emotional regulation are risk factors for poor or

worsening glycaemic control. These findings have significant clinical

Chapter 7: Results

174

implications and demonstrate that clinicians need to take personality into

account when formulating diabetes treatment and management plans.

Figure 7.6: HbA1c values for baseline quintile groups of emotional regulation over three years

Conscientiousness and blood glucose testing trajectories

Results of a mixed-design ANOVA comparing baseline conscientiousness

tertile groups indicated no significant main effect of time on blood glucose

testing blood glucose testing within this cohort, F (1.64, 114.47) =2.05, p= 0.13.

Similarly there was no significant interaction between conscientiousness and

blood glucose testing over time, F (1.64, 114.47) = 0.30, p= 0.74. However, a

significant between-groups effect of conscientiousness on blood glucose

level testing was uncovered, F (1, 70) = 4.05, p = 0.05. These results verify

that higher conscientiousness was consistently related to greater frequency

of blood glucose tests over the course of the research study. Table 7.22

shows the means for the upper and lower tertile groups.

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175

Table 7.22: Mean number of blood glucose tests per fortnight for baseline conscientiousness groups

1st year BGT 2nd year BGT 3rd year BGT

Low Conscientiousness

n = 40

68.95 (25.96) 61.28 (23.09) 60.27 (27.90)

High Conscientiousness

n = 32

78.91 (51.17) 75.53 (31.73) 75.03 (26.99)



These results support prior investigations that have highlighted the role of

conscientiousness in determining diabetes-related self-care behaviour

(Christensen & Smith, 1995; Edwards, 1999; Marks, 2000; Szymborska-

Kajanek et al., 2006). Further research involving larger sample sizes could

help to model relationships and interactions between this trait, blood glucose

testing and glycaemic control over extended periods. Figure 7.7 plots these

scores over the three years of data collection.

Figure 7.7: Mean number of blood glucose tests per fortnight for baseline tertile groups of conscientiousness over three years

Chapter 7: Results

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Emotional regulation and depression trajectories

A mixed design ANOVA revealed no significant main effect of time on

depression within the upper and lower tertile groups for baseline emotional

regulation, F (1.81, 108.80) = 0.60, p = 0.54. No significant interaction was found

between emotional regulation and time, F (1.81, 108.80) = 1.47, p = 0.24 but a

highly significant between-groups effect was found, F (1, 60) = 8.78, p = 0.00.

These results demonstrate that individuals with low levels of emotional

regulation had consistently higher scores for depression over the three

years. Table 7.23 displays the mean depression scores for these groups

over the three-year study.

Table 7.23: Mean depression scores for upper and lower baseline tertiles of emotional regulation

1st year Dep 2nd year Dep 3rd year Dep


n = 36 10.76 (11.18) 8.81 (8.31) 7.50 (7.79)


n = 26 4.07 (4.68) 4.97 (5.39) 4.96 (7.54)



Whilst no significant within-group changes were found in this analysis,

inspection of the means for each group indicated that depression scores for

those low in emotional regulation were within the normal range for the

second and third years (refer to Table 4.1). This indicates that there was

some (albeit non-statistically significant) improvement of psychological

functioning within this group over time. It would be interesting to collect

further rounds of data on depression scores within these groups to examine

whether the changes within depression approached significance over longer

periods. Such results show that further research examining the role of

emotional regulation in determining long-term psychological responses in

youth with type I diabetes would be beneficial. Figure 7.8 graphs the

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177

trajectories of depression scores for the upper and lower baseline tertiles of

emotional regulation over the study period.

Figure 7.8: Mean depression scores for baseline tertile groups of emotional regulation over three years

Emotional regulation and anxiety trajectories

Results of a mixed-design ANOVA employing high and low baseline tertile

groups of emotional regulation demonstrated no significant main effect of

time on anxiety, F (1.80, 107.72) = 1.55, p = 0.22. Furthermore, no significant

interaction between emotional regulation and time was uncovered, F (1.80,

107.72) = 1.52, p = 0.22. These results demonstrate that there were no

significant changes in anxiety levels over time for either group. However, as

expected, a significant between-groups effect was found for emotional

regulation on anxiety, F (1, 60) = 2.40, p = 0.13. Table 7.24 shows that

individuals low in emotional regulation had consistently higher mean anxiety

levels over the three years.

As can be seen in the table below, individuals in the low emotional regulation

group had consistently higher levels of anxiety over time. Like depression,

there were changes in the experience of anxiety that was outside of the

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178

normal range for the low emotional regulation group (refer to Table 4.1).

Mean anxiety scores for this group moved from moderate DASS cut-off in the

first year, to mild in the second and then to normal in the third year. Again,

further rounds of data collection would be helpful to analyse this relationship

further.

Table 7.24: Mean anxiety scores for upper and lower baseline tertiles of emotional regulation

1st year Anx 2nd year Anx 3rd year Anx


n = 36 10.00 (10.42) 8.00 (5.68) 6.83 (6.24)


n = 26 5.73 (5.40) 6.00 (5.58) 6.15 (7.15)



Figure 7.9 illustrates the mean stress scores for the low and high emotional

regulation group over the three data collection periods.

Figure 7.9: Mean anxiety scores for baseline tertile groups of emotional regulation over three years

Chapter 7: Results

179

Emotional regulation and stress trajectories

The association between emotional regulation and stress followed a similar

pattern to depression and anxiety. A mixed-design ANOVA found no

significant main effect of time on stress, F (1.71, 102.85) = 1.29, p = 0.28, no

significant interaction between time and emotional regulation, F (1.71, 102.85) =

1.08, p = 0.34, but a highly significant between-groups effect, F (1, 60) =

10.64, p = 0.00. As table 7.25 reveals, individuals with low emotional

regulation had consistently higher levels of stress. Nevertheless, mean-levels

of stress within both groups were below the norm level proposed by the

DASS researchers (see Table 4.1) for each of year of data collection.

Table 7.25: Mean stress scores for upper and lower baseline tertiles

of emotional regulation

1st year Str 2nd year Str 3rd year Str


n = 36

13.24 (10.41) 12.11 (8.29) 10.44 (7.11)


n = 26

7.67 (6.13) 7.17 (6.49) 7.11 (5.18)



These findings demonstrate that the ability of youth with type I diabetes to

regulate their emotions is important in determining stress levels over time.

Hence, this personality trait could serve as a good predictor of general

psychological outcomes in youth with diabetes over time. Figure 7.10

displays the annual scores for stress within each emotional regulation group.

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180

Figure 7.10: Mean stress scores for baseline tertile groups of emotional regulation over three years

Emotional regulation and avoidance coping trajectories

Analysis of the longitudinal relationships between emotional regulation and

avoidance coping demonstrated no significant main effect of time on

avoidance scores, F (2, 116) = 0.22, p = 0.80, and no significant interaction

between time and emotional regulation, F (2, 116) = 0.06, p = 0.94. This

demonstrates that, for the included sample, avoidance coping did not change

either for the whole group or within individual groups. However, a significant

between-groups effect was found for emotional regulation on avoidance

coping, F (1, 58) = 6.51, p = 0.01. Examination of the means (Table 7.26)

illustrates that individuals in the low emotional regulation group relied more

on avoidance as a way of coping with the demands of their diabetes.

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181

Table 7.26: Mean avoidance coping scores for upper and lower baseline tertiles of emotional regulation

1st year Avo 2nd year Avo 3rd year Avo


n = 35

8.59 (3.34) 8.39 (3.60) 8.39 (3.76)


n = 25

6.73 (2.92) 7.07 (3.24) 6.50 (3.13)


Results presented as mean and standard deviation Means do not include excluded item

These results further emphasise the importance of emotional regulation in

the management of diabetes in youth. The differences between the low and

high emotional regulation groups are represented in Figure 7.11 below.

Figure 7.11: Mean avoidance coping for baseline tertile groups of emotional regulation over three years

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182

Agreeableness and avoidance coping trajectories

Longitudinal analyses demonstrated that levels of agreeableness were also

important in predicting avoidance coping over time. A significant between-

groups effect was found for agreeableness on avoidance, F (1, 67) = 5.83, p =

0.02, with individuals in the low agreeableness group reporting higher levels

of avoidance coping across all three years (Table 7.27). No significant main

effect of time on avoidance was found, F (2, 134) = 0.72, p = 0.49 and there

was no significant interaction between time and agreeableness, F (2, 134) =

0.26, p = 0.77.

Table 7.27: Mean stress scores for upper and lower baseline tertiles of agreeableness

Avoid year 1 Avoid year 2 Avoid year 3

Low Agreeableness

n = 38

8.38 (3.50) 7.72 (3.61) 7.82 (3.56)

High Agreeableness

n = 31

6.56 (2.47) 6.50 (3.13) 6.47 (2.90)


Results presented as mean and standard deviation Means do not include excluded item

These results denote that individuals with diabetes who are cooperative and

compassionate are less likely to rely on avoidance coping strategies over

time. Further research in this area could focus on specific variables that may

underpin this relationship. Figure 7.12 displays the changes in mean-levels

of avoidance coping within the upper and lower tertile groups over the course

of the study.

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183

Figure 7.12: Mean avoidance coping scores for baseline tertile groups of agreeableness over three years

Openness to experience and acceptance coping trajectories

Finally, longitudinal analysis demonstrated no significant main effect of time

on acceptance coping, F (1.84, 124.75) = 0.28, p = 0.76 but a significant

interaction between openness to experience and acceptance coping over

time, F (1.84, 124.75) = 3.11, p = 0.05. A significant between-groups effect was

also found for openness on acceptance, F (1, 68) = 5.27, p = 0.03. These

results demonstrate that levels of acceptance coping were significantly

different between individuals in the low and high baseline tertile groups for

openness and that these differences increased over time. As can be seen in

Table 7.28, acceptance coping increased in the high openness to experience

group over time.

A within-groups, repeated-measures ANOVA demonstrated that the changes

within the high openness to experience group approached significance, F

(1.65, 54.44) = 2.80, p = 0.07. In contrast, no significant changes in acceptance

coping were found for the low openness to experience group F (2, 70) = 0.75,

p = 0.48.

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184

Table 7.28: Mean acceptance coping for upper and lower baseline tertiles of openness

1st year Acc 2nd year Acc 3rd year Acc

Low Openness

n = 36

23.85 (3.98) 23.19 (4.42) 22.68 (4.19)

High Openness

n = 34

24.06 (5.16) 24.94 (3.84) 25.88 (3.11)

Quartile points differ across longitudinal analyses due to FFPI-C T-Score conversions

and small missing data


These results suggest that a person who craves new experiences may be

more likely to come to terms with a diagnosis of type I diabetes over time.

Figure 7.13 graphs the mean scores on acceptance coping for these groups

over the three years of data collection.

Figure 7.13: Mean acceptance coping scores for baseline tertile groups of openness to experience over three years

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185

Summary of longitudinal analyses

Overall, the obtained results demonstrate that five-factor model personality

traits played an important role in determining longitudinal trajectories self-

management outcomes in the study sample. The findings presented here

confirm that baseline personality scores had a continuous effect on certain

management outcomes and these effects where, in some cases, cumulative

over time. This indicates that, at times, personality traits may be a better

predictor of long-term outcomes than other psychosocial predictors that have

previously received attention in the diabetes literature.

Specifically, low levels of emotional regulation, agreeableness,

conscientiousness and openness to experience appear to predict poor self-

management of type I diabetes over time. The relationships between these

personality traits and self-management outcomes are summarised below:

Individuals scoring low on the trait of conscientiousness engage in

consistently less blood glucose testing and have worsening glycaemic

control over time.

Individuals scoring low on emotional regulation tend to have

consistently poorer psychological profiles with higher rates of

depression, anxiety, stress and avoidance coping along with

consistently poorer management of glycaemic levels.

Individuals scoring low on agreeableness consistently rely on

avoidance to cope with diabetes and have worsening glycaemic

control.

Finally, individuals scoring low on openness to experience are less

likely to accept their diabetes whereas those high on this trait appear

to come to accept their condition over time.

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These findings are significant as they underline the importance of personality

in determining self-management of type I diabetes in youth over time.

Reported results show there are specific personality traits that may serve as

red flags for poor psychological wellbeing and deterioration of glycaemic

control during adolescence, and by understanding this, clinicians can identify

youth at risk and provide tailored support that ‘matches’ the individual’s

personality. If these results are replicable, this information could provide the

groundwork for future studies examining the potential of personality-based

interventions in diabetes care. Critically, this could reduce levels of negative

affect within this population and diminish the likelihood of developing

devastating complications. This underlines the importance of further research

within this area

Chapter 8

Conclusions and Future Directions

The present investigation underlines the importance of personality in

determining self-management of type I diabetes in youth over time. This

research offers a significant contribution to current knowledge and the

obtained results merit detailed analysis and interpretation in line with findings

reported from prior studies in this domain. Accordingly, this chapter provides

a summary of the main strengths, key findings, limitations and potential

clinical implications of this study. Particular emphasis is given towards

identifying future avenues of inquiry and providing theoretical explanations

for the obtained results.

Study strengths

When compared to other studies in this area (Skinner et al., 2002; Vollrath et

al., 2007), this research has several strengths that make it one of the most

comprehensive investigations of the role of personality traits in young

people’s glycaemic control and management of type I diabetes. These

strengths add weight to the research findings and provide confidence in the

obtained results.

A major strength of this study is that it is the largest study to employ the five-

factor model as a framework for measuring personality in adolescents with

type I diabetes. The only other study to include all five-factor model traits in

analyses included a sample of 64 juveniles (Vollrath et al., 2007), practically

half the number recruited for the current study (n = 104). Whilst the study

conducted by Skinner et al (2002) consisted of 358 participants, this project

Chapter 8: Conclusion and Future Directions

188

did not specifically target youth and included participants up to 30 years of

age. In all, this means that the current study provides the clearest

representation of relations between five-factor model traits and diabetes self-

management during adolescence. Crucially, the organisational structure of

the five-factor personality model allows the research findings from this study

to be easily contrasted with results from prior studies in this domain.

A second strength of this study is that the research employed a range of

psychosocial, behavioural and physiological measures to index the quality of

participants’ diabetes self-management. As highlighted previously, a

limitation of diabetes research has been that studies have not utilised

multiple measures to operationalise this construct. Prior research in the area

of personality has often relied solely on measures of glycaemic control

(Vollrath et al, 2007) or a small number of adherence behaviours (Lane et al,

2000). By utilising a bio-psychosocial model of health, the current research

builds on the work of others who have endeavoured to conceptualise a

broader view of diabetes self-management in research (Flinders Human

Behavioural & Health Science Unit, 2006; Skinner et al., 2002).

A final strength of this study is that it examined prospective associations

between five-factor personality traits and markers of diabetic self-

management. Review of the literature reveals that the majority of personality

investigations in type I diabetes have been cross-sectional in nature (Lane et

al., 2000; Marks, 2000; Skinner et al., 2002). Indeed, even the results

presented by Vollrath and colleagues (2007) are cross-sectional as these

researchers created aggregate scores from repeated measurements of

personality and glycaemic control. The only longitudinal study prior to the

current research involved retrospective comparisons between personality

and self-management outcomes (Brickman et al, 1996), and this

methodology creates difficulties when making casual claims. Hence, the

current study is an important first as repeated prospective measurements

over multiple time points allows researchers to analyse temporal changes in

outcomes and predict future results.


189

Together, these points indicate that the design and methodology of the

current study represents a gold standard in diabetes-personality research to

date. This shows that the findings of this research deserve attention from

fellow researchers and clinicians working in the field of paediatric type I

diabetes. There were several key outcomes from the current research that

replicate and extend upon prior research results (Lane et al., 2000; Marks,

2000; Skinner et al., 2002; Vollrath et al., 2007) and these could have

significant implications for theory and practice. The following section outlines

the key findings of the current research.

Summary of key findings

The present study provides an abundance of information about personality

and self-management of type I diabetes in Australian youth. Specifically,

there are five key findings that deserve attention. These findings are listed

below and are discussed in detail in the following pages of this thesis.

Key finding #1: Five-factor model personality traits serve as significant

and independent predictors of a variety of self-management outcomes

in youth with type I diabetes.

Key finding #2: Low conscientiousness in youth with diabetes is a risk

factor for sub-standard blood glucose testing, sub-optimal glycaemic

control and worsening glycaemic control over time.

Key finding #3: Low emotional regulation is a risk factor for

consistently poor psychological wellbeing and sub-optimal glycaemic

control. High emotional regulation may also increase the risk of sub-

optimal glycaemic control.

Key finding #4: Low agreeableness is a risk factor for avoidance

coping, sub-optimal glycaemic control and worsening glycaemic

control over time.


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Key finding #5: Low openness to experience is related to less

acceptance of diabetes over time.

Key finding #1: Five-factor model personality traits serve as significant and

independent predictors of a variety of self-management outcomes in youth

with type I diabetes.

The major finding of this research is that personality plays a central role in

self-management of type I diabetes in youth. Correlational and regression

analyses over a period of three years indicate that specific five-factor model

personality traits (conscientiousness, agreeableness, emotional regulation

and openness to experience) can predict a diverse range of outcomes

including glycaemic control, blood glucose testing, depression, anxiety,

stress and coping. These findings are generally in line with the postulated

hypotheses and support past research involving individuals with type I

diabetes (Brickman et al., 1996; Chatzialexiadi et al., 2005; Edwards, 1999;

Gilbert, 1992; Lane et al., 2000; Marks, 2000; Skinner et al., 2002; Vollrath et

al., 2007).

Importantly, results from the current study demonstrate that five-factor model

traits may work in concert to predispose an individual towards poor self-

management. For example, conscientiousness, agreeableness and

emotional regulation were all significant independent predictors of glycaemic

control in the final year of data collection. This suggests that the impact of

one maladaptive personality attribute could be compounded by another

maladaptive attribute. Alternatively, adaptive attributes could help individuals

to compensate for difficulties in other areas. This demonstrates that future

research should investigate the additive effects of personality traits and

whether there are combinations of traits or specific personality profiles that

predict poor self-management of type I diabetes. Indeed, the impact of

personality on self-management may be greater than reported within this

thesis if personality factors interact in this cumulative fashion.


191

Study results also demonstrate that personality may play a stronger role in

determining self-management outcomes than traditional predictor variables

such as age, gender, duration of diabetes or responsibility for diabetes care

(Anderson et al., 1990; Chisholm et al., 2007; Hepburn et al., 1994).

Statistical analyses showed that these demographic factors were often non-

significant predictors of self-management outcomes when entered into

hierarchical regression models along with personality traits. These results

are novel and warrant further attention.

Whilst prior research involving youth with diabetes has shown that

personality and temperament variables can exert their effects on self-

management outcomes independent of demographic and treatment factors

(Vollrath et al, 2007; Weissberg-Benchell & Glasgow, 1997), this appears to

be the first time that personality factors have predicted more of the variance

in outcomes than traditional demographic factors within this population

(Skinner et al., 2002; Vollrath et al., 2007). However, similar results have

been reported in different cohorts. In their study involving adults with type I

diabetes, Lane and colleagues (2000) included age, sex and duration of

diabetes as covariates in their regression models and found that five-factor

model personality traits were stronger predictors of glycaemic control and

self-monitoring of blood glucose levels. Although this suggests that the

current results are valid, replication is required to confirm whether personality

truly plays a greater role in determining self-management than other

demographic factors of interest.

Key finding #2: Low conscientiousness in youth with diabetes is a risk factor

for sub-standard blood glucose testing, sub-optimal glycaemic control and

worsening glycaemic control over time.

Results confirm that conscientiousness plays a significant function in

determining the quality of diabetes management in young people with

diabetes. As predicted, there was a linear relationship between this trait and

factors related to the behavioural control of blood glucose levels. More


192

specifically, youth in this study scoring low on conscientiousness had

significantly higher HbA1c levels and were less rigorous with their blood

glucose testing. These findings are supported by a host of prior research that

has identified conscientiousness as an important factor related to treatment

adherence in chronic health conditions (Bogg & Roberts, 2004; Brickman et

al., 1996; Christensen & Smith, 1995; Friedman et al., 1995; Raynor &

Levine, 2009; Skinner et al., 2002; Vollrath et al., 2007).

Most critically, the current study builds on this past research by

demonstrating that low conscientiousness can predict worsening of

glycaemic management over time. Individuals with low conscientiousness at

study baseline showed significant increases in average HbA1c over the three-

year study period whereas those with high conscientiousness at baseline

maintained stable HbA1c levels. These results are ground-breaking and

indicate that the influence of conscientiousness on diabetes control may be

cumulative over time. If this long-term relationship was upheld over greater

periods (such as a decade) this would likely predict the development of

diabetic complications. Indeed, this trait has been retrospectively associated

with renal deterioration times in individuals with diabetes and this suggests

that such a relationship may exist (Brickman et al., 1996).

These results underline the worth of continued longitudinal research in this

area. By following the current sample over a longer period of time it would be

possible to determine whether the uncovered relationships between self-

management and conscientiousness are maintained through their adult life.

Adolescents tend to become more conscientious as they move into

adulthood (B. W. Roberts, Walton, & Viechtbauer, 2006), and it would be

interesting to see whether any observed increases in conscientiousness

were mirrored by changes in diabetic self-management or glycaemic control.

Key finding #3: Low emotional regulation is a risk factor for consistently poor

psychological wellbeing and sub-optimal glycaemic control. High emotional

regulation may also increase the risk of sub-optimal glycaemic control.


193

The present research highlights that emotional regulation plays an

instrumental, albeit complex, role in self-management of type I diabetes in

youth. Low levels of emotional regulation were related to poorer

psychological profiles as expected. However, the existence of a curvilinear

relationship between this trait and HbA1c is an original finding. Jointly, these

results show that emotional regulation deserves further attention in diabetes

research.

The relationship between emotional regulation and psychological wellbeing is

well documented. This personality trait is often described as a causal

predictor of depression, anxiety, stress and coping and there is typically

substantial overlap between this trait and such outcomes (Carver & Connor-

Smith, 2010; Clark et al., 1994; Kendler et al., 2006; Khan et al., 2005;

Watson et al., 1994). It is thus no surprise that individuals scoring low on

emotional regulation in the current sample reported higher rates of

depression, anxiety, stress and avoidance coping.

Of greater interest is the fact that baseline levels of emotional regulation

were related to consistently higher rates of negative affect over the course of

the three-year study. The temporal stability of this relationship supports

research positing a genetic component of emotional regulation that predicts

risk for longstanding negative affect and psychopathology (Kendler et al.,

2006; Lahey, 2009). Whilst continued longitudinal research is needed to

support this claim, such results indicate that this trait may predict long-term

psychological outcomes within this specific population. Future research

should test this hypothesis and also examine whether targeted interventions

can promote improvements in psychological wellbeing for individuals with

diabetes who score low on this trait.

Sustained work is also needed to better understand the role of emotional

regulation in determining glycaemic control. Although the curvilinear

relationship between emotional regulation and HbA1c reported here provides

an explanation for previously conflicting results (Chatzialexiadi et al., 2005;


194

Gilbert, 1992; Lane et al., 2000; Taylor et al., 2003; Vollrath et al., 2007;

Weissberg-Benchell & Glasgow, 1997), there is still much to be clarified.

Researchers need to understand how these relationships are upheld which

can only be done by replicating these findings and testing theoretical

explanations for the obtained results. At any rate, the current study highlights

that theorists need to consider the relationship between this personality trait

and management of blood glucose levels in greater depth.

Theoretically, the association found between low emotional regulation and

poor glycaemic control makes sense. Results from the current study indicate

that low levels of emotional regulation contribute to consistently poor

glycaemic control over time and this fits with prior work that has linked this

trait to self-care in individuals with diabetes (Chatzialexiadi et al., 2005;

Gilbert, 1992). Furthermore, self-care may interact with psychological

wellbeing in ways not tapped by the measures and analyses utilised in this

study. As mentioned, low levels of emotional regulation can predict co-

morbidity of mental and physical health problems (Lahey, 2009), and this

could contribute to a relationship between low emotional regulation and poor

management of blood glucose. Critically, if such relationships remain stable

over long periods it would be likely that complications would develop for

those very low in emotional regulation.

The relationship between high emotional regulation and poor glycaemic

control also makes some substantive sense. As highlighted previously,

individuals high in emotional regulation often discount health risks (Vollrath et

al., 1999; D. J. Wiebe, Alderfer, Palmer, Lindsay, & Jarrett, 1994) and this

may have contributed to the results of the current study. At each testing

occasion, individuals with very high levels of emotional regulation had poorer

HbA1c results and this suggests that these individuals were deficient in some

aspect of their self-care. Importantly though, high emotional regulation at

baseline was not a significant predictor of control over the course of the

three-year study and HbA1c scores trended downwards for this baseline

group over time. This suggests that further follow-up is needed to determine


195

whether high levels of this trait serve as a stable predictor of long-term

glycaemic control within this cohort.

Together, these results provide preliminary support for a curvilinear

relationship between emotional regulation and glycaemic control. Such

findings are supported by the work of Brickman, Yount, Blaney et al (1996)

who demonstrated that extreme scores on emotional regulation are

predictive of long-term diabetic complications. These results also mirror the

classic Yerkes-Dodson law (1908); where optimal performance is predicted

by moderate levels of emotional arousal. Given these strong theoretical ties

and the repeated measurements employed, the validity of this curvilinear

relationship appears relatively certain. Nevertheless, these results require

replication and the mechanisms underlying this association still remain

unclear.

Whilst the current study has uncovered a non-linear relationship between

emotional regulation and glycaemic control, it has not successfully identified

factors that mediate this relationship. Contrary to hypotheses and prior

research (Chatzialexiadi et al., 2005; Gilbert, 1992), emotional regulation

showed no relationship (either curvilinear or linear) with the included

measures of diabetes self-care. Furthermore, the design and sample size of

the research study did not allow for any detailed analysis of factors that

mediated relationships between personality and self-management outcomes.

This suggests that future research should use more sensitive measures of

self-care and should employ statistical methods such as structural equation

modelling to answer these questions.

Finally, longer-term follow-up is required to determine whether mean-level

and intra-individual changes in emotional regulation are related to significant

improvement or decline in glycaemic control. Research suggests that

individuals generally display increases in emotional regulation from ages 20

to 40 (B. W. Roberts, Walton, & Viechtbauer, 2006) and it would be

worthwhile to track changes in this trait along with glycaemic control in the


196

current sample. If the long-term stability of emotional regulation is high within

this cohort, we may expect those particularly low or high in emotional

regulation at baseline to exhibit signs of diabetic complications later in life.

Key finding #4: Low agreeableness is a risk factor for avoidance coping, sub-

optimal glycaemic control and worsening glycaemic control over time.

The current investigation indicates that agreeableness also substantially

contributes to the quality of diabetes self-management in youth. As

hypothesised, this trait was a significant independent predictor of glycaemic

management. Furthermore, this trait was related to the coping strategies that

the study participants employed. These results suggest that agreeableness

during adolescence may predict both long-term physical and psychosocial

outcomes for people with diabetes.

The negative linear relationships found between agreeableness and HbA1c

are supported by prior research involving individuals with diabetes (Auerbach

et al., 2002; Szymborska-Kajanek et al., 2006; Vollrath et al., 2007). In their

study involving youth with diabetes, Vollrath and colleagues (2007) reported

that high scores on agreeableness were significantly related to lower HbA1c

results when averaged over three time-points. The replication of these

results provides further evidence for the validity of this association.

Unlike Vollrath et al’s (2007) research, the current study found that

agreeableness remained a significant independent predictor of glycaemic

control when entered into hierarchical regressions with other personality

factors. This difference between study findings is notable and is likely due to

the larger sample size of the current study and the associated increases in

statistical power. Such results underline the importance of continued

research in this area utilising larger study cohorts.

The current study also highlights the importance of agreeableness in

determining deterioration of glycaemic control over time. Differences in

HbA1c scores between those rated high and low in agreeableness at baseline


197

increased significantly over the study period with individuals low in

agreeableness showing worsening glycaemic control over the three-year

study period. Like conscientiousness, it appears that this trait may have a

cumulative effect on glycaemic management over time. If this trajectory

continues, it is likely that those low in agreeableness at study baseline would

develop diabetes complications. Clearly, follow-up research is needed to test

whether the strength of the negative association between agreeableness and

HbA1c continues to increase over time. Research shows that individuals

become less agreeable as they move into adulthood (B. W. Roberts, Walton,

& Viechtbauer, 2006), and these mean-level changes in personality have an

unknown impact on glycaemic control at this stage.

Notably, there were no non-linear effects of agreeableness on blood glucose

levels within the study sample over the three-year study period. Such

findings disagree with research that has suggested a negative impact of

agreeableness related characteristics upon glycaemic control (Helgeson et

al., 2007; Hyphantis et al., 2005; Lane et al., 2000). This requires further

investigation, as the personality measure used for the current study did not

allow analyses at a facet level. This makes it impossible to confirm or

disprove the reported positive association between the agreeableness facet

of altruism and HbA1c reported by Lane et al (2000). Based on the current

results, only a negative linear relationship between agreeableness and HbA1c

scores can be confirmed.

Finally, the obtained results indicate that agreeableness may influence

youths’ selection of coping strategies. Significant correlations between

agreeableness and avoidance coping were uncovered for the first and third

years of matched data and mixed design analyses of variance demonstrated

a significant between-groups effect for those high and low on agreeableness

over time. These results suggest that individuals low on agreeableness

consistently rely on avoidance as a coping mechanism in times of stress.

Prior work in this area supports the validity of this research (Carver &

Connor-Smith, 2010; Connor-Smith & Flachsbart, 2007; Hambrick & McCord,


198

2010; Kidachi et al., 2007; Lawson, Bundy, Belcher, & Harvey, 2010). This

indicates that agreeableness could be used to predict poor coping with

diabetes within younger cohorts.

Overall, high levels of agreeableness appear to be a protective factor in

youth with type I diabetes. However, further work is needed to uncover

variables that underpin the relationships reported here. Targeted

interventions could stem from uncovering specific mediating factors and this

underlines the need for further work in this field. As suggested earlier, levels

of social support and the therapeutic relationship between doctor and patient

could be influenced by the trait of agreeableness and this suggests that links

between these factors and personality need to be tested. Similarly,

researchers should focus on whether coping styles mediate relationships

between this trait and glycaemic control.

Key finding #5: Low openness to experience is related to less acceptance of

diabetes over time.

The final key finding from this research is that openness to experience can

impact upon a young person’s management of diabetes. Whilst prior

investigations involving this trait have delivered mixed results (Edwards,

1999; Lane et al., 1988; P. J. Lustman et al., 1991; Marks, 2000; Vollrath et

al., 2007), the current study uncovered significant associations with

acceptance coping over repeated testing occasions. These results are in line

with hypotheses and are supported by prior research on coping (Carver &

Connor-Smith, 2010; Connor-Smith & Flachsbart, 2007; Lawson et al.,

2010).

Results demonstrate that high openness to experience is related to greater

use of acceptance coping strategies in youth with diabetes. Furthermore,

longitudinal analyses indicate that the between-group differences for those

scoring high or low on openness to experience at baseline significantly

increased over the course of the study. Longitudinal increases in acceptance


199

coping for the high openness group approached statistical significance and

this suggests that high openness leads to greater adaptation to diabetes in

adolescence.

These findings make sense. Prior research and theory suggests that people

high in openness tend to accept stressful experiences, framing difficult

situations as a challenge rather than a threat (Carver & Connor-Smith, 2010;

Connor-Smith & Flachsbart, 2007; Lawson et al., 2010). Furthermore, there

appears to be some overlap between this trait and definitions of

psychological hardiness (Smith & Williams, 1992), which is likely related to

acceptance of uncontrollable circumstances such as chronic disease.

These results support the utilisation of a biopsychosocial model of diabetes

self-management in personality-health research and highlight the need for

further work in this area. The current results show that personality can

influence a range of self-management factors other than glycaemic control

and these should be recognised as important outcomes in their own right. By

focusing more on the relationship between openness to experience and

acceptance coping, researchers may be able to identify factors that

contribute to a young person’s quality of life when living with a chronic

disease. Further work employing larger sample sizes and mediational

analyses could help to do this.

Limitations

Whilst the results reported above appear promising, health professionals

must understand the limitations of this study when interpreting these findings.

The size of the study sample, the probability of type I error, rates of missing

data and the tendency towards non-significant relationships in the first year

of data collection should all be taken into account before making treatment or

policy decisions based on this research. Hence, these limitations are critically

discussed below.


200

It is first important to recognise that the reported findings are based on a

comparatively small sample of participants (n = 104). Whilst this study is the

largest of its kind, the sample size was impacted by the limited personnel

available for data collection and the documented difficulties of recruiting

youth for research (Croft et al., 1984; Gattuso et al., 2006; Liese et al., 2008;

H. Roberts, 2000). Consequently, complex statistical modelling of the

completed measures was not undertaken.

It would therefore be worthwhile to conduct a larger-scale study to replicate

the results reported here. A study of this kind would allow for more complex

statistical analyses that could provide greater detail about the temporal

relationships between personality and self-management of diabetes. Such

research should also investigate interactions between personality,

demographic variables and self-management outcomes as prior research

suggests that mediating and moderating effects between these variables

exist (Cameron, Young, & Wiebe, 2007; Skinner et al., 2002).

Secondly, it is important to recognise that a large number of statistical

analyses were carried out on the current study data. Multiple comparisons

increase the risk of type I error (Field, 2009) and this indicates that there is a

small risk that some of the reported significant results capitalised on chance

relationships. Nevertheless, Bonferronni adjustments were used which

minimises the likelihood of type I error and the reported directions of

relationships were largely consistent over the three years of data collection.

This suggests that the results can reasonably be presumed valid and

significant.

Third, the missing quarterly data collected as part of the DRAT study was

beyond acceptable limits. Although, this information could have provided

greater knowledge on associations between personality and self-

management over time, basing a study on this information would be

imprudent due to the potential for sample bias. In contrast, the annual data


201

collections demonstrated excellent completion rates, which indicate that

these data are valid and unbiased.

Finally, there was a lack of significant relationships between personality

variables and self-management outcomes in the first year. Whilst these

results are not strictly a limitation of the study, they are puzzling and require

explanation. Importantly, the lack of significant relationships does not appear

to be due to the different methods employed to collect personality data as the

five-factor model variables showed high temporal stability within participants.

The most plausible explanation for the obtained results is that personality

truly has a cumulative effect over time. Psychological and physical health

issues often develop over long periods (Caltabiano et al., 2002), and it may

take time for the influence of personality to be seen. Indeed, poor health

behaviours must usually be present over long periods to have any real

consequences for the individual. This indicates that we should expect

baseline personality scores to be related to progressive improvement or

decline in outcome variables over time. For the current study, this result was

upheld. In fact, significant differences in self-management outcome scores

were often present for the high and low baseline trait groups in this study.

This suggests that underlying relationships were present in the first year but

these failed to reach significance due to reduced variance in outcomes

measures.

Another explanation for these results could be related to sample size and

measurement error. The current sample was relatively small and it may be

that this did not provide enough power to uncover significant results in the

first year. Measurement error may have also been high at the first testing

occasion as participants were unfamiliar with the study procedures. This

combination may have influenced first year results reducing the strength, and

subsequently, the significance of associations between personality and self-

management outcomes.


202

Overall, this suggests further research is needed to explain results from the

first year of data collection. Researchers should replicate this study to

determine whether similar results are achieved. Furthermore, it would be

worthwhile to follow-up with the current sample to determine whether

association between personality and self-management continue along the

trajectories uncovered here. Such work could pave the way for focused

interventions to assist youth with type I diabetes.

Avenues for intervention

Whilst the above-reported results are preliminary, it is interesting to consider

what these would mean for intervention. Such findings suggest that clinicians

need to be conscious of the personality traits of their young patients and

apply methods of treatment that suit these individuals. Whilst personality-

testing of patients is unlikely (and unnecessary), a greater knowledge of the

relationships between personality and diabetes self-management could help

doctors and diabetes educators to take a more proactive approach towards

treating their patients. Tailored treatments may be particularly successful.

For example, clinicians working with youth who show signs of low

conscientiousness could focus on strategies to improve organisation, time-

management and planning and should implement strategies to increase

frequency of glycaemic testing. To protect against the risk of worsening

glycaemic management, clinicians could also emphasise the benefits of

short-term glycaemic control rather than highlighting negative long-term

outcomes.

When treating patients who show signs of low agreeableness, health care

workers should work to gain the trust of their patients and make them feel

involved in any decision-making processes. Reliance on avoidance as a

mechanism for coping with diabetes should be discouraged and strategies

for healthy conflict resolution should be promoted. By encouraging the

patient to take an active role in the management of their diabetes, clinicians


203

may improve the therapeutic relationship and foster optimal glycaemic

control.

If working with youth low in emotional regulation, doctors and educators

should attempt to promote active coping and reduce negative affect.

Referrals to psychological services may be necessary, particularly as

relationships between glycaemic control and negative affect may be

reciprocal in nature. These individuals should be followed closely over time,

as worsening glycaemic control within this group is likely. Reliance on

avoidance as a coping strategy should be prevented and alternative

approaches emphasised.

When treating youth high in emotional regulation, clinicians may need to

educate about the severity and seriousness of long-term diabetes

complications and also encourage patients to focus on the short-term

advantages of optimal glycaemic control.

Finally, clinicians treating youth low in openness to experience should try to

counsel their patients to accept their diabetes and to incorporate this

experience into their life.

Hopefully, this investigation will lay the groundwork for continued research

into the long-term role of personality in diabetes management that can lead

to evidence-based treatment approaches such as those mentioned above.

Whilst it is unlikely that personality can be altered, clinicians may provide

patients with specialised treatment that could help them to deal with aspects

of their personality. This speaks to the utility of personality research to inform

actual clinical practice and underlines the importance of the current research.

In overview, this research highlights the importance of personality in youths’

self-management of type I diabetes. It also underlines the utility of the five-

factor model of personality integrate and extend upon current knowledge

regarding the role of personality in health. Future research should look to


204

replicate the results reported here, examine these relationships over longer

time periods and posit mediating or moderating pathways between

personality and long-term management of diabetes in young people. By

replicating this study with a larger sample, subtle relationships may be

uncovered which could lead to greater knowledge and specific interventions

aimed at helping youth at risk for poor self-management of diabetes. It would

also be worthwhile to follow the current sample over a longer period of time

to examine whether the prospective links between personality and self-

management outcomes continue along these same trajectories.

.

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List of Appendices

Appendix 1: Newspaper Recruitment Advertisement .................................. 233

Appendix 2: Recruitment Poster ................................................................. 234

Appendix 3: DRAT Information Sheet and Informed Consent Form ........... 235

Appendix 4: Snowball Letter ....................................................................... 237

Appendix 5: Personality Study Information Sheet and Informed Consent Form ...................................................................................... 238

Appendix 6: FFPIC Instructions .................................................................. 240

Appendix 7: Excluded Measures ................................................................. 241

Appendix 8: Personal Information Sheet ..................................................... 242

Appendix 9: Annual Data Collection Form .................................................. 248

Appendix 10: Quarterly Review Form ......................................................... 255

Appendix 11: Diabetes Family Responsibility Questionnaire ...................... 258

Appendix 12: Depression Anxiety Stress Scale .......................................... 260

Appendix 13: DASS 21 Normative Sample Means and Standard Deviations by Sex .................................................................................... 262

Appendix 14: DASS Z Score Conversions .................................................. 263

Appendix 15: Coping with a Disease Inventory ........................................... 264

Appendix 16: Thank You Letter for Postal Participation .............................. 266

Appendix 17: List of Questionnaires for DRAT Postal Participation ............ 267

Appendix 18: Blood Collection Instructions for Participation by Post .......... 268

Appendix 19: Thank You Card .................................................................... 269

Appendix 20: DRAT Newsletter .................................................................. 270

Appendix 21: UWS Ethics Approval ............................................................ 274

Appendix 22: HNE Ethics Approval Letter .................................................. 277

Appendix 23: Missing Data ......................................................................... 279

Appendix 24: CODI Avoidance Inter-item Correlation Matrix ...................... 281

232

Appendix 25: Item-total Statistics for CODI Avoidance ............................... 282

Appendix 26: Exploratory Factor Analysis of CODI Avoidance ................... 283

Appendix 27: Baseline Descriptives for Study Variables ............................. 284

Appendix 28: Frequency Distributions ........................................................ 286

Appendix 29: P-P Plots ............................................................................... 288

Appendix 30: Bivariate Scatterplots ............................................................ 292

Appendix 31: Test-retest correlations for BGL Tests Including Outlier ........ 295

Appendix 32: Correlations for BGL Testing and HbA1c Including Outlier ... 296

Appendix 33: Correlations for BGL Testing and HbA1c Excluding Outlier .. 297

Appendix 34: Participants Falling into DASS Depression Severity Categories ............................................................... 298

Appendix 35: Table of Correlations ............................................................. 299

Appendix 36: Hierarchical Regressions ...................................................... 301

Appendix 1: Newspaper Recruitment Advertisement

233

Does your child have diabetes? Ever wondered why some young people, aged 8 to 19 years, seem to manage their diabetes better than others? We have too. If you are interested in helping us answer this question we would like to invite you and your family to be involved in an exciting new study being conducted by University of Western Sydney and The University of Sydney. To find out more about this study please contact Dr Jane Overland on 02 9515 5930 or [email protected]

Diabetes Research into

Adolescent Transitions

Appendix 2: Recruitment Poster

234

Appendix 3: DRAT Information Sheet and Informed Consent Form

235

Diabetes Research into Adolescent Transitions

Diabetes Research into Adolescent Transitions Project Information Sheet

Why is it that some people with type 1 diabetes are better able to keep their blood glucose levels as close to normal as possible while others struggle? This is a question that is currently being studied by a team of researchers from The University of Western Sydney, The University of Sydney, The Diabetes Centre at Royal Prince Alfred Hospital and Novo Nordisk Pharmaceuticals. The study will involved some 400 young people with type 1 diabetes, living in NSW, aged 8-18, and their families and will focus on the personal, family and environmental factors that may affect diabetes control. Children and young people and their families will be followed for three years so that we can determine what factors have most effect on the management of diabetes. If you agree to participate, one of the research team will visit you once a year. At this meeting, the child or young person with diabetes will be asked to allow a capillary blood sample to be taken for analysis of the blood glucose level and to complete a number of questionnaires relating to their knowledge of diabetes and how they are coping with its effect on their life. We believe that these questionnaires would take perhaps 1 ½ hours to complete. Parents will also be asked to complete some questionnaires relating to their knowledge of diabetes and how they are managing their child’s diabetes. This should take no more than 20 minutes to complete. As well, families (or the child/young person) will be contacted very few months by telephone to gather information on the child’s diabetes control. This would be a very brief call. We must emphasise that your access to services will not be affected in any way by whether you choose to participate in this project or not. You may also change your mind about participating in the project at any time. You should also know that all your responses will be confidential and no names will be used in any reports coming from the project. Should you have any questions about the project please do not hesitate to contact Assoc. Prof. Christine Johnston on 02 4736 0782, Dr Lorraine Smith on 02 9036 7079 or Dr Jane Overland on 02 9515 5930. This study has been approved by the University of Western Sydney Human Ethics Research Ethics Committee. If you have any complaints or reservations about the ethical conduct of this research, you may contact the Ethics Committee through the Ethics Research Officer (Tel: 02 4736 0883). Any issues you raise will be treated in confidence and investigated fully, and you will be informed of the outcome. Thank you for considering this request. We believe that this is a very important project as it will identify ways in which those living with type 1 diabetes could be better supported to ensure that they achieve effective self-management. If you decide to participate, please sign the attached consent form and return it in the enclosed envelope.

Assoc. Prof. Christine Johnston Dr Lorraine Smith Dr Jane Overland

University of Western Sydney The University of Sydney Diabetes Centre, RPAH

Appendix 3: DRAT Information Sheet and Informed Consent Form

236

D iabetes Research into Adolescent Transitions

DIABETES RESEARCH INTO ADOLESCENT TRANSITIONS

Consent to complete additional scales in the project:

If you are willing for you/your child to complete the additional scale in the Diabetes

Research into Adolescent Transitions Project please complete the following:

We understand that:

· I/my child will be asked to complete two additional questionnaires;

· Our names and all identifying information will be removed and that we will not be identifiable in the final report;

· My/my child’s responses on the Five Factor Personality Inventory – Children only will be given to Pro-Ed Inc. (the publishers of the scale), together with my/my child’s age and gender. No identifying information will be provided to Pro-Ed, and

· We may change our minds about participating in this part of the study at any time and that this will in no way affect our access to the services we are receiving or our involvement in the other part of the project.

Parent’s Name: _______________________________________________ Signed__________________________________________ Date________________ Child’s Name: ___________________________________________________ Signed__________________________________________ Date________________

Witness: Signature: Date:

Appendix 4: Snowball Letter

237

Diabetes Research into Adolescent Transit ions

Does your child have diabetes? Ever wondered why some young people, aged 8 to 19 years, seem to manage their diabetes better than others? We have too and so we have just started a three year study which we hope will help us answer that question. We hope that what we find out will make living with diabetes easier for young people and their families. But we need the help of young people with diabetes and their families to do this. The person who gave you this letter has already joined our study and has offered to pass this letter on to other people who might also like to be involved. If you are interested in helping us answer this very important question, we would like to invite you and your family to be part of this exciting new study being conducted by the University of Western Sydney and The University of Sydney. If you agree to participate, a member of our research team will visit you just once a year (for the next three years) at a time and place that is convenient to you. At this visit we will collect a finger prick sample of blood for an A1c and ask you to complete some questionnaires about your knowledge of diabetes and how you are coping with its effect on your life. If you are the young person with diabetes these questionnaire take about 1 hour to complete. If you are the mum or dad, your questionnaires will take about 20 minutes to complete. As well, we would contact you every few months to see how you are going. This would be a brief telephone call. If you do join the study, you should know that you may leave it at any time you wish, that anything you say will be confidential and that no-one will be identifiable in the results we publish. Also, all families who take part in the study will be sent a report of the final results. If you would like to find out more about this study please contact Dr Jane Overland on 02 9515 5930 or email us at [email protected]. The DRAT study team. (Assoc. Prof.) Christine Johnston (Dr) Lorraine Smith (Dr) Jane Overland Mr Daniel Waller Ms Kristy Hatherly Ms Linda Singh Diabetes Centre, RPA Sydney University University of Western Sydney

mailto:[email protected]

Appendix 5: Personality Study Information Sheet and Consent Form

238


Diabetes Research into Adolescent Transitions Project: Additional measure

This is an update to let you know that we would like to include one more questionnaire to the DRAT study. This measure has been added to ensure that we are considering everything that might be important. This is because research on how people manage their diabetes is beginning to suggest that personality may be critical. We are therefore keen to look into this further. To explore the effect of personality, we will be asking the young person with diabetes to fill in the Five Factor Personality Inventory for Children (FFPI-C) when we come to visit you. We also want to send you these questionnaires if you have already received your initial annual interview. These can be completed and sent back to us in the provided reply paid envelope. The FFPI-C will be included in each round of data collection for three years. We estimate that completing this questionnaire will take no more than 20 minutes in all. As with the other measures in the study, your responses will be anonymous. No names or identifying information will be included in the final report. You may, of course, withdraw from the study at any time The Five Factor Personality Inventory for Children (FFPI-C) is new scale that, as far as we are aware, has not been used in Australia before. In order to help test its usefulness we have agreed to pass on the responses to this scale only to Pro-Ed Inc. who publish it. They will not receive any identifying information and will be given responses to the questions on the scale, the gender of the person and age only. This approach helps us create better, more reliable psychological scales. The anonymity and confidentiality of participants will be secure at all times. If you are interested in being involved in this part of the study please complete the consent form on the following page. If you have any questions about this part of the study please do not hesitate to call Assoc. Prof. Christine Johnston on (02) 4736 0782, Dr Lorraine Smith on (02) 9036 7079 or Dr Jane Overland on (02) 9515 5930 if you have any concern or questions regarding the project. Thank you for considering this request and for your support of the Diabetes Research into Adolescent Transitions Project. Yours faithfully,

Daniel Waller

NOTE: This study has been approved by the University of Western Sydney Human Research Ethics Committee. The approval number is HREC 06 - 011. If you have any complaints or reservations about the ethical conduct of this research, you may contact the Ethics Committee through the Research Ethics Officers (Tel.: 4736 0883 or 4736 0884). Any issues you raise will be treated in confidence and investigated fully, and you will be informed of the outcome.

Appendix 5: Personality Study Information Sheet and Consent Form

239


Consent to complete additional scales in the project:

If you are willing for you/your child to complete the additional scale in the Diabetes Research into Adolescent Transitions Project please complete the following: We understand that: I/my child will be asked to complete two additional questionnaires; Our names and all identifying information will be removed and that we will not

be identifiable in the final report; My/my child’s responses on the Five Factor Personality Inventory – Children only will be given to Pro-Ed Inc. (the publishers of the scale), together with my/my child’s age and gender. No identifying information will be provided to Pro-Ed, and We may change our minds about participating in this part of the study at any time and that this will in no way affect our access to the services we are receiving or our involvement in the other part of the project.

Parent’s Name: _______________________________________________ Signed__________________________________________ Date________________ Child’s Name: ___________________________________________________ Signed__________________________________________ Date________________ Witness: Signature: Date:

Appendix 6: FFPI-C Instructions

240


Instructions for Five Factor Personality Inventory – Children (FFPI-C) and Diabetes Management Questionnaire (DMQ)

Dear Family, We would like to thank you again for your help with our research project. Please find attached the Five Factor Personality Inventory – Children (FFPI-C). We would appreciate it if the young person with diabetes in your household could complete this questionnaire. The questionnaire is relatively easy to complete but just in case we thought we should include some instructions. Completing the Five Factor Personality Inventory – Children (FFPI-C) The FFPI-C is a little different to the other questionnaires that you would have completed. The questionnaire consists of 75 items with each item having two statements that are the opposite of each other. There are five circles between these statements. This allows you to choose the one that is closest to what you think and make a decision on how much you agree with it. If you agree with a sentence, colour in the circle closest to that sentence. If you somewhat agree, colour in the second closest circle to that sentence. If unable to decide between sentences, colour in the middle circle. There are no right or wrong answers but try to make a decision and use the middle circle as little as possible. In the example below the person has indicated that they somewhat agree with the statement “I think dogs are nice”.

Example: I think dogs are nice. ○ ● ○ ○ ○ I think dogs are scary. Please complete all of the questions on the inside two pages of the FFPI-C. Please do not pull the questionnaire apart or rip along the perforated lines. Parents helping children with questionnaires Children may need help with understanding the questions and parents should give as much help as they feel the child needs. Some children may find it easier if the statements are read to them. It is important, however, that they say what they really think. For this reason, there is no need to spend a lot of time thinking about the answer. Usually, it is the first answer which is the one that they believe. Returning completed questionnaires We have included a reply paid envelope to allow you to return the responses and consent form to our DRAT! Office. Please make sure you have your name on the questionnaire so that the answers can be properly entered with the rest of your responses on the other questionnaires you have been completing for us.

Important: Consent Form Please note that we need you to read through and sign the attached information and consent form before you complete these questionnaires. This is very important as we will not be able to use any information we receive from you unless this is done.

Appendix 7: Excluded Measures

241

List of Excluded Measures

Self-Description Questionnaires (SDQ I, SDQ II and SDQ III)

Self-concept was measured using the Self-Description Questionnaire (SDQ I) for

children (equivalent to grades 6 and under), the Self-Description Questionnaire (SDQ

II) for adolescents (equivalent to grades 7 - 12), and the Self-Description Questionnaire

(SDQ III) for young adults (equivalent to those who’ve finished high-school) (Marsh,

1988, 1990a, 1990b).

The Self-Description Questionnaires were excluded from analyses for a number of

reasons. First, the three scales (SDQ I, SDQ II and SDQ III) are difficult to compare

across age groups as they use different items and measure different higher-order

factors. Second, the items from the Self-Description Questionnaires could be

considered redundant when measuring personality as they measure some related

domains. Finally these scales were gauged to be predictors rather than outcomes of

self-management and hence were superfluous to the included battery of measures.

Michigan Diabetes Research and Training Centre’s Brief Diabetes Knowledge Test

Child and parental knowledge about type I diabetes was assessed using a modified

version of the Michigan Diabetes Research and Training Centre’s Brief Diabetes

Knowledge Test which is downloadable from the internet (Michigan Diabetes Research

Training Center). The test is a 23-item, multiple-choice measure composed of two

components; a 14-item general diabetes test, and a 9-item insulin-use subscale. The

knowledge test was excluded from analyses based on an extreme ceiling effect

present in scores. This provided little variance to analyse any statistical effects of

knowledge.

Self-Efficacy for Diabetes Scale (SEDS)

The participant’s confidence in their ability to manage their diabetes was measured

using the Self-Efficacy for Diabetes Scale (SEDS) (Grossman, Brink, & Hauser, 1987).

This measure includes 35 items that index self-efficacy in diabetes-specific (24 items),

medical (5 items), and general (6 items) situations. Again, scores showed a strong

ceiling effect and this hampered statistical analyses.

Appendix 8: Personal Information Form

242

PERSONAL INFORMATION FORM PART 1: Information about you, the person with Diabetes

1.Name: _____________________________________________________________ 2.Preferred Contact Details _______________________________________________

Email: _______________________________________________________________

Phone: ______________________________________________________________ 3. Date of Birth: _______________________________________________________ 4. Are you ………………………………… Male Female

5. How old were you when you were diagnosed with diabetes? __years ___months

6. How old are you now? ......................................................... ___________years__________months

7. How many brothers and sisters do you have? … brothers sisters

8. Are you the: a. eldest

b. 2nd eldest

c. 3rd eldest

d. 4th eldest

e. Other (please specify) ____________________________________

9. Does anyone else in your family have Type 1 diabetes? …….No Yes

If Yes please specify: a. Mother

b. Father

ID Number: ________Date:______ Researcher: ___________________

1


243

c. Brother or Sister

d. Grandparent

e. Other (please specify) ______________

10. Are you currently studying? …………………………. No Yes

If Yes, please specify: a. Full time

b. Part time

11. If you are aged 12 or under, are you cared for by someone other than your parents after school hours, e.g. after school care? ………………………..

No Yes

If Yes, please specify ________________________________________

12. Are you currently working …………………No Yes

If Yes, please specify the number of hours per week …….

13. Do you participate in regular sport or exercise? … No Yes

If Yes, please specify the type of sport or exercise and how many hours a week you do that sport or exercise:

Type:________________________________hours/week

Type:________________________________hours/week

Type:________________________________hours/week

Type:________________________________hours/week


244

14. Do you have any of the following problems with your health?

a. Coeliac disease No Yes Don’t Know

b. Addison’s disease No Yes Don’t Know

c. Thyroid disease No Yes Don’t Know

d. Rheumatoid arthritis No Yes Don’t Know

e. Asthma No Yes Don’t Know

f. Other (please specify) ____________________________________

15. Have you used a diabetes chat room in the last 12 months, e.g. Reality Check? No Yes 16. Have you attended a diabetes kids’ camp in the last 12 months? No Yes If Yes, please mark the line below with an X indicating how helpful you

found attending the camp. Very helpful _______________________________ Not at all helpful 17. Have you attended a diabetes support program in the last 12 months? No Yes If Yes, please mark the line below with an X indicating how helpful you found the program. Very helpful ______________________________ Not at all helpful 18. Are you registered with the National Diabetes Supply Scheme? No Yes


245

19. Where do you get your diabetes supplies from?

Please mark an X in the appropriate boxes

Insulin Syringes, pen or pump

Blood glucose strips

a. Pharmacy/Chemist

b. Diabetes Australia

c. Diabetes Clinic

d. Mail Order

e. Other (Please specify)


246

THIS SECTION TO BE COMPLETED BY PARENTS

PART 2: Information about the MOTHER of the family 20. Mother’s age 21. Mother’s ethnic origin ________________________________________________ 22. Mother’s highest educational level achieved a. Primary

b. Secondary

c. TAFE or certificate qualification

d. Tertiary

23. Mother’s work status - currently working ..………… No Yes


b. Part time

24. Mother’s educational status - currently studying..…….. No Yes


b. Part time

PART 3: Information about the FATHER of the family 25. Father’s age 26. Fathers ethnic origin _________________________________________________ 27. Father’s highest educational level achieved a. Primary

b. Secondary

c. TAFE or certificate qualification


247

d. Tertiary

28. Father’s work status - currently working ..….………………..No Yes


b. Part time

29. Father’s educational status - currently studying ..…………. No Yes

If Yes, please specify : a. Full time

b. Part time

PART 4: Information about the house in which you live 30. How many people aged 18 and over live in your house? …………………….. 31. How many people under the age of 18 live in your house? ………………….. 32. Is anyone in your house a member of JDRF? ……………..No Yes 33. Is anyone in your house a member of Diabetes Australia? …No Yes 34. What is your household’s annual income?

a. Less than $31,199

b. $31,200 to $51,999

c. $52,000 to $83,199

d. More than $83,200

Appendix 9: Annual Data Collection Form

248

ANNUAL DATA COLLECTION FORM PART 1: Diabetes Control

Blood Collected for A1c……………………………….Result % 1. Unconscious hypo in the last 3 months No Yes

2. No. of hypos (bsl <3) in the last 2 weeks……………….

3. Admission to hospital with hyperglycaemia in the last 3 months No Yes

4. No. of admissions to hospital for diabetes (hyper- or hypo-glycaemia or stabilization) in last 3 months………………….

5. No. of days off school or work in the last 3 months……………………………

PART 2: Diabetes Management 6. Diabetes Treatment: injections pump

7. If using injections: a. total daily dose (units) prescribed at last review…………..

b. no. of injections prescribed at last review……………………

c. carbohydrate exchanges… variable fixed

d. if fixed, total no. of exchanges a day………………………..

e. meal insulin………………..variable fixed

5 ID Number: ________Date:______ Researcher: ___________________

Time Ins 1 Ins 2 I Ins3

Morning M/T Lunch A/T Dinner Supper

249

Type (name) of Insulin: 1 = 2= 3 = 8. If using a pump: a. total basal dose (units) prescribed at last

review…………

b. insulin to carb ratio prescribed at last review……………………1 unit / grams carb

units/exchange

c. carbohydrate exchanges.. variable fixed

d. if fixed, total no. of exchanges a day…..

e. meal insulin………...variable fixed

All participants: 9. In the last 2 weeks, has the participant taken their insulin or adjusted their insulin as

prescribed: a. all of the time

b. most of the time

c. some of the time

d. none of the time

10. If less than all of the time, please make a comment________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________

11. In the last 2 weeks, has the participant missed injections of insulin or bolus doses: a. all of the time

b. most of the time

c. some of the time

d. none of the time

Time Insulin

250

12. If more than none of the time please make a comment _____________________ _________________________________________________________________ _________________________________________________________________ 13. No. of blood glucose tests over last 2 weeks……Estimated Actual Are blood glucose tests performed …… Randomly [ ] Before meals [ ] After meals [ ]

Paired (before and after a meal) [ ] Before bed [ ] Overnight [ ] To confirm hypo [ ] Other [ ]

If other, please make a comment as to when blood glucose tests are performed and why ______________________________________________________________

_________________________________________________________________ _________________________________________________________________ 14. No. of booked appointments for diabetes review in the last 3 months…….. 15. No. of attendances at diabetes review appointments in last 3 months…… 16. Date(s) of diabetes review(s) in next 3 months (appointment type(s) to be noted): ____________________________________________________________________ ____________________________________________________________________ ____________________________________________________________________ ____________________________________________________________________

251

PART 3: Model of Care 17. Health care professionals seen in the last 12 months HP Attended Wanted Helpful Service type hospital/community Service Type Who attended consult (mins)

to attend

GP [ ] Y/N Y/N child/mother/father

Endocrinologist [ ] Y/N Y/N private /public hospital/community paediatric/adult child/mother/father

Paediatrician [ ] Y/N Y/N private/public hospital/community child/mother/father

Diabetes educator [ ] Y/N Y/N private/public hospital/community paediatric/adult child/ mother/father

Nurse [ ] Y/N Y/N private/public hospital/community paediatric/adult child/ mother/father

Dietician [ ] Y/N Y/N private/public hospital/community paediatric/adult child/ mother/father

Social worker [ ] Y/N Y/N private/public hospital/community paediatric/adult child/ mother/father

Psychologist [ ] Y/N Y/N private/public hospital/community paediatric/adult child/ mother/father

Pharmacist [ ] Y/N Y/N private/public hospital/community paediatric/adult child/ mother/father

Other (please specify)

Attended / wanted to attend Y/N Helpful Y/N

Service Type – private or public; Hospital/Community

Who attended: child/mother/father

Consult (mins)

252

18. Time travelled to diabetes specialist services: a. < 1 hour

b. 1-2 hours

c. 2.1-4 hours

d. > 4 hours

19. Do you have access to 24 hour phone or email advice? No Yes

20. Do you have a team meeting with the people caring for you/your child? a. always Comment: ________________

b. sometimes __________________________

c. never __________________________

21. Is there one professional who takes responsibility for looking after your/your child’s needs? a. no Comment: _________________

b. not sure __________________________

c. yes __________________________

(If yes, who is this person (Diabetes educator etc)?

22. Do you feel that the professionals you see about your/your child’s diabetes know what the others are saying to you?

a. always Comment: ____________________

b. sometimes ____________________________

c. never ____________________________

23. Do you feel that the professionals you see understand your needs and lifestyle? a. always Comment: ____________________

b. sometimes ______________________________

c. never ______________________________

253

24. Do you feel that the professionals you see take your views and needs into account when deciding on treatment? a. always Comment: ___________________

b. sometimes ____________________________

c. never ____________________________

25. Who makes the decisions about what should happen with the management of your/your child’s diabetes?

a. me/my family Comment: _______

b. health care professionals ________________

c. joint decision between me/my family _________________ and health care professionals

26. Do staff really listen to your concerns? a. yes Comment: ____________________

b. to some extent _____________________________

c. no _____________________________

27. Are these concerns taken into account when decisions are made about your/your child’s treatment/management of diabetes?

a. yes Comment:_____________________

b. to some extent _____________________________

c. no _____________________________

28. Do you find that the professionals/service is able to change to meet your changing needs? a. yes Comment: _____________________

b. to some extent ______________________________

c. no ______________________________

254

29. Are you given information about other services and community resources? a. yes Comment: _____________________

b. to some extent ______________________________

c. no _______________________________

30. Are you given opportunities to meet other families/individuals with Type 1 diabetes? a. yes Comment: _____________________

b. to some extent ______________________________

c. no ______________________________

31. Do you think that your language and cultural needs are taken into account as well as they might? a. yes Comment: ____________________

b. to some extent _____________________________

c. no _____________________________ PART 4: Details for next contact in 3 months Preferred day and time of contact: _________________________________________ Preferred contact number: _______________________________________________

Appendix 10: Quarterly Review Form

255

QUARTERLY REVIEW FORM PART 1: Diabetes Control

1. Unconscious hypo in the last 3 months No Yes

2. No. of hypos (bsl <3) in the last 2 weeks……………….

5. Admission to hospital with hyperglycaemia in the last 3 months No Yes

6. No. of admissions to hospital for diabetes (hyper- or hypo-glycaemia or stabilization) in last 3 months………………….

5. No. of days off school or work in the last 3 months…………………………… PART 2: Diabetes Management 6. Diabetes Treatment: ………………………….. injections pump

7. If using injections: a. total daily dose (units) prescribed at last review…..

b. no. of injections prescribed at last review………

c. carbohydrate exchanges……variable fixed

d. if fixed, total no. of exchanges

e. meal insulin variable fixed

Type (name) of Insulin: 1 = 2= 3 =


11

Time Ins 1 Ins 2 I Ins3

Morning M/T Lunch A/T Dinner Supper


256

8. If using a pump: a.total basal dose (units) prescribed at last review……

b. insulin to carb ratio prescribed at last review…………………………1 unit / grams carb

units/exchange

c. carbohydrate exchanges….. variable fixed

d. if fixed, total no. of exchanges a day.

e. meal insulin……... variable fixed

All participants: 9. In the last 2 weeks, has the participant taken their insulin or adjusted their insulin

as prescribed: a. all of the time

b. most of the time

c. some of the time

d. none of the time

10. If less than all of the time, please make a comment ________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ 11. In the last 2 weeks, has the participant missed injections of insulin or bolus doses: a. all of the time

b. most of the time

c. some of the time

d. none of the time

12. If more than none of the time please make a comment ____________________ _________________________________________________________________ _________________________________________________________________

Time Insulin


257

13. No. of blood glucose tests over last 2 weeks…Estimated Actual

Are blood glucose tests performed …… Randomly [ ] Before meals [ ] After meals [ ]

Paired (before and after a meal) [ ] Before bed [ ] Overnight [ ] To confirm hypo [ ] Other [ ]

If other, please make a comment as to when blood glucose tests are performed and why ___________________________________________________________

_________________________________________________________________ _________________________________________________________________ 14. No. of booked appointments for diabetes review in the last 3 months……… 15. No. of attendances at diabetes review appointments in last 3 months…….. 16. Date(s) of diabetes review(s) in next 3 months (appointment type(s) to be noted): _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ PART 3: Details for next contact in 3 months Preferred day and time of contact __________________________________________ Preferred contact number ________________________________________________

Appendix 11: Diabetes Family Responsibility Questionnaire

258

DIABETES FAMILY RESPONSIBILITY QUESTIONNAIRE: DFRQ

Instructions: Below are different tasks or situations that relate to diabetes management in your family. Fill in the circle which best describes the way each task or situation is handled in your family.

Parent(s) take or initiate

responsibility almost all of

the time

Parent(s) and child

share responsibility for this about

equally

Child takes or initiates


the time

1.

Remembering day of clinic appointment.

1 O O O

1

2. Telling teachers about diabetes.

2 O O O

2

3. Remembering to take morning or evening injection.

3 O O O

3

4.

Making appointments with dentists and other doctors.

4 O O O

4

5.

Telling relatives about diabetes.

5 O O O

5

6. Taking more or less insulin according to results of blood sugar or urine tests.

6 O O O

6

7.

Noticing differences in health, such as weight changes or signs of an infection.

7 O O O

7

8.

Telling friends about diabetes. .

8 O O O

8

9. Noticing the early signs of an hypo.

9 O O O

9

10. Giving insulin injections.

10 O O O

10

11

. Deciding what should be eaten when family has meals out (restaurants, friend’s home).

11 O O O 11

12 .

Examining feet and making sure shoes fit properly.

12 O O O 12


2

Appendix 11: Diabetes Family Responsibility Questionnaire

259

Parent(s) take or initiate


the time

Parent(s) and child

share responsibility for this about

equally

Child takes or initiates


the time

13.

Carrying some form of sugar in case of an hypo.

13 O O O

13

14.

Explaining absences from school to teachers or other school personnel.

14 O O O

14

15.

Rotating injection sites.

15 O O O

15

16. Checking expiration dates on medical supplies.

16 O O O

16

17.

Remembering times when blood sugar or urine should be tested.

17 O O O

17

Appendix 12: Depression, Anxiety, Stress Scales

260

DEPRESSION, ANXIETY, STRESS SCALES: 21 ITEM SHORTFORM: (DASS)

Instructions: Please read each statement and fill in the circle which indicates how much the statement applied to you OVER THE PAST WEEK. There are no right or wrong answers. Do not spend too much time on any statement.

Did not

apply to me at all

Applied to me

to some

degree, or

some of the time

Applied to me a

considerable degree, or a good part of

the time

Applied to me very

much, or

most of the time

1.

I found it hard to wind down.

1 O O O O

1

2. I was aware of dryness of my mouth.

2 O O O O

2

3. I couldn’t seem to experience any positive feeling at all.

3 O O O O

3

4.

I experienced breathing difficulty (eg. Excessively rapid breathlessness in the absence of any physical exertion).

4 O O O O

4

5 .

I found it difficult to work up the initiative to do things.

5 O O O O

5

6. I tended to over-react to situations.

6 O O O O

6

7. I experienced trembling (eg. In the hands).

7 O O O O

7

8. I felt that I was using a lot of nervous energy..

8 O O O O

8

9. I was worried about situations in which I might panic and make a fool of myself.

9 O O O O

9

10.

I felt that I had nothing to look forward to.

10 O O O O

10

11. I found myself getting agitated.

11 O O O O

11

12. I found it difficult to relax.

12 O O O O

12

ID Number: _______Date:______ Researcher: ___________________

8

Appendix 12: Depression, Anxiety, Stress Scales

261

Did not

apply to me at all

Applied to me

to some

degree, or

some of the time

Applied to me a

considerable degree, or a good part of

the time

Applied to me very

much, or

most of the time

13.

I felt down-hearted and blue.

13 O O O O

13

14.

I was intolerant of anything that kept me from getting on with what I was doing.

14 O O O O

14

15.

I felt I was close to panic.

15 O O O O

15

16.

I was unable to become enthusiastic about anything.

16 O O O O

16

17.

I felt I wasn’t worth much as a person.

17 O O O O

17

18.

I felt that I was rather touchy.

18 O O O O

18

19. I was aware of the action of my heart in the absence of physical exertion (eg. Sense of heart rate increase, heart missing a beat).

19 O O O O

19

20.

I felt scared without any good reason. .

20 O O O O

20

21

. I felt that life was meaningless.

21 O O O O

21

Appendix 13: DASS 21 Normative Sample Means and Standard Deviations, by Sex

262

DASS 21 Normative Sample Means and Standard Deviations, by Sex

Sex Depression

Anxiety Stress

Mean Std.

Dev

Mean Std.

Dev

Mean Std.

Dev

Males (n = 1044) 6.55 7.01 4.60 4.80 9.93 7.66

Females (n = 1870) 6.14 6.92 4.80 5.03 10.29 8.16

Overall (n = 2914) 6.34 6.97 4.70 4.91 10.11 7.91

(Lovibond & Lovibond, 1995a, p.29, Table 10)

Appendix 14: DASS Z Scores Conversions

263

DASS 21 Z score conversions

Scale Score Z Score

Depression Anxiety Stress

0 1 2 3 4 5 6 7 8 9

10 11 12 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

-0.91 -0.77 -0.62 -0.48 -0.34 -0.19 -0.05 0.09 0.24 0.38 0.53 0.67 0.81 0.96 1.10 1.24 1.39 1.53 1.67 1.82 1.96 2.10 2.25 2.39 2.53 2.68 2.82 2.96 3.11 3.25 3.39 3.54 3.68 3.82 3.97 4.11 4.26 4.40 4.54 4.69 4.83 4.97 5.12

-0.96 -0.75 -0.55 -0.35 -0.14 0.06 0.26 0.47 0.67 0.88 1.08 1.28 1.49 1.69 1.89 2.10 2.30 2.51 2.71 2.91 3.12 3.32 3.52 3.73 3.93 4.13 4.34 4.54 4.75 4.95 5.15 5.36 5.56 5.76 5.97 6.17 6.37 6.58 6.78 6.99 7.19 7.39 7.60

-1.28 -1.15 -1.03 -0.90 -0.77 -0.65 -0.52 -0.39 -0.27 -0.14 -0.01 0.11 0.24 0.37 0.49 0.62 0.74 0.87 1.00 1.12 1.25 1.38 1.50 1.63 1.76 1.88 2.01 2.14 2.26 2.39 2.51 2.64 2,77 2.89 3.02 3.15 3.27 3.40 3.53 3.65 3.78 3.91 4.04

(Lovibond & Lovibond, 1995a, p.42, Table 14)

Appendix 15: Coping with a Disease Inventory

264

COPING WITH A DISEASE INVENTORY: CODI

Instructions: Think of situations when you have been bothered or stressed because of your diabetes. Below you will find a list of ways in which young people may deal with their feelings in these situations. Please tell us how often you usually do the things or have these kinds of thoughts related to your diabetes by filling in the appropriate circle.

NEVER ALWAYS

1.

I am able to manage my diabetes.

1 O O O O O

1

2. I have got used to my diabetes.

2 O O O O O

2

3. I cope well with my diabetes.

3 O O O O O

3

4. I accept my diabetes.

4 O O O O O

4

5. I take my diabetes easy.

5 O O O O O

5

6. I face my situation with humour.

6 O O O O O

6

7. I try to ignore my diabetes.

7 O O O O O

7

8. I pretend to be all right. .

8 O O O O O

8

9. I try to forget my diabetes.

9 O O O O O

9

10. I think about my diabetes.

10 O O O O O

10

11. I believe that faith in God helps me.

11 O O O O O

11

12. I pray that my diabetes will go away.

12 O O O O O

12

13. I learn as much as possible about my diabetes.

13 O O O O O

13

14. I tell myself that even famous people have diabetes.

14 O O O O O

14

ID Number: ______Date:______ Researcher: ___________________

7

Appendix 15: Coping with a Disease Inventory

265

NEVER

ALWAYS

15.

I think of worse situations.

15 O O O O O

15

16. I don’t care about my diabetes.

16 O O O O O

16

17. I think my diabetes is no big deal.

17 O O O O O

17

18. I think my diabetes is not so serious.

18 O O O O O

18

19. I forget about my diabetes.

19 O O O O O

19

20. I cry.

20 O O O O O

20

21. I am frustrated.

21 O O O O O

21

22. I am angry.

22 O O O O O

22

23. I wake up at night and think of terrible things.

23 O O O O O

23

24.

I am ashamed of having diabetes.

24 O O O O O

24

25. I think it is unfair that I have diabetes.

25 O O O O O

25

26. I want to stop having my diabetes.

26 O O O O O

26

27. I hope that my diabetes disappears.

27 O O O O O

27

28. I wish I were healthy.

28 O O O O O

28

Not well at all Very

well

29.

Overall, how well do you think you cope with your diabetes?

29 O O O O O

29

Appendix 16: “Thank you” Letter for Postal Participation

266


c/- Assoc. Prof. Christine Johnston University of Western Sydney Locked Bag 1797 Penrith South DC, NSW, 1797

Dear Family, We would like to thank you again for agreeing to participate in the Diabetes Research into Adolescent Transitions Project. We realize that for some families, like yourselves, it is more convenient to complete the questionnaires in your own time at home and then post them in the reply paid envelope provided. We appreciate the time you will take and look forward to receiving your views and comments. Included in this package are a number of questionnaires which are related to insert name’s knowledge of diabetes and how he/she is coping with its effects on his/her life. We anticipate this should take no longer than one hour to complete. Parents are also asked to complete some questionnaires based on their knowledge of diabetes and how involved they are in managing their child’s diabetes. This should take no longer than 20 minutes to complete. We are also collecting some general information about your family and the care and support you are receiving from health professionals. Should you feel that any of the questions are too personal or sensitive you need not, of course, answer them. The reason we are collecting this type of information is that previous studies have shown that many of these factors play an important role in determining how children and young people manage their diabetes. Finally, we are hoping insert name can provide us with a small blood sample to be taken for analysis of the blood glucose level. The directions for carrying out this task, together with the pipette and vial are included in this letter. The attached table will provide you with an overview of the questionnaires and could be used as a checklist to ensure that we will receive all the information, views and opinions from insert name and your family. If you have any queries about the questionnaires please do not hesitate to contact me and you are also welcome to contact either Assoc. Prof. Christine Johnston on 02 4736 0782, Dr Lorraine Smith on 02 9036 7079 or Dr Jane Overland on 02 9515 5930 if you have any concerns or questions regarding the project. Please be reassured that all responses will be kept confidential and no names will be used in any reports coming from the project. We appreciate you taking the time to participate in this important research project and we look forward to meeting with you again soon. Yours Sincerely, On behalf of the research team

Assoc. Prof. Christine Johnston Dr. Lorraine Smith Dr. Jane Overland University of Western Sydney The University of Sydney Diabetes Centre, RPAH QUESTIONNAIRES FOR DRAT! PROJECT All questionnaires have instructions at the top to explain what to do. When you choose your answer you can tick the circle you want rather than colour it. This may be quicker for you.

Appendix 17: List of Questionnaires for DRAT Postal Participation

267

Diabetes Research into Adolescent Transitions Questionnaire Colour Comment Who will

complete Tick when completed

1 Personal Information Form

White Information about your child and family

Parents or older adolescents

2. DFRQ Pink How tasks related to Diabetes are handled in your family

Parents and child together

3. Michigan Knowledge Test

Yellow Parents/Carers – Column A: Your answer Column B: How you think your child would answer?

Parents

4. Michigan Knowledge Test

Gold The young person’s knowledge of Diabetes

Young Person

5. Annual Data Collection Form

White Information regarding Diabetes control, management and care

Parents

6. SEDS Blue How confident you are in doing some tasks.

Young Person

7. CODI Green Thoughts and feelings about coping with Diabetes

Young Person

8. DASS Grey How you are feeling in general Young Person 9. SDQ1 – For those in Primary School OR

Cream How you feel about school, yourself and family

Young Person

10 SDQ11 – For those in Secondary School or at work

Light Blue How you feel about school, yourself and family

Young Person

12. DMQ Light Blue How you feel about your

Diabetes Young Person

13. FFPI-C How you feel about yourself Young Person Blood Sample for an HbA1c

Please follow directions on the next page.

Please place all completed questionnaires and the solution with the blood sample

in the reply paid envelope provided. We very much appreciate the time you have taken to complete the questionnaires and look forward to receiving your responses.

Appendix 18: Blood Collection Instructions for Participation by Post

268


How to collect your blood for an HbA1c estimation

Included in this blood collecting pack is:

1. a small pipette (this is the thin glass tube attached to the bottom of this page), 2. a labelled sample pot containing a clear solution, 3. a sealable plastic pathology bag, 4. an addressed express post envelope for you to send your questionnaires and

blood sample to the DRAT Study team. Your HbA1c can be measured using a finger prick sample of blood. To collect this blood sample you need to prick your finger exactly the same way you do when you are testing your blood glucose level at home. Once you have pricked your finger, gently squeeze until a small drop of blood

forms. Remove the small pipette attached to the bottom of this letter and then place one end of the pipette into the drop of blood. The pipette will suck up the blood (you may need to tap the end of the pipette into the sample of blood several times for the pipette to become full). Once the pipette is full, open the labelled sampling pot and place the pipette in the clear solution. Now close the lid of the sample pot and shake the pot gently until the solution in the pot turns a pinkish/red colour. Lastly, place the sample pot in the plastic pathology bag provided and seal the bag.

Your sample can now be returned to the DRAT Team in the express post envelope provided. If you have any questions or concerns about collecting this blood sample please contact Dr Jane Overland on 02 9515 5930 or [email protected]. Small pipette

Appendix 19: Thank You Card

269

Appendix 20: DRAT Newsletter

270


271


272


273

Appendix 21: UWS HREC – Ethics Approval

274


275


276

Appendix 22: Hunter New England HREC – Ethics Approval

277

Appendix 22: Hunter New England HREC – Ethics Approval

278

Appendix 23: Missing Data

279

Variable Year

1 Year

2 Year

3

Openness 104 104 104

Conscientiousness 104 104 104

Extraversion 104 104 104

Agreeableness 104 104 104

Emotional regulation 104 104 104

HbA1c 104 104 100

Avoidance Coping 104 102 101

Acceptance Coping 104 102 101

Depression 104 102 100

Anxiety 104 102 100

Stress 104 102 100

No. Blood glucose tests 104 100 103

No. Hypos 104 100 99

Variable Year

1 Year

2 Year

3

No. Unconscious Hypos 104 102 100

No. Hospitalisations 104 102 100

No. Missed Appointments 104 102 98

No. Appointments 104 102 98

Insulin adherence 103 102 100

Gender 104 104 104

Age 104 104 104

Duration of diabetes 104 104 104

Responsibility for care 104 102 101

Geographical location 104 104 104

Ethnicity 94 94 94

Treatment modality 104 104 104

Income 97 97 97

Appendix 23: Missing Data

280

Missing Quarterly Data

Variable Quarterly 1 Quarterly 2 Quarterly 3

No. BGL tests 102 100 96

No. Hypos 100 100 96

Unconscious hypos 101 102 97


No. appointments 101 102 96

Missed appointments 101 102 96



Variable Quarterly 1 Quarterly 2 Quarterly 3

No. BGL tests 96 76 43

No. Hypos 96 76 43

Unconscious hypos 96 76 43


No. appointments 96 76 43

Missed appointments 95 76 43



* All quarterly information was discounted from analyses based on missing data

281

Appendix 24: Inter-item Correlation Matrix for CODI Avoidance Scale

Answer CODI Try ignore diabetes

Answer CODI Pretend to be alright

Answer CODI Try to forget diabetes

Answer CODI Think about diabetes (Reversed)*

Answer CODI Try ignore diabetes 1.00 0.61 0.61 - 0.20

Answer CODI Pretend to be alright 0.61 1.00 0.43 -0.03

Answer CODI Try to forget diabetes 0.61 0.43 1.00 -0.05

Answer CODI Think about diabetes (Reversed)* -0.20 -0.03 -0.05 1.00

282

Appendix 25: Item-total Statistics for CODI Avoidance

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Squared Multiple Correlation

Cronbach's Alpha if Item Deleted


7.61 7.48 0.72 0.55 0.41


7.23 8.65 0.51 0.38 0.57


7.39 9.00 0.52 0.38 0.56


7.36 12.86 0.12 0.06 0.79

* = deleted item

283

Appendix 26: Exploratory Factor Analysis of CODI Avoidance Scale

Factor loading Eigenvalues % of Variance Cumulative %


0.96 2.13 53.25 53.25


0.64 0.99 24.92 78.16


0.64 0.57 14.21 92.37


0.15 0.31 7.63 100.00

Extraction method: Unweighted least squares

* = deleted item

284

Appendix 27: Baseline Descriptives for Study Variables

Variable Data type n Min Max Mean Std. Dev Variance Skew

S.E. Skew Kurtosis

S.E. Kurtosis

K-S statistic

Age Continuous 104 8.17 19.08 12.15 2.77 7.67 0.30 0.24 -0.81 0.47 0.13**

Duration of diabetes Continuous 104 0.33 14.50 4.05 3.21 10.33 1.21 0.24 1.13 0.47 0.12**

Responsibility Continuous 104 18.00 49.00 32.31 5.56 30.95 0.22 0.24 0.08 0.47 0.78

Openness Continuous 104 31.00 71.00 50.76 7.74 59.85 0.21 0.24 0.09 0.47 0.08

Conscientiousness Continuous 104 26.00 67.00 49.84 7.44 55.38 -0.18 0.24 0.57 0.47 0.09

Extraversion Continuous 104 23.00 71.00 47.89 9.31 86.74 -0.01 0.24 0.18 0.47 0.06

Agreeableness Continuous 104 35.00 70.00 54.46 6.24 38.89 -0.27 0.24 0.16 0.47 0.06

Emotional regulation Continuous 104 30.00 67.00 52.35 7.66 58.60 -0.50 0.24 0.13 0.47 0.08

HbA1c Continuous 104 5.70 12.90 8.50 1.25 1.59 0.21 0.24 0.97 0.47 0.09

Avoidance Coping Continuous 104 3.00 15.00 7.17 3.44 11.84 0.59 0.24 -0.65 0.47 0.14**

Acceptance Coping Continuous 102 7.00 30.00 24.30 4.30 18.52 -0.76 0.24 0.48 0.47 0.11**

Depression Continuous 104 0.00 38.00 7.06 9.08 82.40 1.80 0.24 2.79 0.47 0.24**

Anxiety Continuous 104 0.00 36.00 7.29 8.41 70.75 1.67 0.24 2.49 0.47 0.22**

Stress Continuous 104 0.00 36.00 10.02 9.09 82.60 0.88 0.24 -0.12 0.47 0.17**


285

Baseline descriptives for study variables (continued)

Variable Data type n Min Max Mean Std. Dev Variance Skew

S.E. Skew Kurtosis

S.E. Kurtosis

K-S statistic

No. BGL tests Continuous 104 6.00 295.00 73.63 34.15 1165.88 3.15 0.24 17.59 0.47 0.15**

No. Hypos Continuous 104 0.00 17.00 2.45 2.91 8.48 2.65 0.24 9.27 0.47 0.20**

No. Hospitalisations Continuous 104 0.00 1.00 0.05 0.22 0.05 4.29 0.24 16.70 0.47 0.54**

Miss Appointment Continuous 104 0.00 1.00 0.05 0.22 0.05 4.29 0.24 16.70 0.47 0.54**

No. Appointments Continuous 104 0.00 8.00 1.33 0.99 0.98 3.65 0.24 20.33 0.47 0.39**

Gender Nominal 104 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Treatment modality Nominal 104 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Ethnicity Nominal 104 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Unconscious Hypos Nominal 104 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Location Nominal 104 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Insulin adherence Ordinal 103 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Income Ordinal 97 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A


Appendix 28: Frequency Distributions

286

Appendix 28: Frequency Distributions

287

Appendix 29: P-Plots

288


289


290


291

Appendix 30: Bivariate Scatterplots

292


293


294

Appendix 31: Test-retest Correlations for BGL Tests Including Outlier

295

Spearman’s test-retest correlations for BGL tests including outlier


Blood glucose testing 0.55** 0.67** 0.44**


Appendix 32: Correlations for BGL Testing and HbA1c Including Outlier

296

Spearman’s correlations for BGL testing and HbA1c including outlier

Year 1 BGL tests Year 2 BGL tests Year 3 BGL tests

Year 1 HbA1c -0.15 -0.12 -0.19

Year 2 HbA1c -0.08 -0.18 -0.18

Year 3 HbA1c -0.09 -0.19 -0.32**

Appendix 33: Correlations for BGL Testing and HbA1c Excluding Outlier

297

Spearman’s correlations for BGL testing and HbA1c excluding outlier

Year 1 BGL tests Year 2 BGL tests Year 3 BGL tests

Year 1 HbA1c -0.15 -0.12 -0.19

Year 2 HbA1c -0.09 -0.18 -0.18

Year 3 HbA1c -0.11 -0.19 -0.32**

Appendix 34: Participants Falling into DASS Depression Severity Categories

298

Participants falling into DASS depression severity categories

Personality Study

DASS Rating DASS Norm Year 1 Year 2 Year 3

Normal 78.00% 71.15% 74.04% 76.92%

Mild 9.00% 12.50% 9.62% 7.69%

Moderate 8.00% 5.77% 10.58% 6.73%

Severe 3.00% 5.77% 1.92% 0.96%

Extremely severe 2.00% 4.81% 1.92% 3.85%

Missing data 0.00% 0.00% 1.92% 3.85%

Participants falling into DASS anxiety severity categories


Normal 78.00% 65.38% 57.69% 70.19%

Mild 9.00% 6.73% 13.46% 6.73%

Moderate 8.00% 14.42% 17.31% 5.76%

Severe 3.00% 3.85% 8.65% 9.62%


Missing data 0.00% 0.00% 1.92% 3.85%

Participants falling into DASS stress severity categories


Normal 78.00% 72.12% 75.00% 80.77%

Mild 9.00% 10.58% 13.46% 8.65%

Moderate 8.00% 8.65% 5.77% 3.85%

Severe 3.00% 6.73% 3.85% 2.88%


Missing data 0.00% 0.00% 1.92% 3.85%

299

Appendix 35: Table of Correlations

HbA1c11

HbA1c21

HbA1c31

BGTest12

BGTest22 BGTest3

2 Accept1

2 Accept2

2 Accept3

2 Avoid1

2 Avoid2

2 Avoid3

2

CON1 -0.00 -0.25* -0.35** 0.11 0.28** 0.23* 0.11 0.18 0.04 -0.18 -0.27** -0.22*

CON2 -0.03 -0.30** -0.30** 0.18 0.32** 0.28** -0.07 0.09 0.09 -0.10 -0.23* -0.25*

CON3 -0.07 -0.26** -0.32** 0.16 0.31** 0.26** 0.03 0.19 0.08 -0.10 -0.16 -0.21*

AGR1 -0.13 -0.36** -0.28** 0.06 0.22* 0.21* 0.05 0.07 0.16 -0.21* -0.11 -0.19

AGR2 -0.07 -0.31** -0.17 -0.14 0.11 0.05 -0.04 -0.06 0.09 -0.15 -0.11 -0.16

AGR3 -0.22* -0.34** -0.31** -0.06 0.24* 0.17 0.00 0.01 0.09 -0.14 -0.17 -0.29**

EXT1 -0.04 0.11 0.09 0.05 -0.10 -0.06 0.18 0.15 0.11 -0.08 0.00 -0.10

EXT2 0.04 0.03 0.01 0.05 -0.11 -0.06 0.06 0.02 -0.03 -0.05 -0.09 -0.16

EXT3 -0.05 0.01 0.04 0.10 -0.07 0.03 0.18 0.05 0.01 -0.10 -0.09 -0.14

EMO1 -0.07 -0.10 -0.17 0.09 0.18 0.13 0.12 -0.02 0.01 -0.21* -0.31** -0.31**

EMO2 0.08 -0.11 -0.06 0.11 0.18 0.07 0.07 0.13 0.22* -0.16 -0.23* -0.30**

EMO3 -0.05 -0.17 -0.17 0.17 0.16 0.13 0.16 0.27** 0.21* -0.24* -0.24* -0.27**

OPE1 -0.12 -0.20* -0.16 -0.07 0.06 -0.03 0.12 0.19 030** -0.06 -0.02 -0.04

OPE2 -0.10 -0.29** -0.12 -0.00 0.06 0.10 0.14 0.25* 0.23* -0.13 0.05 -0.06

OPE3 -0.17 -0.28** -0.12 -0.02 0.04 0.10 0.20* 0.27** 0.28** -0.16 -0.04 -0.08

* Significant at p< 0.05 **Significant at p < 0.01

1 = Pearson’s product-moment correlation

2 = Spearman’s rho correlation

300

Dep12 Dep2

2 Dep3

2 Anx1

2 Anx2

2 Anx3

2 Stress1

2 Stress2

2 Stress3

2 Hypo1

2 Hypo2

2 Hypo3

2

CON1 -0.16 -0.19 -0.20* -0.15 -0.16 -0.23* -0.05 -0.19 -0.06 -0.13 0.00 -0.01

CON2 -0.13 -0.18 -0.18 -0.06 -0.16 -0.19 -0.07 -0.19 0.01 -0.09 0.10 -0.03

CON3 -0.19* -0.19 -0.07 -0.06 -0.17 -0.19 -0.08 -0.16 0.01 0.03 0.09 0.03

AGR1 -0.18 -0.04 -0.10 -0.17 -0.12 -0.13 -0.15 -0.05 -0.08 -0.21* -0.01 0.08

AGR2 -0.08 -0.06 0.03 -0.12 -0.04 0.04 -0.09 -0.07 -0.03 -0.30** -0.13 -0.07

AGR3 -0.17 -0.05 -0.02 -0.20* -0.13 -0.02 -0.13 -0.11 -0.10 -0.22* -0.12 -0.01

EXT1 -0.09 -0.11 -0.24* 0.00 -0.10 -0.28** -0.02 -0.04 -0.20* 0.08 0.15 0.02

EXT2 -0.09 -0.03 -0.11 0.00 -0.03 -0.06 0.06 0.09 -0.02 -0.01 0.19 0.14

EXT3 -0.18 -0.11 -0.24* -0.03 -0.14 -0.23* -0.07 -0.10 -0.16 0.03 0.11 0.07

EMO1 -0.24* -0.38** -0.32** -0.22* -0.32** -0.27** -0.27* -0.31** -0.37** -0.19 -0.17 -0.12

EMO2 -0.31** -0.38** -0.39** -0.24* -0.30** -0.29** -0.32** -0.31** -0.42** -0.09 0.02 -0.01

EMO3 -0.34** -0.41** -0.34** -0.26** -0.35** -0.37** -0.30** -0.38** -0.40** -0.09 -0.00 -0.02

OPE1 -0.15 0.02 -0.01 -0.07 -0.12 -0.22* -0.10 0.13 0.08 -0.05 -0.02 -0.06

OPE2 -.020* -0.08 -0.04 -0.02 -0.10 -0.21* -0.10 0.06 0.14 -0.05 0.14 0.19

OPE3 -0.22* -0.10 -0.04 -0.04 -0.21* -0.18 -0.12 0.03 0.12 -0.04 0.09 0.07

* Significant at p< 0.05 **Significant at p < 0.01

1 = Pearson’s product-moment correlation

2 = Spearman’s rho correlation

Appendix 36: Hierarchical Regressions

301

Hierarchical multiple regression of 1st year HbA1c

B Std. Error B

Beta t Sig.

Block 1

Constant 7.18 1.52 4.73 0.00**

Responsibility for diabetes tasks 0.03 0.03 0.13 1.09 0.28

Age -0.01 0.05 -0.02 -0.15 0.88

Gender 0.08 0.25 0.03 0.32 0.75


R2 = 0.05, F (4,99) = 1.16, p = 0.34

Block 2

Constant 18.69 4.61 4.05 .000**


Age -0.02 0.05 -0.05 -0.45 0.66

Gender 0.07 0.25 0.03 0.28 0.78



Agreeableness -0.02 0.02 -0.12 -1.15 0.26


Emotional regulation quadratic term 0.00 0.00 2.38 2.46 0.02*

R2= 0.12, F(8,95) = 1.54, p= 0.15

Block 3

Constant 19.47 4.72 4.12 0.00**


Age -0.02 0.05 -0.05 -0.41 0.68

Gender 0.01 0.25 0.00 0.04 0.97



Agreeableness -0.02 0.02 -0.11 -1.07 0.29



Openness to experience -0.02 0.02 -0.09 -0.80 0.43

Extraversion -0.00 0.02 -0.03 -0.29 0.77

R2= 0.12,F(10,93) = 1.32, p= 0.23



302

Hierarchical multiple regression of 2nd year HbA1c

B Std. Error B

Beta t Sig.

Block 1

Constant 9.60 0.87 11.00 0.00**

Responsibility for diabetes tasks -0.04 0.03 -0.17 -1.21 0.23

Age 0.01 0.06 0.02 0.15 0.88

Gender 0.10 0.25 0.04 0.41 0.69


R2= 0.02,F(4,97) = 0.68, p= 0.67

Block 2

Constant 18.08 2.92 6.19 0.00**


Age -0.04 0.06 -0.09 -0.66 0.51

Gender 0.19 0.24 0.08 0.78 0.44



Agreeableness -0.04 0.02 -0.21 -2.06 0.04*


Emotional regulation quadratic term 0.00 0.00 1.41 1.92 0.06

R2= 0.18,F(8,93) = 2.49, p= 0.02*

Block 3

Constant 18.37 3.00 6.13 0.00**


Age -0.02 0.06 -0.04 -0.32 0.75

Gender 0.15 0.24 0.06 0.60 0.55



Agreeableness -0.03 0.02 -0.16 -1.51 0.13




Extraversion 0.01 0.02 0.06 0.61 0.54

R

2= 0.21,F(10,91) = 2.41, p=

0.01*



303

Hierarchical multiple regression of 3rd year HbA1c

B Std. Error B

Beta t Sig.

Block 1

Constant 8.44 0.83 10.21 0.00**


Age -0.01 0.07 -0.02 -0.141 0.89

Gender 0.32 0.26 0.13 1.261 0.21


R2= 0.04,F(4,94) = 0.96, p= 0.43

Block 2

Constant 18.74 3.33 5.64 0.00**


Age -0.06 0.07 -0.14 -0.90 0.37

Gender 0.19 0.25 0.08 0.78 0.44



Agreeableness -0.05 0.02 -0.23 -2.23 0.03



R

2= 0.22, F (8,90) = 3.18, p =

0.00**

Block 3

Constant 18.35 3.43 5.36 0.00**


Age -0.06 0.07 -0.14 -0.90 0.37

Gender 0.21 0.25 0.08 0.82 0.41



Agreeableness -0.04 0.02 -0.21 -2.03 0.05*



Openness to experience 0.00 0.02 0.00 0.03 0.98

Extraversion 0.01 0.02 0.07 0.61 0.54

R

2= 0.22,F(10,88) = 2.54, p=

0.01*



304

Hierarchical multiple regression of 1st year BGL testing

B Std. Error B

Beta t Sig.

Block 1

Constant 141.70 40.89 3.47 0.00**


Age -1.97 1.45 -0.16 -1.36 0.18

Gender -0.69 6.70 -0.01 -0.10 0.92

Duration of diabetes -1.70 1.06 -0.16 -1.60 0.11

R2= 0.06, F(4,99) = 1.49, p= 0.21

Block 2

Constant 78.29 58.59 1.34 0.19


Age -1.69 1.46 -0.14 -1.16 0.25

Gender 0.19 6.69 0.00 0.03 0.98



Agreeableness 0.20 0.57 0.04 0.36 0.72

Emotional regulation 0.00 0.48 0.00 0.00 1.00

R2= 0.09, F(7,96) = 1.40, p= 0.21

Block 3

Constant 83.82 62.82 1.33 0.19


Age -1.74 1.48 -0.14 -1.18 0.24

Gender -0.33 6.95 -0.01 -0.05 0.96



Agreeableness 0.25 0.59 0.05 0.42 0.68



Extraversion 0.10 0.41 0.03 0.23 0.82

R2= 0.10, F(9,94) = 1.10, p= 0.38



305

Hierarchical multiple regression of 2nd year BGL testing

B Std. Error B

Beta t Sig.

Block 1

Constant 96.72 18.54 5.22 0.00


Age 2.39 5.26 0.05 0.46 0.65

Gender -2.93 1.33 -0.30 -2.20 0.03*


R2= 0.11, F(4, 94) = 2.92, p= 0.03*

Block 2

Constant 29.05 28.91

1.01 0.32


Age 3.97 5.22 0.07 0.76 0.44

Gender -2.00 1.30 -0.21 -1.54 0.13



Agreeableness -0.07 0.39 -0.02 -0.19 0.85


R2= 0.21, F (7, 91) = 3.51, p = 0.00**

Block 3

Constant 42.07 31.20

1.35 0.18


Age 4.35 5.23 0.08 0.83 0.41

Gender -2.16 1.32 -0.22 -1.63 0.11



Agreeableness -0.28 0.41 -0.07 -0.69 0.50



Extraversion -0.48 0.33 -0.15 -1.46 0.15

R2= 0.24, F (9, 89) = 3.04, p = 0. 00**



306

Hierarchical multiple regression of 3rd year BGL testing

B Std. Error B

Beta t Sig.

Block 1

Constant 104.95 18.31

5.73 0.00


Age -3.13 1.57 -0.32 -1.99 0.05*

Gender -4.81 5.66 -0.09 -0.85 0.40


R2= 0.07, F(4, 95) = 1.73, p= 0.15

Block 2

Constant 0.42 31.14

0.01 0.99


Age -2.11 1.51 -0.22 -1.40 0.17

Gender -0.75 5.45 -0.01 -0.14 0.89



Agreeableness 0.68 0.46 0.15 1.50 0.14


R2= 0.22, F (7, 92) = 3.69, p = 0.00**

Block 3

Constant 2.29 34.92 0.07 0.95


Age -2.12 1.53 -0.22 -1.39 0.17

Gender -0.97 5.59 -0.02 -0.17 0.86



Agreeableness 0.68 0.47 0.15 1.45 0.15



Extraversion -0.01 0.36 -0.00 -0.04 0.97

R2= 0.22, F (9, 90) = 2.82, p = 0.01*



307

Hierarchical multiple regression of 1st year Hypoglycaemia

B Std. Error B Beta t Sig.

Block 1

Constant 2.35 3.53 0.67 0.51


Age -0.05 0.13 -0.04 -0.36 0.72

Gender -0.71 0.58 -0.12 -1.22 0.23


R

2=0.04, F(4, 99) = 0.93, p=

0.45

Block 2

Constant 8.36 5.08 1.65 0.10


Age -0.05 0.13 -0.05 -0.42 0.68

Gender -0.76 0.58 -0.13 -1.31 0.19



Agreeableness -0.05 0.05 -0.10 -0.99 0.33


R

2=0.06, F(7, 96) = 0.92, p=

0.49

Block 3

Constant 6.85 5.33 1.29 0.20


Age -0.08 0.13 -0.08 -0.66 0.51

Gender -0.68 0.59 -0.12 -1.16 0.25



Agreeableness -0.03 0.05 -0.07 -0.63 0.53



Extraversion 0.07 0.04 0.23 2.04 0.05*

R

2=0.10, F(9, 94) = 1.20, p=

0.31



308

Hierarchical multiple regression of 2nd year Hypoglycaemia

B Std. Error B

Beta t Sig.

Block 1

Constant 2.10 2.23 0.95 0.35


Age 0.04 0.16 0.04 0.24 0.81

Gender -0.33 0.63 -0.06 -0.53 0.60


R2=0.10, F(4, 95) = 0.25, p= 0.91

Block 2

Constant 1.49 3.68 0.40 0.69


Age 0.07 0.17 0.06 0.42 0.68

Gender -0.24 0.65 -0.04 -0.36 0.72



Agreeableness -0.04 0.05 -0.09 -0.81 0.42


R2=0.16, F(7, 92) = 0.34, p= 0.93

Block 3

Constant -1.36 3.98 -0.34 0.73


Age 0.02 0.17 0.02 0.14 0.89

Gender -0.12 0.65 -0.02 -0.18 0.86



Agreeableness -0.03 0.05 -0.08 -0.67 0.50



Extraversion 0.06 0.04 0.15 1.29 0.20

R2=0.26, F(9, 99) = 0.74, p= 0.67



309

Hierarchical multiple regression of 3rd year Hypoglycaemia

B Std. Error B Beta t Sig.

Block 1

Constant 3.85 1.79 2.16 0.03


Age 0.19 0.16 0.21 1.24 0.22

Gender 0.49 0.55 0.09 0.88 0.38


R2=0.06, F(4, 93) = 1.35, p= 0.26

Block 2

Constant 3.15 3.35 0.94 0.35

Responsibility for diabetes tasks -0.14 0.07 -0.34 -2.00 0.05*

Age 0.22 0.16 0.24 1.39 0.17

Gender 0.43 0.58 0.08 0.74 0.46



Agreeableness 0.01 0.05 0.03 0.29 0.77


R2=0.07, F(7, 90) = 0.92, p= 0.50

Block 3

Constant 1.76 3.65 0.48 0.63


Age 0.23 0.161 0.25 1.43 0.16

Gender 0.57 0.59 0.11 0.97 0.34



Agreeableness 0.02 0.05 0.04 0.32 0.75



Extraversion 0.02 0.04 0.05 0.40 0.69

R2=0.09, F(9, 88) = 0.98, p= 0.47



310

Hierarchical multiple regression of 1st year Depression

B Std. Error B

Beta t Sig.

Block 1

Constant 10.50 10.86 0.97 0.34


Age -0.31 0.39 -0.10 -0.79 0.43

Gender -0.20 1.78 -0.01 -0.11 0.91


R

2= 0.06, F(4, 99) = 1.53, p=

0.20

Block 2

Constant 39.75 11.29 3.52 0.00


Age -0.25 0.35 -0.08 -0.73 0.47

Gender -0.57 1.59 -0.03 -0.36 0.72



R

2= 0.26, F(5, 98) = 6.70, p=

0.00**

Block 3

Constant 40.27 15.01 2.68 0.01


Age -0.28 0.35 -0.09 -0.79 0.43

Gender -0.66 1.66 -0.04 -0.40 0.69




Agreeableness -0.01 0.14 -0.01 -0.05 0.96

Extraversion 0.11 0.10 0.11 1.08 0.28


R

2= 0.27, F(9, 94) = 3.84, p=

0.00**



311

Hierarchical multiple regression of 2nd year Depression

B Std. Error B

Beta t Sig.

Block 1

Constant 11.91 4.73 2.52 0.01


Age -0.18 0.35 -0.07 -0.51 0.61

Gender -0.85 1.36 -0.06 -0.62 0.53


R2=0.03,F(4, 97) = 0.71, p= 0.59

Block 2

Constant 30.85 5.80 5.32 0.00


Age -0.31 0.31 -0.13 -0.97 0.33

Gender -1.76 1.24 -0.13 -1.42 0.16



R

2=0.22, F(5, 96) = 5.35, p=

0.00**

Block 3

Constant 27.49 7.74 3.55 0.00


Age -0.43 0.33 -0.18 -1.32 0.19

Gender -1.83 1.27 -0.14 -1.44 0.15




Agreeableness 0.10 0.10 0.10 0.94 0.35

Extraversion 0.13 0.08 0.16 1.62 0.11


R

2=0.26, F(9, 92) = 3.52, p=

0.00**



312

Hierarchical multiple regression of 3rd year Depression

B Std. Error B

Beta t Sig.

Block 1

Constant 2.67 5.17 0.52 0.61


Age 0.13 0.45 0.05 0.30 0.77

Gender 2.54 1.59 0.17 1.59 0.11


R

2=0.03, F(4, 94) = 0.67, p=

0.61

Block 2

Constant 16.66 6.95 2.40 0.02


Age 0.17 0.43 0.06 0.40 0.69

Gender 1.34 1.59 0.09 0.85 0.40



R

2=0.11, F(5, 93) = 2.24, p=

0.06

Block 3

Constant 15.61 10.20 1.53 0.13


Age 0.25 0.45 0.09 0.55 0.59

Gender 1.71 1.63 0.11 1.05 0.30




Agreeableness -0.09 0.14 -0.07 -0.62 0.54

Extraversion -0.00 0.11 -0.00 -0.03 0.97


R

2=0.13, F(9, 89) = 1.44, p=

0.18



313

Hierarchical multiple regression of 1st year Anxiety

B Std. Error B

Beta t Sig.

Block 1

Constant 3.11 10.20 0.31 0.76


Age -0.04 0.36 -0.01 -0.10 0.92

Gender 0.65 1.67 0.04 0.39 0.70


R

2=0.03, F(4, 99) = 0.85, p=

0.50

Block 2

Constant 25.94 11.02 2.35 0.02


Age 0.01 0.34 0.00 0.02 0.98

Gender 0.36 1.56 0.02 0.23 0.82



R

2=0.17, F(5, 98) = 4.09, p=

0.00**

Block 3

Constant 27.33 14.64 1.87 0.07


Age -0.05 0.35 -0.02 -0.13 0.90

Gender 0.57 1.62 0.03 0.35 0.73




Agreeableness -0.07 0.14 -0.05 -0.52 0.61

Extraversion 0.11 0.10 0.12 1.15 0.25


R

2= 0.19, F(9, 94) = 2.44, p=

0.02



314

Hierarchical multiple regression of 2nd year Anxiety

B Std. Error B

Beta t Sig.

Block 1

Constant 15.27 3.68 4.15 0.00


Age 0.07 0.27 0.04 0.26 0.80

Gender 0.39 1.06 0.04 0.37 0.72


R

2=0.10, F(4, 97) = 2.74, p=

0.03

Block 2

Constant 25.39 4.80 5.30 0.00


Age 0.00 0.26 0.00 0.01 0.10

Gender -0.10 1.03 -0.01 -0.10 0.92



R

2=0.43, F(5, 96) = 4.33, p=

0.00**

Block 3

Constant 23.41 6.51 3.60 0.00

Responsibility for diabetes tasks -0.25 0.12 -0.28 -2.01 0.0\5*

Age -0.06 0.27 -0.03 -0.22 0.83

Gender -0.08 1.07 -0.01 -0.08 0.94




Agreeableness 0.03 0.09 0.04 0.37 0.71

Extraversion 0.06 0.07 0.10 0.93 0.36


R

2=0.20, F(9, 92) = 2.51, p=

0.01*



315

Hierarchical multiple regression of 3rd year Anxiety

B Std. Error B

Beta t Sig.

Block 1

Constant 9.74 4.04 2.41 0.02


Age -0.21 0.35 -0.10 -0.59 0.56

Gender 2.21 1.25 0.18 1.78 0.08


R

2= 0.29, F(4, 94) = 2.10, p=

0.09

Block 2

Constant 20.70 5.43 3.81 0.00


Age -0.18 0.34 -0.08 -0.52 0.60

Gender 1.28 1.24 0.10 1.03 0.31



R

2=0.40, F(5, 93) = 3.49, p=

0.01**

Block 3

Constant 14.40 7.82 1.84 0.07


Age -0.24 0.34 -0.11 -0.70 0.49

Gender 1.52 1.25 0.12 1.21 0.23




Agreeableness 0.15 0.11 0.14 1.39 0.17

Extraversion 0.06 0.08 0.07 0.68 0.50


R

2=0.21, F(9, 89) = 2.60, p=

0.01*



316

Hierarchical multiple regression of 1st year Stress

B Std. Error B

Beta t Sig.

Block 1

Constant 11.72 10.90 1.08 0.29


Age -0.20 0.39 -0.06 -0.51 0.62

Gender -1.01 1.79 -0.06 -0.56 0.57


R

2=0.05, F(4, 99) = 1.41, p=

0.24

Block 2

Constant 32.97 12.02 2.74 0.01


Age -0.16 0.37 -0.05 -0.42 0.67

Gender -1.28 1.70 -0.07 -0.75 0.45

Duration of diabetes -0.63 0.27 -0.22 -2.33 0.02*


R

2=0.16, F(5, 98) = 3.66, p=

0.00**

Block 3

Constant 39.61 15.76 2.51 0.01


Age -0.19 0.37 -0.06 -0.51 0.61

Gender -1.32 1.74 -0.07 -0.76 0.45

Duration of diabetes -0.63 0.27 -0.22 -2.34 0.02*



Agreeableness -0.16 0.15 -0.11 -1.09 0.28

Extraversion 0.14 0.10 0.14 1.32 0.19


R

2=0.20, F(9, 94) = 2.55, p=

0.01*



317

Hierarchical multiple regression of 2nd year Stress

B Std. Error B

Beta t Sig.

Block 1

Constant 19.51 5.10 3.83 0.00


Age -0.09 0.37 -0.03 -0.23 0.82

Gender -0.82 1.47 -0.06 -0.56 0.58


R

2=0.05, F(4, 97) = 1.34, p=

0.26

Block 2

Constant 31.60 6.72 4.70 0.00


Age -0.17 0.36 -0.06 -0.46 0.65

Gender -1.40 1.44 -0.10 -0.97 0.33



R

2=0.12, F(5, 96) = 2.55, p=

0.03*

Block 3

Constant 25.27 8.71 2.900 0.01


Age -0.40 0.37 -0.15 -1.08 0.28

Gender -1.07 1.43 -0.07 -0.75 0.45




Agreeableness 0.02 0.11 0.02 0.18 0.86

Extraversion 0.19 0.09 0.22 2.09 0.04*


R

2=0.21, F(9, 92) = 2.68, p=

0.01**



318

Hierarchical multiple regression of 3rd year Stress

B Std. Error B

Beta t Sig.

Block 1

Constant 6.52 4.55 1.43 0.16


Age -0.14 0.39 -0.06 -0.36 0.72

Gender 2.40 1.40 0.18 1.71 0.09


R

2= 0.04, F(4, 94) = 0.96, p=

0.44

Block 2

Constant 20.76 6.02 3.45 0.00


Age -0.10 0.37 -0.04 -0.28 0.78

Gender 1.18 1.38 0.09 0.86 0.39



R

2= 0.15, F(5, 93) = 3.15, p=

0.01*

Block 3

Constant 16.02 8.41 1.90 0.06


Age 0.08 0.37 0.04 0.23 0.82

Gender 1.91 1.34 0.14 1.42 0.16




Agreeableness -0.15 0.11 -0.13 -1.32 0.19

Extraversion 0.02 0.09 0.03 0.24 0.81

Openness to experience 0.19 0.08 0.26 2.39 0.02*

R

2= 0.24, F( 9, 89) = 3.14, p=

0.00**



319

Hierarchical multiple regression of 1st year Avoidance

B Std. Error B

Beta t Sig.

Block 1

Constant 4.22 3.87 1.09 0.28


Age -0.06 0.14 -0.05 -0.44 0.66

Gender 0.42 0.63 0.06 0.66 0.51


R

2=0.07, F(4, 99) = 1.92, p=

0.11

Block 2

Constant 11.80 4.71 2.50 0.01


Age -0.07 0.13 -0.06 -0.49 0.63

Gender 0.31 0.62 0.05 0.50 0.62




R

2=0.14, F(6, 97) = 2.67, p=

0.02*

Block 3

Constant 12.90 5.80 2.22 0.03


Age -0.06 0.14 -0.06 -0.47 0.64

Gender 0.39 0.64 0.06 0.61 0.54




Agreeableness -0.05 0.05 -0.09 -0.85 0.40

Extraversion -0.01 0.04 -0.02 -0.18 0.86


R

2=0.15, F(9, 94) = 1.85, p=

0.07



320

Hierarchical multiple regression of 2nd year Avoidance

B Std. Error B

Beta T Sig.

Block 1

Constant 13.32 2.39 5.56 0.00


Age 0.18 0.18 0.14 1.00 0.32

Gender -0.66 0.69 -0.10 -0.97 0.34


R

2=0. 09,F(4, 95) = 2.35, p=

0.06

Block 2

Constant 20.39 3.77 5.41 0.00


Age 0.11 0.18 0.09 0.63 0.53

Gender -0.93 0.68 -0.13 -1.36 0.18




R

2= 0.15, F(6, 93) = 2.75, p=

0.02*

Block 3

Constant 19.93 4.42 4.51 0.00


Age 0.07 0.18 0.05 0.38 0.71

Gender -0.80 0.71 -0.11 -1.13 0.26




Agreeableness -0.02 0.06 -0.05 -0.41 0.68

Extraversion -0.00 0.05 -0.00 -0.01 0.99


R

2=0.16, F(9, 90) = 1.97, p=

0.05



321

Hierarchical multiple regression of 3rdyear Avoidance

B Std. Error B

Beta T Sig.

Block 1

Constant 10.47 2.31 4.53 0.00


Age -0.15 0.20 -0.13 -0.77 0.45

Gender 0.25 0.71 0.03 0.34 0.73


R

2=0.04, F(4, 92) = 1.00, p=

0.41

Block 2

Constant 19.77 3.54 5.59 0.00


Age -0.18 0.20 -0.16 -0.95 0.35

Gender -0.42 0.70 -0.06 -0.60 0.55




R

2=0.15, F(6, 90) = 2.69, p=

0.02*

Block 3

Constant 24.26 4.43 5.48 0.00


Age -0.18 0.19 -0.15 -0.92 0.36

Gender -0.37 0.71 -0.06 -0.53 0.60




Agreeableness -0.13 0.06 -0.23 -2.16 0.03*

Extraversion -0.00 0.05 -0.01 -0.08 0.94


R

2=0.20, F(9, 87) = 2.37, p=

0.02*



322

Hierarchical multiple regression of 1st year Acceptance

B Std. Error B

Beta t Sig.

Block 1

Constant 26.93 5.22 5.16 0.00


Age 0.09 0.19 0.06 0.48 0.63

Gender -0.77 0.86 -0.09 -0.90 0.37


R

2=0.03, F(4, 99) = 0.80, p=

0.53

Block 2

Constant 20.02 6.51 3.08 0.00


Age 0.10 0.19 0.06 0.53 0.60

Gender -0.67 0.85 -0.08 -0.79 0.43




R

2=0.06, F(6, 97) = 1.07, p=

0.38

Block 3

Constant 18.45 8.04 2.30 0.02


Age 0.09 0.19 0.06 0.45 0.66

Gender -0.63 0.89 -0.07 -0.71 0.48




Agreeableness 0.02 0.08 0.03 0.30 0.77

Extraversion 0.03 0.05 0.07 0.63 0.53


R

2=0.07, F(9, 94) = 0.75, p=

0.67



323

Hierarchical multiple regression of 2ndyear Acceptance

B Std. Error B

Beta t Sig.

Block 1

Constant 22.37 2.67 8.38 0.00


Age 0.12 0.20 0.08 0.60 0.55

Gender -1.41 0.77 -0.18 -1.83 0.07


R

2=0.08, F(4, 97) = 2.00, p=

0.10

Block 2

Constant 19.14 4.24 4.52 0.00


Age 0.15 0.20 0.11 0.76 0.45

Gender -1.31 0.78 -0.17 -1.67 0.10




R

2=0.09, F(6, 95) = 1.48, p=

0.19

Block 3

Constant 19.49 4.87 4.00 0.00


Age 0.09 0.21 0.06 0.43 0.67

Gender -1.11 0.80 -0.14 -1.40 0.17




Agreeableness -0.05 0.06 -0.09 -0.76 0.45

Extraversion -0.03 0.05 -0.07 -0.61 0.54


R2=0,12F(9, 92) = 1.38, p= 0.21



324

Hierarchical multiple regression of 3rdyear Acceptance

B Std. Error B

Beta t Sig.

Block 1

Constant 27.41 2.50 10.96 0.00

Responsibility for diabetes tasks 0.23 0.09 0.40 2.62 0.01**

Age -0.49 0.22 -0.35 -2.28 0.03

Gender -2.74 0.77 -0.34 -3.55 0.00**


R

2=0.15, F(4, 95) = 4.21, p=

0.00**

Block 2

Constant 25.22 3.95 6.39 0.00

Responsibility for diabetes tasks 0.24 0.09 0.40 2.52 0.01*

Age -0.52 0.22 -0.37 -2.35 0.02*

Gender -2.50 0.80 -0.31 -3.12 0.00**




R

2=0.17, F(6, 93) = 3.12, p=

0.01**

Block 3

Constant 22.57 4.93 4.5 0.00

Responsibility for diabetes tasks 0.19 0.09 0.33 2.08 0.04*

Age -0.50 0.22 -0.36 -2.33 0.02*

Gender -2.33 0.79 -0.29 -2.95 0.00**




Agreeableness 0.08 0.07 0.12 1.13 0.26

Extraversion -0.06 0.05 -0.13 -1.24 0.22


R

2=0.24, F(9, 90) = 3.18, p=

0.00**


Five-factor personality traits and trajectories of glycaemic ...

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