Colette Funyui Wembenyui - QUT · Colette Funyui Wembenyui Bachelor of Nursing Submitted in fulfilment of the requirements for the degree of Master of Philosophy (IF80) School of

EXAMINING KNOWLEDGE AND SELF-

MANAGEMENT OF CHRONIC KIDNEY

DISEASE IN A PRIMARY HEALTH CARE

SETTING: VALIDATION OF TWO

INSTRUMENTS

Colette Funyui Wembenyui

Bachelor of Nursing

Submitted in fulfilment of the requirements for the degree of

Master of Philosophy (IF80)

School of Nursing

Faculty of Health

Queensland University of Technology

2017

Examining Knowledge and Self-management of Chronic Kidney Disease in a Primary Health Care Setting:

Validation of Two Instruments i

Keywords

Chronic kidney disease, end stage kidney disease, kidney disease knowledge survey,

patient knowledge, self-efficacy, self-management, self-management instrument,

reliability, validity.

ii Examining Knowledge and Self-management of Chronic Kidney Disease in a Primary Health Care

Setting: Validation of Two Instruments

Dedication

To my father, Tisanjoh Simbo Joseph (23/04/1953 to 02/02/2013) for his love,

care, and firm belief in me. You told me to aim for the stars and I would reach them.

Throughout this journey your words have kept me going. I hope I have made you

proud. To my Grandma, fondly called Mayan; and my aunt, Grace Sekeh Kometa for

their love and support, until their last days.


Validation of Two Instruments iii

Abstract

Background: Chronic kidney disease (CKD) is a debilitating and major health

problem in Australia and globally. If detected early and managed appropriately, CKD

progression can be prevented or delayed. There is growing evidence that self-

management behaviours can improve patient outcomes in people with CKD. To

effectively self-manage, these individuals need to have sufficient knowledge about

their disease and its treatment. There are few suitable instruments that measure

kidney disease-specific knowledge and CKD self-management.

Aims: The primary aims of this study were to evaluate the validity and

reliability of two CKD instruments; namely the Kidney Knowledge Survey (KiKS)

and CKD self-management instrument (CKD-SM) in an Australian population.

The secondary aims were to:

• Describe the characteristics of CKD patients attending a primary

healthcare clinic and to assess CKD knowledge, self-management, and

self-efficacy.

• Test the hypothesised relationship between knowledge, self-management

and self-efficacy.

Methods: The study was cross-sectional, with a test retest protocol conducted

at Inala Primary Care in Queensland, Australia. A total of 77 adults with CKD stages

1-4 completed the self-report CKD-SM comprising 32 items on a 4 point Likert

scale; KiKS (28 items); and the Self-efficacy for Managing Chronic Disease 6-Item

Scale (SEMCD). Demographic and renal clinical characteristics were also collected.

The retest took place one week after the initial completion of the questionnaires.

Results: Most participants had CKD stage 3 (n = 51, 65.4%), half were male

(50.6%), and the age range was 31-88 years (mean = 67.3, SD = 13.2). KiKS scores

ranged from 6 to 25 (mean = 17.4 SD = 4.4). The KiKS had good reliability, the

Kuder-Richardson-20 coefficient was 0.74 (test retest reliability [intraclass

coefficients] = 0.42). Age and household income were significantly associated with

iv Examining Knowledge and Self-management of Chronic Kidney Disease in a Primary Health Care


CKD knowledge compared to gender and marital status (p < 0.05). CKD knowledge

was not related to renal characteristics (p > 0.05).

The internal structure of the 32 item CKD self-management instrument was

examined using exploratory factor analysis. Cross loaded factor items and items

loading < 0.4 were removed from the scale (Kaiser-Meyer-Olkin value = 0.78;

Bartlett's test of sphericity χ2 1262.55, p < 0.001). Four factors consisting of 17 items

with the most meaningful patterns were extracted; this became the Australian version

of the CKD-SM (Aus.CKD-SM). The four factors were named “self-integration”,

“seeking social support”, “adherence to lifestyle modification”, and “problem

solving” respectively. The Aus.CKD-SM had good reliability (Cronbach’s alpha =

0.86, intraclass coefficients = 0.82) and self-management scores ranged from 23 to

67 (mean = 49.78, SD = 9.39). Marital status was significantly related to self-

management compared to age, gender, employment status, or household income (p <

0.01). There was no relationship between self-management behaviours and renal

characteristics (p > 0.05).

The SEMCD scores ranged from 1 to 10 (mean = 7.20, SD = 2.16). Self-

efficacy was significantly related to CKD self-management (p < 0.001), with high

levels of self-efficacy associated with high levels of CKD self-management.

However, there was no relationship between CKD knowledge and CKD self-

management, or self-efficacy.

Conclusion: The validity and reliability of the KiKS and Aus.CKD-SM were

supported in an Australian population with CKD. Study participants across all stages

of their disease reported varying levels of engagement in self-management

behaviours; however, CKD knowledge was unexpectedly low. The disparities in self-

management scores indicate the need for a self-management instrument to identify

those in need of education to enhance self-management. Primary healthcare patients

may benefit from education to improve CKD knowledge and self-management

behaviours, particularly in the earlier stages of the disease.


Validation of Two Instruments v

Table of Contents

Keywords .................................................................................................................................. i

Dedication ................................................................................................................................ ii

Abstract ................................................................................................................................... iii

Table of Contents ......................................................................................................................v

List of Figures ......................................................................................................................... ix

List of Tables ............................................................................................................................x

List of Abbreviations .............................................................................................................. xi

List of Presentations ............................................................................................................... xii

Statement of Original Authorship ......................................................................................... xiii

Acknowledgements ............................................................................................................... xiv

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

1.1 Introduction ....................................................................................................................1

1.2 Background .....................................................................................................................2 1.2.1 Definition and Classification of Chronic Kidney Disease ....................................2 1.2.2 Prevalence of Chronic Kidney Disease ................................................................3 1.2.3 Risk Factors for Chronic Kidney Disease ............................................................4 1.2.4 Causes of Chronic Kidney Disease ......................................................................5 1.2.5 Clinical Manifestations of Chronic Kidney Disease ............................................6 1.2.6 Management of Chronic Kidney Disease .............................................................6

1.3 Research Aim and Questions ..........................................................................................9

1.4 Significance of the Study ................................................................................................9

1.5 Thesis Outline ...............................................................................................................10

1.6 Summary .......................................................................................................................11

Chapter 2: Literature Review ..........................................................................................12

2.1 Introduction ..................................................................................................................12

2.2 Impact of Chronic Kidney Disease ...............................................................................12 2.2.1 Physical Impact ..................................................................................................12 2.2.2 Psychosocial Impact ...........................................................................................13 2.2.3 Socioeconomic Impact .......................................................................................14

2.3 Self-management of Chronic Kidney Disease ..............................................................16

2.4 Chronic Kidney Disease Knowledge ............................................................................21

2.5 Chronic Kidney Disease Self-Efficacy .........................................................................24

2.6 Research Gap ................................................................................................................25

2.7 Summary .......................................................................................................................26

Chapter 3: Theoretical Framework .................................................................................27

3.1 Introduction ..................................................................................................................27

3.2 Self-Management Theories and Definition ...................................................................27

vi Examining Knowledge and Self-management of Chronic Kidney Disease in a Primary Health Care


3.3 Self-Management Skills ............................................................................................... 29 3.3.1 Problem Solving Skills ...................................................................................... 29 3.3.2 Decision Making Skills ...................................................................................... 29 3.3.3 Resource Utilisation Skills ................................................................................. 30 3.3.4 Formation of Patient/Healthcare Provider Partnership ...................................... 30 3.3.5 Taking Action .................................................................................................... 31

3.4 Domains of Self-Management Behaviour .................................................................... 33 3.4.1 Adherence to Dietary Requirements .................................................................. 33 3.4.2 Monitoring and Responding to Alterations in Biochemistry ............................. 34 3.4.3 Adhering to Blood Pressure Regimens .............................................................. 35 3.4.4 Adhering to Medications ................................................................................... 36

3.5 Self-Management Skills and Behaviours ..................................................................... 37

3.6 Theoretical Framework Applied to Current Study ....................................................... 38

3.7 Summary ...................................................................................................................... 43

Chapter 4: Methods .......................................................................................................... 44

4.1 Introduction .................................................................................................................. 44

4.2 Aims ............................................................................................................................. 44

4.3 Research Questions ...................................................................................................... 44

4.4 Design .......................................................................................................................... 44

4.5 Study Setting ................................................................................................................ 47

4.6 Inclusion and Exclusion Criteria .................................................................................. 50

4.7 Sample Size .................................................................................................................. 50

4.8 Instruments ................................................................................................................... 51 4.8.1 Kidney Knowledge Survey ................................................................................ 51 4.8.2 Chronic Kidney Disease Self-Management Instrument..................................... 53 4.8.3 Chronic Disease Self-efficacy ........................................................................... 54 4.8.4 Demographic Questionnaire .............................................................................. 54 4.8.5 Clinical Characteristics Tool ............................................................................. 55

4.9 Procedure and Data Collection..................................................................................... 55

4.10 Data Analysis ............................................................................................................... 56

4.11 Ethical Considerations ................................................................................................. 57

4.12 Summary ...................................................................................................................... 57

Chapter 5: Results ............................................................................................................ 58

5.1 Introduction .................................................................................................................. 58

5.2 Data Preparation and Cleaning..................................................................................... 60

5.3 Sample Characteristics ................................................................................................. 60

5.4 Renal Clinical Characteristics ...................................................................................... 62

5.5 Descriptive Statistics for Key Study Variables ............................................................ 64 5.5.1 Testing the Normality of the Instruments .......................................................... 64 5.5.2 Kidney Disease Knowledge Survey................................................................... 67 5.5.3 Chronic Kidney Disease Self-Management Instrument..................................... 69 5.5.4 Self-Efficacy for Managing Chronic Disease 6-Item Scale ............................... 72 5.5.5 Exploratory Factor Analysis for the CKD-SM .................................................. 72 5.5.6 Chronic Kidney Disease Self-Management Subscales ...................................... 76


Validation of Two Instruments vii

5.6 Reliability of Instruments .............................................................................................83

5.7 Comparing Test and Retest Means ...............................................................................88

5.8 Associations between Chronic Kidney Disease Knowledge, Self-Management, and

Self-Efficacy ...........................................................................................................................89

5.9 Relationships between Demographic Characteristics and CKD Knowledge, CKD Self-

management, and Self-Efficacy ..............................................................................................89

5.10 Relationships between Renal Clinical Characteristics and CKD Knowledge, Self-

Management, and Self-Efficacy ..............................................................................................92

5.11 Summary .......................................................................................................................95

Chapter 6: Discussion .......................................................................................................96

6.1 Introduction ..................................................................................................................96

6.2 Theoretical Framework .................................................................................................96

6.3 Chronic Kidney Disease in Primary Healthcare ...........................................................97

6.4 Main Study Findings.....................................................................................................98 6.4.1 Chronic Kidney Disease Knowledge ..................................................................98 6.4.2 Chronic Kidney Disease Self-Management .....................................................102 6.4.3 Self-Efficacy .....................................................................................................107

6.5 Reliability and Validity of Instruments ......................................................................108

6.6 Summary .....................................................................................................................109

Chapter 7: Conclusions ...................................................................................................110

7.1 Introduction ................................................................................................................110

7.2 Study Strength ............................................................................................................110

7.3 Study Limitations .......................................................................................................111

7.4 Implications for Practice, Education and Research ....................................................111 7.4.1 Implications for Nursing Practice .....................................................................111 7.4.2 Implications for Education ...............................................................................112 7.4.3 Implications for Research .................................................................................113

7.5 Conclusion ..................................................................................................................114

References ............................................................................................................................115

Appendices ...........................................................................................................................131

Appendix 1: Application for Review of Negligible/Low Risk Research Involving Human

Participants ............................................................................................................................131

Appendix 2: Ethics Variation ................................................................................................132

Appendix 3: Site Specific Approval Letter and Forms for Data Collection .........................133

Appendix 4: Author’s Permission to use the CDKD Self-Management Instrument ............142

Appendix 5: Participant Demographic Information Questionnaire ......................................143

Appendix 6: Clinical Characteristics (from medical records review) ...................................145

Appendix 7: Kidney Disease Knowledge Survey (KiKS) ....................................................146

Appendix 8: Chronic Kidney Disease Self-Management Instrument ...................................149

Appendix 9: Self-Efficacy for Managing Chronic Disease Six-Item Scale ..........................152

Appendix 10: Histogram, Normal Q-Q Plots and Box-whisker Plots of Instruments ..........153

viii Examining Knowledge and Self-management of Chronic Kidney Disease in a Primary Health Care


Appendix 11: Patient Information and Consent Forms ........................................................ 154


Validation of Two Instruments ix

List of Figures

Figure 3.1: Self-management skills, modified from Lorig and Holman (2003) pg. 38-

41 .............................................................................................................................32

Figure 3.2: Theoretical Framework Applied to this Study .....................................................41

Figure 5.1: Participants Recruitment Flow Diagram ..............................................................59

Figure 5.2: Histograms for KiKS, CKD-SM and SEMCD .....................................................66

Figure 5.3: Scree Plot for the Aus.CKD-SM Items ................................................................74

Figure 5.4: Exploratory Factor Analysis of the Aus.CKD Self-Management Instrument ......79

Figure 5.5: Histograms for Aus.CKD-SM and Subscales.......................................................83

Figure 5.6: Bland-Altman plots for 1-Week Test-Retest of KiKS, Aus.CKD-SM, and

SEMCD ...................................................................................................................87

x Examining Knowledge and Self-management of Chronic Kidney Disease in a Primary Health Care


List of Tables

Table 4:1 Instrument Validity and Reliability Measures ....................................................... 47

Table 4:2 Comparing the Inala population to the general Australian population ................... 49

Table 5:1: Demographic Characteristics (n = 77) .................................................................. 61

Table 5:2: Renal Clinical Characteristics ............................................................................... 63

Table 5:3: Mean and SD for KiKS, CKD-SM, and SEMCD ................................................. 64

Table 5:4: Kidney Knowledge Survey (KiKS) ...................................................................... 68

Table 5:5: Chronic Kidney Disease Self-Management Instrument ........................................ 70

Table 5:6: Self-Efficacy for Managing Chronic Disease 6-Item Scale .................................. 72

Table 5:7: Factor Loadings for Aus.CKD-SM ....................................................................... 75

Table 5:8: Australian CKD Self-Management Instrument ..................................................... 77

Table 5:9: Reliability Tests of Aus.CKD Self-Management Instrument and Subscales ........ 80

Table 5:10: Internal Consistency Reliability of Instruments (n = 77) .................................... 84

Table 5:11: Intraclass Correlation Coefficients Analysis, Two-Way Random Effects

Model for Consistency, for 1-Week Test-Retest of KiKS, Aus.CKD-SM and

SEMCD ................................................................................................................... 85

Table 5:12: Paired T-test Comparing Knowledge and Self-Management Scores at Two

Time Points ............................................................................................................. 88

Table 5:13: Relationships Between Demographic Characteristics and CKD

Knowledge, Self-Management, and Self-Efficacy ................................................. 91

Table 5:14: Relationships Between Renal Clinical Characteristics and CKD

Knowledge, Self-Management, and Self-Efficacy ................................................. 93


Validation of Two Instruments xi

List of Abbreviations

Aus.CKD-SM Australian Chronic Kidney Disease Self-Management

Instrument

BMI Body Mass Index

BP Blood Pressure

CKD Chronic Kidney Disease

CKD-SM Chronic Kidney Disease Self-Management Instrument

eGFR Estimated Glomerular Filtration Rate

ESKD End-stage Kidney Disease

HbA1c Glycosylated haemoglobin

HDL High Density Lipoproteins

KiKS Kidney Knowledge Survey

SEMCD Self-Efficacy for Managing Chronic Disease 6-Item Scale

xii Examining Knowledge and Self-management of Chronic Kidney Disease in a Primary Health Care


List of Presentations

Oral presentation

Wembenyui C., Bonner A., & Douglas C. (2016). Examining patient’s knowledge

about chronic kidney disease in a primary healthcare setting. 2016 Renal Society of

Australasia Annual Conference, Sea World Gold Coast Conference Centre.

Poster presentation

Wembenyui C., Bonner A., & Douglas C. (2016). Gauging the level of chronic

kidney disease self-management behaviour of patients in a general practice. 2016

Renal Society of Australasia Annual Conference, Sea World Gold Coast Conference

Centre.


Validation of Two Instruments xiii

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature:

Date: November 2017

QUT Verified Signature

xiv Examining Knowledge and Self-management of Chronic Kidney Disease in a Primary Health Care


Acknowledgements

I would like to acknowledge, with thanks, my supervisors, Prof. Ann Bonner

and Assoc. Prof. Clint Douglas for their diverse contributions towards the realisation

of my research project. I would like thank Prof. Ann Bonner for her support as a

supervisor, and her encouragement and mentorship. You stand tall as a role model

that I look up to. My sincere thanks to Assoc. Prof. Clint Douglas for his expertise in

statistical analysis, support in interpreting data, and commitment as my supervisor.

Financial support for data collection, analysis, and tuition was provided by the

Australian Government’s Research Training Scheme and QUT.

My thanks go to Tracey Johnson for facilitating the acquisition of a Site

Specific Approval for data collection at Inala Primary Care in Queensland, Australia.

I would like to thank Dr. Robin Armstrong and RN Chris Bowering for their support

with patient recruitment and conduct of the study. I would also like to thank Kirsty,

the doctors, and all of the other staff at Inala Primary Care for making my time at the

clinic a success.

My thanks also to professional editor, Kylie Morris, who provided copyediting

and proofreading services, according to university-endorsed guidelines and the

Australian Standards for editing research theses.

Thank you to my friend Nguyet Nguyen, who has been a great source of

support throughout this research journey. Thank you to my friends and research

colleagues at QUT.

I would also like to acknowledge the support and encouragement of my

mother, Kuneh Tisanjoh Helen and my siblings.

Finally, I would like to thank my husband Dr. Emmanuel Wembenyui, for his

input and discussion with my research. Emmanuel, Shannon and Lindsay thank you

for your love and support.

Chapter 1: Introduction 1

Chapter 1: Introduction

1.1 INTRODUCTION

Chronic kidney disease (CKD) is increasingly recognised as a major public

health concern in Australia and globally. It is a leading cause of mortality and

morbidity, and poses a significant economic burden to healthcare systems (Evans &

Taal, 2015; Jager & Fraser, 2017). The prevalence of CKD varies across different

populations, with an estimated 8-16% of individuals affected by CKD worldwide

(Jha et al., 2013). Recognition of the early stages of CKD is difficult because

affected individuals are usually asymptomatic (Chadban, 2003; Mathew & Corso,

2009; White et al., 2008). Chronic kidney disease often remains undiagnosed until

there is severe loss of kidney function (Kidney Health Australia, 2015a; Mathew et

al., 2010).

Chronic kidney disease is a treatable disease, and if detected early and

managed appropriately the progression of CKD can be prevented or delayed

(Johnson et al., 2013). Reduced renal function can be detected through a routine

blood test that measures serum creatinine levels, a urinalysis measuring the albumin

creatinine ratio, or through imaging studies (Bonner, 2012; Kidney Health Australia,

2015a; Qaseem et al., 2013). Treatment of CKD involves both pharmacological and

non-pharmacological management. In addition, people with CKD need to make

lifestyle modifications to manage their disease. One strategy to improve patient

outcomes is to enhance self-management (Walker, Marshall, & Polaschek, 2013).

Several studies have shown that effective self-management behaviour, such as

adhering to healthcare recommendations about treatment, can help improve

outcomes (Curtin et al., 2008; Kazawa & Moriyama, 2013; Lin, Wu, Wu, Chen, &

Chang, 2013b). Despite this, in a recent literature review, Bonner et al. (2014a)

identified only five studies that assessed the delivery of self-management

interventions in people with stages 1-4 CKD. These studies differed in methodology

and quality, which led the authors to conclude that the evidence regarding

effectiveness of self-management intervention programs was inconclusive (Bonner et

al., 2014a). There are several instruments available that measure self-management in

chronic diseases (Battersby, Ask, Reece, Markwick, & Collins, 2003; Glasgow,

2 Chapter 1: Introduction

Fisher, Skaff, Mullan, & Toobert, 2007). However, only one study has been

identified that has developed and validated an instrument to measure self-

management in people with CKD (Lin et al., 2013b). The challenge facing health

professionals has to do with understanding self-management in the context of living

with CKD, and specifically, how it can be measured in practice.

Knowledge about CKD is an important factor for self-management. Current

evidence suggests that people with CKD have limited knowledge about their disease

and its treatment (Chen et al., 2011; Costantini et al., 2008; Ong, Jassal, Porter,

Logan, & Miller, 2013; Tuot, Davis, Velasquez, Banerjee, & Powe, 2013). Costantini

et al. (2008) highlighted the need for more disease specific knowledge in people with

early stage CKD to enable them to better manage their disease. Knowledge about

diet, blood pressure, lifestyle modifications, and medications is needed (Ong et al.,

2013).

1.2 BACKGROUND

1.2.1 Definition and Classification of Chronic Kidney Disease

Chronic kidney disease is defined by an estimated glomerular filtration rate

(eGFR) less than 60mL/min/1.73m3 for more than three months, with or without the

presence of kidney damage (Bonner, 2012; Johnson et al., 2013). It can also be

defined by structural or functional abnormalities of the kidney, which must be

present for more than three months, as pathological abnormalities and markers of

kidney damage that may or may not decrease eGFR (Kidney Disease: Improving

Global Outcomes (KDIGO) CKD Work Group, 2013). Kidney damage is evidenced

by the persistent presence of proteinuria, albuminuria, haematuria, or structural

abnormalities detected by imaging tests (Australian Institute of Health and Welfare

[AIHW], 2009; Qaseem et al., 2013). Chronic kidney disease progresses slowly and

insidiously over a period of months or years (Bonner, 2012; Webster, Nagler,

Morton, & Masson, 2017).

Chronic kidney disease is classified into five stages on the basis of kidney

function and damage, one of which is subdivided (Johnson et al., 2013). Stage 1

CKD is described as a normal GFR greater than or equal to 90 mL/min/1.73m2, and

stage 2 CKD as a mild reduction between 60-89 mL/min/1.73m2 (Kidney Health

Australia, 2015a). However, stages 1 and 2 cannot be classified as CKD unless there


are markers of kidney damage, including albuminuria, haematuria, proteinuria,

pathological, or structural abnormalities (Kidney Health Australia, 2015a). Stage 3

CKD is subdivided into 3a and 3b, with 3a indicating a mild to moderate reduction in

eGFR between 45-59 mL/min/1.73m2; and 3b, a moderate to severe reduction in

eGFR between 30-44 mL/min/1.73m2 (Kidney Disease: Improving Global Outcomes

(KDIGO) CKD Work Group, 2013). Stage 4 CKD represents a severe decrease in

renal function with an eGFR between 15-29 mL/min/1.73m2; and the final stage,

stage 5 CKD, is also termed end stage kidney disease (ESKD) with an eGFR less

than 15 mL/min/1.73m2 (Kidney Disease: Improving Global Outcomes (KDIGO)

CKD Work Group, 2013). When kidney function is severely diminished and unable

to sustain life, kidney replacement therapy (KRT – haemodialysis, peritoneal dialysis

or kidney transplantation) is required. Typically, this is when eGFR is less than 10

mL/min/1.73m2 (Chow et al., 2012; Dring & Hipkiss, 2015).

1.2.2 Prevalence of Chronic Kidney Disease

The prevalence of CKD is rising globally. This is partly due to an ageing

population, and an increasing prevalence of diabetes and hypertension (Jager &

Fraser, 2017; Kidney Health Australia, 2015a; National Kidney Foundation, 2010;

Radhakrishnan et al., 2014). More recently, the prevalence of CKD has been reported

to be as much as 10-15% worldwide (Levin et al., 2017). In the United States (US), it

is estimated that more than 10% of the adult population have CKD (Centres for

Disease Control and Prevention, 2014). The prevalence of CKD in Asian countries

varies from 13-17.5% (Li et al., 2011).

In Australia, approximately one out of every 10 adults have evidence of mild to

moderate CKD and is at increased risk of kidney failure or cardiovascular disease

(Johnson et al., 2013; Kidney Health Australia, 2015a). In addition, one in three

Australians have an increased risk of developing CKD (see risk factors below). In

2007, 10% of all deaths in Australia were associated with CKD, and in 2007 and

2008, 13.4% of all hospitalisations were for dialysis (AIHW, 2016a). According to

Chronic Kidney Disease in Queensland (2011), approximately 13% of the adult

population in Queensland have CKD. Chronic kidney disease is more prevalent in

Aboriginal and Torres Strait Islander people than in non-Indigenous Australians, it is

estimated to be 18% (Kidney Health Australia, 2015a). As the prevalence of other

chronic diseases, such as diabetes, hypertension, and cardiovascular diseases (which


are also risk factors for CKD) increase, so too will the prevalence CKD (Kidney

Health Australia, 2015a).

1.2.3 Risk Factors for Chronic Kidney Disease

Chronic kidney disease shares common risk factors with other chronic

diseases, such as cardiovascular disease and diabetes, which are also risk factors for

CKD (AIHW, 2009). The risk factors for CKD can be grouped into three main

categories: fixed, behavioural, and biomedical (AIHW, 2009). The fixed category

includes family history, increasing age, previous kidney disease or injury, low birth

weight, and being male. Behavioural risk factors include tobacco smoking, physical

inactivity, and poor nutrition; while the biomedical category includes diabetes,

hypertension, cardiovascular disease, overweight and obesity, and systemic kidney

inflammation.

Diabetes and hypertension is associated with a significantly increased risk for

developing CKD (Centres for Disease Control and Prevention, 2014; Jha et al., 2013;

Levin et al., 2017; Qaseem et al., 2013; Tanamas et al., 2012). Approximately one in

three adults with diabetes and one in five adults with hypertension have CKD

(Centres for Disease Control and Prevention, 2014). People with hypertension are

five times more likely to have CKD than those without (Tanamas et al., 2012). Both

of these diseases are major causes of CKD and are described in more detail below

(see Section 1.2.4).

Smoking increases the risk of CKD development and progression in the

community (Tomson & Bailey, 2011; Yacoub et al., 2010). In 2010, the AIHW

(2015) estimated that 15% of Australians aged 14 and above smoked. Smoking is

associated with a significant increase in CKD in people with primary hypertension

and diabetes (Yacoub et al., 2010) .

The worldwide prevalence of obesity has been accompanied by an increase in

the incidence of diseases, such as diabetes, hypertension, cardiovascular disease, and

CKD (Eckardt et al., 2013). In Australia, approximately 63% of adults are either

overweight or obese (AIHW, 2016a). The link between CKD and obesity could

partly be due to the involvement of CKD in diabetes and hypertension, which are

both risk factors for CKD (Eckardt et al., 2013). Weight reduction in people can

reduce the risk of CKD (Flesher et al., 2011; Lowth, 2013).


1.2.4 Causes of Chronic Kidney Disease

According to the Chronic Kidney Disease in Queensland registry study, renal

vascular disease, diabetic nephropathy, glomerulonephritis, and genetic renal disease

are the leading causes of CKD prior to commencing KRT (Zhang et al., 2016).

However, reliable data on the causes of CKD has primarily been obtained from

registries of patients with ESKD. The most common causes for the development and

subsequent progression of CKD in Australia are diabetic nephropathy,

glomerulonephritis, and hypertension (AIHW, 2009; ANZDATA Registry, 2016).

Both type 1 and type 2 diabetes mellitus cause CKD (Chen et al., 2011). The

global prevalence of diabetes is 6.4%, approximately 285 million people, and this

figure is projected to rise 7.7% (439 million people) by 2030 (Jha et al., 2013). It is

estimated that approximately 40% of people who have diabetes will go on to develop

CKD (Taal & Evans, 2011). In 2014, diabetes accounted for 37% of all new cases

with ESKD treated with dialysis in Australia (ANZDATA Registry, 2016; Kidney

Health Australia, 2016). Diabetes occurs when the body produces either too little

insulin or no insulin, or the body cannot utilise the insulin it produces. Poorly

controlled blood glucose levels lead to hyperglycaemia, which causes damage to the

glomerular capillaries in the kidney (AIHW, 2009; Kidney Health Australia, 2014).

Initially, this glomerular damage causes small amounts of protein to pass through the

glomerular capillaries and end up in the urine. Later, large amounts of protein are

lost in urine, such that water gets into body tissues and causes swelling, usually in the

face, hands, legs, and feet. If left untreated, the kidney filters become narrow and

clogged, which can lead to kidney failure (Thomas & Bryar, 2013).

Glomerulonephritis is the second leading cause of CKD (Kidney Health

Australia, 2016). Glomerulonephritis describes a wide range of conditions that cause

the glomerulus to become inflamed or damaged, thereby affecting the kidney’s

ability to remove metabolic waste and excess fluid from the body (AIHW, 2009;

Evans & Taal, 2011). In 2014, it accounted for 20% of all commencing dialysis

patients in Australia (ANZDATA Registry, 2016; Kidney Health Australia, 2016).

Hypertension is often a presenting feature of CKD and is an important factor

in the development and progression of CKD (Evans & Taal, 2015; Mobley, 2009). It

is a leading cause of ESKD worldwide. In 2014, hypertension accounted for 13% of

all commencing dialysis patients in Australia (ANZDATA Registry, 2016; Kidney


Health Australia, 2016). Hypertension damages the small blood vessels supplying the

kidneys (Nikolajenko, 2013). Over time, the blood vessel walls become thick and

narrowed (AIHW, 2009), affecting the kidney’s ability to autoregulate blood flow

and GFR (Mobley, 2009).

There are many other causes of CKD, including genetic diseases, infections,

obstruction, drugs, and urological conditions. Together, these other causes account

for approximately 30% of CKD cases (ANZDATA Registry, 2016; Kidney Health

Australia, 2016).

1.2.5 Clinical Manifestations of Chronic Kidney Disease

Chronic kidney disease is a silent disease and people are often asymptomatic

until the disease is well advanced. An individual can lose up to 90% of their kidney

function before symptoms become apparent (Kidney Health Australia, 2016). The

symptoms of CKD may include high blood pressure, changes in urination (reduced

volume, increased frequency or nocturia), peripheral oedema, pain in the kidney area,

fatigue, loss of appetite, difficulty sleeping, headaches, lack of concentration,

pruritus, shortness of breath, nausea and vomiting, or bad breath and a metallic taste

in the mouth. People with CKD stages 1-3 are generally asymptomatic, with

symptoms not typically becoming apparent until stages 4-5. These symptoms tend to

worsen as kidney function deteriorates (Devraj & Wallace, 2013).

1.2.6 Management of Chronic Kidney Disease

The healthcare management of CKD depends on the underlying cause of the

disease, severity of kidney function impairment, and the presence of comorbid health

conditions (e.g., diabetes, cardiovascular disease). The aim of management is to

prevent or delay the progression to ESKD, reduce cardiovascular risk, and improve

quality of life (Jager & Fraser, 2017; Kidney Health Australia, 2015a; Nikolajenko,

2013; Turner, Bauer, Abramowitz, Melamed, & Hostetter, 2012). Treatment

strategies for CKD involve pharmacological and non-pharmacological interventions

(Bautovich, Katz, Smith, Loo, & Harvey, 2014; Flesher et al., 2011; Walker et al.,

2013). However, once an individual reaches ESKD, they must make a decision about

treatment options, which are essentially conservative management or KRT (Bonner

& Douglas, 2014; Dring & Hipkiss, 2015). For the purpose of this study, KRT is not

discussed in detail.


Blood pressure (BP) control is paramount for patients with CKD, firstly to

delay the progression of CKD and secondly to reduce the risk of cardiovascular

disease (Flesher et al., 2011; Judd & Calhoun, 2015; Lowth, 2013; Sakraida &

Robinson, 2009). Target BP should be below 140/90 mmHg (Costantini et al., 2008;

Kidney Health Australia, 2015a); however, for patients with CKD and diabetes or

albuminuria, a BP below 130/80 mmHg is more desirable (Flesher et al., 2011;

Javalk, Fenton, Cohen, & Ferris, 2014; Sakraida & Robinson, 2009). First line

therapy with angiotensin-converting enzyme inhibitors or angiotensin receptor

blocker is generally recommended, due to their BP and albuminuria-lowering effects

(Costantini et al., 2008; Flesher et al., 2011; Kidney Health Australia, 2015a). A

diuretic may be used as an adjunct antihypertensive therapy (Kidney Health

Australia, 2015a; Sakraida & Robinson, 2009). Additional antihypertensive agents,

such as beta blockers and calcium channel blockers, may be prescribed depending on

the cardiovascular indications and comorbidities (Kidney Health Australia, 2015a;

Turner et al., 2012).

For patients with diabetes, good glycaemic control may delay the progression

of CKD (Bautovich et al., 2014; Doulton, Farmer, & Stevens, 2015). It helps prevent

the development of microalbuminuria, as well as delaying the progression of

microvascular complications (Costantini et al., 2008; Doulton et al., 2015; Sakraida

& Robinson, 2009). However, in patients with long standing diabetes, overly

aggressive management of diabetes may increase hypoglycaemic episodes and

should therefore be avoided (Lowth, 2013). Current guidelines suggest people with

established CKD should aim for a haemoglobin A1C level below 7% (Kidney Health

Australia, 2015a; Turner et al., 2012).

Chronic kidney disease is associated with significant alterations in lipid

metabolism (Kidney Health Australia, 2015a; Turner et al., 2012). Hyperlipidaemia,

though not a cause of CKD, may contribute to its progression (Costantini et al.,

2008). Given this, lipid-lowering therapy is recommended for the treatment of

dyslipidaemia in people with CKD (Kidney Health Australia, 2015a; Upadhyay et

al., 2012). However, evidence regarding lipid-lowering therapy independently

slowing the progression of CKD is limited (Costantini et al., 2008; Lowth, 2013).

Despite the limited evidence, patients with CKD may benefit from lipid-lowering


therapy due to an increased cardiovascular risk (Lowth, 2013; Upadhyay et al.,

2012).

Non-pharmacological interventions, also known as lifestyle modification,

remain a critical component of CKD management, regardless of whether medication

therapy is being implemented (Johnson et al., 2013; Kidney Health Australia, 2015a).

Lifestyle modifications, such as smoking cessation, increased physical activity,

weight management, dietary sodium reduction, and moderation of alcohol

consumption, are all important in reducing cardiovascular risks (Flesher et al., 2011;

Javalk et al., 2014; Kidney Health Australia, 2015a; Lowth, 2013). Smoking

cessation should be encouraged, as evidence suggests it exaggerates the risk of

cardiovascular disease in people with CKD (Flesher et al., 2011; Lin, Tsai, Lin,

Hwang, & Chen, 2013a). Weight reduction should be encouraged in overweight or

obese individuals; with a target body mass index (BMI) of 18.5 to 24.9 kg/m2 and a

waist circumference of no more than 94cm in men and 80cm in women (Kidney

Health Australia, 2015d). Moderate intensity exercise for at least 30 minutes most

days of the week is recommended for overall health and to reduce the risk of CKD.

Regular physical activity, such as brisk walking, should be encouraged, as it reduces

cardiovascular risks and blood pressure, both of which increase mortality risks in

people with CKD (Eskridge, 2010). Dietary sodium restriction (2 grams a day) is

crucial for people with CKD to reduce BP and albuminuria (Bautovich et al., 2014;

Flesher et al., 2011; Judd & Calhoun, 2015; Lee, Kim, Cho, & Kim, 2013).

Reduction in alcohol intake has been shown to prevent hypertension in individuals

with normal or high normal blood pressure (Sesso, Cook, Buring, Manson, &

Gaziano, 2008). Alcohol consumption should be limited to no more than two

standard drinks in men and no more than one in women per day (Eskridge, 2010).

Chronic kidney disease is a complex disease requiring life-long management.

Self-management is key to improving outcomes in those with the disease. People

with CKD should be offered education and information on CKD and its treatment.

However, to date, no studies have been undertaken to evaluate measures of CKD

knowledge and CKD self-management in Australia.


1.3 RESEARCH AIM AND QUESTIONS

The primary aim of this study was to evaluate the validity and reliability of two

CKD instruments: the Kidney Knowledge Survey (Wright, Wallston, Elasy, Ikizler,

& Cavanaugh, 2011) and CKD self-management instrument (Lin et al., 2013b) in an

Australian CKD population.


• Describe the characteristics of patients attending a primary healthcare

CKD clinic and to assess CKD knowledge, self-management, and self-

efficacy.


and self-efficacy.

In order to achieve these aims, this study sought to answer the following

research questions:

1. Is the CKD self-management instrument (CKD-SM) a valid and reliable

measure of self-management behaviours in an Australian population with

CKD?

2. Is the Kidney Knowledge Survey (KiKS) a valid and reliable measure of

CKD knowledge in an Australian population with CKD?

1.4 SIGNIFICANCE OF THE STUDY

Chronic kidney disease is a major health problem in Australia. CKD is a

debilitating disease that impacts significantly on individuals, families, and society as

a whole. With the increasing prevalence of diabetes, hypertension, obesity, and an

ageing population, the prevalence of CKD will only continue to rise (Braun, Sood,

Hogue, Lieberman, & Copley-Merriman, 2012; Jager & Fraser, 2017; Kidney Health

Australia, 2015a; Radhakrishnan et al., 2014). All stages of CKD are associated with

increased risk of cardiovascular disease, which a leading cause of morbidity and

mortality in patients with CKD (Couser, Remuzzi, Mendis, & Tonelli, 2011). In the

most severe form of CKD (stage 5 CKD or ESKD), individuals require costly kidney

replacement therapy in the form of dialysis or kidney transplantation to sustain life.

The progression of CKD to ESKD not only decreases an individual’s health-related

quality of life, it also increases the use of health care resources, as well as posing a


significant financial burden on health care systems (Couser et al., 2011; Essue,

Wong, Chapman, Li, & Jan, 2013; Kidney Health Australia, 2015c; Levin et al.,

2017).

Research shows that effective self-management behaviours can slow CKD

progression and improve health outcomes (Bonner et al., 2014a; Costantini et al.,

2008; Curtin et al., 2008; Lin et al., 2013b). Knowledge about CKD is an important

factor in self-management (Johnson et al., 2016; Ong et al., 2013; Wu, Hsieh, Lin, &

Tsai, 2016). However, people with often lack adequate knowledge relating to their

disease (Devraj et al., 2015; Devraj & Gordon, 2009; Enworom & Tabi, 2015;

Wright et al., 2011).

In order to improve CKD knowledge and increase self-management in people

with CKD, it is important to be able to measure knowledge and self-management.

However, the development of such measures are limited and existing scales have not

been validated in an Australian population. For this reason, it is important that

research is conducted to provide healthcare providers with evidence-based measures

of CKD knowledge and self-management. Second, the results from the self-

management instrument will provide insight into how individuals with CKD manage

their disease on a daily basis. Third, the findings from the Kidney Knowledge Survey

provide more information regarding what people with CKD know about their

disease, and this can be used to guide patient education and educational programs.

Finally, it is hoped that the validation of these instruments will inform healthcare

providers about better management of CKD by identifying the areas that education

should focus on.

1.5 THESIS OUTLINE

This thesis is divided into seven chapters. This chapter has outlined the

background of CKD, the context of the study, and its purpose, in addition to also

describing the significance of the study. Chapter 2 provides a review of the literature

on the burden associated with CKD, self-management of CKD, and CKD

knowledge. Chapter 3 discusses the theoretical framework that underpins this study,

followed by a description of the factors affecting CKD self-management. Chapter 4

discusses the methodology and research design, including the study setting,

sampling, data management, and analysis. Ethical issues, together with health and


safety considerations relating to the conduct of research are also discussed. Chapter 5

provides a detailed overview of the study findings; while Chapter 6 presents the

discussion, interpretation, and evaluation of the study findings. Finally, Chapter 7

presents the conclusion, discusses the study’s limitations, and provides

recommendations for future studies.

1.6 SUMMARY

Chronic kidney disease poses a significant health problem in Australia and

globally. This chapter provided a brief overview of CKD, its risk factors, causes, and

management strategies. People with CKD need to manage their disease on a daily

basis and these individuals need to be knowledgeable about their disease in order to

be able to effectively self-manage. However, studies to validate measures of CKD

knowledge and CKD self-management have not been undertaken in Australia. The

following chapter is a literature review on the impact of CKD, CKD self-

management, CKD knowledge, and self-efficacy.

12 Chapter 2: Literature Review

Chapter 2: Literature Review

2.1 INTRODUCTION

This chapter begins with a discussion of the physical, psychosocial, and

socioeconomic impact of CKD. This chapter also reviews literature on the following

topics: self-management of chronic diseases, and more specifically, CKD self-

management, CKD knowledge, and CKD self-efficacy amongst people with CKD. In

addition, literature on CKD self-management and CKD knowledge measures are

explored. Finally, this chapter highlights the gaps in knowledge from the literature

review.

2.2 IMPACT OF CHRONIC KIDNEY DISEASE

Being diagnosed with CKD places a significant burden on individuals, their

families, health care systems, employers, and society as a whole (Braun et al., 2012).

The impact of CKD increases as the disease progresses and is associated with higher

rates of morbidity, mortality, and increased utilisation of healthcare resources (Evans

& Taal, 2015; Thorp, Eastman, Smith, & Johnson, 2006). The impact of CKD can be

grouped into three main categories: physical, psychosocial, and socioeconomic.

2.2.1 Physical Impact

Chronic kidney disease adversely affects the physical health of individuals.

People with CKD may experience a range of burdensome symptoms and physical

alterations in body image (Bonner & Douglas, 2014). Physical changes in body

image are usually due to external changes in skin, mobility, body weight, medication

side effects, and kidney replacement access devices (Bonner & Douglas, 2014;

Öyekçin, Gülpek, Sahin, & Mete, 2012).

One of the most burdensome symptoms of CKD is fatigue, with 70-97% of the

population with CKD reporting a lack of energy and feelings of tiredness (Bonner,

Wellard, & Caltabiano, 2010). A systematic review of symptom burden in CKD

found that the prevalence of symptom burden was high in all stages of the disease

(Almutary, Bonner, & Douglas, 2013). Fatigue or lack of energy was found in 81%,

Chapter 2: Literature Review 13

drowsiness in 75%, pain in 65%, pruritus in 61%, and dry skin in 57% (Almutary et

al., 2013).

In another study, the most commonly reported symptoms by patients with CKD

were cognitive impairment, dementia, sleep disturbance, pain, and emotional and

physical dysfunction, with physical dysfunction being the most prevalent and

debilitating symptom (Braun et al., 2012). The prevalence of sleep disorders is

significantly high in chronic kidney disease, for example, in a study that measured

the prevalence of sleep disorders in 124 newly diagnosed CKD patients, 89.5% of the

patients reported sleep disorders (De Santo, Bartiromo, Cesare, & Cirillo, 2008). The

prevalence of reduced physical functioning in advanced CKD and in older adults has

been also noted in the literature (Cruz et al., 2011; Odden, 2010). However, physical

functioning has been found to be reduced in CKD as early as in stages 1-3 (Cruz et

al., 2011). Pain significantly impacts the quality of life of people with CKD, as it can

lead to decreased functional capacity and avoidance of social activities (Kafkia,

Chamney, Drinkwater, Pegoraro, & Sedgewick, 2011). Thus, it is important for

nurses to understand the physical burden of CKD in order to support patients in

managing their symptoms.

2.2.2 Psychosocial Impact

Chronic kidney disease is associated with decreased quality of life, which

significantly impacts on the psychosocial functioning of individuals. Depression and

anxiety can occur due to changes from being healthy to experiencing a long-term

illness, the continuous burden of physical symptoms, fear of dialysis, uncertainty

about disease outcome, and negative experiences with the healthcare system (Curtin

& Mapes, 2001). For example, a study to investigate the prevalence of depression

and anxiety and their association with quality of life in 208 pre-dialysis CKD patients

found that pre-dialysis CKD patients had a high prevalence of depression (47.1%)

and anxiety (27.6%), which were associated with a decreased quality of life (Lee et

al., 2013). Other psychological stressors reported by individuals may include denial,

anger, sadness, fearfulness, helplessness, frustration, loss of control, and feelings of

guilt (Harwood, Wilson, Locking-Cusolito, Sontrop, & Spittal, 2009; Javalk et al.,

2014).

The prevalence of depression is higher in people with CKD than in the general

population (Vecchio, Palmer, Tonelli, Johnson, & Strippoli, 2012). Approximately


one-quarter of people with CKD are diagnosed with depression compared to 5-9% in

women and 2-3% in men (Vecchio et al., 2012). Depression can be a response to a

multitude of factors, including limited capacity for self-expression, productivity, and

social involvement; negative body image; and fear of uncertainty about the future

(Curtin & Mapes, 2001). Loss is an important psychological issue for people with

CKD (Bautovich et al., 2014). People with CKD often report loss of identity, role,

employment, and body image, all of which are risk factors for depression. These

individuals experience grief for these losses, including the loss of their lives before

CKD. The psychological burden of CKD could be alleviated by early identification

and intervention.

2.2.3 Socioeconomic Impact

Chronic kidney disease is associated with significant socioeconomic burden,

which is felt by the individual, their family, and society as a whole. Furthermore,

people with CKD may experience social isolation, relationship difficulty, decreased

autonomy, and financial difficulties (Javalk et al., 2014). People with CKD may have

difficulty re-establishing normal relationships, as it interferes with an individual’s

ability to work, engage in social activities, participate in family, and enjoyment of

hobbies (Bonner & Douglas, 2014; Kidney Health Australia, 2009). Many people

suffer loss of income due to unemployment, which affects themselves and their

families. Loss of income leads to changes in spending patterns, as household

finances are directed towards care and welfare costs (Jha et al., 2013). An Australian

study found that there remains a high out-of-pocket cost associated with the care and

management of CKD despite the availability of a comprehensive government funded

healthcare system (Fraser et al., 2013). Similarly, in another Australian study

involving 247 participants, 75% of the study participants reported financial hardship

associated with CKD (Essue et al., 2013). Examples of hardships described include

the inability to pay utility bills, medication costs, co-payments for hospital

appointments, and even missing hospital appointments or not filling out

prescriptions. Essue et al. (2013) reported the average out of pocket cost per three

months for a CKD patient to be $907.

Chronic kidney disease also impacts on the quality of life of family members.

It disrupts family life and imposes a substantial burden on family members, as family

members need to adjust and take on different roles and responsibilities (Gayomali,


Sutherland, & Finkelstein, 2008). This is because people with CKD often rely on

their family and friends for support in managing their disease. In addition, people

with CKD experience various psychological problems that may in turn adversely

affect their family members (Gayomali et al., 2008), who are often unprepared and

feel inadequate in their role as carers (Tong, Sainsbury, & Craig, 2008). Evidence

suggests that family members may feel isolated, resentful, frustrated, anxious, guilty,

and hostile (Bodenheimer, Lorig, Holman, & Grumbach, 2002; Tong et al., 2008).

In couples, sexual relationships may be affected, because people with CKD may

experience both physical and psychological changes that affect sexual functioning

(Theofilou, 2012).

At a societal level, the most obvious effect is the enormous financial cost and

loss of productivity associated with CKD. This disease poses a significant financial

burden to health care systems around the world. For example, in England, the total

expenditure on kidney care, including CKD, to the National Health Service is

estimated at £1.64 billion in 2009 to 2010, with the cost of CKD estimated at £1.44

to £1.45 billion (Kerr, Bray, Medcalf, O'Donoghue, & Matthews, 2012). In the

United States (US), the annual total cost of CKD per person across all stages of the

disease ranges from $1,183 to $35,292 (Sood et al., 2011). The cost of treating

milder forms of CKD is said to be more than double the total cost of ESKD. In 2007,

the Medicare expenditure on CKD in the US exceeded $60 billion compared to $25

billion spent on ESKD (Couser et al., 2011). In Australia, the best available evidence

for the economic impact of CKD is of people attending dialysis. The annual cost to

the Australian Government is estimated at $1 billion, with the cost of treating ESKD

from 2009 to 2020 estimated to be approximately $12 billion (Kidney Health

Australia, 2015c).

Resource utilisation and costs to healthcare systems increase with CKD

progression and increased disease severity. A US study investigating health resource

utilisation among patients with CKD stages 1-4 found that CKD has direct and

indirect costs that have a ripple effect on the workplace (Sullivan, 2007). Having this

disease causes loss of productivity due to disability or absenteeism, which generally

accounts for up to 25% of health-related costs of CKD (Sullivan, 2007). According

to Sood et al. (2011), more than 10 hours of work time is lost per week to CKD.


As can be seen from the above discussion, CKD affects multiple domains in

the lives of those with the disease. Self-management is therefore required, not only to

manage disease-related problems, but also to manage overall health and wellbeing.

2.3 SELF-MANAGEMENT OF CHRONIC KIDNEY DISEASE

Chronic kidney disease is a complex disease and management is an ongoing

challenge for health care professionals (Curtin, Mapes, Schatell, & Burrows-Hudson,

2005; Curtin et al., 2008; Ong et al., 2013). Effective management of CKD requires

collaboration between healthcare providers and patients. CKD is a lifelong disease

and people with it bear the primary responsibility for its day-to-day management

(Curtin et al., 2005; Curtin et al., 2008). These individuals therefore require skills,

training, and education to perform CKD self-management (Costantini et al., 2008).

According to Lorig and Holman (2003) it is impossible for an individual not to

manage, whether an individual decides to engage in healthful behaviours or not to

actively engage in managing their disease, reflects their approach to self-

management.

The effective management of CKD depends largely on the person’s

performance, as they need to take responsibility for their own treatment on a daily

basis (Ong et al., 2013; Thomas, Kanso, & Sedor, 2008). This may involve lifestyle

modification (smoking cessation, weight reduction, increased physical activity, and

limiting alcohol intake), taking medications (antihypertensive drugs, glycaemic

agents, statins, etc.), and performing self-monitoring activities (weight, blood

glucose levels, blood pressure, etc.) (Javalk et al., 2014).

The term ‘self-management’ has been used to describe an individual’s active

participation in their treatment (Lorig & Holman, 2003). Curtin and Mapes (2001)

defined self-management as “patients’ positive efforts to oversee and participate in

their health care to optimise health, prevent complications, control symptoms,

marshal medical resources, and minimise intrusion of the disease into their preferred

lifestyles” (p. 386). According to Corbin and Strauss (1988), self-management refers

to the tasks that individuals living with a chronic condition must undertake. These

tasks include medical or behavioural management, such as taking medications, or

maintaining a special diet; role management, including creating and maintaining new

healthful behaviours; and emotional management, which requires dealing with


feelings of frustration, anger, fear, depression, as well as adjusting to life with a

chronic illness.

Following interviews with long-term dialysis patients, two broad domains of

self-management were identified: self-management of healthcare and self-

management of everyday life (Curtin et al., 2005). The first domain ‘self-

management of healthcare’ includes five interdependent dimensions:

communication, partnership in care, self-care activities, self-advocacy, and

medication or treatment adherence (Curtin et al., 2005; Curtin et al., 2008).

Communication is a foundation of self-management because individuals must report

their symptoms to healthcare providers, ask questions to promote independent

problem solving, and must in turn receive information, support, and guidance from

their health provider (Curtin et al., 2005; Curtin et al., 2008). The second domain

‘self-management of everyday life’ refers to the achievement and maintenance of

normality in as many aspects of daily life as is possible (Curtin et al., 2005). These

two domains are in agreement with the three self-management tasks by Corbin and

Strauss (Novak et al., 2008). For instance, the medical or behaviour management

task would correspond with the self-management activities and behaviours

undertaken in the self-management of health care domain. The role and emotional

management tasks would correspond with behaviours geared towards the

achievement and maintenance of normality in as many aspects of daily life in the

self-management of everyday life domain (Curtin et al., 2005).

Researchers have studied the effects of self-management interventions on

health outcomes of patients with various chronic diseases: diabetes, asthma, arthritis,

chronic obstructive pulmonary disease, and CKD (Buszewicz et al., 2006; Chen et

al., 2011; Ciaccio & Portnoy, 2009; De Santo et al., 2008; Kazawa & Moriyama,

2013; Vecchio et al., 2012). Kazawa and Moriyama (2013) examined the effects of a

self-management skills acquisition program on people with diabetic nephropathy

living in Japan (n = 30) in a pre- and post-test study. They found that self-efficacy,

self-management behaviours, and glycohaemoglobin improved as a result of the

program. However, renal function remained unchanged, because the study duration

(six months) was not long enough to determine whether renal function improved or

worsened (Kazawa & Moriyama, 2013). Ciaccio and Portnoy (2009) examined the

effects of self-management on medication adherence and found that self-


management improved medication adherence and clinical markers of asthma.

Another randomised controlled trial aimed at evaluating the effectiveness of a self-

management program on primary care patients with osteoarthritis showed a reduction

in anxiety and improved perceived self-efficacy; however, the effect on pain,

physical functioning, or contact with primary care was minimal (Buszewicz et al.,

2006). A systematic review of 29 randomised controlled trials assessing the

effectiveness of self-management education programs in people with osteoarthritis

showed self-management education programs may slightly improve self-

management skills, pain, osteoarthritis symptoms, and function, but may not improve

active and positive engagement in life or quality of life (Kroon et al., 2014). In

another systematic review, Zwerink et al. (2014) demonstrated that self-management

interventions can improve health-related quality of life, reduce hospital admissions,

and improve dyspnoea in people with chronic obstructive pulmonary disease.

There is growing evidence that self-management is associated with improved

health outcomes in people with CKD (Bonner et al., 2014a; Chen et al., 2011; Curtin

et al., 2008; Lin et al., 2013a; Lin et al., 2013b). In a pilot study, Lin et al. (2013a)

developed a self-management education program and evaluated its effects on self-

efficacy, self-management behaviour, and CKD progression in people in the early

stages of the disease living in Taiwan (n = 37). The study demonstrated a significant

increase in self-efficacy, but no improvement in self-management behaviour; with

participants’ eGFR remaining stable throughout the study. The authors argued that

the stability in the eGFR was as a result of the self-management education program,

which prevented the deterioration of kidney function (Lin et al., 2013a). In a

randomised controlled study, Chen et al. (2011) examined the impact of self-

management support on CKD progression in Taiwanese people with CKD stages 3-5

(n = 54) and found that self-management support may delay CKD progression and

reduce morbidity associated with CKD stages 3-5. In another randomised controlled

trial, Flesher et al. (2011) examined the effects of a comprehensive cooking and

exercise program on the progression of CKD with a focus on self-management in a

Canadian population (n = 40). The study showed a 61% improvement in the

intervention group, in four of five endpoint measures (urinary sodium, blood

pressure, eGFR, urinary protein, and total cholesterol) compared to the control group.

In this study, the endpoints with the greatest change in the intervention group were


urinary sodium and blood pressure. However, the improvement in eGFR, urinary

protein and total cholesterol in the experimental group was not reflected in the

individual participants, which may be due to the small sample size (Flesher et al.,

2011).

Enworom and Tabi (2015) conducted a two-part non-experimental study to

retrospectively evaluate the effectiveness of a Medicare kidney disease education

program on clinical outcomes (n = 49) and assess knowledge of self-management

behaviours (n = 98) in a US population with stage 4 CKD. Results showed that the

provision of kidney disease education to people with stage 4 CKD was associated

with improved clinical outcomes. Participants of the kidney disease education

program experienced a slower decline in GFR compared to non-kidney disease

education participants (1.3 mL/min/1.73m2 lost versus 7.5 mL/min/1.73m2).

There have been four systematic reviews on self-management of interventions

in people with various stages of CKD. A systematic review of 22 randomised

controlled trials of educational interventions in people with kidney disease identified

only one study that involved those not yet on dialysis (Mason, Khunti, Stone,

Farooqi, & Carr, 2008). A second systematic review specifically focused on the

effectiveness of nursing interventions to improve self-management in people

receiving haemodialysis (Reid, Hall, Boys, Lewis, & Chang, 2011). There have also

been two systematic reviews of self-management interventions in people with CKD

stages 1-4 (Bonner et al., 2014a; Welch et al., 2015).

Bonner et al. (2014a) included studies that assessed whether self-management

interventions improved patient outcomes. The outcomes assessed were adherence,

knowledge, CKD progression, health literacy, self-efficacy, health-related quality of

life, and hospitalisations. Of the five studies in the review, three assessed knowledge.

There were large effect sizes for between-group differences at follow-up for

knowledge (1-2 weeks later) in two of these studies, while in a third study CKD

knowledge was found to be higher at six months than at baseline but below baseline

at 12 months using within group analysis (Bonner et al., 2014a).

Two studies assessed health-related quality of life and both studies showed

evidence that self-management may improve health-related quality of life (Bonner et

al., 2014a). Self-management was assessed as an outcome in two studies, with both

suggesting that self-management interventions may improve patient outcomes. Two


different instruments were used to measure self-management and neither were

specific for CKD (Bonner et al., 2014a). In one study, the authors adapted an existing

instrument that is not available in English (Choi & Lee, 2012), while the second

study used a self-management questionnaire by the Stanford School of Medicine

Patient Education Research Centre (Flesher et al., 2011). Only one study assessed

hospitalisations as an outcome where participants who received self-management

interventions had significantly fewer hospitalisations (18.50%) compared to the

control group (Bonner et al., 2014a). None of the reviewed studies assessed health

literacy, self-efficacy, and patient reported adherence, although four studies reported

data on clinical indicators and all other causes of mortality as objective adherence

measures (Bonner et al., 2014a). Overall, these authors found that there is

inconsistent evidence for the effectiveness of self-management programs and

differences in the mode of delivery, intensity, content, and duration of programs

(Bonner et al., 2014a).

In a similar review, Welch et al. (2015) found seven studies (two were the

same as (Bonner et al., 2014a)) and sought to identify knowledge gaps and future

directions for research. The authors found that efforts to improve knowledge, self-

management, and self-efficacy were included in all studies. However, educational

content was incomplete, self-management interventions were varied and limited in

scope, while strategies to improve self-efficacy were largely underdeveloped. There

were numerous methodological limitations, including flaws in study designs,

recruitment difficulties, sample size limitations, attrition rates, and limited

descriptions of processes used in intervention delivery. In addition, study outcomes

were diverse, and findings were inconclusive (Welch et al., 2015).

Both Bonner et al. (2014a) and (Welch et al., 2015) found few studies that

focused on the early stages of CKD to improve self-management. Only one study

included in these reviews occurred in Australia, indicating the need for more

Australian research on CKD self-management interventions. However, to assess

CKD self-management interventions, a valid and reliable instrument is needed.


Measuring Chronic Kidney Disease Self-Management

There are several instruments available that measure self-management in

chronic diseases (Battersby et al., 2003; Glasgow et al., 2007). For instance, the

Partners in Health Scale (Battersby et al., 2003), developed in Australia is a generic

(non-disease specific) scale developed to assess self-management of chronic

diseases. However, this scale is not suitable for measuring CKD self-management

behaviours because it is less sensitive to clinically important changes in ones’ health

as a result of an intervention. Disease-specific instruments to measure self-

management have been developed for type 2 diabetes mellitus (Lin, Anderson,

Chang, Hagerty, and Loveland-Cherry (2008), chronic obstructive pulmonary disease

(Glasgow et al., 2007), and heart failure (Riegel et al., 2004). Only one CKD specific

self-management instrument is currently available (Lin et al., 2013b); however, the

usefulness and reliability of this scale in other CKD populations has not yet been

reported.

2.4 CHRONIC KIDNEY DISEASE KNOWLEDGE

Chronic kidney disease knowledge includes a general understanding of the

disease, its risk factors, causes, appropriate treatment, consequences, and individual

CKD status (Plantinga, Tuot, & Powe, 2010). Knowledge about CKD is an important

factor in self-management. Research has shown that education to improve knowledge

relating to a disease condition, especially if geared towards lifestyle modification, is

necessary for behaviour change to occur (Chow et al., 2012).

Health literacy has been identified as a key determinant of chronic disease self-

management, including CKD (Devraj & Gordon, 2009). It describes a person’s

ability to gain access to, understand, and use health information to make appropriate

decisions about health and medical care (Campbell & Duddle, 2010). Low health

literacy adversely affects health outcomes and may impact on an individual’s ability

to access preventative health services, understanding of the disease and treatment

options, poorer self-reported health, and increased utilisation of health care resources

(Campbell & Duddle, 2010; Devraj & Gordon, 2009). Hence, optimising health

literacy may enhance self-efficacy.

To effectively self-manage, people with CKD need to have sufficient

knowledge about their disease. However, research shows that knowledge about


kidney disease is limited in people with CKD, even among those cared for by

nephrologists (Devraj et al., 2015; Enworom & Tabi, 2015; Finkelstein et al., 2008;

Ong et al., 2013; Wright Nunes et al., 2011). Finkelstein et al. (2008) measured

perceived knowledge of therapeutic options for ESKD in a cohort of 676 individuals

with stages 3-5 CKD cared for by nephrologists in the United States and found that

only 23% of the participants had extensive knowledge of their disease, while a

staggering one-third had little or no knowledge about their disease. Similarly, Ong et

al. (2013) found that people with CKD often reported having limited knowledge

about certain aspects of the disease, including diet, blood pressure, and medication

management. Other studies have demonstrated that people with early stages of CKD

lack knowledge of the disease (Chen et al., 2011; Levey & Coresh, 2012) and that

having more CKD-specific knowledge will enable them to better manage their

disease (Levey & Coresh, 2012).

Wright Nunes et al. (2011) studied the relationships between perceived and

objective knowledge in people with CKD attending a nephrology clinic. It was found

that perceived knowledge was very limited among people with CKD under the care

of a nephrologist, and the association between perceived and objective knowledge

was low to moderate. More than half of the participants reported having limited

knowledge about the functions of the kidney, symptoms of CKD, medications that

can improve kidney health, medications that are harmful to the kidneys, and foods

that should be avoided with reduced kidney function. Moreover, of the participants

who frequently visited a nephrologist (three or more visits in a year), a quarter

reported knowing little or nothing about why they were referred to a nephrologist

(Wright Nunes et al., 2011).

Wright et al. (2011) developed the Kidney Knowledge Survey (KiKS) to

measure kidney disease specific knowledge in people with CKD not yet requiring

KRT. Participants attending a nephrology specialty clinic in the USA (n = 401)

completed the survey. This study found that patients had low knowledge about many

topics important to CKD self-care (Wright et al., 2011). Only 19% of patients

identified urinary protein as a marker of kidney damage; 40% the role of the kidney

in glucose control; and knowledge of some symptoms of progressing kidney disease

was also limited, with only 22% correctly identifying that there may be no symptoms

as CKD progresses. More recently, Devraj and Wallace (2013), developed the low-


literacy Chronic Kidney Disease Self-Management Knowledge Tool; however, it has

not yet been administered to people with kidney disease, including those with CKD.

Devraj et al. (2015) assessed the relationship between health literacy and

kidney function in people with CKD stages 1-4 referred to an outpatient nephrology

clinic. The study measured health literacy, CKD self-management knowledge, CKD

awareness, and eGFR. Findings from this study showed a small but significant

relationship between health literacy and kidney function (p = 0.002).

There has been little research conducted in Australia regarding knowledge of

CKD. White et al. (2008) conducted an interviewer-administered cross-sectional

survey to examine the understanding of the causes of kidney disease and recall of

kidney function testing in a cohort of 852 Australian adults. Knowledge about the

risk factors of kidney disease was low, with only a few (< 10%) identifying diabetes

and hypertension as risk factors for developing kidney disease. Recall of kidney

function testing was also found to be limited, with only 32% recalling ever being

tested for kidney disease. This study highlights the importance of patient education to

improve recognition of risk factors and preventative interventions in CKD (White et

al., 2008).

A cross-sectional study was conducted to evaluate knowledge about CKD

among patients presenting to a nephrology outpatient clinic for the first time (Burke,

Kapojos, Sammartino, & Gray, 2014). Two hundred and ten patients were surveyed,

using open-ended questions about their understanding of CKD causes, symptoms,

and management. The study showed that new patients referred to a nephrology clinic

had very limited knowledge of CKD. A majority (82%) of the sample reported

receiving inadequate education from their referring physician, 16% were unsure of

the reason for the referral, 40% did not know the causes of CKD, and 51% were

unsure of its management. In conclusion, the authors argued that improving primary

care provider’s knowledge and recognition of CKD will lead to better patient

education prior to referral (Burke et al., 2014).

In a follow-up to the above mentioned study, 95 patients from the original

cohort were re-surveyed using the same open-ended questions used in the initial

questionnaire to determine whether knowledge about CKD improved 12 months after

attending a nephrology clinic (Gray, Kapojos, Burke, Sammartino, & Clark, 2016).

This study found that after one year of attending a nephrology clinic and having


access to educational material in the waiting room, patient knowledge of CKD

remained limited, with only marginal improvements from baseline results. At 12

months, there was a reduction in ‘unsure’ responses regarding the reasons for referral

(5% compared to 20%, p = 0.002) and fewer participants reported being unsure of

the meaning of CKD (37% compared to 57%, p = 0.005). There were also fewer

‘unsure’ responses regarding the management of CKD (38% compared to 57%, p =

0.004). The authors argued that one year of attendance to a nephrology clinic is not

enough to improve CKD knowledge, suggesting that a more structured,

individualised, and repetitive educational program delivered by a multi-disciplinary

team may be more effective in improving patient knowledge (Gray et al., 2016).

From the review of literature above, it can be seen that only a few studies have

measured CKD knowledge in people with the disease and only two studies have been

conducted in Australia (Burke et al., 2014; Gray et al., 2016). In both of these

studies, measurement of CKD-specific knowledge was not robustly assessed. The

questions used have not been validated. The participants were surveyed using open-

ended questions developed following of a review of studies assessing kidney disease

understanding and impressions obtained based on clinical experience by staff (Burke

et al., 2014). The questions assessed patients’ perceived reason for referral, and

knowledge of the causes, symptoms, treatments, and outcomes of kidney disease.

Questions relating to kidney function, medications, blood pressure targets, and other

topics essential to maintaining kidney health were not included. In addition, assessors

relied on patient recall of previous nephrologist consultations and CKD education.

To guide the development of educational support to improve CKD self-management,

robust assessment of individuals’ knowledge about CKD is required. The validated

kidney disease-specific knowledge questionnaire (KiKS) (Wright et al., 2011),

described in Chapter 4 (Section 4.8.1) was used to assess CKD knowledge in this

study.

2.5 CHRONIC KIDNEY DISEASE SELF-EFFICACY

Self-efficacy is an individual’s belief in his or her capacity to accomplish a

specific behaviour; it reflects confidence in their ability to exercise control over

events that affect their lives (Bandura, 1989). The higher an individual’s confidence

in their ability to perform an action is, the more likely they are to follow through with


the action (Bandura, 1997). Thus, people with high self-efficacy will have better self-

management.

Self-efficacy is associated with improved self-management behaviours and

medication adherence in people with earlier stages of CKD (Curtin et al., 2008;

Wierdsma et al., 2011). Curtin et al. (2008) examined the association between

perceived self-efficacy in 174 people with CKD and their self-management

behaviours. Self-efficacy was measured using the five-item Perceived Efficacy in

Patient-Physician Interaction Questionnaire (PEPPI) short-form and the 7 De Novo

self-efficacy items. The five categories of self-management behaviours measured

were communication with caregivers, partnership in care, self-care activities, self-

advocacy, and medication adherence. The authors found a positive association

between perceived self-efficacy and four out of the five categories of self-

management behaviours (controlling for patient age, education, diabetes status,

hypertension, serum creatinine, physical, and mental functioning). Higher perceived

self-efficacy was associated with increased communication with caregivers,

partnership in care, self-care, and medication adherence. Curtin et al. (2008)

concluded that people are more likely to seek therapeutic self-care and other self-

management measures during periods of illness and vulnerability.

Wierdsma et al. (2011) investigated the effect of discussing self-efficacy scores

in relation to long-term medication use in 54 adults with CKD in the Netherlands,

using a pre-test and post-test design. The study found that discussing self-efficacy

scores regarding long-term medication use led to increased self-efficacy. To date, no

studies have reported the level of self-efficacy of Australians with CKD.

2.6 RESEARCH GAP

The review of current literature shows that self-management may delay or even

halt progression of CKD to advanced stages, and consequently, terminal renal

failure. However, research about CKD self-management is limited. Available

literature on the topic is inconsistent and hampered by significant methodological

flaws (Bonner et al., 2014a; Welch et al., 2015). Despite the importance of kidney

disease knowledge in enhancing CKD self-management, available evidence indicates

limited kidney disease knowledge among the CKD population. In order to improve

self-management, an understanding of CKD self-management and knowledge is


important. Of the measures available to assess kidney disease knowledge, only one

has been vigorously developed and psychometrically assessed (Kidney Knowledge

Survey) (Wright et al., 2011). At the present time, the CKD-SM is the only

instrument identified in the literature (Lin et al., 2013b). However, both measures

have yet to be robustly assessed in the Australian CKD population; hence, the need

for this study.

2.7 SUMMARY

This chapter reviewed self-management in people with early stage chronic

kidney disease. Self-management has been associated with positive health outcomes

and could delay the progression of CKD. Management of CKD is complex and

multifaceted; individuals with CKD need to make lifestyle modifications to manage

their disease. However, people with CKD often lack adequate knowledge relating to

their disease and self-management. Studies about CKD self-management are

inconsistent, and programs differ in content, intensity, and duration. It is therefore

crucial that there is greater understanding of self-management to enable optimum

management of CKD. Prior to undertaking such studies, valid and reliable

instruments to measure CKD self-management and CKD knowledge are required.

The next chapter presents the theoretical framework that underpins this study.

Chapter 3: Theoretical Framework 27

Chapter 3: Theoretical Framework

3.1 INTRODUCTION

Nurse researchers use conceptual models or theoretical frameworks to design

and conduct their investigations (Mock et al., 2007). Theoretical frameworks allow

the researcher to establish orderly connections between observations and facts, which

can make research findings more useful and generalisable (Polit & Beck, 2012).

Theoretical frameworks also guide the development and testing of nursing

interventions and theories (Mock et al., 2007). These theories often provide the basis

for predicting and controlling situations (Polit & Beck, 2012). In addition, theoretical

frameworks help stimulate new research and the extension of knowledge (Polit &

Beck, 2012).

Self-management is based on the principle that successful management of

chronic disease requires active patient participation in their own care on a regular and

long-term basis (Chen et al., 2011; Curtin & Mapes, 2001). Several self-management

theories have been put forward to enhance the understanding of self-management of

chronic disease (Grey, Knafl, & McCorkle, 2006; Lorig & Holman, 2003; Novak et

al., 2008; Ryan & Sawin, 2009; Schulman-Green et al., 2012). This chapter begins

with a brief overview of self-management history and definition. The study’s

theoretical framework, based on the Lorig and Holman (2003) self-management

skills and informed by Ong et al. (2013) behavioural domains of CKD self-

management, is then presented. This is followed by a discussion regarding why these

self-management skills and behavioural domains complement each other, and how

CKD knowledge and self-efficacy are likely to influence self-management. Finally,

the rationale for the chosen frameworks and their limitations is discussed.

3.2 SELF-MANAGEMENT THEORIES AND DEFINITION

The term ‘self-management’ was first used by Thomas Creer in 1976 in his

publications about the rehabilitation of chronically ill children (Lorig & Holman,

2003; Novak, Costantini, Schneider, & Beanlands, 2013). Creer acknowledged that

his work was based on Bandura’s self-efficacy theory and suggested that the term

self-management was an indication that individuals are active participants in their

28 Chapter 3: Theoretical Framework

own care (Lorig & Holman, 2003; Novak et al., 2013). In 1988, Corbin and Strauss

further developed the concept of self-management and delineated three main tasks:

medical or behavioural management, role management, and emotional management

(Bodenheimer et al., 2002; Lorig & Holman, 2003). Medical management tasks

involve taking medications, changing diet, or blood pressure self-monitoring. Role

management tasks involve creating and maintaining new meaningful behaviours or

life roles. Lastly, emotional management involves coping with feelings of sadness,

anger, fear, frustration, and depression, which are common in chronic illness

(Bodenheimer et al., 2002; Lorig & Holman, 2003). Corbin and Strauss’s (1988)

work was based on problems perceived as important by individuals living with

chronic illness (Lorig & Holman, 2003).

Lorig and Holman (2003) expanded on the three self-management tasks and

identified five core self-management skills: problem solving, decision making,

resource utilisation, forming of a patient-health care provider partnership, and taking

action. However, the processes of self-management do not necessarily occur in a

linear manner; self-management tasks and skills overlap, affecting each other (Lorig

& Holman, 2003; Schulman-Green et al., 2012). Individuals with chronic diseases

sometimes have shifting perspectives between focusing on psychosocial needs and

wellness (Lorig & Holman, 2003). Similarly, Schulman-Green et al. (2012) reported

that for one individual with a new diagnosis of a chronic disease, the initial focus

may be on illness needs, while for another person it may necessitate exploring and

expressing emotions first, before focusing on illness needs. For example, it may take

considerable time for an individual newly diagnosed with CKD and their family to

understand its implications (Costantini et al., 2008), and as such they may not be

ready to process complex information (Schulman-Green et al., 2012). The above

discussions reveal that self-management is a complex and dynamic process.

For the purposes of this study, self-management is an approach to chronic

disease management in which individuals actively engage in managing their disease

and the consequences of living with the disease (Drury & Aoun, 2014; LeBlanc &

Jacelon, 2016; Schulman-Green et al., 2012). It is different to self-care, which

generally refers to the adoption of a healthy lifestyle, or preventative strategies

undertaken to promote or maintain health and wellness (LeBlanc & Jacelon, 2016;

Schulman-Green et al., 2012).


The theoretical framework for this study is based on Lorig and Holman (2003)

core self-management skills. This model of self-management has been evaluated on a

variety of chronic conditions, such as arthritis, diabetes, and heart and lung disease

(Lorig, Sobel, Ritter, Laurent, & Hobbs, 2001; Lorig et al., 1999), translated into

many languages, and has been adopted in many countries (Grady & Gough, 2014).

Self-management interventions depend on the assumptions that: (1) individuals with

different chronic diseases share similar disease-related concerns and problems; (2)

these individuals will acquire the skills and confidence (self-efficacy) needed to

manage their diseases on a day-to-day basis; and (3) increased confidence and

knowledge in self-management will lead to improved health outcomes and decreased

utilisation of health care resources (Lorig et al., 1999).

3.3 SELF-MANAGEMENT SKILLS

As mentioned above, there are five core self-management skills. The following

section discusses these skills.

3.3.1 Problem Solving Skills

Problem solving skills are not about teaching people the solution to their

problems, but rather about giving them the basic skills to problem-solve. These skills

enable problem identification, the generation of possible solutions, solution

implementation, and evaluation of results (Lorig & Holman, 2003). People with

CKD can be taught this process, whereby a problem is identified, a set of potential

solutions for problem resolution is generated, the most appropriate solution is

selected and implemented, and the effectiveness is evaluated. There is some evidence

regarding the use of problem solving interventions in diabetes self-management

(Glasgow et al., 2007; Phillips & Knuchel, 2011). In the context of CKD, effective

problem solving is crucial due to the complexities associated with managing the

disease, coupled with competing demands of everyday life. For example, problem

solving may involve recognising the emotional burden associated with living with

CKD and developing strategies to manage emotional responses and integrating

disease management into daily life (Novak et al., 2013).

3.3.2 Decision Making Skills

Those with a chronic disease have to make decisions on a day-to-day basis in

response to changes in their health condition (Lorig & Holman, 2003). Every day,


individuals have to make decisions about their diet, exercise, and taking medications

(Kafkia et al., 2011). To do this, individuals must have adequate knowledge to

manage common changes (Lorig & Holman, 2003; Ong et al., 2013). Ong et al.

(2013) related this to food management, whereby an individual with CKD may be

uncertain about the applying their knowledge about their renal diet to specific blood

results or symptoms. In addition, those with CKD may know their blood pressure

targets and how to monitor their blood pressure at home, but may not know how to

use this information to increase their chances of delaying disease progression.

Individuals can be taught how to identify these gaps and how to manage them.

3.3.3 Resource Utilisation Skills

Finding and utilising resources is an integral part of self-management

(Schulman-Green et al., 2012). Many self-management programs involve the use of

resources, such as the internet, library, and community resource guides (Lorig &

Holman, 2003). However, these programs do not teach participants how to use these

resources. Self-management teaches these individuals how to seek out the most

appropriate resources and how to use them (Lorig & Holman, 2003). For best results,

it is important for these individuals to access several potential resources at a time, to

further increase their knowledge and abilities (Gucciardi, Smith, & DeMelo, 2006;

Lorig & Holman, 2003). Self-management resources also include individuals (family

members, friends, and healthcare providers), spiritual resources, and social and

transportation services (Novak et al., 2013; Schulman-Green et al., 2012). The

activation of resources may vary over time depending on the severity of the illness

and an individual’s ability and willingness to manage their disease (Schulman-Green

et al., 2012). To effectively use these resources requires the formation of

patient/healthcare provider partnerships and for patients to learn how to navigate the

health care system (Lorig & Holman, 2003; Novak et al., 2013).

3.3.4 Formation of Patient/Healthcare Provider Partnership

The formation of partnership and collaboration between healthcare providers

and individuals with chronic diseases is crucial to self-management (Lorig &

Holman, 2003). People with CKD need to actively participate in decision making

regarding the management of their disease. In a partnership, healthcare professionals

provide information about health-related options, such as benefits and risks, and the

individual provides information about what is most important and practical for them


in their situation (Ong et al., 2013; Poulos & Antonsen, 2005). Shared decision

making occurs when individuals with chronic disease and healthcare professionals

jointly decide on the best treatment option for the individual (Bodenheimer et al.,

2002; Novak et al., 2013). For this to occur, a supportive healthcare team is required,

to enhance a patient’s ability to perform these tasks (Lorig & Holman, 2003).

3.3.5 Taking Action

Taking action involves learning the skills required to change a behaviour

(Lorig & Holman, 2003). People with CKD self-manage activities relating to food,

blood pressure, blood results, and medication management (Ong et al., 2013).

Activities surrounding food management include grocery shopping, cooking, eating,

and dining out. Blood pressure management involves monitoring blood pressure,

taking blood pressure medications, and diet management to control blood pressure.

Blood result management involves obtaining, organising, and monitoring results and

incorporating these into other aspects of care. Finally, medication management

involves taking medications as prescribed, obtaining information about the

medications, and avoiding prohibited medications. To be able to manage these tasks,

these individuals need to set goals and develop an action plan (Lorig & Holman,

2003). For example, creating a personal journal with set goals and plans, such as a

blood pressure target and plans to achieve the target (Ong et al., 2013). Key to the

achievement of an action plan is self-efficacy, an individual’s confidence in his or

her ability to accomplish set goals (Bodenheimer et al., 2002). Hence, people with

CKD and their health care providers need to collaboratively decide on an action plan

because they are designed to give individuals confidence in managing their disease

(Bodenheimer et al., 2002). Taking action is based on Bandura’s self-efficacy theory

(Costantini, 2006; Lorig & Holman, 2003).

Figure 3.1 depicts the relationship between the different core self-management

skills; and was adopted from Lorig and Holman (2003). The individual with CKD

negotiates self-management through the core skills of problem solving, decision

making, finding and utilising resources, forming partnerships with healthcare

providers to enable informed choices about treatment, and taking action to manage

problems as they arise.


Figure 3.1: Self-management skills, modified from Lorig and Holman (2003) pg.

38-41

Lorig and Holman’s (2003) self-management skills were developed as part of

the Stanford chronic self-management program at Stanford University in the 1990s

(Department of Health Victoria, 2008; Lorig et al., 1999). The program was initially

developed for arthritis self-management, and was later extended to a range of chronic

diseases, as they require the same self-management skills. The program is group-

based, with a very structured content delivered to 10-15 participants weekly for two

and half hours over a six weeks period (Department of Health Victoria, 2008; Lorig

et al., 1999). The group environment reduces isolation, thereby facilitating self-

efficacy. The use of highly trained lay people with chronic conditions to facilitate the

program enables the participants to build self-confidence through learning and

sharing. The program focuses strongly on goal-setting and problem-solving skills.

Despite the popularity of the Lorig and Holman’s (2003) model of self-

management, there are some limitations associated with the program (Department of

Core self-management skills

Problem solving Decision making

Resource

utilisation

Patient-health care

provider partnership

Taking action


Health Victoria, 2008; Drury & Aoun, 2014; LeBlanc & Jacelon, 2016; Schulman-

Green et al., 2012). First, group environments do not suit everyone; moreover, there

is reduced capacity to address individual needs in a group environment. Drury and

Aoun (2014) suggested that a detailed assessment of the individual with chronic

disease is required to determine readiness for change in order to facilitate self-

management support. Second, the very structured content makes it difficult to

address individual learning needs, styles, and speeds. Engaging in self-management

is an ongoing challenge, and personal factors such as age, functional status,

perceived ability to manage their disease, education, and psychosocial and

socioeconomic status all affect an individual’s ability to self-manage (LeBlanc &

Jacelon, 2016). Finally, participants need to find ongoing peer contact after the

completion of the six week course (Department of Health Victoria, 2008). Chronic

disease management requires ongoing partnerships with healthcare professionals,

follow-up visits are therefore essential to foster self-management (LeBlanc &

Jacelon, 2016). As this current study is not an intervention to improve self-

management behaviours, only the self-management skills from Lorig and Holman’s

(2003) model was used to guide instrument testing.

3.4 DOMAINS OF SELF-MANAGEMENT BEHAVIOUR

People with CKD are faced with numerous challenges, including making major

changes in diet, managing multiple medications, and frequent blood pressure

monitoring. Four main behavioural domains have been identified in CKD self-

management, namely: adherence to dietary requirements, monitoring and responding

to alterations in biochemistry, adhering to blood pressure regimens, and adhering to

medications (Ong et al., 2013). The following sections explain each of these.

3.4.1 Adherence to Dietary Requirements

Dietary interventions have a critical role in CKD management, and managing

the complex diet and fluid requirements can be a challenging task for people with

CKD (Palmer et al., 2015). Consultation with a dietician (if available) is helpful in

understanding the optimum diet for people with renal disease, such as foods that may

or may not be appropriate for the individual and the effect of dietary changes on

medical prognosis (Phillips & Knuchel, 2011). Sodium, phosphate, protein, and fluid

intake may need to be controlled depending on the stage of the disease. However,


adhering to the dietary requirements is often burdensome due to constant decision-

making about food and drink choices, adapting to complex eating patterns, existing

cultural practices, in addition to the competing demands of CKD and other related

diseases (Palmer et al., 2015).

Adherence to dietary requirements reflects food management behaviour. Ong

et al. (2013) described food management as activities surrounding grocery shopping,

cooking, eating, and eating out. Grocery shopping starts with planning ahead, this

may involve making a shopping list and sticking to it or eating before going to the

grocery store to curb impulse buying. While at the store, people with CKD should

focus on buying fresh foods, and avoid packaged or convenience foods, as these tend

to be much higher in sodium and are less rich in vitamins (Johnson et al., 2013;

Kidney Health Australia, 2015b; Queensland Health, 2014). Reading food labels may

help with the identification of certain nutrients that should be consumed in reduced

amounts, such as protein, phosphate, and sodium. In addition, processed foods

should be avoided, as these contain high amounts of sodium and phosphate.

Flavourings, such as herbs, spices, lemon, garlic, and pepper should be used in place

of salt when cooking (Kidney Health Australia, 2015b; Queensland Health, 2014). It

is important to plan ahead when eating out; this may involve calling the restaurant

and explaining that you are following a special diet, or making special requests about

food preparation and servings (National Kidney Foundation, 2010; Renal Resource

Centre, 2011).

3.4.2 Monitoring and Responding to Alterations in Biochemistry

Regular blood checks are required to monitor kidney function and the effects

reduced kidney function has on other systems (e.g., haematological). The level of

kidney function is determined by GFR, which is obtained from the measurement of

serum creatinine (Johnson et al., 2013). Monitoring and responding to alterations in

biochemistry are blood management behaviours. Ong et al. (2013) described blood

management as activities such as obtaining, organising, and monitoring results, and

linking the results to other aspects of care.

The timing and frequency of CKD monitoring and follow up depends on

disease severity and risk of progression (Kidney Health Australia, 2015a). During

stages 1 and 2, blood testing can be done every 12 months. For those with stage 3a or

3b, every 3 to 6 months, while for advanced stages of CKD (stage 4 and 5) blood


testing should be every 1 to 3 months (Kidney Health Australia, 2015a). After having

a blood test, individuals must return to medical or nurse practitioners for a review of

the results. During the visit, the individual gains understanding about their disease,

treatment plan, and ongoing evaluations (Poulos & Antonsen, 2005). Blood test

results may show a rapid decline in kidney function, in which case an urgent clinical

review is required. Alterations in biochemistry may require adjustments to

medication doses to levels that match kidney function, commencing new

medications, or adopting a different approach to disease management. This may

mean a referral to a nephrologist (or nurse practitioner) for preparation for KRT if

indicated (Kidney Health Australia, 2015a).

3.4.3 Adhering to Blood Pressure Regimens

Blood pressure (BP) management is the cornerstone of CKD management. In

people with CKD, BP should consistently be maintained below 140/90 mmHg, or

130/80 mmHg in those with albuminuria or diabetes (Kidney Health Australia,

2015a). Clinical management of hypertension is often based on clinic-based blood

pressure readings. However, these readings can be inaccurate, leading to over

diagnosis of hypertension due to white-coat hypertension and under diagnosis of

hypertension due to masked hypertension (Cohen, Huan, & Townsend, 2014;

Doulton et al., 2015). To achieve BP targets, a more accurate assessment of BP is

required. Cohen et al. (2014) suggested that home BP monitoring provides improved

diagnostic accuracy and can reduce misclassification of hypertension in people with

CKD.

Adhering to blood pressure regimens involves monitoring blood pressure,

taking blood pressure medications, and managing diet to control blood pressure (Ong

et al., 2013). In addition to clinic-based BP monitoring, individuals with CKD should

be trained to self-monitor their BP at home and record their own data (Cohen et al.,

2014; Doulton et al., 2015). A good BP monitoring device should provide accurate

and reliable measurements. In addition, the cuff should properly fit the upper arm,

because if it is too tight it will give a false high BP reading (Cohen et al., 2014). The

recorded data can then be taken to their next clinic visit, where it can be added to the

patient’s health record (Cohen et al., 2014).

Multiple antihypertensive agents are used in CKD to keep BP under control.

For individuals to effectively self-manage, the process starts with getting the


prescription dispensed, then taking the medication consistently as prescribed, and

finally, continuing to take the medication until otherwise instructed by a health care

provider (Chang & Winkelmayer, 2010). In addition, lifestyle modifications are also

required, including adhering to a low sodium diet (Johnson et al., 2013), smoking

cessation, and increasing activity (see Chapter 1, p. 8).

3.4.4 Adhering to Medications

Individuals with CKD often require multiple medications to control symptoms,

manage comorbid conditions (e.g., diabetes, hyperlipidaemia), and treat alterations

due to declining required renal function (e.g., anaemia and acidosis) (Rifkin &

Winkelmayer, 2010). Adherence to medication regimens is essential for keeping

these conditions under control and reducing disease progression. Medication

management involves the patient taking prescribed medications, avoiding

medications that are contraindicated, and developing an understanding of their

medications (Ong et al., 2013).

People with stage 3 or 4 CKD are frequently prescribed about six to eight

different medications to take every day; managing multiple medications can be

challenging (Devraj & Wallace, 2013). Keeping medications organised (e.g., pill

organiser, Webster pack) facilitates the process of taking medications as prescribed

in people with CKD. Forming daily habits, such as taking medications at the same

time each day or linking them to daily events such as meal time or bed time will

make adhering to a medication schedule easier (Raymond, Wazny, & Sood, 2011).

Some medications are harmful to the kidneys and as such should be avoided in

people with CKD. These medications are usually associated with acute interstitial

nephritis (Evans & Taal, 2015). Examples of such medications include non-steroidal

anti-inflammatory drugs (NSAIDS), diuretics, proton pump inhibitors, and anti-

retroviral drugs (Evans & Taal, 2015).

People with CKD should talk to their health care providers to get an

understanding of all their prescribed medications. When prescribing medication, the

prescriber should provide the patient with all necessary information about the

medication, including the name, the rationale, its purpose, and common side effects

(Brown & Bussell, 2011). Learning the names of all the medicines being taken, why

they are necessary, and what to expect during treatment can increase adherence to


medication regimens (Brown & Bussell, 2011). When people know what to expect

from a medication it is easier for them to manage the side effects. These individuals

should be advised to report any unwanted or unpleasant side effects to their health

care provider.

3.5 SELF-MANAGEMENT SKILLS AND BEHAVIOURS

The following sections provide the rationale and assumptions for the

theoretical framework used in this study, notably self-management skills (Lorig &

Holman, 2003) and self-management behavioural domains (Ong et al., 2013). The

self-management skills and self-management behavioural domains were chosen

because they complement each other. The acquisition of self-management skills

facilitates the performance of self-management behaviours (Phillips & Knuchel,

2011).

Chronic kidney disease is a lifelong disease that requires ongoing maintenance

of a care regimen, including adherence to diet, blood pressure monitoring, and

responding to alterations in biochemistry and medication. Problem solving skills are

a strong determinant of how an individual respond to self-management behaviours

(Lorig & Holman, 2003). In a descriptive study involving people with mild to

moderate CKD, a study participant described how problem-solving skills were

successfully applied to modify CKD treatment through self-monitoring (Costantini et

al., 2008). The participant was able to identify that her prescribed Lasix dose was

causing dehydrating and following discussions with the prescriber, the Lasix dose

was reduced (Costantini et al., 2008). People with CKD require support to problem

solve around high-risk situations that may threaten adherence to diet, blood pressure,

and medication regimens (Eskridge, 2010; National Kidney Foundation, 2004; Ong

et al., 2013).

People with CKD have to make decisions on a daily basis about engaging in

self-management behaviours (Costantini et al., 2008). Through problem solving, an

individual generates strategies to deal with a problem, and must make decisions

about which action to take (Lorig & Holman, 2003). People with CKD may be asked

to eat or limit certain food types, and as such, need to make decisions about their

food choices. They also need to make decisions about exercise. For example, how

much exercise is enough? When decision making is shared between the patient and


health care provider, a patient/health professional partnership is formed, and there is

a greater likelihood of adherence to self-management behaviours (Bodenheimer et

al., 2002; Ong et al., 2013). Asking the patient about their problem can help health

professionals to understand adherence from a patient’s point of view (Bodenheimer

et al., 2002; Raymond et al., 2011). For example, an individual not taking their

medication as directed may be due to the medication side effects they have

experienced. Working collaboratively with the patient is important to create an

individualised care plan (Raymond et al., 2011).

Finding and using resources is both a skill and behaviour (Lorig & Holman,

2003). Resource activation is a self-management behaviour that includes identifying

and accessing resources such as healthcare, community, spiritual, and social support

(Novak et al., 2013). Using the internet to download, track, and record CKD clinical

characteristics can help people with CKD to monitor their own progress (Ong et al.,

2013).

Taking action involves learning skills about how to act on problems (Lorig &

Holman, 2003; Novak et al., 2013). Key to changing behaviour is the creation of a

short-term action plan (Lorig & Holman, 2003; Novak et al., 2013). For example, in

helping a patient to meet a blood pressure target, the healthcare provider and patient

can work collaboratively to create an action plan that limits sodium intake to 2000

mg per day. If this is accomplished, the patient may later propose a revised action

plan to further limit sodium intake to 1500mg a day. In people with CKD,

performing self-management behaviours, such as adhering to renal dietary

requirements, monitoring and responding to alterations in biochemistry, adhering to

blood pressure regimens, and taking medications as prescribed are all part of taking

action to self-manage their disease.

3.6 THEORETICAL FRAMEWORK APPLIED TO CURRENT STUDY

Figure 3.2 illustrates the relationship between patient’s characteristics, CKD

knowledge, self-efficacy, and CKD self-management. A person’s characteristics are

likely to influence their knowledge of CKD and self-efficacy, which in turn is likely

to influence CKD self-management.

Patient characteristics, such as age, gender, level of education, and

socioeconomic status, are likely to influence their knowledge about a disease. Low


levels of disease knowledge have been associated with old age, lower socioeconomic

status, ethnicity, and lower levels of educational achievement (Fraser et al., 2013;

Hocking, Laurence, & Lorimer, 2013). Male gender and non-married status have also

been associated with low CKD knowledge (Fraser et al., 2013).

Lorig and Holman’s (2003) self-management framework draws from

Bandura’s theory of self-efficacy (Lorig & Holman, 2003; Ryan & Sawin, 2009). An

individual’s self-efficacy has a major role with regards to how goals, tasks, and

challenges are approached (Bandura, 1986). Individuals are more likely to engage in

activities for which they have high self-efficacy than in those they do not (van der

Bijl & Shortridge-Baggett, 2001). The concept of self-efficacy lies at the centre of

Bandura’s (1986, 1989) social cognitive theory, which is based on the principle that

in almost every situation, an individual’s actions and reactions, including social

behaviours and cognitive processes, are gained through observation.

Self-efficacy enhancement is considered a key component of self-management.

This can be achieved through the performance of skills mastery or action planning,

modelling of behaviours, reinterpretation of symptoms, and social persuasion (Lorig

& Holman, 2003). However, the current study does not test the full social cognitive

theory. Nonetheless, it does measure the perceived level of self-efficacy in people

attending the primary health care clinic to determine convergent validity.

Knowledge about CKD and self-efficacy are likely to influence CKD self-

management. People with CKD are expected to make lifestyle modifications and

adhere to treatment regimens to slow down the progression of the disease to ESKD

(Lopez‐Vargas et al., 2014). These individuals need to be knowledgeable about their

disease and have confidence in their ability to adhere to therapy (see Section 2.5). In

addition, those with high self-efficacy are more likely to engage in self-management

behaviours and vice versa. Self-efficacy has been associated with increased

communication with caregivers, partnerships in care, self-care, and medication

adherence in people with CKD (Curtin et al., 2008).

However, it is important to acknowledge the complexity of information for

people with CKD to achieve a realistic partnership with healthcare professionals

(Ormandy, 2008). People with CKD often report lack information particularly in the

early stages of their disease and difficulties in understanding available information


(Costantini et al., 2008). In addition, having the skills to self-manage does not

automatically translate into the performance of self-management behaviours. These

individuals need support and education to understand their and to integrate self-

management into their daily lives (Costantini et al., 2008).


Figure 3.2: Theoretical Framework Applied to this Study


3.7 SUMMARY

In this chapter, the theoretical framework for this study was developed and

drawn from the five self-management skills and four self-management behavioural

domains from (Lorig & Holman, 2003) and (Ong et al., 2013) respectively. The

theoretical framework for this study captures the theoretical relationships between

the characteristics of an individual, CKD knowledge, self-efficacy, and CKD self-

management behaviours. The following chapter describes the research design and

how the study was conducted.

44 Chapter 4: Methods

Chapter 4: Methods

4.1 INTRODUCTION

This chapter describes the research design and methods used to measure CKD

self-management and knowledge. First, the research aims and questions are stated;

followed by an explanation of the research design, study setting, study participants,

data collection, and the instruments used in the study. The ethical considerations for

this study are then presented, and the chapter concludes by addressing strategies for

data management and analysis.

4.2 AIMS

The primary aims of this study were to evaluate the validity and reliability of

two CKD instruments; namely the Kidney Knowledge Survey (KiKS) and CKD self-

management instrument (CKD-SM) in an Australian population.


• Describe the characteristics of CKD patients attending a primary

healthcare clinic and to assess CKD knowledge, self-management, and

self-efficacy.


and self-efficacy.

4.3 RESEARCH QUESTIONS

1. Is the Kidney Knowledge Survey (KiKS) a valid and reliable measure of

CKD knowledge in an Australian population with CKD?

2. Is the CKD self-management instrument (CKD-SM) a valid and reliable

measure of self-management behaviours in an Australian population with

CKD?

4.4 DESIGN

The study used a descriptive cross-sectional design, with test-retest research

protocol. The test-retest reliability of a research instrument is a measure of

Chapter 4: Methods 45

consistency of the instrument over time (Elkin, 2012; Polit & Beck, 2012). It

involves the administration of an instrument to the same individuals on at least two

occasions (Polit & Yang, 2015). Structured questionnaires designed to assess CKD

knowledge, self-management, and self-efficacy were administered on two separate

occasions to persons with CKD stages 1-4. The test-retest reliability was used to

assess the stability of the instruments at different times. It is important at this point to

explain psychometric testing of instruments.

Psychometric testing of the instruments

Instruments designed to measure patient-reported outcomes must be evaluated

to allow more appropriate conclusions about the measurement properties of the

instrument (Dowrick, Wootten, Murphy, & Costello, 2015; Mokkink et al., 2010).

Reliability and validity are two key constructs required for all instruments.

Reliability assesses the extent to which a measurement is free from

measurement error and produces stable results for repeated measurements (Mokkink

et al., 2010; Polit & Yang, 2015). To determine the reliability of instruments, internal

consistencies are calculated. Internal consistency refers to the degree to which the

items in a scale measure the same construct (Dowrick et al., 2015; Mokkink et al.,

2010; Polit & Yang, 2015). For instruments yielding continuous data, such as the

CKD-SM and Self-efficacy for Managing Chronic Disease 6-Item Scale (SEMCD),

internal consistencies are usually calculated using Cronbach’s alpha coefficients. For

instruments with dichotomous responses, such as the KiKS, internal consistency is

determined by calculating the Kuder-Richardson-20 (KR-20) coefficient. Regardless

of the technique, an instrument is considered reliable when its Cronbach’s alpha or

KR-20 coefficient is 0.70 or greater (de Vet, Terwee, Mokkink, & Knol, 2011; Polit

& Yang, 2015). However, internal consistency values exceeding 0.9 are not

recommended, as this may indicate redundancy of items (de Vet et al., 2011;

Dowrick et al., 2015; Polit & Yang, 2015). Scores less than 0.7 are sometimes

acceptable in the literature; however, internal consistency values of 0.6 or below

generally indicate poor reliability (Polit & Yang, 2015).

Another measure of reliability is the test-retest reliability, which is the extent

that scores for an individual have not changed between two time points (Mokkink et

al., 2010; Polit & Yang, 2015). In this study, the intraclass coefficients of the KiKS,

CKD-SM and SEMCD were calculated to determine test-retest reliability.


Measurement error, which is a component of reliability, ought to also be determined;

it refers to the systemic and random error in a score that is not attributed to the true

value of the construct (Mokkink et al., 2010; Polit & Yang, 2015). Measurement

errors for the KiKS, CKD-SM, and SEMCD were visualised using Bland-Altman

plots to determine limits of agreement.

Next, validity is important to evaluate in patient-reported outcome measures.

Validity is the extent to which an instrument measures what it intends to measure

(Mokkink et al., 2010; Polit & Yang, 2015). Face and content validity are subjective

evaluations of validity (Mokkink et al., 2010; Polit & Yang, 2015). Face validity is

the degree to which an instrument appears to measure what it purports to measure;

while content validity is concerned with how well an instrument measures what it is

supposed to measure (Mokkink et al., 2010; Polit & Yang, 2015). As the instruments

used in this study were already developed, face and content validity were not

evaluated. However, the CKD-SM was modified with permission from the

instrument developer during a group supervisory meeting, which included the

researcher, two supervisors and one other research student. The CKD-SM was then

sent to two expert clinicians (nurse practitioners) to evaluate its content validity.

Construct validity was then assessed by investigating the internal relationships

between the instruments and the theoretical concepts. Exploratory factor analysis was

one method for assessing construct validity that was used to determine the validity of

the CKD-SM.

Lastly, convergent validity, a sub-class of construct validity, is designed to test

the degree to which constructs that are expected to be related, are in fact related

(Polit & Yang, 2015). To determine convergent validity, the relationships between

CKD knowledge, self-management, and self-efficacy were investigated. The

instrument validity and reliability testing are summarised below in Table 4.1. The

theoretical framework for the study described in Chapter 3 (see Section 3.6) was

used to guide the hypothesis testing. More details about the instruments and data

analysis is described in forthcoming sections of this chapter.


Table 4:1 Instrument Validity and Reliability Measures

Instruments

(current study)

Reliability Validity

KiKS • Internal consistency reliability

- KR-20 coefficient

• Test-retest reliability

- Intraclass correlation

coefficient

• Construct validity

- Bivariate analysis

CKD-SM • Internal consistency reliability

- Cronbach’s alpha

• Test-retest reliability

- Intraclass correlation

coefficient

• Construct validity

- Exploratory factor

analysis

- Bivariate analysis

• Convergent validity

with SEMCD

4.5 STUDY SETTING

The study was conducted at Inala Primary Care in Queensland, Australia. Inala

Primary Care is located in an area of high socio-economic deprivation. The following

paragraphs provide a description of the Inala population using the recently released

2016 Australian Census.

In the 2016 census, the population of Inala was 58,495 and comprised of 51.3%

male and 48.7% female (Australian Bureau of Statistics, 2016). Aboriginal and

Torres Strait Islander (ATSI) people made up 4.5% of the population. The median

age of the people in Inala was 33 years.

The Queensland suburb of Inala is ethnically and culturally diverse. Just over

half of people living in Inala were born in Australia (Australian Bureau of Statistics,

2016). Other top countries of birth included Vietnam (9.6%), New Zealand (5.1%),

England (2.7%), India (1.5%) and Samoa (1.4%). Half of the population speak a

language other than English at home, with Vietnamese (14.7%) being the most

predominant language spoken. Over half (55.8%) of the people aged 15 and over in

Inala were employed full time, 28.4% worked part time, while 11% were

unemployed. The most common occupations included Professionals 14.5%,

Technicians and Trade Workers 14.4%, Labourers 14.3%, Clerical and

Administrative Workers 13.4%, and Community and Personal Service Workers


13.1%. The median weekly individual and household incomes were $535 and $1287

respectively (Australian Bureau of Statistics, 2016).

In comparing the Inala population to the general Australian population, in

2016, there was a higher proportion of males in Inala than the general Australian

population (Australian Bureau of Statistics, 2016). The Inala population was more

ethnically and culturally diverse, with a higher proportion people born overseas, and

those who spoke a language other than English at home (Australian Bureau of

Statistics, 2016). In addition, there was a higher proportion of ASTI people in Inala

than the general Australian population. Full-time employment rates were similar in

the two population groups although Inala had a higher rate of unemployment. There

were more Professionals in Inala than the general Australian population. Finally, in

relation to income and education, the general Australian population had higher

median weekly income and were more educated (Australian Bureau of Statistics,

2016). Table 4.2 below shows how the population of Inala compared to the general

Australian population based on the 2016 census report (Australian Bureau of

Statistics, 2016).


Table 4:2 Comparing the Inala population to the general Australian population

Characteristics Inala Australia

People (%)

Male

Female

ATSI

51.3

48.7

4.5

49.3

50.7

2.8

Median age (years)

33

38

Country of Birth (%)

Australia

Vietnam

New Zealand

England

India

Samoa

56.2

9.6

5.1

2.7

1.5

1.4

66.7

0.9

2.2

3.9

1.5

0.1

English only spoken at home (%)

53.7

72.7

Language other than English spoken at home

(%)

35.5 22.2

Working full-time (%)

55.8 57.7

Unemployed (%)

11 6.9

Occupation (%)

Professionals

Managers

Technicians and trade workers

Labourers

14.5

7.7

14.4

14.4

22.2

13

13.5

9.5

Median weekly income ($)

Individual

Household

535

1,287

662

1,438

Highest level of educational attainment (%) Year 12

(19%)

Bachelor degree

and above (22%)

Inala Primary Care is a not for profit organisation that provides screening and

health care services to the local community. Inala Primary Care also has a range of


clinics, including the Keeping Kidneys Clinic, which is designed to support people

with CKD in the community and reduce the number of people requiring access to

dialysis. Patients who attended the Keeping Kidneys Clinic are referred to the clinic

by their general practitioners, people with stable CKD are sometimes referred to the

clinic from the Princess Alexandra Hospital Nephrology Services (i.e. decanting

from tertiary to primary care). The Keeping Kidneys Clinic supports people with

CKD very early on in their disease right through to those on conservative care

pathways. This kidney care is delivered by upskilled general practitioners and

practice nurses supported by the Renal Clinical Nurse Consultant and allied health

professionals. An on-site nephrologist (half a day per week) supervises the general

practitioner with treatment plans.

4.6 INCLUSION AND EXCLUSION CRITERIA

Participants were eligible to participate in the study if they had been diagnosed

with CKD stages 1-4, were at least 18 years of age, could read or speak English,

were able to give informed consent, and were willing to participate in the study. It

was beyond the scope of this study to have instruments translated into other

languages. Participants were excluded if they had ESKD, were receiving dialysis, or

were cognitively impaired. In this study cognition was assessed by the general

practitioner or practice nurse.

4.7 SAMPLE SIZE

Individuals with CKD who attended the Keeping Kidneys Clinic at Inala

Primary Care were approached to take part in the study. There is no general

agreement about the sample size required for a validation study (Polit & Yang,

2015). Recommended sample sizes vary widely, ranging from at least 50 to several

hundred (Polit & Yang, 2015). According to Kline (2013), a minimum sample size of

100 study participants is recommended for validation studies. However, it has been

argued that a sample size of 50 subjects is adequate for reliability studies (Polit &

Yang, 2015; Streiner, Norman, & Cairney, 2015). A total of 78 participants were

recruited in this study. Fifty-four indicated their willingness to take part in the re-test

and were mailed the questionnaires however, only 32 questionnaires were returned to

the researcher. Sousa and Rojjanasrirat (2011) recommend a minimum 20% of the

sample for retesting instruments. In this study, 41.6% (32) of the study sample took


part in the retesting phase. Typically, a time interval of one to two weeks is often

recommended between the two testing time points (Institute for Health and Care

Research, 2010; Leong & Austin, 2006). The time interval should not be too short in

order to prevent participants remembering what they previously answered, nor

should it be too long, because the participants’ scores may have changed (Institute

for Health and Care Research, 2010). Furthermore, intervals longer than two weeks

might result in significant alterations in the attributes being measured (Leong &

Austin, 2006). For example, in this study, participants’ knowledge may have changed

during that period or their confidence in self-management may have increased.

However, the intent of this study was not to improve CKD knowledge or self-

management but rather to validate these instruments. In line with the above

justifications, the retest took place one week following the initial testing.

4.8 INSTRUMENTS

Five instruments were used in this study to measure CKD knowledge, self-

management, and self-efficacy at baseline and one week later. A demographic

questionnaire and clinical characteristics instrument were also used.

4.8.1 Kidney Knowledge Survey

The KiKS (Appendix 7) was used to measure participants’ knowledge about

CKD. This instrument was chosen because it was designed to measure kidney

disease specific knowledge in people with CKD not yet requiring KRT (Wright et al.,

2011). Furthermore, this instrument has been used to assess knowledge in people

with CKD and has been shown to be valid and reliable (Enworom & Tabi, 2015;

Johnson et al., 2016; Wright et al., 2011), although not yet in an Australian

population.

The original Kidney Knowledge Survey

The KiKS consists of 28-items, with knowledge questions related to CKD and

its progression (Wright et al., 2011). Initially, approximately 100 questions were

generated covering many topics relevant to self-care practices and prevention of

CKD progression (Wright et al., 2011). The authors used a combination of experts in

various areas of kidney disease (nephrologists, nurses, dieticians, research personnel

and a kidney educator) and previously described methods, to review both the content

of the kidney knowledge questions and patient perspectives of disease specific


information needs. Experts in health literacy, scale validation and psychometric

analysis were also consulted for method input (Wright et al., 2011). The items were

reviewed for face and content validity and redundancy using an iterative process and

ultimately reduced to 34 kidney knowledge questions. The questions were then field

tested in a small group of clinical and non-clinical staff for clarity and content, and

no additional suggestions were made. Six items were further removed after factor

analysis, leaving 28 items. The 28 items are grouped into three sections to measure

general knowledge about kidney disease, functions of the kidneys, and symptoms of

CKD and kidney failure. The instrument comprises items requiring ‘yes’ or ‘no’

responses, or choosing the correct answer from multiple choices. Each correct

answer was awarded one point, while each wrong answer was awarded a zero, with

the total score ranging from 0-28 points. Knowledge scores were calculated by

adding the number of correct responses. Higher scores indicated better knowledge of

CKD. This instrument has been validated in the United States (Wright et al., 2011).

The internal consistency reliability was determined by Kuder-Richardson -20 (KR-

20) reliability coefficient. The KR-20 coefficient of the KiKS was 0.72, with a mean

score of 18.48 ± 4.2 (range, 3.08-26.88).

Instrument construct validity of the KiKS was determined through an a priori

model of hypothesised associations between patient characteristics and knowledge

about CKD (Wright et al., 2011). Wright et al. (2011) were also informed by

associations observed in other chronic disease knowledge scales such as diabetes

(Huizinga et al., 2008; Rothman et al., 2005) and HIV (Osborn, Davis, Bailey, &

Wolf, 2010). Construct validity was supported if knowledge scores were associated

with patient characteristics. Bivariate analysis showed knowledge scores were

associated with age, formal education, health literacy, kidney education class

participation, knowing someone else with CKD, and awareness of their own

diagnosis (Wright et al., 2011). More recently, the KiKS has been used in another

study to measure knowledge of CKD in a sample of patients who had CKD stage 3

with coexisting diabetes and hypertension in the US (Welch et al., 2016). The KiKS

has been pilot tested in an Australia study (Bonner et al., 2014b); however, the study

results have yet to be published.


4.8.2 Chronic Kidney Disease Self-Management Instrument

Self-management was measured using the CKD-SM (Appendix 8) developed

by Lin et al. (2013b). The CKD-SM is the only published instrument that has been

developed to assess the self-management behaviours of people with CKD. Initially,

the authors identified six dimensions for CKD self-management behaviours that were

used to generate a pool of items: learning skills and knowledge about disease,

interaction with health professionals and significant others, problem solving, self-

care, self-integration, and emotional management (Lin et al., 2013b). The six

dimensions were based on dimensions developed during the development of the

diabetes self-management instrument by Lin et al. (2008), other literature (Curtin et

al., 2005; Gallant, 2003; Hill-Briggs, 2003; Lorig & Holman, 2003), and clinical

experience. Factor analysis supported four subscales with 29 items. The subscales

included ‘self-integration’, ‘problem solving’, ‘seeking social support’, and

‘adherence to recommended regimen’ (Lin et al., 2013b). The first subscale, self-

integration, reflects on how an individual can achieve a balanced life by adjusting

their lifestyle to incorporate recommended treatment regimens and self-management

activities. The second subscale, problem solving, reflects on the individuals’ ability

to seek out resources and gain disease-specific knowledge to solve problems relating

to their disease. The third subscale, seeking social support, reflects the individuals’

ability to seek support from significant others to deal with problems resulting from

their disease. The fourth subscale, adherence to recommended regimens, describes

how an individual follows healthcare advice on recommended treatment regimens.

The CKD-SM appears to have been informed by Lorig and Holman (2003) self-

management skills; however, the authors do not explicitly address how the self-

management skills informed their study (Lin et al., 2013b).

The CKD-SM was developed in a Taiwanese population (Lin et al., 2013b).

The responses to each item are graded on a four-point Likert scale (1 = never, 2 =

sometimes, 3 = often, and 4 = always). The maximum total score is 116. Content

validity was assessed by eight experts including nephrologists, a dietician, case

manager, and nurse educators specialised in CKD practice. Content validity index,

the extent of agreement between experts (Waltz, Strickland, & Lenz, 2005), was

0.89. Construct validity was determined through exploratory factor analysis. The four

subscales accounted for a total variance of 60.51%.


Cronbach’s alpha was used to assess the internal consistency reliability of the

CKD-SMI and the four subscales. The Cronbach’s alpha for the 29-item CKD-SM

total scale was 0.95, while that of the four subscales ranged from 0.77-0.92,

demonstrating good internal consistency. The test-retest reliability coefficient at two

weeks was 0.72, indicating relative stability. However, this instrument has not been

tested in other populations, including in Australia.

Following permission from the instrument developers (Lin et al., 2013b), the

CKD-SMI was modified to include medication self-management behaviours, with

four items added. The modified CKD-SM, comprising 32 items, was then sent to two

expert clinicians (nurse practitioners) involved in teaching CKD self-management

behaviours for feedback. No further changes were made to the modified instrument.

4.8.3 Chronic Disease Self-efficacy

Self-efficacy was measured the using SEMCD (Appendix 9) developed by

(Lorig et al., 2001). This widely used six item instrument was derived from several

self-efficacy scales developed and tested for the Chronic Disease Self-Management

study (Lorig et al., 2001). The SEMCD is less burdensome for patients. It compasses

several domains that are common across many chronic diseases, including symptom

management, communication with healthcare providers, and emotional and role

functioning. Each item is scored on a 10-point Likert scale ranging from “not at all

confident” (1) to “totally confident” (10), with a total score ranging from 6-60. The

scale is scored by calculating the mean of at least four of the six items, therefore

allowing for a maximum of two missing items. A higher number indicates higher

self-efficacy. The high internal consistency reliability of 0.91 and moderate

correlation (r = 0.58) with the general self-efficacy scale indicated that validity and

reliability of the SEMCD were acceptable (Lorig et al., 2001).

4.8.4 Demographic Questionnaire

The demographic questionnaire (Appendix 5) consisted of nine items designed

to collect data about the characteristics of the sample population. The study

participants were asked to provide details about their age, gender, level of education,

marital status, employment status, current occupation, household income, ethnicity,

and living arrangements.


4.8.5 Clinical Characteristics Tool

Participants’ clinical characteristics (Appendix 6) were obtained through a

review of medical records. These included weight, height, body mass index (BMI),

eGFR, serum potassium, calcium, phosphate, serum creatinine, albumin,

glycosylated haemoglobin (HbA1c), high density lipoproteins (HDL), and low

density lipoprotein levels (LDL). A list of current medications, including over the

counter medications were also captured to establish participants’ baseline data.

4.9 PROCEDURE AND DATA COLLECTION

Potential study participants were identified by clinical staff at the Keeping

Kidneys Clinic. The researcher then approached interested participants in the clinic

waiting room and provided a brief overview of the study. Study participants were

recruited using convenience sampling, a non-probability method of sampling where

study participants are selected due to the ease of access and proximity to the

researcher (Polit & Beck, 2012). After agreeing to participate, each participant was

asked to sign a consent form (see Appendix 11).

Baseline data for each participant were collected through the completion of a

self-report demographic questionnaire and a review of medical records. The

participants were also given the three instruments (KiKS, CKD-SM and SEMCD) to

collect self-report data on knowledge, self-management behaviours, and self-

confidence. Re-testing took place one week after the completion of the

questionnaires. Participants did not receive a photocopy of the first test and neither

were they informed of their scores. Participants who were willing to participate in the

re-test were mailed the questionnaires and a pre-paid envelope to return the

completed questionnaires to the researcher.

Following the recruitment of study participants, the researcher explained the

purpose of the study, data collection procedures, and the potential risks and benefits

of participating in the study. Participants were advised that participation was

voluntary, and they could withdraw from the study at any time, without giving any

explanation and without it affecting their relationship with and the treatment

provided by the Inala Primary Care clinicians. After receiving written informed

consent, participants were provided with the self-administered questionnaires to

complete in a quiet room in the clinic, with the researcher available nearby to read


the questions out loud if required. The questionnaires took approximately 30 to 45

minutes to complete. Participants who were willing to participate in the re-test

indicated their interest by ticking the box allocated for this purpose on the consent

form (see Appendix 11). One week later the questionnaires were mailed to

participants, with a reply pre-paid envelope also provided. All participants received a

$10 shopping voucher as recognition of their involvement in the study.

4.10 DATA ANALYSIS

Data entry and analysis were conducted using IBM SPSS Statistics version

22.0. Participants’ demographic and renal characteristics, as well as the mean scores

of the KiKS, CKD-SM and SEMCD were analysed using descriptive statistics.

Cronbach’s alpha coefficients were calculated to determine the internal consistency

of the CKD-SM and SEMCD. The internal consistency of the KiKS was assessed

using the KR-20 coefficient. Cronbach’s alpha and KR-20 coefficients > 0.70 were

considered satisfactory (Polit & Beck, 2012; Polit & Yang, 2015). Test re-test

reliability was determined by calculating the intraclass correlation coefficient one

week after initial completion of the survey. Bland-Altman plots were used to show

the limits of agreement for the KiKS, CKD-SM and SEMCD at test and retest.

Exploratory factor analysis was also used to evaluate the construct validity of

the CKD-SM. The principal component analysis method was used to examine the

internal structure of the CKD-SM. Bartlett’s test of sphericity and the Kaiser-Meyer-

Olkin test for sampling adequacy were performed on the data. The number of factors

to be retained was determined by having an eigenvalue above 1, inspection of the

scree plot, and by interpreting the resulting factor structure.

Construct validity was also determined through a priori hypothesis test

according to the study’s theoretical framework. Relationships between

demographic/renal clinical characteristics and patient-reported outcomes (CKD

knowledge, self-management, and self-efficacy) were determined using an

independent sample t-test if data were normally distributed or Mann-Whitney test if

data were not normally distributed.

Finally, correlation coefficients and analysis of variance were used to examine

the relationships between CKD knowledge, self-management, and self-efficacy. It

was hypothesised that knowledge of CKD is associated with self-efficacy and self-


management, and increased self-efficacy will lead to improvements in self-

management; thus, analysis of variance was conducted to test this hypothesis. The

outcomes for each instrument were reported separately to avoid any potential bias.

Details of data preparation are discussed in Section 5.2 of the next chapter.

4.11 ETHICAL CONSIDERATIONS

Ethics approval (Human Research Low Risk) was sought from the Queensland

University of Technology Human Research Ethics Committee (QUT Ethics

Approval Number 1500000071) (see Appendix 1 and 2). In addition, a Site Specific

Approval was obtained from Inala Primary Care (see Appendix 3). Signed informed

consent was obtained from all study participants before any data were collected.

To maintain confidentiality of records during the study and in the publication

of results, data about the study participants were de-identified. Personal identification

information, such as names and addresses, were removed and each participant was

assigned a unique code to protect their identity.

The completed questionnaires were kept in a secured location and the data

were entered into IBM SPSS v22. Following data entry, all hard copies of participant

surveys, including participant identifiable information were stored in a locked filing

cabinet in School of Nursing at QUT. Only the principal researcher and supervisors

had access to the stored data. Electronic data and documentation were also backed-up

regularly on the student’s university drive, which is password protected.

4.12 SUMMARY

This chapter presented the research design and method. First, a description of

the research aims and questions was given, and an overview of the psychometric

testing of instruments was then provided. The research instruments: the KiKS, CKD-

SM and SEMCD were described, and the hypothesised relationships between CKD

knowledge, self-efficacy, and self-management were tested. This chapter also

provided the ethical considerations of this study. The following chapter presents the

study’s results.

58 Chapter 5: Results

Chapter 5: Results

5.1 INTRODUCTION

This chapter presents the results of the study conducted to evaluate the validity

and reliability of the KiKS and CKD-SM in an Australian CKD population. The

chapter begins with a description of the participants’ demographic and renal clinical

characteristics, CKD knowledge, self-management, and self-efficacy. This is

followed by the results of exploratory factor analysis examining the internal structure

of the CKD-SM. The results of the reliability and validity testing of the KiKS, CKD-

SM and SEMCD are then presented. The chapter concludes by examining the

relationships between the different variables.

Data collection occurred between June and December 2015. Of the 112

individuals with CKD who attended the clinic during this period, 104 were eligible

for recruitment. The reasons for exclusion were: unable to read or speak English (n =

5), visual impairment (n = 2), and cognitive impairment (n = 1). In addition, 26

individuals indicated that they did not wish to participate in the study.

Of the 78 participants who took part in the study, one participant was excluded

from the analysis because they did not complete the majority of the surveys, and thus

had a lot of missing data. The participant recruitment flow diagram is presented in

Figure 5.1.

Chapter 5: Results 59

Figure 5.1: Participants Recruitment Flow Diagram

Total number of patients

attending clinic (n = 112)

Recruited (n = 78)

Excluded for not meeting criteria (n = 8)

• Unable to read/speak English (n = 5)

• Visual impairment (n = 2)

• Cognitive impairment (n = 1)

Analysis (n = 77)

• Excluded due to incomplete

data (n = 1)

Retest (n = 32)

Approached (n = 104)

Test (n = 78)

Declined to participate (n = 26)


5.2 DATA PREPARATION AND CLEANING

Prior to analysing the data, a coding manual was prepared to enter data into

IBM SPSS Statistics version 22.0. Each item or question in the questionnaires was

assigned a unique variable name. In addition, numbers were assigned to each of the

responses from the questionnaires. Data cleaning was then performed by the

researcher to check for errors, with 20% of entries randomly selected and cross-

checked against the original data from the questionnaires. Data were also checked for

missing values, outliers, and inconsistencies using frequencies for categorical

variables and descriptive statistics for continuous variables. No missing data were

found in the demographic data and responses to the KiKS and CKD-SM. However,

there were some missing renal clinical data that could be accounted for because

specific investigations were not indicated for all participants or blood test results

were not found in medical records. In these cases, data analysis was only performed

on those with available data. Any questionable data were checked against the raw

data from the questionnaires and were corrected accordingly.

5.3 SAMPLE CHARACTERISTICS

Demographic characteristics for the 77 participants who completed the

questionnaires are summarised in Table 5.1. There was a nearly equal distribution of

males (n = 39, 50.6%) and females (n = 38, 49.4%). Participant ages ranged from 31

to 88 years (mean = 67.26, SD = 13.19), with the majority aged 70 to 79 (41.6%).

Almost half of the participants were married (49.4%). Among the participants, 13

(16.9%) had completed primary school, 17 (22.1%) had completed secondary school,

32 (41.6%) had completed high school, and 8 (10.4%) had a diploma. The remaining

participants (9.1%) had completed either a bachelor or post graduate degree. Most of

the participants (n = 49, 63.6%) were retired, while 15 (19.5%) were employed. The

majority of participants (62.3%) reported an annual household income of less than

$24,999. Fifty-six (72.7%) participants were Caucasian and eight (10.4%) were

either of Aboriginal or Torres Strait Islander origin. Most of households (74%)

comprised of two or more people. As shown in Table 5.1 below, the participants in

this study represented the Inala population in terms of gender, education,

employment, household income and ethnicity.


Table 5:1: Demographic Characteristics (n = 77)

Characteristics n (%)

Gender Male

Female

39 (50.6)

38 (49.4)

Age (years)

Range: 31 – 88

Mean = 67.26

SD = 13.19

≤ 39

40 – 49

50 – 59

60 – 69

70 – 79

≥80

4 (5.2)

6 (7.8)

9 (11.7)

16 (20.8)

32 (41.6)

10 (13.0)

Marital status Single

Married

Widowed

Divorced

Separated

10 (13.0)

38 (49.4)

15 (19.5)

12 (15.6)

2 (2.6)

Education

Primary school

Secondary school

High school

Diploma

Bachelor degree

Post graduate degree

13 (16.9)

17 (22.1)

32 (41.6)

8 (10.4)

5 (6.5)

2 (2.6)

Employment status Employed

Unemployed

Retired

Other

15 (19.5)

10 (13.0)

49 (63.6)

3 (3.9)

Household income Less than $24,999

$25,000 to $49,999

$50,000 to $74,999

$75,000 to $99,999

$100,000 to 149,999

48 (62.3)

17 (22.1)

6 (7.8)

3 (3.9)

3 (3.9)

Ethnicity

Aboriginal or Torres Strait Islander

White/Caucasian

Other

8 (10.4)

56 (72.7)

13 (16.9)

Number of people in your

household

One

Two or more

20 (26.0)

57 (74.0)


5.4 RENAL CLINICAL CHARACTERISTICS

Most participants had either CKD stage 3A (28.6%) or stage 3B (36.4%). The

mean eGFR was 47.51 (SD = 19.03). A third of the participants (33.8%) had a SBP

>140. Blood work results obtained from clinical records indicated that most

participants had normal values for potassium (80.5%), calcium (89.6%), phosphate

(96.1%), albumin (90.9%), HbA1c (57.1%), HDL (70.1%), and LDL (84.4%).

However, some participants had no records for calcium (1.3%), HbA1c (39%), HDL

(10.4%), and LDL (6.5%). Using BMI, the majority of participants (n = 50, 64.9%)

were clinically obese, 18 (23.4%) were overweight, and only eight (11.7%) were

within the normal range. In relation to current prescribed medications, the mean

number was 9.7. Eight (10.4%) participants reported taking three different

medications or less, 22 (28.6%) reported four to seven different medications, and 47

(61%) reported taking at least eight different medications on a daily basis. The

majority of participants were prescribed antihypertensive drugs (87%), statins

(75.5%), and analgesics (68.8%). Thirty-four (44.2%) participants took diabetic

medications and 23 (29.9%) were on diuretics. The participants’ renal characteristics

are summarised in Table 5.2 below.


Table 5:2: Renal Clinical Characteristics

Clinical test results (Mean, SD) Range n %

eGFR (mL/min/1.73m2)*

Mean = 47.51

SD = ± 19.03

CKD stage 1 (≥ 90) 7 9.1

CKD stage 2 (60 – 89) 8 10.4

CKD stage 3A (45 – 59) 22 28.6

CKD stage 3B (30 – 44) 28 36.4

CKD stage 4 (15 – 29) 12 15.6

Blood Pressure (mmHg)

Mean SBP = 135.68

SD = ± 19.43

Mean DBP = 73.05

SD = ± 11.91

< 120/80 16 20.8

120/80 – 140/90 35 45.5

>140/90 26 33.8

Potassium (mmol/l)

Mean = 4.54

SD = ± 0.48

3.5 – 5.0 62 80.5

> 5.0 15 19.5

Calcium (mmol/l)

Mean = 2.37

SD = ± 0.15

< 2.25 7 9.2

2.25 – 2.65 69 89.6

No test 1 1.3

Phosphate (mmol/l)

Mean = 1.11

SD = ± 0.69

0.8 – 1.5 74 96.1

> 1.5

3 3.9

Albumin (g/l)

Mean = 39.12

SD = ± 3.75

< 35 7 9.1

35 – 50

70 90.9

HbA1c (%)*

Mean = 7.47

SD = ± 1.51

4.2 – 6.5 44 57.1

> 6.5 3 3.9

No test 30 39.0

HDL (mmol/l)*

Mean = 1.26

SD = ±0.43

≤ 0.9 15 19.5

> 0.9 54 70.1

No test 8 10.4

LDL (mmol/l)*

Mean = 2.20

SD = ±0.83

≤ 2.0 65 84.4

> 2.0 7 9.1

No test 5 6.5

BMI (kg/m2)*

Mean =33.03

SD = ±8.52

18.5 – 24.9 9 11.7

25 – 30 18 23.4

> 30 50 64.9

Current medications

Mean = 9.71

SD = ±4.82

≤ 3 8 10.4

4 – 7 22 28.6

≥ 8 47 61.0

*eGFR: estimated Glomerular Filtration Rate (mL/min/1.73m2); SBP: Systolic Blood Pressure; DBP:

Diastolic Blood Pressure; HbA1c: Glycated haemoglobin; HDL: High Density Lipoprotein; LDL:

Low Density Lipoprotein; BMI: Body Mass Index


5.5 DESCRIPTIVE STATISTICS FOR KEY STUDY VARIABLES

The maximum possible score for the KiKS is 28. In this study, participants

scores ranged from 6 to 25 (mean = 17.40, SD = 4.44). The CKD-SM has a

minimum and maximum score of 32 and 128 respectively. Participants’ self-

management scores ranged from 51 to 125 (mean = 91.34, SD =17.32). Participants’

item scores ranged from 1 to 10 (mean = 7.20, SD = 2.16) on the SEMCD. The

instrument responses for this study are summarised in Table 5.3 below.

Table 5:3: Mean and SD for KiKS, CKD-SM, and SEMCD

Instruments Items Range Mean SD

Potential Actual

KiKS 28 0 – 28 6 – 25 17.40 4.44

CKD-SM 32 32 – 128 51 – 125 91.34 17.32

SEMCD 6 1 – 10 1.8 – 10 7.20 2.16

5.5.1 Testing the Normality of the Instruments

The normality of the KiKS, CKD-SM and SEMCD was assessed using

skewness and kurtosis, and the Shapiro-Wilk test. A Shapiro-Wilk test p-value >

0.05 indicates the sample data are not significantly different from a normal

population, meaning the data are normally distributed (Polit, 2010). Results of the

Shapiro-Wilk tests showed the p-values for the total KiKS score and total CKD-SM

scores were 0.07 and 0.26, respectively. However, the p-value was < 0.001 for the

total SEMCD score, suggesting the data were not normally distributed.

Skewness and kurtosis were both divided by their standard errors, and results

greater than ± 1.96 suggested non-normal distributions (Barton & Peat, 2014).

Applying this rule for the KiKS gave -1.63 for skewness and -0.65 for kurtosis, both

with ± 1.96 limits, suggesting a normal distribution. Skewness and kurtosis were

both negative, indicating that the data were moderately skewed to the left and

kurtotic. The CKD-SM showed -0.89 for skewness and -1.24 for kurtosis, indicating

moderate left skewness and kurtosis, and a normal distribution. The skewness and


kurtosis for the SEMCD were -2.93 and -0.11, respectively. The skewness of -2.93

was well above the ± 1.96 limits, suggesting a great departure from normality. The

negative values indicated the data were skewed to the left.

Histograms, boxplots, and Q-Q plots were used to check the graphical

distribution for each instrument. Visual inspection of the histogram confirmed that

the scores for the KiKS and CKD-SM were normally distributed, while those for the

SEMCD were not normally distributed (see Figure 5.2). Boxplots were also used to

check for outliers, as they can have a significant effect on correlation coefficients,

thereby reducing the accuracy of the results. There were no outliers for both the

KiKS and CKD-SM; however, the SEMCD boxplot highlighted an outlier. Details of

these are provided in Appendix 10.


Figure 5.2: Histograms for KiKS, CKD-SM and SEMCD


5.5.2 Kidney Disease Knowledge Survey

Eleven items were most frequently answered correctly. Six items had

percentage correct scores >80%: item 2 (81.8%), item 7 (83.1%), item 8 (88.3%),

item 9 (88.3%), item 17 (80.5%), and item 19 (87.0%). However, only 9.1% of

participants knew why too much protein in urine is not good for the kidney (item 3).

In addition, only a third (33.8%) of the participants knew that kidneys had no role in

blood sugar regulation (item 16), and 28.6% of participants knew that people with

CKD may have no symptoms at all (item 28). A summary of the KiKS responses are

presented in Table 5.4.


Table 5:4: Kidney Knowledge Survey (KiKS)

CKD Knowledge Items

Correct

(%)

Wrong

(%)

1 On average, your blood pressure should be: 63.6 36.4

2 Are there certain medications your doctor can prescribe to help

keep your kidney(s) as healthy as possible?

81.8 18.2

3 Why is too much protein in urine not good for the kidney? 9.1 90.9

4 Select the one medication from the list below that a person with

chronic kidney disease should avoid:

59.7 40.3

5 If the kidney(s) fail, treatment might include: 54.5 45.5

6 What does “GFR” stand for? 59.7 40.3

7 Are there stages of chronic kidney disease? 83.1 16.9

8 Does chronic kidney disease increase a person’s chances for a

heart attack?

88.3 11.7

9 Does chronic kidney disease increase a person’s chances for

death from any cause?

88.3 11.7

This section is about WHAT THE KIDNEY DOES.

10 Does the kidney make urine? 61.0 39.0

11 Does the kidney clean blood? 77.9 22.1

12 Does the kidney keep bones healthy? 50.6 49.4

13 Does the kidney keep a person from losing hair? 75.3 24.7

14 Does the kidney help keep red blood cell count normal? 71.4 28.6

15 Does the kidney help keep blood pressure normal? 70.1 29.9

16 Does the kidney help keep blood sugar normal? 33.8 66.2

17 Does the kidney help keep potassium levels in the blood normal? 80.5 19.5

18 Does the kidney help keep phosphorus levels in the blood

normal?

68.8 31.2

This section is about SYMPTOMS.

19 Increased fatigue? 87.0 13.0

20 Shortness of breath? 64.9 35.1

21 Metal taste/bad taste in the mouth? 51.9 48.1

22 Unusual itching? 57.1 42.9

23 Nausea and/or vomiting? 53.2 43.8

24 Hair loss? 71.4 28.6

25 Increased trouble sleeping? 64.9 35.1

26 Weight loss? 44.2 55.8

27 Confusion? 41.6 58.4

28 No symptoms at all? 28.6 71.4


5.5.3 Chronic Kidney Disease Self-Management Instrument

The responses for the CKD-SM are summarised in Table 5.5. The highest

scoring item was item 30 (mean = 3.92, SD = 0.32) and the lowest scoring item was

item 4 (mean = 1.79, SD = 1.02).


Table 5:5: Chronic Kidney Disease Self-Management Instrument

Items Mean SD

1 When I have questions about my disease, I discuss what I have to do with my family and friends. 1.90 0.98

2 I would ask about the possible reasons for the decline in my kidney function. 2.39 1.07

3 I inform my family and friends about my kidney treatment plan (such as medication changes, lifestyle

changes).

2.44 1.20

4 I share my personal experience of kidney disease with other patients who have kidney disease. 1.79 1.02

5 I understand the meaning of my kidney function blood tests (such as creatinine, eGFR). 2.51 1.11

6 When my blood pressure is high (more than 140/90), I try to find out the possible reasons. 2.71 1.18

7 To prevent the increased workload on my kidneys, I am able to control what I eat. 2.81 1.01

8 I follow the kidney diet suggested by my doctor or nurse or dietician. 2.78 1.13

9 I solve problems related to my kidney disease by using various sources (such as calling my nurse or

doctor, using the internet, Google, kidney support group).

2.16 1.15

10 When I am feeling upset or frustrated, I discussed my feelings with others. 2.03 0.93

11 I incorporate my kidney disease treatment into my life. 2.87 1.08

12 I avoid habits that worsen my kidney function (such as smoking, consuming alcoholic drinks, overly

salty food).

3.21 0.94

13 I follow health professionals’ recommendations about exercise. 2.78 0.93

14 I keep track of my symptoms and early warning signs (blood sugar levels, weight, shortness of breath,

swelling in feet).

3.18 0.88

15 I follow health professionals’ recommendations about eating a balance diet. 3.00 0.95

16 I ask doctors or nurses questions to clarify my kidney treatment plan. 2.90 1.06

17 I follow health professionals’ recommendations about not smoking. 3.43 1.08

18 I have changed my lifestyle to prevent my kidney disease from getting worse. 3.21 0.89

19 I seek help from others when I am feeling upset or frustrated. 2.03 1.01

20 I keep my kidneys healthy by maintaining my overall health. 3.03 0.90

21 I stop bad habits that are harmful to my kidneys (such as smoking, consuming overly salty food and 3.18 1.06


alcohol).

22 I take steps to understand the risk factors associated with chronic kidney disease (such as high blood

pressure, diabetes, smoking, obesity).

3.22 0.91

23 I control my body weight according to the advice from doctors or nurses. 2.87 0.95

24 I make good choices about the type and amount of food I eat when I am not at home (such as at the

shops, church, parties, eating out).

2.95 1.01

25 I can adjust my daily routine to follow my kidney disease treatment plan when I am not at home (such

as travelling, holidays).

2.82 1.04

26 When my body has new or worsening physical symptoms (such as foot swelling, severe headache,

passing extra urine at night), I try to find out the cause.

3.08 1.06

27 I still take all my medications even when I am not at home. 3.73 0.72

28 I feel I am able to attend social events (such as weddings, parties, church) even though I have kidney

disease.

3.35 0.99

29 I seek out information about chronic kidney disease from a range of sources (such as internet, flyers,

brochures, books, kidney support group).

2.26 1.09

30 I take my medications as prescribed by my doctors or nurses or pharmacist. 3.92 0.32

31 I take action when my early warning signs and symptoms get worse. 3.60 0.75

32 When I have questions about my kidney disease, I discuss what to do with my doctors or nurses or

pharmacist.

3.49 0.91


5.5.4 Self-Efficacy for Managing Chronic Disease 6-Item Scale

Overall, the participants reported good self-efficacy. The item mean scores

ranged from 6.99 ± 2.53 to 7.38 ± 2.31 (Table 5.6).

Table 5:6: Self-Efficacy for Managing Chronic Disease 6-Item Scale

Items Mean SD

1 How confident are you that you can keep the fatigue caused by

your disease from interfering with things you want to do?

7.21 2.39

2 How confident are you that you can keep the physical

discomfort or pain of your disease from interfering with things

you want to do?

7.38 2.31

3 How confident are you that you can keep the emotional distress

caused by your disease from interfering with things you want to

do?

7.26 2.48

4 How confident are you that you can keep any other symptoms or

health problems you have from interfering with things you want

to do?

7.06 2.56

5 How confident are you that you can do the different tasks and

activities needed to manage your health condition so as to

reduce your need to see a doctor?

6.99 2.53

6 How confident are you that you can do things other than just

taking medication to reduce how much your illness affects your

everyday life?

7.08 2.66

5.5.5 Exploratory Factor Analysis for the CKD-SM

Prior to performing factor analysis, data were evaluated to determine

factorability. One item (item 27) with a low SD to mean score was removed from the

CKD-SM following inspection of the data. Exploratory factor analysis was then

conducted to examine the internal structure of the remaining 31 items of CKD-SM

and to explore the interrelationships among the items. There were three major steps

involved in the exploratory factor analysis, namely: evaluating the factorability of

data, factor extraction, and factor rotation (Polit & Yang, 2015).

In evaluating the factorability of the data, the sample size and strength of the

relationship among the items should be considered (Polit & Yang, 2015). Generally,


larger samples are recommended to rule out spurious correlations. However, the

sample size of this study (77) was less than the recommended or at least five cases

for each item for factor analysis (Pallant, 2013; Polit & Yang, 2015). The raw data

were transformed into a correlation matrix and inspected for the number of sizable

correlations. The correlation matrix should consist mainly of coefficients of 0.3 or

greater for factor analysis to be appropriate. Inspection of the correlation matrix

revealed large numbers of coefficient values that were equal to or greater than 0.3.

Sampling adequacy was evaluated using Bartlett’s test of sphericity and Kaiser-

Meyer-Olkin test. The Kaiser-Meyer-Olkin value was 0.78, well above the

recommended 0.6 for factor analysis, indicating good sampling adequacy (Polit &

Yang, 2015). In addition, Bartlett’s test of sphericity was statistically significant (χ2

501.89, df = 136, p < 0.001), supporting factorability of the data (Pallant, 2013; Polit

& Yang, 2015). To determine the number of factors to be extracted, the principal

component analysis extraction method was used. It revealed nine components with

eigenvalues greater than one, explaining 69.82% of the variance. Visual inspection of

the scree plot showed a clear break after the fourth component (see Figure 5.3).


Figure 5.3: Scree Plot for the Aus.CKD-SM Items

It was decided to retain four components for further investigation. This was

further supported by the fact that only four components accounted for at least 5% of

the variance in the data matrix (Polit, 2010). Using principal component analysis,

four factors with 18 items were extracted from the CKD-SM. Cross loading items or

items with a factor loading of less than 0.4 were removed from the scale (Polit, 2010;

Polit & Yang, 2015). However, one factor had only two items loading above 0.4.

Factor analysis was rerun to improve the interpretability of the subscales, with

generalised least squares extraction and oblimin rotation yielding the best solution in

terms of parsimony and conceptual meaning of the underlying factors (Table 5.7).


Table 5:7: Factor Loadings for Aus.CKD-SM

Items Factor Loadings

1 2 3 4

Factor 1: Self-integration

4 To prevent increased workload on my kidneys 0.85 0.24 0.09 0.20

11 I control my body weight 0.73 0.04 0.01 0.08

9 I keep my kidneys healthy 0.71 0.05 0.06 0.14

12 Making good choices about food 0.66 0.16 0.01 0.27

13 Adjusting daily routine to follow 0.63 0.06 0.17 0.00

Factor 2: Seeking support

3 Sharing experience with other patients 0.09 0.82 0.02 0.03

1 Discussing with family and friends 0.16 0.68 0.01 0.08

2 Informing family and friends about kidney

treatment

0.01 0.57 0.09 0.05

5 I solve problems related to my kidneys 0.21 0.54 0.00 0.02

Factor 3: Adherence to lifestyle modification

6 Avoiding habits that worsen my kidney disease 0.18 0.05 0.75 0.06

7 Follow recommendation about not smoking 0.02 0.01 0.69 0.08

10 Stopping bad habits harmful to the kidneys 0.07 0.05 0.65 0.01

8 Changed lifestyle to prevent kidney disease

getting worse

0.47 0.14 0.29 0.24

Factor 4: Problem solving

16 Taking action when signs and symptoms worsen 0.12 0.05 0.14 0.87

17 Discussing with doctors or nurses 0.15 0.13 0.08 0.59

15 Taking all medications, even when not at home 0.17 0.10 0.16 0.51

14 Finding out the cause of new or worsening

symptoms

0.10 0.10 0.10 0.49

Generalised least squares method, oblimin rotation.

All factor loadings 0.29 and above are in bold.

According to Polit and Beck (2012) and Polit and Yang (2015), different

extraction methods produce small differences in the final factor structure. Four

factors consisting of 17 items with the most meaningful patterns were extracted. One

item (“I have changed my lifestyle to prevent my kidney disease from getting

worse”), which loaded onto Factor 1, did not appear to conceptually fit this subscale,

and the same item was also cross loaded onto Factor 3. Based on conceptual grounds,

a decision was made for it to be retained in Factor 3. The four factors consisting of

17 items constitute the modified CKD-SM, now renamed the Australian Chronic


Kidney Disease Self-Management instrument (Aus.CKD-SM), described in the next

section.

5.5.6 Chronic Kidney Disease Self-Management Subscales

The four factors were named “self-integration”, “seeking social support”,

“adherence to lifestyle modification”, and “problem solving” respectively. These

four factors accounted for 53.30% of the total variance in CKD self-management.

Factor 1, self-integration, included five items and accounted for 30.03% of the

variance, with factor loadings ranging from 0.63-0.85. This factor describes self-

management activities, lifestyle adjustments, and implementation of recommended

regimens by the individual to achieve a balanced life. Factor 2, seeking social

support, included four items and accounted for 9.54% of the variance, with factor

loadings ranging from 0.54-0.82. This factor focuses on the actions an individual

takes to seek out resources or support from others to cope with their CKD. Factor 3,

adherence to lifestyle modification, included four items and accounted for 7.49% of

the variance, with factor loadings ranging from 0.29-0.75. This factor describes the

extent to which an individual adopts lifestyle modification associated with CKD.

Factor 4, problem solving, included four items and accounted for 6.23% of the

variance with factor loadings ranging from 0.49-0.87. This factor reflects an

individual’s ability to acquire CKD specific knowledge and to seek out information

from various resources to help manage their CKD. From this point forward, further

analysis was conducted using the 17 item Aus.CKD-SM (see Table 5.8).


Table 5:8: Australian CKD Self-Management Instrument

Items Mean SD

3 I share my personal experience of kidney disease with other

patients who have kidney disease.

1.79

1.02

1 When I have questions about my disease, I discuss what I have to

do with my family and friends.

1.90 0.98

5 I solve problems related to my kidney disease by using various

sources (such as calling my nurse or doctor, using the internet,

Google, kidney support group).

2.16 1.15

2 I inform my family and friends about my kidney treatment plan

(such as medication changes, lifestyle changes).

2.44 1.20

4 To prevent the increased workload on my kidneys, I am able to

control what I eat.

2.81 1.01

13 I can adjust my daily routine to follow my kidney disease

treatment plan when I am not at home (such as travelling,

holidays).

2.82 1.04

11 I control my body weight according to the advice from doctors or

nurses.

2.87 0.95

12 I make good choices about the type and amount of food I eat

when I am not at home (such as at the shops, church, parties,

eating out).

2.95 1.01

9 I keep my kidneys healthy by maintaining my overall health. 3.03 0.90

14 When my body has new or worsening physical symptoms (such

as foot swelling, severe headache, passing extra urine at night), I

try to find out the cause

3.08 1.06

10 I stop bad habits that are harmful to my kidneys (such as

smoking, consuming overly salty food and alcohol).

3.18 1.06

8 I have changed my lifestyle to prevent my kidney disease from

getting worse.

3.21 0.89

6 I avoid habits that worsen my kidney function (such as smoking,

consuming alcoholic drinks, overly salty food).

3.21 0.94

7 I follow health professionals’ recommendations about not

smoking.

3.43 1.08

17 When I have questions about my kidney disease, I discuss what

to do with my doctors or nurses or pharmacist.

3.49 0.91

16 I take action when my early warning signs and symptoms get

worse.

3.60 0.75

15 I still take all my medications even when I am not at home. 3.73 0.72

The internal consistency reliability of the factors (subscales) was determined

using Cronbach’s alpha. The Cronbach’s alpha for the total Aus.CKD-SM was 0.86.


The Cronbach’s alpha of the subscales ranged from 0.72-0.85. The results of the

EFA for the Aus.CKD-SM are presented in Figure 5.4.


Figure 5.4: Exploratory Factor Analysis of the Aus.CKD Self-Management Instrument


The results of the descriptive analysis (score ranges, mean, and SD) and

Cronbach’s alphas for the Aus.CKD-SM are presented in Table 5.9.

Table 5:9: Reliability Tests of Aus.CKD Self-Management Instrument and

Subscales

Subscales and instrument No. of

items

Score range Mean ±SD Cronbach’s

α Potential Actual

Factor 1: Self-integration 5 1-4 1-4 2.91±0.76 0.85

Factor 2: Seeking support 4 1-4 1-4 2.09±0.82 0.75

Factor 3: Adherence to

lifestyle modification

4 1-4 1.5-4 3.29±0.67 0.72

Factor 4: Problem solving 4 1-4 1-4 3.47±0.65 0.74

Aus.CKD-SM total score 17 17-68 23-67 49.78±9.39 0.86

Histograms were used to check the graphical distribution for Aus.CKD-SM

and the subscales. Visual inspection of the histograms showed the scores for the

Aus.CKD-SM and subscale 1 were normally distributed. However, those for the

subscales 2, 3, and 4 were not (see Figure 5.5).




Figure 5.5: Histograms for Aus.CKD-SM and Subscales

5.6 RELIABILITY OF INSTRUMENTS

The internal consistency reliability of the KiKS was tested using the Kuder-

Richardson-20 (KR-20) coefficient, because its measures the reliability of surveys

with dichotomous responses (de Vet et al., 2011; Polit & Yang, 2015). All of the

KiKS items had only one correct response, with the exception of item 5 (asking

participants to select two potential treatments for kidney failure), which required two

responses to be correct. Cronbach’s alpha was used to test for internal consistency in

the Aus.CKD-SM and SEMCD. The KR-20 of the KiKS was 0.74, and the

Cronbach’s alpha of the Aus.CKD-SM and the SEMCD were 0.86 and 0.93

respectively. The scores from the current study and original instrument studies are

summarised below (see Table 5.10).


Table 5:10: Internal Consistency Reliability of Instruments (n = 77)

Instruments KR-20 Cronbach’s alpha

KiKS (Wright et al., 2011) 0.72

KiKS (current study) 0.74

CKD-SM (Lin et al., 2013b) 0.95

Aus.CKD-SM (current study) 0.86

SEMCD (Lorig et al., 2001) 0.91

SEMCD (current study) 0.93

Next, the intraclass correlation coefficients (ICCs) of the KiKS, Aus.CKD-SM

and SEMCD were measured following the retest involving a sub-sample from the

main study (n = 32), because the instruments yielded continuous scores (Polit &

Yang, 2015). According to Polit and Yang (2015), when developing a new

instrument, the researcher should aim for a test-retest reliability of at least 0.80.

However, for subsequent measurements, a retest reliability of 0.70 is acceptable

(Polit & Yang, 2015). The Aus.CKD-SM and SEMCD both had good ICC; 0.82 (p <

0.01) and 0.78 (p < 0.01) respectively. The ICC for the KiKS was below the

acceptable level of 0.70 (0.42, p = 0.07). The ICCs for the three instruments are

presented in Table 5.11.


Table 5:11: Intraclass Correlation Coefficients Analysis, Two-Way Random Effects Model for Consistency, for 1-Week Test-

Retest of KiKS, Aus.CKD-SM and SEMCD

Instruments

Intraclass

Correlationb

95% Confidence Interval F Test with True Value 0

Lower Bound Upper Bound Value df1 df2 Sig

KiKS

Single Measuresd

Average Measurese

.27a

.42c

-.08

-.18

.56

.72

1.74

1.74

31

31

31

31

.07

.07

Single Measuresd .70a .47 .84 5.68 31 31 <0.01

Aus.CKD-SM

Average Measurese

.82c

.64

.93

5.68

31

31

<0.01

SEMCD

Single Measuresd

Average Measurese

.64a

.78c

.38

.55

.81

.89

4.58

4.58

31

31

31

.31

<0.01

<0.01 a. The estimator is the same, whether the interaction effect is present or not.

b. Type C ICCs using a consistency definition. The between-measure variance is excluded from the denominator variance.

c. This estimate is computed assuming the interaction effect is absent, because it is not estimable otherwise.

d. Single Measures: values for single items on each scale

e. Average Measures: average values across all items on each scale


Bland-Altman plots were created for the KiKS, Aus.CKD-SM, and SEMCD to

show the level of agreement between the two measurements at test-retest (Polit &

Yang, 2015). The mean difference for the KiKS scores between Time 1 and Time 2

was -0.91 and the SD of the difference was 5.30. The Aus.CKD-SM mean difference

between scores for Time 1 and Time 2 was -0.19 and the SD of the difference was

6.05. The mean difference for the SEMCD scores between Time 1 and Time 2 was -

0.44 and the SD of the difference was 11.71. Using linear regression analysis and

visual inspection of the Bland-Altman plots found no proportional bias between the

two time points for measurement of the KiKS, Aus.CKD-SM, and SEMCD (see

Figure 5.6).


Figure 5.6: Bland-Altman plots for 1-Week Test-Retest of KiKS, Aus.CKD-SM,

and SEMCD


5.7 COMPARING TEST AND RETEST MEANS

The scores for the 32 participants who completed the KiKS, Aus.CKD-SM,

and SECDM during the initial testing (Time 1) and at re-test (Time 2; one week

later), are presented in Table 5.12. The average Time 1 score was 17.59 (SD = 4.88)

and Time 2 score was 18.50 (SD = 3.83) for the KiKS. The average Aus.CKD-SM

Time 1 score was 51.09 (SD = 9.32) and Time 2 score was 51.28 (SD = 7.97).

Normality testing revealed both CKD knowledge and self-management scores were

normally distributed; hence, paired t-test was used to compare the means of the KiKS

and Aus.CKD-SM at the two time points. There were no significant differences

between both CKD knowledge (t31 = -0.97, p = 0.34) and self-management scores

(t31 = -0.18, p = 0.86) at Time 1 and Time 2. These results indicate that neither CKD

knowledge or self-management improved after the initial testing.

Table 5:12: Paired T-test Comparing Knowledge and Self-Management Scores

at Two Time Points

Variables Mean Number Std. Deviation Std. Error

Mean

KiKS Time 1

KiKS Time 2

17.59

18.50

32

32

4.88

3.83

0.86

0.68

Aus.CKD-SM Time 1

Aus.CKD-SM Time 2

51.09

51.28

32

32

9.32

7.97

1.65

1.41

Paired Differences

Mean

difference

SD

SE

Mean

95% CI of the

Difference

t

df

Sig.(2-

tailed) Lower Upper

KiKS -0.91 5.30 0.94 -2.82 1.00 -0.98 31 0.34

Aus.CKD-SM

-0.19

6.05

1.07

-2.37

1.99

-0.18

31

0.86

The test of normality revealed that self-efficacy scores were not normally

distributed at both Time 1 and Time 2. Therefore, the Wilcoxon signed-ranked test

was used to compare the means of the SEMCD scores at two time points. The

Wilcoxon signed-ranked test showed there were no significant improvements in self-


efficacy scores from Time 1 to Time 2 (Z = -0.59, p = 0.56). The mean self-efficacy

score was 46.0 for Time 1 and 48.0 Time 2.

Pair sample correlations for the KiKS, Aus.CKD-SM and SEMCD for Time 1

and Time 2 were also examined. There were significant relationships between

Aus.CKD-SM scores at Time 1 and Time 2 (r = 0.77, p < 0.01) and also for self-

efficacy scores at both time points (r = 0.64, p < 0.01). Self-efficacy scores were not

significantly related at Time 1 and Time 2 (r = 0.27, p = 0.12).

5.8 ASSOCIATIONS BETWEEN CHRONIC KIDNEY DISEASE

KNOWLEDGE, SELF-MANAGEMENT, AND SELF-EFFICACY

Following the theoretical relationships proposed in Chapter 3, correlations

between CKD knowledge and CKD self-management behaviour were examined

using Pearson’s correlation coefficient. There was no relationship between CKD

knowledge and self-management behaviour (r = 0.02, p = 0.90). On examination of

the association between CKD self-management and self-efficacy, a moderate

positive relationship was found between CKD self-management and self-efficacy (r

= 0.37, p < 0.01), with high levels of self-efficacy associated with high levels of

CKD self-management. The relationship between CKD knowledge and self-efficacy

was also examined. However, there was no relationship between CKD knowledge

and self-efficacy, and the two variables were not significantly correlated (r = 0.07, p

= 0.57).

5.9 RELATIONSHIPS BETWEEN DEMOGRAPHIC CHARACTERISTICS

AND CKD KNOWLEDGE, CKD SELF-MANAGEMENT, AND SELF-

EFFICACY

Bivariate analysis was performed to examine the relationships between CKD

knowledge, CKD self-management, and self-efficacy, and the various demographic

characteristics (see Table 5.13). Independent sample t-test and Mann-Whitney test

were used to compare the means of CKD knowledge, CKD self-management, and

self-efficacy by demographic clinical characteristics.

There was no significant difference between knowledge, self-management, and

self-efficacy for most of the independent variables, although CKD knowledge was

significantly greater in people aged < 60 than those > 60 (19.21 vs 16.81), and those

who had a household income ≥ $25,000 compared to those with a household income


< $25,000 per annum (19.03 vs 16.42). Levels of CKD self-management were

significantly higher in people who were married than those who were not (52.26 vs

46.41). Being married and having a household income ≥ $25,000 per annum were

associated with higher levels of self-efficacy than being unmarried (45.39 vs 41.08)

and having a household income under $25,000 per annum (41.08 vs 39.56).


Table 5:13: Relationships Between Demographic Characteristics and CKD Knowledge, Self-Management, and Self-Efficacy

Demographics Categories Number KiKS Aus.CKD-SM SEMCD

Gender Male

Female

39

38

17.51±5.23

17.29±3.53

48.59±9.15

50.03±8.68

42.28±12.40

44.16±13.64

Age Under 60

Above 60

19

58

19.21±4.21#

16.81±4.39

48.32±10.50

49.62±8.38

47.42±10.42

41.83±13.51

Marital status Married

Unmarried

38

39

17.29±4.56

17.51±4.38

52.26±6.45**

46.41±10.01

45.39±10.74**

41.08±14.66

Education Primary school

Secondary school and

above

13

64

16.62±4.56

17.56±4.44

47.08±8.76

50.33±9.48

42.38±14.02

43.38±12.86

Household income Under $25,000 pa

$25,000 pa and above

48

29

16.42±4.23#

19.03±4.31

49.17±10.04

49.52±6.73

39.56±14.26**

49.24±7.43

Ethnicity White/Caucasian

Not White/Caucasian

56

21

17.41±4.42

17.38±4.61

50.27±7.92

46.71±10.87

43.73±13.48

41.81±11.69

Note: CKD: chronic kidney disease; CKD-SM: chronic kidney disease self-management; pa: per annum; # Mann-Whitney p<0.05; *Independent t-test p<0.05;

**p<0.01


5.10 RELATIONSHIPS BETWEEN RENAL CLINICAL

CHARACTERISTICS AND CKD KNOWLEDGE, SELF-

MANAGEMENT, AND SELF-EFFICACY

Bivariate analysis was performed to examine the relationships between CKD

knowledge, CKD self-management, and self-efficacy, and various renal clinical

characteristics (eGFR, BP, BMI, and current number of medications). Independent

sample t-test and Mann-Whitney test were used to compare the means of CKD

knowledge, CKD self-management, and self-efficacy by renal clinical characteristics

(See Table 5.14).

Knowledge and self-management behaviours were not associated with renal

characteristics (p > 0.05). Although, when the relationship between the renal

characteristics and the individual Aus.CKD-SM subscales were examined, self-

integration was significantly related to BMI (p < 0.01). Self-efficacy was

significantly related to BMI and number of medications (p < 0.01). However,

relationships between self-efficacy and CKD stages were not significant.


Table 5:14: Relationships Between Renal Clinical Characteristics and CKD Knowledge, Self-Management, and Self-Efficacy

Renal characteristics

Categories Number KiKS Aus.CKD-SM SEMCD

CKD stages CKD stages 1- 3A

CKD stage 3B or 4

37

40

16.73±5.29

18.03±03.43

48.84±9.16

49.73±8.73

45.73 ± 12.53

40.86 ± 13.10

Blood pressure Normal blood pressure

Hypertension

51

26

16.98±4.44

18.23±4.42

49.73 ± 9.59

48.46 ± 7.43

43.78 ± 12.94

42.08 ± 13.22

BMI Normal or overweight

Obese

27

50

17.56±4.89

17.32±4.23

51.11 ± 8.26

48.32 ± 9.15

48.26 ± 11.84##

40.48 ± 13.02

Medications 1 to 7

8 or more

30

47

17.57±4.34

17.30±4.55

48.97 ± 9.29

49.51 ± 8.72

49.67 ± 13.02**

39.09 ± 13.71

Note: CKD: chronic kidney disease; CKD-SM: chronic kidney disease self-management; BMI: body mass index; ## Mann-Whitney p<0.01; **Independent t-test

p<0.01


5.11 SUMMARY

This chapter presented the results for this study. The study participants had

limited knowledge about their disease. People with CKD across all stages engaged in

self-management behaviours to some extent. Most participants reported good

confidence in managing their disease. Exploratory factor analysis was conducted on

the CKD-SM from which four factors comprising of 17 items were extracted. These

four factors formed the Australian CKD Self-Management instrument (Aus.CKD-

SM). The reliability and validity of the KiKS, Aus.CKD-SM, and SEMCD were

established. The study found that CKD self-management was significantly related to

self-efficacy (p < 0.01), with high levels of self-efficacy associated with high levels

of CKD self-management. However, there was no relationship between CKD

knowledge and CKD self-management, as well as self-efficacy. The study findings

are discussed in next chapter.

96 Chapter 6: Discussion

Chapter 6: Discussion

6.1 INTRODUCTION

The aim of this study was to evaluate the validity and reliability of the KiKS

and CKD-SM in an Australian population with CKD attending a primary healthcare

clinic. This chapter first discusses how the result of this study fit with the theoretical

framework. The results are then discussed alongside existing evidence from the

literature about patient disease-specific knowledge, self-management behaviours, and

chronic disease self-efficacy. These discussions later guide the proposal of practical

implications of the study findings in Chapter 7.

6.2 THEORETICAL FRAMEWORK

This study was informed by Lorig and Holman’s (2003) self-management

skills and Ong et al.’s (2013) self-management behavioural domains for people with

CKD (see Chapter 3). This model of self-management suggests improvements in an

individual’s behaviours can be made through the acquisition of the five core self-

management skills: problem solving, decision making, resource utilisation, formation

of patient/healthcare provider partnerships, and taking action. According to the

study’s theoretical framework, a person’s demographic characteristics are likely to

influence their knowledge about CKD. Having adequate knowledge about CKD is

likely to increase a person’s self-efficacy, which is a precondition for behaviour to

occur. Chronic kidney disease knowledge, together with increased self-efficacy, is

likely to improve self-management through the acquisition of the core self-

management skills needed to perform CKD self-management behaviours.

The results of this study found that age and annual household income were

associated with higher levels of CKD knowledge than gender, marital status, and

education level. Other studies have found that older age, lower socioeconomic status,

and lower educational levels were associated with lower levels of CKD knowledge

(Devraj et al., 2015; Fraser et al., 2013). The results from this study will help

healthcare providers to identify people who are at risk of knowledge deficit, so that

strategies can be implemented to improve their understanding of CKD, and thus,

health outcomes.

Chapter 6: Discussion 97

However, this study was unable to demonstrate the association between CKD

knowledge and self-efficacy or self-management. This finding was surprising, as

higher kidney disease-specific knowledge has been found to improve self-

management behaviours in other studies (Devraj & Gordon, 2009; Enworom & Tabi,

2015). A possible explanation for this is that the participants in this study may have

thought they were good self-managers. Tout and Plantinga (2011) suggested that

those with low CKD knowledge, but who feel confident enough to self-manage their

disease are likely to make poor decisions about their health, and this is based on their

inadequate understanding of the disease. One way to increase confidence (self-

efficacy) with being able to self-manage and adhere to treatment regimens is to

increase knowledge about the disease and its treatment. However, this study did find

that self-efficacy was associated with self-management, which is consistent with

other studies (Curtin et al., 2008; Lin et al., 2012). Despite the mixed results of this

study, improving disease-specific knowledge in people with CKD is likely to

increase self-efficacy, and consequently, self-management behaviours (Wu et al.,

2016).

6.3 CHRONIC KIDNEY DISEASE IN PRIMARY HEALTHCARE

Due to the increasing numbers of people with earlier stages of CKD, it is

important that identification and management of CKD occur in primary healthcare

settings (Walker et al., 2013). The participants recruited in this study were generally

similar to those of other studies involving Australian adults with CKD in terms of

age, gender, employment status, and had similar proportions of people with CKD

stages 1- 3 (Burke et al., 2014; Gray et al., 2016). However, the current study had

more participants from diverse backgrounds, including those who identified as either

Aboriginal or Torres Strait Islander, compared to the other Australian studies (Burke

et al., 2014; Gray et al., 2016).

According to the World Health Organization, good health or ill health is

determined by the conditions in which people are born, grow, live, and work

(Commission on Social Determinants of Health, 2008). Because of their potent and

underlying effects, social indicators, such as education, income, employment

conditions, power, and social support, can either strengthen or undermine an

individual’s health (AIHW, 2016b). Factors related to social disadvantage, such as

low income, low socio-economic status, neighbourhood deprivation, and minority


groups are strongly associated with higher rates of CKD (Couser et al., 2011; Essue

et al., 2013; Jha et al., 2013; Morton et al., 2016; Nicholas, Kalantar-Zadeh, &

Norris, 2015). The sample in this study is consistent with these other studies. An

individual’s socioeconomic status is likely to affect their access to quality cheap

food, cost of medication, and access to resources to support a healthy lifestyle.

Therefore, an understanding of an individual’s social and cultural environment

provides a social context to the challenges of CKD self-management.

This study has provided further knowledge about the demographic and renal

characteristics of people with CKD attending primary healthcare settings in

Australia; particularly in socially disadvantaged areas, such as Inala. Given the

higher prevalence of CKD in older people and in those with lower socioeconomic

status, increasing awareness in these populations together with careful monitoring of

the disease may slow the progression of CKD to more advanced stages. It may also

reduce the burden on the healthcare system due to the high cost of kidney

replacement therapy.

6.4 MAIN STUDY FINDINGS

6.4.1 Chronic Kidney Disease Knowledge

Overall, this study has shown that CKD knowledge is low among people with

early stages of CKD. The KiKS scores were lower than expected, despite the fact

participants were recruited from a dedicated CKD (Keeping Kidneys) clinic. It is

worth noting that the knowledge deficits identified in this study are consistent with

other studies conducted in Australia (Burke et al., 2014; Gray et al., 2016; Lopez‐

Vargas et al., 2014; White et al., 2008) and elsewhere (Enworom & Tabi, 2015;

Finkelstein et al., 2008; Johnson et al., 2016; Levey & Coresh, 2012; Ong et al.,

2013; Wright et al., 2011). When compared to the original study (Wright et al.,

2011), the level of CKD knowledge in this study was lower, although the scores were

similar to a more recent study (Johnson et al., 2016). The higher KiKS scores

reported by Wright et al. (2011) could be attributed to the fact that the participants in

that study were younger, recruited from a hospital-based nephrology clinic, and

included those receiving dialysis, who tend to know more about their disease.

Although Johnson et al. (2016) included dialysis patients, some of the participants


were recruited from primary care clinics and they were generally similar to those in

the current study in terms of age, gender, and employment status.

The first section of the KiKS deals with general knowledge about BP targets,

medications of potential harm or benefit to the kidneys, treatment options for kidney

failure, and other topics important to preserving kidney function. This study found

that this section was poorly answered; indicating that understanding of topic areas

important for self-management is likely to be problematic. Many of the study

participants did not know the correct blood pressure target for people with CKD.

Wright Nunes et al. (2011) also reported limited knowledge of BP targets among

patients treated in a nephrology clinic. Hypertension is not only a risk factor for

CKD, it is also a contributing factor of CKD progression (AIHW, 2014; Evans &

Taal, 2015). In another Australian study of people attending primary care services,

only a small fraction were able to identify hypertension as a risk factor for CKD

(White et al., 2008). Blood pressure monitoring and antihypertensive therapy are

often needed to control hypertension in those with CKD (Costantini et al., 2008;

Flesher et al., 2011; Ong et al., 2013). Therefore, it is necessary that education about

BP targets be given at the same time as education about how BP is controlled. When

patients have a sufficient level of understanding about key topics and the treatment,

they are more likely to adhere to treatment plans.

The majority of participants in this study were unaware that the presence of

protein in urine is not only a sign of kidney damage, but if left untreated, may

accelerate progression to ESKD. This result is similar to other studies in the US

(Johnson et al., 2016; Wright et al., 2011). Studies have revealed that people with

CKD want to know more about their disease, especially what can be done to protect

current kidney function (Campbell & Duddle, 2010; Costantini et al., 2008;

Ormandy, 2008) and information about medications, education about tests, and blood

test results (Lopez‐Vargas et al., 2014; Ormandy, 2008). Surprisingly, knowledge

deficits were also identified in questions relating to the avoidance of nonsteroidal

anti-inflammatory (NSAIDs) medications, treatment for kidney failure, and

understanding of GFR.

Nearly half of the participants in this study did not know that NSAIDs should

be avoided when CKD is diagnosed. There seems to be more knowledge about the

avoidance of NSAIDs in the US (Enworom & Tabi, 2015; Wright et al., 2011). The


use of NSAIDs is associated with CKD progression (Gooch et al., 2007) and is

common in individuals with moderate to severe CKD (Plantinga et al., 2011).

However, in Australia, NSAIDs are easily purchased over-the counter in

supermarkets to treat mild to moderate pain. It is important that special attention to

education about pain management be given to the CKD population.

The meaning of GFR as a method to test kidney function was not understood

by participants in this study. The results of this study are consistent with those of the

Tanamas et al. (2012) study, which found that only a third of the participants recalled

ever having their kidney function tested, while another third indicated they had never

had their kidney function tested (White et al., 2008). Similar results have also been

reported elsewhere (Enworom & Tabi, 2015; Wright et al., 2011). Lopez‐Vargas et

al. (2014) found that patients are often frustrated by their inability to understand

kidney blood test results and desire an explanation of these results. Priority should be

given to education in the earlier stages of CKD about how kidney function is

measured and what the results mean.

The KiKS is also able to assess knowledge about normal kidney function. This

study found that many participants did not know that the kidneys make urine and are

essential for bone health. Most incorrectly identified that the kidneys were involved

in blood glucose regulation. A recent survey of the UK general public showed that

knowledge about the functions of the kidneys was much lower than the current study

population (Slevin & Taylor, 2014). Knowledge about CKD symptoms was also

limited in this study. A majority of the participants did not know that there may be no

symptoms as CKD progresses. Similar results have previously been reported (Chow

et al., 2012; Enworom & Tabi, 2015; Wright et al., 2011). People also often express

disbelief when they receive a diagnosis of CKD due to the lack of physical

symptoms (Lopez‐Vargas et al., 2014), further highlighting the need for more

education about the asymptomatic nature of CKD. Costantini et al. (2008) found that

the absence of signs and symptoms of CKD could be a barrier to self-management,

because individuals could not make the connection between the necessity of taking

medications even when feeling well. Prompt recognition of CKD symptoms will

enable people to seek immediate care from their health care provider, thereby

slowing and/or halting disease progression.


Health literacy is having the skills to obtain, understand, and use health

information to make appropriate decisions about healthcare (Campbell & Duddle,

2010; Devraj & Gordon, 2009; Fraser et al., 2013), and low health literacy is a

significant issue in many chronic diseases, including CKD. Chronically ill

individuals with low levels of health literacy have little understanding of their

condition or its management (Sakraida & Robinson, 2009), and have difficulties

functioning in health care setting (Johnson, 2015). Health literacy is particularly

important for people with CKD due to the complex nature of the disease, which

requires active participation and the acquisition of self-management skills (Jain &

Green, 2016). Active participation involves following advice on dietary restrictions,

adhering to complex medication regimens, or coordinating transport to attend

medical appointments. A low level of health literacy is common among people with

CKD and has been associated with limited kidney disease knowledge (Dageforde &

Cavanaugh, 2013; Wright et al., 2011), low socio-economic status, and poor health

outcomes (Fraser et al., 2013). Limited health literacy is a significant issue in the

CKD population and should not be underestimated. Its prevalence varies from 10 to

50% (Dageforde & Cavanaugh, 2013), with an overall prevalence of approximately

23% (Fraser et al., 2013). Understanding an individual’s level of health literacy is

crucial to improving the quality of their experience when communicating with health

professionals. Furthermore, the patient’s baseline knowledge may serve as a

foundation to provide further education and enhance understanding of CKD.

The results of this study also showed that CKD knowledge differed due to age,

marital status, and annual household income. Chow et al. (2012) also found that

people who were older, had a lower monthly income, and lower educational status

were more likely to have limited CKD knowledge. Surprisingly, in this study, there

were no differences due to gender or level of education. Participants who had

secondary school education or higher and those who were female gender showed a

tendency towards having more CKD knowledge; however, these differences were not

statistically significant. This could be explained by the overall low levels of CKD

knowledge among the study participants. In addition, this study found no

relationships between CKD knowledge and renal characteristics, such as stage of

CKD, BP, BMI, and current number of medications. The low levels of CKD

knowledge identified in this study indicate that those attending the Keeping Kidneys


Clinic were either not receiving adequate education from their primary health care

providers or that they did not understand the information that was provided. Another

possible indication might be because the primary healthcare professionals at the

Keeping Kidneys Clinic were not aware of some of the CKD knowledge and for this

reason might not have provided the patients with those knowledge.

6.4.2 Chronic Kidney Disease Self-Management

The modified version of the CKD-SM was reduced through exploratory factor

analysis to the 17-item Aus.CKD-SM comprising four sub-scales. These scales were

self-integration, seeking support, adherence to lifestyle modifications, and problem

solving. The first subscale, self-integration, focuses on how an individual with CKD

incorporates self-management activities and treatment regimens into their daily life.

To successfully self-integrate, these individuals need support that forms the

foundation of factor 2. The second subscale, seeking support, describes the

individual’s actions in seeking out resources and/or support from others to cope with

their disease. Individuals with CKD need to make adjustments to their lifestyles to

manage their disease. The third subscale, adherence to lifestyle modifications,

describes the extent to which an individual follows healthcare advice for lifestyle

modification to control CKD. It includes the avoidances of behaviours that worsen

kidney function, such as cigarette smoking, adopting a low salt diet, moderating

alcohol consumption, and engaging in physical activity. As with other chronic

diseases, those with CKD face problems relating to their disease, treatment, and

personal life. The final subscale, problem solving, reflects a person’s ability to seek

out information from various resources and acquire disease specific knowledge to

help manage their CKD.

Subscale alignment with self-management skills

Of the four subscales of the Aus.CKD-SM, only one (problem-solving

subscale) explicitly aligns with one of the five core self-management skills described

by Lorig and Holman (2003) (i.e., problem solving, decision making, resource

utilisation, patient-healthcare provider partnerships, and taking action). This subscale

includes problem solving items such as finding out the cause of new and worsening

symptoms, taking action when symptoms get worse, and discussing questions and

concerns with healthcare professionals. Self-management is problem based (Lorig &

Holman, 2003). Problem solving involves seeking solutions aimed at maintaining


health (Novak et al., 2013). In the case of CKD, individuals who have poor problem-

solving skills need to be taught how to generate possible solutions to their problems,

as well as decision-making.

Conceptually, the Aus.CKD-SM subscale of self-integration captures decision

making and taking action skills Lorig and Holman (2003). In the self-integration

subscale, the individual with CKD learns how to live with their disease. This can be

achieved by developing strategies to manage the disease while concurrently taking

care of self to maintain overall health (Costantini et al., 2008; Curtin et al., 2005).

People with chronic diseases are required to make decisions on a daily basis in

response to changes in their health condition (Lorig & Holman, 2003). For those with

CKD, this involves making decisions about food and dining out, monitoring their

disease, exercise, and renegotiating days around medical appointments. Costantini et

al. (2008) reported varying levels of patient involvement in decision making relating

to healthcare. These individuals need to work collaboratively with their healthcare

providers to negotiate treatment demands. Taking action is the implementation of

decisions. By integrating CKD into the preferred lifestyle, the individual is taking

action.

The seeking support subscale of the Aus.CKD-SM reflects the skills associated

with forming patient healthcare-provider partnerships and resource utilisation

described by Lorig and Holman (2003). People with CKD need to find and utilise

various resources to support self-management. Self-management resources include

individuals (family members, friends, and healthcare providers), community,

spiritual, and social services (Novak et al., 2013; Schulman-Green et al., 2012). The

items in the seeking support subscale include seeking support from family and

friends by discussing CKD treatment, as well as sharing experiences with other

patients with CKD. As part of self-management, these individuals are also required

to engage in self-monitoring, and report symptoms, experiences, and concerns to

their healthcare providers; in turn, they receive information, support, and guidance

on how to self-manage (Curtin et al., 2005; Lorig & Holman, 2003). However, the

literature suggests that people with CKD selectively reported only the symptoms

deemed most serious to healthcare providers in order not to disturb them (Curtin &

Mapes, 2001). Similarly, those with the disease sometimes avoid seeking support

from family and friends for fear of being a burden (Harwood, Locking-Cusolito,


Spittal, Wilson, & White, 2005). Poor support systems may be a barrier to self-

management. As such, these individuals need supportive relationships and guidance

to access and use community-based resources (Lorig & Holman, 2003; Novak et al.,

2013).

The Aus.CKD-SM adherence to lifestyle modification subscale also reflects the

decision making and taking action dimensions of self-management by Lorig and

Holman (2003). Adherence to lifestyle modification is a component of self-

integration. Chronic kidney disease management is complex and treatment

recommendations usually involve fluid and diet restrictions (Costantini, 2006;

Eskridge, 2010; Johnson et al., 2013). Those with the disease are required to monitor

diet and fluids, as well as having a generally healthy lifestyle, which includes

smoking cessation, reducing alcohol consumption, and engaging in regular physical

activity. These individuals are required to make daily decisions in relation to positive

and negative health-related behaviours, and must take action to implement changes

geared towards improving CKD and overall health. Despite having the best of

intentions, some individuals find it difficult to adhere to lifestyle modification

recommendations, and as such, require guidance to overcome challenges associated

with self-management.

Overall, the results of this study show that the participants had adequate levels

of CKD self-management, although there were some variations in the levels of

engagement in the four subscales of self-management behaviours. Problem-solving

behaviours were the most frequently performed, followed by adherence to lifestyle

modification, self-integration, and seeking support. Lin et al. (2013b) also found

seeking support to be the least performed self-management behaviour, whereas the

most performed self-management behaviour was adherence to recommended

treatment regimens. Problem-solving involved the performance of behaviours, such

as taking action when CKD signs and symptoms worsened, discussing questions with

doctors/nurses, and taking all medications, even when not at home.

There is little research about the problem-solving skills of CKD patients. In a

longitudinal study designed to examine the feasibility and effectiveness of a self-

management program on patients with early stages of CKD, Lin et al. (2013a) found

significant improvements in CKD self-management behaviours following

instructions on how to self-monitor daily activities and identify problems related to


disease management. The study found that certain routine practices, such as the

consumption of overly salty food or eating a high protein diet, impacted on kidney

function, and after receiving information, participants decided to problem-solve by

abstaining from these harmful practices (Lin et al., 2013a). In a randomised

controlled trial conducted in Australia involving those with CKD to assess the

feasibility and impact of a medication self-management intervention, barriers and

concerns with regards to taking medications were identified (Williams, Manias,

Walker, & Gorelik, 2012). Prescription refills emerged as a possible solution for

improving medication adherence in those with CKD, who often have to take multiple

medications (Williams et al., 2012). Interventions designed to improve problem-

solving skills have been shown to improve health-related outcomes in a number of

chronic diseases, such as arthritis (Barlow, Turner, & Wright, 2000), heart disease

(Kato et al., 2016), and stroke (Parke et al., 2015). In people with type 2 diabetes,

problem-solving skills have been associated with significant improvements in HbA1c

(Fitzpatrick, Schumann, & Hill-Briggs, 2013). Since management of CKD to slow

progression of the disease also requires adherence with diet, medications, and

lifestyle modifications, which are in common with other chronic disease populations,

enhancing problem-solving skills may also reap similar health-related benefits.

This study found that adherence to lifestyle modification was the second most

performed self-management behaviour. This included the performance of behaviours

such as avoiding habits that worsen kidney function, following recommendations

about not smoking, stopping bad habits that are harmful to the kidneys, and changing

lifestyle to prevent kidney disease from getting worse. The report of adherence to

lifestyle modification in this study was higher than other studies. Previous studies

have found that those with CKD often report difficulties and loss of control when

adjusting their daily life and lifestyle modifications to manage the disease (Costantini

et al., 2008; Eskridge, 2010). However, those who adhered to a combination of

lifestyle modification factors involving smoking cessation, consuming a healthy diet,

engaging in regular exercise, and maintaining a healthy weight were more likely to

reduce their risk of death due to cardiovascular events (National Kidney Foundation,

2015; Tan & Johnson, 2008). Adherence to lifestyle modification is important in

CKD management; however, this relies largely upon the individual’s willingness to

maintain health-promoting behaviours. The high degree of adherence reported in this


study might suggest participants’ willingness to adhere to lifestyle modification

advice and positive intention to self-manage their disease.

In this study, self-integration was the third most performed self-management

behaviour. It involved controlling diet to prevent increased workload on the kidneys,

managing body weight, maintaining overall health, making good dietary choices, and

adjusting daily routines to follow a treatment plan. These results indicated that

participants were motivated to engage in self-care activities. Several studies have

demonstrated that risk factors for CKD can be reduced through individuals actively

engaging in self-management activities (Flesher et al., 2011; Walker, Marshall, &

Polaschek, 2014). Those with CKD can optimise wellness by integrating their

disease and self-management activities into their daily life (Lin et al., 2012; Lin et

al., 2013b). However, CKD self-management is a complex process and may be

especially challenging for older adults, who often have to manage multiple comorbid

conditions (Bowling et al., 2017). Despite genuine efforts to integrate treatment

recommendations into preferred lifestyles, those with CKD often find doing so to be

time consuming and difficult to master (Costantini et al., 2008). Offering guidance

on how to integrate treatment recommendations into daily life could be beneficial in

improving CKD self-management.

This study found that seeking support was the least performed self-

management behaviour. It involves sharing personal experiences with other patients,

discussing questions with family and friends, informing family and friends about the

treatment plan, or solving disease related problems. This result is consistent with Lin

et al. (2013b). Yet, support from family, friends, and community groups could have a

positive influence on CKD self-management (Havas, Bonner, & Douglas, 2016;

Thomas-Hawkins & Zazworsky, 2005). Family and friends provide practical and

emotional support to facilitate self-management. However, some individuals may

choose not to seek support from family and friends for fear of being a burden

(Harwood et al., 2005). Members of the health care team can be a valuable source of

support for patients and their families through the formation of patient-healthcare

provider partnerships. The provision of self-management support, such as health

education, patient education, telephone based-support, and group support have been

found to reduce disease progression and hospitalisation in people with CKD (Chen et

al., 2011). In people with diabetes, having higher levels of social support has been


associated with better glycaemic control, increased knowledge, enhanced treatment

adherence, and improved quality of life (Strom & Egede, 2012).

Patient reported self-management in this study must be understood within a

social context, as the ability to self-manage is dependent on the social determinants

of health. Education, income, and socioeconomic status are social determinants of

health outcomes in those with chronic diseases. The participants in this study may

have perceived themselves to be functioning well within their social constraints (e.g.,

limited income to buy quality food or fill medication prescriptions). Understanding

how these social constraints influence self-management behaviours may be

beneficial to the provision of self-management support. The results of this study

suggest that improving self-management support may enhance patient-healthcare

provider partnerships, and lead to adherence to a CKD treatment regimen, and

improved health outcomes. Self-management support is invaluable in the context of

CKD, especially in the early stages of the disease to provide the necessary guidance

and encouragement required to implement or maintain new behaviours and make

informed health decisions. Healthcare professionals are in a good position to provide

such support through ongoing assessment of an individual’s readiness to learn and

embrace behaviour change.

6.4.3 Self-Efficacy

Participants in this study reported high levels of self-efficacy, which is higher

than in other studies using the same instrument (Freund, Gensichen, Goetz,

Szecsenyi, & Mahler, 2011; Lorig et al., 2001). It is possible that the high levels of

self-efficacy reported in this study was due to the robust CKD model of care at the

Keeping Kidneys Clinic. When an individual has high self-efficacy to perform a

specific task, he or she is more likely to initiate the task, put more effort into it, and

persist to see it through in the face of adversities (Bandura, 1986, 1997). Increased

self-efficacy has been shown to be associated with positive changes in CKD self-

management behaviours, such as increased communication with caregivers,

partnership in care, self-care activities, and medication adherence in CKD patients

(Curtin et al., 2008). Wierdsma et al. (2011) also found an association between

increased CKD self-efficacy and medication adherence. Healthcare professionals can

assist with increasing an individual’s self-efficacy by assessing how confident they

are in accomplishing particular self-management tasks on a scale of 0 to 10. If the


individual reports a confidence level less than 7, then the healthcare professional

should work in partnership with the person to overcome barriers until confidence is

higher (Coleman & Newton, 2005; Thomas-Hawkins & Zazworsky, 2005). Thus,

enhancing self-efficacy through CKD self-management support may be beneficial in

slowing CKD progression.

6.5 RELIABILITY AND VALIDITY OF INSTRUMENTS

This study evaluated the validity and reliability of two kidney disease-specific

instruments: the KiKS and Aus.CKD-SM. The KiKS was shown to be a valid and

reliable measure of knowledge in an Australian sample. It had a good internal

consistency and was similar to the original KiKS. These results, however, indicated

that the scores improved after retesting. This may be explained by the possibility that

some participants may have looked for the correct responses (e.g. in-patient

education material, websites) or simply may have had time to reflect on their past

performance and refine their response. According to Polit and Yang (2015),

regardless of the stability of the measure, traits such as knowledge change over time.

The Aus.CKD-SM was shown to be a valid and reliable measure of self-

management in the sample population. The instrument demonstrated good internal

consistency and stability. Good construct validity was also demonstrated with the

four Aus.CKD-SM factors accounting for the most variance. Despite the Cronbach’s

alpha for the Aus.CKD-SM being lower than that of the reference CKD-SM (Lin et

al., 2013b), overall, the Aus.CKD-SM and subscales all had a Cronbach’s alpha well

above the recommended minimum of 0.7 for reliability (Polit, 2010; Polit & Yang,

2015). The Aus.CKD-SM can be used to assess how people with CKD in Australia

self-manage their disease and the results could be used to develop tailored

interventions to better support the self-management of an individual.

The secondary aim of this study was to test the hypothesised relationships

between CKD knowledge, self-efficacy, and self-management. For this reason, the

validity and reliability of the SEMCD was evaluated. The SEMCD was found to be a

valid and reliable measure of self-efficacy in those with CKD. The instrument had

excellent internal consistency, similar to the original (Lorig et al., 2001), and the

German version of the SEMCD (Freund et al., 2011). Upon examining the

relationships between CKD knowledge, self-efficacy, and self-management, self-


efficacy was found to be significantly related to self-management, with high levels of

self-efficacy associated with high levels of self-management. However, there was no

relationship between CKD knowledge and self-efficacy or self-management.

6.6 SUMMARY

The validity and reliability of the KiKS was supported in an Australian

population with CKD. Knowledge about CKD was lower than expected. The

Australian sample had limited understanding of many topics important to the self-

management of their disease, and limited knowledge of kidney function and

symptoms of CKD. The Aus.CKD-SM was a valid and reliable instrument for

measuring self-management in people with CKD. Study participants were more

likely to engage in problem solving, adherence to lifestyle modification, and self-

integration self-management behaviours, as opposed to seeking support from family,

friends, or health care providers. The participants reported adequate levels of self-

efficacy and, self-efficacy scores were positively related to self-management scores.

The final chapter of this thesis concludes with the strengths and limitations of this

study, together with implications of the findings and recommendations for future

research.

110 Chapter 7: Conclusions

Chapter 7: Conclusions

7.1 INTRODUCTION

This study evaluated the validity and reliability of the KiKS and CKD-SM in

an Australian population with CKD attending a primary healthcare clinic. The study

results were presented and discussed in the previous chapters. This chapter analyses

the strengths and limitations of the study, as well as presenting the implications of

the findings and proposing recommendations for future research.

7.2 STUDY STRENGTH

This study has a number of strengths. It is the first study to evaluate the validity

and reliability of the KiKS and Aus.CKD-SM in an Australian population with CKD.

The study was informed by a theoretical framework comprising Lorig and Holman’s

(2003) self-management skills and Ong et al.’s (2013) self-management behavioural

domains for CKD. The study followed rigorous methodology for the validation of

instruments. The use of validated instruments to measure disease-specific

knowledge, self-management, and self-efficacy levels in people with CKD

strengthens the results of this study. These results will contribute to the

understanding of the level of kidney disease knowledge, CKD self-management, and

self-efficacy among those attending primary care CKD clinics in Australia. The

study also followed the correct procedures for test-retest studies, such as using an

adequate sample size for retest, and having an adequate time interval between the

initial completion of the questionnaires and the retest. The sample size was adequate

for this study due to the time frame for data collection, although Polit and Yang

(2015) recommend a larger sample be used to ensure stability in item covariation

estimates.

It is important to note that this study, conducted in a primary care CKD clinic

serving a socially disadvantaged group, involved an often under researched and

understudied population, who are potentially at risk of poorer outcomes. Because of

the population being serviced, recruitment was slower than anticipated, with only one

participant being recruited in some weeks. The participants could self-administer the

questionnaires but if required the researcher could read the questions out aloud. For

Chapter 7: Conclusions 111

this reason, the study was more inclusive of those with potentially lower literacy

levels. Finally, the diversity in age, gender, and multicultural background of the

study population, which included Aboriginal or Torres Strait Islanders, reflects the

demographic profile of Australia’s CKD population.

7.3 STUDY LIMITATIONS

Despite the strengths of this study, there are several limitations. First, a

convenience sample was recruited from a single clinic located in a

socioeconomically disadvantaged area, which may have introduced sampling bias.

Second, due to the cross-sectional design of this study, causality and associations

cannot be conclusively determined. Third, the use of self-reported questionnaires

(CKD-SM and SEMCD) may have led to overestimation of behaviours due to

participants’ tendency to provide socially desirable responses (Kimberlin &

Winterstein, 2008; Li, Jiang, & Lin, 2013). Fourth, there were no

discussion/considerations of the culturally and linguistically diverse (CALD) and

Indigenous population in this study. In addition, data on languages spoken other than

English and years since CKD diagnosis were not collected which in hindsight should

have been since these could impact on CKD knowledge and self-management

Finally, the participants who agreed to take part in the retest may have sought

assistance from a family member (or even checked answers to the knowledge

questions on the internet) because it was completed at home.

7.4 IMPLICATIONS FOR PRACTICE, EDUCATION AND RESEARCH

There are several important implications of this study to nursing practice,

education, and research. These implications, together with the proposed

recommendations, are discussed in the following paragraphs.

7.4.1 Implications for Nursing Practice

The present study indicates that disease-specific knowledge was generally poor

in people diagnosed with CKD attending the primary care clinic. This suggests that

further education, as well as the type, amount, and resources used to educate patients

should be reviewed. It is also important to be aware that some patient characteristics

(i.e., age, gender, education level, and socioeconomic status) could be associated

with low levels of health literacy, which may impact on improving understanding


about CKD. In addition, the provision of inadequate and ineffective educational

methods could result in insufficient knowledge transfer. Thus, nurses (particularly

practice nurses working in primary health care) ought to be better prepared to

provide education that is targeted to the individual. The instruments validated in this

study could be used as a “readiness tool kit” in in the CKD population for self-

management, CKD knowledge and self-efficacy. Using the KiKS as an assessment

tool could be useful for identifying areas of low patient knowledge in people with

CKD.

Chronic kidney disease management is complex, requiring individuals to take

multiple medications and make significant lifestyle changes. Health care

professionals need to establish priorities when helping patients manage their CKD.

Medication regimens should be simplified to improve medication adherence,

especially in individuals with multiple co-morbidities.

Chronic kidney disease is a progressive disease; thus, it is important that

individuals with CKD are identified and taught how to self-manage their disease in

the early stages of their disease. The validated Aus.CKD-SM could be used to

identify individuals in need of support to acquire sufficient self-management skills.

7.4.2 Implications for Education

This study has implications for education for both patients and nurses. The

results of this study show that the education provided by health care professionals at

a dedicated CKD clinic is not sufficient to achieve adequate understanding of CKD.

Education ought to focus on risk factors, functions of the kidney, and CKD

symptoms. When patients are prescribed medications, education should include the

reason for the prescription, possible adverse effects, the importance of adherence, as

well as the consequences of non-adherence. Similarly, education on BP monitoring

should include BP targets and what to do if BP is consistently outside these targets.

Education about both the disease and how to self-manage the disease should be given

in plain language to facilitate understanding.

The study findings also indicate that the participants were less likely to seek

support, perhaps because resources were not readily available (although this is

unlikely given the specific focus of the Keeping Kidney Clinic) or that they were

reluctant to seek support. Apart from support from family and friends, healthcare

Chapter 7: Conclusions 113

providers may facilitate self-management by providing easy access to health-care

advice, follow-up telephone conversations, and linking patients to useful resources.

Family members could be invited to consultations to receive education on CKD and

the importance of self-management. In addition, those with CKD could be directed to

the Kidney Health Australia website (http://kidney.org.au/) and taught how to

navigate it to retrieve relevant information about their disease (if appropriate, given

the circumstances of individuals); alternatively, hard copy information in various

languages could be sourced by health care professionals and given to patients. The

Kidney Health Australia website also has information about CKD support groups.

Teaching self-management is time-consuming, requiring multiple and ongoing

contact with healthcare professionals for goal setting, education on self-monitoring,

problem solving skills, and evaluation of progress. Newly diagnosed CKD patients

may be more receptive to education than those who have had a longer diagnosis, as

these individuals are more likely to believe taking action might improve their health

status. This study was unable to determine the longevity of CKD, therefore future

research is warranted.

Nurses interact with patients at key time points throughout their illness

trajectory, such as during admission, medication changes, and upon discharge from

healthcare services. These interactions make nurses ideally placed to impart

information and everyday life skills to people with CKD, thereby empowering them

to self-manage their disease. The instruments validated in this study could serve as a

‘readiness tool kit’ for the CKD population. To be able to educate patients, nurses

also need to be knowledgeable about CKD, its trajectory, and its management;

ongoing professional education is therefore important for nurses, particularly those

working in primary healthcare.

7.4.3 Implications for Research

Several implications for further research have arisen from this study. Future

research should consider further testing of the validity and reliability of the KiKS and

Aus.CKD-SM, with a larger sample recruited from various primary healthcare

locations across Australia to increase the generalisability of the tools. In addition,

future research involving a larger sample maybe useful to develop clinical cut-points

that indicate the levels at which some or more intensive education is required for

patients. The role of disease-specific knowledge, self-efficacy, and self-management

http://kidney.org.au/


and its impact on the rate of progression of CKD is also an area that requires further

examination. Future studies ought to consider specific population groups such as the

CALD and Indigenous population. In addition, reporting language spoken other than

English and years since CKD diagnosis, and their impact on CKD knowledge and

self-management is a worthy consideration. A longitudinal study may help to

determine the causal relationship between the level of CKD knowledge and the

factors influencing such knowledge uptake. Similarly, a longitudinal study involving

CKD self-management may help illustrate the causal associations between levels of

self-management and related influencing factors. The delivery of appropriate and

timely self-management interventions that maximise an individual’s confidence to

engage in self-management behaviours and remain motivated to follow healthcare

professional advice about adherence to recommended regimens is required.

7.5 CONCLUSION

Gaps in the literature suggest the need for validated instruments to measure the

levels of both CKD knowledge and self-management. This study evaluated the

validity and reliability of the KiKS and Aus.CKD-SM in an Australian population

with CKD attending a primary healthcare CKD clinic. The validity of both the KiKS

and Aus.CKD-SM were both confirmed. This study also described the characteristics

of patients attending a primary healthcare clinic and measured CKD knowledge, self-

management, and self-efficacy. While individuals across all stages of their disease

were engaged in some level of self-management, CKD knowledge was unexpectedly

low. The optimal levels of self-efficacy reported in this study reflect the participants’

desire to gain more disease-specific knowledge and skills to self-manage their CKD.

Finally, this study has made recommendations for practice, education, and research.

References 115

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Appendices 131

Appendices

Appendix 1: Application for Review of Negligible/Low Risk Research

Involving Human Participants

QUT Research Ethics Unit Tue 17/03/2015 4:07 PM To: Ann Bonner; Clint Douglas; Colette Wembenyui; Cc: Janette Lamb;

Dear Prof Ann Bonner and Mrs Colette Wembenyui Project Title: Self-management in people with chronic kidney disease (CKD) stages 2-4: Validation of CKD knowledge and self-management measures Ethics Category: Human - Low Risk Approval Number: 1500000071 Approved Until: 17/03/2017 (subject to receipt of satisfactory progress reports) We are pleased to advise that your application has been reviewed and confirmed as meeting the requirements of the National Statement on Ethical Conduct in Human Research. I can therefore confirm that your application is APPROVED. If you require a formal approval certificate please advise via reply email. CONDITIONS OF APPROVAL Please ensure you and all other team members read through and understand all UHREC conditions of approval prior to commencing any data collection: > Standard: Please see attached or go to http://www.orei.qut.edu.au/human/stdconditions.jsp > Specific: None apply Decisions related to low risk ethical review are subject to ratification at the next available UHREC meeting. You will only be contacted again in relation to this matter if UHREC raises any additional questions or concerns. Whilst the data collection of your project has received QUT ethical clearance, the decision to commence and authority to commence may be dependent on factors beyond the remit of the QUT ethics review process. For example, your research may need ethics clearance from other organisations or permissions from other organisations to access staff. Therefore the proposed data collection should not commence until you have satisfied these requirements. Please don't hesitate to contact us if you have any queries. We wish you all the best with your research. Kind regards Janette Lamb on behalf of Chair UHREC Office of Research Ethics & Integrity Level 4 | 88 Musk Avenue | Kelvin Grove

p: +61 7 3138 5123

e: [email protected] w: http://www.orei.qut.edu.au

132 Appendices

Appendix 2: Ethics Variation

Ethics variation - approved - 1500000071

QUT Research Ethics Advisory Team

Tue 19/01, 3:22 PMAnn Bonner;Colette Wembenyui;Janette Lamb

Inbox

Dear Prof Ann Bonner and Mrs Colette Wembenyui

Approval #: 1500000071

End Date: 17/03/2017

Project Title: Self-management in people with chronic kidney disease

(CKD) stages 2-4: Validation of CKD knowledge and self-management measures

This email is to advise that your variation has been considered by the

Chair, University Human Research Ethics Committee. This HREC is

constituted and operates in accordance with the National Health and Medical

Research Council's (NHMRC) National Statement on Ethical Conduct in Human

Research (2007).

Changes have made to info-consent (attached) as there were a couple of

small errors.

Whenever you submit your amended documents, you must change the date on the

document name to the date you made the changes.

Please ensure you use the attached versions when conducting your research.

Approval has been provided for the inclusion criteria: CKD stage 1.

PLEASE NOTE:

RESEARCH SAFETY -- Ensure any health and safety risks relating to this

variation have been appropriately considered, particularly if your project

required a Health and Safety Risk Assessment.

CONFLICTS OF INTEREST -- If this variation will introduce any additional

perceived or actual conflicts of interest please advise the Research Ethics

Unit by return email.

Please don't hesitate to contact us if you have any questions.

Regards

Janette Lamb / Debbie Smith

on behalf of Chair UHREC

Office of Research Ethics & Integrity

Level 4 | 88 Musk Avenue | Kelvin Grove

p: +61 7 3138 5123 / 3138 4673

e: [email protected]

w: http://www.orei.qut.edu.au

Appendices 133

Appendix 3: Site Specific Approval Letter and Forms for Data Collection

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Appendix 4: Author’s Permission to use the CDKD Self-Management

Instrument

Hi Dear Colette Wembenyui

Greetings!

I am pleased that you are interested in the CKD-SM instrument we developed.

Would you mind telling me about your academic background and what study you are planning to use

the CKD-SM instrument.

Attached please find the material you need. I authorize you to use this instrument. However, I would

remind you that please be sure to cite the reference when you report the study results.

I wish you well in your work and your studies.

Best regards,

Chiu-Chu

Chiu-Chu Lin, PhD RN

Professor,

School of Nursing, Kaohsiung Medical University, Taiwan

http://www.kmu.edu.tw/

Email address: [email protected]

Tel: 886-7-3121101 ext 2611

Fax: 886-7-3218364

>

>

> 2016-10-25 8:42 GMT+08:00 秋菊(chiu-chu) <[email protected]>:

>

Appendices 143

Appendix 5: Participant Demographic Information Questionnaire

1. What is your gender?

Male

Female

2. What is your age? ………......years

3. What is your marital status?

Single

Married

Widowed

Divorced

Separated

4. What is the highest degree or level of school you have completed?

Primary school

Secondary school

High school

Diploma

Bachelor's degree

Master's degree

Doctorate degree

Other: please specify……………………

5. Are you currently...?

Employed for wages

Self-employed

Unemployed

A homemaker

Retired

Other: please specify……………………

6. What best describes your current occupation?

• Industry

• Non-profit (religious, arts, social assistance, etc.)

• Government

• Health Care

• Education

• Other: Please specify

7. What category best describes your annual household income?

Less than $24,999

$25,000 to $49,999

$50,000 to $74,999

$75,000 to $99,999

$100,000 to $149,999

$150,000 or more

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8. What is your ethnicity?

Aboriginal or Torres Strait Islander

African

Asian

Pacific Islander

Hispanic/Latino

White/ Caucasian

Other: Please specify……………………….

9. How many people live in your household? ………………

Appendices 145

Appendix 6: Clinical Characteristics (from medical records review)

1. Weight

2. Height

3. BMI

4. BP

5. eGFR

6. Potassium

7. Calcium

8. PO4

9. Serum creatinine

10. Albumin

11. HbA1c

12. HDL

13. LDL

Current medications (list)

- include prescribed and

over the counter

medications

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Appendix 7: Kidney Disease Knowledge Survey (KiKS)

This survey aims to find out how well you understand chronic kidney disease.

Please answer every question by marking the appropriate box.

If you are unsure about how to answer, please choose one best answer that applies

to you.

1. On average, your blood pressure should be:

□ 160/90

□ 150/100

□ 170/80

□ Lower than 130/80

2. Are there certain medications your doctor can prescribe to help keep your

kidney(s) as healthy as possible?

□ Yes

□ No

3. Why is too much protein in the urine not good for the kidney?

□ It can scar the kidney

□ It is a sign of kidney damage

□ It is a sign of kidney damage AND can scar the kidney

□ It can cause an infection in the urine

□ All of the above

4. Select the ONE MEDICATION from the list below that a person with CHRONIC

kidney disease should AVOID:

□ Lisinopril

□ Tylenol

□ Motrin / Ibuprofen

□ Vitamin E

□ Iron Pills

5. If the kidney(s) fail, treatment might include (FOR THIS QUESTION you can

PICK up to TWO ANSWERS):

□ Lung biopsy

□ Haemodialysis

□ Bronchoscopy

□ Colonoscopy

□ Kidney transplant

6. What does "GFR" stand for?

□ Glomerular Filtration Rate - tells us level of kidney function

□ Good Flow Renal - tells us about flow of urine from kidney

□ Gain For Real - tells us if your kidney function is improving

□ Glucose Function Rate - tells us about your blood sugar level

7. Are there stages of CHRONIC kidney disease?

□ Yes

□ No

Appendices 147

8. Does CHRONIC kidney disease increase a person's chances for a heart attack?

□ Yes

□ No

9. Does CHRONIC kidney disease increase a person's chance for death from any

cause?

□ Yes

□ No

This section is about WHAT THE KIDNEY DOES. Please select one answer to each

question below.

Yes No

10. Does the kidney make urine? □ □

11. Does the kidney clean blood? □ □

12. Does the kidney help keep bones healthy? □ □

13. Does the kidney keep a person from losing hair? □ □

14. Does the kidney help keep red blood cell counts

normal?

□ □

15. Does the kidney help keep blood pressure normal? □ □

16. Does the kidney help keep blood sugar normal? □ □

17. Does the kidney help keep potassium levels in the

blood normal?

□ □

18. Does the kidney help keep phosphorus levels in the

blood normal?

□ □

This section is about SYMPTOMS. Please select from the list, all of the symptoms a

person might have if they have chronic kidney disease or kidney failure.

Yes No

19. Increased fatigue? □ □

20. Shortness of breath? □ □

21. Metal taste / bad taste in the mouth? □ □

22. Unusual itching? □ □

23. Nausea and / or vomiting? □ □

24. Hair loss? □ □

25. Increased trouble sleeping? □ □

26. Weight loss? □ □

27. Confusion? □ □

28. No symptoms at all? □ □

Appendices 149

Appendix 8: Chronic Kidney Disease Self-Management Instrument

There are a number of questions in relation to how you feel and deal with chronic kidney disease, please select one of four response that best

reflects your real situation in the last three months.

1: Never 2: Sometimes 3: Often 4: Always

QUESTION NEVER SOMETIMES OFTEN ALWAYS

1. When I have questions about my disease, I discuss what I have to do with my

family and friends.

2. I would ask about the possible reasons for my decline in my kidney function.

3. I inform my family and friends about my kidney treatment plan (such as

medication changes, lifestyle changes).

4. I share my personal experience of kidney disease with other patients who

have kidney disease.

5. I understand the meaning of my kidney function blood tests (such as

creatinine, eGFR).

6. When my blood pressure is high (more than 140/90), I try to find out the

possible reasons.

7. To prevent the increased workload on my kidneys, I am able to control what I

eat.

8. I follow the kidney diet suggested by my doctor or nurse or dietician.

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9. I solve problems related to my kidney disease by using various sources (such

as calling my nurse or doctor, using the internet, Google, kidney support

group).

10. When I am feeling upset or frustrated, I discussed my feelings with others.

11. I incorporate my kidney disease treatment into my life.

12. I avoid habits that worsen my kidney function (such as smoking, consuming

alcoholic drinks, overly salty food).

13. I follow health professionals’ recommendations about exercise.

14. I keep tract of my symptoms and early warning signs (blood sugar levels,

weight, shortness of breath, swelling in feet).

15. I follow health professionals’ recommendations about eating a balance diet.

16. I ask doctors or nurses questions to clarify my kidney treatment plan.

17. I follow health professionals’ recommendations about not smoking.

18. I have changed my lifestyle to prevent my kidney disease from getting worse.

19. I seek help from others when I am feeling upset or frustrated.

20. I keep my kidneys healthy by maintaining my overall health.

21. I stop bad habits which are harmful to my kidneys (such as smoking,

consuming overly salty food and alcohol).

Appendices 151


22. I take steps to understand the risk factors associated with chronic kidney

disease (such as high blood pressure, diabetes, smoking, obesity).

23. I control my body weight according to the advice from doctors or nurses.

24. I make good choices about the type and amount of food I eat when I am not at

home (such as at the shops, church, parties, eating out).

25. I can adjust my daily routine to follow my kidney disease treatment plan

when I am not at home (such as travelling, holidays).

26. When my body has new or worsening physical symptoms (such as foot

swelling, severe headache, passing extra urine at night), I try to find out the

cause.

27. I still take all my medications even when I am not at home.

28. I feel I am able to attend social events (such as weddings, parties, church)

even though I have kidney disease.

29. I seek out information about chronic kidney disease from a range of sources

(such as internet, flyers, brochures, books, kidney support group).

30. I take my medications as prescribed by my doctors or nurses or pharmacist.

31. I take action when my early warning signs and symptoms get worse.

32. When I have questions about my kidney disease, I discuss what to do with my

doctors or nurses or pharmacist.

152 Appendices

Appendix 9: Self-Efficacy for Managing Chronic Disease Six-Item Scale

We would like to know how confident you are in doing certain activities. For each of

the following questions, please choose the number that corresponds to your

confidence that you can do the tasks regularly at the present time.

1. How confident are you that you can

keep the fatigue caused by your

disease from interfering with the

things you want to do?

____________________________

not at all | | | | | | | | | | totally

confident 1 2 3 4 5 6 7 8 9 10 confident


keep the physical discomfort or pain

of your disease from interfering with

the things you want to do?

____________________________




keep the emotional distress caused by

your disease from interfering with the

things you want to do?

______________________________




keep any other symptoms or health

problems you have from interfering

with the things you want to do?

______________________________




do the different tasks and activities

needed to manage your health

condition so as to reduce you need to

see a doctor?

______________________________




do things other than just taking

medication to reduce how much your

illness affects your everyday life?

______________________________



Appendices 153

Appendix 10: Histogram, Normal Q-Q Plots and Box-whisker Plots of

Instruments

154 Appendices

Appendix 11: Patient Information and Consent Forms

Appendices 155

156 Appendices

Documents

Colette Funyui Wembenyui - QUT · Colette Funyui Wembenyui Bachelor of Nursing Submitted in fulfilment of the requirements for the degree of Master of Philosophy (IF80) School of