Thông tin tài liệu
PSYCHOSOCIAL DETERMINANTS OF
INTENTIONAL AND UNINTENTIONAL
NONADHERENCE IN PATIENTS UNDERGOING
AUTOMATED PERITONEAL DIALYSIS AND
CONTINUOUS AMBULATORY PERITONEAL
DIALYSIS
YU ZHENLI
A THESIS SUBMITTED
FOR THE DEGREE OF MASTER
OF SOCIAL SCIENCES
NATIONAL UNIVERSITY OF SINGAPORE
2011
Acknowledgments
I would like to express my deep thanks to my supervisor Dr. Konstantina Griva
for offering me the opportunity to work with her and enlightening me on my research.
Her dedication to health research energizes me and helps me find my sense of direction in
my life. I am also very grateful to Jo-an, Augustine and Zhihui for spending much of their
precious time on proofreading this thesis. In addition, my sincere thanks go to Ivy for
providing me great assistance in the recruitment process. Finally, I owe my sincere
thanks to my good friends, Anastasia, Jannah and Jean for working together with me and
being there for me at the most needed times.
i
Table of Contents
Acknowledgments ............................................................................................................... i
Table of Contents ................................................................................................................ ii
Summary ............................................................................................................................ iv
List of Tables ...................................................................................................................... v
List of Figures .................................................................................................................... vi
List of Appendices ............................................................................................................ vii
Chapter One ........................................................................................................................ 1
Introduction ......................................................................................................... 1
End Stage Renal Disease ................................................................................. 1
Health Beliefs .................................................................................................. 9
Emotional Distress......................................................................................... 11
Quality of Life ............................................................................................... 14
Nonadherence ................................................................................................ 17
Determinants of Nonadherence ..................................................................... 26
Limitations of Previous Studies..................................................................... 30
Study Objectives ............................................................................................ 31
Study Hypotheses .......................................................................................... 31
Chapter Two ..................................................................................................................... 33
Methodology ..................................................................................................... 33
Participants .................................................................................................... 33
Study Instruments .......................................................................................... 35
Study Languages............................................................................................ 43
Data Analysis................................................................................................. 44
Ethics ............................................................................................................. 48
Chapter Three ................................................................................................................... 49
ii
Results ............................................................................................................... 49
Demographics ................................................................................................ 49
Health Beliefs ................................................................................................ 52
Emotional Distress......................................................................................... 54
Quality of Life ............................................................................................... 57
Prevalence of Nonadherence ......................................................................... 59
Factors Associated With Self-reported Nonadherence.................................. 65
Factors Associated With Nonadherence Based on Biochemical Markers .... 81
Chapter Four ..................................................................................................................... 86
Discussion ......................................................................................................... 86
Overview ....................................................................................................... 86
Health Beliefs ................................................................................................ 86
Emotional Distress......................................................................................... 88
Quality of Life ............................................................................................... 92
Prevalence of Nonadherence ......................................................................... 96
Determinants of Nonadherence ................................................................... 102
Clinical Recommendations .......................................................................... 114
Study Strengths and Limitations.................................................................. 116
Future Studies .............................................................................................. 118
Chapter Five .................................................................................................................... 120
Conclusion ....................................................................................................... 120
References ....................................................................................................................... 121
iii
Summary
The purpose of this study was to evaluate the prevalence of nonadherence and
associated psychosocial factors (i.e., health beliefs, emotional distress and quality of life)
in patients undergoing peritoneal dialysis (PD). Intentional (e.g., reasoned decision) and
unintentional (e.g., forgetting) nonadherence were investigated as separate constructs and
compared. It was a cross-sectional survey conducted with 144 PD patients. Nonadherence
to all three main components of the therapeutic regimen, i.e., performing dialysis
exchanges, taking medication as instructed and restricting dietary intake, was common,
with dietary guidelines the most difficult to adhere to. Intentional nonadherence occurred
more frequently than unintentional nonadherence for dialysis and diet; intentional and
unintentional nonadherence to medication were equivalent. Nonadherence was strongly
affected by psychosocial factors. More specifically, patient satisfaction was the most
important predictor of intentional nonadherence to dialysis, whereas environment quality
of life was the strongest predictor of unintentional nonadherence to dialysis. Self-efficacy
was the strongest predictor of intentional and unintentional nonadherence to both
medication and diet.
iv
List of Tables
Table 1: Nonadherence Rates Documented in PD Patients .............................................. 20
Table 2: Number of Items, Reliability Coefficients and Concepts Measured by the
KDQOL-SF Domains ....................................................................................................... 40
Table 3: Number of Items, Reliability Coefficients and Concepts Measured by the
WHOQOL-BREF Domains .............................................................................................. 41
Table 4: Distribution of Missing Values........................................................................... 44
Table 5: Demographical Characteristics of APD and CAPD Patients ............................. 50
Table 6: Clinical Characteristics of APD and CAPD Patients ......................................... 51
Table 7: Health Beliefs Results in APD and CAPD Patients ........................................... 53
Table 8: Emotional Distress Results in APD and CAPD Patients .................................... 55
Table 9: Quality of Life Results in APD and CAPD Patients .......................................... 58
Table 10: Self-reported Nonadherence Outcomes in APD and CAPD Patients ............... 63
Table 11: Nonadherence Based on Biochemical Markers in APD and CAPD Patients ... 64
Table 12: Comparisons of Self-reported Nonadherence Between Different Subgroups .. 67
Table 13: Spearman Rank Correlations Between Self-reported Nonadherence and
Selected Variables............................................................................................................. 69
Table 14: Multivariate Correlates of Self-reported Nonadherence to Dialysis Guidelines
........................................................................................................................................... 72
Table 15: Multivariate Correlates of Self-reported Nonadherence to Medication
Guidelines ......................................................................................................................... 73
Table 16: Multivariate Correlates of Self-reported Nonadherence to Dietary Guidelines 75
Table 17: Spearman Rank Correlations Between Emotional Distress and Health Beliefs76
Table 18: Factors Affecting Nonadherence Based on Biochemical Markers in Univariate
Analyses ............................................................................................................................ 82
Table 19: Factors Affecting Nonadherence Based on Biochemical Markers in
Multivariate Analyses ....................................................................................................... 85
Table 20: WHO Identified Categories Affecting Nonadherence and Significant Predictors
of Nonadherence in Our Study ....................................................................................... 103
v
List of Figures
Figure 1. Illustration of Hemodialysis Procedure ............................................................... 3
Figure 2. Illustration of Peritoneal Dialysis Procedure ....................................................... 5
Figure 3. Flowchart of the Recruitment Process. .............................................................. 34
Figure 4. A Simple Mediation Model. .............................................................................. 46
Figure 5. Anxiety Score Distribution in All Patients. ....................................................... 54
Figure 6. Depression Score Distribution in All Patients ................................................... 55
Figure 7. Loneliness Score Distribution in All Patients ................................................... 56
Figure 8. Distribution of All Patients’ Frequencies of Overall Deviation From Different
Aspects of the Therapeutic Regimen ................................................................................ 61
Figure 9. Distribution of All Patients’ Frequencies of Intentional and Unintentional
Deviation From Different Aspects of the Therapeutic Regimen. ..................................... 62
Figure 10. Health Belief Mediators of the Relationship Between Anxiety and Intentional
Nonadherence to Medication. ........................................................................................... 77
Figure 11. Health Belief Mediators of the Relationship Between Depression and
Intentional Nonadherence to Medication .......................................................................... 78
Figure 12. Health Belief Mediators of the Relationship Between Anxiety and
Unintentional Nonadherence to Diet ................................................................................ 79
Figure 13. Health Belief Mediators of the Relationship Between Depression and
Unintentional Nonadherence to Diet ................................................................................ 80
Figure 14. Health Belief Mediator of the Relationship Between Anxiety and Intentional
Nonadherence to Diet. ...................................................................................................... 80
vi
List of Appendices
Appendix A: Permission to Use Figure 1 and Figure 2 .................................................. 149
Appendix B: Participation Information Sheet................................................................. 152
Appendix C: Consent Form ............................................................................................ 156
Appendix D: Demographics Questionnaire .................................................................... 157
Appendix E: Medical Form ............................................................................................ 159
Appendix F: Permission to Use Table 2 ......................................................................... 161
Appendix G: Permission to Use Table 3......................................................................... 163
Appendix H: Nonadherence Measures ........................................................................... 165
Appendix I: Research Ethics Approval........................................................................... 167
vii
CHAPTER ONE
Introduction
End Stage Renal Disease
End stage renal disease (ESRD) is the final stage of chronic kidney disease
(CKD), marked by a glomerular filtration rate (GFR) of less than 15 ml/min/1.73 m2.
Patients with ESRD have kidneys failing to effectively remove wastes, keep appropriate
levels of electrolytes (e.g., sodium, potassium, calcium, magnesium) and reabsorb
glucose, blood proteins (e.g., albumin) and other small molecules (Kaazempur-Mofrad,
Vacanti, Krebs, & Borenstein, 2004). Reported incident rates of ESRD varied from 13 in
Bangladesh to 557 per million population in Morelos and reported prevalent rates of
ESRD varied from 110 in Philippines to 2311 per million population in Taiwan in 2008
(United States Renal Data System [USRDS], 2010). Diabetes mellitus is a major cause of
ESRD, accounting for more than 40% of ESRD incident cases in most countries (USRDS,
2010). Other important causes of ESRD include glomerulonephritis and high blood
pressure. Symptoms common in ESRD patients include fatigue/tiredness, pruritus,
constipation, anorexia, pain, sleep disturbance, anxiety, dyspnea, nausea, restless legs and
depression (Murtagh, Addington-Hall, & Higginson, 2007). Transplant and dialysis are
renal replacement therapy (RRT) choices for ESRD patients to partially restore their
kidney functions and sustain life.
Transplant
Transplant is the most ideal form of treatment for ESRD patients. Prevalent rates
of functioning grafts worldwide varied from 29 in Romania to 572 per million population
in Norway in 2008 (USRDS, 2010). The corresponding rate in Singapore was 344.5
1
(Singapore Renal Registry [SRR], 2010). The 1- and 5-year survival rates for transplant
patients in Singapore were 97.5% and 91.5% respectively (SRR, 2010). In transplant, a
healthy kidney donated by a relative or others is placed in the body to take over the work
of the old, dysfunctional kidney. Patients are required to continuously take
immunosuppressants to prevent the body from rejecting the new kidney after
transplantation. Apart from this, transplant patients live a relatively normal life, with
much less fluid and dietary restrictions and clinical visits when compared to dialysis
patients (Christensen & Ehlers, 2002).
Despite the good clinical and psychological outcomes, kidney transplantation
remains underutilized mainly due to a shortage of kidney donors. Only 23% of the treated
ESRD patients worldwide were living with a functioning transplanted kidney at the end
of 2004 (Grassmann, Gioberge, Moeller, & Brown, 2005). Furthermore, kidney
transplantation is only an option for a select group of patients. Medical contraindications
and high comorbidity burden limit the patient pool. Legislation may also preclude elderly
patients, the fastest rising segment of the renal population (SRR, 2010), from transplant
candidacy. In Singapore, patients over 60 are not eligible transplant candidates (Vathsala
& Chow, 2009). National data indicates that most transplant kidneys (68%) are from
deceased donors (SRR, 2010) and patients need to wait for a median of 9.44 years for
deceased-donor renal transplants (Vathsala & Chow, 2009). Thus most ESRD patients
need dialysis to sustain life.
Hemodialysis
Hemodialysis (HD) is the predominant dialysis modality used in most parts of the
world (USRDS, 2010). Approximately 89% of dialysis patients were undergoing HD and
2
mann et al., 2005). In
11% underggoing peritooneal dialysiis (PD) at thhe end of 20004 (Grassm
S
Singapore,
85.7% of diialysis patieents were trreated with H
HD and 14..3% with PD
D in 2008
(
(SRR,
20100). Prevalennt rates of H
HD worldwiide varied ffrom 103 inn Philippinees to 2097
p million populationn in Taiwann in 2008 (U
per
USRDS, 20010). The ccorrespondinng rate in
S
Singapore
w
was 908.6 (SRR,
(
20100). The 1- annd 5-year survival
s
ratees for HD ppatients in
S
Singapore
w
were 89.2% and 58.9% respectivelyy (SRR, 20110).
Figgure 1. Illusstration of hhemodialysis proceduree.
Note. Used with perm
N
mission from
m the Nationnal Institutee of Diabetees and Digeestive and
K
Kidney
Disseases, Natioonal Instituttes of Healtth (see Apppendix A). R
Retrieved August
A
16,
2
2011,
from http://kidneey.niddk.nihh.gov/kudiseeases/pubs/hhemodialysiis/images/diialysis.gif.
The basic HD pprocedure iss illustratedd in Figure 11. The undeerlying mechanism of
H is simillar to PD. B
HD
Blood and ddialysate aree separated by a semippermeable m
membrane.
S
Substances
(e.g., wasstes, toxinss, excessivee water) diffuse
d
from
m a regionn of high
c
concentratio
on (blood) into one off lower conncentration ((dialysate) and
a are subbsequently
r
removed
froom the bodyy together with
w the diaalysate. In H
HD, this difffusion proceess occurs
o
outside
the bbody relyinng on a HD machine.
m
HD
D patients usually
u
go too hospitals or
o dialysis
c
centers
to aattend dialyssis sessionss and are paassive recipients of treaatment. Stanndard HD
s
sessions
aree 4 to 5 hourrs, 3 times a week (Madduell et al., 22003).
3
Peritoneal Dialysis
Peritoneal dialysis (PD) is a home-based renal therapy involving patients’ active
participation. Patients or carers receive trainings about how to perform PD exchanges
from PD nurses and are supposed to perform the procedures independently at home after
the training period. Prevalent rates of PD worldwide varied from 2 in Bangladesh to 846
in Hong Kong per million population in 2008 (USRDS, 2010). The corresponding rate in
Singapore was 163.6 (SRR, 2010). The 1- and 5-year survival rates for PD in Singapore
were 81.1% and 26.7% respectively (SRR, 2010).
The basic PD procedure is demonstrated in Figure 2. PD uses patient’s
peritoneum as a natural semipermeable membrane. While the diffusion process occurs
outside the body of a HD patient, it occurs in the abdomen of a PD patient. A catheter
(soft tube) is inserted into the abdomen of a PD patient and this operation makes the
patient vulnerable to peritonitis. Dialysate flows into the peritoneal cavity through the
catheter, stays there (patients are ambulatory during this period) absorbing wastes, toxins
and excess water from the blood and then is drained out of the body together with the
wastes. Then the infusion process begins again and the procedure repeats. The draining
and infusion process is called an exchange, taking 30 to 60 minutes depending on the
patient’s health status.
These repeated exchanges can be performed either manually by patients
(continuous ambulatory peritoneal dialysis, CAPD) or automatically using a mechanical
devise over night (automated peritoneal dialysis, APD). As opposed to intermittent
schedules of HD, PD is a continuous treatment that is performed daily. A typical CAPD
4
p
prescription
n involves 4-6 manual eexchanges ddaily typicallly every 3 or
o 4 hours. A
APD lasts
f 9 to 12 hhours while the patient sleeps at nigght.
for
Figurre 2. Illustraation of periitoneal dialyysis proceduure.
Note. Used with perm
N
mission from
m the Nationnal Institutee of Diabetees and Digeestive and
K
Kidney
Disseases, Natioonal Instituttes of Healtth (see Apppendix A). R
Retrieved August
A
16,
2
2011,
from http://kidneey.niddk.nihh.gov/kudiseeases/pubs/pperitoneal /images/Perittoneal.gif.
D
Dialysis
vs. Transplantt
Diallysis is not as effectivee as transplaant. Projecteed years of life are 3 too 17 years
l
longer
for trransplant paatients than those on thhe waiting llist (Wolfe eet al., 1999)). Dialysis
p
patients
also have worrse outcomes than trannsplant patiients in term
ms of qualiity of life
(
(Basok
et aal., 2009; L
Lee, Morgann, Conway, & Currie, 2005; Maglakelidze, P
Pantsulaia,
T
Tchokhonel
lidze, Manaagadze, & C
Chkhotua, 22011; Ogutm
men et al., 22006; Panaagopoulou,
H
Hardalias,
B
Berati, & F
Fourtounas, 2009), althhough thesee results shoould be vieewed with
c
caution
as transplant patients
p
tennd to be yoounger and healthier tthan dialysiis patients
(
(Bakewell,
H
Higgins, & Edmunds, 22001; Niu & Li, 2005; O
Ogutmen ett al., 2006).
5
PD vs. HD
Clinical outcomes, namely mortality, morbidity and hospitalization, are
comparable between PD and HD (Harris, Lamping, Brown, & Constantinovici, 2002;
Keshaviah, Collins, Ma, Churchill, & Thorpe, 2002; Selgas et al., 2001). A study in
Singapore found higher mortality in diabetic patients on PD than HD but not in nondiabetic patients (Noshad, Sadreddini, Nezami, Salekzamani, & Ardalan, 2009). But the
generalization of study is called into question as it only recruited 60 PD and 60 HD
patients. There is good evidence indicating that PD enables better preservation of residual
renal function (Moist et al., 2000; Oreopoulos, Ossareh, & Thodis, 2008) and is
associated with less cognitive decline (Conde et al., 2010).
A limiting factor of PD use is peritonitis (Bender, Bernardini, & Piraino, 2006).
Peritonitis is probably the most important reason for PD technique failure and drop-out
from PD programs, contributing to approximately16% death in PD patients (Davenport,
2009; Kawaguchi et al., 2003; Li et al., 2010). There is a misconception that PD patients
are more vulnerable to infections than HD patients due to peritonitis. In fact, the overall
risks of infection are similar for PD and HD, although infection types are different
(Aslam, Bernardini, Fried, Burr, & Piraino, 2006).
PD and HD populations have distinct characteristics. PD population tend to be
younger, married, healthier and more educated (Ahlmen, Carlsson, & Schonborg, 1993;
Little, Irwin, Marshall, Rayner, & Smith, 2001; Marron et al., 2005; Ponz Clemente et al.,
2010; Stack, 2002). There is an increasing emphasis on expanding the penetration and
utilization rate of PD. In Hong Kong, 79.4% patients are on CAPD (USRDS, 2010).
Jalisco and Morelos are two places in Mexico where more than half of the dialysis
6
patients are on PD as well (USRDS, 2010). Since PD is less expensive, increasing the use
of PD has significant effect on government budget. It has been estimated that if PD
utilization increases to 40% in Singapore, savings to government will be around $ 25
million per year (Walker, Chen, & Bhattacharyya, 2007). Although PD tends to be
favored by the younger patients who are fully ambulatory and independent, it is important
to recognize that currently there is a shift in recommending and placing less independent
patients on PD regimes (Dimkovic & Oreopoulos, 2008). Offering PD to older patients
can be supported by informal or formal carers who oversee dialysis procedures or by
assisted PD schemes that involve daily visits by community nurses at patients’ home to
initiate PD exchanges (Jassal & Watson, 2011).
APD vs. CAPD
Research to date has focused predominantly on comparing medical endpoints
between APD and CAPD, leaving the psychosocial outcomes poorly understood (Guney
et al., 2010). Clinical studies cannot confirm a clear superiority of one modality over
another, with most studies documenting equivalent outcomes in the two groups in terms
of survival, technical failure, hospitalization, peritonitis, dialysis adequacy, clearance,
hernias rates and the decline of residual renal function (Balasubramanian, McKitty, &
Fan, 2011; Mehrotra, 2009; Mehrotra, Chiu, Kalantar-Zadeh, & Vonesh, 2009; Michels,
Verduijn, Boeschoten, Dekker, & Krediet, 2009; Tang & Lai, 2007).
APD may be more beneficial for certain groups, such as high transporters whose
peritoneal membrane allows for rapid solute transport (Johnson et al., 2010). In addition,
peritonitis risk is reduced in APD with Luer connections compared with CAPD with a
disconnect system, as shown in two randomized controlled trials (Piraino & Sheth, 2010).
7
Another advantage of APD is its ability to reduce intra-abdominal pressure (Enoch,
Aslam, & Piraino, 2002) which is frequently intolerable for some elderly. Known
concerns regarding APD involve inadequate removal of sodium and poor hypertension
control (Ortega et al., 2001; Rodriguez-Carmona, Perez-Fontan, Garca-Naveiro,
Villaverde, & Peteiro, 2004). Individualization of APD based on patient characteristics
may modify these risk factors (Brunkhorst, 2005).
The percentage of APD in PD users is rising steadily in recent years in many parts
of the world, like Canada, US, Singapore and Switzerland (Blake, 1999; Dell'Aquila,
Berlingo, Pellanda, & Contestabile, 2009; Mehrotra, 2009) In Singapore, incident rate of
PD patients choosing APD had increased from 3% in 1999 to 50.5% in 2008 (SRR, 2010).
Patients are motivated to choose APD mainly due to the autonomy it provides instead of
medical considerations (Mehrotra, 2009). APD is especially appealing to young and
independent patients (Badve et al., 2008; Balasubramanian et al., 2011; Fine & Ho, 2002;
Johnson et al., 2010). A main factor limiting APD use is the high cost associated with the
machine. APD on average costs 20% more than CAPD (Dell'Aquila et al., 2009).
Since APD is performed at night and less onerous, it is especially favorable for
two groups of patients. The first group includes students and employers whose day time
activities demand minimum disruptions (Dell'Aquila et al., 2009; Liakopoulos &
Dombros, 2009). With the use of APD, patients do not need to interrupt their study or
work several times a day to perform exchanges. APD also allows patients to avoid the
embarrassing experience of performing exchanges in front of peers and thus is more
appealing to patients.
8
The second group of patients who can gain great benefits from APD includes
those who are highly dependent on others for their treatment, such as children and the
elderly (Dell'Aquila et al., 2009; Liakopoulos & Dombros, 2009). Since only two
connections are required for APD each day (vs. multiple connections in CAPD), it is
easier for employed carers to perform these exchanges without major life disruptions. For
elderly patients without good social support but still want to receive treatment at home, it
is less expensive for APD patients to hire nurses to visit their house since APD requires
fewer visits. Elderly patients on APD in nursing homes have more time to take part in
day time activities and this greatly facilitates their rehabilitations (Dimkovic &
Oreopoulos, 2008).
Health Beliefs
Past studies on patients’ health behaviors tend to depict patients as passive
recipients of medical advice which is given to patients’ best interest (Donovan, 1995).
Doctors feel frustrated about patients’ inability to stick to treatment plans which give rise
to various adverse outcomes such as elevated hospitalization, morbidity and mortality
(Vermeire, Hearnshaw, Van Royen, & Denekens, 2001). These disappointing outcomes
motivate researchers to reexamine patients’ involvement in their therapy decisions and to
take into consideration patients’ beliefs. Different models have been proposed to explain
how patients’ beliefs affect their health decisions, such as the Health Belief Model (HBM)
(Rosenstock, 1974), Theory of Planned Behaviour (TPB) (Ajzen & Fishbein, 1980),
Common Sense Model (CSM) (Leventhal, Diefenbach, & Leventhal, 1992) and
Medication Adherence Model (MAM) (Johnson, 2002). This study focused on two
9
models that receive significant research attention, namely Horne and Weinman’s (1999)
necessity-concerns model and Bandura’s (1977) self-efficacy theory.
Beliefs About Medicines
Horne and Weinman (1999) proposed a necessity-concerns model to understand
medication-taking behaviors among patients with chronic illnesses. The basic idea is that
patients do not follow doctors’ advice without questioning, but perform elaborate
calculations based on their beliefs about medicines. Main considerations include
perceived usefulness/necessity of the prescribed medication and perceived disruptive
effects/injuries caused by the medication. Patients are inclined to take their prescribed
medication when the perceived necessity exceeds perceived concerns. Otherwise patients
may adjust or skip medication to suit their needs. Necessity or concern beliefs can also
work independently. For instance, if patients experience dangerous drug interactions as a
result of taking their prescribed medication, they tend to adjust medication doses no
matter how important they perceive the medication to be.
Self-efficacy
Self-efficacy is a key concept in Bandura’s social learning theory and is defined
as “people’s beliefs in their capabilities to exercise control over their own level of
functioning and over events that affect their lives” (Bandura, 1991, p. 257). The effect of
self-efficacy in determining health behaviors is well-established in literature and this
theory has been successfully applied in different settings such as smoking relapse
prevention, pain management, weight control and rehabilitation from myocardial
infarction (O'Leary, 1985). Dialysis patients experience various stressors caused by their
disease and treatment. The top five stressors among dialysis patients are limitation of
10
physical activity, decrease in social life, uncertainty about the future, fatigue and muscle
cramps (Lok, 1996). Patients’ with high self-efficacy are very likely to face these
stressors directly, actively search for effective ways to minimize the influence of these
stressors, set high goals for their behaviors to maintain health and remain resilient when
confronted with physiological or social barriers (Bandura, 1977).
Emotional Distress
Emotional distress has received considerable attention in dialysis patients.
Patients on dialysis have been reported to spend about six hours of their day (not
including sleeping hours) in negative affective states (Song et al., 2011). A lot of factors,
such as the intrusive nature of renal treatment (Griva, Davenport, Harrison, & Newman,
2010), high burden of symptoms (Murtagh et al., 2007), impaired daily functioning
(Cook & Jassal, 2008) and severe sleep problems (Guney et al., 2010) may give rise to
high emotional distress in this group. Emotional distress can be further worsened by
patients’ reluctance to get evaluation and treatment for it (Wuerth, Finkelstein, &
Finkelstein, 2005), which may be especially pronounced in Singapore where there is a
high level of stigma attached to mental illness (Lai, Hong, & Chee, 2001)
Depression
Typical major depression symptoms include “depressed mood, anhedonia,
appetite or weight change, sleep disturbance, fatigue, psychomotor disturbance, feelings
of worthlessness or guilt, impaired concentration and suicidal thoughts” (Koenig, George,
Peterson, & Pieper, 1997, p. 1378). The golden standard for diagnosis of clinical
depression is the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition
11
(DSM-IV) criteria. The Beck Depression Inventory (BDI) and Hospital Anxiety and
Depression Scale (HADS) are the most widely used self-administered questionnaires for
screening depression. Around 80% of patients classified as depressed based on BDI or
HADS meet DSM-IV criteria (Atalay et al., 2010; Herrero et al., 2003; Wuerth et al.,
2005).
Depression is the most common psychiatric disorder seen in dialysis patients
(Ibrahim & El Salamony, 2008). The prevalence of major depression in patients on renal
replacement therapy has been estimated to be from 20% to 30%, considerably higher than
general populations (Atalay et al., 2010; Fukunishi et al., 2002). Since many symptoms of
depression, such as appetite loss, fatigue and impaired concentration, may be renal
disease manifestations, physicians may overlook the presence of depression among
dialysis patients.
Age, gender, smoking, comorbidities, serum IL-6, albumin, perceived illness
effects and social support are useful in predicting depression (Hung et al., 2011; Kimmel
et al., 1995; Osthus et al., 2010; Patel, Shah, Peterson, & Kimmel, 2002). Depression is
associated with various adverse outcomes such as death, hospitalization and peritonitis
(Diefenthaeler, Wagner, Poli-de-Figueiredo, Zimmermann, & Saitovitch, 2008; Hedayati
et al., 2008; Troidle et al., 2003). For instance, it has been recognized that patients who
scored 16 or higher on BDI had a 2.7-fold increased risk of mortality than those who
scored lower on BDI (Chilcot, Davenport, Wellsted, Firth, & Farrington, 2011). It is
unclear why depression is associated with poor outcomes, probably through impairing
immune functioning, nutritional status and self-care abilities (Kimmel, Weihs, &
Peterson, 1993). In addition, depressed patients are more likely to have poor sleep quality
12
(Guney et al., 2008) and sexual dysfunction (Lew-Starowicz & Gellert, 2009).
Depression may also affect patients’ intention to withdraw from dialysis treatment
(Christensen & Ehlers, 2002).
Anxiety
Anxiety is characterized by feelings of uncertainty, tension, helplessness,
inadequacy, self-consciousness, concentration difficulties, feeling flushed, perspiring,
damp hands, irregular breathing, racing heartbeat, and dry mouth (Endler, Parker, Bagby,
& Cox, 1991). In contrast to the prosperity of studies on depression, anxiety in dialysis
patients receives little attention, even though anxiety is also associated with poor
outcomes such as lowered quality of life and increased likelihood of sexual dysfunction
(Sayin, Mutluay, & Sindel, 2007; Steele et al., 1996; Vasilieva, 2006; Vazquez et al.,
2005). Conventional measures of anxiety include the Hospital Anxiety and Depression
Scale (HADS) and the State-Trait Anxiety Inventory (Spielberger, 1985). The prevalence
of anxiety was reported to be 18.6% in a recent study involving 97 adult HD and PD
patients (Partridge & Robertson, 2011). Around one third of the HD patients were
diagnosed with anxiety in the study of Taskapan et al. (2005). Few studies investigated
the prevalence of anxiety in PD patients. A study in Singapore reported 13% depression
and 50% anxiety in 30 CAPD patients (Lye, Chan, Leong, & van der Straaten, 1997).
Loneliness
Loneliness is defined as "to the extent that a person's network of social
relationships is smaller or less satisfying than the person desires" (Peplau & Perlman,
1979, p. 101). Perceptions of loneliness have rarely been recorded in dialysis patients,
although it is reasonable to expect its high prevalence in this group, since dialysis patients
13
tend to have decreased social life and frequently face family problems and marriage
malaises due to dialysis (Lok, 1996; Rapisarda et al., 2006). One phenomenological study
identified loneliness as an important theme in patients’ experience with dialysis (Herlin &
Wann-Hansson, 2010). Another study assessed loneliness among CAPD patients together
with their carers and detected low level of loneliness in both groups (Asti, Kara, Ipek, &
Erci, 2006). But this study used a relatively young patient sample (mean age around 45)
and it may not be appropriate to generalize findings in this sample to the elderly patients
who tend to have decreased economic and social resources (Buemi et al., 2008).
Emotional Distress on APD vs. CAPD
It is unclearly whether APD offers patients better psychological adjustment than
CAPD, since only three studies with small sample sizes compared emotional outcomes in
APD and CAPD patients and reported mixed results. One study reported equivalent
depression rates in the two groups (Guney et al., 2010). Another study reported lower rate
of depression in APD than CAPD patients (Griva et al., 2010). Similarly, de Wit, Merkus,
Krediet and de Charro (2001) reported that APD patients were less depressed and anxious
than CAPD patients.
Quality of Life
Quality of life is defined by the World Health Organization (WHO) as
“individuals’ perception of their position in life in the context of the culture and the value
systems in which they live and in relation to their goals, expectations, standards and
concerns” (Harper & Power, 1998, p. 551).
14
Quality of life measures can be divided into generic and disease-specific. Generic
instruments measure concepts that are relevant to everyone’s well-being (e.g., life
satisfaction), can be applied in different populations and allow comparisons across
different groups (Patrick & Deyo, 1989). Disease specific measures are used only in
limited populations, but are more sensitive in detecting small quality of life changes
associated with specific conditions (e.g., severity of disease) (Valderrabano, Jofre, &
Lopez-Gomez, 2001). The 36-item Short-Form Health Survey (SF-36), EuroQOL 5
Dimension (EQ-5D), and World Health Organization Quality of Life Instrument, Short
Form (WHOQOL-BREF) are the top three used generic quality of life measures, whereas
the Kidney Disease Quality of Life instrument (KDQOL) and its shortened versions
(KDQOL-SF, KDQOL-36) are the most commonly used measures for assessing disease
specific quality of life in ESRD patients (Glover, Banks, Carson, Martin, & Duffy, 2011).
The current study measured both generic and disease specific quality of life, as
recommended in literature (Valderrabano et al., 2001).
Quality of life impairment is predominant in dialysis patients when compared
with healthy individuals (Maglakelidze et al., 2011; Osthus et al., 2010; Sayin et al.,
2007) , especially in the physical health domain (Bohlke et al., 2008; Brown et al., 2010;
de Wit et al., 2001). Psychological factors, such as health beliefs and social
support/deprivation, have been found to be important in predicting quality of life
(Bakewell, Higgins, & Edmunds, 2002; Theofilou, 2011; Wight et al., 1998). Nonpsychological factors, such as age, gender, hospitalization, number of comorbid diseases,
primary kidney disease, nutritional status and dialysis adequacy may also influence
15
quality of life (de Wit et al., 2001; Fructuoso, Castro, Oliveira, Prata, & Morgado, 2011;
Senol, Sipahioglu, Ozturk, Argun, & Utas, 2010).
The importance of quality of life is increasingly appreciated, not only because it is
inversely correlated with hazards of hospitalization and mortality (DeOreo, 1997;
Valderrabano et al., 2001), but also because it evaluates the effectiveness of treatment
based on patients’ subjective feelings (Fructuoso et al., 2011). Dialysis patients are
willing to trade less living time for better quality of life (Jhamb et al., 2011; Tsevat et al.,
1998), corresponding to Socrates’ adage that “The really important thing is not to live,
but to live well”. Nephrologists also place more weight on quality of life than mortality
and morbidity in recommending dialysis modalities (Mendelssohn, Mullaney, Jung,
Blake, & Mehta, 2001). Various efforts have been initiated to improve patients’ quality of
life, such as adjusting dialysis prescription, controlling comorbidities, treating anemia
and alleviating depression (Ross, Hollen, & Fitzgerald, 2006).
Quality of Life on APD vs. CAPD
Although APD is expected to offer patients better quality of life due to its less
onerous nature (Balasubramanian et al., 2011), this hypothesis is not well-supported in
literature. Five studies compared quality of life outcomes between APD and CAPD
patients, with four of them suggesting equivalent quality of life between the two groups
based on SF- 36 scores (Balasubramanian et al., 2011; Bro et al., 1999; de Wit et al.,
2001; Guney et al., 2010) and one suggesting worse physical but better mental quality of
life in APD patients (Diaz-Buxo, Lowrie, Lew, Zhang, & Lazarus, 2000). The last study
(Diaz-Buxo et al., 2000) did not control for critical covariates (e.g., comorbidity, time on
dialysis) and thus its results should be viewed with caution. A recent longitudinal study
16
examined quality of life in 119 APD and 105 CAPD patients with SF-36 and reported
worse baseline quality of life in CAPD patients, but no differences were found after a
year (Balasubramanian et al., 2011). Thus it is quite possible that PD modality per se is
not a significant predictor of quality of life after patients adapt to it.
Nonadherence
Definition and Measurement of Nonadherence
Any deviation from doctor’s treatment instructions was viewed as nonadherence
in this study. In contrast to abundance of research on quality of life and emotional
adjustment, there has been little focus on treatment nonadherence. Nonadherence to
treatment regime is a key contributor to poor survival in patients treated with dialysis,
probably in the same order of importance as medical indicators (Bander & Walters, 1998).
Dialysis regimen is extremely complicated and time-consuming, involving regular
clinical visits, attending dialysis sessions, taking a variety of medications, limiting water
intake and paying great attention to food choices. As treatment complexity has been cited
as the most important reason affecting patients’ nonadherence (Donovan, 1995), it is not
unexpected that it is easy for dialysis patients to be nonadherent.
Nonadherence studies in dialysis patients are greatly hindered by a lack of
consistent standards for measuring nonadherence. Common measures of nonadherence
include: (a) report from patients or medical staff, (b) biological and biochemical markers,
(c) electronic monitoring and (d) checking medication refill status and inspection of
dialysate delivery records. Each method has its own drawbacks. The most widely used
method is self-report, a cost-effective way of measuring nonadherence (George,
17
Mackinnon, Kong, & Stewart, 2006), though there are doubts about the accuracy of selfreport (Horne & Weinman, 1999; Vlaminck, Maes, Jacobs, Reyntjens, & Evers, 2001).
For instance, Haynes et al. (1980) reported that patients underestimated their
nonadherence by 17% in self-reports when compared with pill count. Common biological
and biochemical markers used to measure nonadherence include interdialytic weight,
phosphorus, potassium and albumin levels (Karamanidou, Clatworthy, Weinman, &
Horne, 2008; Kugler, Maeding, & Russell, 2011). The validity of using biochemical
markers to indicate nonadherence is challenged by using arbitrary, instead of theory
supported, cut-off values to divide patients into adherence and nonadherence groups and
by factors irrelevant with nonadherence such as residual renal function, dialysis
prescriptions, disease conditions and demographic characteristics (Denhaerynck et al.,
2007). Electronic devices such as the Medication Event Monitoring System (MEMS,
Aardex, Switzerland) and the Home-Choice Pro card (Baxter Healthcare Corporation,
Deerfield, Illinois, USA) are capable of providing reliable measures of nonadherence
(Chua & Warady, 2011; Sevick et al., 1999), but the associated high costs limit their wide
application. Medication refill rates (Gincherman, Moloney, McKee, & Coyne, 2010) and
dialysate delivery records (Fine, 1997) cannot reveal whether patients actually utilize the
medication or dialysate (e.g., patients may give the medication or dialysate to others) or
whether patients use them correctly. The current study used the combination of selfreport (used in a non-threatening way) and biochemical markers (i.e., serum potassium,
phosphate and albumin levels) to detect nonadherence and this design has been suggested
as powerful at detecting nonadherence (George et al., 2006; Inui, Carter, & Pecoraro,
1981).
18
Another key problem in past studies is treating nonadherence as an
unidimensional concept, reflected by using a composite score to indicate overall
nonadherence (Lin & Liang, 1997; Pakpour et al., 2010; Sayin et al., 2007; Vives et al.,
1999). However, patients do not perceive different aspects of the therapeutic regimen
(e.g., dialysis, medication, diet, fluid) as equally important and have differing levels of
difficulty in managing separate treatment components (Smith et al., 2010; Stack et al.,
2010). In studies measuring nonadherence to several components of the therapeutic
regimen simultaneously, nonadherence rates were found to be different and affected by
different factors (Karamanidou, Clatworthy, et al., 2008; Sensky, Leger, & Gilmour,
1996). Therefore, it is more reasonable to assess nonadherence to different aspects of the
therapeutic regimen as separate constructs. Studies reporting nonadherence rates to three
main components of the therapeutic regimen (i.e., dialysis, medication and diet) among
PD patients are listed out in Table 1.
19
20
Note. NA = nonadherence.
USA
Warren et al.,
1994
121 CAPD
USA
64 PD
191 PD
19 APD
Italy
Italy
173 CAPD
Hong Kong
Russo et al.,
2006
119 HD 51 PD
USA
42 APD
75 HD, 14 CAPD
USA
Juergensen et al., 2004
39 HD, 15 PD
67 / 26 CAPD
30 PD
Israel
USA
Holley et al.,
2006
Katzir et al.,
2010
Kutner et al.,
2002
Lam et al.,
2010
Neri et al.,
2002
Nolph et al.,
1995
Canada
Fine, 1997
35 PD
China
Brazil
92 PD
USA
Figueiredo et al.,
2005
49 PD
USA
51 pediatric APD
15 CAPD 5 APD
USA
USA
50 PD
Sample
Italy
Area
Chua et al.,
2011
Amici et al.,
1996
Bernardini et al.,
1997
Bernardini et al.,
1998
Bernardini et al.,
2000
Chen et al.,
2006
Author,
year of publication
Performing less than 90% of prescribed exchanges
Excessive dietary protein
intake (DPI) < 0.8 g/kg/d or > 1.2 g/kg/d
Following a prescription variable (duration/ number of
cycles/number of sessions/dialysate volume) < 95% of
the time.
Performing less than 90% of prescribed exchanges
Performing less than 90% of prescribed exchanges
Performing less than 90% of prescribed exchanges
Definition of NA
Not clear
Laboratory data
Home visit
Laboratory data
Creatinine excretion (CrEx) > 1.24
Dialysis: not compliant with the exchange protocol
procedures
Medication: incongruence between
drugs at home and prescribed in the clinical file
Creatinine excretion (CrEx) > 1.24
Baxter PD Link software Missing PD sessions in 90 days
Self-report / laboratory Missing at least one treatment / shortening at least one
data
treatment / PO4 > 7.5mg/dl
Mild, moderate, severe or very severe deviation from
Self-report
therapeutic regimen
Self-report
Self-report / records
of dialysate delivery and Using less than 90% of prescribed dialysate
pick-up
Not to fill a prescription / Not to take specific
Self-report
medications
Baxter Home-Choice Delivered dialysis volume/prescribed dialysis
PRO Card
volume)*100 < 95%
Self-report
Baxter Home-Choice
PRO Card
Laboratory data
Self-report
Self-report
Self-report
PD Adequest software Software model
Method
Table 1Equation 1
Nonadherence Rates Documented in PD Patients
26%
23%
Male / Female:
17% / 5%
53%
7%
HD:19% / 31% / 19%
PD:30% / 4% / 10%
17%
12% / 5%
30%
4%/8%/18%/22%
30%
35%
40%
22%
Dialysis
25%
17%
HD / CAPD:
27% / 2%
30% / 21%
Medication
% NA
20
62%
77.1%
Diet
Nonadherence to Dialysis Procedures
It is life-threatening if dialysis patients fail to perform dialysis exchanges as
prescribed, since wastes, toxins and excess water may accumulate in the body and disturb
its hemodynamic status. Despite its importance, nonadherence to dialysis prescriptions is
a common problem. Reported rates of nonadherence to dialysis prescriptions in PD
varied from 4% to 53% (Amici et al., 1996; Bernardini, Nagy, & Piraino, 2000;
Bernardini & Piraino, 1997, 1998; Chua & Warady, 2011; Figueiredo, Santos, &
Creutzberg, 2005; Fine, 1997; Kutner, Zhang, McClellan, & Cole, 2002; Lam, Twinn, &
Chan, 2010; Neri, Viglino, Cappelletti, Gandolfo, & Barbieri, 2002; Nolph et al., 1995;
Russo et al., 2006; Warren & Brandes, 1994). The corresponding rates in HD varied from
0% to 32% (Bleyer et al., 1999; Block, Hulbert-Shearon, Levin, & Port, 1998; DeOreo,
1997; Hecking et al., 2004; Kutner et al., 2002; Leggat et al., 1998; Sherman, Cody,
Matera, Rogers, & Solanchick, 1994; Taskapan et al., 2005).
Factors associated with nonadherence to dialysis prescriptions include smoking,
younger age and ethnicity with blacks reporting more nonadherence than whites (Kimmel
et al., 1995; Kutner et al., 2002; Leggat et al., 1998; Unruh, Evans, Fink, Powe, & Meyer,
2005). Perceived negative effects of treatment on daily life and less control perception
over future health were identified as predictors of shortening behaviors in one study
(Kutner et al., 2002). Nonadherence to dialysis has been found to be associated with
higher mortality and lower likelihood of kidney transplantation in HD (Unruh et al.,
2005). Data on PD patients showed that nonadherence to dialysis is associated with
technique failure, inadequate dialysis, increased peritonitis rates and hospitalizations
(Bernardini et al., 2000; Bernardini & Piraino, 1998).
21
Nonadherence to Medication
In addition to performing exchanges regularly, dialysis patients are expected to
take multiple tablets to control their phosphate levels (dialysis procedure is unable to
remove phosphate from the body adequately) and manage symptoms and comorbid
diseases. Dialysis pill burden is ranked as one of the highest among chronic illnesses
(Chiu et al., 2009). Patients take 10 to 12 different types of medications and one fourth of
dialysis patients take more than 25 pills per day (Chiu et al., 2009; Manley et al., 2004).
Medications have distinct requirements for mode, timing and amount of intake. The
complexity of medication regimen significantly increases patients’ likelihood of
nonadherence (Chiu et al., 2009).
A total of 2% to 30% PD patients fail to take their prescribed medication as
instructed (Holley & DeVore, 2006; Katzir et al., 2010; Lam et al., 2010; Russo et al.,
2006), whereas 17% to 99% HD patients do not adhere to their prescribed medication
(Curtin, Svarstad, & Keller, 1999; Lin & Liang, 1997). Age, pill burden, health literacy,
health beliefs, personality, social support, and patient satisfaction have been cited as
important factors affecting medication nonadherence (Browne & Merighi, 2010;
Karamanidou, Clatworthy, et al., 2008). Important barriers to medication adherence
include non user-friendly drug compound, feeling of discomfort, forgetfulness,
polypharmacy and patient ignorance (Lindberg & Lindberg, 2008).
Inadequate control of phosphorus level is linked with several risk factors for
cardiovascular disease, such as elevated blood pressure, hyperkinetic circulation,
increased cardiac work, and high arterial tensile stress (Marchais, Metivier, Guerin, &
London, 1999). While hyperkalemia (high potassium) is a common problem among HD
22
patients, hypokalemia (low potassium) is profound among PD patients because there is
greater filtration of potassium from blood to dialysate during the dialysis process in PD
than HD (Factor, 2007; Khan, Bernardini, Johnston, & Piraino, 1996). Potassium < 3.5
mmol/l is associated with increased mortality, risk of peritonitis and poor nutritional
status (Chuang, Shu, Yu, Cheng, & Chen, 2009; Szeto et al., 2005) and can be managed
effectively with potassium supplements or increasing dietary potassium intake. No
studies have investigated nonadherence to potassium supplements among PD patients.
Nonadherence to Diet
Because dialysis does not restore functioning levels comparable to a health kidney,
dietary restrictions are often used together with medications to prevent the increment of
certain elements, such as sodium, phosphorus and protein, in the body. Dietary restriction
is the most distressing part of dialysis regimen (Durose, Holdsworth, Watson, &
Przygrodzka, 2004; Lam et al., 2010), probably because it involves profound alterations
to individuals’ lifestyles. Only two studies investigated dietary nonadherence in PD
patients and found that 62% to 77.1% of patients did not follow their dietary guidelines
(Chen, Lu, & Wang, 2006; Lam et al., 2010). The prevalent rates of dietary nonadherence
in HD patients varied from 24% to 81.4% (Kara, Caglar, & Kilic, 2007; Kugler,
Vlaminck, Haverich, & Maes, 2005; Lin & Liang, 1997; Vlaminck et al., 2001).
Factors affecting dietary nonadherence have not been adequately examined. One
study in Hispanic patients identified knowledge of diet, language, food consumption
frequency, socioeconomic status, family support and attitudes toward the renal diet as
important factors related to dietary nonadherence (Morales Lopez, Burrowes, Gizis, &
Brommage, 2007). Another study revealed that younger male patients and smokers were
23
more likely to be nonadherent to diet and fluid restrictions (Kugler et al., 2005). Since
poor understanding with dietary contents was an important reason for nonadherence,
using menu suggestion was found effective in reducing dietary nonadherence (Chen et al.,
2006). Nonadherence with salt and fluid was a critical reason for PD drop-out
(Kawaguchi et al., 2003).
Intentional and Unintentional Nonadherence
Increasing knowledge is a standard way employed to reduce nonadherence in
intervention programmes. However, nonadherence is problematic even among those with
good knowledge (Lee & Molassiotis, 2002; Nerbass et al., 2010). Clarifying causes of
nonadherence and intervening accordingly may be more cost-effective. Two broad
categories have been proposed to classify causes of nonadherence: intentional and
unintentional (Clifford, Barber, & Horne, 2008). This “intentional-unintentional”
typology is predominantly used in investigating medication-taking behaviors (Daleboudt,
Broadbent, McQueen, & Kaptein, 2010; Unni & Farris, 2011). Both forms of
nonadherence have been observed in dialysis patients (McCarthy, Cook, Fairweather,
Shaban, & Martin-McDonald, 2009; Nerbass et al., 2010; Polaschek, 2007), although no
attempt has been made to document their prevalent rates. Unintentional nonadherence is a
passive process, like forgetting to take medication or failing to recognize what are
contained in food when eating. Patients are usually not aware of their deviation from
treatment guidelines when it occurs. Factors such as complexity of treatment and disease
severity contribute to unintentional nonadherence (Schuz et al., 2011). Intentional
nonadherence is an active, decision-making process. Patients deliberately adjust their
regimen to suit their needs, like forgoing medications to avoid side effects. Intentional
24
nonadherence is especially likely to occur if patients experience consequences as a result
of adherence (e.g., dangerous drug interactions) or if patients are not well-informed and
henceforth feel uncertain about the effectiveness of treatment (Schuz et al., 2011). There
is good evidence suggesting that unintentional nonadherence occurs more often than
intentional nonadherence in other populations (Rees, Leong, Crowston, & Lamoureux,
2010; Sewitch et al., 2003; Unni & Farris, 2011).
A phenomenological study in another type of chronic illness identified
forgetfulness, accidentally overdose and the unavailability of medication as reasons for
unintentional nonadherence and intentional nonadherence was mainly caused by side
effects, social activities, eating out, drinking alcohol or traveling (Eliasson, Clifford,
Barber, & Marin, 2011). To the best of our knowledge, no studies concerning ESRD
patients have distinguished intentional and unintentional nonadherence.
Nonadherence on APD vs. CAPD
Only one known study compared nonadherence difference between APD and
CAPD patients (Bernardini et al., 2000). Home visit supply inventories were used to
evaluate nonadherence to dialysis exchanges in this study and PD modality was identified
as an independent predictor of nonadherence, with more nonadherence reported in CAPD
than APD patients.
Limitations of Previous Studies on Nonadherence in PD Patients
As can be seen from Table 1, nonadherence levels in PD patients are relatively
understudied compared to outcomes such as quality of life. Seventeen studies could be
retrieved but these present several limitations. Most of these studies focused on
nonadherence to dialysis and medication, overlooking nonadherence in relation to dietary
25
recommendations for PD patients. Although changes in diet are essential for management
of conditions to ensure good clinical outcomes, little is known on rates of nonadherence
with respect to dietary recommendation in this population. Recruited study samples were
very small, which limits generalizability of findings. Only four studies had sample sizes
above 100. Methodological criteria to define nonadherence in some studies are
questionable. For instance, two studies defined nonadherence as creatinine excretion
(CrEx) > 1.24 and this was later found to be an unreliable marker of nonadherence (Blake,
Spanner, McMurray, Lindsay, & Ferguson, 1996). No previous studies used traditional
biochemical markers (e.g., potassium, phosphate) to measure nonadherence in PD
patients. Moreover, no studies have compared nonadherence outcomes in APD and
CAPD. The majority of studies were forced to merge between APD and CAPD groups
due to small sample sizes or were only based on CAPD patients. Nonadherence rates in
APD patients were hence either not assessed or reported together with CAPD patients, so
the question of which PD modality may be associated with less nonadherence remains
largely unanswered. Lastly, no studies in PD patients have looked at intentional and
unintentional nonadherence despite their important implications for intervention.
Determinants of Nonadherence
Previous studies on determinants of nonadherence tend to focus on demographical
and clinical variables, overlooking the effects of psychosocial variables (Karamanidou,
Clatworthy, et al., 2008; Russell, Knowles, & Peace, 2007). However, identifying
demographical and clinical associates of nonadherence is of limited use in clinical
applications as these factors are usually not modifiable (Sensky et al., 1996). Moreover, it
26
has been suggested that psychosocial factors are stronger determinants of nonadherence
than demographical and clinical variables (Karamanidou, Clatworthy, et al., 2008).
Therefore, it is imperative to examine the effects of psychosocial factors on
nonadherence. This study focuses on three psychosocial variables: health beliefs,
emotional distress and quality of life.
Health Beliefs
Little data is available about the effects of beliefs about medicines on
nonadherence in ESRD patients. However, the association between beliefs about
medicines and nonadherence to medication is well-supported in other populations
(Daleboudt et al., 2010; Horne & Weinman, 1999; Schuz et al., 2011; Unni & Farris,
2011). In a study involving 324 patients from different chronic conditions, the difference
between perceived necessity of the prescribed medication and perceived concerns about
the medication (e.g., side effects, long-term dependence) was found to be an independent
predictor of medication nonadherence, accounting for a good portion (19%) of the
variance (Horne & Weinman, 1999). Concern beliefs and necessity beliefs appear to have
different roles in determining intentional and unintentional nonadherence. Concern
beliefs have been reported to affect both intentional and unintentional nonadherence
(Daleboudt et al., 2010; Unni & Farris, 2011), whereas necessity beliefs were mainly
associated with intentional nonadherence (Schuz et al., 2011; Unni & Farris, 2011).
There is ample evidence suggesting that self-efficacy is correlated with
nonadherence to fluid, medication and diet in dialysis patients (Brady, Tucker, Alfino,
Tarrant, & Finlayson, 1997; Christensen, Wiebe, Benotsch, & Lawton, 1996; Eitel,
Friend, Griffin, & Wadhwa, 1998; Lindberg & Fernandes, 2010; Oka & Chaboyer, 2001;
27
Rosenbaum & Ben-Ari Smira, 1986; Schneider, Friend, Whitaker, & Wadhwa, 1991;
Zrinyi et al., 2003). The impact of self-efficacy on nonadherence to dialysis exchanges is
however, yet to be determined. A recent study consisting of 133 HD patients reported
significantly less fluid intake in patients with high self-efficacy than patients with low
self-efficacy (Lindberg, Wikstrom, & Lindberg, 2010). Similarly, another study involving
a large group of HD patients associated self-efficacy with dietary nonadherence based on
self-report and biochemical markers (Zrinyi et al., 2003).
Emotional Distress
Depression has been found to be associated with nonadherence (Brownbridge &
Fielding, 1994; Cukor, Rosenthal, Jindal, Brown, & Kimmel, 2009; De-Nour & Czaczkes,
1976). Depression may have a direct effect on nonadherence as symptoms may manifest
as reduced appetite, excessive fatigue and a lack of energy which limit patients’ ability to
adhere (McCarthy et al., 2009). Depression may also have an indirect effect on
nonadherence through beliefs and cognitions. Depressed patients tend to have negative
thoughts and feel hopeless and despair about self, world and future (Dekker et al., 2011).
It is very likely that depressed patients devaluate their ability to cope with their diseases
(i.e., having low self-efficacy), underestimate the effectiveness of their treatment and
hold exaggerated concerns about possible disruptive effects of their treatment and thus
show nonadherence behaviors (DiMatteo, Lepper, & Croghan, 2000). Given that previous
intervention programmes targeting depression tend to have low response rates (Wuerth et
al., 2005), identifying mediators between depression and nonadherence has the potential
to find a more direct and appealing way to help depressed patients with elevated
nonadherence.
28
Health beliefs appear to be important mediators between depression and
nonadherence in other populations (Chao, Nau, Aikens, & Taylor, 2005; Sacco et al.,
2007; Sacco et al., 2005; Schoenthaler, Ogedegbe, & Allegrante, 2009). For instance, a
study involving 445 patients with diabetes reported that depression affected
nonadherence mainly via perceived side effects, perceived general barriers, and selfefficacy (Chao et al., 2005). Given that depression is significantly associated with health
beliefs (Devins et al., 1982; Tsay & Healstead, 2002) and health beliefs, as stated above,
are commonly associated with nonadherence. It is highly probable that self-efficacy also
mediates the depression-nonadherence relationship in dialysis patients. This hypothesis
was tested in this study. Anxiety was also found to be associated with nonadherence
(Brownbridge & Fielding, 1994). The relationships among anxiety, health beliefs and
nonadherence are rarely explored in literature and were also examined in this study.
Quality of Life
Only two studies associated quality of life with nonadherence (DeOreo, 1997;
Pakpour et al., 2010) and the direction of this association is not clear. DeOreo (1997)
studied 1000 HD patients and revealed that physical quality of life was higher, but mental
quality of life was lower in patients who skipped more than two treatments per month
than other patients. In contrast, Pakpour et al. (2010) reported a positive association
between physical quality of life and adherence and no association between mental quality
of life and adherence in a group of 250 HD patients.
29
Limitations of Previous Studies
Previous studies have shed some light on outcomes related to PD yet present with
several shortcomings that limit generalizability of findings to other PD populations.
The majority of studies comparing outcomes in APD and CAPD have focused
exclusively on clinical endpoints such as peritonitis, morbidity and mortality.
Psychological and behavioral outcomes, such as emotional distress, quality of life, and
nonadherence, have largely been overlooked.
Studies that explored psychological outcomes have very small sample sizes (not
exceeding 70) and some merge across APD and CAPD into one group. There are also
conceptual and method limitations in the measures of nonadherence such as using
unreliable biochemical markers (e.g., creatinine excretion).
Most of the studies on PD patients have focused on nonadherence yo dialytic
prescriptions (e.g., missing exchanges, shortening dialysis time) or prescribed medication,
overlooking the lifestyle aspects of treatment such as dietary recommendations. No
studies have explored intentional and unintentional nonadherence.
The factors that may explain/predict nadherence difficulties and self-care
behaviors in patients maintained on PD are also not well understood. Psychosocial factors,
which are more proximal predictors of nonadherence and more amendable to
interventions, receive little attention in literature than demographical and clinical
variables.
Furthermore, to the best of our knowledge, there have been no studies on
psychosocial outcomes in PD patients in Singapore. It is hard to extrapolate or generalize
30
experience gained in other countries to Singapore due to the local variation in the
important factors (e.g., sociopolitical, economic, cultural).
Study Objectives
To assess the effect of PD modality (APD or CAPD) on health beliefs,
emotional distress and quality of life
To document overall, intentional and unintentional nonadherence rates to
different aspects of the therapeutic regimen (i.e., dialysis, medication and diet)
To identify psychosocial determinants of nonadherence
Study Hypotheses
Two main hypotheses were suggested for this study based on previous findings:
Hypothesis 1 focuses on the prevalence of nonadherence among PD patients.
Based on what has been discussed above, we proposed Hypothesis 1a that nonadherence
to dietary restrictions is higher than nonadherence to medical aspects of treatment
regimes, namely dialysis exchanges and medication. The exact differences between the
three components could not be predicted, since past studies used different definitions and
measures of nonadherence. Hypothesis 1b is that unintentional nonadherence is expected
to occur more than intentional nonadherence, as explained above.
Hypothesis 2 is regarding determinants of nonadherence. Hypothesis 2a is that
positive health beliefs (i.e., high necessity beliefs, low concern beliefs and high selfefficacy) are expected to be associated with less nonadherence; Hypothesis 2b that more
emotional distress is expected to be associated with more nonadherence.
31
No apriori directional hypotheses were formulated regarding psychosocial (i.e.,
health beliefs, emotional distress, quality of life and nonadherence) differences between
APD and CAPD groups as well as the association between quality of life and
nonadherence, as there is no clear theoretical or empirical evidence.
32
CHAPTER TWO
Methodology
Participants
This was a cross-sectional study. Participants were recruited from the Peritoneal
Dialysis Center, Singapore General Hospital (SGH) from October 2010 to June 2011.
Inclusion Criteria
Patients were recruited if they met the following criteria:
1. In the PD programme for a minimum of 3 months;
2. Aged 21 years or over;
3. Able to communicate verbally with research assistants;
4. Able to provide informed consent.
Recruitment Process
Patients were randomly approached by research assistants while awaiting
consultations with nephrologists at the PD center. All approached patients were given a
short introduction about the purpose and procedure of our study. After a brief screening,
patients failing to meet the inclusion criteria were excluded from participation. The rest
of the eligible patients were given an information sheet listing out details of the study
(see Appendix B). It was made clear to the patients that participation was strictly
voluntary and confidential. Any additional questions were answered appropriately before
patients made their decisions concerning participation. The overall recruitment process is
shown in Figure 3.
33
Patients approached
n = 199
Eligible
n = 183
Agreed to participate
n = 146
Included
n = 144
Excluded
n = 16
Declined
n = 37
Dropped-out
n=2
Figure 3. Flowchart of the recruitment process.
One hundred and ninety nine patients were approached at the PD center. Of the
199, 16 failed to meet the inclusion criteria and were excluded. Of the eligible 183, 37
declined invitation to participate. Of the 146 who agreed to participate, n = 2 patients
dropped out due to sudden decline in health status. Thus, our final sample consisted of
144 PD patients (response rate = 79%). Written informed consent (see Appendix C) was
obtained from patients prior to questionnaire administration. Standard to research
conducted in busy clinical settings, study assessments were scheduled based on patients’
preference, availability and convenience. Questionnaires were therefore completed and
returned to research in the following ways (data only available for n = 132 patients):
1. Completing the questionnaires at the hospital while awaiting consultations with
nephrologists or medicines (n = 33).
34
2. Bringing the study information sheet and questionnaires back to further
consider about it and posting their questionnaires back to us if they wanted to (n = 37).
3. Indicating a preferred time and allowing a research assistant to administer a
home visit (n = 62).
Study Instruments
Demographics
Demographics (age, gender, marital status, ethnicity, education, employment
status, housing condition and time on dialysis) were collected with self-designed
questions (see Appendix D). Patients were also inquired about who were taking a more
active role (self vs. carer) in managing their disease.
Medical Information
Medical information (prescribed medicines, comorbid diseases, primary cause of
ESRD, PD modality, creatinine, potassium, phosphate, albumin, hemoglobin and Kt/V,
see Appendix E) was abstracted from medical records by nursing staff in the participating
clinic.
Charlson Comorbidity Index (CCI). CCI score was calculated using the validated
method suggested by Beddhu et al. (2002) for PD patients. Included comorbid diseases
were coronary artery disease, heart failure, peripheral vascular disease, cerebrovascular
disease, dementia, chronic pulmonary disease, connective tissue disorder, peptic ulcer
disease, diabetes, hemiplegia, moderate or severe renal disease, tumor, leukemia,
lymphoma, liver disease, metastatic solid tumor and AIDS. One point was added to the
CCI score for each decade of > 40 years of age. Patients were classified into low,
35
moderate and high comorbidity groups based on being positioned before the 33rd,
between 33rd and 66th, and after the 66th percentiles of CCI scores respectively.
Health Beliefs Measures
The Beliefs About Medicines Questionnaire (BMQ).Patients’ medicine beliefs
were assessed with the BMQ (Horne, Weinman, & Hankins, 1999). It consists of a
necessity subscale and a concerns subscale (5 items for each), measuring perceived
necessity/usefulness about taking the prescribed medication and perceived disruptive
effects/dependence effects caused by the medication, respectively. Aggregate scores
(range = 5−25) were used, with higher scores indicating more necessity or concern
beliefs. The difference between the necessity score and the concerns score was calculated
for each individual. If this value was positive, it indicated more necessity beliefs over
concern beliefs. Otherwise, it indicated necessity beliefs equal to or lower than concern
beliefs. Both subscales showed high reliability in our study. Necessity subscale
Cronbach’s α = .81 and concerns subscale Cronbach’s α = .77.
Self-efficacy. It was assessed with questions adapted from the Self Efficacy for
Managing Chronic Disease Scale developed by Lorig et al. (2001). It consists of a set of
13 items, asking patients to rate their confidence in accomplishing various tasks involved
in managing their illness on a 10-point Likert scale (e.g., “How confident are you that
you can perform your PD exchanges as instructed by your doctor?” 1 = not at all
confident, 10 = totally confident). The first six items (Cronbach’s α = .89) were from the
original scale, measuring generic self-efficacy. We also designed additional seven items,
one item measuring dialysis self-efficacy, two measuring medication self-efficacy
(Cronbach's α = .61) and four measuring dietary self-efficacy (Cronbach's α = .87). Mean
36
scores (range = 1−10) were used, with higher scores denoting higher levels of selfefficacy.
Emotional Distress Measures
The Hospital Anxiety and Depression Scale (HADS). Symptoms of depression and
anxiety were assessed using the HADS (Zigmond & Snaith, 1983). HADS is a widely
used instrument, consisting of an anxiety subscale (7 items) and a depression subscale (7
items). Patients were asked to rate their degree of anxiety and depression in the past 14
days on a 4-point Likert scale (0 = yes, definitely, 3 = no, not at all). Aggregate score for
each subscale was derived, ranging from 0 to 21. A higher score signifies higher level of
anxiety or depression. Both the depression and anxiety subscales demonstrated
satisfactory reliability. Anxiety subscale Cronbach’s α = .83. Depression subscale
Cronbach’s α = .76. The aggregate scores were classified into three categories: normal
(0−7), borderline (8−10) and abnormal (11−21) (Thompson et al., 2000). HADS has been
validated and applied successfully in measuring anxiety and depression among ESRD
patients (Loosman, Siegert, Korzec, & Honig, 2010; Riezebos, Nauta, Honig, Dekker, &
Siegert, 2010). This measure was preferred over other instruments as it precludes somatic
symptoms such as fatigue, anorexia, and weight loss (Fernandes et al., 2010) which may
confound with symptoms related to renal condition and uremia.
The Revised UCLA Loneliness Scale. Loneliness was assessed with the Revised
UCLA Loneliness Scale (Russell, Peplau, & Cutrona, 1980). It includes 20 items.
Patients responded to the questions on a 4-point Likert scale. The aggregate score (range
= 20−80) was calculated, with higher score indicating a higher degree of loneliness. This
scale had high reliability on the study (Cronbach’s α =.85). The loneliness scores were
37
classified into four categories: low (20−34), moderate (35−49), moderately high (50−64)
and high (65−80) (Perry, 1990).
Quality of Life Measures
Three measures were used to capture all aspects of quality of life: generic health
related quality of life, disease specific quality of life and subjective global quality of life.
The Kidney Disease Quality of Life Short Form (KDQOL-SF). Disease specific
quality of life was assessed with the KDQOL-SF (Hays et al., 1997), a special instrument
targeting ESRD patients. The original questionnaire includes the 36-item Short-Form
Health Survey (SF-36), supplemented by kidney disease-specific questions. SF-36 was
replaced by its shorter version, the 12-item Short-Form Health Survey (SF-12) in our
study to reduce burden of completion for respondents. SF-12 is a generic health related
quality of life measure. Two summary scores, physical component summary score (PCS)
and mental component summary score (MCS), were calculated using the standard way
(Ware, Kosinski, Keller, & Institute, 1998). The eight domain scores used in SF-36 were
not calculated, as suggested in the SF-12 manual (Ware et al., 1998). Conveniently,
Singapore’s SF-36 PCS and MCS norms are available (Thumboo et al., 2001) and a
calculator is provided online (http://www.singhealth.com.sg/Research/
HealthServicesResearch/OurServices/OutcomesResearch/Documents/HSRSingHealthSF
36calculator-v3-beta.xls) to calculate PCS and MCS norms after adjusting for age, gender
and ethnicity. It has been justified to use SF-36 norms to interpret SF-12 results (Brown
et al., 2010). The kidney disease specific part includes 31 items to measure disease
specific quality of life, encompassing 6 domains: symptoms, effects of kidney disease,
burden of kidney disease, patient satisfaction, staff encouragement and social support. All
38
6 domain scores were transformed to be varying from 0 to 100. Higher scores signify
better quality of life for all KDQOL-SF domains. Table 2 provides a summary for the
KDQOL domains regarding their number of items, reliability coefficients on the study
and interpretations. All KDQOL-SF subscales used in this study showed acceptable
reliability (range = .66 to. 85). KDQOL-SF has been verified in 1180 Singapore HD
patients and showed satisfactory psychometric properties (Joshi, Mooppil, & Lim, 2010).
The World Health Organization Quality of Life Instrument, Short Form
(WHOQOL-BREF). Subjective global quality of life was measured with the WHOQOLBREF (Herrman et al., 1998). This instrument includes 26 items. Twenty four out of the
26 items were used to assess patients' quality of life in four domains: physical health,
psychological health, social relations, and environment. Each domain score ranged from
4 to 20, with higher score signifying better quality of life. Table 3 displays the number of
items, reliability coefficients and interpretations for the four domains. All domains
showed acceptable reliability, with Cronbach’s α ranging from .61 to .81. WHOQOLBREF has been validated in 23 countries consisting of 11,830 sick and well adults
(Skevington, Lotfy, & O'Connell, 2004) and the norms reported in this article were
compared with our WHOQOL-BREF results.
39
Table 2: Number of Items, Reliability Coefficients and Concepts Measured by KDQOL-SF Domains
Number of Items, Reliability Coefficients and Concepts Measured by the KDQOL-SF
Domains
No. of items Cronbach’s α
Concept measured
SF-12 PCS
6
.66
Physical component summary score
SF-12 MCS
6
.72
Mental component summary score
Symptom list
12
.85
Extent that patients are bothered by
dialysis-related symptoms such as chest
pain, cramps and itchy skin
Effects of kidney
8
.85
disease
Extent that patients are bothered by daily
life issues such as fluid restriction, dietary
restriction, inability to
dependence on medical staff
Burden of kidney
4
.79
travel
and
Extent that patients are bothered by time
disease
consumed by dialysis, its intrusiveness and
Patient satisfaction
burden on family
Patients’ satisfaction with care received for
1
dialysis
Staff
2
.84
patients to be independent and support
patients in dealing with their kidney disease
encouragement
Social support
Extent to which renal staff encourage
4
.71
Patients’ satisfaction with togetherness and
support from family and friends
Note. KDQOL-SF = Kidney Disease Quality of Life Short Form. SF-12 = 12-item ShortForm Health Survey. PCS = physical component summary score. MCS = mental
component summary score. Cited from “Psychosocial predictors of non-compliance in
haemodialysis and peritoneal dialysis patients,” by N. G. Kutner, R. Zhang, W. M.
McClellan, & S. A .Cole, 2002, Nephrology, Dialysis, Transplantation, 17, p. 95. Used
with permission from the Oxford University Press (see Appendix F).
("WHOQOL-BREF Introduction, Administration, Scoring and Generic Version of the
Assessment,
Field
Trial
Version,"
1996
)
40
Table 3: Number of Items, Reliability Coefficients and Concepts Measured by WHOQOL-BREF Domains
Number of Items, Reliability Coefficients and Concepts Measured by the WHOQOLBREF Domains
Domain
Concepts measured
Physical health
Activities of daily living
7 items,
Dependence on medicinal substances and medical aids
Cronbach’s α = .80
Energy and fatigue
Mobility
Pain and discomfort
Sleep and rest
Work Capacity
Psychological health Bodily image and appearance
6 items,
Negative feelings
Cronbach’s α = .81
Positive feelings
Self-esteem
Spirituality / Religion / Personal beliefs
Thinking, learning, memory and concentration
Social relations
Personal relationships
3 items
Social support
Cronbach’s α = .61
Sexual activity
Environment
Financial resources
8 items
Freedom, physical safety and security
Cronbach’s α = .76
Health and social care: accessibility and quality
Home environment
Opportunities for acquiring new information and skills
Participation in and opportunities for recreation / leisure activities
Physical environment (pollution / noise / traffic / climate)
Transport
Note. WHOQOL-BREF = World Health Organization Quality of Life Instrument, Short
Form. Cited from “WHOQOL-BREF Introduction, Administration, Scoring and Generic
Version of the Assessment, Field Trial Version,” by M. Rapley, 1996, World health
Organization, p. 7. Used with permission from the World Health Organization (see
Appendix G).
41
Nonadherence Measure
No single method of measuring nonadherence is perfect. For this study, we
elected to use self-report as the primary outcome and serum concentrations of potassium,
phosphate and albumin as a secondary outcome.
Self-reported nonadherence. A dialysis specific measure (see Appendix H) was
designed for the purpose of this study to assess self-reported nonadherence to the PD
regimens. The scale contains 9 items, measuring nonadherence to prescribed dialysis
exchanges (3 items), medication (3 items) and diet (3 items). The three questions used to
assess medication nonadherence were adapted from Horne and Weinman (1999):
1. “How often do you follow this regime?” (overall nonadherence)
2. “Some people forget to take their medicines. Overall, how often does this
happen to you?” (unintentional / accidental nonadherence)
3. “Some people decide to miss out a dose of their medication or adjust it to suit
their own needs. Overall, how often do you do this?” (intentional / deliberate
nonadherence)
Similar formats were used for measuring nonadherence to dialysis and diet. But
for nonadherence to diet, one more item was added before the three questions: “Have you
been asked to follow a diet?” If patients answered “no” to this question, they did not need
to answer the rest questions regarding nonadherence to diet. Potential responses to each
question are from 1 (= never) to 5 (= very often). The score for the first item (“How often
do you follow this regime?”) was reversed. Thus higher scores indicated greater
nonadherence for all questions. The overall nonadherence scale showed good reliability
in this study (Cronbach's α = .77). Nonadherence was defined as at least rarely deviating
42
from treatment regimen. Two additional questions were developed in accordance with the
criteria defined in the USRDS (1997) survey of dialysis morbidity and mortality to assess
patients' behavioral nonadherence to dialysis guidelines:
1. “During the last 4 weeks, how many times have you skipped/missed one of your
PD sessions/exchanges?”
2. “During the past 4 weeks, how many times have you shortened your PD session
by 10 minutes?”
These two items were treated as stand alone items for the analyses, with higher scores
signifying more frequent nonadherence to dialysis exchanges/regimen.
Nonadherence based on biochemical markers. Three biochemical markers (i.e.,
potassium, phosphate and albumin) were used to estimate nonadherence to diet and
medication. Nonadherence was defined as levels outside clinical cut-offs/ targets:
potassium < 3.5 mmol/l (signifying nonadherence to dietary guidelines or taking
potassium supplements), phosphate > 1.78 mmol/l (signifying nonadherence to dietary
guidelines or phosphate binders), and albumin < 3.5 g/dl (signifying malnutrition and
thereby dietary nonadherence). These cut-off values have been widely used in previous
studies (Moe et al., 2005; Plantinga et al., 2004; Szeto et al., 2005) and were confirmed
by the key PD consultants in patient care as clinical targets for the patient population.
Study Languages
Patients were free to choose their preferred language in this study. All study
instruments were available in English and Chinese versions. The HADS, UCLA
loneliness Scale, KDQOL-SF and WHOQOL-BREF have been translated into Chinese
43
and validated in other studies (Fang, Hao, & Li, 1999; Leung, Ho, Kan, Hung, & Chen,
1993; Li, 1998; Liu, 1993). The BMQ and demographical, self-efficacy and adherence
questionnaires have no standardized Chinese versions available. They were translated by
a research assistant proficient in both English and Chinese. All patients who could only
speak Chinese dialects (e.g., Hokkien) or Malay were interviewed with assistance from
their family members or by research assistants.
Data Analysis
Missing Data
Table 4 shows the distribution of missing items. As missing items were small in
number and randomly distributed, mean imputations were used if at least half of the items
in the same domain were answered by the participants (missing Kt/V values were not
imputed).
Table 4: Distribution of Missing Values
Distribution of Missing Values
Frequency
Percent
Kt/V
4
2.8
BMQ
6
0.4
UCLA loneliness
1
0.03
SF-12
2
0.1
WHOQOL-BREF
3
0.08
Nonadherence measures
1
0.08
Note. BMQ = Beliefs about Medicines Questionnaire. UCLA loneliness = Revised
UCLA Loneliness Scale. SF-12 = 12-item Short-Form Health Survey. WHOQOL-BREF
= World Health Organization Quality of Life Instrument, Short Form.
44
Statistical Analysis
Descriptives. Descriptive statistics included medians, means and standard
deviations for continuous variables and frequencies and percentages for categorical
variables.
Univariate analyses. Kolmogorov-Smirnov tests were used to assess the
normality of variable distribution. Means were compared with independent t tests, paired
t tests, ANOVA or ANCOVA and percentages were compared with χ2 tests unless
otherwise specified. When data were not normally distributed, non-parametric tests
(Mann-Whitney tests, Wilcoxon signed-rank tests, Kruskal-Wallis tests or Fisher's exact
tests) were used instead and noted. Spearman rank correlation coefficients were
calculated to denote the correlations among variables.
Multivariate analyses. Hierarchical stepwise multiple linear regressions were used
to identify important predictors of self-reported nonadherence levels. Hierarchical
stepwise logistic regressions were used to identify significant predictors of nonadherence
based on biochemical markers. Entry and removal criteria were fixed at p = .05 and p
= .10 respectively. The large number of variables measured increased the risk of type I
error. Therefore, the first stage of model selection involved initial screening using
univariate tests of associations (e.g., ANOVAs, Mann–Whitney tests, correlations or χ2
tests as appropriate) to identify demographical, clinical and psychosocial variables
associated with nonadherence indices. A forward stepwise procedure was used to select
variables from those that were significant at .05 on initial screening.
Mediation analyses. Figure 4 is used to illustrate how to test the existence of
mediation effect. X represents the predictor, Y represents the dependent variable and M
45
represents the mediator. Based on the suggestions of Barron and Kenney (1986), the X-Y
relationship is totally mediated by M if (1) there is a significant relationship between X
and Y before controlling for M (total effect, represented by c path in Figure 4); (2) M is
significantly associated with X (a path); (3) the influence of M on Y (b path) is
significant; (4) the effect of X on Y becomes zero after controlling for M (direct effect,
represented by cʹ path). Indirect effect is the difference between total effect and direct
effect and it estimates the effect of X on Y via M.
c
Y
X
M
b
a
Y
X
cʹ
Figure 4. A simple mediation model.
We tested mediation effects with the use of a SPSS macro (available at
www.quantpsy.org ) provided by Preacher and Hayes (2008). It performs the four steps
recommended by Baron and Kenny (1986) simultaneously. The significance of indirect
effect can be estimated through a nonparametric method (bootstrapping) contained in this
Macro. It produces a 95% confidence interval (CI) for indirect effect. If this 95% CI does
not include zero, it indicates that indirect effect is significantly different from zero. This
statistic method (bootstrapping) is highly recommended in literature (Shrout & Bolger,
2002), because it does not require normal distribution, reduces the probability of Type I
46
error, and allows for multiple mediators to be tested simultaneously after controlling for
covariates (Buffardi & Campbell, 2008). Parameters were estimated based on 1000
bootstrap samples in the current study.
Specifically, we created and tested all possible mediation effects of health belief
variables (i.e., medicine beliefs and/or self-efficacy) on the relationship between
emotional distress (i.e., depression, anxiety or loneliness) and nonadherence in three steps,
similar to previous studies (Buffardi & Campbell, 2008; Sebire, Standage, &
Vansteenkiste, 2009). Firstly, if an emotional factor significantly correlated with one of
the
six
nonadherence
measures
(intentional/unintentional
nonadherence
to
dialysis/medication/diet) after controlling for covariates, this emotional factor was chosen
as X (the predictor) and the corresponding nonadherence measure was chosen as Y (the
dependent variable). Secondly, if a health belief variable significantly correlated with
both X and Y after controlling for covariates, it was chosen as M (the mediator). If there
was more than one health belief variable meeting this criteria, they were entered into the
same model and tested simultaneously in order to reduce number of tests and type I error.
Thus a mediation model in our study contained only one predictor and one dependent
variable, but allowed for multiple mediators. Finally, we used the SPSS macro provided
by Preacher and Hayes (2008) to examine the significance of the total effect, direct effect
and indirect effect of emotional distress (X) on nonadherence (Y).
Statistical significance level was set at .05 for all procedures. Bonferroni αadjustment was applied for all post-hoc comparisons. All the procedures were performed
using the Statistical Package for Social Sciences (SPSS) version 17.0 (SPSS Inc, Chicago,
IL, USA).
47
Required sample size. Required sample sizes were calculated with G*power 3.1
(Faul, Erdfelder, Buchner, & Lang, 2009) corresponding to two primary study goals: (a)
comparing APD and CAPD outcomes (using independent t tests or Mann-Whitney tests)
and (b) identifying predictors of intentional and unintentional nonadherence (using
multiple linear regressions). A sample size of 64 for each group is required to detect a
medium effect size of d = .5, = .05, power (1) = .8 for two-tailed independent t test.
A sample size of 67 for each group is required to detect a medium effect size of d = .5,
= .05, power (1) = .8 for two-tailed Mann-Whitney test. For linear regression with 17
predictors, a sample size of 146 is required to detect a medium effect size of 2 = .15,
= .05, power (1) = .8.
Ethics
This study was approved by Centralized Institutional Review Board, SingHealth
Research Facilities (see Appendix I).
("Seventh Report of the Singapore Renal Registry, 2007/2008," 2010; "USRDS 2010 Annual Data Report," 2010; "The
USRDS Dialysis Morbidity and Mortality Study: Wave 2," 1997)
48
CHAPTER THREE
Results
Demographics
Fifty eight automated peritoneal dialysis (APD) and 86 continuous ambulatory
peritoneal dialysis (CAPD) patients participated in the study (response rate = 79%).
Table 5 summarizes the patients' demographic characteristics. The median age of
the patients was 59 years (range = 21−89 years), close to the median age of 60 years for
Singapore prevalent PD patients (SRR, 2010). Forty three percent of the patients were
male and most patients (72%) were married.
Comparisons between PD subgroups indicated that APD patients had been on PD
for less time, χ2(2, N = 144) = 13.90, p = .001, and were significantly more likely to be
employed, χ2(1, N = 144) = 6.66, p = .01 compared to CAPD respondents. No differences
were found for other demographics (age, gender, marital status, ethnicity, education level,
employment status and housing condition) between the two groups. These case-mix
differences (i.e., time on dialysis and employment status) were entered as covariates in
ANCOVA tests conducted to compare APD and CAPD patients, as long as they were
associated with the outcome in question.
49
Table 5: Demographical Characteristics of APD and CAPD Patients
Demographical Characteristics of APD and CAPD Patients
Total
APD
CAPD
(n =144)
(n = 58)
(n = 86)
58.0 ± 12.9
55.6 ± 14.5
59.6 ± 11.5
.08
62 (43)
26(45)
36(42)
.74
103 (72)
37(64)
66 (77)
.13
41 (28)
21(36)
20 (23)
Chinese
102 (71)
42 (72)
60 (70)
Malay/Indian/Other
42 (29)
16 (28)
26 (30)
Low
54 (38)
20(34)
34 (39)
Intermediate
54 (38)
19(33)
35 (41)
High
36 (24)
19(33)
17 (20)
Employed
38 (26)
22(38)
16 (19)
Unemployed
106 (74)
36(62)
70 (81)
1−4 rooms flats
88 (61)
31(53)
57 (66)
5 rooms flats
35 (24)
16(28)
19 (22)
Condominiums or above
21 (15)
11(19)
10 (12)
Less than 1 year
32 (22)
21(36)
11 (13)
1 to 2 years
20 (14)
10(17)
10 (12)
More than 2 years
92 (64)
27(47)
65 (75)
Age in years
Male
p
Marital status
Married
Widowed/divorced/single/
other
Ethnicity
.85
Education Level
.23
Employment status
.01
Housing condition
.29
Time on dialysis
.001
Note. Data expressed as M ± SD or n (%). APD = Automated Peritoneal Dialysis. CAPD
= Continuous Ambulatory Peritoneal Dialysis.
50
Table 6: Clinical Characteristics of APD and CAPD Patients
Clinical Characteristics of APD and CAPD Patients
Total
APD
CAPD
(n =144)
(n = 58)
(n = 86)
36 (25)
16 (28)
20 (23)
.70
Pill burden
10.3 ± 2.6
10.2 ± 2.7
10.3 ± 2.5
.77a
CCI
5.6 ± 2.0
5.3 ± 2.2
5.7 ± 1.9
.33a
Low ( ≤ 5)
72(50)
30(52)
42(49)
.88
Moderate ( 6)
30(21)
11(19)
19(22)
High ( ≥ 7)
42(29)
17(29)
25(29)
Diabetes
62(43)
28(48)
34(39)
Hypertension
28(19)
8(14)
20(23)
Glomerulonephritis
38(27)
16(28)
22(26)
Other
16(11)
6(10)
10(12)
Creatinine (mmol/l)
0.87 ± 0.31
0.89 ± 0.33
0.85 ± 0.29
.39
Potassium (mmol/l)
4.1 ± 0.7
4.1 ± 0.7
4.1 ± 0.6
.76
Phosphate (mmol/l)
1.60 ± 0.54
1.68 ± 0.57
1.55 ± 0.51
.18
2.9 ± 0.5
2.9 ± 0.6
2.9 ± 0.5
.74
Hemoglobin (g/dl)
10.76 ± 1.69
10.83 ± 1.58
10.72 ± 1.77
.72
Kt/V
2.33 ± 0.99
2.39 ± 1.26
2.29 ± 0.76
.44a
Dependence on carer
p
Primary cause of ESRD
Albumin (g/dl)
.52
Note. Data expressed as M ± SD or n (%).APD = Automated Peritoneal Dialysis. CAPD
= Continuous Ambulatory Peritoneal Dialysis. Dependence on carer = carer
accomplishing most treatment tasks (e.g., performing dialysis). Pill burden = number of
prescribed medicines. CCI = Charlson Comorbidity Index. ESRD = End Stage Renal
Disease.
a
Mann-Whitney test.
51
Clinical Characteristics
Table 6 displays patients' clinical characteristics. Seventy five percent of the
patients were relatively independent, requiring minimum or no assistance in their
treatment activities. Twenty five percent relied on their carers to accomplish most or all
of the treatment tasks (e.g., performing dialysis). A median of 10 types of medicines were
prescribed to the patients (M = 10.3, SD = 2.6, range = 5−17). Based on 33rd (≤ 5) and
67th (> 6) percentiles of the CCI scores, 50% patients had moderate to high comorbidity.
The most common causes of ESRD were diabetes (43%), glomerulonephritis (27%) and
hypertension (19%). Patients were adequately dialyzed but malnourished, as suggested by
a mean Kt/V level of 2.33 (SD = 0.99) and a mean albumin level of 2.9 (SD = 0.5) g/dl,
respectively. There were no significant differences between APD and CAPD patients on
any of the clinical parameters recorded (see Table 6).
Health Beliefs
BMQ
Patients expressed strong beliefs in the necessity of their prescribed medication
(M = 20.5, SD = 3.9) and a moderate level of concerns about the disruptive effects of the
medication (M = 16.1, SD = 5.1) (Table 7).
The necessity-concerns difference score was calculated through subtracting the
concerns score from the necessity score for each individual. This difference score was
positive (more positive evaluation of necessity beliefs vs. concern beliefs; necessity
outweighing any concerns) in 73%, zero (equal necessity and concern beliefs) in 12% and
negative (negative evaluation of need of medication vs. concerns; concerns outweighing
52
perceived need) in 15% of the patients. Mann-Whitney tests indicated no differences
between APD and CAPD patients on necessity and concern beliefs.
Table 7: Health Beliefs Results in APD and CAPD Patients CAPD Patients
Health Beliefs Results in APD and CAPD Patients
Total
APD
CAPD
(n =144)
(n = 58)
(n = 86)
Necessity
20.5 ± 3.9
20.1 ± 4.0
20.7 ± 3.9
.21a
Concerns
16.1 ± 5.1
16.7 ± 4.7
15.7 ± 5.4
.40a
Generic SE
5.9 ±2.0
6.0± 2.3
5.8 ± 1.8
.76
Dialysis SE
8.9 ± 1.7
8.7 ± 2.1
9.1 ± 1.3
.53a
Medication SE
7.8 ± 1.9
7.2 ± 2.2
8.2 ± 1.6
.01a
Dietary SE
7.3 ± 2.1
7.1 ± 2.2
7.5 ± 2.0
.23a
p
BMQ
SE
Note. Data expressed as M ± SD. Higher value indicates more necessity/concern beliefs
or higher self-efficacy. APD = Automated Peritoneal Dialysis. CAPD = Continuous
Ambulatory Peritoneal Dialysis. BMQ = Beliefs about Medicines Questionnaire. SE =
self-efficacy.
a
Mann-Whitney test.
Self-efficacy
Patients reported high self-efficacy in following dialysis (M = 8.9 out of possible
10, SD = 1.7), medication (M = 7.8, SD = 1.9) and dietary guidelines (M = 7.3, SD = 2.1)
and moderate generic self-efficacy in managing their illness (M = 5.9, SD = 2.0).
Mann-Whitney tests indicated that mean medication self-efficacy was higher in
CAPD (Mdn = 8.5) patients than APD (Mdn = 7.2) patients, U = 1873.0, p = .01. This
was further confirmed by a ANCOVA test after controlling for time on dialysis and
employment status, F(1, 139) = 10.00, p = .002. Generic, dialysis and dietary self-
53
e
efficacy
on the other haand were noot significanntly differennt between thhe two PD ssubgroups
(
(Table
7).
Emotioonal Distresss
H
HADS
HAD
DS scores are
a displayedd in Table 88. Mean anxxiety score was
w 7.0 (SD
D = 5.0), in
t normal range (0−77). Mean deepression sccore was 9..1 (SD = 4.6), in the bborderline
the
r
range
(8−100) based on the classificcation schem
me of Thom
mpson et al. ((2000).
Usinng 8 as cut--off value, possible annxiety was detected
d
in 41% (n = 59) of all
p
patients
(Figgure 5). Annxiety rates among
a
APD
D and CAPD
D groups weere 41% (n = 24) and
4
41%
(n = 35), respectively (Tablee 8). Possiblle depressioon was detected in 62%
% (n = 90)
o all patiennts (Figure 6).
of
6 Depressiion rates am
mong APD aand CAPD ggroups weree 64% (n =
3 and 62%
37)
% (n = 53), respectively
r
y (Table 8).
26%
15%
Normal (0--7)
Boderrline (8-10)
59%
Abnormal ((11-21)
Figgure 5. Anxiiety score ddistribution iin all patientts.
54
38%
40%
22%
N
Normal (0-7)
Boderline (8-10)
Abbnormal (11-221)
Figuure 6. Depression scoree distributioon in all patiients.
T
Table
8: Em
motional Disstress Resullts in APD aand CAPD P
Patients
E
Emotional
D
Distress Ressults in APD
D and CAPD
D Patients
Total
D
APD
C
CAPD
(n =144)
(n = 558)
(nn = 86)
H
HADS
Anxxiety
7.0 ± 5.0
7.0 ± 4.8
7.00 ± 5.0
0.98a
Normal (0-7)
85 (59)
34 (559)
51 (59)
1.00b
Borderlinee (8-10)
22 (15)
9 (15)
13 (15)
Abnormal (11-21)
37 (26)
15 (226)
22 (26)
9.1 ± 4.6
9.3 ± 4.5
9.00 ± 4.7
0.59a
Normal (0-7)
54 (38)
21 (336)
33 (38)
0.59b
Borderlinee (8-10)
32 (22)
11 (119)
21 (24)
Abnormal (11-21)
58 (40)
26 (445)
32 (38)
36.5 ± 10.1
36.7 ± 10.2
36.33 ± 10.1
0.86c
Low (20-334)
62 (43)
27 (447)
35 (41)
0.75d
Moderate ((35-49)
67 (47)
24 (441)
43 (50)
Moderatelyy high (50-664)
13 (9)
6 (10)
7 (8)
High (65-880)
2 (1)
1 (22)
1 (1)
H
HADS
Deprression
U
UCLA
Loneeliness
p
Note. Data eexpressed aas M ± SD oor n (%). AP
N
PD = Autom
mated Peritooneal Dialyssis. CAPD
= Continuouus Ambulattory Peritoneeal Dialysiss. HADS = H
Hospital Annxiety and Depression
D
S
Scale.
UCL
LA lonelinesss = Revisedd UCLA Lonneliness Scaale.
a
Mann-Whiitney test. bχ2test. cindeppendent t tesst. dFisher’ss exact test.
55
Therre were no differencess in either the levels of depression or anxieety (using
c
continuous
ween APD
HADS scores) or freqquencies of depression or anxiety cases betw
a
and
CAPD
D patients. These com
mparisons were
w
perform
med with Mann-Whittney tests
(
(supplemen
nted with AN
NCOVA tests) using continuous
c
H
HADS scorres and χ2 tests
t
using
c
categorized
HADS scorres (see Tabble 8).
R
Revised
UC
CLA Loneliness Scale
Baseed on the classification
c
n scheme oof Perry (19990), the m
mean lonelinness score
3
36.5
(SD = 10.1) was inn moderate range (35−
−49). Amongg all patientts, about hallf (n = 67)
r
reported
mooderate loneeliness and additional 10% (n = 115) reportedd moderatelly high or
v
very
high degree
d
of lonneliness (Fiigure 7). Fiffty three peercent of AP
PD (n = 31)) and 59%
C
CAPD
patieents (n = 511) reported at least mooderate degrree of loneliiness (Tablee 8). Both
t mean looneliness scoore (tested w
the
with independent t test)) and the freequency of loneliness
c
cases
(testedd with Fisheer’s exact teest) did not ddiffer betweeen APD annd CAPD paatients.
%
9%1%
43%
47%
Low (20-344)
Moderatee (35-49)
M
Moderately
highh (50-64)
H
High (65-80)
Figure 7. L
Loneliness sccore distribuution in all ppatients.
56
Quality of Life
KDQOL-SF
Table 9 reports quality of life levels for the total PD sample as well as APD and
CAPD subgroups. Results on the generic health related quality of life measure, i.e. SF-12,
indicated that patients' mean PCS score of 35.7 (SD = 9.2) was significantly lower than
the mean MCS score of 43.6 (SD = 11.0), t(143) = −7.16, p < .001. Both the mean PCS
and MCS scores were significantly lower than the age-, gender- and ethnicity-adjusted
Singapore norms (Thumboo et al., 2001) in one sample t tests (PCS norm 51.2, t[143] =
−20.16, p < .001; MCS norm 50.3, t[143] = −7.25, p < .001), indicating that patients'
quality of life was impaired in our sample, especially in the physical domain/well-being.
Among kidney disease specific quality of life subscales, staff encouragement (M
= 72.2, SD = 30.7) and social support (M =71.6, SD = 21.6) scored the highest (indicative
of better quality of life scores), while on the other hand burden of kidney disease (M =
32.4, SD = 26.5) scored the lowest. Thus, patients perceived a relatively high level of
support from medical staff, family members and friends. They also felt severely bothered
by time spent on dialysis, its intrusiveness and burden on family.
Mann-Whitney or t tests were performed to compare KDQOL differences
between APD and CAPD. APD (Mdn = 79.2) and CAPD (Mdn = 70.8) patients only
scored differently on symptoms score, with marginally significantly less symptoms
reported by APD patients, Mann-Whitney test U = 2005.5, p = .05. However, ANCOVA
tests showed no quality of life differences (including symptoms) between the two groups
after controlling for covariates.
57
Table 9: Quality of Life Outcomes in APD and CAPD Patients
Quality of Life Results in APD and CAPD Patients
Total
APD
CAPD
(n =144)
(n = 58)
(n = 86)
SF-12 PCS
35.7 ± 9.2
36.7 ± 9.9
34.9 ± 8.7
.26
SF-12 MCS
43.6 ± 11.0
44.0 ± 10.3
43.4 ± 11.6
.73
Symptoms
68.8 ± 19.4
72.0 ± 19.7
66.7 ± 18.9
.05a
Effects of kidney disease
67.0 ± 22.9
69.1 ± 22.9
65.6 ± 23.0
.37
Burden of kidney disease
32.4 ± 26.5
34.2 ± 27.2
31.2 ± 26.1
.57a
Patient satisfaction
65.7 ± 22.1
63.5 ± 21.0
67.2 ± 22.8
.46a
Staff encouragement
72.2 ± 30.7
74.6 ± 26.7
70.6 ± 33.1
.83a
Social support
71.6 ± 21.6
70.3 ± 22.4
72.5 ± 21.2
.43a
Physical health
11.7 ± 3.2
11.7 ± 3.1
11.6 ± 3.3
.93a
Psychological health
13.0 ± 3.2
12.7 ± 3.4
13.3 ± 3.0
.28
Social relations
13.2 ± 3.4
13.0 ± 3.6
13.4 ± 3.3
.40a
Environment
13.6 ± 2.5
13.4 ± 2.9
13.8 ± 2.3
.32
p
KDQOL-SF
WHOQOL-BREF
Note. Data expressed as M ± SD. Higher values indicates better quality of life. APD =
Automated Peritoneal Dialysis. CAPD = Continuous Ambulatory Peritoneal Dialysis.
KDQOL-SF = Kidney Disease Quality of Life Short Form. SF-12 = 12-item Short-Form
Health Survey. PCS = physical component summary score. MCS = mental component
summary score. WHOQOL-BREF = World Health Organization Quality of Life
Instrument, Short Form.
a
Mann-Whitney test.
WHOQOL-BREF
The international mean scores (Skevington et al., 2004) for the physical health,
psychological health, social relations and environment domains were 16.2 (SD = 2.9),
15.0 (SD = 2.8), 14.3 (SD = 3.2) and 13.5 (SD = 2.6). The corresponding four domain
scores in our study were 11.7 (SD = 3.2), 13.0 (SD = 3.2), 13.2 (SD = 3.4) and 13.6 (SD =
58
2.5), with the first three scores significantly lower than the norms (t[143] = −16.88, p
< .001; t[143] = −7.51, p < .001; t[143] = −3.80, p < .001 respectively) and the last score
equivalent to the normative mean (t[143] = 0.55, p = .58). Thus WHOQOL-BREF results
indicated that patients’ physical, psychological and social relations, but not environment,
quality of life was poorer than international norms.
APD and CAPD patients did not score differently on any of the WHOQOL-BREF
domains (Mann-Whitney, t tests or ANCOVA tests).
Prevalence of Nonadherence
Self-reported Nonadherence
The percentages of patients reporting “never”, “rarely”, “sometimes”, “often” or
“very often” deviating from their treatment recommendations for dialysis, medication and
diet are depicted in Figure 8. A total of n = 12 patients reported no dietary restrictions, so
dietary nonadherence outcomes were based on the rest 132 patients. Twenty percent, 47%
and 75% of the patients reported at least rarely deviating from dialysis, medication and
diet guidelines respectively. Mean dialysis nonadherence score was the lowest (M = 1.3,
SD = 0.6), followed by medication (M = 1.6, SD = 0.8) and diet (M = 2.2, SD = 0.9)
(Table 10).
The percentages of patients reporting intentionally and unintentionally deviating
from guidelines regarding dialysis exchanges/schedule, medication and diet (“never”,
“rarely”, “sometimes”, “often” or “very often”) are shown in Figure 9.
Nonadherence was defined as at least rarely deviating from treatment guidelines.
According to this criterion, 28%, 58% and 81% of the patients were classified as
59
intentional nonadherent to dialysis, medication and diet, whereas 19%, 71% and 73%
were classified as unintentional nonadherent to dialysis, medication and diet, respectively.
Wilcoxon signed-rank tests showed that patients reported more intentional (Mdn =
1.0) than unintentional (Mdn = 1.0) nonadherence to dialysis, z = 3.07, p = .002; patients
also reported more intentional (Mdn = 3.0) than unintentional (Mdn = 2.0) nonadherence
to diet, z = 3.48, p < .001; no significant difference was found between intentional (Mdn
= 2.0) and unintentional (Mdn = 2.0) nonadherence to medication, z = 1.73, p = .08.
Mann-Whitney tests showed that APD and CAPD patients reported similar
nonadherence levels (overall, intentional or unintentional) in all three domains (Table 10).
These results were confirmed by ANCOVA tests after controlling for covariates.
χ2 test indicated that a significantly higher percentage of CAPD patients (23%)
reported shortening treatment sessions when compared with APD patients (2%), χ2(1, N =
144) = 12.89, p < .001.
60
60%
40%
20%
0%
Percentages of patients
80%
60%
40%
20%
0%
80%
60%
40%
20%
0%
Never
Rarely
Dialysis
Sometimes
Medication
Often
Very often
Diet
Figure 8. Distribution of all patients’ frequencies of overall deviation from different
aspects of the therapeutic regimen.
61
60%
40%
20%
0%
Percentages of patients
80%
60%
40%
20%
0%
80%
60%
40%
20%
0%
Dialysis
Medication
Diet
Figure 9. Distribution all patients’ frequencies of intentional and unintentional deviation
from different aspects of the therapeutic regimen.
Note. Intentional nonadherence on the left and unintentional nonadherence on the right of
each stack.
62
Table 10: Self-reported Nonadherence Outcomes in APD and CAPD Patients
Self-reported Nonadherence Results in APD and CAPD Patients
Total
APD
CAPD
(n = 144)
(n = 58)
(n = 86)
Dialysis
1.3 ± 0.6
1.2 ± 0.6
1.3 ± 0.6
.76
Medication
1.6 ± 0.8
1.7 ± 0.8
1.6 ± 0.8
.14
Dietb
2.2 ± 0.9
2.4 ± 0.9
2.2 ± 0.9
.27
Dialysis
1.4 ± 0.8
1.3 ± 0.6
1.5 ± 0.8
.07
Medication
2.0 ± 1.0
2.0 ± 1.0
2.0 ± 1.1
.72
Dietc
2.6 ± 1.1
2.7 ± 1.1
2.6 ± 1.1
.58
Dialysis
1.2 ± 0.5
1.2 ± 0.4
1.2 ± 0.5
.91
Medication
2.1 ± 0.9
2.2 ± 0.9
2.0 ± 0.9
.20
Dietb
2.4 ± 1.1
2.5 ± 1.1
2.3 ± 1.1
.32
Skipping PD sessions
13 (9)
3 (5)
10 (12)
.24
Shortening PD sessions
21 (15)
1 (2)
20 (23)
< .001
pa
Overall nonadherence
Intentional nonadherence
Unintentional nonadherence
Note. Data expressed as M ± SD or n (%). Higher value indicates more nonadherence.
APD = Automated Peritoneal Dialysis. CAPD = Continuous Ambulatory Peritoneal
Dialysis.
a
Mann-Whitney or χ2 test. bn = 132 because 12 patients reported no dietary restrictions. cn
= 131 because 12 patients reported no dietary restrictions and one did not answer this
question.
Nonadherence Based on Biochemical Markers
Three biochemical markers, potassium, phosphate and albumin, were used to
assess clinical nonadherence in this study. Table 11 includes reference values for the
three biochemical markers, their clinical meanings and nonadherence rates (i.e.
percentages of patients with values outside these targets).
63
Twenty seven (19%) patients had potassium level < 3.5 mmol/l, indicating
nonadherence to diet. This might also reflect nonadherence to the prescribed medication
as n = 20 out of the 27 low in potassium patients were prescribed with potassium
supplements.
Forty eight (33%) patients had phosphate level > 1.78 mmol/l, which reflects a
combination of poor adherence to diet and phosphate binders. Based on medical records,
all respondents but one were prescribed with phosphate binders.
One hundred and twenty seven (88%) patients did not achieve target albumin
levels. Low albumin reflects poor nutritional status, suggesting that most patients were
not eating appropriately.
Table 11: Nonadherence Based on Biochemical Markers in APD and CAPD Patients
Nonadherence Based on Biochemical Markers in APD and CAPD Patients
Target levels
Potassium
≥ 3.5 mmol/l
Phosphate
≤ 1.78mmol/l
Albumin
≥ 3.5 g/dl
NA, n (%)
Out-target values signify
NA to…
dietary guidelines/
potassium supplements
dietary guidelines/
phosphate binders
dietary guidelines
Total
APD
CAPD
p
27 (19) 12 (21) 15 (17)
.67
48 (33) 23 (40) 25 (29)
.21
127 (88) 50 (86) 77 (90)
.60
Note. APD = Automated Peritoneal Dialysis. CAPD = Continuous Ambulatory Peritoneal
Dialysis. NA = nonadherence.
Nonadherence rates based on biochemical markers were equivalent between APD
and CAPD patients (see Table 11). Interestingly, n = 12 patients reported that they were
not given any dietary recommendations as part of their treatment. There were however no
differences in biochemical levels/outcomes (i.e., potassium, phosphate and albumin)
64
between this group and the group of patients who reported having been given dietary
recommendations.
Factors Associated With Self-reported Nonadherence
Univariate Analyses
Self-reported nonadherence comparison between different subgroups. Kruskal–
Wallis and Mann-Whitney tests were used to compare nonadherence levels in subgroups
with different demographical and clinical profiles (gender, marital status, ethnicity,
educational level, employment status, housing, time on dialysis, dependence on carer and
primary cause of ESRD).
Results indicated significant differences for ethnicity, employment status,
education, time on dialysis and primary cause of ESRD (Table 12).
The Chinese were less likely to forget taking medications (Mdn = 2.0) than nonChinese (Mdn = 3.0), U = 1689.0, p = .04.
Employed patients (Mdn = 3.0) were significantly more likely to intentionally
deviate from their dietary demands than unemployed patients (Mdn = 2.5), U = 1259.5, p
= .05.
Three nonadherence measures, namely intentional nonadherence to medication
and diet and unintentional nonadherence to medication, varied with education level (H[2]
= 11.24, p = .003; H[2] = 6.59, p = .04; H[2] = 6.84, p = .03, respectively). Bonferronicorrected Mann-Whitney tests (adjusted α = .05/3 = .017) followed up the results and
revealed that patients with intermediate education showed more both intentional and
unintentional nonadherence to medication than patients with low education (U = 994.5, p
65
= .002; U = 1089.0, p = .017, respectively), whereas patients with high education only
showed more intentional nonadherence to medication than patients with low education (U
= 669.0, p =. 007).
Time on dialysis had significant impact on intentional nonadherence to dialysis,
H(2) = 8.58, p = .01. When this was followed up by Bonferroni-corrected Mann-Whitney
tests (adjusted α = .05/3 = .017), both patients on dialysis for more than two years and
patients on dialysis between one to two years were found to be significantly more likely
to intentionally violate their dialysis guidelines (U = 1143.0, p = .015; U = 205.5, p
= .004 respectively) relative to patients who had been on PD for less than one year.
Primary cause of ESRD was closely linked with intentional nonadherence to
medication and diet (H[3] = 12.78, p = .005; H[3] = 15.63, p = .001, respectively).
Bonferroni-corrected Mann-Whitney tests (adjusted α = .05/6 = .008) were used to make
pairwise comparisons. Patients with other cause of ESRD were significantly more likely
to be intentionally nonadherent to medication and dietary guidelines than patients with
hypertension as primary cause of ESRD (U = 96.5, p = .001; U = 61.0, p = .001
respectively) and patients with diabetes as primary cause of ESRD (U = 281.5, p = .005;
U = 190.5, p = .003 respectively). In addition, patients with glomerulonephritis as
primary cause of ESRD showed significantly more intentional nonadherence to dietary
guidelines than patients with hypertension as primary cause of ESRD (U = 261.0, p
= .004).
66
Table 12: Comparisons of Self- reported Nonadherence Between Different Subgroups
Comparisons of Self-reported Nonadherence Between Different Subgroups
Dependent variables
(n = 144)
Intentional NA to
dialysis
Intentional NA to
medication
Unintentional NA to
medication
Intentional NA to
dietc
Factors
Time on dialysis
Less than 1 year
1 to 2 years
More than 2 years
Education Level
Low
Intermediate
High
Primary cause of
ESRD
Diabetes
Hypertension
Glomerulonephritis
Other
Ethnicity
Chinese
Non-Chinese
Education Level
Low
Intermediate
High
Employment status
Employed
Unemployed
Education Level
Low
Intermediate
High
Primary cause of
ESRD
Diabetes
Hypertension
Glomerulonephritis
Other
n
NA score,
M ± SD
32
20
92
1.1 ± 0.4
1.6 ± 0.9
1.5 ± 0.8
.01a
54
54
36
1.6 ± 1.0
2.1 ± 1.0
2.2 ± 1.1
.003a
62
28
38
16
1.9 ± 1.0
1.6 ± 0.8
2.2 ± 1.1
2.6 ± 1.0
.005a
102
42
2.0 ± 0.8
2.4 ± 1.1
.04b
54
54
36
1.9 ± 0.9
2.3 ± 0.9
2.2 ± 0.8
.03a
33
98
2.9 ± 0.9
2.6 ± 1.1
.05b
49
51
31
2.3 ± 1.1
2.8 ± 1.1
2.9 ± 1.1
.04a
58
26
34
13
2.5 ± 1.2
2.2 ± 1.0
2.9 ± 1.0
3.5 ± 0.8
.001a
p
Note. APD = Automated Peritoneal Dialysis. CAPD = Continuous Ambulatory Peritoneal
Dialysis. NA = nonadherence. ESRD = end stage renal disease.
a
Kruskal–Wallis test. bMann-Whitney test. cn = 131 because 12 patients reported no
dietary restrictions and one did not answer this question.
67
Correlates of self-reported nonadherence measures. Spearman rank correlation
coefficients were calculated to investigate the influence of continuous variables (age, pill
burden, CCI, biochemical marker values, BMQ, self-efficacy, HADS anxiety and
depression , UCLA loneliness, KDQOL-SF and WHOQOL-BREF scores) on selfreported nonadherence measures. Significant correlates of self-reported nonadherence
measures are presented in Table 13.
Older age was associated with less nonadherence as indexed by inverse
associations with unintentional nonadherence to medication and diet (rs = −.29, p < .001;
rs = −.19, p = .03, respectively) and intentional dietary nonadherence (rs = −.24, p = .007).
Comorbidity (assessed with CCI) was associated with less nonadherence in some
domains (see Table 13).
Patients’ cognitions were significantly associated with nonadherence in the
expected direction. Strong necessity beliefs were associate with less intentional
nonadherence to medication (rs = −.19, p = .03), whereas higher concern beliefs were
associated with nonadherence on almost all measures (all ps < .05; see Table 13 for full
results). Higher self-efficacy scores were associated with less nonadherence in most
domains as indexed with inverse correlation with nonadherence scores ( see Table 13).
Emotional distress (greater symptoms of depression or anxiety) increased
nonadherence. Quality of life levels (as measured with KDQOL-SF symptoms, effects of
kidney disease and patient satisfaction, WHOQOL-BREF environment) were also
significantly associated with nonadherence measures (significant rs ranging from −.17 to
−32, all ps < ,05, see Table 13 for full results); the overall pattern of results showed that
68
better quality of life was associated with less nonadherence to treatment
recommendations.
Table 13: Correlates of Self-reported Nonadherence in Spearman Rank Correlations
Spearman Rank Correlations Between Self-reported Nonadherence and Selected
Variables
Intentional NA
Unintentional NA
Dialysis Medication Diet
Dialysis Medication Diet
**
Age
−.16
−.14
−.24
−.13
−.29***
−.19*
CCI
−.08
−.13
−.20*
−.09
−.19*
−.16
Necessity
−.06
−.19*
−.06
−.04
−.15
−.08
Concerns
−.20*
−.30***
−.27** −.16
-.21*
−.38***
Dialysis
−.19*
−.16
−.07
−.25**
−.20*
Medication
−.12
−.35***
−.27** −.04
−.32***
−.40***
Diet
−.13
−.37***
−.38*** −.14
−.26**
−.52***
Anxiety
−.12
−.21*
−.23** −.12
-.16
−.32***
Depression
−.12
−.20*
−.16
−.10
-.11
−.21*
−.28**
−.18*
−.12
−.19*
−.14
−.14
−.17*
−.19*
−.18*
−.17*
−.08
−.21*
−.32***
−.16
−.10
−.26**
−.18*
−.26**
−.30***
−.16
−.18*
−.28**
−.16
−.25**
BMQ
Self-efficacy
−.08
HADS
KDQOL-SF
Symptoms
Effects of kidney t
disease
Patient satisfaction
WHOQOL-BREF
Environment
Note. Spearman rank correlation. NA = nonadherence. CCI = Charlson Comorbidity
Index. BMQ = Beliefs about Medicines Questionnaire. HADS = Hospital Anxiety and
Depression Scale. KDQOL-SF = Kidney Disease Quality of Life–Short Form.
WHOQOL-BREF = World Health Organization Quality of Life Instrument, Short Form.
*
p < .05. **p < .01. ***p < .001.
69
Multivariate Analyses
Six hierarchical stepwise multiple linear regressions were performed to identify
important demographical, clinical and psychosocial multivariate correlates of selfreported intentional and unintentional nonadherence to different aspects of therapeutic
regimen (dialysis intentional and unintentional, medication intentional and unintentional
and diet intentional and unintentional). Only variables associated with self-reported
nonadherence measures in univariate analyses were included in the models. In all
regression analyses, demographics (age, ethnicity, education level and employment status)
were entered on the first step, followed by medical variables (modality, time on dialysis,
CCI and primary cause of ESRD) on the second step and psychosocial variables (BMQ
necessity and concerns, self-efficacy scores, HADS depression and anxiety scores,
KDQOL-SF symptoms, effects of kidney disease, patient satisfaction and WHOQOLBREF environment) on the final step. Categorical variables were coded as dummy
variables as appropriate. If one dummy variable was selected by the regression model, all
related dummy variables were kept in the final model. Forward selection method was
used for entry of variable into respective blocks. The problem of multicollinearity (strong
linear relationship among two or more predictors in regression model) was assessed with
the Variance Inflation Factor (VIF) value. VIF value more than 10 indicates
multicollinearity (Myers, 1990). VIF values in the current study varied from 1.00 to 2.79,
indicating no multicollinearity problem.
The final regression models explained low to moderate proportions of variance in
self-report nonadherence scores (cumulative R2 = .11 to .31). Psychosocial variables (ΔR2
= .07 to .31) independently accounted for more variance in self-reported nonadherence
70
scores relative to demographical (ΔR2 = .00 to .09) and clinical variables (ΔR2 = .00 to .08)
(see Table 14 to Table 16)
Intentional nonadherence to dialysis exchanges. Education, modality, KDQOLSF patients satisfaction and WHOQOL-BREF environment were significant predictors of
intentional nonadherence to dialysis exchanges in the final regression model, accounting
for R2 = .19, R2adj = .16 of the variance, F(5, 138) = 6.32, p < .001. High education (vs.
low education) (β = .22, p = .01, ΔR2 = .03) and CAPD (β = .22, p = .006, ΔR2 = .03)
were positively associated with intentional nonadherence to dialysis guidelines, whereas
patient satisfaction (β = −.21, p = .02, ΔR2 = .09) and WHOQOL environment (β = −.21,
p = .02, ΔR2 = .04) were inversely associated with intentional nonadherence to dialysis
guidelines (Table 14).
Unintentional nonadherence to dialysis. The final regression model (F[3, 140] =
5.92, p = .001) explained R2 = .11, R2adj = .09 of the variance in unintentional
nonadherence to dialysis, with time on dialysis and WHOQOL environment quality of
life being significant at the final step. More than 2 years on dialysis (vs. less than 1 year)
(β = .22, p = .02, ΔR2 = .04) and lower environment quality of life score (β = −.27, p
= .001, ΔR2 = .07) were associated with greater unintentional nonadherence
( i.e,.forgetfullness) to dialysis exchanges (Table 14).
71
Table 14: Multivariate Correlates of Self-reported Nonadherence to Dialysis Guidelines
Multivariate Correlates of Self-reported Nonadherence to Dialysis Guidelines
Dependent variable: intentional nonadherence to dialysis guidelines
Step
1
Predictor
R2
R2adj
ΔR2
ΔF
p
.03
.02
.03
2.48
.09
−0.34 −.22** .06
.05
.03
4.74
.03
10.23
B
β
Intermediate education −0.04 −.03
(vs. low education)
High education
−0.39 −.22*
(vs. low education)
2
3
CAPD (vs. APD)
KDQOL-SF patient
*
−0.007 −.21
.15
.13
.09
−0.06 −.21*
.19
.16
.04
satisfaction
WHOQOL-BREF
< .001
environment
Dependent variable: unintentional nonadherence to dialysis guidelines
Step
2
Predictor
R2
R2adj
ΔR2
ΔF
p
.04
.03
.04
2.89
.06
−0.05 −.27** .11
.09
.07
11.56
.001
B
β
1 to 2 years on dialysis −0.05 −.04
(vs. less than 1year)
More than 2 years on
−0.21 −.22*
dialysis
(vs. less than 1 year)
3
WHOQOL-BREF
environment
Note. Hierarchical stepwise multiple linear regressions were used. APD = Automated
Peritoneal Dialysis. CAPD = Continuous Ambulatory Peritoneal Dialysis. KDQOL-SF =
Kidney Disease Quality of Life–Short Form. WHOQOL-BREF = World Health
Organization Quality of Life Instrument, Short Form.
*
p < .05. **p < .01.
72
Intentional nonadherence to medication. In the final regression model, three
variables, namely primary cause of ESRD, medication self-efficacy and BMQ concerns,
emerged as significant multivariate correlates of intentional nonadherence to medication,
explaining R2 = .24, R2adj =.21of the variance, F(5, 138) = 8.55, p < .001 (Table 15).
Diabetes (β = −.38, p = .003, ΔR2 = .01) and hypertension (β = −.30, p = .008, ΔR2 = .06)
as primary causes of ESRD (vs. other cause of ESRD), as well as medication selfefficacy (β = −.32, p < .001, ΔR2 = .13) were negatively associated with intentional
nonadherence to medication. Concern beliefs (β = .19, p = .02, ΔR2 = .03) were positively
correlated with intentional nonadherence to medication.
Unintentional nonadherence to medication. Two demographic variables (age and
ethnicity) and one psychosocial factor (medication self-efficacy) remained significant in
predicting unintentional nonadherence to medication (i.e., medication forgetfulness) at
the final step of the regression, F(3, 140) = 9.58, p < .001, R2 = .17. R2adj = .15 (Table 15).
All of them were negatively correlated with unintentional nonadherence to medication
(age β = −.20, p = .01, ΔR2 = .05; Chinese β = −.18, p = .02, ΔR2 = .04; self-efficacy β =
−.28, p < .001, ΔR2 = .08).
Intentional nonadherence to dietary guidelines. The final regression model only
included age and dietary self-efficacy, F(2, 128) = 12.83, p < .001, R2 = .17, R2adj = .15
(Table 16). Both age (β = −.18, p = .03, ΔR2 = .04) and dietary self-efficacy (β = −.36, p =
1.78 mmol/l) were significantly more likely
to be younger (Mann-Whitney U = 1400.0, p < .001), female (χ2[1, N = 144] = 7.49, p
= .007), employed (χ2[1, N = 144] = 6.45, p = .02), and having less comorbidity (MannWhitney U = 1650.0, p = .005).
Patients nonadherent to albumin (< 3.5 g/dl) were significantly more likely to be
older (Mann-Whitney U = 705.0, p = .02), unemployed (Fisher’s exact test, p = .02),
living in poorer housing conditions (Fisher’s exact test, p = .01), having more
comorbidity (Mann-Whitney U = 680.5, p = .01), worse physical quality of life (SF-12
PCS t[142] = −2.13, p = .04) and worse mental quality of life (SF-12 MCS MannWhitney U = 740.0, p = .04).
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Table 18: Factors Affecting Nonadherence Based on Biochemical Markers in Univariate Analyses
Factors Affecting Nonadherence Based on Biochemical Markers in Univariate Analyses
Adherent
Nonadherent
p
Potassium
Potassium
≥ 3.5 mmol/l
< 3.5 mmol/l
(n = 117)
(n = 27)
25 (21)
11 (41)
.05
Pill purden
10.1 ± 2.7
11.1 ± 1.8
.02a
CCI
5.4 ± 2.0
6.4 ± 1.9
.01a
Phosphate
Phosphate
≤ 1.78 mmol/l
> 1.78 mmol/l
(n = 96)
(n = 48)
Age
60.8 ± 12.0
52.4 ± 13.0
Male
49 (51)
13 (27)
.007
Employed
19 (20)
19 (40)
.02
5.8 ± 1.8
5.0 ± 2.3
Albumin
Albumin
≥ 3.5 g/dl
< 3.5g/dl
(n = 17)
(n = 127)
48.5 ± 17.6
59.3 ± 11.7
.02a
9 (53)
29 (23)
.02b
1−4 rooms flats
5 (29)
83 (65)
.01b
5-rooms flats
7 (42)
28 (22)
Condominum or above
5 (29)
16 (13)
4.4 ± 2.1
5.7 ± 1.9
.01a
SF-12 PCS
40.1 ± 11.1
35.1 ± 8.8
.04
SF-12 MCS
48.4 ± 8.8
43.0 ± 11.2
.04a
Dependence on carer,
CCI
Age
Employed
< .001a
.005a
Housing condition
CCI
Note. Data expressed as M ± SD or n (%). ESRD = End Stage Renal Disease. CCI =
Charlson Comorbidity Index. SF-12 = 12-item Short-Form Health Survey. PCS =
physical component summary score. MCS = mental component summary score.
a
Mann-Whitney test. bFisher’s exact test.
82
Multivariate Analyses
Clinical nonadherence indices (based on potassium, phosphate and albumin
clinical targets) were subsequently regressed to demographic, clinical and psychosocial
variables in three separate hierarchical stepwise logistic regression models. Only
variables associated with nonadherence based on biochemical markers in univariate
analyses (reported earlier) were entered as potential predictors. In all regression analyses,
demographics (age, gender, employment status and housing condition) were entered on
the first step, followed by medical variables (dependence on carer, pill burden and CCI)
on the second step and psychosocial variables (SF-12 PCS and SF-12 MCS) on the final
step. Nagelkerke R2, similar to R2 in linear regression, was calculated to assess how much
variance in nonadherence (based on biochemical markers) could be explained by the
regression models. Forward: likelihood ratio method was used for variable selection.
Table 19 presents the final regression models.
Potassium < 3.5 mmol/l. The only variable that was significant in predicting
potassium < 3.5 mmol/l at the final step of the logistic regression was CCI, accounting
for Nagelkerke R2 = .07 of the variance (Omnibus χ2[1, N = 144] = 6.05, p = .01). One
score increment on CCI was associated with 30% more likelihood of potassium < 3.5
mmol/l (OR = 1.30, 95% CI = [1.05, 1.62], p = .02). This model correctly classified
potassium status in 81% of the cases.
Phosphate > 1.78 mmol/l. Only age and gender were significant in predicting
phosphate > 1.78 mmol/l at the final step of the logistic regression, accounting for
Nagelkerke R2 = .18 of the variance, Omnibus χ2(2, N = 144) = 19.63, p < .001. One year
increment of age was associated with 5% less likelihood of phosphate > 1.78 mmol/l (OR
83
= 0.95, 95% CI = [0.92, 0.98], p = .001). Female was 1.5 times more likely than men to
have phosphate > 1.78 mmol/l (OR = 2.53, 95% CI = [1.15, 5.55], p = .02). The final
model correctly classified phosphate status in 70% of the cases.
Albumin < 3.5 g/dl. Albumin level < 3.5 g/dl was explained by age and housing
condition, Omnibus χ2(3, N = 144) = 20.87, p < .001, Nagelkerke R2 = .26. One increment
on age increased the likelihood of albumin < 3.5 g/dl by 8% (OR = 1.08, 95% CI = [1.03,
1.12], p = .001). Patients living in 5-rooms flats and Condominiums or above were 85%
less likely to have albumin level < 3.5 g/dl when compared to patients living in 1-4 rooms
flats (OR = 0.15, 95% CI = [0.04, 0.57], p = .006; OR = 0.15, 95% CI = [0.03, 0.65], p
= .01). The final model correctly classified albumin status in 90% of the cases.
84
Table 19: Predictors of Nonadherence Based on Biochemical Markers in Multivariate Analyses
Factors Affecting Nonadherence Based on Biochemical Markers in Multivariate Analyses
Dependent variable: potassium < 3.5 mmol/l
Step
2
Predictor
B
−.26*
CCI
OR
Nagelkerke R2
χ2
p
1.30
.07
6.05
.01
Dependent variable: phosphate > 1.78 mmol/l
Step
1
B
OR
Nagelkerke R2
χ2
p
Age
−0.05**
0.95
.13
19.63
< .001
Female (vs. male)
−0.93*
2.53
.18
Predictor
Dependent variable: albumin < 3.5 g/dl
Step
1
B
OR
Nagelkerke R2
χ2
p
Age
−0.07**
1.08
.13
20.87
1.78 mmol/l, comparable to previous findings (Hecking et al., 2004;
Leggat et al., 1998; O'Connor, Jardine, & Millar, 2008). Both potassium and phosphate
levels are traditional biochemical markers used to indicate nonadherence to medication
and diet (Denhaerynck et al., 2007). Nonadherence rates estimated by biochemical values
were lower than self-reports which were 47% for medication and 75% for diet. This was
96
in line with the notion by Kugler et al. (2011) that self-report, when used in an
unthreatening way, was more capable of detecting minor nonadherence than biochemical
markers. The rate of nonadherence to albumin was 88%, remarkably higher than previous
studies (Russell et al., 2008; Sehgal, Leon, & Soinski, 1998). This is a cause for concern,
given that serum albumin level is a robust associate of high mortality and morbidity in
patients with and without renal diseases (Goldwasser & Feldman, 1997; Owen, Lew, Liu,
Lowrie, & Lazarus, 1993).
Nonadherence based on biochemical values were not associated with all selfreport nonadherence scores, consistent with previous studies (Chiu et al., 2009; Kaveh &
Kimmel, 2001; Lee & Molassiotis, 2002). One reason for the lack of concordance
between the two methods is likely to be due to differences in time measurement. It is also
important to note that biochemical levels are likely to be influenced by a variety of
factors such as comorbidity, dialysis adequacy, residual renal function, time at which the
tests were performed and acid-based and hormonal status (Denhaerynck et al., 2007). In
addition, biochemical values may be affected by “white coat adherence” noted in
different populations which means patients tend to have good adherence several days
preceding a clinic visit and much less satisfactory adherence in other days (Podsadecki,
Vrijens, Tousset, Rode, & Hanna, 2008).
Comparison of Nonadherence to Dialysis, Medication and Diet
Nonadherence to dialysis exchanges was high. This is most probably supported by
patients’ high awareness about the importance of dialysis, family enforcement, and the
comfort of doing dialysis at home. PD patients do not need to be concerned about
transportation, a common cause of nonadherence in HD patients (Latham, 1998).
97
Moreover, some patients are more motivated to adhere to dialysis because they hold the
misconception that dialysis can compensate for their violations in other domains of the
therapeutic regimen (Smith et al., 2010).
Nonadherence to medication fell between nonadherence to dialysis and diet. As
mentioned above, patients in our study had elevated level of concerns about the
disruptive effects (e.g., side effects, drug dependence) of their prescribed medication
when compared to other populations (Horne & Weinman, 1999; Rees et al., 2010).This
might be an important contributor of medication nonadherence in this group. In addition,
patients may be discouraged to take medication when they hold the belief that dialysis is
enough to keep their health, as medicines produce no immediate symptomatic relief and
they frequently receive information regarding the effects of medicines from alternative
therapists (e.g., practitioner of Chinese medicine), other patients and friends that conflicts
with physicians’ suggestions (Donovan & Blake, 1992; Karamanidou, Clatworthy, et al.,
2008).
Dietary nonadherence was the greatest of the three, consistent with previous
findings (Lam et al., 2010; Leggat et al., 1998). This supports the notion that
nonadherence is very likely to occur when it requires major changes in lifestyle (Arenas
et al., 2010). Following dietary guidelines is especially challenging for patients in
countries where there is a huge discrepancy between traditional eating preferences and
dietary needs for dialysis patients (Lam et al., 2010; Park, Choi-Kwon, Sim, & Kim,
2008). Eating out is common in Singapore, as testified by the abundance of food courts
and restaurants in the country. However, commercially prepared food usually contain
high fat, cholesterol and salt (Ang & Foo, 2002) and is considered unhealthy for dialysis
98
patients. Thus, patients need to make food choices cautiously when they eat out because
the alternative would be to eat unpalatable food at home. This restricted diet can be very
distressing for patients. Nonadherence is especially likely to occur among patients with
low frustration tolerance (De-Nour & Czaczkes, 1972). Even for patients who succeed in
adjusting to renal dietary plans, dietary abuse can appear in social occasions because the
collectivist culture in Singapore expects people to cater to the needs of others and
suppress their own needs, such as in the case of “Guanxi” (McCarthy et al., 2009;
Triandis, 2001). For patients with unsuccessful dietary adherence, guiding them to cope
with frustration and to be more assertive may be helpful.
Intentional and Unintentional Nonadherence
Intentional nonadherence to each domain of the treatment regimen was
considerable, 28%, 58% and 81% for dialysis, medication and diet respectively.
Unintentional nonadherence rates were also high, 19%, 71% and 73% for dialysis,
medication and diet, respectively.
Hypothesis 1b that unintentional nonadherence is more common than intentional
nonadherence was not supported by our data based on mean scores, in contrast with
findings in other chronic illness conditions (Rees et al., 2010; Sewitch et al., 2003; Unni
& Farris, 2011). Part of the discrepancy may be related to mode of administration. The
questionnaire developed by Morisky, Green and Levine (1986) was used to assess
intentional and unintentional nonadherence in other studies. It asks patients about
nonadherence directly (e.g., “Do you ever forget to take your medicines?”). Patients may
feel more comfortable to cite forgetfulness to justify their nonadherence rather than
admitting deliberate deviations since the former is less blamable (Unni & Farris, 2011).
99
We have opted for patient self-report administration to an independent researcher not
involved with patients’ care in a confidential setting outside the clinic to minimize social
desirability bias. All items were carefully phrased in a nonjudgmental manner to validate
experience and facilitate disclosure (e.g., “Some people forget to take their medicines.
Overall, how often does this happen to you?”). Such an approach has been recommended
as effective in reducing response bias in self-report (Vlaminck et al., 2001). As there is no
previous work on intention/unintentional nonadherence in ESRD patients, further work is
needed to examine if the discrepancy may be somehow related to this population per se
rather than the methodology.
It is noteworthy that although intentional nonadherence seems puzzling to health
providers, it may be more understandable when viewed from patients’ perspectives.
Intentional nonadherence can be positively reinforced in daily life. For instance, Eliasson
et al. (2011) interviewed a group of chronic myeloid leukemia patients and reported that
patients’ intentional nonadherence to medication could be encouraged by the reduction of
symptoms following altering drugs’ doses and by perceived doctors’ unintentional
reassurance that occasional deviations would not lead to detrimental consequences.
Moreover, nonadherence to HD regimen and remaining sick had been found helpful for
patients to gain attention from others and to resolve family problems (De-Nour &
Czaczkes, 1972). On the other hand, adherence can be negatively reinforced. Patients
may experience no adverse effects when reducing or stopping therapy for some time, but
may feel tiredness, itching and headaches caused by the treatment (Polaschek, 2007).
Renal nurses commented in the study of McCarthy et al. (2009) that PD patients were at
greater risk of being marginalized by their social network if they followed their treatment
100
guidelines as instructed. For instance, if patients kept their rooms very clean, it might
discourage others from visiting due to concerns about causing infections to patients. In
addition, PD patients have more chances to intentionally modify some aspects of their
therapy such as starting time and the length of dialysis, since they are not dependent on
the fixed schedule in dialysis centers (Polaschek, 2007).
Patients’ attributes of foresight and foreplanning have been proven to be an
important predictor of unintentional nonadherence (Daleboudt et al., 2010). However,
this may not play a major role in this study, as even though cognitive decline is common
among older dialysis patients (Tyrrell, Paturel, Cadec, Capezzali, & Poussin, 2005), they
did not report more forgetfulness than younger patients. In addition, unintentional
nonadherence could be caused by patients’ poor comprehension about their treatment and
language barriers (Browne & Merighi, 2010). Education from pharmacists has been
found to be effective in reducing patients’ difficulty about remembering medicines
(Sathvika, Naraharib, Gurudevb, & Parthasarathia, 2009).
Comparison of Nonadherence Between APD and CAPD Patients
APD patients reported lower level of nonadherence than CAPD patients. More
specifically, APD was associated with less intentional nonadherence to dialysis
exchanges than CAPD in multivariate analysis and it was less common for APD patients
to shorten their treatment in univariate analysis. Bernardini et al. (2000) also evaluated
nonadherence to dialysis prescriptions in these two groups and noted elevated
nonadherence in CAPD patients than APD patients. This result is not surprising, given
the distinct CAPD and APD demands. CAPD involves performing several exchanges
manually during the day time, whereas APD is performed at night by a mechanical
101
device when patients are sleeping. Thus CAPD patients may face more barriers in terms
of time (e.g., going out for no more than 4 hours), space (e.g., employers searching for
space to perform exchanges) and physiological status (e.g., continuous inter-abdominal
pressure) than APD patients (Liakopoulos & Dombros, 2009).
Determinants of Nonadherence
Our results show that nonadherence is a multi-faceted phenomenon, concurrently
determined by the interplay of five categories identified by WHO (Sabaté, 2003) as
critical for nonadherence. They are: social-economic factors, health care system related
factors, condition-related factors, therapy-related factors and patient-related factors. The
relationship between these five categories and predictors spotted in our study is displayed
in Table 20.
Demographics and Nonadherence
Our study identified a set of demographical variables (age, gender, ethnicity,
education and housing condition) associated with nonadherence based on multivariate
analyses. They generally made small contributions to the prediction of nonadherence
outcomes, except when nonadherence was based on phosphate and albumin levels.
Several systematic reviews report that demographic variables (excluding age and
smoking) are inconsistent correlates of nonadherence (Karamanidou, Clatworthy, et al.,
2008; Russell et al., 2007).
102
Table 20:WHO Identified Categories Affecting Nonadherence and Significant Predictors in Our Study
WHO Identified Categories Affecting Nonadherence and Significant Predictors of
Nonadherence in Our Study
Five categories affecting nonadherence
Social-economic factors
Significant predictors of nonadherence
in our study
Demographics (age, gender,
ethnicity, education level and
housing condition)
WHOQOL-BREF environment
Health care system-related factors
KDQOL-SF patient satisfaction
Condition-related factors
Comorbidity
Primary cause of ESRD
PD modality
Time on dialysis
BMQ concerns
Self-efficacy
HADS depression
HADS anxiety
Therapy-related factors
Patient-related factors
Age. Patients who were older had less unintentional nonadherence to medication,
less intentional nonadherence to dietary demands and less likelihood of phosphate > 1.78
mmol/l than younger patients in the current sample. Older age is a consistent predictor of
less nonadherence to all aspects of the medical regimen in other studies (Ismail, Hakim,
Oreopoulos, & Patrikarea, 1993; Kugler et al., 2005; Lam et al., 2010). The association
between younger age and nonadherence may reflect the practical and logistic challenges
in sustaining care and following treatment as the younger patients are more likely to be
occupationally and socially active (van der Mei et al., 2007). Younger patients also find it
difficult coming into terms with their sickness and tend to have poor tolerance about
103
limitations associated with treatment (Dimkovic & Oreopoulos, 2000; GonsalvesEbrahim, Sterin, Gulledge, Gipson, & Rodgers, 1987; Herlin & Wann-Hansson, 2010).
Nonadherence can help younger patients to deny their dependence on dialysis and the
severity of their condition (De-Nour & Czaczkes, 1972). This can be further exacerbated
by younger patients’ perception of a lack of support from family members, doctors,
nurses, and technicians (Oka & Chaboyer, 1999).
Older age was also found to be associated with reduced albumin level in our study,
although due in part to albumin level tending to decrease steadily with age (Greenblatt,
1979; Salive et al., 1992). It is also plausible that older patients do not take in adequate
amount of protein due to reduced appetite (Park et al., 2008).
Gender. Females were 1.5 times more likely to have phosphate > 1.78 mmol/l
than males, indicating possible dietary or medication violations. Another study also
reported a higher level of fluid nonadherence among females than males (Chilcot,
Wellsted, & Farrington, 2010). In contrast, other studies suggested that males are more
nonadherent to all aspects of renal regimen than females (Bame, Petersen, & Wray, 1993;
Lam et al., 2010; O'Connor et al., 2008; Setoguchi, Choudhry, Levin, Shrank, &
Winkelmayer, 2010). Asefzadeh, Asefzadeh and Javadi (2005) revealed that female
patients were less likely to be adherent than male patients because of forgetfulness,
doubts about the importance and necessity of medicines and religious considerations. In
the current study, females did not report more nonadherence in survey but showed
elevated phosphate levels. Chen et al. (2006) suggested that some patients adhere to their
medical regimen wrongly, such as when patients think that they are duly following
doctors’ instruction but in fact their methods are not correct. This is probably the case
104
with some female patients in our study. Interestingly, females are also more responsive to
educational interventions about nonadherence (Barnett, Li Yoong, Pinikahana, & Si-Yen,
2008). Another reason to explain the gender difference on self-report and phosphate level
might be that females feel more reluctant about reporting nonadherence than males, since
females tend to have more concerns about others’ evaluation of them than males
(Hoffman, 1977).
Ethnicity. The Chinese were less likely to forget taking their prescribed
medication in our study than non-Chinese. The cohesion of Chinese families might play
an important role in this. Closer ties amongst family members can facilitate patients’
medication adherence through providing financial resources, increasing motivation,
educating patients about the dosing and effects of medicines, setting reminders or directly
administrating medications (Fredriksen-Goldsen et al., 2011).
Education level. Patients with high education (post-secondary school) reported
more intentional deviation from dialysis guidelines than patients with low educational
level (no or primary school) in our study, whereas the difference between patients with
intermediate education (secondary school) and patients with low education was not
significant. Our finding is in contrast with other studies such as Lam et al. (2010) which
reported a negative association between education level and nonadherence to dialysis,
fluid and dietary restrictions. Comparing patients on different educational levels revealed
that 39% patients with high education were still working while only 15% patients with
low education were still working. Patients with high educational level were also younger
than patients with low education level (data not shown). Thus patients with high
education level might be occupationally and socially more active and their daily activities
105
might have more conflicts with the rigorous demands of their medical regimen. Patients
with busy or unpredictable schedules are found to be less likely to be adherent in other
studies (Browne & Merighi, 2010).
Housing condition. Patients living in more expensive property (5-rooms
flats/condominiums or above) had a lower risk of malnutrition (indicated by albumin
level) than patients living in less expensive property (1-4 rooms flats). Housing condition
was used as a rough indicator of patients’ financial status in our study, since there were
many missing values for monthly family income. Poor financial status is a barrier to
adequate protein nutrition amongst dialysis patients, since renal diet can be expensive to
maintain (Sehgal et al., 1998; Soinski, Kelly, & Komaransky, 1993).
Clinical Variables and Nonadherence
The association between PD modality and nonadherence has been discussed
above. Other factors affecting nonadherence involved time on dialysis, comorbidity and
primary cause of ESRD. Clinical factors were also found to be poor predictors of
nonadherence in literature (Karamanidou, Clatworthy, et al., 2008).
Time on dialysis. Patients on PD for more than two years were more likely to
intentionally deviate from their dialysis guidelines than patients treated with PD for less
than one year. Difficulty in sustained motivation in long-term dialysis patients is
commonly documented (Lam et al., 2010). It has also been suggested that patients follow
doctors’ advice initially when they are unfamiliar with their disease and treatment. As
they become more knowledgeable about their treatment gradually through their
observations and body reactions, they intentionally adjust certain aspects of their
treatment to feel better or to regress back to their previous lifestyle (Polaschek, 2007).
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Comorbidity. Comorbidity was the only factor associated with potassium level
below clinical target. A positive relationship between comorbidity and nonadherence has
also been observed in other studies (Pang, Ip, & Chang, 2001; Setoguchi et al., 2010).
Patients with higher number of comorbid diseases may have more physical or emotional
difficulties about being adherent to potassium. Dependence on carers for shopping and
cooking also predispose patients to nonadherence in literature (Lee & Molassiotis, 2002;
Sehgal et al., 1998), but our study could not confirm these results.
Primary cause of ESRD. Patients with diabetes and hypertension as primary cause
of ESRD showed less intentional nonadherence to medication than patients with other
causes of ESRD (e.g, systemic lupus erythematous). Diabetic status was also found
associated with less nonadherence in another study (Christensen, Benotsch, Wiebe, &
Lawton, 1995). It has been observed in different populations that patients stop taking
medicines as soon as there is a reduction of symptoms (Addington, 1979; Britten, 1994).
Given that both diabetes and hypertension are asymptomatic, it is very likely that patients
with these conditions are more aware about the importance of being adherent to
medication regimen regardless of symptoms due to previous experience with diabetic and
hypertension regimen.
It is noteworthy that PD patients in our study had a very high pill burden, taking a
median of 10 types of medication per day. Contrary to two previous studies (Chiu et al.,
2009; Neri et al., 2011), our study detected no association between pill burden and
medication nonadherence. This discrepancy may be due to the fact that the other two
studies used the exact number of tablets (dosages per day), whereas we only evaluated
the number of prescribed medicines and this parameter might be less sensitive.
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Psychosocial Variables and Nonadherence
Health beliefs. Hypothesis 2a that positive health beliefs (i.e., high necessity
beliefs, low concern beliefs and high self-efficacy) are associated with less nonadherence
was partially confirmed. Self-efficacy and concern beliefs had a major impact on
nonadherence to diet and medication, but not on nonadherence to dialysis exchanges. The
lack of association between dialysis nonadherence and health beliefs may be due to the
fact that dialysis presents a more immediate gratification of their illness, thus involving
less rational decision making.
In our study higher level of concern beliefs were associated with more intentional
nonadherence to medication. This is in line with studies in other patient populations
(Daleboudt et al., 2010; Lowry, Dudley, Oddone, & Bosworth, 2005). It is
understandable that patients’ intentional nonadherence to medication was affected by
concern beliefs in our sample. Dialysis patients may become more sensitive to the costs
associated with taking medication (e.g., side effects, drug dependence) after they have
already experienced multiple losses such as functional decline, unemployment and
reduced social life (Chan, Brooks, Erlich, Chow, & Suranyi, 2009). Thus the need to
prevent further losses takes over and leads to intentional deviations from medication
guidelines. The observed inverse association between concern beliefs and unintentional
nonadherence to diet is noteworthy as unintentional nonadherence is not typically thought
of as an active process (Clifford et al., 2008). Other studies (Daleboudt et al., 2010; Unni
& Farris, 2011) also reported that concern beliefs played an important role in
unintentional nonadherence to medication. It seems that concerns may somehow lead to
108
patients attaching low priority to treatment which may lead to more forgetfulness. More
work using qualitative method is needed to explore these relationships.
Some concerns may be fueled by a lack of understanding of how the drugs work,
it is also important to remember that these concerns may be well validated by patients’
subjective experience and not solely founded by a lack of understanding or poor
knowledge (Schuz et al., 2011). The finding of the role on concerns beliefs driving
nonadherence highlights the need to correct patients’ misperceptions and worries about
side effects. Corrective actions need to be taken either in the form of educating patients
about drug effects or when the situation calls, changing prescribed drug (there may for
instance be another hypertensive agent that the health care professions could prescribe if
one is not that well tolerated) so as to reduce risk of intentional deviation from prescribed
medication regimen.
Self-efficacy influenced most nonadherence measures (intentional/unintentional
nonadherence to medication/diet) in our study. This is in line with the finding of Smith et
al. (2010) that sense of self-responsibility and perceived capability of self-restriction are
critical facilitators of adherence. There is overwhelming theoretical and empirical
evidence on the role of self-efficacy in guiding adherence and self-care management in
populations with different chronic illnesses (Lindberg & Fernandes, 2010; Mo & Mak,
2009; Taal, Rasker, Seydel, & Wiegman, 1993; Zrinyi et al., 2003). Self-efficacy training
in HD patients has been found to be effective in reducing weight gains (Tsay, 2003).
Incorporating self-efficacy into a self-management programme for PD patient achieved
successful effects in terms of enhancing volume status, quality of life, rehabilitation
status, and reducing nonadherence to dialysis and dietary guidelines (Su et al., 2009).
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Dialysis patients face a range of adherence barriers such as debilitating symptoms,
a lack of motivation and conflicts between therapy and other life activities (Polaschek,
2007). The importance of self-efficacy is that it “determines whether coping behavior
will be initiated, how much effort will be expended, and how long it will be sustained in
the face of obstacles and aversive experiences” (Bandura, 1977, p. 191). Thus patients
with stronger self-efficacy are very likely to view their treatment demands as challenging
tasks to be mastered, actively participate in solving their problems and remain resilient
when their efforts do not achieve desirable effects immediately. On the other hand,
patients with low self-efficacy are very likely to judge treatment tasks as too difficult for
them and thus give up quickly when they encounter barriers in managing their treatment
or when they do not see effects of adherence in short term (Bandura, 1989).
Bandura (1977) proposed four sources of self-efficacy: mastery experience,
vicarious experience, verbal persuasion and physiological states. Strategies corresponding
to these postulates can be effective in enhancing self-efficacy in PD patients, such as: (a)
providing adequate training to ensure that patients master necessary knowledge and skills
and helping patients achieve successes at the initiation of dialysis; (b) using social model
and asking successful patients to share their adherence experiences and tips so other
patients can learn from them; (c) reassuring patients that they are capable of
accomplishing the tasks; (d) helping patients to reduce stress and view emotional and
physiological arousals as facilitators instead of barriers to adherence (Bandura, 2006;
Locke & Latham, 2002).
Given the importance of cognitive factors (i.e., beliefs about medicines and selfefficacy), it is imperative to take them into account in interventions aimed at reducing
110
nonadherence. Little success is found in previous educational programmes which
increased patients’ understanding about treatment but failed to change patients’ health
beliefs and self-efficacy (Cummings, Becker, Kirscht, & Levin, 1981; Karamanidou,
Weinman, & Horne, 2008; Tanner et al., 1998).
Emotional distress and nonadherence. Hypothesis 2b that more emotional distress
is associated with more nonadherence was confirmed in univariate analyses, but negated
in multivariate analyses after controlling for covariates. The relationship between
depression and nonadherence is well supported in different studies (Brownbridge &
Fielding, 1994; Cukor et al., 2009; De-Nour & Czaczkes, 1976). A rigorous review
reported a considerable relationship between depression and nonadherence across
different chronic illnesses, with depressed patients three times as likely as non-depressed
patients to be nonadherent (OR = 3.03, 95% CI = [1.96, 4.89]) (DiMatteo et al., 2000).
However, the association between anxiety and nonadherence is ambiguous in current
literature (DiMatteo et al., 2000). The association between anxiety and nonadherence in
dialysis patients was confirmed in one study (Brownbridge & Fielding, 1994) but negated
in three other studies (Christensen, Moran, Lawton, Stallman, & Voigts, 1997; Katz et al.,
1998; Schneider et al., 1991).
A significant finding of our study is that emotional distress only affects
nonadherence indirectly through cognitions. More specifically, depression and anxiety
affect nonadherence through increasing patients’ concerns about the adverse effects of
medicines and lowering patients’ self-efficacy about achieving treatment goals. This
result has important clinical implications, since it suggests that modifying beliefs may be
a feasible way to reduce nonadherence in depressed patients.
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Quality of life and nonadherence. Two quality of life parameters turned out to be
important in predicting nonadherence outcomes: KDQOL-SF patient satisfaction and
WHOQOL-BREF environment.
Patient satisfaction was the most important predictor of intentional nonadherence
to dialysis in our study. Patients’ satisfaction with their nephrologists has also been found
associated with better attendance at HD sessions (Kovac, Patel, Peterson, & Kimmel,
2002). Low satisfaction with care can fuel patients’ nonadherence. A tense doctor-patient
relationship lowers the perceived authority of health providers. Patients view those bythe-book treatments given by the doctors as not to their best interest and have a tendency
to use their subjective experience to modify their treatments (Allen, Wainwright, &
Hutchinson, 2011). For instance, patients experiment with their medication to find out
minimum dosage workable for them and to control side effects instead of following
doctor’s advice blindly (Donovan, Blake, & Fleming, 1989; Vermeire et al., 2001). In
addition, nonadherence could be a way of “acting out”, used by passive-aggressive
patients to express their underlying hostility towards the medical team who did not treat
them well (De-Nour & Czaczkes, 1972). Furthermore, patients are apt to conceal their
nonadherence behaviors from health providers, and when they do so, it blocks out
necessary discussion regarding the appropriateness of such behaviors (Allen et al., 2011;
Donovan & Blake, 1992). The importance of patients’ faith in their doctors is testified by
reports of healthy physician-patient relationship related to adhering to treatment plans
even when facing severe physical and social stressors (Eliasson et al., 2011).
Good environment quality of life decreased nonadherence to dialysis guidelines.
Patients with better environmental quality of life are more likely to have adequate
112
resources (e.g., financial, informational) to secure their needs, gain easy accessibility to
health care when problems arise, have better chances to participate in recreational
activities to alleviate stress and a more favorable home environment (e.g., safety, ease of
transportation, low noise). All these factors can help patients establish a positive attitude
towards dialysis which has been found to be important in motivating patients to perform
exchanges. This is well illustrated by the comment of a patient in the study of Polascheck
(2007): “If life is going well, then dialysis is not a problem.”
Comparison of Determinants of Intentional and Unintentional Nonadherence
There is theoretical and empirical evidence to show that intentional nonadherence
to medication is largely driven by patients’ motivation and beliefs (Branin, 2001; George,
Kong, & Stewart, 2007). These are factors largely amenable to change. In contrast,
unintentional nonadherence to medication is thought to be the result of a passive process
that is less strongly associated with individuals' beliefs and more closely related to
patients’ skills or their abilities to follow their medication regimen (e.g. age, manual
dexterity, cognitive impairment, health literacy) or treatment factors (e.g. dose,
complexity) (Clifford et al., 2008).
However, predictors of intentional and unintentional nonadherence were not
clearly separated in our study. Intentional nonadherence was determined by the interplay
of age, education, PD modality, primary cause of ESRD, patient satisfaction,
environment quality of life, concern beliefs and self-efficacy, whereas unintentional
nonadherence was affected by age, ethnicity, time on dialysis, environment quality of life,
concern beliefs and self-efficacy. Thus, age, environment quality of life, concern beliefs
and self-efficacy were all predictors of both intentional and unintentional nonadherence.
113
One possible reason could be that there was moderate to high overlap between patients
reporting intentional nonadherence and unintentional nonadherence (Spearman
correlation, rs = .35 for dialysis, rs = .56 for medication, rs = .67 for diet, all ps < .001).
This is not unexpected since factors such as a busy life schedule can simultaneously
contribute to intentional and unintentional nonadherence. Nonadherence is not a
dichotomy of intentionality. For instance, patients forgetting to take medication at lunch
may intentionally increase dosage at dinner (Eliasson et al., 2011).
Clinical Recommendations
It has been advocated that views concerning nonadherence should be expanded
from merely focusing on patients as cause of nonadherence to considering the
interactions between patient, health provider and the wider health care system (Kammerer,
Garry, Hartigan, Carter, & Erlich, 2007). We agree that health providers are essential in
preventing patients’ nonadherence. In order to do this, there are several suggestions.
Firstly, the extensiveness of nonadherence should be noted, especially regarding dietary
restrictions. Few patients discuss their nonadherence behaviors with physicians without
prompting (Allen et al., 2011; Donovan & Blake, 1992). This may give physicians the
wrong impression that few patients are nonadherent (McHorney, 2009). Patients also feel
frustrated if doctors do not check upon them regarding their therapy (Polaschek, 2007).
Thus it is crucial for physicians to become aware of this issue and provide consultations
for patients claiming difficulties about following doctors’ instructions. As shown in our
results, patients are willing to acknowledge nonadherence when asked in a nonthreatening, non-authoritative way. Secondly, intentional and unintentional nonadherence
114
are different phenomena. The intentionality of nonadherence should be considered in
designing interventional programmes to reduce nonadherence. It should be pointed out
that it is common to suggest behavioral strategies such as timer-alarms and medication
boxes to patients reporting forgetfulness about therapy (Unni & Farris, 2011). Our results
showed that these strategies alone are not likely to be effective without considering
patients’ beliefs (self-efficacy and beliefs about medicines). Thirdly, it is notable that
nonadherence is determined by a complex interplay of social-economic factors (age,
gender, ethnicity, education level, housing condition and environment quality of life),
health care team related factors (patient satisfaction), condition-related factors
(comorbidity and primary cause of ESRD), therapy-related factors (PD modality and time
on dialysis) and patient-related factors (medicine beliefs, self-efficacy, depression and
anxiety). Social-economic, condition and therapy related factors are less modifiable than
other factors. Patients with younger age, female gender, non-Chinese ethnicity, high
education, poor housing condition, poor environment quality, high comorbidity, primary
cause of ESRD not being diabetes or hypertension, and longer time on dialysis should be
monitored for risk of nonadherence. For patients consistently showing nonadherent
behaviors while on CAPD, switching to APD may be a solution (Raj, 2002). Patients’
cognitions, emotions and satisfaction with care are also critical determinants of
nonadherence and they are more modifiable in interventions. Health providers can play
an important role in reducing nonadherence through ways such as opening up
communication regarding nonadherence, addressing patients’ concerns about medication
and providing assurance that patients have adequate self-care abilities. A specific strategy
suggested in literature that can be quite useful in this population is regular phone calls
115
from health providers (physicians, nurses or pharmacist) (Eliasson et al., 2011). Phone
calls serve as reminders for patients to follow treatment guidelines and at the same time
enhance patients’ relationship with medical staff and reduce patients’ uncertainty
regarding their disease and treatment. Implementing this strategy has been proven to be
effective in improving quality of life, resolving patients’ concerns and reducing
nonadherence among PD patients and patients with other chronic conditions (Chow &
Wong, 2010; Clifford, Barber, Elliott, Hartley, & Horne, 2006; Eliasson et al., 2011).
Another more time-consuming strategy is negotiated care in which patients take an active
role making decisions regarding their prescriptions. This strategy produced marked
outcomes in a group of diabetic PD patients, reducing patient nonadherence to dietary salt
and fluid restriction from 80.5% to 23.8% (Quan et al., 2006).
Study Strengths and Limitations
This study has several important strengths. Firstly, our study sample was highly
representative of Singapore PD patients. It comprised almost one quarter of all local PD
patients and had similar age, gender, ethnicity and primary cause of ESRD as the overall
PD population in Singapore. Selection bias was minimized in our study. Participants were
not limited to those who could read and completed the questionnaires by themselves.
Patients could participate in our study through different ways based on their preferences
for time, location and language. This prevented loss of patients due to poor health
condition, illiteracy, visual problems and language barriers. Secondly, this is the first
systematic study to investigate prevalence and predictors of intentional and unintentional
nonadherence in PD patients. A series of determinants of intentional and unintentional
116
nonadherence were identified, thus offering multiple choices for targeting nonadherence.
In addition, our study provided critical information regarding health beliefs, emotional
distress, quality of life and nonadherence for patients on APD and CAPD. This can
facilitate better management of PD programme and help patients’ selection of PD
modality.
Limitations of our study should be noted. Regression models in our study only
explained low to moderate variance in nonadherence outcomes. More than half of the
variance was not tapped in our study. Our study failed to assess some important
associates of nonadherence such as cognitive decline (Daleboudt et al., 2010) and
smoking status (Kugler et al., 2011; Russell et al., 2007) as well as factors beyond
patients’ control such as cost of medicines (Hirth, Greer, Albert, Young, & Piette, 2008).
In addition, we did not assess patients who did not show up for their regular clinics. Since
patients who miss their clinic appointments are also more likely to be nonadherent with
other aspects of the therapeutic regimen (Daleboudt et al., 2010), nonadherence rates in
our study might be underestimated. Moreover, we used adapted questionnaires in
assessing dialysis, medication and dietary self-efficacy and nonadherence. Since the
psychometric properties of these two questionnaires have not been tested in other studies,
findings regarding nonadherence and self-efficacy should be interpreted with caution.
It should also be noted that about half of the participants were interviewed by
research assistants. Interviewed patients may tend to give favorable responses due to
social desirability issue. However, all analyses did not control for patients’ participation
method because there were 12 missing items for this variable. These missing items would
lead to loss of power and increase type II error in data analyses. Our current sample size
117
(n = 144) is powerful enough to detect medium effect sizes but underpowered to detect
small effect sizes based on G*power 3.1 (Faul et al., 2009) calculations. Post-hoc power
(1) = .83, .81, .79 for independent t, Mann-Whitney tests and regression analyses
respectively when fixing = .05 and effect sizes to be medium. A sample size of 394 for
each group is required to detect a small effect size of d = .2, = .05, power (1) = .8 for
two-tailed independent t test. A sample size of 412 for each group is required to detect a
small effect size of d = .2, = .05, power (1) = .8 for two-tailed Mann-Whitney test.
For linear regression with 17 predictors, a sample size of 1000 is required to detect a
small effect size of 2 = .02, = .05, power (1) = .8. Thus, it is necessary to continue
the research with more participants in order to detect small effect sizes.
Future Studies
Our study makes the first attempt to distinguish intentional and unintentional
nonadherence in ESRD patients and to identify associated factors. More studies are
required to test whether findings in our study can be repeated in PD patients from other
regions or be generalized to other ESRD populations (e.g., HD, transplant patients).
Moreover, since this is an observational study, we can only evaluate factors affecting
absolute nonadherence. It has been recognized that factors maintaining nonadherence are
different from factors changing nonadherence (Friend, Hatchett, Schneider, & Wadhwa,
1997). Longitudinal studies are needed to describe the dynamics of nonadherence as the
renal disease progresses and identify relevant predictors.
Our study reported high prevalence of intentional nonadherence. However,
intentional nonadherence does not always appear to be related with detrimental clinical
118
consequences. Some studies paradoxically identified a positive relationship between
mortality and adherence (Park et al., 2008). This was interpreted using “reasoned
adherence”, that is, patients decide to follow treatment guidelines only to a certain point
to balance their treatment needs and their daily life needs (Nevins, 2002). This kind of
behavior may reduce prescriptions errors by physicians, free patients from the helpless
role and hence provide survival advantages (Leggat, 2005; O'Brien, 1990). More studies
are required to ascertain the relationship between intentional nonadherence and clinical
outcomes.
Another direction worth mentioning is regarding the influence of carers on
patients’ outcomes. Since PD is a home based therapy, carers are closely involved in
patients’ treatment such as moving dialysis fluids, performing exchanges, and making
critical medical decisions (Fan, Sathick, McKitty, & Punzalan, 2008). Carers of PD
patients are vulnerable to poor mental health and have more diminished quality of life
than those of HD patients (Belasco, Barbosa, Bettencourt, Diccini, & Sesso, 2006; Fan et
al., 2008; Shimoyama et al., 2003). The implication of this on patients’ outcomes has
been completely overlooked. It is possible that caring for PD patients may give rise to
carer burnout which may result in suboptimal care or switching to in-center HD.
119
CHAPTER FIVE
Conclusion
This study assessed health beliefs, emotional distress, quality of life and
nonadherence in a representative cohort of APD and CAPD patients. High rates of
nonadherence were observed for all three aspects of the medical regimen, i.e., dialysis,
medication and diet, with dietary nonadherence being the most remarkable. Intentional
nonadherence occurred more commonly than unintentional nonadherence, In addition to
nonadherence, PD patients also exhibited unfavorable attitudes toward treatment regimen,
poor emotional outcomes, and compromised quality of life. APD patients were more
adherent than CAPD patients and CAPD patients reported higher self-efficacy about
managing their medicines. No other differences between APD and CAPD patients could
be demonstrated. More importantly, this study identified important determinants of
nonadherence. The strongest predictors of self-reported nonadherence tend to be
psychosocial factors instead of demographical and clinical factors. Health belief factors
were found to be mediators in the relationship between emotional distress and
nonadherence. These significant findings have important implications for the successful
management of PD programme. Efforts aimed at improving patients’ communication
with medical providers and addressing patients’ concerns about therapy or doubts about
their own self-management capability are very likely to be effective in terms of reducing
nonadherence and maximizing treatment effects in PD populations.
120
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Append
dix A
Permission
n to Use Figgure 1 and Figure 2
Appeendix 1
149
150
151
Appendix B
Participation Information Sheet
Appendix B:Participation Information Sheet
1. Study Information
Protocol Title:
An evaluation of psychosocial and behavioural outcomes in patients on peritoneal
dialysis regimes and their carers
Principal Investigator & Contact Details:
Dr. Marjorie Foo Wai Yin,
Department of Renal Medicine,
Singapore General Hospital,
Tel: (65) 63214436
Email: marjorie.foo.w.y@sgh.com.sg
2. Purpose of the Research Study
You are invited to participate in a research study. It is important to us that you first
take time to read through and understand the information provided in this sheet.
Nevertheless, before you take part in this research study, the study will be
explained to you and you will be given the chance to ask questions. After you are
properly satisfied that you understand this study, and that you wish to take part in
the study, you must sign this informed consent form. You will be given a copy of
this consent form to take home with you.
You are invited because we are approaching patients with kidney disease who are
receiving peritoneal dialysis (PD) under the care of the Renal Clinicians at
Singapore General Hospital. Individuals who are over the age of 21 years, have
been on PD for at least 90 days and are willing to participate are welcome. Patients
judged by the senior staff members as too ill or likely to be distressed by taking
part in the present study will be excluded from participating.
This study is carried out to explore patients’ experience with their dialysis regime,
diet and medication and to explore any difficulties they may be facing in their life
and overall adjustment due to their condition and/or treatment. This study hopes to
recruit 200 participants from Singapore General Hospital over a period of 24
months.
3. What procedures will be followed in this study
The study will be conducted over a period of 24 months. You only need to
participate once. If you decide to participate, you will need to sign a consent form
and complete 8 questionnaires which assess treatment nonadherence, beliefs about
medication, mood, quality of life, social isolation and sociodemographic domains.
The questionnaires will either be completed independently by you (i.e. selfadministered) or administered by the research assistant in the form of a structured
interview, according to your preference. This segment will span approximately 55
to 70 minutes, depending on your reading and response speed. In addition, your
152
medical records will be reviewed based on your consent.
Medical data will also be retrieved from your medical records. These will include:
Blood pressure ratings, dialysis adequacy delivery indices (Kt/V); albumin,
hemoglobin, potassium, phosphate, and peritoneal dialysis regime/dose.
Information regarding, primary cause of kidney failure and date that you started on
peritoneal dialysis will also be recorded.
4. Your Responsibilities in This Study
You will be required to complete 8 questionnaires asking questions about your
treatment, mood and overall well-being. If you encounter difficulties in
understanding the questions, you may seek assistance. Should you feel
uncomfortable or unwell during the assessment process, kindly inform the
investigator and the assessment will be terminated without delay.
5. Possible Risks and Side Effects
We do not anticipate that answering the questionnaires entails any risk or are likely
to cause any discomfort or distress to the participants. The questionnaires are
selected on the basis of good psychological principles and are non-invasive. It is
therefore not expected that the questionnaires would cause any harm, risk or
discomfort to the respondent.
6. Possible Benefits from Participating in the Study
Most participants enjoy the opportunity to express their opinions about their
experience and reflect on their behaviors and/or difficulties in managing the
demands of illness and treatment.
We hope and anticipate that patients will find sharing their experience useful. We
also hope that this will also benefit future patients and carers. Your feedback and
the knowledge gained from this study will help us evaluate the care delivered and
if needed design a support intervention program for PD patients and carers to assist
them in better manage their condition and improve their well-being. By gaining a
better insight and understanding in patients and carers’ experiences, we will be
able to develop a program specific to patients and carers’ needs in the local
context.
7. Costs & Payments if Participating in the Study
Participation by both you and your carer will be collectively rewarded by a token
fee of SGD 20. This will be covered by MOE ACRF start up research grant to Dr
Konstadina Griva (Co- Investigator) and will be administered by the research
assistants upon completion of questionnaires.
8. Voluntary Participation
Your participation in this study is voluntary. You may stop participating in this
study at any time. Your decision not to take part in this study or to stop your
participation will not affect your medical care or any benefits to which you are
entitled. If you decide to stop taking part in this study, you could do this without
hesitation.
153
Your doctor, the Investigator and/or the Sponsor of this study may stop your
participation in the study at any time if they decide that it is in your best interests.
They may also do this if you do not follow instructions required to complete the
study adequately. If you have other medical problems or side effects, the doctor
and/or nurse will decide if you may continue in the research study.
In the event of any new information becoming available that may be relevant to
your willingness to continue in this study, you (or your legally acceptable
representative, if relevant) will be informed in a timely manner by the Principal
Investigator or his/her representative.
9. Compensation for Injury
The questionnaires have been carefully chosen to be completely non-invasive –
none of the questionnaires is likely to pose any risk of harm or injury to you. As
such there are no compensation arrangements in place.
10. Confidentiality of Study and Medical Records
Information collected for this study will be kept confidential. Your records, to the
extent of the applicable laws and regulations, will not be made publicly available.
However, Singapore Health Services (SHS) Centralised Institutional Review
Board (CIRB) and Ministry of Health will be granted direct access to your original
medical records to check study procedures and data, without making any of your
information public. By signing the Informed Consent Form, you (or your legally
acceptable representative, if relevant) are authorizing such access to your study
and medical records.
Data collected and entered into the Case Report Forms are the property of
Singapore General Hospital and National University of Singapore. In the event of
any publication regarding this study, your identity will remain confidential.
11. Who To Contact if You Have Questions
If you have questions about this research study or there are any injuries sustained
during the course of this study, you may contact,
Dr. Marjorie Foo Wai Yin, Principal Investigator
Department of Renal Medicine,
Singapore General Hospital,
Tel: (65) 63214436
Email: marjorie.foo.w.y@sgh.com.sg
Dr Griva Konstadina, Co-Investigator
Department of Psychology,
National University of Singapore
Tel: (65) 65163561
Email: psygk@nus.edu.sg
The study has been reviewed by the SHS CIRB (the central ethics committee) for
154
ethics approval.
If you want an independent opinion of your rights as a research subject you may
contact the SingHealth CIRB Secretariat at 6323-7515.
If you have any complaints about this research study, you may contact the
Principal Investigator or the SingHealth CIRB Secretariat.
155
Appendix C
Consent Form
Appendix C: Consent Form
Protocol Title:
An evaluation of psychosocial and behavioural outcomes in patients on peritoneal
dialysis regimes and their carers
Principal Investigator & Contact Details:
Dr. Marjorie Foo Wai Yin, Principal Investigator
Department of Renal Medicine,
Singapore General Hospital,
Tel: (65) 63214436
Email: marjorie.foo.w.y@sgh.com.sg
I voluntarily consent to take part in this research study. I have fully discussed and
understood the purpose and procedures of this study. This study has been explained to
me in a language that I understand. I have been given enough time to ask any questions
that I have about the study, and all my questions have been answered to my satisfaction.
_______________________
____________________
Name of Participant
Signature Date
Witness Statement
I, the undersigned, certify to the best of my knowledge that the participant signing this
informed consent form had the study fully explained in a language understood by him /
her and clearly understands the nature, risks and benefits of his / her participation in the
study.
_______________________
____________________
Name of Witness
Signature Date
Investigator Statement
I, the undersigned, certify that I explained the study to the participant and to the best of
my knowledge the participant signing this informed consent form clearly understands the
nature, risks and benefits of her participation in the study.
_______________________ _______________________
Name of Investigator /
Signature
Person administering consent
_ ____________________
Date
156
Appendix D
Demographics Questionnaire
Appendix D: Patient Demographic Information
[1]
What is your date of birth? (please write in MM/YYYY) __________________
[2]
What is your first language? (please write in) _______
[3]
How would you describe your ethnic background?
1
3
5
7
[4]
Chinese
2
Indian
4
White
6
Do not wish to answer
____________
Malay
Asian Other
Other
How would you describe your relationship status?
1
Married
2
Divorced
3
Widowed
4
Single
5
Living with partner
6
Other
[5]
What is your highest educational qualification? (please write in)
________________________
[6]
Which of the following responses best characterises your current work activity or
employment status?
1
2
3
4
employed full-time
5
employed part-time
6
self-employed
7
unemployed, laid off involuntarily 8
retired
looking after home and family
student
other/none of the above
[7]
What approximately is the current estimated monthly income of your overall
family? (Please remember your answers are confidential)
1
2
3
4.
5.
6.
$ 0 -$ 2,000
$ 2,001 - $ 4,000
$ 4,001 - $ 6,000
$ 6,001 – above
don’t know
don’t wish to answer
157
[8]
Which of the following best describes your housing:
1
2
3
4
5
6
[9]
How long have you been on peritoneal dialysis? _______________________
1
2
3
4
1-2 HDB flat
3-4 HDB flat
HDB 5 room/executive/maisonette
Condominium, including executive condominium and private
apartment
Terrace/ Semi – Detached / Bungalow
other (please specify__________________________________)
Less than 1 month
6 to 12 months
13 to 24 months
More than 24 months (2 years)
158
Appendix E
Medical Form
Appendix E: Patient Medical Information
I. Comorbidities (please tick any that apply and list any additional)
Yes
No
Bone Disease
Hypertension
Diabetes (without end of organ damage)
Diabetes (with end of organ damage)
Myocardial infarction
Congestive Heart Failure
Peripheral Vascular disease
Cerebrovascular disease
Connective tissue disease
Dementia
Hemiplegia
Leukemia
Malignant lymphoma
Malignant solid tumor in the last 5 years
(metastatic)
AIDS
Peptic Ulcer Disease
Liver disease (mild)
Liver disease (moderate)
Liver disease (severe)
Renal disease (mild)
Renal disease (moderate)
Renal disease (severe)
Please list any additional comorbid conditions_____________________________
____________________________________________________________________
159
II. Treatment Details
Primary kidney disease diagnosis
CAPD
Type of current dialysis modality
(Tick the appropriate column)
APD
III. Biochemical data (most recent 2 values recorded)
Date1 ____________________
Time1
Date2 ____________________
Time2
Time1
Creatinine
Potassium
Phosphate
Albumin
Hemoglobin
Kt/V
Time2
IV. Medication details (oral and injectable) – please list prescribed medication (inc EPO)
_____________________________________________________________________
_____________________________________________________________________
_____________________________________________________________________
_____________________________________________________________________
_______________________________________________________
160
Appendix F
Perrmission too use Table 2
Appeendix 6
161
162
Append
dix G
Perrmission to Use Table 3
Equaation 2
163
164
Appendix H
Nonadherence Measures
Appendix G: Nonadherence Measures
QUESTIONS ABOUT PD EXCHANGES
How often do you follow this
regime?
1
Very often
2
3
Often Sometimes
4
5
Rarely
Never
4
5
Rarely
Never
4
5
Rarely
Never
Some people forget to perform
PD exchanges/ session. Overall,
how often does this happen to
1
2
3
Very often Often Sometimes
you?
Some say that they decide to skip
one of their PD exchanges/session
or adjust the length to suit their
own needs. Overall, how often do
1
2
3
Very often Often Sometimes
you do this?
During the last 4 weeks how many time have skipped/missed one of your PD
sessions/exchanges? _____________________________________________________
During the past 4 weeks how many times have you prolonged/shorten your PD session
by 10 minutes ?____________________________________________________
QUESTIONS ABOUT TAKING YOUR MEDICINES
How often do you follow this
regime?
Some people forget to take their
medicines. Overall, how often
does this happen to you?
1
2
3
Very often Often Sometimes
1
2
3
Very often Often Sometimes
4
5
Rarely
Never
4
5
Rarely
Never
4
5
Rarely
Never
Some people decide to miss out a
dose of their medication or adjust
it to suit their own needs. Overall,
1
2
3
Very often Often Sometimes
how often do you do this?
165
QUESTIONS ABOUT DIET
Have you been asked to follow a
diet?
How often do you follow these
dietary recommendations?
Some people forget to follow
dietary recommendations.
Overall, how often does this
happen to you?
2
NO
(ignore questions below)
1
YES
1
2
3
Very often Often Sometimes
1
2
3
Very often Often Sometimes
Some people decide to adjust their
1
2
3
diet to suit their own needs.
Overall, how often do you do
Very often Often Sometimes
this?
4
5
Rarely
Never
4
5
Rarely
Never
4
5
Rarely
Never
166
Appen
ndix I
Reseearch Ethn
nics Approvval
Appendix H: Research Ethnics Approval
A
A
167
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[...]... and intervening accordingly may be more cost-effective Two broad categories have been proposed to classify causes of nonadherence: intentional and unintentional (Clifford, Barber, & Horne, 2008) This intentional- unintentional typology is predominantly used in investigating medication-taking behaviors (Daleboudt, Broadbent, McQueen, & Kaptein, 2010; Unni & Farris, 2011) Both forms of nonadherence. .. distinguished intentional and unintentional nonadherence Nonadherence on APD vs CAPD Only one known study compared nonadherence difference between APD and CAPD patients (Bernardini et al., 2000) Home visit supply inventories were used to evaluate nonadherence to dialysis exchanges in this study and PD modality was identified as an independent predictor of nonadherence, with more nonadherence reported in. .. roles in determining intentional and unintentional nonadherence Concern beliefs have been reported to affect both intentional and unintentional nonadherence (Daleboudt et al., 2010; Unni & Farris, 2011), whereas necessity beliefs were mainly associated with intentional nonadherence (Schuz et al., 2011; Unni & Farris, 2011) There is ample evidence suggesting that self-efficacy is correlated with nonadherence. .. unintentional nonadherence despite their important implications for intervention Determinants of Nonadherence Previous studies on determinants of nonadherence tend to focus on demographical and clinical variables, overlooking the effects of psychosocial variables (Karamanidou, Clatworthy, et al., 2008; Russell, Knowles, & Peace, 2007) However, identifying demographical and clinical associates of nonadherence. .. users is rising steadily in recent years in many parts of the world, like Canada, US, Singapore and Switzerland (Blake, 1999; Dell'Aquila, Berlingo, Pellanda, & Contestabile, 2009; Mehrotra, 2009) In Singapore, incident rate of PD patients choosing APD had increased from 3% in 1999 to 50.5% in 2008 (SRR, 2010) Patients are motivated to choose APD mainly due to the autonomy it provides instead of medical... 2006) Nonadherence with salt and fluid was a critical reason for PD drop-out (Kawaguchi et al., 2003) Intentional and Unintentional Nonadherence Increasing knowledge is a standard way employed to reduce nonadherence in intervention programmes However, nonadherence is problematic even among those with good knowledge (Lee & Molassiotis, 2002; Nerbass et al., 2010) Clarifying causes of nonadherence and intervening... dialysis, probably in the same order of importance as medical indicators (Bander & Walters, 1998) Dialysis regimen is extremely complicated and time-consuming, involving regular clinical visits, attending dialysis sessions, taking a variety of medications, limiting water intake and paying great attention to food choices As treatment complexity has been cited as the most important reason affecting patients ... study in another type of chronic illness identified forgetfulness, accidentally overdose and the unavailability of medication as reasons for unintentional nonadherence and intentional nonadherence was mainly caused by side effects, social activities, eating out, drinking alcohol or traveling (Eliasson, Clifford, Barber, & Marin, 2011) To the best of our knowledge, no studies concerning ESRD patients. .. are ambulatory during this period) absorbing wastes, toxins and excess water from the blood and then is drained out of the body together with the wastes Then the infusion process begins again and the procedure repeats The draining and infusion process is called an exchange, taking 30 to 60 minutes depending on the patient’s health status These repeated exchanges can be performed either manually by patients. .. patients nonadherence (Donovan, 1995), it is not unexpected that it is easy for dialysis patients to be nonadherent Nonadherence studies in dialysis patients are greatly hindered by a lack of consistent standards for measuring nonadherence Common measures of nonadherence include: (a) report from patients or medical staff, (b) biological and biochemical markers, (c) electronic monitoring and (d) checking ... dietary guidelines the most difficult to adhere to Intentional nonadherence occurred more frequently than unintentional nonadherence for dialysis and diet; intentional and unintentional nonadherence. .. quality of life was the strongest predictor of unintentional nonadherence to dialysis Self-efficacy was the strongest predictor of intentional and unintentional nonadherence to both medication and. .. Clarifying causes of nonadherence and intervening accordingly may be more cost-effective Two broad categories have been proposed to classify causes of nonadherence: intentional and unintentional
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Xem thêm: PSYCHOSOCIAL DETERMINANTS OF INTENTIONAL AND UNINTENTIONAL NONADHERENCE IN PATIENTS UNDERGOING AUTOMATED PERITONEAL DIALYSIS AND CONTINUOUS AMBULATORY PERITONEAL DIALYSIS
