OLDER ADULTS WITH DIABETES AND CANCER: IMPACT ON DIABETES SELFMANAGEMENT
By
Denise Soltow Hershey

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Nursing
2011

ABSTRACT
OLDER ADULTS WITH DIABETES AND CANCER: IMPACT ON DIABETES SELFMANAGEMENT
By
Denise Soltow Hershey
Background: Self-management is an important component of diabetes and cancer care. Eight
to 18% of individuals with cancer will have preexisting diabetes. Individuals with diabetes who
develop cancer have higher mortality and morbidity rates when compared to those without
diabetes. Framework: This study was guided by the framework for heart failure selfmanagement and family variables by Dunbar et al (2008). Aims: The primary aim of this study
was to examine the relationship between individual, clinical and behavioral characteristics in
adults 50 and older who have preexisting diabetes and are receiving chemotherapy for a solid
tumor cancer at baseline and after a minimum of 8 weeks of chemotherapy. A secondary aim for
this study was to identify challenges or issues related to diabetes self-management in adults who
are undergoing chemotherapy. Methods: This pilot study utilized a baseline self-administered
survey and a follow up phone survey 8 weeks later. The Diabetes Complications Index, Katz
Comorbidity scale, Confidence in Diabetes Scale, outcome expectancies subscale of the MultiDiabetes Questionnaire and the Hospital Anxiety and Depression Scale were utilized at baseline,
in addition to other descriptive questions. The Symptoms of Illness Checklist and Self-care
Inventory were utilized at both baseline and 8 weeks to assess symptom severity and diabetes
self-management behaviors. The 8 week survey included two open-ended questions that asked
patients about challenges or issues with their diabetes self-management, which were tape
recorded and then transcribed for analysis. Correlations and a linear regression model were
performed for both the baseline and 8 week time intervals to determine factors which influenced

diabetes self-management. A paired t-test was utilized to compare diabetes self-management at
baseline and 8weeks to determine if there was a difference in the level of performance. Sample:
The sample consisted of 34 adults 50 and older with pre-existing diabetes who were receiving
chemotherapy for a solid tumor cancer or lymphoma. Individuals were recruited from 8 different
community cancer centers in Michigan and Ohio. Results: Age, years with diabetes and level
of diabetes self-efficacy where all positively correlated with performance of diabetes selfmanagement behaviors at baseline. Age and level of diabetes self-efficacy are significant
(p<.05) predictors for baseline self-management. At 8 weeks, years with diabetes, level of
diabetes severity and diabetes self-efficacy were found to be positively correlated with diabetes
self-management behaviors. Diabetes severity, number of comorbidities, ethnicity and baseline
self management were noted to be significant (p<.05) predictors. A paired t- test indicated
significant difference between baseline and 8 week self-management, with individuals having
lower scores, indicating they performed less self-management after a minimum of 8 weeks of
chemotherapy, with significant (p<.05) differences for the specific behaviors of diet and
exercise. Themes Identified were: prioritization, health issues and self-management issues.
Implications: Implications for practice include increasing awareness among nurses and health
care providers who care for this population regarding the challenges and issues of managing 2
competing chronic conditions, and the need for increased patient education regarding the
management of diabetes while undergoing chemotherapy. Further research needs to look at the
relationship between glycemic control, diabetes self-management and health related outcomes in
this population. Development of guidelines and quality indicators for patients with diabetes and
cancer need to be developed, which improve health related outcomes, survivorship and overall
health related quality of life for this population.

Copyright by
DENISE SOLTOW HERSHEY
2011

This dissertation is dedicated to my husband and best friend
Scott Hershey, for all of his support, patience’s, and encouragement in helping
me to achieve my goal.

v

ACKNOWLEDGEMENTS
I would like to acknowledge Dr. Barbara Given for her guidance, expertise, and support
during this process. Dr. Given has been a mentor for many years. Her words of encouragement,
the knowledge she has shared, and the time and commitment she has given me over the past five
years have allowed me to achieve this goal. I look forward to continuing to work with and learn
from Dr. Barbara Given as I grow in my new role as a nurse scientist.
I would also like to acknowledge all of the other members of my dissertation and
guidance committee: Dr. Charles Given, Dr. William Corser, Dr. Alex Von Eye and Dr Jodi
Holtrop. Each of these individuals has shared with me their expertise and has given me much
guidance, support and encouragement over these past five years.
I would like to acknowledge the various sources of funding I received which supported
my doctoral education as well as my dissertation research. Specifically I would like to
acknowledge the funding I received from: the Michigan Nurse Corp Initiative provided by the
Michigan Department of Community Health and the Department of Community Health and
Economic Growth; the Alpha Psi Chapter of Sigma Theta Tau Research Award; awards from
the Graduate School at Michigan State University which included a Summer Dissertation
Fellowship and a Dissertation Completion Fellowship; and awards from the Michigan State
University College of Nursing which included the Gilbert and Leona Schuman Endowed
Scholarship and the George and Lorimar Parsons Nursing Scholarship.
I would like to acknowledge the four undergraduate research scholars who helped with
data entry and the transcribing of tapes for this study. These students are Kelsey Soronen,
Lauren Pavlic, Sarah Mroczek and Kelsey Obrecht. Thanks for all of your hard work and
dedication to this project.

vi

I would also like to acknowledge Jan Tipton CNS, from the University of Toledo Cancer
Center in Toledo Ohio, for all of the hours she put in assisting me with IRB applications for this
site, as well as the time she put in recruiting, and interviewing participants for this study.
Without Jan’s assistance, I would still be recruiting subjects and not yet defending this
dissertation.
In addition I need to acknowledge all of the sites which allowed me to recruit patients
from and the nurses at these sites who identified and recruited individuals for this study. These
sites are: Sparrow Regional Cancer Center – Lansing, Michigan, MSU Breslin Cancer Center –
Lansing, Michigan, Red Cedar Oncology – Lansing, Michigan, Allegiance Health Cancer Center
– Jackson, Michigan, Great Lakes Cancer Institute - Flint, Michigan, West Michigan Cancer
Center - Kalamazoo, Michigan, Munson Medical Center Infusion Clinic – Traverse City, MI and
the University of Toledo Cancer Center - Toledo, Ohio.
Lastly I would like to acknowledge several individuals who have provided me support
and encouragement throughout this process. First I would like to acknowledge my husband,
Scott, for all of hours reading and editing papers, his encouraging comments, and for sharing this
journey with me. Second I need to thank and acknowledge my sister, Deborah, for reviewing
and editing this dissertation. She is much better at grammar and punctuation than I am. I would
also like to acknowledge Beth Byma PhD, who started this program with me, and has provided
me with support and encouragement when needed, and who has become a good friend. I would
also like to acknowledge all of the other classmates and students in the program for each and
every one of them have given me support, feedback, and encouragement when needed.

vii

TABLE OF CONTENTS

LIST OF TABLES………………………………………….………………………………...…..x
LIST OF FIGURES………………………………………………………………………...……xi
CHAPTER 1
BACKGROUND AND SIGNIFICANCE………………………………………………………..1
Self-management of Chronic Disease……….…………………………………...………..2
Diabetes Treatment and Self-management…….……………………………..…………...3
Cancer Treatment and Self-management….…………………………….…….…………..4
Cancer and Diabetes Self-management……………………………….…………….…….6
Significance…….…………………………………………………….……………….…..9
Study Aims………………………………………………………………………….…...11
Influence of Individual, Clinical and Behavioral Characteristics on Self-management....11
CHAPTER 2
CONCEPTUAL FRAMEWORK………………………………………………………………..14
CHAPTER 3
INTEGRATED REVIEW OF THE LITERATURE…………………………………………….25
Diabetes Self-management………………………………..……………………………..26
Cancer Self-management…………………….………………………………….……….36
Self-management of Multi-morbidities……….………………………………….………38
Cancer and Diabetes………………………….………………………………………….43
Summary……………………………………..…………………………………………..45
CHAPTER 4
METHODS………………………………………………………………………………………48
Study Subjects……………………………………………………………..…………….48
Design for Sampling………………………………………………………..……………48
Recruiting Subjects…………………………………………………….……..………….49
Study Settings………………………………………………………………...………….49
Data Collection…………………………………………………………..………………52
Study Measures………………………………………………………….……….………52
Individual Characteristics………………………………………………………………..54
Clinical Characteristics…………………………………………………………………..54
Behavioral Characteristics……………………………………………………………….56
Diabetes Self-management Behaviors…………………………………………………...58
Impact of Cancer on Diabetes Self-management………………….………...…………..58
Approach to Statistical Analysis…………………………………..…..…………………59
Sample Size Estimate – Power Analysis……………………………...…………………62
Quality Control and Data Management…………………………………..……….……..62
Potential Difficulties and Limitations……………………………………………………63
viii

Protection of Human Subjects…….………………………………...…………...………64
Risk to Human Subjects…………………………………………………………...…….64
Adequacy of protection against risk…….……………………………….………………65
Women and Minorities…………….………………………………..………...…………66
CHAPTER 5
RESULTS………………………………………………………………………………………..68
Study Measures……………………………………………………...………………..….69
Sample……………………………………………………………………..….………….76
Results & Analysis………………………………………………….………..…………..82
Aim 1……………………………………………………………….……………..……..82
Aim 2…………………………………………………………….………..……………..86
Additional Analysis……………………………………………..………….……………88
Aim 3……………………………………………………………..……….……………..92
Power………………………………………………………………..…..……………….96
Summary……………………………………………………………..…..………………96
CHAPTER 6
DISSCUSSION AND IMPLICATIONS……………………………………………...………..98
Discussion Results Aim 1…………………………………………………..……………98
Discussion Results Aim 2 ……………………….………………….….……….……...100
Discussion Results Additional Analysis………………………………….….…………105
Discussion Results Aim 3…………………………………………………..………..…108
Discussion of Results in Relation to Conceptual Framework…….……………………110
Limitations…………………………………………………………….………….…….112
Implications for Nursing Practice……………………………………....………….…...116
Implication of Results for Research……………………………………………….……117
Implication of Results for Policy……………………………………………………….119
Conclusions/Summary……………………………………………………………...…..121
APPENDICES
APPENDIX A – Baseline Survey Instrument………………………………………….124
APPENDIX B – 8 week Survey Instrument……..………………………………..……136
APPENDIX C – Study Consent Form……….….….……………………………….….142
BIBLIOGRAPHY………………………………………………………………………………149

ix

LIST OF TABLES
Table 1: Variables & Measurement for study…………………………………………………...53
Table 2: Internal consistency reliability statistics for study instruments at baseline……………75
Table 3: Internal consistency reliability statistics for study instruments at 8 weeks……………76
Table 4: Sample characteristics, N and % for categorical variables at baseline and 8 weeks…...78
Table 5: Sample characteristics, N, µ, & sd for continuous variables at baseline and 8 weeks…80
Table 6: Correlation estimates of individual characteristics and baseline diabetes selfmanagement behaviors………………………………………………………………….84
Table 7: Correlation estimates of clinical characteristics and baseline diabetes self-management
behaviors………………………………………………………………………………..85
Table 8: Correlation estimates of behavioral characteristics and baseline diabetes selfmanagement behaviors………………………………………………………………….86
Table 9: Coefficients for final linear regression model indicating significant predictors of
diabetes self-management at baseline……………………………………………………86
Table 10: Correlation estimates of baseline individual characteristics and 8 week diabetes selfmanagement behaviors…………………………………………………………………88
Table 11: Correlation estimates of baseline clinical characteristics, 8 week symptom severity,
8 week cancer impact on diabetes and 8 week diabetes self-management behaviors...89
Table 12: Correlation estimates of baseline behavioral characteristics and 8 week diabetes selfmanagement behaviors…………………………………………………………………90
Table 13: Coefficients for final linear regression model indicating significant predictors of
diabetes self-management at 8 weeks………………………………………………….90
Table 14: Baseline and 8 week means and standard deviations for cancer specific symptoms...91
Table 15: Paired t-test for Symptom Severity and Diabetes self-management at baseline and
8 Weeks……………………………………………………….……………………….91
Table 15: Paired t –test for specific paired self-management items at baseline and 8 weeks…...92
Table 16: Themes with definitions and indicators………………………………………………93

x

LIST OF FIGURES

Figure 1: Dunbar et al (2008) Framework for heart failure self-management and family
variables. Certain solid lines were changed to broken lines in order to indicate
areas and relationships for future research by this investigator. Solid lines indicate relationships being explored by this study……………………………..………14
Figure 2: Study Framework adapted from the framework for heart failure self-management
and family variables by Dunbar et al. (2008)…………………………………………17
Figure 3: Consort table of study flow and enrollment indicating number approached, enrolled
and completion of study………………………………………………………………..77
Figure 4: Histogram showing distribution of diabetes self-efficacy measure…………………..81
Figure 5: Histogram showing distribution of diabetes self-management measure at baseline….82
Figure 6: Histogram showing distribution of diabetes self-management measure at 8 weeks….83
Figure 7: Revised framework depicting direct or mediating effect of cancer specific
characteristics on diabetes self-management………………………………………..113

xi

Chapter 1
Background and Significance
Diabetes and Cancer are two of the most prevalent diseases in adults 65 years of
age and older (Cohen, 2007; Jack, Airhihenbuwa, Namageyo-Funa, Owens, & Vinicor, 2004;
Leak, Davis, Houchin, & Mabrey, 2009). An estimated 1.4 million individuals in the U.S. will
be diagnosed with cancer this year (Leak, et al., 2009), and 63% of all newly diagnosed cancers
will be in individuals 65 or older ("JAHF 2006 Annual Report," 2006). One in 5 adults over 65
have some form of diabetes (Jack, et al., 2004). Diabetes occurs in 8 – 18% of individuals with
cancer (Leak, et al., 2009; Psarakis, 2006; Visovsky, Meyer, Roller, & Poppas, 2008).
Self-management is an important component of disease management programs for both
cancer and diabetes (Fu, LeMone, & McDaniel, 2004; Gallagher, Donoghue, Chenoweth, &
Stein-Parbury, 2008; Sousa, Hartman, Miller, & Carroll, 2009). The dual self-management of
cancer and diabetes will be required for many of these individuals. The majority of selfmanagement studies focus on the issues or barriers to self-management related to a single
chronic illness (Bayliss, Steiner, Fernald, Carne, & Main, 2003) . Few studies address the issue
of self-management in individuals with multiple morbidities (Kerr et al., 2007). The number of
comorbidities an individual has is identified as a barrier to effective self-management (Bayliss,
Ellis, & Steiner, 2007; Kerr, et al., 2007; Nagelkerk, Reick, & Meengs, 2006). Studies
exploring the impact or relationship cancer has on the performance of self-management
behaviors for another, were not found.
Individuals with diabetes and cancer may experience competing demands related to
symptoms, self-care requirements and effects of treatment which can impact their overall selfmanagement of one or both disorders. Individuals with more than one chronic illness may
prioritize one chronic condition over the other, or the symptoms of one disorder may impact the
1

actual performance of self-management behaviors required for another disorder (Kerr, et al.,
2007; Krein, Heisler, Piette, Makki, & Kerr, 2005). Competing demands for chronic disease
self-management are considered the rivalry between the self-management requirements of two or
more chronic health conditions, as well as the individual, behavioral and clinical characteristics
specific to the individual. Competing demands may develop from symptoms and care
requirements associated with cancer and its treatment and impact the actual performance of
required self-management activities for diabetes. Overall self-management has been noted to be
poorer in individuals with comorbid chronic pain and depression (Gonzalez et al., 2007; Krein, et
al., 2005; Lee et al., 2009). Based on the literature, it can be hypothesized that cancer and its
treatment may have a negative impact on diabetes self-management in older adults with diabetes
who are undergoing chemotherapy for the treatment of cancer.
The main purpose of this exploratory study is to examine the relationship between
individual, clinical, and behavioral characteristics and the performance of diabetes selfmanagement behaviors in older adults receiving outpatient chemotherapy for cancer. The
secondary purpose of this study is to identify common issues or concerns related to being able to
continue self-management of diabetes which may contribute to the development of competing
demands in older adults undergoing chemotherapy for cancer. The following sections will
further explore the concept of self-management and the impact cancer may have on the selfmanagement of diabetes.
Self-Management of Chronic Disease
The importance of self-management of chronic disease was highlighted in the Institute of
Medicines report, Crossing the Quality Chasm (2001). This report cited self-management as one
of the essential components of chronic disease management programs (Crossing the quality

2

chasm: A new health system for the 21st century, 2001). Older adults with comorbidities will
participate in some type of self-management activity to manage their chronic conditions
(Gallagher, et al., 2008). Most older adults with a chronic illness will need to make decisions
regarding their diet, level and type of exercise, and following prescribed medication regimens.
For the older adult with a chronic illness, self-management is essential for maintaining functional
ability, prevent complications, and to slow or prevent progression of the disorder. The
participation in self-management activities for chronic disease by older adults has been shown to
alleviate symptoms, and improve physical and mental health outcomes (Harvey et al., 2008).
Diabetes Treatment and Self-Management
Self-management activities for individuals with diabetes includes the monitoring of blood
sugars, regular foot exams and the adjustment of medications based on their blood sugars
(Bodenheimer, Lorig, & Holman, 2002; Funnell, 2008). Older adults with diabetes will usually
need to monitor their blood sugars, administer insulin and/or take oral medications, monitor their
diet and follow an exercise program to assist in the management of and prevent complications
associated with diabetes (Funnell, 2008). Older adults with diabetes need to monitor symptoms
which could be caused by elevated or low glycemic levels. Dose adjustment of insulin may be
required in order to manage glycemic levels. Diet and exercise programs may also need to be
adjusted in order to maintain glycemic control. Daily foot inspections and regular eye exams are
also an essential component of the diabetic’s self-management plan. The individual with
diabetes will need to perform multiple different self-management activities in order to manage
their diabetes at home.
The performance of self-management activities for diabetes has been shown to improve
glycemic control and decrease complications associated with the disease (Heisler, Piette,

3

Spencer, Keiffer, & Vijan, 2005; Lee, et al., 2009; Whittemore, Melkus, & Grey, 2005). A lack
of attention to diabetes self-management in individuals with cancer can impact overall glycemic
control and increase the risk of infection and other complications associated with cancer and/or
diabetes (Psarakis, 2006). This possible lack of attention may be due to cancer related
symptoms, other individual, clinical or behavioral characteristics or self-management
requirements for cancer. The following section will explore cancer treatment and selfmanagement.
Cancer Treatment and Self-Management
Solid tumor cancers, i.e. breast, lung colon etc, and lymphomas comprise the majority of
all diagnosed cancers in the U.S. ("Cancer Facts and Figures," 2009). Patients with cancer may
undergo multiple treatment modalities, including surgery, radiotherapy, chemotherapy and/or
endocrine therapy (Tadman & Roberts, 2007). Each type of treatment is done either in
conjunction with another or individually over different time frames (Tadman & Roberts, 2007).
Dosing used for each of these modalities is dependent upon the cancer being treated (Tadman &
Roberts, 2007).
Chemotherapy is one of the most common forms of cancer treatment used today, and is
considered to be safe and effective in older adults (Green & Hacker, 2004).

Frequency and

strength of dosing are dependent upon type of and stage of cancer, age of the patients, as well as
the presence of comorbidities (Tadman & Roberts, 2007). The duration of treatment is also
dependent on type of cancer and type of chemotherapy being used; many patients may receive
chemotherapy for a minimum of 3 – 6 months or longer (Tadman & Roberts, 2007). This
extended duration of treatment and the normal physiologic changes associated with aging may
impact how the older adult tolerates chemotherapy treatments and their tolerability to the side-

4

effects associated with chemotherapy (Green & Hacker, 2004). The associated symptoms, such
as fatigue, nausea and loss of appetite which occur with chemotherapy, may have an impact on
an individual’s ability to continue to self-manage an existing chronic condition, such as diabetes.
Common symptoms experienced in older adults undergoing chemotherapy are pain,
fatigue, depression, nausea and vomiting, and loss of appetite. These symptoms in older cancer
patients are usually overlapping (Rao & Cohen, 2004). The presence of comorbidities in older
adults with cancer can increase the severity and number of symptoms experienced and decrease
level of physical functioning (Reiner & Lacasse, 2006). The symptoms experienced by older
adults with cancer who are receiving chemotherapy have been shown to impact their activities of
daily living (Reiner & Lacasse, 2006). Older cancer patients with more advanced disease and
comorbidities have an increased risk for poorer physical function, higher levels of depression,
lower quality of life and a higher risk of death (Deimling, Bowman, & Wagner, 2007; Heidrich,
Egan, Hengudomsub, & Radolph, 2006; Mystakidou et al., 2006; Reiner & Lacasse, 2006).
Self-management is one of the key strategies utilized by cancer patients to manage their
cancer and cancer treatment related symptoms (Fu, et al., 2004). Over the past few years the role
of the patient in relation to their cancer care has changed (Tadman & Roberts, 2007). Patients
are encouraged to manage their lives, deal with their treatment processes and manage outcomes
associated with this treatment (Tadman & Roberts, 2007). Decreased physical function and
depression, in addition to the symptom burden experienced have all been noted to be barriers to
self-management by adults with multi-morbidities (Bayliss, Ellis, et al., 2007; Bayliss, et al.,
2003). The associated symptoms which occur with chemotherapy may have an impact on an
individual’s ability to continue to self-manage an existing chronic condition, such as diabetes.

5

The following section will explore the relationship between cancer and diabetes selfmanagement.
Cancer and Diabetes Self-Management
The literature on cancer has shown the impact chemotherapy can have on an individual of
any age. Chemotherapy is associated with an increase in symptoms such as pain and fatigue,
increased risk for infection and altered nutritional status (Tadman & Roberts, 2007). Older
adults with comorbidities such as diabetes and cancer are more likely to experience fatigue,
weakness, impaired movement and functioning and have an increased risk for falls (Green &
Hacker, 2004). As the population ages, the prevalence of the co-existence of cancer and diabetes
will become more common (Smyth & Smyth, 2005). Individuals with diabetes are at increased
risk for developing cancer (Attili et al., 2007; Extermann, 2007) and increased mortality after
developing cancer than those without diabetes (Attili, et al., 2007).
Individuals with diabetes and cancer are at increased risk of having poorer outcomes
when compared to individuals with cancer only (Psarakis, 2006). The presence of cancer and its
associated treatment may negatively influence outcomes associated with the treatment and selfmanagement of diabetes (Leak, et al., 2009). The effects of chemotherapy may create a
competing demand between cancer and diabetes self-management due to the effects of symptoms
associated with chemotherapy. The impact from symptoms such as fatigue, pain, loss of
appetite, nausea and vomiting may affect an individual’s ability to continue to perform selfmanagement activities for their diabetes. The increase in symptoms associated with
chemotherapy and a decline in physical function may impact the ability to exercise and the
ability to perform tasks such as blood glucose monitoring (Leak, et al., 2009; Psarakis, 2006).

6

Older adults with diabetes who are undergoing chemotherapy for cancer may experience
increased symptoms, and decline in physical function (Green & Hacker, 2004; Psarakis, 2006).
The older adult with cancer may experience the competing effects of symptoms related to cancer
or it treatment with existing comorbidities (Kerr, et al., 2007; Terret, Zulian, Naiem, & Albrand,
2007; Zeber et al., 2008). Cancer patients experience a myriad of symptoms which may impact
their ability to continue to perform self-management activities for existing chronic conditions
such as diabetes (Jerant, von Friederichs-Fitzwater, & Moore, 2005; Terret, et al., 2007).
The impact cancer and treatment with chemotherapy has on the older adult’s ability to
self-manage existing chronic conditions such as diabetes is unknown. Certain chemotherapy
regimens can lead to hyperglycemia and the development of symptoms such as nausea, vomiting,
and fatigue. These symptoms may influence diet and exercise regimens impacting glycemic
control (Psarakis, 2006). Individuals may have difficulty in being able to follow diet and
exercise plans due to symptoms associated with their cancer and its treatment and decide to no
longer perform self-management activities. Individuals may also find it more difficult to manage
their blood sugars due to effects of treatment causing hyperglycemic or hypoglycemic episodes.
The symptom burden experienced by individuals undergoing treatment for cancer may
produce levels of fatigue and pain which interferes with ability to exercise, glucose monitoring,
taking medications as prescribed and following an appropriate diet. Symptom burden associated
with cancer related fatigue has been noted to impact physical functioning and ability to perform
activities of daily living (Hofman, Ryan, Figueroa-Moseley, Jean-Pierre, & Morrow, 2007).
Self-management activities associated with administering medications was negatively impacted
in individuals receiving radiotherapy, due to the symptom distress they experienced (Oberst,
Hughes, Chang, & McCubbin, 1991). One can conclude that symptoms associated with

7

chemotherapy would have a negative impact on the continued performance of self-management
activities in individuals with cancer and diabetes who are undergoing chemotherapy.
Older adults have to be able to self-manage their cancer and a preexisting comorbidity
such as diabetes while undergoing chemotherapy; they may experience changes or perceive
changes in their health status, mood and number of symptoms they experience on a daily basis
(Psarakis, 2006; Reiner & Lacasse, 2006). The changes in overall health status experienced by
individuals with cancer and diabetes may cause them to change how they self-manage their
diabetes. The individual with diabetes and cancer will need to perform additional selfmanagement activities related to their cancer and its treatment. The self-management of cancer
related symptoms may be prioritized over taking care of and performing self-management
activities for diabetes (Kerr, et al., 2007). Lack of attention to diabetes self-management can
impact overall glycemic control. Individuals with poorer glycemic control, which results in
higher blood glucose levels, are at increased risk for infection and other complications associated
with cancer and/or diabetes (Leak, et al., 2009; Psarakis, 2006). It is unknown how diabetes selfmanagement is impacted by cancer related symptoms and individual, clinical or behavioral
characteristics of the individual, and if the competing demands of cancer causes a “lack of
attention” to diabetes self-management.
The impact that cancer and its treatment has on an older adult’s ability to continue to selfmanage their diabetes is not known, specific studies exploring this relationship were not found
by this author. The literature suggests diabetes self-management maybe negatively impacted in
individuals with cancer undergoing treatment with chemotherapy (Leak, et al., 2009; Psarakis,
2006). One can hypothesize older adult’s place less emphasis on self-managing their diabetes
due to the symptoms they experience associated with their cancer and cancer treatment.

8

Kerr et

al (2007) noted how much a patient’s diabetes self-management is impacted by another condition
is dependent upon the burden and severity of the competing disorder. Individuals may prioritize
a disorder that is more severe or life-threatening over another which is perceived as less severe
(Kerr, et al., 2007; Piette & Kerr, 2006). No studies were found which addressed the impact
cancer and its treatment has on the self-management of diabetes.

In order to begin to

understand how cancer and its treatment impacts an older adults ability to continue to selfmanage their diabetes an exploratory study will be developed.
Significance
One of the initiatives of The U.S. Department of Health and Human Services is in regards
to individuals who have multiple chronic conditions (MCCs). One of the main focuses of this
initiative is to assist individual with MCCs with self-management, in order to reduce the burden
of MCC to society and improve the health of individuals with MCCs (Barone et al., 2010). As
the population ages the number of individuals with diabetes and cancer will most likely increase.
There is a gap in the literature regarding the impact a new chronic condition can have on the selfmanagement of an existing condition such as diabetes. There is also a lack of knowledge
regarding the impact cancer and its treatment may have on the self-management of existing
chronic conditions in older adults.
Individuals with diabetes who develop cancer are more likely to be admitted to the
hospital, develop an infection or die from their cancer (Barone et al., 2008; Srokowski, Fang,
Hortobagyi, & Giordano, 2009). A pre-existing diagnosis of diabetes is present in 8 – 18% of
individuals who are diagnosed with cancer (Psarakis, 2006). It is unknown why these
individuals have higher mortality and complication rates when compared to individuals without
diabetes. It has been hypothesized that glycemic levels may play a role in the development of

9

complications for individuals with diabetes who are undergoing chemotherapy (Attili, et al.,
2007). Self-management behaviors positively influence glycemic control in individuals with
diabetes (Gold et al., 2008; Hartz et al., 2006; Scain, Friedman, & Gross, 2009). Individuals who
perform fewer self-management activities for their diabetes have poorer glycemic control (Hartz,
et al., 2006). Poorer glycemic control in individuals with diabetes is also associated with poorer
outcomes, increased infection rates and increased morbidity and mortality (Blonde, 2007;
Krinsley, 2008; Patel, 2008; Sousa, et al., 2009). Nurses and other healthcare providers need to
be aware of the occurrence of potential competing demands for self-management in adults with
more than one chronic condition, particularly cancer and diabetes. The impact that cancer and its
treatment has on diabetes self-management may influence the glycemic control patients have
during this period.
This study seeks to develop a beginning understanding of how self-management for
diabetes may be impacted by cancer and its treatment, and how self-management for an existing
comorbidity may be impacted by a new chronic illness. In addition, this exploratory study will
begin to identify the relationship between factors such as symptoms; self-efficacy and outcome
expectancies have on the performance of self-management activities for diabetes in this
population. This exploratory study will contribute to the knowledge base regarding the impact
cancer has on an older adults ability to continue to successfully self-manage their diabetes while
undergoing chemotherapy and identify specific concerns or issues related to diabetes selfmanagement in this population. Results from this study can be used to develop future studies
which explore the relationship between self-management of diabetes in individuals with cancer
and glycemic control, as well as, the development of nursing interventions to assist in the
improvement of overall health related quality of life in this population.

10

In order to address this gap in knowledge and exploratory study will be developed to
explore the relationship between cancer treatment and diabetes self-management. This study
will be guided by the following primary and secondary aims:
Study Aims
Primary Aims.
1) Among patients with pre-existing diabetes who are either starting or are currently
receiving outpatient chemotherapy for a solid tumor cancer how does clinical
characteristics associated with diabetes and cancer and behavioral characteristics relate to
the performance of diabetes self-management behaviors while controlling for individual
characteristics.
2) Among patients with pre-existing diabetes who have completed a minimum of 8 weeks of
outpatient chemotherapy, how does baseline clinical and behavioral characteristics,
baseline diabetes self-management behaviors and symptom burden after 8 weeks of
chemotherapy relate to the performance of diabetes self-management behaviors while
controlling for individual characteristics.
Secondary Aim.
1) To identify common challenges or concerns regarding the self-management of diabetes in
older adults after completing at least 8 weeks of outpatient chemotherapy.
Influence of Individual, Clinical and Behavioral Characteristics on Self-Management
As identified in the aims, the antecedents of clinical and behavioral characteristics, and
the co-variate of individual characteristics can have an influence on the performance of selfmanagement behaviors (Dunbar, Clark, Quinn, Gary, & Kaslow, 2008). Previous research
related to self-management has identified a positive or negative influence of these characteristics
on the actual performance of self-management behaviors (Bayliss, Ellis, et al., 2007; Dunbar, et
al., 2008; Gallant, Spitze, & Prohaska, 2007; Nagelkerk, et al., 2006). The following sections
will discuss each of these characteristics and their relationship to self-management behaviors.
Individual Characteristics. Age, sex, level of education, marital status, race and

11

Ethnicity and socio-economic status have all been noted to influence the performance of selfmanagement behaviors for chronic disorders (Jenerette & Phillips, 2006; Jerant, et al., 2005;
McDonald-Miszczak & Wister, 2005; Nagelkerk, et al., 2006). The amount of influence age and
sex has on the actual performance of self-management activities is unclear (MacInnes, 2008).
The effect each of these has may be dependent upon the specific activity required. Women are
more likely to follow prescribed medication regimens, where men are more likely to follow
prescribed exercise regimens (Burnette, Mui, & Zodikoff, 2004; MacInnes, 2008). Individuals
of who are a minority, have lower levels of education, belong to a lower financial bracket, or live
alone are more likely to experience more barriers associated with the performance of selfmanagement activities (Bayliss, Ellis, et al., 2007; Bayliss, et al., 2003; Dunbar, et al., 2008;
Gallant, et al., 2007; MacInnes, 2008; Nagelkerk, et al., 2006; Tang, Brown, Funnell, &
Anderson, 2008).
Clinical Characteristics. The complexity of the disorder, the plan of care, the
concordant or discordant nature of comorbidities, and the symptom experience associated with
different chronic conditions have all been shown to impact self-management behaviors (Bayliss,
Ellis, et al., 2007; Bayliss, et al., 2003; Kerr, et al., 2007; Piette & Kerr, 2006). Difficulty in
recognizing and managing symptoms associated with different conditions, is an issue for
individuals with more than one comorbidity (MacInnes, 2008). The overall burden of disease is
greater in individuals with multiple comorbidities and is considered a barrier for effective selfmanagement of coexisting comorbidities (Bayliss, Ellis, et al., 2007). The longer an individual
has a disorder, the more likely they are able to recognize symptoms and use self-management
strategies effectively for their existing comorbidity (MacInnes, 2008). Clinical characteristics
can influence the actual performance of self-management activities. It is unclear how the

12

development of a new comorbidity, such as cancer, may impact or influence the actual
performance of self-management activities for an existing comorbidity, such as diabetes.
Behavioral characteristics. Factors which influence how one behaves or performs an
activity such as self-efficacy, outcome expectancies, and depression/anxiety play an essential
role in the performance of self-management behaviors by adults with a chronic condition. Each
of these have been found to be correlated with the actual performance of self-management
behaviors for a disorder (Bai, Chiou, & Chang, 2009; Conn, 1998; Williams & Bond, 2002).
Individuals with lower levels of self-efficacy, higher levels of depression/anxiety or feel that the
performance of self-management behaviors will not prevent progression of their disease, or
complications associated with their disease, are less likely to perform self-management activities
for their comorbidity (Aljasem, Peyrot, Wissow, & Rubin, 2001; Bai, et al., 2009; Chlebowy &
Garvin, 2006; Conn, 1998; Hall, Rodin, Vallis, & Perkins, 2009; Luyster, Hughes, & Gunstad,
2009; Williams & Bond, 2002).
The existing literature identified several individual, clinical and behavioral characteristics
that can have an influence on the performance of self-management activities for comorbidities.
Only a brief picture of how each of these may impact self-management behaviors was presented
here. Each of these and their relationship to the performance of self-management behaviors will
be explored further in Chapters 2 and 3.

13

Chapter 2
Conceptual Framework
This exploratory study will be guided by the framework for heart failure selfmanagement and family variables by Dunbar et al (2008), see figure 1. It is important when
studying self-management that one considers all factors which can influence the actual
performance of self-management behaviors. Literature has shown the actual performance of
self-management behaviors can be influenced by individual specific characteristics such as age,
disease specific factors such as type of disorder, number of comorbidities and their associated
treatment, as well as psychological or behavioral factors such as self-efficacy.
Individual
Characteristics

Proximal Outcomes:
Adherence
Functional Status

Clinical
Characteristics

Prerequisite
Knowledge &
Skills

Psychological Status
Self
Management
Behaviors

Distal Outcomes:
Hospital Admissions
ED visits
Health Resource Use

Behavioral
Characteristics

Cost

Family Outcomes:
Caregiver Burden
Family Context

Support, Self-Efficacy, Knowledge and Skills, Family
Structure, Functioning

Caregiver Health
(MH)

Figure 1: Dunbar et al (2008) Framework for heart failure self-management and family
variables. Certain solid lines were changed to broken lines in order to indicate areas and
relationships for future research by this investigator. Solid lines indicate relationships being
explored in this study.
14

The Dunbar et al (2008) framework for heart failure self-management with family
variables was chosen for this study for it includes the majority of factors which can impact selfmanagement behaviors as well as the outcomes associated with the performance of selfmanagement behaviors. This framework uses a structure-process-outcome format, to identify the
antecedents to the processes of self-management behaviors and outcomes of HF selfmanagement, and was developed by Dunbar et al (2008) in order to depict what is currently
known in the literature regarding the relationships between antecedents to self-management
behaviors and outcome of self-management behaviors.
The framework in addition depicts the influence the family context has on the selfmanagement behaviors of individuals with heart failure (Dunbar, et al., 2008). Dunbar et al
(2008) noted family was a strong influencing factor for self-management, since most selfmanagement occurs under the influence of family. The family context includes family support
and functioning, efficacy, and the family structure and knowledge and skills (Dunbar, et al.,
2008).
Antecedents to self-management behaviors include individual, clinical and behavioral
characteristics, prerequisite knowledge and skills and the family context. For the purpose of this
study only the antecedents of individual, clinical and behavioral characteristics, as well as the
process variable of self-management behaviors will be addressed. The model components
omitted from this study will provide a foundation for future work to be developed, based on the
results from this study.
Individual characteristics include socio-demographics such as age, sex, and education
level. Severity of illness, such as stage or classification of illness, number of comorbidities and
plan of care including self-care regimens are part of clinical characteristics. Behavioral

15

characteristics are considered the individual’s ability to attend to behaviors, motivation, selfefficacy, and mood states. These antecedents influence self-management behaviors for heart
failure. Outcomes are considered proximal, distal or family related. Proximal outcomes include
adherence, functional status, and psychological status. Distal outcomes include hospital
admissions, ED visits, health resource use and cost. Caregiver burden and caregiver health make
up the family outcomes. All outcomes are influenced by the self-management behaviors of the
individual.
As previously mentioned, the framework for heart failure self-management with family
variables (Dunbar, et al., 2008) provides the guidance for this study. The following framework
based on the Dunbar model, (see Figure 2), will be used to guide this study. The model for this
study excludes the antecedent components of prerequisite knowledge and skills, and the family
context, as well as the proximal, distal and family outcomes identified in the original model.
These components/variables are not being considered for this current exploratory study, will be
used as future studies are developed based on the findings of this study.
For the purposes of this study, the framework uses the structural components or
antecedents of individual, clinical and behavioral characteristics, which influence the process of
self-management behaviors at baseline and at 8 or more weeks of receiving chemotherapy. In
this framework the process component of self-management behaviors at baseline is also used as
an antecedent to self-management behaviors at 8 weeks or more of chemotherapy. The
following sections will further define each of the model components and their relationship to
self-management behaviors or changes in self-management behaviors.

16

Individual Characteristics:
Age, Sex, Annual Family
Income, Marital Status,
Race/Ethnicity, Living
Arrangements

Diabetes Self-Management
Behaviors at Baseline

Clinical Characteristics:
Number of comorbidities,
Baseline and 8 week symptom
burden, total number of
medications
Diabetes specific:
Length of Illness, type, type of
medications, severity
Cancer specific:
Type, stage, treatment type,
length of time on chemo

Diabetes Self-Management
Behaviors after minimum of 8
weeks of chemotherapy

Behavioral Characteristics:
Aim 1:

Diabetes Self-Efficacy, Diabetes Outcome
Expectancies, Depression/Anxiety

Aim 2:

Figure 2: Study framework adapted from the framework for heart failure self-management with family
variables by Dunbar et al (2008)
17

Diabetes Self-Management Behaviors
The outcome component of this model is the performance of diabetes self-management
behaviors. Self-management is used as part of many chronic disease plans of care. Diabetes,
Chronic Obstructive Pulmonary Disease (COPD) and congestive heart failure (CHF) all require
patients to perform some type of self-management regime (Chen, Chen, Lee, Cho, & Weng,
2008; Funnell, 2008; Washburn & Hornberger, 2008). Individuals with Diabetes will need to
monitor and maintain their condition in between visits with their health care provider.
Individuals with Diabetes will usually have to monitor their blood sugars, administer insulin
and/or take oral medications, monitor and follow a diabetes specific diet, and follow an exercise
program to assist in the management of and prevent complications related to their diabetes
(Funnell, 2008). Proper self-management can improve diabetes outcomes, if individuals are
able to sustain the complex array of self-management behaviors required of them (Heisler,
Smith, Hayward, Krein, & Kerr, 2003).
Heisler et al (2003) described self-management as a set of skilled behaviors used by
patients to sustain complicated medication, diet and exercise regimens. Individuals with
Diabetes, who are on insulin, may need to adjust their dose based on their blood sugars, take
several medications at different times during the day, and/or take medications based on specific
blood glucose levels (Nathan et al., 2006; Ripsin, Kang, & Urban, 2009; Sigal, Kenny,
Wasserman, Castaneda-Sceppa, & White, 2006). The sustainment of these complicated
regimens is part of self-management regimens for many individuals with diabetes (Heisler,
Smith, et al., 2003). For individuals with diabetes, monitoring and following a specified diet and
regular exercise plan to assist in glycemic control is an important self-management behavior.
Diets may need to be adjusted depending on the level of exercise individuals participate in.

18

Exercise programs for individuals with diabetes include cardiovascular exercises such as walking
or jogging, as well as resistance training with weights (Sigal, et al., 2006). The ability to perform
and maintain these complicated regimens is not only influenced by an individual’s PSE, and
individual characteristics, but also their clinical characteristics. Individuals who already have
one chronic disease to manage may find it difficult to continue their self-management routine
when they develop a second chronic condition, such as cancer, which may be more life
threatening, and also require them to perform self-management activities.
Most individuals with cancer who are undergoing chemotherapy will experience
symptoms of fatigue, nausea, vomiting, diarrhea and decreased appetite which can impact diet
and exercise regimens for the self-management of diabetes (Leak, et al., 2009; McCoubrie,
Jeffrey, Paton, & Dawes, 2005; Smyth & Smyth, 2005). Cancer specific medications for
managing symptoms and chemotherapy regimens may impact glucose levels. Many cancer
treatment protocols will use glucocorticosteroids as part of the treatment regimen which can
cause glucose levels to increase (Leak, et al., 2009). This increase in glucose levels may require
additional medication management by the patient in order to control.

Patients may need to

learn additional skills in order to maintain and manage their diabetes, as well as skills to manage
their cancer.
The learning and acquiring skills in order to carry on and actively participate in the care
of one’s chronic disease is considered a component of self-management (Blakeman, Macdonald,
Bower, Gately, & Chew-Graham, 2006; Bodenheimer, Lorig, Holman, & Grumback, 2002;
Lorig, 1993; Lorig & Homan, 2003). Gallant et al (2008) described self-management as
activities undertaken by individuals to control or minimize the impact a disease or condition can
have on their physical health status and functioning, and to help them cope with the psychosocial

19

consequences of living with a chronic illness. Self-management includes the responsibility of
being able to interpret the effects of diseases and treatments in order to be able to use
medications properly, change behaviors in response to symptom changes, interpret and report
symptoms accurately and to adjust to the social and economic consequences of having a chronic
illness (Holman & Lorig, 2004). Self-management occurs when individuals engage in activities
to control or reduce the impact of their disease, and can be described as a daily routine that is
modified by the patients perceived needs, physical abilities and sources of support (Bayliss,
Edwards, Steiner, & Main, 2008; Bourbeau, 2008; Grey, Knafl, & McCorkle, 2006). For the
purposes of this study self-management behaviors will be considered the activities individuals
perform to maintain their diabetes. The performance of the activities which compromise selfmanagement behaviors for diabetes are influenced by the structural or antecedent components of
the framework. These relationships will be explored in the following sections.
Individual Characteristics
The structural component of individual characteristics comprises the variables of age,
sex, marital status, race/ethnicity, living arrangements, financial and educational levels. Each of
these individual characteristics may play a role in regards to the performance and/or the
continued performance of self-management behaviors (Bayliss et al., 2007; Bayliss, et al., 2003;
Dunbar, et al., 2008; MacInnes, 2008). The performance of self-management behaviors tend to
increase as an individual ages (Grey, et al., 2006; MacInnes, 2008). Individuals who are
considered elderly or frail may perform self-management behaviors less, and be more dependent
upon spouses or caregivers to provide or assist with recommended self-management behaviors
(Brewer-Lowry, Arcury, Bell, & Quandt, 2010; Grey, et al., 2006). Women are more likely to
ensure that a spouse or partner is adherent and performing recommended self-care activities vs.

20

performing them themselves (Grey, et al., 2006). The nature or amount of self-management
behaviors performed may also vary by other demographic variables such as race, educational and
income levels (Dunbar, et al., 2008; Grey, et al., 2006).
Each of these socio-demographic variables which make up individual characteristics can
have a positive or negative influence on the performance of recommended self-management
behaviors. The role each of these variables has on the continued performance of selfmanagement behaviors for one disorder, when they are faced with the management of a new
disorder is unclear. Based on the known positive or negative impact each of these can have on
the performance of self-management behaviors, it is essential that they be considered when
looking at the effect one condition may have on the performance of self-management behaviors
for another. For the purposes of this study individual characteristics are considered the sociodemographic variables which can either have a positive or negative influence on the performance
of self-management behaviors for diabetes in individuals undergoing treatment for cancer.
Clinical Characteristics
Clinical characteristics are those variables that are associated with the individuals general
health and specific health conditions. General health variables include the number of
comorbidities or chronic conditions an individual has and the symptoms they experience
associated with these conditions. Specific health conditions, for this study, include diabetes and
cancer. Diabetes specific variables include length of illness, diabetes type, number of diabetes
related medications, and diabetes severity. Cancer specific variables include type, stage, number
of cancer related medications, chemotherapy treatment regimen and length of time the individual
has been on chemotherapy.

21

Clinical characteristics have been noted to influence the performance of self-management
behaviors either positively or negatively (Aljasem, et al., 2001; Janz et al., 2007; Kerr, et al.,
2007; Mann, Ponieman, Leventhal, & Halm, 2009; Molasiottis, Stricker, Eaby, Velders, &
Coventry, 2008; Rao & Cohen, 2004). The longer an individual has a chronic condition, such as
diabetes, the more likely they are to perform self-management behaviors (MacInnes, 2008). The
more symptoms an individual experiences and the overlapping or competing effects of these
symptoms with required self-management behaviors can negatively impact their performance
(Cleeland et al., 2000; Hofman, et al., 2007; Kerr, et al., 2007; Oberst, et al., 1991; Reiner &
Lacasse, 2006). The greater the number of medications an individual takes, the more complex
their treatment plan and the severity of their illness can also have a negative impact on the
performance of self-management behaviors (Bayliss, Ellis, et al., 2007; Mann, Ponieman,
Leventhal, & Halm, 2009; Piette & Kerr, 2006).
In summary, clinical characteristics are those variables which are associated with an
individual’s health or disease state. These variables in general have been shown to negatively
impact the performance of self-management behaviors, with the exception of length of time the
individual has had the condition. The amount of influence these variables have on the actual
performance of self-management behaviors maybe influenced by certain behavioral
characteristics, these will be explored in the following section.
Behavioral Characteristics
Behavioral characteristics are factors which influence how one may react to, manage a
specific problem, and their confidence to do so. Motivation, mood states, ability and selfefficacy (SE) have all been identified as behavioral characteristics which influence selfmanagement behaviors (Dunbar, et al., 2008). For the purposes of this study perceived Diabetes

22

self-efficacy (PDSE), outcome expectancies (OE), depression and anxiety will be utilized to
explore the relationship to the performance of self-management behaviors for diabetes in adults
undergoing chemotherapy.
Higher levels of depression and anxiety, lower levels of SE and/or OE, have been shown
to negatively impact the performance of self-management behaviors (Aikens, Perkins, Piette, &
Lipton, 2008; Aljasem, et al., 2001; Chlebowy & Garvin, 2006; Egede, Ellis, & Grubaugh, 2009;
Williams & Bond, 2002). Individuals who have higher levels of self-efficacy and feel like they
can have a positive impact on their outcomes are more likely to perform required selfmanagement behaviors (Aljasem, et al., 2001; Chlebowy & Garvin, 2006). Individuals who have
higher levels of self-efficacy and feel they have some control over their outcomes, exhibit higher
levels of confidence and feel they are able to carry out the required behaviors (Aljasem, et al.,
2001; Sousa, et al., 2009).
In summary, behavioral characteristics are factors associated with ones mood, confidence
and beliefs about being able to carry out a particular activity and that the impact the performance
of a particular activity will have on their overall health state. Behavioral characteristics can have
a negative or positive influence on the performance of self-management behaviors for diabetes,
dependent on their level. It is not clear how these variables will impact the performance of selfmanagement behaviors after being on chemotherapy for 8 weeks or more. It can be hypothesized
that individuals who have higher levels of SE and OE, and lower levels of depression and
anxiety at baseline, will be more likely to continue to perform self-management behaviors for
their diabetes.
The framework for this study uses components of the framework for heart failure selfmanagement and family variables by Dunbar et al (2008) to identify how self-management

23

behaviors may be impacted in older adults with diabetes while undergoing treatment for cancer.
In the framework for this study the individual, clinical and behavioral characteristics have a
positive or negative influence on the performance of self-management behaviors for diabetes at
baseline and after being on chemotherapy for at least 8 weeks. By understanding the relationship
between clinical, behavioral and individual characteristics and the performance of selfmanagement behaviors for diabetes in individuals undergoing treatment for cancer can help
clinicians caring for this population, in order to improve health related quality of life outcomes.
What is known in regards to self-management of diabetes, and the factors which may influence
this will be further explored in Chapter 3.

24

Chapter 3
Integrated Review of the Literature
In 1998, Wagner published a model of improvement of chronic illness care, which is
known as the Chronic Care Model. One of the main constructs of this model is self-management
support (Wagner, 1998). The importance of self-management was also highlighted in the
Institute of Medicine’s report, Crossing the Quality Chasm (Crossing the quality chasm: A new
health system for the 21st century, 2001). This reported cited self-management as one of the
essential components of chronic disease management programs, in order for patients and their
families to be able to acquire skills needed to make lifestyle changes. Over the past several years
the concept of self-management of comorbidities or chronic disease, such as diabetes mellitus
(DM), congestive heart failure (CHF) and arthritis, has been the focus of multiple research
studies (Albert, Buchsbaum, & Li, 2007; Aljasem, et al., 2001; Atak, Tanju, & Kose, 2008; Chen
& Wang, 2007; Chriss, Sheposh, Carlson, & Riegel, 2004; Jerant, et al., 2005; McDonaldMiszczak & Wister, 2005; Nagelkerk, et al., 2006).
Self-management of symptoms has also been the focus of cancer studies investigating
how individuals manage their cancer and cancer treatment related symptoms (Borthwick,
Knowles, McNamara, O'Dea, & Stroner, 2003; Chou, Dodd, Abrams, & Padilla, 2007; Hoffman
et al., 2009; Magnan, 2004; Miaskowski et al., 2004; Williams et al., 2006; Wydra, 2001). More
recently researchers have started to investigate the impact of multiple comorbidities has on an
individuals ability to self-manage their chronic conditions (Bayliss, Edwards, Steiner, & Main,
2008; Bayliss, Ellis, et al., 2007; Bayliss, et al., 2003; Borg, Ingalill, & Blomqvist, 2006; Kerr, et
al., 2007; Schoenberg, Leach, & Edwards, 2009). This literature does not specifically address
the impact that one comorbidity may have on the self-management of another, such has the
impact cancer has on diabetes self-management. Self-management is an essential part of chronic
25

disease management and individuals with multiple morbidities need to be able to juggle
managing both conditions in order to achieve desirable outcomes for all of their conditions, and
maintain their overall quality of life (Bayliss, Bosworth, et al., 2007; Bayliss, Ellis, et al., 2007).
Over the past few years the relationship between cancer and diabetes has been the topic
of only a few published articles (Leak, et al., 2009; Psarakis, 2006; Smyth & Smyth, 2005,
Extermann, 2007). None of these articles specifically address the impact cancer and its treatment
has on the self-management of diabetes. The importance of diabetes self-management and
glycemic control in the cancer patient is emphasized. In order to develop care processes that will
ensure continued self-management of diabetes in the older adult who is undergoing treatment for
cancer, and understanding of how diabetes self-management is impacted needs to be developed.
The following sections will present an integrative review of the literature focusing on the
self-management of diabetes, cancer and multimorbidities, and how each is influenced by
individual, clinical and behavioral characteristics of the individual. The current state of the
science regarding the association of preexisting diabetes and cancer, the impact of having
diabetes and cancer on health related outcomes, and the influence cancer has on diabetes selfmanagement will also be explored.
Diabetes Self-Management
The self-management of diabetes is an essential component of the patient’s plan of care.
Individuals with diabetes are required to maintain a sometimes complex regimen in order to
achieve good glycemic control and prevent complications (Sousa, Zauszniewski, Lea, & Davis,
2005). Poor glycemic control in individuals with diabetes has been associated with the
development of cardiovascular and peripheral vascular disease and kidney disease which can be
life threatening. In addition, poorly controlled diabetes can have an impact on HRQOL

26

outcomes, such as physical, social, emotional and cognitive functioning, pain and overall well
being (Honish, Westerfield, Ashby, Momin, & Phillippi, 2006). The individual with diabetes
will usually perform some type of self-management activity on a daily basis. These activities
include monitoring of blood sugars, administering of insulin and/or taking of oral medications,
monitoring of diet regimens and the following of an exercise program to assist in the
management of and prevention of complications related to their diabetes (Funnell, 2008). The
actual performance of diabetes self-management behaviors has been shown to be influenced by
various factors. These factors include individual, behavioral and clinical characteristics of the
individual with diabetes.
Individual Characteristics
Age. The influence of an individual’s age varies among studies and the specific selfmanagement behavior being assessed. Older adults overall tend to have fewer barriers associated
with self-management of diabetes when compared to younger adults (Glasgow, Hampson,
Strycker, & Ruggiero, 1997; Vijan et al., 2005; Wen, Shepherd, & Parchman, 2004). Age has
been found to be a significant predictor of diet self-care with older adults reporting higher levels
of diet self-care in a cross-sectional study by Wen et al (2004). Vijan et al (2005) also found diet
was less burdensome for older adults when compared to younger adults. Honish, Westerfield,
Ashby et al (2006) noted an individual is more likely to be compliant with diabetes treatment as
they age. Aljasem et al (2001) noted age was negatively correlated with binge eating, adjusting
diet and vigorous exercise, and positively correlated with following an ideal diet. In this study
younger individuals were more likely to adjust their diet and participate in vigorous exercise,
where older individuals were less likely to binge eat, and more likely to follow an ideal diet.
Age has been noted to have a positive or negative influence on the performance of diabetes self-

27

management behaviors. The relationship of age to the continued performance of selfmanagement behaviors in older adults undergoing chemotherapy is unknown.
Sex. In relation to self-care behaviors, women with diabetes are more likely to participate in
exercise and men are less likely to binge eat (Aljasem, et al., 2001). Women are also more likely
to report significant disruption of their social lives due to their diabetes (Mann, et al., 2009a).
Other studies which have included gender as a variable has not reported any significant
differences in self-management behaviors in relation to gender (Coates & Boore, 1998; Toljamo
& Hentinen, 2001). These studies that have not found an association between gender and selfmanagement behaviors for diabetes did not look at specific behaviors. Gender differences for
diabetes self-management may be behavior specific.
Race/Ethnicity. Few studies have looked at diabetes self-management in specific to
race, ethnicity and culture. Aljasem et al (2002) noted race was negatively correlated with
skipping medications and Caucasians were less likely to skip their medications than other races.
The specific relation of race to performance of self-management behaviors is unclear.
Ethnicity and culture can positively or negatively influence self-management of diabetes
(Ponzo et al., 2006; Weiler & Crist, 2009; Wong, Stewart, & Furler, 2009).

Ethnicity and

culture has an influence on diet, exercise and overall performance of self-management behaviors
for diabetes (Castro, O'Tolle, Brownson, Plessel, & Schauben, 2009; Lanting et al., 2008;
Sowattanangoon, Kotchabhakdi, & Petrie, 2009; Weiler & Crist, 2009). Lanting et al (2008)
studied ethnic differences in outcomes of diabetes in a study conducted in the Netherlands. This
study compared native Dutch individuals to immigrants from Turkey or Morocco. Immigrants
viewed their Diabetes more seriously, and reported a more positive attitude and higher levels of
social support than Dutch participants. Barriers were higher among the immigrants in relation to

28

diet management, where Dutch individuals had more barriers associated with taking prescribed
medications and monitoring of blood glucose levels.
Weiler and Crist (2009) noted in the Latino population, cultural aspects of family, social
expectations/perception of “illness” and the social stigma of disease all could have a positive or
negative influence on the performance of self-management behaviors for diabetes. Culture also
plays a strong influence in how Asians manage their diabetes. In the Thai, Chinese and other
Asian populations diabetes is managed according to cultural beliefs and norms (Sowattanangoon,
et al., 2009; Wong, et al., 2009; Xu, Toobert, Savage, Pan, & Whitmer, 2008). Race and
ethnicity have both been shown to be strong influencing factors for diabetes self-management.
The role race and ethnicity play in regards to maintaining self-management behaviors for
diabetes when an individual is faced with a new chronic condition such as cancer is missing in
the literature.
Financial. An individuals income level can be an influencing factor to how well they
comply with and perform required self-management behaviors. Peyrot, Rubin, Kruger and
Travis (2010) noted individuals who had higher incomes were less likely to omit or skip an
insulin dose. Nagelkerk et al (2006) and Utz et al (2006) both identified cost of supplies and
limited resources as barriers to performing diabetes self-management behaviors. Income level
has also been noted to have an influence regarding certain behavioral characteristics associated
with diabetes self-management. Figaro, Elasy, BeLue, Speroff and Dittus (2009), noted
individuals with higher levels socioeconomic status also had higher levels of outcome
expectations and self-efficacy, which influenced their compliance with diabetes self-management
behaviors. Income level plays a direct role on the performance of self-management behaviors by
influencing the individual’s ability to afford supplies necessary to carry out self-management

29

activities. Income level also plays an indirect or mediating role through its association with selfefficacy and outcome expectancies. These behavioral characteristics will be further explored
later in this chapter.
Education Level. Findings regarding level of education are consistent across studies.
Individuals with lower education perceive more difficulty with diabetes self-management
(Aljasem, et al., 2001; Mann, et al., 2009a; Rhee et al., 2005). Mann et al (2009) noted
individuals with less than high school education were more likely to believe that they actually
had diabetes, they felt they only had diabetes when glucose levels were high. Individuals who
did not perceive their glucose levels as being high were less likely to take medications as
prescribed. Aljasem et al (2001) noted individuals with higher levels of education tested their
blood glucose more frequently. Current research has shown that the education level of the
individual can have an impact on the performance of diabetes self-management behaviors, with
individuals with higher levels of education being more likely to perform diabetes selfmanagement activities.
Marital Status. Self-management behaviors for diabetes can be influenced by spouses
and other family members (De Ridder, Schreurs, & Kuijer, 2005; Ponzo, et al., 2006). Having a
good support person such as a spouse who is willing to engage with the patient in regards to selfmanagement is identified as a factor which improves self-management behaviors in individuals
with diabetes (Nagelkerk, et al., 2006). Tolijamo & Hentinen (2001) noted individuals who lived
alone had poorer Hbg A1c’s than those living with a family or partner. Individuals who perceive
higher levels of support from spouses and other family members who may be living with them
are less likely to neglect self-management behaviors (Toljamo & Hentinen, 2001; Wen, et al.,
2004). The influence of marital status is dependent upon how much spousal support the

30

individual perceives. The presence of other individuals in the home can have a positive effect on
the performance of diabetes self-management behaviors.
Summary
Many of the studies which have explored the relationship between individual
characteristics and self-management of diabetes have been qualitative or used a descriptive,
correlational or cross-sectional design. These studies show individuals who have had the disease
longer, are older, have higher education, and higher levels of income are more likely to have
better diabetes self-management. Race/ethnicity/culture, marital status, sex and living
arrangements also play a role in the self-management behaviors for diabetes in adults. In
addition, many of these studies excluded individuals who had life threatening conditions, or
complex or competing chronic illnesses. None of these studies specifically addressed issues that
may occur when an individual develops a new competing comorbid condition such as cancer.
Are individuals with diabetes likely to sustain their level of self-management behaviors over
time when faced with a new chronic illness that may be perceived as more life threatening, and
causes the development of competing demands related to the addition of a new complex
condition, which adds to the complexity of the disease management regimen?
Clinical Characteristics
Across the diabetes self-management literature four different clinical characteristics
which impact diabetes self-management can be identified. These are: diabetes related
complications, number of comorbidities, competing nature of comorbidities and the diabetes
regimen (Aljasem, et al., 2001; Kerr, et al., 2007; Utz et al., 2006). The length of time an
individual has had diabetes can also be a factor which influences how well they perform diabetes
self-management behaviors. Longer duration of diabetes has been noted to be significantly

31

associated with lower diabetes self-management (Ponzo, et al., 2006). Reasons for this
association may be due to the facts that individuals who have had diabetes longer may have more
diabetes related complications, developed other competing comorbid conditions and may have a
more complex regimen to follow in order to manage their diabetes.
Aljasem et al (2001) noted skipping meals was higher among individuals with more
diabetes related complications. Kerr et al (2007) noted that the greater number of comorbidities
and individual had, the lower was their diabetes self-management ability and prioritization.
Kerr et al (2007) also noted that individuals with discordant conditions, such as pulmonary
disorders or heart disease had lower self-management ability. Utz et al (2006) and Morrow et al
(2008) both conducted qualitative studies exploring diabetes self-management. Participants in
both of these studies also cited the discordant nature of both chronic and acute illness impacted
their self-management. The management of discordant competing conditions impacted their
ability to control glucose levels and follow their self-care regimen due to the interference from
medications and/or other prescribed treatments for the competing conditions (Morrow, Haidet,
Skinner, & Naik, 2008; Utz, et al., 2006).
The complexity of the self-management regimen itself has also been noted by individuals
with diabetes as a barrier to effective self-management. (Mann, et al., 2009; Utz, et al., 2006;
Vijan, et al., 2005). In a study by Mann et al (2009) which sought to identify potentially
modifiable factors related to poor medication adherence among individuals with diabetes found
that regimen complexity correlated with poor adherence to taking medications. In a qualitative
study by Utz et al (2006), the complexity of knowing what to do to manage diabetes, and fitting
the complex regimen into their life were identified as barriers to effective self-management by
rural African Americans who participated in this study. Regimen complexity may be confusing

32

to some individuals with diabetes, when it comes to making decisions regarding management of
high or low blood glucose levels, and how best to adjust other components of their regimen
(Mann, et al., 2009; Utz, et al., 2006). For individuals who have a more complex regimen or
who’s regimen complexity changes, they may be less likely to perform self-management
activities as recommended.
Summary
Consistently across studies, the complexity of plan of care and the number of
comorbidities have been identified as a barrier to diabetes self-management. Very few studies
have looked at the relationship between comorbidities and actual diabetes self-management. The
study by Kerr et al (2007) was the only study found by this investigator which explored the
relationship between comorbid conditions and diabetes self-management. This study used a
cross-sectional observational design and did not look at changes in self-management behaviors in
adults when a new chronic condition develops. Kerr et al (2007) did consider cancer to be a
discordant condition, which can have an impact on diabetes self-management. From the
literature it is unknown if individuals will maintain the same level of diabetes self-management
when faced with a new complex comorbid condition.
Behavioral Characteristics
Self-Efficacy. Self-efficacy has been noted to be an important influential factor related
to successful diabetes self-management and has been found to be positively correlated with the
performance of self-management behaviors (Aljasem, et al., 2001; Sousa, et al., 2005; Wen, et
al., 2004;Williams & Bond, 2002). Only one study found by this author found no significant
relationship between self-efficacy and performance of self-management behaviors for diabetes
(Chlebowy & Garvin, 2006). Individuals with high diabetes self-efficacy are more likely to

33

follow an ideal diet and adjust their insulin as need, are less likely to skip testing of blood
glucose levels and taking of their medications (Aljasem, et al., 2001;Williams & Bond, 2002).
Self-efficacy has also been noted to be a significant predictor of exercise self-care in individuals
with diabetes (Wen, et al., 2004;Williams & Bond, 2002).
An individual’s level of self-efficacy is an important factor associated with the
performance of self-management behaviors for diabetes. For individuals with higher levels of
self-efficacy are more likely to perform self-management behaviors and have higher levels of
self-management than those with lower levels of self-efficacy. What role does self-efficacy play
in individuals who have diabetes and then develop a new chronic condition such as cancer? Are
these individuals still likely to maintain the same level of self-management for their diabetes or
will this change? Some studies have looked to see if the level of self-efficacy in individuals with
diabetes could be changed through education. Studies have found the level of self-efficacy will
improve or increase with education and as self-efficacy improves so does diabetes selfmanagement. (Atak, et al., 2008; Shah & Booth, 2009; Trief, Teresi, Eimicke, Shea, &
Weinstock, 2009). Studies which address the association between diabetes specific self-efficacy
and ability to maintain self-management behaviors when faced with a new chronic condition are
missing in the literature.
Outcome Expectancies. Very few studies have included outcome expectancies (OE) of
the individual as part of the study. OE has been noted to be significantly related to the
performance of self-care behaviors (Chlebowy & Garvin, 2006; Mann, et al., 2009;Williams &
Bond, 2002). The more an individual believes that their overall diabetes management impacts
their outcomes, the more likely they are to perform self-management behaviors (Chlebowy &
Garvin, 2006). All of these studies are descriptive or correlational in design, using a cross-

34

sectional survey approach. None of these studies report information regarding comorbidities,
and if there is a moderating or mediating effect between OE and other comorbidities on the
performance of diabetes self-management activities. The influence OE has on the performance of
diabetes self-management behaviors in individuals with diabetes, who have developed a new
competing illness, such as, cancer is currently unknown.
Anxiety/Depression. Both depression and anxiety have been noted to have a negative
impact on the performance of self-management behaviors for diabetes (Aikens, et al., 2008; Bai,
et al., 2009; Ciechanowski, Katon, & Russo, 2000; Egede, et al., 2009; Gonzalez, et al., 2007;
Hall, et al., 2009; Lin et al., 2004; Luyster, et al., 2009). Individuals who with higher levels of
depression have poorer glycemic control and are less likely to follow the recommended diet and
exercise regimens (Aikens, et al., 2008; Bai, et al., 2009; Ciechanowski, et al., 2000; Gonzalez,
et al., 2007; Hall, et al., 2009). The presence of anxiety may increase the likelihood of avoidance
behaviors due to fear associated with adverse outcomes (Hall, et al., 2009). The impact anxiety
and depression has on diabetes self-management in individuals who are undergoing treatment for
cancer is unknown. Existing studies have either excluded individuals with life threatening
illnesses, or complex comorbidities such as cancer from the study, or information regarding
comorbidities was not considered during the analysis.
Summary
Behavioral characteristics play an important role in regards to how well or how much an
individual will perform self-management behaviors for their diabetes. The role these play in the
continued performance of self-management activities when an individual is faced with a new and
complex illness such as cancer is unknown. This study seeks to begin to fill this gap by
exploring the relationship between diabetes self-management behaviors in individuals who are

35

undergoing treatment for a recently diagnosed cancer, and the behavioral characteristics of selfefficacy, outcome expectancies and depression/anxiety. Like diabetes, individuals with cancer
also perform self-management activities. Cancer self-management will be explored in the next
section.
Cancer Self-Management
The literature related to self-management in cancer, primarily focuses on the management
of cancer and cancer treatment related symptoms. Cancer and its treatment can cause a myriad of
symptoms in which patients will perform self-management strategies for (Dodd, 1988; Janz, et
al., 2007; Miaskowski, et al., 2004;Williams, et al., 2006), Individuals undergoing
chemotherapy have reported experiencing up to 14 different symptoms (Chou, et al., 2007). In a
study by Janz et al (2007) that explored the symptom experience of women following primary
breast cancer treatment, over half the women in the study (study N= 1372) reported symptoms
related to fatigue, hot flashes, sleep disturbance and general pain. Given et al (2001) in a study
which examined the co-occurrence and patterns of change among symptoms in newly diagnosed
cancer patients found pain and fatigue were more likely to occur within 40 days of concluding
chemotherapy, radiation and/or surgery. Patients with 3 or more comorbid conditions had
significantly more symptoms.
Common symptoms associated with chemotherapy that have been reported are lack of
energy, hair loss, dry mouth, nausea, feeling sad, sleep difficulty, mouth sores, loss of appetite,
numbness in fingers and/or toes, and fatigue (Chou, et al., 2007;Williams, et al., 2006). Rao &
Cohen (2004) noted symptoms in elderly cancer patients usually overlap and include nausea,
vomiting, pain, depression, fatigue, insomnia, loss of appetite, and increased anxiety. Fatigue is
one of the most commonly reported symptoms in older cancer patients (Rao & Cohen, 2004).

36

Many older adults will have pre-existing comorbid disease when diagnosed with cancer
(Rao & Cohen, 2004; Reiner & Lacasse, 2006). The severity of symptoms experienced in the
older cancer patient is associated with having three or more pre-existing comorbidities (Reiner &
Lacasse, 2006). Kurtz et al (2001) noted patients with higher symptom severity scores had more
comorbid conditions, were more depressed and experienced greater physical functioning deficits.
Many older adults with cancer will use self-management strategies to manage their symptoms.
Self-management strategies for these symptoms include making dietary changes, taking
medications, resting or taking a nap (Williams, et al., 2006). The use of self-management
strategies have been found to provide relief of these symptoms (Borthwick, et al., 2003; Dodd,
1988; Magnan, 2004). Studies have found that the use of self-management strategies will
provide up to moderate relief for many of these cancer related symptoms (Chou, et al., 2007;
Dodd, 1988; Magnan, 2004; Miaskowski, et al., 2004).
Actual factors which influence the use of self-management activities for symptoms in
cancer patients is scarce in the literature. A recent study by Hoffman et al (2009) found
perceived self-efficacy for fatigue self-management predicted greater physical function status,
and mediated the relationship between cancer related fatigue severity and physical function
status. This study did not look at the relationship between perceived self-efficacy and actual
performance of fatigue self-management strategies.
Summary
The cancer literature which addresses self-management primarily focuses on the selfmanagement of symptoms associated with cancer and its treatment and highlights the nature and
frequency of symptoms in adults with cancer undergoing treatment. The presence of cancer
related symptoms can also impact the overall physical functioning of the individual with cancer

37

which could impact ability to perform certain self-management activities, such as exercise for
diabetes. Several researchers have looked at the use of different interventions in order to improve
the symptom experience for individuals receiving cancer treatment. The impact of having to
perform self-management activities for these symptoms on the performance of self-management
activities for diabetes is unknown. Will individuals with diabetes be less likely to continue to
perform self-management behaviors for their diabetes in the presence of these symptoms and
other requirements for cancer care? Kerr et al (2007) noted that patients with diabetes who have
an illness that they perceive more severe or life threatening such as cancer, will ignore their
diabetes and required self-management behaviors. The impact of having multi-morbidities on
self-management will be explored in the following section.
Self-management of Multi-Morbidities
The literature regarding the self-management of multi-morbidities by older adults is
limited. Over the past decade various researchers have started to study this phenomenon. The
majority of individuals 65 and older will have more than one chronic condition they will need to
self-manage. Bayliss et al (2003) conducted a qualitative study to identify perceived barriers to
self-management in individuals with multiple chronic conditions. Barriers to self-management
were related to individual, clinical and behavioral characteristics. Each of these characteristics
will be further explored and then an overall summary will be presented at the end of the section.
Individual Characteristics. Characteristics related to cost/financial status, social
support and age have been noted to be barriers to self-management in individuals with multiple
morbidities (Bayliss, J. Ellis, & J. Steiner, 2007; Bayliss, Ellis, & Steiner, 2008; Bayliss, Steiner,
Fernald, Crane, & Main, 2003). Bayliss et al (2008) noted that age was related to increased

38

disease burden in individuals with multiple morbidities. This increased disease burden has been
noted to interfere with the self-management for a chronic illness.
The financial burden of caring for multiple chronic conditions has been noted in more
than one article (Bayliss, et al., 2007, 2008;Bayliss, et al., 2003; Jerant, et al., 2005; Piette &
Kerr, 2006; Schoenberg, et al., 2009). Financial constraints noted in the literature were related to
increased out of pocket cost due to the increased number of medications and dietary
recommendations (Bayliss, et al., 2003; Schoenberg, et al., 2009). In a qualitative study by
Schoenberg et al (2009) participants expressed challenges related to medication and dietary
adherence due to finances.
Social support that is perceived to be lacking has also been noted to create a barrier in the
management of multiple morbidities by adults. Bayliss et al (2008) in a qualitative study which
explored process of care desired by elderly individuals with multi-morbidities, identified sources
of support as factor which modified the individuals daily routine. The reason for this impact on
actual self-management was not identified in this study. Jerant et al (2005) in a qualitative study
which explored perceived barriers to self-management noted lack of support from family as
being a barrier. Specific barriers identified by participants in the study were: family members
who did not perceive the individual as being “sick” and spouses who were not supportive of
weight loss efforts or who would cook inappropriate foods (Jerant, et al., 2005). Social support
which is perceived as negative or lacking can impact how well a patient performs selfmanagement behaviors for one or more chronic conditions.
Clinical Characteristics. The impact or relationship of clinical characteristics
associated with having multiple comorbidities on self-management has been noted frequently in
the literature (Bayliss, et al., 2007, 2008; Bayliss, et al., 2003; Jerant, et al., 2005; Kerr, et al.,

39

2007; Noel, Frueh, Larme, & Pughs, 2005; Piette & Kerr, 2006; Proctor, Hasche, MorrowHowell, Shumway, & Snell, 2008; Schoenberg, et al., 2009). These clinical characteristics
include the total number of medications the individual takes, the compounding effects, and the
number of, as well as the severity of the symptoms, the individual experiences that are associated
with his comorbidities. The overall disease burden of the different comorbidities and the
concordance or discordance of the comorbidities is also considered a clinical characteristic which
can act as a barrier to effective self-management.
The total number of medications required by individuals with multiple morbidities was
identified as a barrier to self-management in a study by Noel et al (2005). Barriers associated
with the total number of medications were related to the difficulty of the medication regimen,
and the adverse effects associated with the medications (Noel, et al., 2005). Similarly,
participants in a qualitative by Bayliss et al (2003) noted the compounding effects, therapeutic
effects and side effects of medications presented a barrier to self-management. In this same
study, participants noted the schedule and actual coordination of having to take multiple
medications, also created a barrier to self-management of their different co-morbidities (Bayliss,
et al., 2003).
The compounding effects of multiple comorbid conditions have been noted most in the
literature as a barrier to self-management for the different multimorbidities. These compounding
effects can be placed into three different categories: disease burden, disease concordance and
symptoms. Disease burden: the burden of dominant effect of a single condition was noted by
Bayliss et al (2003) as a barrier to self-management. This compounding effect or increased
burden impacted the ability to exercise in individuals with diabetes due to difficulty in breathing.
Kerr et al (2007) noted that higher burden associated with multiple morbidities was associated

40

with lower self-management. This disease burden is associated with the concordance or
discordance of the multiple conditions an individual may have and the symptoms associated with
each disorder.
Disease Discordance: Discordant condition are conditions which do not follow a similar
disease management plan and are not part of the same overall pathophysiologic risk profile of an
individual (Piette & Kerr, 2006). Hypertension and Diabetes would be considered concordant,
diabetes and cancer would be discordant. Individuals with discordant conditions have lower selfmanagement prioritization and lower self-management ability (Kerr, et al., 2007). This
discordance between medical problems can cause a competition for time, attention and priority
for older adults and impact their self-management of one or more of their chronic conditions
(Proctor, et al., 2008).
Symptoms: Patients with multiple morbidities also experience multiple symptoms, and
the compounding effects of their conditions may increase their total symptom severity and
symptom burden (Bayliss, et al., 2007, 2008; Jerant, et al., 2005; Noel, et al., 2005). Bayliss et al
(2007) noted depressive symptoms created a barrier to the self-management of chronic
conditions in older adults with multiple morbidities. The risk of, or presence of depression
increases in individuals with comorbid conditions (Egede, 2005; Simon, 2001), and the presence
of depression in individuals with diabetes has been noted to decrease adherence to diet, exercise
and medication regimens (Egede, et al., 2009; Gonzalez, et al., 2007). Depression has also been
associated with poorer perceived health status and functional ability (Egede, et al., 2009).
Symptoms of decreased mobility, physical limitations, fatigue, pain, dizziness, shortness of
breath and insomnia were noted by Noel et al. (2005), as being issues for individuals with multimorbidities which can impact self-management.

41

Behavioral Characteristics (self-efficacy).

Bayliss et al (2003, 2007) noted

level of self-efficacy to be a barrier to self-management in individuals with multiple morbidities.
Wasson, Johnson and Mackenzie (2008) in a cross-sectional longitudinal survey looking at
factors associated with self-management found individuals with higher confidence were more
likely to perform self-management behaviors. Individuals with persistent high levels of selfefficacy were better engaged in everyday activities and self-management (Wasson et al., 2008).
Gallagher, Donoghue, Chenoweth and Stein-Parbury (2008) in a prospective descriptive study
looking at the relative influence of self-efficacy on self-management behaviors in older adults
with chronic illness found self-efficacy to predict both concurrent and future self-management.
Individuals with higher levels of self-efficacy were more likely to sustain their self-management
behaviors (Gallagher et al., 2008).
Self-efficacy has also found to partially mediate the negative relationship of having more
than one chronic health condition and ability to exercise (Krein, Heisler, Piette, Butchart, &
Kerr, 2007). Krein et al (2007) in a cross-sectional survey examined how chronic pain affected
difficulty with recommended self-management activities, found higher self-efficacy was
associated with significant lower odds of reporting difficulty with exercising. Self-efficacy plays
an essential role in the performance of self-management behaviors. Individuals with higher
levels of self-efficacy are more likely to engage in self-management behaviors when compared
to those with lower levels of self-efficacy.
Summary
The limited nature of the studies regarding the relationship of self-management in
individuals with multimorbidities identifies barriers similar to those in individuals with a single
morbidity such as diabetes: age, financial status, social support and self-efficacy. In addition,

42

this literature identifies specific issues regarding having multiple conditions: symptoms,
complexity of care, disease concordance and overall disease burden as barriers to selfmanagement. These few studies have been mainly qualitative in nature and have not actually
explored the relationship between these variables and/or their impact on self-management
behaviors in adults with diabetes. Further studies need to be done in order to address how much
the self-management for one chronic condition may be impacted by another and what factors
might be associated with changes in self-management behaviors in this population. None of these
studies have specifically looked at the impact cancer may have on continued self-management of
diabetes or other chronic conditions.
Cancer & Diabetes
Studies which explore the relationship between cancer and diabetes self-management are
absent in the literature. Most literature looking at the relationship of diabetes and cancer focuses
on complications or outcomes associated with cancer and its treatment (Attili, et al., 2007;
Barone, et al., 2008; Srokowski, et al., 2009). Attili et al (2007) noted individuals with diabetes
with good glycemic control are at equal risk for complications when compared with non
diabetics, diabetes is associated with an increase in mortality and poor response rates to
treatment. In a literature review by Psarakis (2006) noted individuals with diabetes and cancer
have a poorer prognosis, higher infection rates and shorter remission periods than those without
diabetes. Hyperglycemia in diabetes was noted to be one of the factors associated with these
poorer outcomes (Psarakis, 2006).
The glucocorticoids used in cancer treatment protocols impact glucose levels in
individuals with cancer and diabetes (Leak, et al., 2009; Psarakis, 2006). In addition to
glucocorticoids, the possible impact symptoms and changes in diabetes self-management may

43

also affect glycemic control. Nausea and vomiting may impact the patients ability to eat and
drink impacting their ability to continue to follow their diabetic diet. Fatigue and pain may have
an impact on the individuals activity level. Patients undergoing chemotherapy with diabetes are
at risk for hypoglycemic as well as hyperglycemic episodes which can impact their symptom
severity and how they self-manage their diabetes (Leak, et al., 2009; McCoubrie, et al., 2005).
Individuals who are on insulin to control their diabetes need to be willing to make
necessary changes to their insulin regimens, as well dietary and exercise regimens used to
manage their diabetes (Leak, et al., 2009; Psarakis, 2006). Individuals with diabetes and cancer
may also need to increase the frequency of blood glucose monitoring, especially if they are on
steroids, or are having nausea, vomiting, or changes in appetite and food tolerance (Psarakis,
2006). In order to achieve the best outcomes for individuals with diabetes and cancer, health
care providers need to have an understanding of the complexities of both diseases (Smyth &
Smyth, 2005). A collaborative team approach between the patient’s primary diabetes care
provider and their oncology provider needs to be developed in order to prevent and minimize
complications and quality of life in individuals with cancer and diabetes (Smyth & Smyth, 2005).
Individuals who have good glucose control have been shown to have lower infection rates than
those with poor glucose control while undergoing treatment for cancer (Psarakis, 2006).
In summary, this literature which addresses the relationship between cancer and diabetes
primarily focuses on the possible interaction between cancer treatment and diabetes outcomes
such as glycemic control. The literature also identifies the increased risk of mortality, morbidity
and infection in individuals with diabetes and cancer (Barone, et al., 2008, 2010; Folsom,
Anderson, Sweeney, & Jackobs, 2004; Giovanucci et al., 2010; Meyerhardt et al., 2003). One
non-research article did begin to highlight the importance of continued diabetes self-management

44

by individuals undergoing treatment for cancer (Psarakis, 2006). None of the studies or articles
have actually looked at changes in diabetes self-management in patients undergoing treatment
for cancer. In order to understand how to best care and support individuals with diabetes and
cancer, an understanding needs to be developed regarding the patients perceptions of how their
cancer has impacted their ability to care for their diabetes. In addition, a understanding of what
specific factors may influence or predict changes in the self-management of diabetes in adults
with cancer needs to be developed.
Summary
Diabetes and cancer are two common chronic diseases in today’s society. The number of
individuals who have both cancer and diabetes may steadily increase over the next several years
as the population ages. Both diabetes and cancer commonly occur in individuals over the age of
50. Self-management is considered an essential component in the plan of care for individuals
with diabetes and/or cancer. Several factors can influence if an individual will perform
recommended self-management behaviors. Individual, clinical and behavioral characteristics of
an individual all can influence the actual performance of self-management behaviors.
For the individual with diabetes, individual or socio-demographic factors have been
shown to have either a positive or negative influence on the performance of self-management
activities. Clinical characteristics such as disease severity, length of time with the illness,
symptom burden and other comorbidities all can influence how well an individual performs selfmanagement behaviors. What is not as well known is how a change in the patients clinical
situation may impact their self-management, specifically the development of a new and possibly
competing disorder, such as cancer. It has been hypothesized that individual with diabetes will

45

prioritize a disease that they consider more severe or life threatening over their diabetes, and be
less likely to follow their recommended regimen (Kerr, et al., 2007; Piette & Kerr, 2006).
Behavioral factors such as depression, self-efficacy and outcome expectancies all have
been shown to play a role in the performance of self-management activities. How these impact
or are related to the continued performance of self-management activities in individuals with
diabetes who are undergoing treatment for cancer is unknown. One can hypothesize that
individuals who have higher levels of self-efficacy, outcome expectancies and lower levels of
depression and anxiety will more likely continue to perform their self-management activities for
their diabetes, while they are undergoing chemotherapy for cancer treatment.
We know that individuals with cancer and diabetes have higher mortality rates, are more
likely to develop complications such as infections, and are more likely to be hospitalized when
compared to those with cancer only (Barone, et al., 2008; Giovanucci, et al., 2010; Psarakis,
2006). It has been hypothesized that glycemic control may play a role in regards to how well a
patient may respond to or tolerate treatment (Giovanucci, et al., 2010; Psarakis, 2006). The
performance of self-management behaviors by individuals with diabetes has been shown to
improve glycemic control (Honish, et al., 2006; Sousa, et al., 2005). On can hypothesize that if
the performance of self-management behaviors decrease in individuals while they are
undergoing chemotherapy, they may have worse glycemic control and be more likely to develop
complications. It is important to determine how cancer and its treatment may impact diabetes
self-management in adults while they are undergoing chemotherapy.
The literature in this chapter identified many of the factors which can influence the actual
performance of self-management behaviors for individuals with diabetes or cancer. The
development of cancer in individuals with pre-existing diabetes may produce a competition or

46

interaction between recommended treatments and self-management behaviors that may impact
their ability to adhere to treatment or their ability to comply with recommended self-management
behaviors (Terret, et al., 2007). As nurses and health care providers we need to understand the
competition which may occur in older adults, between cancer and co-morbidity self-management
in order to provide safe and efficacious care to the older adult population with cancer. This
exploratory study seeks to fill the gaps in our knowledge and add to the current research in
relation to the impact cancer has on self-management of diabetes in older adults receiving
chemotherapy.

47

Chapter 4
Methods
This pilot study utilized a written self-administered survey at baseline and a phone survey
8 weeks later. The phone survey included two open-ended questions. Written surveys were
given to the potential participants by nurses at each of the study sites. These surveys were part
of a packet which also included two consent forms, a study brochure and a self-addressed
stamped envelope for the participant to return the written survey and a signed consent form to the
study investigator. Phone surveys were completed by the study investigator 8 weeks after the
participant was enrolled in the study. The open ended questions were tape recorded with the
participants permission for the purpose of transcription for analyses.
Study Subjects
Inclusion Criteria. The sample consisted of individuals who were50 years of age or
older with Type I or II diabetes and a diagnosis of a solid tumor cancer, who were eligible for or
currently undergoing outpatient intravenous or oral chemotherapy. In addition participants
needed to be on daily insulin or an oral hypoglycemic for their diabetes, be able to read, write
and speak English, be able to follow verbal and written instructions and have access to and be
able to use a telephone.
Exclusion Criteria. Participants were excluded if they were unable to hear or use a
telephone as well as individuals who were unable to understand written and verbal
communication or had a self-reported history of Alzheimer’s or Dementia.
Design for Sampling
A convenience sample of individuals meeting the study criteria was obtained from eight
different cancer centers in the states of Michigan and Ohio. A rolling enrollment was utilized
until an adequate number of participants had been recruited.
48

Recruiting subjects. Recruitment began once IRB approval from Michigan State
University and the individual cancer centers was obtained. Potential participants were identified
by specific clinic staff who had agreed to assist in the recruitment of participants. These
individuals were provided with a check list of the inclusion and exclusion criteria to assist them
in identifying potential participants. Final screening for participation was done by the study
investigator after the consent had been received. Once a participant was identified, the clinic staff
provided them with a study information packet, which included a study brochure, consent form,
initial survey and self-addressed stamped envelope to return the completed survey and consent to
the study investigator. Cancer center staff had an educational session provided by the study
investigator regarding the study protocol, inclusion and exclusion criteria. If a participant
agreed to participate they were asked to return the signed consent along with the completed
initial survey and contact information to the study investigator.
In addition to the recruitment by staff members in the clinic, flyers were posted in the
waiting areas and exam rooms of the clinics informing patients about the study. The flyers asked
the patients if interested in participating to discuss with their provider or clinic nurse. In
addition, fliers were posted in the conference room where the classes were taught as well as in
the waiting rooms.
Study Settings. Sparrow Regional Cancer Center located on the Campus of the Sparrow
Health System in Lansing, Michigan treats approximately 9500 individuals per year, and is
accredited by the American College of Surgeons Commission on Cancer. The Sparrow Regional
Cancer Center, utilizes a multidisciplinary approach employing combinations of cutting-edge
techniques in treating cancer. The regional cancer center at sparrow utilizes oncological surgery,
chemotherapy, radiation and drug therapies as part of their individualized treatment programs for

49

each individual. The Sparrow Regional Cancer Center has an extensive clinical trials program
and is a member of the Michigan Cancer Research Consortium and is a state leader in clinical
trials. Through this consortium, access to the innovative protocols at M.D. Anderson, Mayo
Clinic and the National Cancer Institute are made available to patients seen and treated at the
cancer center.
MSU Breslin Cancer Center, also located in Lansing, Michigan, is a full service
outpatient based community cancer care facility and sees approximately 900 individuals per
year. MSU Breslin Cancer Center is a member of the Great Lakes Cancer Institute (GLCI). The
GLCI is a non-profit partnership between Michigan State University and McLaren Health Care.
The MSU Breslin Cancer Center utilizes an interdisciplinary team approach for coordinated,
comprehensive, individualized treatment planning and has a dedicated chemotherapy outpatient
infusion center. The MSU Breslin Cancer Center has a clinical trials office which matches
patients who qualify for regional or nationally approved clinical trials. The MSU Breslin Cancer
Center has received the 10 star Community Investor Award through the American Cancer
Society, Great Lakes Division.
Allegiance Health Cancer Center is located in Jackson Michigan, and provides cancer
care to over 800 individuals per year. The cancer center at Allegiance Health is part of the Ray
H. Clark Cancer Research Program and has an affiliation with the University of Michigan
(UOM) Cancer Center Network. As part of the UOM cancer network, Allegiance has access to
University-based clinical trials, allowing individuals to be treated close to home with the latest
therapies. Oncologist at Allegiance health provides chemotherapy, immunotherapy and hormone
therapy options to patients for over 30 years. The cancer center at Allegiance Health has been
awarded the American College of Surgeon Commission on Cancer Care certification. Allegiance

50

Health also participates in many national clinical trials. Allegiance Health Cancer Center is one
of 50 participating research programs which has a collaborative relationship with the NCI
clinical trial programs through the Michigan Cancer Research Consortium Community Clinical
Oncology Program.
West Michigan Cancer Center (WMCC) located in Kalamazoo, Michigan is a
collaborative oncology practice between Borgess Medical Center, and Bronson Methodist
Hospital. WMCC averages over 80,000 patient visits a year. WMCC provides a broad range of
diagnostic and treatment services, including onsite radiation and chemotherapy. WMCC
participates in multiple clinical trials and is a member of the National Cancer Institute.
Red Cedar Oncology is a private oncology practice located in East Lansing, MI.
The practice consists of one oncologist, a physician assistant and a nurse practitioner who
specialize in oncology care. Red Cedar Oncology has approximately 3840 patient visits per year.
The office participates in various clinical trials, and provides onsite chemotherapy.
Munson Medical Center Infusion Clinic is part of the Munson Healthcare organization
and is located in Traverse City, MI. The infusion clinic sees approximately 8,000 patients per
year, and provides multiple services, including chemotherapy administration. The Infusion clinic
participates in many clinical trials supported by the National Cancer Institute and is a member of
the Grand Rapids clinical Oncology Program.
University of Toledo Cancer Center (UTCC) is located on the campus of the University
of Toledo Medical Center in Toledo, Ohio. UTCC is a comprehensive cancer center which
provides evaluation, diagnosis and treatment for all forms of cancer. UTCC has approximately
1056 new patients per year which are seen for cancer treatment. UTCC offers and participates in

51

multiple clinical trials and collaborates with the Jacobson Center for Clinical & Translational
Research.
Great Lakes Cancer Institute – Flint (FCC) is located in Flint, Michigan. FCC provides
comprehensive state of the art cancer treatment, including radiation and chemotherapy. FCC is
accredited by the commission on cancer and the American college of surgeons. FCC sees over
800 patients per year. FCC participates in multiple clinical trials and is a member of the Clinical
Cancer Research Center, which is a collaborative between all of the Great Lakes Cancer Institute
Centers.
Data Collection
The initial survey was written at a Flesh-Kincaid 8th grade reading level, and the reading
level was checked by using the Flesh-Kincaid readability assessment tool in Micro-soft word and
determined to be at an 8th grade level. Once the consent and initial survey was obtained the
study investigator contacted the potential subject to reconfirm their willingness to participate in
the study and further establish if they meet study criteria, and set up times for the subsequent
survey to be administered by telephone. This interview took approximately 20 minutes.
Participants were contacted at 6 weeks, to set up a time for the follow up survey to be completed.
Study Measures
This study utilized multiple different established instruments to measure each of the
study variables. Table 1 provides a summary of the variables and the instruments being used to
assess each of the variables in the study.

52

Table 1:
Variables & Measurement for study
α

Variable
type/Collection
Time
Individual Characteristics
Socio-demographics: (Age, sex, education
level, income status, race/ethnicity marital
status and living arrangement)
Clinical Characteristics
Co-morbidities
Type and Number
Generic Symptoms –
Symptoms of Illness check List
Diabetes: Length of Illness,
Insulin or non-insulin, Number of
medications,
Diabetes Severity: Diabetes Complications
Index (DCI)
Cancer: Type and Stage,
Type of Chemo, Chemo frequency/Schedule,
Cancer Meds
Behavioral Characteristics:
Perceived Diabetes
Self-Efficacy: Confidence in Diabetes SelfCare Scale
Outcome Expectancies: Outcome
Expectancies subscale of the
Multidimensional Diabetes Questionnaire
(MDQ)
Depression/Anxiety: Hospital Depression
and Anxiety Scale (HADS):
Depression subscale
Anxiety subscale
Diabetes Self-Management Behaviors
Self-Care Inventory-R

Cancer Impact on Diabetes Self-management
Scale (Based on the illness Intrusiveness
Rating Scale)
Total - Baseline
Total –8 weeks

# of
items

Covariates/
Baseline

6

3 minutes

Predictor/
Baseline
Predictor/
Baseline and 8
week follow up
Predictor/
Baseline

10

5 minutes

3

1 minute

Predictor/
Baseline
Predictor/
Baseline

10

10 minutes

4

2 minute

Predictor/
Baseline

20

.90

10 Minutes

Predictor/
Baseline

6

.86

3 minutes

7
7

.89
.86

5 minutes
total for
both scales

15

.87

10 minutes

Predictor/
Baseline
Outcome –
Baseline & 8
week follow up
Predictor – 8
week follow up

33

.86

Estimated
Time to
Complete

5 minutes

2 minutes

140
53
53

5

65 minutes
22 minutes

Individual Characteristics. Individual characteristics included the socio-demographic
variables of age, sex, education level, income status, marital status, race and
ethnicity. Age was measured as a continuous variable based on patients date of birth. Sex was
treated as a dichotomous categorical variable, identifying male or female sex. Education level
was measured by asking the participant to classify their highest level of education on a 7 point
scale, ranging from no formal education to completed graduate/professional degree. Marital
status was measured with a 5 point scale, which asks the individual to identify their current
marital status. Income status was measured using a 7 point scale asks individuals what their
combined household income was the prior year and ranges from less than $24,999 to $150,000
or above. For race, individuals were asked to identify what race they consider themselves,
Caucasian/White, African American/Black, Mexican American/Hispanic/Chicano, Native
American/Alaskan, Oriental/Asian/Pacific Islander or Other. Ethnicity was assessed by asking
individuals to identify themselves as either Hispanic or Latino or not Hispanic or Latino.
Clinical Characteristics. Diabetes specific and Cancer specific clinical characteristics
were obtained. In addition, general symptoms and number of comorbidities were also obtained.
Comorbidities were assessed using a modified version of the Katz’s Comorbidity Questionnaire
(Katz, Chang, Sangha, Fossel, & Bates, 1996). This questionnaire asked patients if they have a
specific condition and has been used in older patients (Katz et al., 1996). This questionnaire
allowed for simple counting of the chronic diseases using a self-report method. The original
Katz comorbidity questionnaire was tested in 170 patients and was found to have a test-retest
reliability of .91 and had a spearman correlation of .63 was noted between the comorbidity
questionnaire and the Charlson index (Katz et al., 1996). Comorbidities will be recorded as a
total number for analysis.

54

General symptoms at baseline were obtained using the Symptoms of Illness Checklist
(SIC). The SIC was developed to be a comprehensive measurement of physical illness, that
could be used to measure psychological influences on physical health (Stowell, Hedges, Key, &
Bloch, 2009). This tool measures 33 different common symptoms associated with illness and
disease, the frequency of the symptom and the interference of the symptom on daily function.
The frequency of the symptom is measured by asking the number of days over the past two
months they had experienced the symptom on a 6 point scale, 0 being none and 5 being daily.
For this study participants were asked how many days they have experienced the symptom over
the past 2 weeks. The interference with daily function is measured by having the patient rank on
a 4 point scale, in which 1 is no interference with daily activities and 4, severe interference. The
SIC has a Cronbachs alpha of .86 (Stowell et al., 2009). This instrument was tested in 520
individuals from four different samples, the mean age ranged from 22.7 with a sd of 2.8 years to
45.8 with a sd of 14.1 years. Participant self-report in one of the samples was compared with
scores from physician interviews in order to establish concurrent-criterion validity. The scores
from patient self-report were similar to those obtained from the physician interviews. The
Cohen’s Kappa coefficients for the presence or absence of symptoms ranged from .48 – 1.0 for
all items (Stowell et al., 2009). This tool was selected for this study for it assesses symptoms
associated with both chronic disease and cancer. Baseline symptoms will be entered as total
number of symptoms as well as a total severity score. A severity score for each symptom is
obtained by multiplying the frequency by the level of impact or interference. Then all severity
scores are summed to form a total severity score.
Diabetes specific clinical characteristics included measurements for length of illness, type
and number of medications. Length of illness was obtained by asking patients how many years

55

they have had diabetes and was treated as a continuous variable. Type of diabetes was based on
if they are insulin dependent or non-insulin dependent and was classified as a categorical
variable. Number of medications was measured by asking patients how many different
medications they take for their diabetes and was treated as a continuous variable. In addition,
individuals were asked the total number of medications they take daily, in addition to their
diabetes medications and was entered as a continuous variable.
Cancer specific clinical characteristics included type and stage of cancer, type of
chemotherapy, number of medications they are on for their cancer and cancer symptoms. For
cancer type and stage, patients were asked what type of cancer they have, and if they have early
or late stage cancer. Type of chemotherapy was classified as intravenous, oral or both. Patients
were also asked if they had been treated with radiation or were currently receiving radiation
treatments. Number of medications was measured by self-report by asking patients how many
different medications they currently take on a daily or as needed basis for their cancer.
Medication frequency was assessed as a categorical variable.
Behavioral Characteristics. Perceived Diabetes self-efficacy was measured using a
modified version of the Confidence in Diabetes Self-Care Scale (CIDS) (van der Ven et al.,
2003). The CIDS is a reliable and valid measure that has been used to measure diabetes specific
self-efficacy in type I diabetics. The CIDS measures a person’s confidence in ability to perform
20 different diabetes specific self-management behaviors.

For each item participants were

asked to rate on a 5 point likert scale ranging from 1, No, I am sure I cannot to 5, Yes, I am sure I
can. All items are preceded with “I believe I can……”. Specific items asked include: check my
blood glucose at least two times daily, adjust my insulin when I am sick, and keep my blood
glucose in the normal range when under stress. The CIDS has a internal consistency reliability

56

of .90 and a test-retest reliability coefficient of .85. The CIDS has been validated in both U.S.
and Dutch populations (van der Ven et al., 2003). For this study an additional question was
added addressing the patients belief that they can take their medications as prescribed in order to
adapt the questionnaire to individuals with type II diabetes who are not on insulin. A total
perceived self-efficacy score was developed by summing all of the items and was considered a
continuous variable.
OE was measured with the OE subscale of the Multidimensional Diabetes Questionnaire
(MDQ) (Talbot, Nouwen, Gingras, Gosselin, & Audet, 1997). The OE subscale consisted of 6
items which measured the patients perceptions of the effects of self-management behaviors on
metabolic control and prevention of complications and has documented validity and reliability
(Cronbachs alpha of .86) (Talbot, et al., 1997). All items were measured on a 10 point scale. A
total OE score was developed by summing all of the items.
Depression/Anxiety was measured by using the Hospital Anxiety and Depression Scale
(HADS). The HADS consisted of a total of 14 items measured on a 4 point likert scale, ranging
from 0 - 3. Seven of the items screen for the presence of depression and the other 7 screen for
the presence of anxiety (Snaith, 2003). Each subscale was summed individually, scores of 0 – 7
for either subscale is indicative of normal range, or not having the condition. Score of 8 – 10
indicates mild disorder, 11 – 14 moderate disorder and 15 to 21 indicates presence of severe
disorder (Fossa, Dahl, & Loge, 2003). The coefficient alpha is .89 for the depression subscale
and .86 for the anxiety subscale (Olsson, Mykletun, & Dahl, 2005). The HADS anxiety scale
has a sensitivity of .89 and specificity of .75, the depression subscale has a sensitivity of .80 and
specificity of .88 (Olsson, et al., 2005).

57

Diabetes Self-Management Behaviors. The Self-Care Inventory – R (SCI-R) (Weinger,
Butler, Welch, & La Greca, 2005) was used to measure diabetes self-management. The SCI-R is
a 15 item self-report measure which assesses patient perceptions regarding their adherence to
diabetes self-care recommendations. The SCI-R measures how well individuals have followed a
prescribed regimen for diabetes care over the past month. Specific items included: check blood
glucose with monitor, take the correct dose of diabetes pills or insulin, take the diabetes pills or
insulin at the right time, eat meals/snacks on time, exercise, and adjust insulin dosage based on
glucose values, food and exercise. Individuals were asked to rate each behavior on a 5 point
Likert scale, ranging from 1, never do it, to 5, always do this as recommended without fail
(Weinger et al., 2005).
The SCI-R has been tested and validated in individuals with either type I and type II
diabetes. The internal consistency reliability coefficient was 0.87 and had a reliability
coefficient in individuals with type I diabetes of 0.84 and type 2 diabetes of 0.85. Convergent
validity of the SCI-R shows a positive correlation with self-esteem and self-efficacy, and is
negatively correlated with diabetes related emotional distress and anxiety (Weinger et al., 2005).
The SCI-R also has demonstrated ability to respond to change (Weinger et al., 2005).
Impact of Cancer on Diabetes Self-management. The Impact cancer has on diabetes
self-management was measured using a modified Illness Intrusiveness scale (Devins, 2010). The
Illness Intrusiveness Scale (IIS) is a 13 item scale which asks respondents to rate the degree to
which their illness interferes with their quality of life. The IIS asks respondents to rate how
much their illness or its treatment interferes with aspects of an individual’s life, such as diet and
exercise, on a 7 point Likert scale, with 1 reflecting not very much and 7 reflecting very much

58

(Devins, 2010). The IIS has an internal consistency which ranges from .80 to .90, with a testretest reliability of .80 (Devins, 2010).
The scale was modified to ask how much the patient’s cancer and its treatment interfered
with different aspects of their diabetes self-management, utilizing the same 7 point Likert scale.
The modified scale consisted of 5 questions which addressed different aspects of diabetes selfmanagement, diet, exercise, managing blood sugars, taking medications and self-management
activities in general. The modified instrument reliability coefficient was calculated is reported in
chapter 5.
Approach to Statistical Analysis
Data analysis for the quantitative portion of the study was done using PASW 18 ("PASW
Statistics ", 2009). Atlas.ti ("Atlas.ti," 2009) was used to analyze the open ended question for
this study. The main focus of this pilot study was to examine the relationship among individual,
clinical and behavioral characteristics and diabetes self-management behaviors at baseline and at
8 weeks after starting chemotherapy. Initially the frequency distributions of descriptive variables
(means, median, percentages and standard deviations) were computed. This general descriptive
data was used to report socio-demographic characteristics, cancer and diabetes characteristics,
and behavioral characteristics of the sample. In addition, to the frequency distribution, measures
of central tendency, skewness and variability were assessed for all study variables. The use of
correlations and linear regression was used to analyze the data. The following equation was
used: yi = β0 + β1x i1 + β2xi2 + ……..+ βjxij + єi. The specific analysis plan for each aim is
outlined in the following section:
Aim 1: Among patients with pre-existing diabetes who are either starting, or are
currently receiving outpatient chemotherapy for a solid tumor cancer how does clinical

59

characteristics and behavioral characteristics relate to the performance of diabetes selfmanagement behaviors while controlling for individual characteristics.
The Pearson-r was run between each of the independent variables and the dependent
variable (diabetes self-management behaviors) in order to determine if a positive or negative
relationship between these variables at baseline. Here the independent variables included
clinical characteristics (symptoms, number of comorbidities, type of diabetes, diabetes severity,
number of medications, cancer type, stage, type of chemo & number of cancer medications),
level of depression/anxiety, self-efficacy and outcome expectancies. A linear regression model
was used to determine which of the independent variables were statistically related to diabetes
self-management behavior performance at baseline, while controlling for the covariates which
consisted of individual characteristics of age, sex, marital status, living arrangements,
race/ethnicity, and income status. The equation for this analysis was: self-management
behaviors-baseline = clinical characteristics + level of depression + level of anxiety + level of
self-efficacy + level of outcome expectancies + individual characteristics.
Aim 2: Among patients with pre-existing diabetes who have completed a minimum of 8
weeks of outpatient chemotherapy how does baseline clinical and behavioral characteristics,
self-management behaviors for diabetes at baseline, cancer impact on diabetes and symptom
burden at 8 weeks relate to the performance of diabetes self-management behaviors while
controlling for individual characteristics. The Pearson-r was run between each of the
independent variables and the outcome variable (diabetes self-management at 8 weeks after
starting chemotherapy) to determine if there is a positive or negative relationship between these
variables. Here independent variables included clinical characteristics, level of
depression/anxiety, self-efficacy, outcome expectancies, baseline self-management behaviors,

60

and impact of cancer on diabetes self-management and symptom burden at 8 weeks. A linear
regression model was used to determine which of the independent variables are statistically
related to the level of diabetes self-management behaviors at 8 weeks after starting
chemotherapy while controlling for the covariates of individual characteristics. The equation for
this analysis was: diabetes self-management behaviors at 8 weeks = clinical characteristics+
level of depression + level of anxiety + level of self-efficacy + level of outcome expectancies +
self-management behaviors at baseline + cancer impact on diabetes + symptom burden at 8
weeks + cancer impact on diabetes + individual characteristics.
In addition to the primary analysis, a sub-analysis using paired t-test was performed to
assess if there were significant differences between the means of baseline and 8 week symptom
severity. Cancer specific symptoms of fatigue, pain, appetite and nausea were also compared
between baseline and 8 weeks. A comparison was also done between the means of baseline and
8 week diabetes self-management activities to see if there was change in relation to the
performance of self-management behaviors.
Aim 3: To identify common challenges or concerns regarding the self-management of
diabetes in older adults after completing at least 8 weeks of outpatient chemotherapy. Specific
research questions: 1) Based on your experiences over the past 8 weeks, what do you think have
been some of the challenges or issues you have had to face in regards to managing your
diabetes?; and 2) In general describe how your diabetes has been affected or impacted since you
started undergoing chemotherapy for your cancer?
This aim was analyzed using Atlas.ti version 6 ("Atlas.ti," 2009). The responses to the
single open ended question were transcribed and then uploaded in to Atlas.ti version 6
("Atlas.ti," 2009) for analysis. Each individual transcript was read at least twice and then coded.

61

The transcripts were compared for similar codes, in order to identify common concerns or issues
across participants. All transcripts were coded and analyzed by the study investigator. The
study investigator used thematic analysis approach for coding the transcripts. During the coding
process the study investigator kept a study log regarding personal reflections, analytical and
theoretical decisions.
Sample size estimates – Power Analysis
A post hoc analysis was done in order to determine power. In estimating the potential
power of this study, the applicant reviewed the literature in regards to reported effect sizes for
the outcome variable of self-care behaviors, using the SCI-R. Two studies were identified,
which reported effect sizes ranging from small (.16) (Garcia, 2008) to moderate (.37) (Wallace et
al., 2009). Power calculations were done using the program G-power (Faul, 2008). Calculations
were based on using a f-test for a linear model. Calculations were run using an effect size of .16,
α=0.05, Power (1-β) = 0.8, which produced a required sample size of 70. The calculations were
repeated using and effect size of .37, which produced a required sample size of 17.
Quality Control and Data Management
To ensure that quality and consistency of the collected data for this study the following
measures were taken: 1: Development of a procedure manual: This manual included recruitment
procedures, enrollment procedures, and consent procedures and forms, all instruments and forms
to be utilized in the study, approach to managing and analyzing data, interview protocols, and
methods for evaluating interviews throughout the data collection period. 2: Training and
evaluation of interviewers, in case there were interviewers in addition to the study investigator
for the 8 week collection times. This training included the study investigator conducting practice
interviews which will be taped and evaluated to ensure that the questions and interview methods

62

are applied correctly. Every interview conducted at 8 weeks was tape recorded, in order to
obtain and develop a transcript for the qualitative section of the study. 3: Data management: all
collected data was entered into PASW by student volunteers. A codebook was developed that
linked the study variables to variable names in PASW. All interview forms will have an
identification number which will correspond with the subject ID variable in PASW. Random
checks will be made to ensure that data was entered completely and correctly by the study
investigator. All tapes of the data obtained from responses to the open-ended question, were
transcribed into a word document. This word document was uploaded into and Atlas.ti for
purpose of analysis. 4: Data collection sites were approached by the study investigator, once
agreement was reached regarding their participation in the project, the study investigator
provided educational training to the staff that were identifying potential participants regarding,
inclusion and exclusion criteria, and study protocols. The study investigator also made periodic
contacts throughout the study with the sites to assess for any problems associated with
recruitment and to help problem solve, to ensure that recruitment of participants is consistent
across sites. 5: The study investigator addressed with the site coordinators any recruitment issues
which arose, if it was identified that recruiters were not following protocols, the issues were
discussed with them and a plan to address the issues was developed, if problems continued to
occur the sites were removed from the study.
Potential Difficulties & Limitations:
It is acknowledged by the study investigator that participant recruitment could be a
potential difficulty. Since, participants are not going to be consented by the sites, and will take
packets home to review, complete and return to the study investigator it is possible that surveys
will not be returned. The study investigator will work with the sites to ensure that packets are

63

being given out appropriately and will check with them weekly to see how many have been
distributed in order to determine rate of return. If return rates are significantly low, the study
investigator may need to consider having patients consented by the study sites and then follow
up with enrolled participants by phone in order to complete the baseline survey. In addition, the
study investigator acknowledges that additional sites may need to be recruited, in order to ensure
that an adequate sized sample is recruited within the study period.
The study investigator met regularly with her committee to discuss issues regarding
recruitment, in order to identify other recruitment strategies as necessary and to identify when
additional sites may be needed. In order to control for possible attrition of subjects after the
baseline survey is completed, the study investigator will mail a reminder card four weeks prior to
the follow up phone survey. In addition to reminding the participant of the follow up survey, the
card will also ask the patient to contact the study investigator if the previously scheduled date
would need to be rescheduled.
Protection of Human Subjects
Risk to Human Subjects:
Human Subjects Involvement and Characteristics: Participants in this study included
individuals 50 years of age or older who have diabetes and a recent diagnosis of cancer, who
were eligible to receive chemotherapy or who were already on chemotherapy. Individuals who
were cognitively impaired or unable to use a telephone were not eligible for participation in this
study. Subjects were recruited from a total of 7 different cancer centers, 6 in lower Michigan
and 1 in Ohio. Sites were responsible for identifying potential participants and supplying them
with study information and a consent form. If a subject was willing to participate in the study
they were required to return a signed consent form to the study investigator.

64

Sources of Materials: Upon receipt of the consent form and completed initial survey the
study investigator contacted the participant to set up the 8 week telephone interviews. The initial
survey obtained socio-demographic information, information regarding their diabetes and cancer
treatment, level of diabetes perceived self- efficacy and questions regarding their current selfmanagement behaviors. During the 8 week interviews questions regarding symptoms and selfmanagement of their diabetes were asked. In addition, during the 8 week interview two openended questions were asked and tape recorded. The survey instruments did not include the
subjects name or other identifying information. All consent forms, which had subjects names,
ID numbers and contact information were kept in a locked file which only the study investigator
had access too.
Potential risks: Subjects were not placed at physical or financial risk by their participation in
this study. Participants could choose to withdraw from participation in this study at the time the
study investigator contacted them to complete the interview. They also could elect to have the
study investigator contact them at an alternate time if they were are unable to complete the
questionnaire at that time. There is a potential risk for the subjects to incur stress related to
answering questions and to the time it may involve.
Adequacy of Protection Against Risks
Recruitment and informed consent: Recruiters were employees of the participating cancer
centers. All recruiters were trained by the study investigator in regards to the inclusion and
exclusion criteria for study participants and general information regarding the purpose of the
study. Once a potential participant was identified by a recruiter, they gave the patient a packet
with information regarding the study, contact information for the study investigator, a consent
form and stamped envelope to be used to return the signed consent form to the study

65

investigator. When the study investigator contacted the subject to complete the survey, she
reconfirmed consent for participation.
Protection against risk: All subjects were assured by the study investigator regarding the
confidentiality of all information given during the interview. They were informed that the
information will not be shared in any way resulting in their identification. Data was reported as
aggregate only and not individually. They were also informed that only the consent form would
have any identifying data on it, and that only the study investigator had access to these forms.
Subjects were given the opportunity to ask questions about the study and the process to ensure
they understand any potential risk of their involvement in the study.
Women and Minorities
Women: Approximately 48% of all new cancers will occur in women (Jemal et al., 2009).
Women make up approximately 50% of the population in the United States ("USA quickfacts
from the US census bureau," 2009). 10.2 % of women over the age of 20 will have diabetes
which is only slightly less than total estimated percentage of men with diabetes (11.2%) ("Total
prevalence of diabetes and prediabetes," 2009). Based on these facts, the study investigator
made all attempts to have similar percentages of women with diabetes and cancer in this study.
Minorities: African Americans make up approximately 12.8% of the population in the U.S.
and 14.3 % of the population in Michigan ("USA quickfacts from the US census bureau," 2009).
Asians make up approximately 2.4% of the population Michigan, were individuals of Latino or
Hispanic origin make up approximately 4.0% of the population in Michigan ("USA quickfacts
from the US census bureau," 2009) African American men have a 18% higher incidence rate of
cancer when compared to white men (Jemal et al., 2009). African-American women have a 6%
lower incidence rate for cancer when compared to white females (Jemal et al., 2009) 9.8% of

66

non-Hispanic whites and 14.7% of non-Hispanic blacks in the U. S. will have Diabetes. Based
on these facts, the study investigator made all attempts to recruit similar percentages of
minorities with diabetes and cancer into this study.

67

Chapter 5
Results
The primary purpose of this study was to explore the relationship between clinical and
behavioral characteristics and the performance of diabetes self-management behaviors in adults
50 and older, with preexisting diabetes and undergoing chemotherapy for cancer over an 8 week
time period. The secondary purpose of this study was to identify specific issues adults with
diabetes who were undergoing chemotherapy experience in relation to managing their diabetes.
This study sought to address the following aims:
Primary Aims:
1. Among patients with pre-existing diabetes who are either starting, or are currently
receiving outpatient chemotherapy for a solid tumor cancer how does clinical
characteristics associated with diabetes and cancer and behavioral characteristics relate to
the performance of diabetes self-management behaviors while controlling for individual
characteristics.
2. Among patients with pre-existing diabetes who have completed a minimum of 8 weeks of
outpatient chemotherapy, how does baseline clinical and behavioral characteristics,
baseline diabetes self-management behaviors and symptom burden after 8 weeks of
chemotherapy relate to the performance of diabetes self-management behaviors while
controlling for individual characteristics.
Secondary Aim:
1. To identify common challenges or concerns regarding the self-management of diabetes in
older adults after completing at least 8 weeks of outpatient chemotherapy.

The following sections will present the results from this study. Sample characteristics,
and descriptive analyses of each aim will be presented. In addition, reliability of study
instruments, and scoring information will be presented. Interpretation and discussion of the
results and implications will be presented in chapter 6.

68

Study Measures
Operational definitions for all study variables were provided in chapter 4. The following
section will discuss the actual measurement and scoring for each variable. Reliability coefficient
for each of the study instruments for this study will be presented. Reliability data for each
instrument for this study can be found in tables 2 & 3.
Predictors/Covariates
Individual characteristics.
Age.

Each participant reported their date of birth. Their age in years was calculated

based on the date of enrollment into the study and treated as a continuous variable for analysis.
Sex. Each participant self-reported their sex as either male or female. They were scored
as 1.00 for male and 2.00 for female in SPSS. Sex was treated as a categorical variable for
analysis.
Household Income. Income was a self-reported variable. Participants were asked to
select the total household income which best reflected them. Income was categorized into 5
different categories ranging from less than or equal to $49,999 per year to great than or equal to
$200,000 per year. Income was treated as a categorical variable for analysis.
Marital Status. Each participant was asked to report their current marital status. They
were to choose the category which best reflected them. Categories included never married,
married, divorced/separated, widowed and living together. Marital Status was treated as a
categorical variable for analysis.
Living arrangements. Individuals were asked to report their current living arrangements
based on established categories. These categories were live alone, live with spouse/significant

69

other, live with family member other than spouse/significant other and live with non-family
member.
Race. Each participant was asked to select the category which best fit their race.
Categories included, Caucasian/White, African American/Black, Mexican
American/Hispanic/Chicano, Native American/Alaskan and other. Race was treated as a
categorical variable for analysis purposes.
Ethnicity. Each participant was asked to select the ethnicity which best fit them.
Categories were Hispanic or Latino, or non-Hispanic or Latino. Ethnicity was treated as a
categorical variable.
Clinical characteristics.
Number of comorbidities. Participants were asked if they had specific comorbidities
based on the Katz Comorbidity Index (Katz, et al., 1996). These comorbidities included
hypertension, asthma, chronic lung disease other than asthma, congestive heart failure, heart
attack, stroke, neurological condition, arthritis, and emotional, nervous or psychiatric problem.
Participants were also asked to list any other chronic condition they currently had other than their
cancer or diabetes. Each condition was coded 1.00 for yes, and 0.00 for no. For other, the study
investigator reviewed what was written to ensure that they reported a chronic condition other
than what had already been reported. A variable titled total other was then developed and the
number of other conditions was entered. A total number of comorbidities other than diabetes and
cancer was developed by summing all comorbidities and total other together. This variable was
treated as a continuous variable for analysis.
Symptom Burden. The Symptom of Illness Checklist (Stowell, et al., 2009) was used to
collect data on 33 different symptoms common in illness. This instrument was described in

70

chapter 4. This instrument is scored by individuals selecting a frequency for each symptom and
an interference level. In order to develop a total symptom burden (severity) score the frequency
score for each symptom was multiplied by its interference score. The total severity scores for
each item were summed to create a total symptom burden score. This instrument was
administered at both baseline and eight weeks. A Cronbachs alpha for this instrument for this
study was determined to be .86 at both baseline and 8 weeks. This is consistent with the reported
reliability of the instrument (Stowell, et al., 2009). Reliability coefficients were also calculated
for the instruments subscales of frequency and interference for baseline and 8 weeks. The
frequency subscale had an α = .77 and .76 respectively, and the interference subscale had an α =
.82 and .79 respectively.
Number of years with diabetes. Participants were asked to self-report the total number of
years they had diabetes. This variable was considered a continuous variable for analysis.
Type of diabetes. Participants were asked to select if they had Type I or Type II diabetes.
Type I diabetes was coded as 1.00 and type II coded as 2.00 for analysis. Diabetes type was
treated as a categorical variable for purposes of analysis.
Diabetes treatment. Participants were asked to select the type of medications they were
on for their diabetes. Participants could choose oral medication, insulin or oral and insulin both.
Diabetes treatment type was treated as a categorical variable for purpose of analysis. Insulin was
coded as a 1.00, oral medications coded as a 2.00 and both insulin and oral was coded as a 3.00.
Total number of medications. Participants were asked to report the number of
medications they took for their diabetes, their cancer, and their other comorbidities. The
reported number of medications for each category was summed to develop a total number of
medications. This variable was treated as a continuous variable for analysis.

71

Diabetes Severity. Diabetes severity was measured using the Diabetes Complication
Index (Fincke et al., 2005). This instrument was described in chapter 4. A Cronbachs alpha was
computed for this instrument for this study (α = .69). Prior psychometrics have not been
reported for this instrument. For each item participants were asked to choose yes if they had the
problem and no if they did not. Items were coded as 1.00 for yes and 0.00 for no. Items for
coronary artery disease (CAD), cerebrovascular disease (CVD), peripheral vascular disease
(PVD), neuropathy, foot problems and eye problems were all summed to obtain a total severity
score. Higher scores were indicative of having more diabetes associated complications, and were
interpreted to mean greater diabetes severity. CAD, CVD, neuropathy and foot problems were
all computed variables based on answers to specific questions.
CAD was determined by summing patient selected answers to blockage in blood flow to
your heart, angina and myocardial infarction. The sum of 3 was considered positive for having
CAD. CVD disease was computed by summing scores for questions that asked regarding if the
individual had ever had a stroke or a transient ischemic attack (TIA). The sum of 1 or more was
considered positive for having CVD. Neuropathy was computed by summing answers to
questions which asked regarding presence of peripheral neuropathy and autonomic neuropathy.
A sum of 1 or greater was positive for the presence of neuropathy. Foot problems was computed
by summing answers to the questions regarding ulcers on the feet or lower legs, gangrene and
amputated feet or toes. A sum of 1 or greater was positive for having foot problems.
Cancer type. Participants were asked to identify the type of cancer they had from a list of
common cancers. These included breast, lung, colon, liver, pancreas, ovarian, bladder,
gynecological, gastrointestinal, lymphoma and prostate. If the type of cancer they had was not on

72

the list, they were asked to write in their diagnosis. Cancer type was treated as a categorical
variable for purposes of analysis.
Cancer Stage. Participants were asked to self-report there cancer stage as either early or
late. This variable was coded as 1.00 for early and 2.00 for late, and was considered a
categorical variable for analysis purposes.
Length of time on chemo. The total number of chemo days was determined by computing
the total number of days from their self-reported chemo start date, and the date they completed
the study. This variable was reported in total days, and was treated as a continuous variable for
analysis.
Cancer impact on diabetes self-management. Cancer impact on diabetes selfmanagement scale was described in chapter 4. This instrument was developed for this study,
based on the illness intrusiveness scale (Devins, 2010). The instrument consisted of 5 items and
scores for all 5 items were summed in order to obtain a total impact score. The Cronbachs alpha
for this instrument was  = .85 for this study. This variable was treated as a continuous variable
for purposes of analysis.
Behavioral characteristics.
Diabetes self-efficacy. The Confidence in Diabetes Self-Care Scale (van der Ven, et al.,
2003), was modified to measure diabetes self-efficacy in this population. This scale was
described in chapter 4. The original scale had 20 items, and additional item was added which
addressed the individual’s belief in their ability to take medications as prescribed, in order to
accommodate individuals with type II diabetes who are not on insulin. A internal consistency
reliability coefficient for this single concept instrument was  = .86 for this study. This alpha is
slightly lower than the reported reliability of α=.90.

73

Scoring of the Confidence in Diabetes Self-Care Scale was done by transforming all total
scores to a 0 – 100 scale. This was done by taking the patients raw score minus the lowest
possible score and then dividing by the possible score range, and then multiplying by 100 (van
der Ven, et al., 2003). The patient’s raw score was determined by summing all the items
together to create a total diabetes self-efficacy score. This variable was treated as a continuous
variable for analysis.
Outcome expectancies. Diabetes outcome expectancies was measured using the outcome
expectancies subscale of the multidimensional diabetes questionnaire. This instrument was
discussed in chapter 4. The original survey utilized a 0 – 100 scale. For this study a 0 -10 scale
was utilized, and then the total raw score was converted to a 0 – 100 range, utilizing the same
method described for the confidence in diabetes self-care scale. The Cronbachs alpha for this
scale was determined to be  = .87 for this study, which is consistent with reported reliabilities
for this instrument (Talbot, et al., 1997). This variable was treated as a continuous variable for
analysis.
Depression/Anxiety. Depression and anxiety was measured using the Hospital Anxiety
and Depression Scale (Snaith, 2003). Information regarding this instrument was provided in
chapter 4. For scoring items 1, 3, 5, 6, 8, 10, 11, and 13 were reverse coded once the
questionnaire was completed and data was entered into SPSS. Total anxiety score is developed
by summing items 1, 3, 5, 7, 9, 11, and 13 and a total depression score is developed by summing
items, 2, 4, 6, 8, 10, 12 & 14. The Cronbachs alpha for this scale was  = .86 for all items, and 
= .81 for the anxiety subscale and  = .69 for the depression subscale for this study. The
reliabilities obtained for this study are lower than the originally reported reliabilities for the

74

subscales, which are anxiety α=.89 and depression α=.86 (Snaith, 2003). This variable was
treated as a continuous variable for analysis.
Dependent Variable
Diabetes self-management. Diabetes self-management was measured with the Self-care
Inventory-Revised (Weinger, et al., 2005). This instrument was described in chapter 4. This
instrument consisted of 15 items which measure self-care activities for diabetes, and is scored by
summing all items and then converting the raw score to a score ranging from 0 – 100. This was
done by taking the patients raw score and subtracting the lowest possible score, and then dividing
by the score range, which is then multiplied by 100
Table 2:
Internal consistency reliability statistics for study instruments at baseline
Instrument
N of
µ
Variance
Items
Symptoms of Illness Checklist*
60
31.53
410.39

sd



20.26

.86

Symptom Frequency subscale

30

23.09

203.18

14.25

.77

Symptom Interference subscale

30

8.29

48.76

6.98

.82

Diabetes Complications Index**

13

1.79

4.17

2.04

.69

Confidence in Diabetes Self-Care Scale

21

88.03

173.60

13.17

.86

6

55.52

58.32

7.64

.87

14

25.18

55.90

7.47

.86

Anxiety Subscale

7

12.94

19.99

4.47

.81

Depression Subscale

7

11.78

11.67

3.41

.69

15

43.97

97.48

9.87

.74

Outcomes Expectancies Scale
Hospital Anxiety and Depression Scale

Self-care Inventory Revised

*The following items were removed due to zero variance: cold sores frequency and interference,
menstrual problems frequency and interference.
**Item for Stroke was removed due to zero variance
(Weinger, et al., 2005). The patient’s raw score was determined by summing all the items
together to create a total self care score. This variable was treated as a continuous variable for
analysis. An internal consistency reliability coefficient was calculated for this instrument, and
75

was determined to have a  = 74 at baseline and .80 at the 8 week interval. Both the baseline and
8 week reliability scores obtained were lower than those originally reported for this instrument
(α=.87).
Table 3:
Internal consistency reliability statistics for study instruments at 8 weeks
Instrument
N of
µ
Variance
Items
Symptoms of Illness Checklist*
58
37.37
452.62

sd



21.27

.87

Symptom Frequency subscale

31

28.11

224.25

14.98

.76

Symptom Interference subscale

27

9.26

53.28

7.30

.79

5

16.81

80.23

8.96

.85

14

40.59

127.79

11.30

.80

Cancer Impact on Diabetes Scale
Self-care Inventory Revised**

*The following items were removed due to zero variance: Menstrual problems frequency and
interference, rash, sinus, cold sore and lymph interference
**The following item was removed due to zero variance: checking ketones
Sample
Once IRB approval was obtained from MSU and the other sites for this study (see
appendix for IRB approval letters) a total of 34 patients who met inclusion criteria were enrolled
in this study, once IRB approval was received from Michigan State University, and the
individual cancer centers (see appendix for approval letters). A total of 55 baseline surveys were
distributed. Out of those, 35 were returned (64%). Of those who returned there baseline
surveys, 29 (85%) subjects completed the 8 week follow up survey. Reasons for noncompletion
included: 2 deceased, 1 unable to complete due to placement into an extended care facility, and
two individuals did not respond to requests for follow up.

See figure 3 for consort table.

Of the participants who were eligible and enrolled in the study, 52.9% (n=18) were female,
61.8% (n=21) were married. The majority were Caucasian (85.3%, n= 28), most had some
college education or completed college (66.5%, n= 26). The majority had type II diabetes

76

(94.1%, n= 32), and late stage cancer (61.8%, n= 22). The most common cancers were breast
(29.4%, n=10), pancreas (14.7%, n= 5) and lung (11.8%, n=4). The majority was receiving
outpatient IV chemotherapy (55.9%, n=19), 29.4% (n= 10) were receiving both IV chemo and
radiation.
Of the participants who completed the study, 58.6% (n=17) were female, 69 % (n= 20)
were married, and most had some college or completed college (71.4%, n= 21). The majority
were Caucasian (79.3%, n= 23), had type II diabetes (93.1%, n= 27), late stage cancer (65.5%,
n= 19) and were receiving IV chemotherapy (58.6%, n= 17). See tables 4 & 5 below for further
sample information.

Approached and received packet
55 (100%)

Consented and Returned baseline
survey
35 (64%)

Eligible for Study

Yes
34 (97%)

No
1 (3%)

Completed 8 week
survey
29 (85%)
Figure 3: Consort table of study flow and enrollment indicating number approached,
enrolled and completion of study

77

Figures 4, 5 & 6, present sample distribution information for the outcome variable of
diabetes self-management behaviors, and for the behavioral characteristic of diabetes selfefficacy in the form of a histogram. For diabetes self-management behaviors at baseline the
skewness was .108, and at 8 weeks was .202. The Kurtosis was -1.066 at baseline and -.173 at 8
weeks. Diabetes self-efficacy had a skewness of -.753 and kurtosis of .957.
Table 4:
Sample characteristics, N and % for categorical variables at baseline and 8 weeks:
Baseline
8 Weeks
Variable
N
%
N
%
Sex
Male
16
47.1
12
41.4
Female
18
52.9
17
58.6
Race
Caucasian/White
28
82.4
23
79.3
African American/Black
5
14.7
5
17.2
Mexican American/Hispanic Chicano
1
2.9
1
3.4
Ethnicity
Hispanic or Latino
1
2.9
1
3.4
Not Hispanic or Latino
33
97.1
28
96.6
Education Level
No formal education or completed grade school
Some high school
Completed high school
Some college or technical training
Complete college
Marital Status
Never Married
Married
Divorced/Separated
Widowed
Living Together

1
1
6
15
11
3
21
4
5
1

2.9
2.9
17.6
44.1
32.4
8.8
61.8
11.8
14.7
2.9

1
1
6
12
9
3
20
2
4
0

3.4
3.4
20.7
41.4
31.0
10.3
69
6.9
13.8
0.0

Living Arrangements
Live Alone
Live with spouse/significant other
Family member other than spouse/significant other
Non-family member

4
22
7
1

11.8
64.7
20.6
2.9

3
21
5
0

10.3
72.4
17.2
0

78

Table 4: Continued
Sample characteristics, N and % for categorical variables at baseline and 8 weeks:
Baseline
8 weeks
Variable
N
%
N
%
Household income
≤ $49,999
17
50.0
15
51.7
$50,000 – $99,999
11
32.4
8
27.6
$100,000 – $149,999
1
2.9
1
3.4
$150,000 – $199,999
1
2.9
1
3.4
Type of Diabetes:

Type I
Type II

2
32

5.9
94.1

2
27

6.9
93.1

Insulin
Oral
Insulin & Oral

9
21
4

26.5
61.8
11.8

5
20
4

17.2
69.0
13.8

Breast
Colon
Lung
Liver
Gynecological
Gastrointestinal
Pancreas
Lymphoma
Other

10
2
4
2
1
1
5
2
7

29.4
5.9
11.8
5.9
2.9
2.9
14.7
5.9
20.6

10
2
4
1

34.5
6.9
13.8
3.4

1
4

3.4
13.8

7

24.1

Early
Late

12
22

35.3
64.7

10
19

34.5
65.5

IV Chemotherapy
Oral Chemotherapy
IV & Oral Chemotherapy
IV Chemotherapy & Radiation
IV, Oral Chemotherapy & Radiation
Other

19
1
1
10
1
2

55.9
2.9
2.9
29.4
2.9
5.9

17
1
1
9

58.6
3.4
3.4
31.0

1

3.4

Diabetes Medication Type

Cancer type

Cancer Stage
Cancer Treatment Type

79

Table 5:
Sample characteristics, N, µ, & sd for continuous variables at baseline and 8 weeks
Baseline
Variable
n
min
max
ū
sd
n
min
Age*
34
50
92 64.88 10.14 29
50
Number of years with
Diabetes*

8 weeks
max
ū
92
65.31

Sd
9.68

34

1.00

20.00

9.04

5.11

29

1.00

20.00

8.5

4.72

Total number of Comorbidities
other than Diabetes*

34

0.0

5.0

2.67

1.55

29

0.0

5.0

2.75

1.59

Diabetes Severity Score*

34

0.0

4.00

1.26

1.30

29

0.0

4.0

1.27

1.29

Symptom Burden**

34

0.0

78.00

24.62

21.09

29

0.0

116.0

28.41

25.38

29

50

288

124.17

63.66

3.00

21.00

10.68

5.16

Total number of chemotherapy
days***
Total number of medications*

34

Diabetes Self-efficacy*

2.00

21.00

10.14

5.08

29

34

41.38 100.00

81.14

13.45

29

33.33 100.00

77.72

15.58

Diabetes Outcome
Expectancies*

34

27.78 100.00

91.83

13.95

29

27.78 100.00

90.93

14.74

Anxiety Level*

34

7.00

24.00

13.26

5.03

29

7.00

24.00

13.37

5.07

Depression Level*

34

7.00

20.00

11.76

3.36

29

7.00

20.00

11.79

3.36

29

5.00

35.00

16.03

8.57

29

15.63

81.25

47.73

15.69

Cancer Impact on Diabetes
Self-management***
Diabetes Self-care Score**

34

26.56

81.25

51.79

*Data collected at baseline only
**Data collected at baseline and 8 weeks
***Data collected at 8 weeks only

80

14.98

Figure 4: Histogram showing distribution of diabetes self-efficacy measure

81

Figure 5: Histogram showing distribution of diabetes self-management measure at baseline
Results and Analysis
Aim 1. Among patients with pre-existing diabetes who are either starting, or are
currently receiving outpatient chemotherapy for a solid tumor cancer how does clinical
characteristics associated with diabetes and cancer and behavioral characteristics relate to the
performance of diabetes self-management behaviors while controlling for individual
characteristics.
Correlation analysis was utilized to determine if specific individual, clinical and behavioral
characteristics were either positively or negatively related to the performance of diabetes self-

82

management behaviors at baseline (see tables 6, 7, 8 for correlation results). Age, DSE and
number of years with diabetes were all positively correlated with the performance of selfmanagement behaviors at baseline. As age, level of DSE and years with diabetes increased so
did the performance of diabetes self-management behaviors.

Figure 6: Histogram showing distribution of diabetes self-management measure at 8 weeks

83

Table 6

Education Level

r

Race

.037

-.059

-.297

.021 -.222

.127

.108

-.073

.440*

1 -.332

.000 -.066

-.097

.415

1

-.662* -.191

-.003

-.165

.076

.040

.058

1

.382*

.125

1

-.172

Race

r

1

Ethnicity
.185

r

Living
Arrangements
Baseline Diabetes
Self-Management
*p < .05

-.289

r

Marital Status

1

r

Ethnicity

Baseline
Diabetes SelfManagement

r

Living
Arrangements

Age

Age

r

.162

Education
Level

Sex
Sex

Marital Status

Correlation estimates of individual characteristics and baseline diabetes self-management
behaviors (N=34)

-.063

1

r

1

A linear regression model was also run at baseline to assess for possible significant
predictors of diabetes self-management at baseline. In order to run this model, variables were
blocked by characteristic type, individual, clinical and behavioral, and backward regression was
utilized. A total of 19 iterations were completed before the final model was achieved. The final
model had a R=.705, R square = .497, R square change of -.006, F= 14.84 with a p=.000. The
adjusted R square for the final model was .46, indicating that the model explained approximately
46% of the variance. The initial model had an R = .792 and R square of .628. Diabetes self-

84

Table 7

r

Diabetes Severity

r

Number of
Comorbidities
Symptom Severity

.077

-.010

-.056

.004

1

-.431*

-.074

.003

.228

.001

.332

.139

.173

-.198

1

.327

.107

-.155

-.205

-.135.

.128

.106

.402*

1

.428*

.459*

-.162

.117

-.013

.244

.349

1

.124

-.441*

.260

-.152

.583*

.211

1

-.168

.201

.113

.137

-.176

1

-.125

.045

-.058

.083

1

.131

.062

.077

1

-.155

-.109

1

.070

r

r

Number of
medications

Cancer
Treatment

Cancer Type

Baseline
Diabetes SM

.066

Cancer Stage

.003

r

r

1

Diabetes
Years

.184

r

Number of
medications
Baseline Diabetes
SM
*p <.05

.040

r

Cancer Treatment

-.166

r

Cancer Stage

.147

r

Cancer Type

Symptom
Severity

Diabetes
Medication
Diabetes Years

Number of
comorbidities

r

Diabetes
Severity

Diabetes Type

Diabetes
Medication

Diabetes Type

Correlation estimates of clinical characteristics and baseline diabetes self-management behaviors (n=34)

1

85

Table 8

Baseline
Diabetes SelfManagement

Depression

Anxiety

Outcome
expectancies

Diabetes SelfEfficacy

Correlation estimates of behavioral characteristics and baseline diabetes self-management
behaviors (N=34)

Diabetes Self-Efficacy

r

1 .382* -.238

-.128

.540*

Outcome Expectancies

r

1 -.114

-.210

.274

Anxiety

r

1

.547*

-.114

Depression

r

1

-.137

Baseline Diabetes Selfr
Management
*bolded items indicate statistically significant correlations
efficacy and age were noted to be significant predictors of diabetes self-management behaviors
at baseline (see table 9 for regression coefficients). Individuals with higher levels of diabetes
self-efficacy and older age were more likely to perform self-management behaviors than those
who were younger with lower levels of self-efficacy.
Table 9
Coefficients for final linear regression model indicating significant predictors of diabetes selfmanagement at baseline
ƅ
Std. error
Standardized ƅ
t
p
Constant
-38.911
16.82
-2.31 .028
Diabetes
.581
.144
.523
4.04 .000
Self-Efficacy
Age
.67
.191
.452
3.49 .002
Aim 2. Among patients with pre-existing diabetes who have completed a minimum of 8
weeks of outpatient chemotherapy, how does baseline clinical and behavioral characteristics,
baseline diabetes self-management behaviors and symptom burden after 8 weeks of

86

1

chemotherapy relate to the performance of diabetes self-management behaviors while
controlling for individual characteristics.
Correlations analysis was utilized to determine if any predictors were positively or
negatively correlated with diabetes self-management behaviors at 8 weeks. A linear regression
model was run to determine what predictors contributed significantly to the performance of selfmanagement behaviors at eight weeks while controlling for baseline self-management. Tables
10, 11 and 12 present the results of the correlation analysis.
At eight weeks none of the baseline individual characteristics were found to be
significantly correlated with performance of self-management behaviors at eight weeks. The
baseline clinical characteristics of number of years with diabetes and level of diabetes severity
were significantly correlated with the performance of self-management behaviors at 8 weeks.
Behavioral characteristics found to be significantly correlated with self-management behaviors at
8 weeks were baseline self-management behaviors and level of diabetes self-efficacy.
A linear regression model was run to determine if specific independent variables
predicted performance of self-management activities at 8 weeks. A total of 15 iterations were
completed before the final model was achieved. The final model had a R = .939, R square =
.882, R square change = -009, F = 17.831, and p = .000. The adjusted R square was .83, the
model accounted for 83% of the variance. The first model had a R = .989 and a R square = .977.
Baseline self-management, number of years with diabetes, total number

87

Table 10

Education Level

r

Race

r

Ethnicity

r

Marital Status

r

Living
Arrangements

-.013

-.195 -.261

.036

-.142

.068

.112

.056

.025

.-.219

.285

.262
-.149

Ethnicity

Marital Status

.128

r

8 week Diabetes
Self-Management

8 week
Diabetes SelfManagement

1

Living
Arrangements

r

Race

Education
Level

Age

-.040 -.092

1

1 -.306 -.059

.151

1

r

-.081

1

Sex

.111

1 -.662*

Age

Sex

Correlation estimates of baseline individual characteristics and 8 week diabetes selfmanagement behaviors (n= 29)

.125

.225

.024 -.272
1 -.317

.334

r

1

*p<.05
of comorbidities and ethnicity were all noted to be significant (p<.05) predictors of performance
of self-management behaviors at 8 weeks (see Table 13 for regression coefficients).
Additional Analysis
In order to assess if cancer and its treatment had an impact on the performance of
diabetes self-management behaviors, and to assess if the patients symptom burden significantly
changed from baseline to 8 weeks, a paired t-test was utilized to compare the means of items
that were collected both at baseline and 8 weeks.

There were no significant differences found

between baseline and 8 week means for total symptom severity (baseline µ = 23.37, sd = 21.86,
8 week µ = 28.52, sd = 26.17). Further analysis was done comparing symptom severity means at
baseline and 8 weeks for cancer specific symptoms: pain, fatigue, nausea and appetite. No
significant differences were found. For this sub-analysis pain was the combined severity scores
88

8 wk DM SM

CIDS

# medications

# chemodays

CA treatment

CA Stage

CA Type

8wk TSSS

Baseline
TSSS

# Co-morbids

TDSS

DM Years

DM Meds

DM Type

Table 11
Correlation estimates of baseline clinical characteristics, 8 week symptom severity, 8 week cancer impact on diabetes and 8
week diabetes self-management behaviors (n= 29)

DM type
r 1 .23 -.23 .04
.21
-.00 -.38*
.05
.09
-.05
.26
-.04 -.21 -.04
DM Meds
r
1 -.38* -.09
-.01
.29
.19
.12
.35
-.17
-.13
.19 -.20
.48*
DM years
r
1 .34
.15
-.09
.26
-.32
-.18
-.12
.23
.27
.14 .60*
DM Severity
r
1
-.23
.29
.11
-.13
.25
.25 .43*
.45*
.55*
.38*
# of
r
1
.12
.19
.322
-.06
.10
.07
.36
-.46
.63*
comorbids
TSSS
r
1
-.19
.24
.25
-.09
.15
.36
.13
.63*
8 week TSSS r
1
-.28
.07
-.17
.26 .48*
.25
.41*
CA type
r
1
-.13
.03
-.09
-.09 -.31 -.13
CA stage
r
1
.09
.07
.06
.26
.06
CA Treatment r
1
-.17
.05 -.19 -.24
# chemodays
r
1
-.00 -.21
.19
# Medications r
1 -.02
.34
CIDS
r
1
.17
8wk DM SM r
1
*p < .05
DM = Diabetes, TDSS = Total Diabetes Severity, TSSS = Total symptom severity, CA= Cancer, CIDS = Cancer Impact on
Diabetes, SM = Self-management behaviors

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Table 12

Outcome Expectancies

Depression
Baseline Diabetes SelfManagement
8 week Diabetes SelfManagement
*p < .05

r

8 week
diabetes selfmanagement

Baseline
Diabetes SelfManagement

.614*

-.028

-.135

.282

.119

.550*

-.125

-.134

-.022

-.165

1

r

.655*

1

r

-.046

1

r

-.172

1

r

Anxiety

.286

1

Depression

r

Anxiety

Diabetes Self-Efficacy

Outcome
expectancies

Diabetes SelfEfficacy

Correlation estimates of baseline behavioral characteristics and 8 week diabetes selfmanagement behaviors

.726*
1

Table 13
Coefficients for final linear regression model indicating significant predictors of diabetes selfmanagement at 8 weeks
b
Std. error
Standardized b
t
p
Constant
81.52
16.66
4.89
.000
Ethnicity
-39.15
6.66
-.471 -.5872
.000
Years with Diabetes
1.26
.28
.37
4.50
.000
Number of Comorbidities
3.59
.76
.37
4.69
.000
Baseline DM Self-management
.513
.081
.502
6.29
.000
behaviors
Diabetes Self-efficacy
.072
.134
.061
.538
.597
Diabetes Outcome Expectancies
-.071
0168
-.039
-.425
.676
Anxiety
-.287
.321
-.094
-.894
.382
Depression
.111
.540
.024
.206
.839
*bolded items represent those found to be significant

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Table 14
Baseline and 8 weeks means and standard deviations for cancer specific symptoms
Symptom
N
µ
sd
Fatigue Severity
29
4.48
.73
Fatigue Severity 8 weeks
4.69
.78
Appetite
29
1.44
2.57
Appetite 8 weeks
2.03
3.06
Nausea
29
.58
1.68
Nausea 8 weeks
1.76
3.35
Pain
29
7.89
9.16
Pain 8 weeks
6.03
8.55

Table 15
Paired t-test for Symptom Severity and Diabetes self-management at baseline and 8 weeks
(n=29)
Paired differences
Paired Variables
µ
sd
t
p
Symptom Severity Baseline
-4.21
20.61
-.109
.281
Symptom Severity 8 weeks
Diabetes Self-Management Baseline
5.71
11.61
2.65
.013
Diabetes Self-Management 8 weeks

backpain, abdominal pain, muscle aches and joint pain. The symptom inventory used for this
study did not ask a general pain question. Even though there were not significant differences, it
was noted that individuals had higher means for nausea, appetite, and fatigue at 8 weeks when
compared to baseline, indicating that these symptoms had increased in severity. Overall
individuals had a lower mean score for pain, indicating that they experienced less pain at 8
weeks. See table 14.
There were significant differences (p = .013) in regards to the overall performance of
diabetes self-management behaviors (baseline µ = 51.40, sd = 16.01, 8 week µ= 45.69, sd =
17.47), indicating patients performed less activities to manage their diabetes after a minimum of
8 weeks of chemotherapy, then they did at baseline. (See Table 15)

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In order to assess if there were specific self-management behaviors that were performed
either more or less after 8 weeks of chemotherapy, a paired t-test was done to compare each of
the items on the self-care inventory. There were significant differences between baseline and 8
week measures for certain specific self-care activities. These items included eating meals and
snacks on time (baseline µ = 3.96, sd =.90, 8 week µ = 3.41, sd = 1.11), treating low blood
glucose with carbohydrates (baseline µ=2.34, sd =2.07, 8 week µ=1.34, sd 1.95) and exercise
(baseline µ=2.86, sd=1.06 and 8 week µ=2.41, sd=1.08). At 8 weeks participants were less
likely to eat on time and less likely to exercise. The significant difference in treating low blood
sugar maybe a reflection of how the form is scored, for if an individual did not have a low blood
sugar in the 2 weeks prior to completing the form, they item was scored as 0. (See Table 16)
Table 16
Paired t –test for specific paired self-management items at baseline and 8 weeks (n=29)
Paired differences
Paired baseline & 8 week variable
µ
sd
t
p
Check blood glucose
-.03
1.05
-.17
.86
Record blood glucose
-.00
1.28
.00
1.00
Check ketones
.34
1.08
1.72
.09
Take correct dose of pills or insulin
.21
.98
1.14
.26
Take pills or insulin at right time
.14
1.03
.72
.47
Eat correct food portions
.31
1.00
1.67
.11
Eat meals/snacks on time
.55
1.02
2.91
.007
Keep food records
.10
1.18
.47
.64
Read food labels
-.17
1.28
-.72
.47
Treat low blood glucose with carbohydrates
1.00
2.36
2.28
.030
Carry quick acting sugar
.103
1.52
.37
.72
Come in for clinic appointments
.48
2.99
.87
.39
Wear medic alert ID
.14
.92
.81
.42
Exercise
.44
1.05
2.28
.030
Adjust insulin dosage
.03
1.21
.15
.879
*bolded items represent items which were statistically significant
Aim 3: To identify common challenges or concerns regarding the self-management of
diabetes in older adults after completing at least 8 weeks of outpatient chemotherapy.

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Thematic analysis was done to analyze the transcribed tapes in order to identify common
themes. Participants were asked two open-ended questions asking them to discuss the challenges
and issues regarding taking care of their diabetes while undergoing chemotherapy, and how they
felt their diabetes was impacted overall by their cancer treatment. A data driven approach to
analyzing the data was utilized. In using this method, raw data is examined for codes and themes
across subjects. The investigator draws upon their thoughts, ideas, perceptions and knowledge of
prior theories and research in the development of themes (Devins, 2010). During the analysis of
the transcribed tapes, the investigator utilized reflexivity regarding preconceived ideas, feelings
and emotions that may have impacted analysis by keeping memos of these thoughts and others
that may have influenced the coding of the transcripts, as well as reasons for changing or
merging a particular code.
Three themes were identified during analysis of the tapes: self-management issues, health
issues and prioritization. Table 17 provides a definition for each theme and the indicators (a
description of when the theme occurs). The theme of self-management issues included issues
Table 17
Themes with definitions and indicators:
Theme
Definition
Self-management
Specific diabetes self-management
issues
behaviors which may be negatively
or positively impacted by cancer
and/or cancer treatment
Health Issues
Positive or negative factors which
can impact an individual’s overall
health while undergoing treatment
for cancer
Prioritization
Factors which influence or impact
how an individual will care for their
diabetes while undergoing cancer
treatment

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Indicators
Exercise impact, Diet
Management, Medicine
Management, Glucose
monitoring
Glycemic/blood sugar
issues, symptom issues, no
reported impact
Provider influence, personal
choice, need for help

related to several specific diabetes self-management behaviors, exercise, diet, medicine
management and glucose monitoring. The impact chemotherapy had on exercise was noted to be
negative. As noted by the following quotes: “not able to exercise like I usually would be able to.
Ya know, if you’re tired, you’re not going to do very much” and “because of tiredness, I am not
able, or willing to exercise as much as I did before the chemo and surgery and treatment.”
Several individuals noted a negative impact on their diets as well: “When I do eat my blood
sugar spikes to 400 -500 so then I don’t want to eat so I just drink a lot of fluids”; “Challenge is
my diet, um because during the cancer, you want to eat comfort foods, and most comfort foods
are really not diabetic-friendly” and “No appetite, you don’t have an appetite so you wait,
deliberately wait and eat when you’re hungry.”
A few individuals reported challenges and changes in how they manage their diabetic
medications in order to achieve better glycemic control. “Using a sliding scale for insulin was a
challenge”, and “The challenges were to become more, um efficient and um more regular about
checking sugars and managing whatever medication need to be given according to the blood
sugar level”.
Glucose monitoring was another area noted by individuals that changed due to their
cancer and its treatment. “I started taking my readings before every meal and every morning and
every night. So I was taking it 5 times a day when I first started out. And I was only taking it
once before” and “Diabetes is easily managed if I monitor my numbers”.
The second theme identified was health issues associated with the cancer and its
treatment. Many individuals reported issues with glycemic control while receiving
chemotherapy. Symptoms were also frequently reported as a side-effect of the chemotherapy. A
few individuals did not feel that the cancer and its treatment had any impact on their diabetes.

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Individuals who expressed issues with glycemic control, manly reported issues with
elevated blood sugars and inability to have consistency in their blood glucose readings: “The
numbers tend to bounce around an awful lot….I can be 300 one night and then 140”, “I never
know what it’s going to be. It’s always high and I worry about I being so high that I am going to
go into a coma” and the chemotherapy itself, Taxol in particular, would push sugar up over 200
the days that I would have it”.
Symptoms were also frequently reported as common issues which occurred while
undergoing treatment for chemotherapy. “So yucky meaning you feel like you don’t have any
energy or sick all the time.” “I’d say the biggest thing is the way it’s affected the appetite. You
know, where a lot of things didn’t roll off on the taste and then you start eating and it’s like you
hit a wall and its, you know, I can’t eat anymore,” and “chemo throws everything out of wack. It
makes me light headed, or mostly tired. Loss of appetite, loss of hair, ups the stress level”.
A few individuals reported no impact on their diabetes or health while receiving
treatment. “I’ve had no problems at all. Chemo has had almost no impact. I took my pills this
morning and I had my cereal this morning that I like, but it’s pretty acceptable. I would say that
it’s had very little impact,” and “I haven’t had bad side effects from chemo, so I have been able
to eat normally. I have remained active, and the chemo really hasn’t gotten me down.”
The third theme identified was prioritization, this theme included positive and negative
issues related to provider influence, need for help and personal choice. Provider influence was
noted to be both positive and negative. “my family doctor sent me to an endocrinologist and
she’s been trying to get it under control” and “He just said chemo drugs usually interfere and
cause, you know, your diabetes to go all over,…was comfortable waiting till I was done with
chemo, as long as the random ones weren’t going very high”

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Individuals were also noted to be making personal choices about the management of their
diabetes while they were undergoing treatment for their cancer. “I am really conflicted and I am
pretty much putting the diabetes on the back burner, I mean I watch it but I am focusing on what
I should do for the cancer right now” and “I haven’t given up on my diabetes because the whole
time up until now, making sure that I am doing the right thing and my diabetes isn’t going to be
affected by my cancer….. I made up my mind that one was as important as the other.” One
individual reported help from their caregiver in order to continue to manage their diabetes, as
noted by the following statement from the caregiver “He hasn’t been able to give it (insulin) to
himself like he used to, I do the work.”
Through the use of thematic analysis, several themes and issues were identified. Overall
the issues identified had a negative impact on diabetes self-management. Further discussion of
these themes and how they may link to some of the quantitative results will be explored and
discussed in chapter 6.
Power:
A post hoc analysis utilizing the program G-power (Faul, 2008), in order to determine the
studies power. This analysis was run for the matched pair analysis (paired t-test) and for the
linear regression model at 8 weeks. The paired t-test analysis revealed a effect size of .30, and a
power (1- err prob) of .46. The linear regression model revealed a effect size of .88 and a
power (1- err prob) of .99.
Summary:
This chapter presented the results from both the quantitative and qualitative analysis.
Based on the results, cancer and its treatment can have an impact on the management of diabetes
in older adults. Level of self-management behaviors which are recommended to be performed

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daily such as monitoring blood sugars, diet and exercise were found to be significantly different
between baseline and after being on chemotherapy for at least 8 weeks. Clinical factors such as
symptom severity, years with diabetes, number of comorbidities and number of medications all
may impact the continued management of diabetes in older adults who are receiving
chemotherapy. Individuals with diabetes express issues with glycemic control, as well as the
ability to follow diet and exercise regimens while undergoing chemotherapy. Further
interpretation and discussion of these results will be presented in chapter 6.

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Chapter 6
Discussion & Implications
This study focused on the relationship between individual, clinical and behavioral
characteristics and the performance of diabetes self-management behaviors in adults 50 and older
who were undergoing chemotherapy for a solid tumor cancer or lymphoma. This final chapter
will present an interpretation of the results, the relationship of the results to the conceptual
framework, and current research literature and limitations of the study. The implications for
nursing practice, research and policy will also be presented.
Discussion of Result for Aim 1
Aim 1: Among patients with pre-existing diabetes who are either starting, or are currently
receiving outpatient chemotherapy for a solid tumor cancer how does clinical characteristics
associated with diabetes and cancer and behavioral characteristics relate to the performance of
diabetes self-management behaviors while controlling for individual characteristics.
Initially correlations where performed, and age, number of years with diabetes and level
of DSE was found to be positively correlated with the performance of self-management
behaviors for diabetes at baseline. A linear regression model was also performed. Age and level
of self-efficacy were also found to be predictors of diabetes self-management activities at
baseline in adults undergoing treatment for cancer. The older the adult, the more likely they are
to perform recommended self-management activities for their diabetes. Other studies have also
noted age to be a predictor of performance for diabetes self-management behaviors (Honish,
Westerfield, Ashby, Momin, & Phillippi, 2006; Wen, Shepherd, & Parchman, 2004). Wen et al
(2004) noted older Hispanic individuals were more likely to be compliant with diet selfmanagement behaviors when compared to younger adults with diabetes. Honish et al (2006)

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found as a person ages their level of compliance with diabetes self-management activities
increases. The mean age of participants in this study was 44.71 with a sd = 9.67 (Honish, et al.,
2006). It is possible that older adults, who no longer are caring for young children, and who may
be retired have less demands on their time, and are better able to adhere to and perform
recommended self-care behaviors than adults who may have other competing demands.
Unlike past research, this study found a positive correlation between years with diabetes
and performance of diabetes self-management activities, indicating the longer an individual has
diabetes the more likely they are to perform diabetes self-management behaviors. Ponzo et al
(2006) noted the longer an individual had diabetes, the less likely they were to follow and
perform recommend self-management activities for diabetes. Ponzo et al (2006) hypothesized
that the negative relationship between years with diabetes and performance of self-management
activities may be due to the development of diabetes related complications, competing comorbid
conditions and more complex treatment regimens. The current study did not find a significant
correlation between the number of comorbid conditions, number of medications or level of
diabetes severity and the performance of diabetes self-management. Based on the findings from
this study, the hypothesis proposed by Ponzo et al (2006) is not supported. This difference may
be due to the difference in the populations studied. Ponzo et al (2006) studied Italian men and
women who were 1st generation Italian immigrants with type II diabetes. Ponzo et al (2006) did
not report how long these individuals had been in the United States, it is possible that the
adjustment to a new culture also impacted their ability to perform diabetes self-management
activities. Further research needs to be done in order to better understand the relationship
between other comorbidities and diabetes specific clinical characteristics and the performance of
diabetes self-management behaviors.

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Level of DSE was also found to be significantly correlated with and predictive of
performance of self-management behaviors for diabetes. As level of DSE increased, so did the
performance of self-management behavior. This finding is consistent with the diabetes literature
(Aljasem, Peyrot, Wissow, & Rubin, 2001; Sousa, Zauszniewski, Lea, & Davis, 2005; Williams
& Bond, 2002). Higher levels of self-efficacy in individuals with diabetes has been associated
with improved diet, glucose monitoring, taking of medications and exercise (Aljasem, et al.,
2001; Wen, et al., 2004; Williams & Bond, 2002). Individuals with higher levels of self-efficacy
will usually feel more capable and confident in their ability to perform and carry out specific
activities. Individuals with diabetes with higher levels of diabetes self-efficacy when face with a
competing condition such as cancer, may still feel they are capable and have the confidence to
manage their diabetes.
Based on the findings for Aim 1, adults who have preexisting diabetes who are older,
have higher levels of DSE and have had diabetes longer are more likely to perform diabetes selfmanagement behaviors at baseline, or early in their treatment with chemotherapy. Nurses and
other healthcare providers who are caring for individuals with diabetes and cancer who are or
will be receiving chemotherapy need to understand this relationship in order to assist patients in
caring for both of these disorders. Patients who have lower levels of DSE or who have not had
diabetes longer may need extra support or assistance early in their treatment process in order to
ensure they continue to follow self-management recommendations for their diabetes.
Discussion Results for Aim 2
Aim 2: Among patients with pre-existing diabetes who have completed a minimum of 8
weeks of outpatient chemotherapy, how does baseline clinical and behavioral characteristics,
baseline diabetes self-management behaviors and symptom burden after 8 weeks of

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chemotherapy relate to the performance of diabetes self-management behaviors while controlling
for individual characteristics.
A correlation analysis and linear regression model were performed to determine the
relationship between individual, clinical and behavioral characteristics and diabetes selfmanagement behaviors at 8 weeks. Unlike the baseline model, age was not found to be
correlated with, or predictive of self-management behaviors. Age may be a predictor early in the
chemotherapy process, as indicated by the baseline model, the longer an individual is on
chemotherapy, they draw from their years of experience with diabetes and management of
diabetes to help them sustain and continue to perform diabetes self-management behaviors. In
addition this study only looked at individuals 50 and older; age may have been found to be
predictive if younger adults were included in the study.
As with the baseline correlations, DSE was found to be positively correlated with
diabetes self-management activities after 8 weeks of chemotherapy. However, DSE was not
found to be predictive of the performance of self-management activities after 8 weeks of
chemotherapy in the linear regression model, which differs from the baseline model. It is
possible that level of DSE at baseline does not sustain self-management behaviors overtime in
individuals with a competing chronic condition. Individuals with diabetes may need a selfefficacy booster intervention while undergoing chemotherapy in order to sustain DSE and
possibly improve the performance of self-management behaviors at baseline. A consideration
has to be given to the possibility that DSE is not what is needed to sustain performance of selfmanagement activities in this population. It may be a more general self-efficacy, or combined
self-efficacy for both cancer and diabetes in this population that would possibly be predictive of
or sustain better performance diabetes self-management behaviors. It is also entirely possible

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that level of self-efficacy may not have anything to do with performance of self-management
behaviors in adults with competing chronic conditions. In order to determine this, further
research which looks at the relationship between self-efficacy and the performance of selfmanagement behaviors in individuals with discordant or competing chronic conditions needs to
be conducted.
As with the baseline correlation results, years with diabetes continued to be positively
correlated with the performance of self-management activities for diabetes at 8 weeks. In the
baseline regression model, years with diabetes was not found to be predictive of the performance
of diabetes self-management activities. Years with diabetes was found to be a significant
predictor of the performance of diabetes self-management behaviors at 8 weeks. The longer an
individual had diabetes the more likely they were to continue to perform self-management
activities over time while undergoing chemotherapy. One possible explanation for this is that
individuals who have had diabetes longer have had to overcome other barriers or challenges in
regards to caring for their diabetes in the past and have developed a process to manage their
diabetes when facing a new challenge or barrier. Individuals who have had diabetes longer may
already have a support system as well as an established routine in place to assist them with
managing their diabetes while undergoing chemotherapy. Individuals who have had diabetes
longer do not find the effects or challenges of chemotherapy to be a barrier to the performance of
diabetes self-management behaviors. Individuals, who have had diabetes longer, may also have
a better understanding of the importance of maintaining and caring for their diabetes, than those
who have not had diabetes as long.
At 8 weeks, the number of comorbidities an individual had, their baseline level of
diabetes self-management and their ethnicity were found to be predictive of level of performance

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of self-management behaviors. Level of diabetes severity was also found to be positively
correlated with performance of self-management activities, which was different from the
baseline data. Individuals with higher levels of diabetes severity were more likely to perform
self-management behaviors after a minimum of 8 weeks of chemotherapy. Past research which
has looked at the relationship between diabetes related complications and performance of selfmanagement activities in individuals, have found a negative correlation which differs from this
study. These differences may be related to how diabetes complications and severity were
measured. Aljasem et al (2001) used medical exam and chart audit to determine the presence of
diabetes related complications, this study utilized self-report. Individuals in this study may have
misreported complications associated with their diabetes, which a medical chart audit would
have clarified. It is also possible, older adults with higher diabetes severity levels are more
likely to perform self-management activities while being faced with a new condition such as
cancer and treatment with chemotherapy, in order to prevent further complications. In addition,
individuals with whose diabetes is more severe, or who may have more diabetes related
complications, are more likely to be older and have had diabetes longer.
The number of comorbidities an individual had was found to be predictive of the
performance of diabetes self-management comorbidities at 8 weeks. Individuals with more
comorbidities performed more diabetes self-management activities than those with lower levels.
The literature has proposed that the number of comorbidities an individual has creates a barrier
to self-management (Bayliss, Ellis, & Steiner, 2007). Kerr et al (2007) reported discordant or
competing comorbidities are more likely to impact the self-management of diabetes in adults
with multiple morbidities. It can be hypothesized that in individuals who have diabetes who are

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undergoing chemotherapy, have already dealt with adjusting their self-management behaviors to
accommodate the management of a new chronic condition.
Ethnicity was also found to be predictive of diabetes self-management behaviors at 8
weeks. The majority of the sample was classified as non-Hispanic or Latino, with only one
participant being Hispanic or Latino. Being Hispanic or Latino was noted to be predictive of
lower levels of self-management at 8 weeks. These findings are based on only one individual
and cannot be generalized to the population. Ethnicity in past studies has been found to have
either a positive or negative effect on overall performance of diabetes self-management
behaviors (Castro, O'Tolle, Brownson, Plessel, & Schauben, 2009; Lanting et al., 2008;
Sowattanangoon, Kotchabhakdi, & Petrie, 2009). Future studies need to include more diverse
ethnic backgrounds as well as racial backgrounds in order to better assess the role ethnicity and
race plays in regards to the performance of self-management activities in this population.
Summary
At 8 weeks the clinical characteristics of number of comorbidities and years with diabetes
were found to be predictors of diabetes self-management behavior, versus the individual
characteristic of age and the behavioral characteristic of DSE at baseline. DSE continue to be
correlated with self-management behaviors at 8 weeks. Individuals in this study with higher
levels of self-management at baseline continued to have higher levels of diabetes selfmanagement after a minimum of 8 weeks of chemotherapy. Clinical characteristics have been
hypothesized to negatively impact self-management behaviors. The findings from this study
indicate individuals with more comorbidities and more years with diabetes are more likely to
perform diabetes self-management behaviors, while undergoing chemotherapy. These findings
do not tell us if these individuals performed diabetes self-management activities at lower levels

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when compared to baseline. This study did not find cancer specific clinical characteristics to be
correlated or predictive of the performance of diabetes self-management behaviors. This could
be due to the fact that most patients were early in their cancer care trajectory. It is possible that
cancer specific clinical characteristics later in the care trajectory could impact diabetes selfmanagement. Further research needs to be done before we can conclude that cancer and its
treatment is not predictive of the performance of diabetes self-management behaviors. This
study only looked at individuals over a short period of time, and it is possible that there may be
an accumulative effect related to cancer symptoms, treatment type and number of treatment days
which may only be detected after a longer treatment period.
Discussion of Additional Analysis
A paired t-test was performed in order to determine if there were differences between
baseline and 8 week levels of self-management behaviors and symptom burden, in order to
determine if the effects of chemotherapy had an impact overtime. A significant difference was
not found in relation to level of symptom burden individuals were experiencing. This may be
due to the fact that individuals were already undergoing chemotherapy at the time they were
enrolled in the study, new symptoms or changes in symptoms associated with their cancer and its
treatment were already occurring at baseline.
In further analysis, cancer specific symptoms were compared. These symptoms included
pain, appetite, nausea and fatigue. There were differences noted between the 8 week and
baseline means, for these symptoms these findings were not significant. Individuals overall
reported higher levels of fatigue, nausea and poorer appetite’s at 8 weeks when compared to
baseline. It is important to note that fatigue is also a common symptom of diabetes; it is unclear
if the increase in fatigue was due to the cancer treatment, or the individual’s diabetes.

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Individuals reported issues with glycemic control during this time which could have also
impacted the patient’s level of fatigue. Further research needs to further explore the relationship
between diabetes, cancer and the development of fatigue in individuals undergoing treatment.
A significant difference was found between the level of diabetes self-management
activities performed at baseline and those performed at 8 weeks. Indicating chemotherapy may
have an impact on an individual’s ability to perform self-management behaviors overtime.
Individuals overall had higher levels of diabetes self-management at baseline when compared to
diabetes self-management levels at 8 weeks after starting chemotherapy. The mean score for
self-management activities at baseline was 51.40 and at 8 weeks was 45.69, higher scores
reflected higher levels of diabetes self-management behavior performance. This difference was
determined to be significant with p < .05. Previous literature has noted individuals with
discordant conditions may have lower self-management ability, and prioritize the selfmanagement of one disease over the other (Kerr et al., 2007) and the symptoms of one disorder
may interfere with the ability to perform self-management activities for another disorder
(Bayliss, et al., 2007; Jerant, von Friederichs-Fitzwater, & Moore, 2005).

Differences in levels

of overall symptom severity were not found to be a factor related to the performance of diabetes
self-management behaviors. Further analysis may need to be done which looks at cancer
specific symptoms, only, not just all symptoms associated with illness, in order to have a better
understanding of this relationship and the impact symptoms may have on the performance of
diabetes self-management behaviors. The decrease in the performance of self-management
activities for diabetes in this study may be due to the impact cancer related symptoms may have
on the ability to follow diet and exercise programs, and how the individual may prioritize caring

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for their diabetes when faced with a new discordant, complex and life threatening condition such
as cancer.
Specific diabetes self-management behaviors were also found to be significantly different
between baseline and 8 week time periods. These included eating meals and snacks on time,
treating low blood glucose with carbohydrates and exercise. Individuals were less likely to eat
meals and snacks on time and to exercise at 8 weeks when compared to baseline measures. This
difference can possibly be explained by the data obtained from the open ended questions where
patients reported difficulty with their diets and ability to exercise due to the side effects
associated with their chemotherapy. Differences in symptom burden at baseline and 8 weeks
were not detected by the paired t-test. It is possible that a difference, could have been detected if
the sample size was larger, there was a difference in the number of individuals who started the
study versus those who completed (N = 34 at baseline, and N = 29 at 8 weeks). A different
symptom assessment tool may have been more sensitive in determining differences related to
cancer specific symptoms only, versus all symptoms associated with illness. Further analysis
may need to be done that looks at cancer specific symptoms such as pain, fatigue, and nausea to
see if there is a specific relationship between these and the performance of specific diabetes selfmanagement behaviors.
The treatment of low blood sugar was less likely to occur after a minimum of 8 weeks of
chemotherapy, when compared to the baseline measure. As mentioned in the results section, if
an individual had not incurred a low blood sugar in the 2 weeks prior to completing the final
survey they were scored a 0 for that item, instead of 1 through 5. This difference is most likely
due to the fact that many individuals experienced issues with hyperglycemia versus
hypoglycemia during this time. Many individuals reported issues with glycemic control, when

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asked about how they felt the cancer had impacted their diabetes. This will be explored further
in the discussion of results for Aim 3.
Summary
This analysis indicated chemotherapy may have a negative impact on the overall
performance of diabetes self-management behaviors in older adults. Specifically related to the
effects chemotherapy may have on diet management and exercise. Cancer and diabetes are
considered discordant disorders since they do not follow a similar disease management plan.
Consistent with the literature, the discordance between two conditions can create a competition
for time, attention and priority and impact the self-management of one or both of the conditions
(Kerr, et al., 2007; Piette & Kerr, 2006; Proctor, Hasche, Morrow-Howell, Shumway, & Snell,
2008).
Discussion Results Aim 3:
The purpose of Aim 3 was to identify common challenges or concerns, identified by
individuals with both diabetes and cancer, regarding the self-management of diabetes after
completing at least 8 weeks of outpatient chemotherapy using open ended questions. Three
common themes were identified in individuals with preexisting diabetes who were undergoing
chemotherapy; these were self-management issues, health issues and prioritization. Selfmanagement issues identified were impact on exercise, diet, the management of medications and
the monitoring of blood glucose. Diet and exercise were both negatively impacted by the
chemotherapy. Individuals acknowledged that the effects related to symptoms associated with
their chemotherapy decreased their appetite, and the stress associated with dealing with cancer
increased their likelihood of not eating correctly. The symptom of fatigue was often mentioned
as a factor which impacted their ability to exercise regularly.

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The level of prioritization of diabetes self- management was also noted to be a theme
found among the participants in this study. Individuals acknowledged that taking care of and
dealing with their cancer was more important than caring for their diabetes. Individuals
expressed being overwhelmed and preoccupied with their cancer, which contributed to them
thinking less about caring for their diabetes. This lack of prioritization may also help to explain
differences between baseline and 8 week performance of self-management behaviors. Patients
were also influenced by their healthcare providers in regards to how much emphasis they placed
on their diabetes management. Some individuals reported they were informed by their
healthcare provider to not worry about their diabetes while being treated for the cancer, for the
cancer treatment will probably cause problems with their blood sugars. In conversations with
various health care providers, neither oncologist nor primary care providers felt comfortable in
regards to managing diabetes during treatment and/or they did not feel that the management of
diabetes was important while individuals were receiving chemotherapy. This indicates a need
for increased education of healthcare providers regarding the relationship between diabetes and
cancer and the importance of managing both diseases simultaneously.
The majority of the patients in this study did report issues with glycemic control and
frustration with not being able to control their blood sugars. Through additional side observation
by the investigator it was noted that several of the patients in the study required hospitalization
during this 8 week period. Approximately 23% (n= 8) of the participants required
hospitalization for problems including infections and cardiac related issues. In previous studies
(Barone et al., 2008; Peairs et al., 2011) individuals with diabetes who have cancer have an
increased risk for hospitalization.

Diabetes is a risk factor for the development of infection and

cardiac disease. Chemotherapy can also alter and have a negative effect on the immune and

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cardiac systems. Individuals with diabetes and cancer undergoing chemotherapy are probably at
higher risk for the development of complications when compared to cancer patients without
diabetes. Due to this increased risk for hospitalization, it is important for healthcare providers to
understand the importance of managing both the cancer and the diabetes. Improved coordination
of care between oncologist and diabetes care providers needs to be a priority.
Summary
From the data collected from the open-ended questions, it is evident that cancer and
chemotherapy has an impact on diabetes self-management, with 24 out of the 29 patients at 8
weeks reporting some type of impact on their diabetes self-management after 8 weeks of
chemotherapy. Specifically the impact of cancer related symptoms on performance of diabetes
self-management behaviors and the treatment effects from the chemotherapy and steroids they
were on causing elevated glycemic levels. Five individuals reported no impact on their diabetes
self-management. It is unknown if these individuals had high or low levels of diabetes selfmanagement at the beginning. It is possible that they did not perform routine self-management
prior to starting chemotherapy, and or had poor glycemic control when they first started
treatment for their cancer. The level of performance of self-management activities may not
equate to improved or adequate glycemic control in this population. Further research is needed
to determine the actual impact cancer treatment has on glycemic control in older adults with both
diabetes and cancer.
Discussion of Results in Relation to Conceptual Framework
The conceptual framework for this study was based on the framework for heart failure
self-management with family variables by Dunbar et al (2008). This frame work utilized a
structure-process-outcome format. The framework for this study utilized the structure

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components of individual, clinical and behavioral characteristics and the process component of
diabetes self-management behaviors at baseline and 8 weeks. The results of this study did show
that the individual characteristic of age had an influence and was related to self-management
behaviors at baseline. Ethnicity may have an influence on diabetes self-management at 8 weeks,
due to the fact that most individuals were from the same ethnicity, further studies would need to
be done to determine the true nature of this relationship.
Clinical characteristics of years with diabetes, number of comorbidities and diabetes
severity were all found to be either correlated with or predictive of self-management behaviors
for diabetes at 8 weeks. Years with diabetes was correlated with self-management behaviors at
baseline, was not found to be a significant predictor for baseline self-management behaviors.
DSE was the only behavioral characteristic to be positively correlated with baseline and 8 weeks
self-management behaviors, and predictive of baseline self-management.
This framework was specifically developed to look at heart failure self-management; this
study shows that some of the same characteristics which influence self-management for heart
failure also influence diabetes self-management. Based on the results of this study, components
of individual, clinical and behavioral characteristics all play a role in the performance of diabetes
self-management in individuals undergoing treatment for cancer. Due to the small sample size it
is possible that not all relationships were detected. It is unclear if behavioral characteristics have
an influence on the performance of self-management activities at 8 weeks. In addition the entire
model was not tested in this study.
The framework as depicted in chapter two, also did not account for the influence cancerself-management may have on the performance of diabetes self-management behaviors. This
model needs to be changed in order to take into consideration the role cancer self-management

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Individual Characteristics:
Age, Gender, Annual Family
Income, Marital Status, Living
Arrangements, Race/Ethnicity

Diabetes SelfManagement
Behaviors - Baseline

Clinical Characteristics:
Number of comorbidities,
Symptom burden – diabetes and
other comorbidities
Diabetes specific:
Length of illness, type, medication
type, severity
Cancer Specific Characteristics
Cancer Symptom burden (8weeks)
Type of treatment and treatment
protocol
Cancer type & stage
Cancer meds – type and number

“Cancer self-management”

Diabetes SelfManagement
Behaviors – 8 weeks or
more after starting
chemotherapy

Behavioral Characteristics:
Self-efficacy, outcome expectancies,
depression/anxiety

Aim 1:
Aim 2:

Figure 7: Revised framework depicting the influence of cancer self-management may have between baseline
and 8 week diabetes self-management

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activities may have in regards to the performance of diabetes self-management behaviors in
adults undergoing chemotherapy for cancer. Figure 7 is a depiction of a revised model
reflecting this possible change.
Study Limitations
This study is limited by the small sample size. The small sample limits the ability to
generalize this to all patients with diabetes and cancer. Future studies need to improve on the
recruitment of patients. One of the barriers for improved recruitment was the lack of having
patient’s consented onsite. The return rate for the surveys was adequate, more patients could
have been captured by having someone consent them onsite. Phone calls could have been placed
in order to remind them to complete the survey, or a phone survey could have been utilized in
order to collect the data.
A second limitation of this study is that all date was self-reported. Self-report is a
method frequently used in research. However, questions are open to individual interpretation by
the respondent; the individual may not interpret the question correctly causing them to answer
differently than intended. The baseline data for the study was collected by a self-administered
written survey in which respondents returned and the 8 week follow up data was collected by a
follow up phone survey. Potentially individuals may have answered differently on the written
survey vs. the phone survey where they were actually responding to the study investigator.
Future studies need to consider using one approach to data collection, to ensure consistency.
A third limitation is the lack of information regarding actual glycemic control individuals
in the study had prior to starting chemo and during the data collection period. Many individuals
reported problems with elevated glucose during this time. It is unclear if the increase in
glycemic levels was new, or if individuals had prior issues with glycemic control and were more

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aware of glycemic levels during this time. Future studies need to consider adding measures of
glycemic control such as HgBA1c’s, blood sugar diaries, and/or average daily glucose
measurements.
Medical audit and clinical data in conjunction with self-report measures could be used to
assess and confirm level of glycemic control, diabetes severity, years with diabetes, number and
type of comorbidities, and the actual number of other medications, and the type of medications
individuals are taking, as well as cancer specific clinical characteristics. This study collected
information regarding the number of other medications individuals were taking, by knowing
what types of medications individuals are on that may be impacting their glycemic control would
be important to control for. Steroids are used in conjunction with the treatment of certain
cancers, specifically lung cancer, as well as they are sometime used to help with symptoms
associated with chemotherapy. The use of steroids is known to cause and elevation in blood
glucose levels.
A fourth limitation of this study is that the same diabetes self-care measure was used at
baseline and 8 weeks. It is possible that the baseline measure increased the participant’s
awareness regarding diabetes self-management and they improved their diabetes selfmanagement behaviors while participating in the study.
A fifth limitation of this study was the short time period between the collection of
baseline and final 8 week data. Eight weeks was selected to ensure that individuals had at least
received 2 cycles of their chemotherapy. It is possible that individuals may experience a greater
impact later on in treatment. It is also unknown if individuals would return back to prechemotherapy or baseline levels of diabetes self-management after they have adjusted to the
cancer treatment regimen.

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A sixth limitation of this study, was the instrument used to measure symptom severity.
This instrument was originally designed to measure common symptoms of illness and was
selected for its ability to measures symptoms associated with other comorbidities and chronic
conditions along with cancer related symptoms. The tool measured severity by multiplying the
frequency of the symptom by the level of interference the symptom caused in the individuals
daily life. This is not a true measure for symptom severity; it is unknown how the patient would
actually describe that symptom on a severity rating scale. Some individuals may have had a
symptom for the same number of days, but experienced the symptom at different levels of
intensity. Future research needs to utilize symptom tools that specifically measure the severity
or intensity of the symptoms, and be specific to the disease being studied.
The final limitation to this study relates to the analysis and interpretation of the open
ended questions. Thematic analysis was utilized to identify the themes; the data was only
reviewed by one reviewer. In order to state these were the actual themes, and there weren’t other
possible themes, the data should have been reviewed by at least one other reviewer. The themes
compared and discussed, and then agreement reached between the two reviewers on what the
actual themes were. Member checking could also have been utilized to ensure that the themes
were interpreted correctly. Member checking involves taking the identified and defined themes
back to the respondents for confirmation and agreement. In addition the questions utilized where
worded in a way that members would focus on issues with their diabetes management. Due to
this, members did not discuss other issues or feeling about their cancer or cancer treatment and it
is possible that other factors could have been identified that may have been a potential barrier to
the self-management of diabetes with these expanded questions.

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Implications for Nursing Practice
The combined results of the quantitative analysis and the open ended questions indicated
clinical issues that nurses and other health care providers need to be aware of in this population.
Issues include: problems with glycemic control; barriers to being able to adhere to diet and
exercise regimens; and the fact that both patients and healthcare providers may not find diabetes
management to be a priority while undergoing chemotherapy. In addition oncologist and
oncology nurses may not understand diabetes management, and what self-management behaviors
patients with diabetes should perform on a daily basis.
Oncology nurses need to be aware of the problems individuals with diabetes and cancer
may encounter, and encourage patients who are undergoing chemotherapy to continue to manage
their diabetes. Nurses and other health care providers who care for individuals with both
diabetes and cancer need to be made aware of the issues this population of patient’s encounters.
Through conference presentations, in-services and publications the awareness of these
individuals could possibly be increased. The level of knowledge oncology nurses have
regarding managing diabetes in the oncology patient needs to be assessed. Based on this
knowledge, tailored inservices and training sessions can be developed to meet the specific needs
of these healthcare providers.
Interventions need to be developed in order to assist oncology nurses in regards to
helping patients manage their diabetes while they are undergoing chemotherapy. The oncology
nurse will have frequent contact with these individuals while they are receiving treatment.
Depending on the type of cancer, chemotherapy treatments can occur anywhere from weekly to
monthly. The oncology nurse can frequently assess if individuals are having issues with their

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diabetes management, and refer them to the appropriate provider if necessary for further
intervention.
Implications for nursing and other health care providers include both health care provider
directed interventions and patient directed interventions. In order to improve health related
outcomes in this population oncology nurses, oncologists and primary diabetes providers need to
be aware of the issues; knowledgeable about possible interventions that could be effective in
improving outcomes in this population; and work together to provide coordinated care that
focuses on managing both the diabetes and cancer simultaneously. Nursing interventions could
include: 1) educational pamphlets that can be given to patients explaining the importance of
continuing to manage their diabetes; 2) development of protocols for how to manage blood
sugars, which may include utilizing insulin to scale based on blood sugar readings; 3)
information regarding how best to manage their diet when they are dealing with side-effects from
the chemotherapy; 4) strategies for exercising while undergoing chemotherapy and 5)
coordinating care with a diabetic nurse practitioner or educator, and/or the patients primary care
provider. Care guidelines for oncologist, oncology nurses, endocrinologist, advance practice
nurses, diabetes educators and primary care providers who are caring for individuals with cancer
and diabetes need to be developed and disseminated to ensure that these patients with diabetes
and cancer can achieve the best possible diabetes and cancer outcomes.
Implications for Research
This study highlights the need for further research which investigates the relationship
between diabetes and cancer in regards to the impact cancer and its treatment has on diabetes
outcomes, as well as the impact diabetes has on cancer related outcomes. Research needs to
occur in several areas: outcomes, intervention development and translation. Once we understand

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the relationship between cancer and diabetes and vice versa, and have tested and proven
interventions which improve outcomes, we may be able to transfer these to other individuals
with competing or discordant conditions, such as congestive heart failure (CHF) and cancer, or
diabetes and CHF. The following paragraphs will explore further each of these areas.
In the area of outcomes, further understanding of the relationship between glycemic
control and its relationship to cancer related outcomes such as survivorship, mortality and
morbidity, as well as health related outcomes of physical function, health care utilization and
symptom burden need to be developed. Research looking at the relationship of self-management
and glycemic control in individuals undergoing treatment for cancer, and health related outcomes
needs to be developed. It is unclear if the impact on self-management is part of the reason for
poor glycemic control and poorer cancer related outcomes in individuals with diabetes and
cancer. In addition does diabetes and level of glycemic control alter the effectiveness of the
chemotherapy and other treatments for cancer? Do patients with diabetes need a different
treatment protocol to treat their cancer than individuals who do not have diabetes. We need to
conduct studies which will improve our understanding of the underlying pathophysiological
mechanisms which occur in individuals with diabetes and cancer, in order to develop
interventions that will improve survivorship and overall quality of life for individuals with
diabetes and cancer.
Interventions need to be developed which focus on improving health related quality of
life, diabetes self-management, glycemic control, and the dual management of diabetes and
cancer.

Specific treatment protocols need to be developed and tested which will provide

guidance to oncology nurses, oncologists and primary care providers in order to improve

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glycemic control, decrease complications, treatment side effects and adverse reactions,
hospitalizations, and improve survivorship in this population.
Once these interventions have been developed and tested, they then need to be translated
to the clinical practice arena. Research will need to be done that allows for the testing of
interventions that have been shown to have an impact on health related outcomes and glycemic
control in individuals with cancer and diabetes, to other populations. Clinical research needs to
be done in order to develop evidence based protocols which improve glycemic control, treatment
side effects and potential adverse reactions in individuals with diabetes while undergoing
chemotherapy for cancer. A randomized control trial which compares various treatment
protocols for managing diabetes needs to be developed. These trials should look at multiple
outcomes, in order to develop a treatment guideline for individuals with diabetes and cancer.
Outcomes should include glycemic control, complication rates such as infections and
hospitalizations as well as quality of life indicators. In addition the information gained from
research needs to be translated to the individual patients through the development of guidelines
which can assist patients in the self-management of their diabetes while undergoing
chemotherapy.
Implications for Policy
This study highlights the need for the development of policies and protocols which can
improve outcomes for individuals with multimorbidities. Currently diabetes and cancer are
managed separately, as this study noted; providers may prioritize the management of one
disorder over the other. One of the goals of cancer care is long term survivorship, and improved
quality of life for individuals with cancer. Diabetes care strives for prevention or delay of
potential complications, optimal glycemic control, as well as improved quality of life. Quality

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indicators for diabetes management in individuals with cancer who are undergoing treatment
need to be developed. These indicators should include acceptable glycemic levels, measured by
HgBA1c or average daily glucose levels, complication rates for infection occurrence and
hospitalizations related to both conditions, diabetes and cancer, and the response to treatment.
Policies and protocols need to be developed which allow for better coordinated care for
individuals with diabetes and cancer. Currently we don’t have guidelines which address how to
best care for individuals with cancer and diabetes. As stated in the research implications section,
specific interventions and protocols need to be developed and tested.
In order to be successful in providing higher quality of care to this population,
stakeholders such as payers and policy makers need to be considered.

Our protocols and

guidelines need to ensure the highest quality of care at the lowest cost. Payers and policy makers
need to see the benefit of supporting quality indicators which could be part of a pay for
performance system in individuals with diabetes and cancer. Does improved care coordination
actually translate to less cost overall. The goal would be to demonstrate improved care
coordination can decrease complications, hospitalizations, and possibly the loss of productive
days in individuals with cancer and diabetes, which could translate to lower cost, better outcomes
and improved quality of life.
One of the aims of the Patient Protection and Affordable Care Act (PPACA) of 2010 was
to improve care coordination for individuals with multiple chronic conditions, such as diabetes
and cancer (Justice, 2010). The PPACA establishes demonstration projects within the Medicare
program to test models for delivery of healthcare services and payment reform which include
Medical Homes and Accountable Care Organizations (ACO) (Justice, 2010). These medical
homes and ACO’s could be mechanisms to evaluate and support care coordination services for

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individuals with diabetes and cancer, to see if utilization of services, hospitalization rates,
complications such as infections, cardiac events, and health care costs decrease, and outcomes
improve.
Conclusion/Summary
The primary purpose of this study was to identify factors which may influence diabetes
self-management in adults 50 and older who were undergoing chemotherapy for a solid tumor
cancer. The secondary purpose was to identify specific issues or challenges individuals with
diabetes and cancer faced in regards to caring for their diabetes while undergoing chemotherapy.
The results indicated that age and level of self-efficacy were predictors of baseline selfmanagement in this population. After at least 8 weeks of chemotherapy, years with diabetes,
number of comorbidities, and baseline diabetes self-management were predictors of better selfmanagement when compared to individuals with fewer years, fewer comorbidities and lower
baseline self-management.
A sub-analysis was performed in order to identify if there was a change between baseline
self-management and diabetes self-management after at least 8 weeks of chemotherapy.
Individuals in this study did have lower self-management scores at 8 weeks, indicating that
chemotherapy and or its side-effects had an impact on the management of their diabetes. This
impact was directly seen on the self-management areas of diet and exercise.
Through qualitative data, individuals identified issues with diet and exercise, due to
impact of symptoms such as nausea, vomiting, loss of appetite and fatigue. This data also
revealed that patient may not prioritize caring for their diabetes when faced with cancer and
undergoing treatment. Glycemic control during cancer treatment was an issue for many of the

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participants, with most reporting problems with hyperglycemia, and difficulty in keeping their
blood glucoses with in a normal range.
Prior to this study, studies that specifically addressed the impact of a new discordant
condition (cancer) on an existing chronic disease (diabetes) were very limited or scarce in the
literature. Studies specifically looking at the impact on self-management were not found. This
study contributes to the science by adding knowledge regarding how one condition can have a
negative impact on another, and increasing the need for better care coordination and
management. Treatment protocols need to be developed which can provide for improved care
for patients with competing chronic conditions in order to improve health related quality of life
outcomes. Nurses can play an essential role in the development and implementation of these
outcomes and can take the lead in regards to research and development of interventions that can
influence nurse-sensitive outcomes, such as symptoms and self-care.

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APPENDICES

123

APPENDIX A

Baseline Survey Instrument

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Diabetes and Cancer Self-management Study Survey Tool: (Baseline)
Thank-you for your willingness to participate in this study, this survey will ask you several
questions about your diabetes, cancer, symptoms you may be having, and what you do to
care for your diabetes. The survey should take you 45 – 60 minutes to complete. Once you
have completed the survey, place in the provided envelope along with your signed consent
form and return to the study investigator. Once again, thank you for taking part in this
study, once the study investigator receives your completed survey and consent; she will be
contacting you to set up the follow up phone interview.
The following are basic questions about you, please answer each question:
1. Gender: (circle one)
a. Male
b. Female
2. What is your date of birth: _ _ / _ _ / _ _ _ _ (mm/dd/yyyy) (write in date)
3. What is your highest level of education completed? (circle one)
a. No formal education or completed grade school
b. Completed some high school
c. Completed high school
d. Completed some college or technical training
e. Completed college
4. What is race do you consider yourself? (circle one)
a. Caucasian/White
b. African American/Black
c. Mexican American/Hispanic/Chicano
d. Native American/ Alaskan
e. Other: (specify):____________________
5. What is your ethnic background: (circle one)
a. Hispanic or Latino
b. Not Hispanic or Latino
6. What is your current marital status: (circle one)
a. Never Married
b. Married
c. Divorced/Separated
d. Widowed
e. Living Together
7. What is your current living arrangement: (circle one)
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a.
b.
c.
d.

Live alone
Live with spouse/significant other
Live with a family member other than your spouse or significant other
Live with a non-family member

8. How many individuals live in your home:_________________ (Write in number)
9. What was your combined household income last year? (circle one)
a. Less than 49,999
b. $50,000 – 99,999
c. $100,000 – 149, 999
d. $150,000 – 199,999
e. $200,000 or above
The following questions are specific about your Diabetes:
1. What type of Diabetes do you have: (circle one)
a. Type I (non-adult onset, Juvenile Diabetes)
b. Type II (Adult onset)
2. What types of medications do you take for your Diabetes: (circle one)
a. Insulin
b. Oral medications to lower your blood sugar
c. Both Insulin and oral medications
3. How long have you had Diabetes: ______________(write in number of years)
4. How many medications do you take total for your diabetes: ______(write in number)
5. How many other medications (those for health conditions other than your diabetes or
cancer) do you take:______(write in number)

The following questions are about conditions which are possibly related to your diabetes,
please answer YES or NO to each of the following items: (Based on the Diabetes
Complications Index)
Yes
1.

Has a doctor ever told you that you have a blockage in the blood flow to
your heart? Such blockage can lead to chest pain; also called angina.

2.

In the past 6 months have you had chest pain or pressure?
If yes, was the chest pain or pressure brought on by physical activity or
stress?
126

No

If yes, Was the chest pain or pressure relieved by rest or nitroglycerine?
3.

Has a doctor ever told you that you have had a TIA? This is also called
“Transient Ischemic Attack” or “warning stroke” or have you ever
developed sudden, stroke like symptoms, for example, weakness on one
side of your body, difficulty speaking, drooping of one side of our mouth,
drooling or trouble seeing, which completely returned to normal within a
day?

4.

Has a doctor ever told you that you have blockages in the blood vessels,
arteries to your legs, also called peripheral vascular disease or during the
past 6 months, have you had leg cramps or pain in your calf while
walking, which was relieved by rest?

5.

During the past 6 months have you had no feeling or numbness in your
feet?

6.

During the past 4 weeks, have you had loss or bowel control or diarrhea
while sleeping?

7.

During the past 6 months have you had ulcers on your toes, feet or lower
legs?

8.

Have you ever had gangrene on any of your toes?

9.

Have you ever had any part of your toes or feet amputated because of
diabetes?

10. Has a doctor ever told you that you have retinopathy or diabetic eye
disease, or do you now have cataracts?
The following questions are about health conditions you currently have other than your
diabetes or cancer, please answer YES or NO to each of the following items: (Based on the
Katz Comorbidity Index)
Yes
1. Has a doctor ever told you that you have high blood pressure or
Hypertension?
2. Has a doctor ever told you that you have Asthma?
3. Has a doctor ever told you that you have a chronic lung condition
other than Asthma (such as COPD or Emphysema) ?
4. Has a doctor ever told you that you have congestive heart failure?
5. Has a doctor ever told you that you had a heart attack, coronary heart
disease, angina or other heart problems?
6. Has a doctor ever told you that you had a stroke?
7. Has a doctor every told you that you have a neurological condition
such as Parkinson’s, seizure disorder or multiple sclerosis?
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No

Yes

No

8. Has a doctor every told you that you have arthritis or rheumatism?
9. Has a doctor ever told you that you have a emotional, nervous or
psychiatric problem?
10. Do you have any other health conditions no already mentioned?
IF yes: what are they: (please write in)

The following questions are about symptoms (based on the Symptoms of Illness Checklist),
you may be experiencing related to your diabetes or other health problems: Next to each
symptom, there are two columns, titled “Number of days symptom was present” and
“Interference with “DAILY ACTIVITIES”. For each symptom, please place an X in the box
which reflects the number of days you had the symptom over the past month and place an X in
the box which reflects how much the symptom interfered with your daily activities.
Number of days
symptom was present
during month

Severely
Interfered

Considerably
Interfered

Slightly
Interfered

Did not
Interfere

15 – 30 days

128

8 – 14 days

4 – 7 days

1 – 3 days

Did Not Have
SYMPTOM
Sore Throat
High Blood Pressure
Ear Problems (Ear ache or pain,
ringing or buzzing in ears etc)
Muscle aches or pain not due to
strenuous exercise or joints
Joint (not muscle) problems
(stiffness, pain, swelling etc)
Cough due to illness
Respiratory problems other than
cough (wheezing, trouble
breathing, shortness of breath, etc)

Interference with
Daily activities

Number of days
symptom was present
during month

Severely
Interfered

Considerably
Interfered

Slightly
Interfered

Did not
Interfere

15 – 30 days

129

8 – 14 days

4 – 7 days

1 – 3 days

Did Not Have
SYMPTOM
Back and neck problems (back
aches, backpain, etc)
Sleeping Problems (trouble falling
asleep, insomnia, etc.)
Abdominal pain, (due to ulcers,
acid indigestions, appendicitis etc)
Feeling exhausted or fatigued
Blood in feces (stool)
Skin rash anywhere on the body
Urinary problems (painful
urination, blood in urine, etc)
Lightheaded, faint, dizzy
Chest pain
Constipation
Diarrhea
Eye problems (redness, impaired,
discharge or blurry vision, etc)
Dental Problems (bleeding or
discomfort in gums, teeth or
mouth, canker sores, etc)
Sinus Problems
Nasal Problems (runny nose,
congested nasal passages, etc)
Nausea (stomach sickness,
inclination to vomit, etc)
Headaches (migraine or other)
Fever
Swollen ankles or feet
Muscle twitching
Cold sores

Interference with
Daily activities

Number of days
symptom was present
during month

Interference with
Daily activities
Severely
Interfered

Considerably
Interfered

Slightly
Interfered

Did not
Interfere

15 – 30 days

8 – 14 days

4 – 7 days

1 – 3 days

Did Not Have
SYMPTOM
Numbness/tingling in hands or
feet
Menstrual Problems
Change in appetite (loss of
appetite, overeating, etc)
Swollen glands in neck

The following questions are about your Cancer and Cancer Care:
1. What type of Cancer do you have? (circle all that apply)
a. Breast
b. Colon
c. Lung
d. Bladder
e. Liver
f. Gynecological (other than ovarian)
g. Gastrointestinal (other than Liver or pancreas)
h. Pancreas
i. Lymphoma
j. Prostate
k. Ovarian
l. Other: ___________________list type
2. What stage is your cancer? (circle one)
a. Early
b. Late
3. Have you had cancer before? (circle one)
a. Yes
b. No
4. Do you have metastasis or your cancer is in more than one place in your body? (circle one)
a. Yes
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b. No
5. What type of treatment are you receiving for your cancer? (circle all that apply)
a. Chemotherapy (intravenous)
b. Radiation
c. Oral Chemotherapy
d. Other: ______________(write in)
6. When did you or will you start your chemotherapy treatments: ________________(write in
date of first treatment)

If you are RECEIVING INTRAVENOUS CHEMOTHERAPY TREATMENTS (if not,
skip question 6)
7. How often do you receive your intravenous treatments? (circle one)
a. Once a month
b. Twice monthly
c. Weekly
d. Twice weekly
e. Other_______________
If you are RECEIVING RADIATION TREATMENTS (if not, skip question 7)
8. How often do you receive your radiation treatments? (circle one)
a. Once a month
b. Twice monthly
c. Weekly
d. Twice weekly
e. Daily
f. Other_______________

If you are TAKING an ORAL CHEMOTHERAPY AGENT (if not, skip question 8)
9. How often do you take your oral chemotherapy pill: (circle one)
a. Weekly
b. Every other week
c. Twice a week
d. Every other day
e. Daily
f. Other:________________
10. Do you take any medications for your cancer or cancer related symptoms:
a. yes
b. no
If YES: How many different medications do you take: ___________(please write in
number)
131

The following questions ask about how much you believe you can perform certain activities
to manage your diabetes. These are measured on a 1 – 5 scale, with 1 meaning „No, I am
sure I cannot‟, and 5 being „Yes, I am sure I can‟. Select a number from 1 – 5 which best
reflects your ability to perform each of the activities. (Based on the Confidence in Diabetes
Self-care Scale)
1. I believe I can plan my meals and snacks according
to dietary guidelines.

1

2

3

4

5

2. I believe I can check my blood glucose at least two
times a day

1

2

3

4

5

3. I believe I can detect high levels of blood glucose in
time to correct

1

2

3

4

5

4. I believe I can detect low levels of blood glucose in
time to correct

1

2

3

4

5

5. I believe I can treat a high blood glucose correctly

1

2

3

4

5

6. I believe I can treat a low blood glucose correctly

1

2

3

4

5

7. I believe I can keep daily records of my blood
glucose

1

2

3

4

5

8. I believe I can decide when its necessary to contact
my doctor or diabetes educator

1

2

3

4

5

9. I believe I can ask my doctor questions about my
treatment plan

1

2

3

4

5

10. I believe I can keep my blood glucose in the normal
range when under stress

1

2

3

4

5

11. I believe I can check my feet for sores or blisters
every day

1

2

3

4

5

12. I believe I can inform colleagues/others of my
diabetes if needed

1

2

3

4

5

13. I believe I can ask friends or relatives to help with
my diabetes

1

2

3

4

5

14. I believe I can keep my medical appointments

1

2

3

4

5

15. I believe I can exercise two or three times weekly

1

2

3

4

5

16. I believe I can figure out what foods to eat when
dining out

1

2

3

4

5

17. I believe I can read and hear about diabetes
complications without getting discouraged

1

2

3

4

5

18. I believe I can take my medications as prescribed

1

2

3

4

5

132

Please answer the following questions if you are on
Insulin (if not skip)
19. I believe I can perform the prescribed number of
daily insulin injections

1

2

3

4

5

20. I believe I can adjust my insulin for exercise,
traveling or celebrations

1

2

3

4

5

21. I believe I can adjust my insulin when I am sick

1

2

3

4

5

The following questions are about how much you think performing specific activities is
important to controlling your diabetes. Rate your answers based on a scale of 1 to 10,
with 1 meaning „Not at all important‟, and 10 meaning „Very important‟. (Based on the
Outcomes Expectancy subscale of the Multidimensional diabetes Questionnaire)
1. To what extent do you think that following your diet is
important to controlling your diabetes?

1 2 3 4 5 6 7 8 9 10

2. To what extent do you think that taking your medication
as recommended (pills, Insulin) is important for
controlling your diabetes?

1 2 3 4 5 6 7 8 9 10

3. To what extent do you think that exercise is important for
controlling your diabetes?

1 2 3 4 5 6 7 8 9 10

4. To what extent do you think that measuring your blood
sugar is important for controlling your diabetes?

1 2 3 4 5 6 7 8 9 10

5. To what extent do you think that following your diabetes
treatment (diet, medication, blood sugar testing, exercise)
is important for controlling your diabetes?

1 2 3 4 5 6 7 8 9 10

6. To what extent do you think following your diabetes
treatment (diet, medication, blood sugar testing, exercise)
is important for delaying and/or preventing long term
diabetes complications (problems related to eyes, kidneys,
heart or feet).

1 2 3 4 5 6 7 8 9 10

The following questions are about your mood and feelings. Read every sentence, then
circle the answer that best describes how you have been feeling during the LAST WEEK.
You do not have to think too much to answer, your first thought is the most important.
I feel tense or “wound up”

Most of the time
A lot of the time
From time to time (occasionally)
Not at all
Definitely as much

I still enjoy the things I used to enjoy:
133

I get a sort of frightened feeling as if
something awful is about to happen:

I can laugh and see the funny side of
things:

Worrying thoughts go through my mind:

I feel cheerful:

I can sit at ease and feel relaxed:

I feel as if I am slowed down:

I get a sort of frightened feeling like
“butterflies” in the stomach:

I have lost interest in my appearance:

I feel restless as I have to be on the move:

I look forward with enjoyment to things:
134

Not quite as much
Only a little
Hardly at all
Very definitely and quite badly
Yes, but not too badly
A little, but it doesn’t worry me
Not at all
As much as I always could
Not quite so much now
Definitely not so much now
Not at all
A great deal of the time
A lot of the time
From time to time, but not often
Only occasionally
Not at all
Not often
Sometime
Most of the time
Definitely
Usually
Not often
Not at all
Nearly all the time
Very often
Sometimes
Not at all
Not at all
Occasionally
Quite often
Very often
Definitely
I don’t take as much care as I should
I may not take quite as much care
I take just as much care
Very much indeed
Quite a lot
Not very much
Not at all
As much as I ever did

Rather less than I used to
Definitely less than I used to
Hardly at all
Very often indeed
Quite often
Not very often
Not at all
Often
Sometimes
Not often
Very seldom

I get sudden feelings of panic:

I can enjoy a good book or radio/TV
program:

The following questions are about the activities you do, base on what you have been
advised to do to manage your diabetes. Select a number from 1 to 5 that best reflects: How
have you followed your diabetes treatment plan in the past 2 weeks? (Based on the Self-care
Inventory-revised).
Never
1. Check blood
glucose with
monitor
2. Record blood
glucose
results
3. If type 1:
Check
ketones when
glucose is
high

Rarely

Sometimes

Usually

Always

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

6. Eat the
correct food
portions

1

2

3

4

5

7. Eat

1

2

3

4

5

4. Take correct
dose of
diabetes pills
or insulin
5. Take diabetes
pills or
insulin at the
right time

135

Have type
2 diabetes

Never

Rarely

Sometimes

Usually

Always

8. Keep food
records

1

2

3

4

5

9. Read food
labels

1

2

3

4

5

meals/snacks
on time

10. Treat low
blood glucose
with just the
recommended
amount of
carbohydrate
11. Carry quick
acting sugar
to treat low
blood glucose

1

2

1

2

3

3

4

5

4

Never had
low blood
glucose

5

12. Come in for
clinic
appointments

1

2

3

4

5

13. Wear a Medic
Alert ID

1

2

3

4

5

14. Exercise

1

2

3

4

Did not
have
appointme
nt

5

15. If on insulin:
adjust insulin
dosage based
on glucose
values, food
and exercise

1

2

3

4

5

Not on
Insulin

Thank-you for completing the survey, you will be receiving a call from the study
investigator after she receives the completed survey and consent form in the mail, this
should be within 1 – 2 weeks after the survey has been mailed.

136

APPENDIX B

Eight Week Survey Instrument

137

Eight Week Telephone Survey: Inform the participants that their answers are being audio
taped.
The following questions are about symptoms (based on the Symptoms of Illness Checklist),
you may be experiencing: Next to each symptom, there are two columns, titled “Number of
days symptom was present” and “Interference with “DAILY ACTIVITIES”. For each
symptom, please place an X in the box which reflects the number of days you had the symptom
over the past month and place an X in the box which reflects how much the symptom interfered
with your daily activities.
Number of days
symptom was present
during month

Severely
Interfered

Considerably
Interfered

Slightly
Interfered

Did not
Interfere

15 – 30 days

138

8 – 14 days

4 – 7 days

1 – 3 days

Did Not Have

SYMPTOM
Sore Throat
High Blood Pressure
Ear Problems (Ear ache or pain,
ringing or buzzing in ears etc)
Muscle aches or pain not due to
strenuous exercise or joints
Joint (not muscle) problems
(stiffness, pain, swelling etc)
Cough due to illness
Respiratory problems other than
cough (wheezing, trouble
breathing, shortness of breath, etc)
Back and neck problems (back
aches, backpain, etc)
Sleeping Problems (trouble falling
asleep, insomnia, etc.)
Abdominal pain, (due to ulcers,
acid indigestions, appendicitis etc)
Feeling exhausted or fatigued
Blood in feces (stool)
Skin rash anywhere on the body
Urinary problems (painful
urination, blood in urine, etc)
Lightheaded, faint, dizzy
Chest pain

Interference with
Daily activities

Number of days
symptom was present
during month

Severely
Interfered

Considerably
Interfered

Slightly
Interfered

Did not
Interfere

15 – 30 days

8 – 14 days

4 – 7 days

1 – 3 days

Did Not Have

SYMPTOM
Constipation
Diarrhea
Eye problems (redness, impaired,
discharge or blurry vision, etc)
Dental Problems (bleeding or
discomfort in gums, teeth or
mouth, canker sores, etc)
Sinus Problems
Nasal Problems (runny nose,
congested nasal passages, etc)
Nausea (stomach sickness,
inclination to vomit, etc)
Headaches (migraine or other)
Fever
Swollen ankles or feet
Muscle twitching
Cold sores
Numbness/tingling in hands or feet
Menstrual Problems
Change in appetite (loss of
appetite, overeating, etc)
Swollen glands in neck

Interference with
Daily activities

The following questions are about the activities you do, base on what you have been
advised to do to manage your diabetes. Select a number from 1 to 5 that best reflects: How
have you followed your diabetes treatment plan in the past 2 weeks? (Based on the Self-care
Inventory-revised).
Never
1. Check blood
glucose with
monitor

Rarely

Sometimes

Usually

Always

1

2

3

4

5

2. Record blood
139

glucose results

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

6. Eat the correct
food portions

1

2

3

4

5

7. Eat
meals/snacks
on time

1

2

3

4

5

8. Keep food
records

1

2

3

4

5

9. Read food
labels

1

2

3

4

5

3. If type 1:
Check ketones
when glucose is
high
4. Take correct
dose of
diabetes pills or
insulin
5. Take diabetes
pills or insulin
at the right time

10. Treat low blood
glucose with
just the
recommended
amount of
carbohydrate
11. Carry quick
acting sugar to
treat low blood
glucose

1

2

3

4

5

1

2

3

4

Have type
2 diabetes

Never had
low blood
glucose

5

12. Come in for
clinic
appointments

1

2

3

4

5

13. Wear a Medic
Alert ID

1

2

3

4

5

14. Exercise

1

2

3

4

5

1

2

3

4

5

Did not
have
appointme
nt

15. If on insulin:
adjust insulin
dosage based
on glucose

140

Not on
Insulin

values, food
and exercise

The following items ask about how much your cancer and/or its treatment interferes with
different aspects of your diabetes self-management. Please circle the one number that best
describes your current status. Rate each item based on a 1 – 7 scale with 1 being equal to not
very much to 7 being equal to very much.
How much does your cancer and its treatment interfere with:
1.

The things you eat and drink to manage your diabetes:
(not very much) 1 2 3 4 5 6 7 (very much)

2. Exercising:
(not very much) 1

2 3

4 5

6

7 (very much)

3. (Controlling, Managing, Monitoring??? – not sure of the best word to use here) your
blood glucose levels:
(not very much) 1 2 3 4 5 6 7 (very much)
4. Taking your diabetes medications at the right time:
(not very much) 1 2 3 4 5 6 7 (very much)
5. Performing self-management activities and caring for your diabetes in general:
(not very much) 1 2 3 4 5 6 7 (very much)

Please answer the following questions:
Based on your experiences over the past 8 weeks, what do you think have been some of the
challenges or issues you have had to face in regards to managing your diabetes?

In general describe how your diabetes has been affected or impacted since you started
undergoing chemotherapy for your cancer?

141

APPPENDIX C

Study Consent Form

142

Research Participant Information and Consent Form
You are being asked to participate in a research project. Researchers are required to provide a consent
form to inform you about the study, to convey that participation is voluntary, to explain risks and benefits
of participation, and to empower you to make an informed decision. You should feel free to ask the
researchers any questions you may have.

Study Title: Older Adults with Diabetes and Cancer: Impact on Diabetes Self-Management
Researcher and Title: Denise Soltow, MSN, FNP-BC, Doctoral Candidate
Barbara Given, RN, PhD, FAAN, Associate Dean for Research
Department and Institution: College of Nursing, Michigan State University
Address and Contact Information: Mail: Michigan State University, College of Nursing -PhD
Program, 417B West Fee Hall, E. Lansing, MI 48824. Phone: (toll free) 1-877-595-6426.
1. PURPOSE OF RESEARCH:
You are being asked to participate in a research study to investigate how diabetes selfmanagement may be impacted or changed in older adults who are undergoing treatment for
cancer. You have been selected as a possible participant in this study because you have a
prior history of diabetes and you are being treated with either insulin or an oral medication.
You have also been, recently diagnosed with cancer and are eligible to receive either
intravenous or oral chemotherapy to treat your cancer or are currently receiving
chemotherapy. From this study, the investigators hope to better understand issues or
problems associated with diabetes self-management while being treated for cancer, so that
new treatments which may improve the care process for individuals with diabetes and cancer.
In the entire study, a total of 60 individuals are being asked to participate. Your participation
in this study will take about 45 - 60 minutes to complete the initial written survey and 20
minutes for a follow up survey to be completed over the phone 8 weeks later.
2. WHAT YOU WILL DO:
For this study you will be required to complete an initial written questionnaire and return in
the provided self-addressed stamped envelope. After the study investigator has received your
initial questionnaire she will contact you by telephone at the number you provided in order to
confirm your willingness to participate in the study and to set up an approximate time for
your follow up telephone survey 8 weeks later. The follow up survey will take
approximately 20 minutes. Once the follow up survey is completed you will have no further
requirements for this study.
Since, every individual responds differently to their cancer treatment, if due to side effects
from the treatment you are unable to complete the follow up survey at the prior established
time, the study investigator will ask if you are willing to set up another time within the next
week in order to complete the survey.
If you would like to receive a final summary of the results of this study, please indicate to the
study investigator at the initial contact, and after the study is completed, a final summary will
143

be mailed to you. Your participation in this study is entirely voluntary and compensation for
your participation will not be provided.
4. POTENTIAL BENEFITS:
You will not directly benefit from your participation in this study. However, your
participation in this study may contribute to the understanding of how cancer and its
treatment may impact diabetes management and care for older adults. This understanding
may lead to new interventions which could help to improve health related outcomes for
individuals with diabetes and cancer in the future.
5. POTENTIAL RISKS:
As with any research study, there may be additional risks to the participant that are currently
unforeseeable. Potentially individuals participating in this study may develop some distress
due to the questions regarding the management of their diabetes. All individuals approach
how they manage their diabetes differently, and if for some reason you develop concerns,
you will be advised to contact your diabetes care provider in order to discuss. It is not
anticipated that any of the questions would produce more than a minimal level of distress. If
for some reason, you develop a high level of distress, you will be given resources for help.
Contact numbers for local crisis centers are included in the packet you received when you
were asked to participate in the study.
6. PRIVACY AND CONFIDENTIALITY:
The data for this project are being collected confidentially. Only the researcher will be able
to link data to you. Your name and contact information will be kept separate from your
completed surveys. Your surveys will only have an ID number which the researcher can use
to link to your contact information. Once the study is completed, your contact information
will be destroyed.
The phone interview, which will occur at 8 weeks after you have enrolled in this study, will
be audio taped. These tapes will not have any identifying data other than your study ID on
them. These tapes will be kept in a locked file in which the researcher will be the only
person who has access. The researcher will conduct the phone interview in a private setting,
in order to keep the conversation private.
Information about you will be kept confidential to the maximum extent allowable by law
unless there is a danger to yourself or others. Only the principle investigators and the
Michigan State University Institutional Review Board (IRB) will have access to records
which have identifying data. All records will be kept for a minimum of 3 years, and will be
stored in a lock file in the College of Nursing Research Center.
The results of this study may be published or presented at professional meetings, but the
identities of all research participants will remain anonymous.
7. YOUR RIGHTS TO PARTICIPATE, SAY NO, OR WITHDRAW

144

Participation in this research project is completely voluntary. You have the right to say no.
You may change your mind at any time and withdraw. If you decide to withdraw, the care
you receive for your diabetes and/or your cancer will not be affected.
You may choose not to answer specific questions or to stop participating at any time.
Choosing not to participate or withdrawing from this study will not make any difference in
the quality of any treatment you may receive or benefits to which you are otherwise entitled.
8. COSTS AND COMPENSATION FOR BEING IN THE STUDY:
It is possible you may incur a cost if you use a cell phone for your primary phone. For, you
may incur charges for minutes used if you go over the minutes included in your cell phone
plan. If you use a phone other than a cell phone, there are no potential costs to you for
participating in this study. You will not receive money or any other form of compensation for
participating in this study.
9. THE RIGHT TO GET HELP IF INJURED:
If you are injured as a result of your participation in this research project, Michigan State
University will assist you in obtaining emergency care, if necessary, for your research related
injuries. If you have insurance for medical care, your insurance carrier will be billed in the
ordinary manner. As with any medical insurance, any costs that are not covered or in excess
of what are paid by your insurance, including deductibles, will be your responsibility. The
University’s policy is not to provide financial compensation for lost wages, disability, pain or
discomfort, unless required by law to do so. This does not mean that you are giving up any
legal rights you may have. You may contact study investigator Denise Soltow at 1-877-5956426 with any questions or to report an injury.
10. CONTACT INFORMATION FOR QUESTIONS AND CONCERNS
If you have concerns or questions about this study, such as scientific issues, how to do any
part of it, or to report an injury, please contact the researcher Denise M. Soltow or Barbara
Given by mail at Michigan State University, College of Nursing, PhD Program, 417B West
Fee Hall, E. Lansing, MI 48824. Phone: 1-877-595-6426.
If you have questions or concerns about your role and rights as a research participant, would
like to obtain information or offer input, or would like to register a complaint about this
study, you may contact, anonymously if you wish, the Michigan State University’s Human
Research Protection Program at 517-355-2180, Fax 517-432-4503, or e-mail irb@msu.edu or
regular mail at 207 Olds Hall, MSU, East Lansing, MI 48824.

145

11. DOCUMENTATION OF INFORMED CONSENT
Your signature below means that you voluntarily agree to participate in this research study.
There are two copies of this consent form in your packet, one for you to sign and return if
you wish to participate in this study, and one for you to keep for your records.

Signature______________________________________________Date________________

146

Please complete the following contact information:
Name: (printed)________________________________________________

Phone: __ __ __ - __ __ __ - __ __ __ __
(Area code)

Best time to Call:
Day of week: (circle all that apply) Sunday Monday Tuesday Wednesday Thursday
Friday Saturday
Time of Day: (circle all that apply) Morning

Afternoon

147

Evening

BIBLIOGRAPHY

148

Bibliography
Aikens, J., Perkins, B., Piette, J., & Lipton, B. (2008). Association between depression and
concurrent type 2 diabetes outcomes varies by diabetes regimen. Diabetic Medicine, 25,
1324-1329.
Albert, N. M., Buchsbaum, R., & Li, J. (2007). Randomized study of the effect of video
education on heart failure healthcare utilization, symptoms, and self-care behaviors.
Patient Education & Counseling, 69, 129-139.
Aljasem, L., Peyrot, M., Wissow, L., & Rubin, R. (2001). The impact of barriers and selfefficacy on self-care behaviors in type 2 diabetes. The Diabetes Educator, 27, 393-403.
Atak, N., Tanju, G., & Kose, K. (2008). The effect of education on knowledge, self management
behaviours and self efficacy of patients with type 2 diabetes. Australian Journal of
Advance Nursing, 26, 66-74.
Atlas.ti (Version 6). (2009) [computer software]: Atlas.ti Scientific Software Development
GrmbH.
Attili, V., Bapsy, P., Dadhich, H., Batra, U., Lokanatha, D., & Babu, K. (2007). Impact of
diabetes on cancer chemotherapy outcome: A retrospective analysis. International
Journal of Diabetes in Underdeveloped Countries, 27, 122-128.
Bai, Y., Chiou, C., & Chang, Y. (2009). Self-care behaviour and related factors in older people
with type 2 diabetes. Journal of Clinical Nursing, 18, 3308-3315.
Barone, B., Yeh, H., Snyder, C., Peairs, K., Stein, K., Derr, R., et al. (2008). Long-term all-cause
mortality in cancer patients with preexisting diabetis mellitus: A systematic review and
meta-analysis. JAMA, 300, 2754-2764.
Barone, B., Yeh, H., Snyder, C., Peairs, K., Stein, K., Derr, R., et al. (2010). Postoperative
mortality in cancer patients with preexisting diabetes. Diabetes Care, 33, 931-939.
Bayliss, E., Bosworth, H., Noel, P., Wolff, J., Damush, T., & Mciver, L. (2007). Supporting selfmanagement for patients with complex medical needs: Recommendations of a working
group. Chronic Illness, 3, 167-175.
Bayliss, E., Edwards, A., Steiner, J., & Main, D. (2008). Processes of care desired by elderly
patients with multimorbiditeis. Family Practice, 25, 287-293.
Bayliss, E., Ellis, J., & Steiner, J. (2007). Barriers to self-management and quality-of-life
outcomes in seniors with multimorbidities. Annals of Family Medicine, 5, 395-402.

149

Bayliss, E., Ellis, J., & Steiner, J. (2009). Seniors' self-reported multimorbidity captured
biopsychosial factors not incorporated into two other data-based morbidity measures.
Journal of Clinical Epidemiology, 62, 550 -557.e1.
Bayliss, E., Steiner, J., Fernald, D., Carne, L., & Main, D. (2003). Descriptions of barriers to
self-care by persons with comorbid chronic diseases. Annals of Family Medicine, 1, 1521.
Blakeman, T., Macdonald, W., Bower, P., Gately, C., & Chew-Graham, C. (2006). A qualitative
study of gp's attitudes to self-managment of chronic disease. British Journal of General
Practice, 56, 407-414.
Blonde, L. (2007). State of diabetes care in the United States. American Journal of Managed
Care, 13, S36-40.
Bodenheimer, T., Lorig, K., & Holman, H. (2002). Patient self-management of chronic disease in
primary care. JAMA, 19, 2469-2475.
Bodenheimer, T., Lorig, K., Holman, H., & Grumback, K. (2002). Patient self-managment of
chronic disease in primary care. JAMA, 288, 2469-2475.
Borg, C., Ingalill, H., & Blomqvist, K. (2006). Life satisfaction among older people (65+) with
reduced self-care capacity: the relationship to social, health and financial aspects. Journal
of Clinical Nursing, 15, 607 - 618.
Borthwick, D., Knowles, G., McNamara, S., O'Dea, R., & Stroner, P. (2003). Assessing fatigue
and self-care strategies in patients receiving radiotherapy for non-small cell lung cancer.
European Journal of Oncology Nursing, 78, 231 - 241.
Bourbeau, J. (2008). Clinical decision processes and patient engagement in self-management.
Disease Management and Health Outcomes, 16, 327-333.
Brewer-Lowry, A., Arcury, T., Bell, R., & Quandt, S. (2010). Differentiating approaches to
diabets self-management of multi-ethnic rural older adults at the extremes of glycemic
control. The Gerontologist, 50, 657 - 667.
Burnette, D., Mui, A. C., & Zodikoff, B. D. (2004). Gender, self-care and functional status
among older persons with coronary heart disease? A national perspective. Women &
Health, 39, 65-84.
Cancer Facts and Figures. (2009). American Cancer Society Retrieved March 6, 2009, from
http://www.cancer.org/downloads/STT/500809web.pdf
Castro, S., O'Tolle, M., Brownson, C., Plessel, K., & Schauben, L. (2009). A diabetes selfmanagement program designed for urban american indians. Preventing chronic Disease:
Public Health Research, Practice and Policy, 6, 1-7.
150

Chen, K., Chen, M., Lee, S., Cho, H., & Weng, L. (2008). Self-management behaviours for
patients with chronic obstructive pulmonary disease: a qualitative study. Journal of
Advanced Nursing, 64, 595-604.
Chen, S., & Wang, H. (2007). The relationship between physical function, knowledge of disease,
social support and self-care behavior in patients with rheumatoid arthritis. Journal of
Nursing Research, 15, 183-192.
Chlebowy, D., & Garvin, B. (2006). Social support, self-efficacy, and outcome expectations:
Impact on self-care behaviors adn glycemic control in caucasian and african american
adults with type 2 diabetes. The Diabetes Educator, 32, 777-786.
Chou, F., Dodd, M., Abrams, D., & Padilla, G. (2007). Symptoms, self-care, and quality of life
of Chinese American patients with cancer. Oncology Nursing Forum, 34, 1162-1167.
Chriss, P. M., Sheposh, J., Carlson, B., & Riegel, B. (2004). Predictors of successful heart failure
self-care maintenance in the first three months after hospitalization. Heart & Lung: The
Journal of Acute and Critical Care, 33, 345-353.
Ciechanowski, P., Katon, W., & Russo, J. (2000). Depression and diabetes: Impact of depressive
symptoms on adherence, function and costs. Archives of Internal Medicine, 160, 32783285.
Cleeland, C. S., Mendoza, T., Wang, X., Chou, C., Harle, M., Morrissey, M., et al. (2000).
Assessing symptom distress in cancer patients: The M.D. anderson symptom inventory.
Cancer, 89, 1634-1646.
Coates, V., & Boore, J. (1998). The influence of psychological factors on the self-management
of insulin dependent diabetes mellitus. Journal of Advanced Nursing, 27, 528 - 537.
Cohen, H. (2007). The cancer aging interface: a research agenda. American Society of Clinical
Oncology, 25, 1945-1948.
Conn, V. (1998). Older women: Social cognitive theory correlates of health behavior. Women &
Health, 26, 71-85.
Conn, V., & Russell, C. (2005). Self-management of chronic illnesses among aging adults.
Journal of Gerontological Nursing, 31, 4.
Crossing the quality chasm: A new health system for the 21st century. (2001). United States of
America: National academy of Sciences. Chapter 1.
De Ridder, D. T. D., Schreurs, K. M. G., & Kuijer, R. G. (2005). Is spousal support always
helpful to patients with asthma or diabetes? A prospective study. Psychology & Health,
20, 497-508.
151

Deimling, G., Bowman, K., & Wagner, L. (2007). The effects of cancer-related pain and fatigue
on functioning of older adult, long-term cancer survivors. Cancer Nursing, 30, 421 - 433.
Devins, G. (2010). Using the illness intrusiveness ratings scale to understand health-related
quality of life in chronic disease. Journal of Psychosomatic Research, 68, 591-602.
Dodd, M. (1988). Patterns of self-care in patients with breast cancer. Western Journal of Nursing
Reserach, 10, 7 - 24.
Dunbar, S., Clark, P., Quinn, C., Gary, R., & Kaslow, N. (2008). Family influences on heart
failure self-care and outcomes. Journal of Cardiovascular Nursing, 23, 258-265.
Egede, L. (2005). Effect of comorbid chronic diseases on prevalence and odds of depression in
adults with diabetes. Psychosomatic Medicine, 67, 46-51.
Egede, L., C., E., & Grubaugh, A. (2009). The effect of depression on self-care behaviors and
quality of care in a national sample of adults with diabetes. General Hospital Psychiatry,
31, 422-427.
Extermann, M. (2007). Interaction between comorbidity and cancer. Cancer Control, 14, 13 - 22.
Faul, F. (2008). G*Power (Version 3.0.) [computer software]: http://www.psycho.uniduesseldorf.de/aap/projects/gpower/.
Figaro, M., Elasy, T., BeLue, R., Speroff, T., & Dittus, R. (2009). Exploring socioeconomic
variations in diabetes control strategies: Impact of outcome expectations. Journal of the
National Medical Association, 101, 18-23.
Fincke, B., Clark, J. A., Linzer, M., Spiro, A., Miller, D. R., Lee, A., et al. (2005). Assessment of
long-term complications due to type 2 diabetes using patient self-report: The diabetes
complications index. Journal of Ambulatory Care Management, 28, 262-273.
Folsom, A., Anderson, K., Sweeney, C., & Jackobs, D. (2004). Diabetes as a risk factor for death
following endometrial cancer. Gynecologic Oncology, 94, 740-745.
Fossa, S., Dahl, A., & Loge, J. (2003). Fatigue, anxiety and depression in long-term survivors of
testicular cancer. Journal of Clinical Oncology, 21, 1249-1254.
Fu, M., LeMone, P., & McDaniel, R. (2004). An integrated approach to an analysis of symptom
management in patients with cancer. Oncoloty Nursing Forum, 31, 65-70.
Funnell, M. M. (2008). Standards of care for diabetes: what's new, what's different. Nursing, 38,
47-49.

152

Gallagher, R., Donoghue, J., Chenoweth, L., & Stein-Parbury, J. (2008). Self-management in
older patients with chronic illness. International Journal of Nursing Practice, 14, 373382.
Gallant, M. P., Spitze, G. D., & Prohaska, T. R. (2007). Help or hindrance? How family and
friends influence chronic illness self-management among older adults. Research on
Aging, 29, 375-409.
Garcia, A. (2008). Clinical and life quality differences between mexican american diabeteic
patients at a free clinic and a hospital-affiliated clinic in Texas. Public Health Nursing,
25, 149-158.
Giovanucci, E., Harlan, D., Archer, M., Bergenstal, R., Gapstur, S., Habel, L., et al. (2010).
Diabetes and cancer: A consensus report. CA: A Cancer Journal for Clinicians, 60, 207221.
Given, C., Given, B., Azzouz, F., Kozachik, S., & Stommel, M. (2001). Predictors of pain and
fatigue in the year following diagnosis among elderly cancer patients. Journal of Pain
and Symptom Management, 21, 456-466.
Glasgow, R., Hampson, S., Strycker, L., & Ruggiero, L. (1997). Personal-model beliefs and
social-environmental barriers related to diabetes self-management. Diabetes Care, 20,
556 - 561.
Gold, R., Yu, K., Liang, L., Adler, F., Balingit, P., Luc, P., et al. (2008). Synchronous provider
visit and self-management education improves glycemic control in Hispanic patients with
long-standing type 2 diabetes. Diabetes Educator, 34, 990-995.
Gonzalez, J., Safren, S., Cagliero, E., Wexler, D., Delahanty, L., Wittenberg, E., et al. (2007).
Depression, self-care, and medication adherence in type 2 diabetes. Diabetes Care, 30,
2222-2227.
Green, J. M., & Hacker, E. D. (2004). Chemotherapy in the geriatric population. Clinical Journal
of Oncology Nursing, 8, 591 - 597.
Grey, M., Knafl, K., & McCorkle, R. (2006). A framework for the study of self- and family
management of chronic conditions. Nursing Outlook, 54, 278-286.
Hall, P., Rodin, G., Vallis, M., & Perkins, B. (2009). The consequences of anxious temperament
for disease detection, self-management behavior and quality of life in type 2 diabetes
mellutis. Journal of Psychosomatic Research, 67, 297-305.
Hartz, A., Kent, S., James, P., Xu, Y., Kelly, M., & Daly, J. (2006). Factors that influence
improvement for patients with poorly controlled type 2 diabetes. Diabetes Research &
Clinical Practice, 74, 227-232.

153

Harvey, P. W., Petkov, J. N., Misan, G., Fuller, J., Battersby, M. W., Cayetano, T. N., et al.
(2008). Self-management support and training for patients with chronic and complex
conditions improves health-related behaviour and health outcomes. Australian Health
Review, 32, 330-338.
Heidrich, S., Egan, J., Hengudomsub, P., & Radolph, D. (2006). Symptoms, symptom beliefs
and quality of life of older breast cancer survivors: A comparative study. Oncology
Nursing Forum, 33, 315 - 322.
Heisler, M., Piette, J., Spencer, M., Keiffer, E., & Vijan, S. (2005). The relationship between
knowledge of recent HbA1c values and diabetes care understanding and selfmanagement. Diabetes Care, 28, 816-822.
Heisler, M., Smith, D. M., Hayward, R. A., Krein, S., & Kerr, E. (2003). How well do patients'
assessments of their diabetes self-management correlate with actual glycemic control adn
receipt of recommended diabetes services? Diabetes Care, 26, 738-743.
Heisler, M., Vijan, S., Anderson, R., Ubel, P., Bernstein, S., & Hofer, T. (2003). When do
patients and their physcians agree on diabetes treatment goals and strategies, and what
difference does it make. Journal of General Internal Medicine, 18, 893-902.
Hoffman, A., von Eye, A., Gift, A., Given, B., Given, C., & Rothert, M. (2009). Testing a
theoretical model of percieved self-efficacy for cancer related fatigue self-management
and optimal physical function status. Nursing Research, 58, 32 - 41.
Hofman, M., Ryan, J., Figueroa-Moseley, C., Jean-Pierre, P., & Morrow, G. (2007). Cancerrealted fatigue: The scale of the problem. The Oncologist, 12, 4 - 10.
Holman, H., & Lorig, K. (2004). Patient self-management: A key to effectiveness and efficiency
in care of chronic disease. Public Health Reports, 119, 239-243.
Honish, A., Westerfield, W., Ashby, A., Momin, S., & Phillippi, R. (2006). Health-related
quality of life and treatment compliance with diabetes care. Disease Management, 9(4),
195-200.
Jack, L., Airhihenbuwa, C., Namageyo-Funa, A., Owens, M., & Vinicor, F. (2004). The
pychosocial aspects of diabetes care: Using collaborative care to manage older adults
with diabetes. Geriatrics, 59, 26-32.
Janz, N. K., Mujahid, M., Chung, L. K., Lantz, P. M., Hawley, S. T., Morrow, M., et al. (2007).
Symptom experience and quality of life of women following breast cancer treatment.
Journal of Women's Health, 16, 1348-1361.
Jenerette, C., & Phillips, R. (2006). An examination of differences in intr-personal resources,
self-care management, and health outcomes in older and younger adults with sickle cell
disease. Southern Online Journal of Nursing Research, 7, 1-24.

154

Jerant, A., von Friederichs-Fitzwater, M., & Moore, M. (2005). Patients' percieved barriers to
active self-management of chronic conditions. Patient Education and Counseling, 57,
300 - 307.
The John A. Hartford Foundation (JAHF) Annual Report. (2006). Retrieved from
http://www.jhartfound.org/pdf%20files/JAHF%202006%20Annual%20Report.pdf
Katz, J., Chang, L., Sangha, O., Fossel, A., & Bates, D. (1996). Can comorbidity be measured by
qeustionnaire rather than medical record review. Medical Care, 34, 73-84.
Kerr, E., Heisler, M., Krein, S., Kabeto, M., Langa, K., Weir, D., et al. (2007). Beyond
comorbidity counts: How do comorbidity type and severity influence diabetes patients
treatment priorities and self-managment. Journal of General Internal Medicine, 12, 1600
- 1640.
Krein, S., Heisler, M., Piette, J., Makki, F., & Kerr, E. (2005). The effect of chronic pain on
diabetes patients' self-management. Diabetes Care, 28, 65-70.
Krein, S. L., Heisler, M., Piette, J. D., Butchart, A., & Kerr, E. A. (2007). Overcoming the
influence of chronic pain on older patients' difficulty with recommended selfmanagement activities. Gerontologist, 47, 61-68.
Krinsley, J. S. (2008). Glycemic variability: a strong independent predictor of mortality in
critically ill patients. Critical Care Medicine, 36, 3008-3013.
Kurtz, M., Kurtz, J., Stommel, M., Given, C., & Given, B. (2001). Physical functioning and
depression among older persons wtih cancer. Cancer Practice, 9, 11-18.
Justice, D. (2010) Long term services and supports and chronic care coordination: Policy
advances enacted by the patient protection and affordable care act. Briefing: a
publication of the national academy for state health policy, April. Retrieved from
www.nashp.org.
Lanting, L. C., Joung, I. M., Vogel, I., Bootsma, A. H., Lamberts, S. W., & Mackenbach, J. P.
(2008). Ethnic differences in outcomes of diabetes care and the role of self-management
behavior. Patient Education & Counseling, 72, 146-154.
Leak, A., Davis, E., Houchin, L., & Mabrey, M. (2009). Diabetes management and self-care
education for hospitalized patients with cancer. Clinical Journal of Oncology Nursing,
13, 205-210.
Lee, H., Chapa, D., Kao, C., Jones, D., Kapustin, J., Smith, J., et al. (2009). Depression, quality
of life, and glycemic control in individuals with type 2 diabetes. Journal of the American
Academy of Nurse Practitioners, 21, 214-224.

155

Lin, E., Katon, W., Von Korff, M., Rutter, C., Simon, G., Oliver, M., et al. (2004). Relationship
of depression and diabetes self-care, medication adherence and preventative care.
Diabetes Care, 27, 2154-2160.
Lorig, K. (1993). Self-management of chronic illness; a model for the future. Generations, 17,
11-15.
Lorig, K., & Homan, H. (2003). Self-management education: History definitions, outcomes and
mechanisms. Annals of Behavioral Medicine, 26, 1-7.
Luyster, F., Hughes, J., & Gunstad, J. (2009). Depression and anxiety symptoms are associated
with reduced dietary adherence in heart failure patients treated with an implantable
cardioverter defibrillator. Journal of Cardiovascular Nursing, 24, 10-17.
MacInnes, J. (2008). Factors affecting self-care in heart failure: a literature review. British
Journal of Cardiac Nursing, 3, 293-299.
Magnan, M. (2004). The effectiveness of fatigue-related self-care methods and strategies used by
radiation oncology patients. Self-care, Dependent-care and Nursing, 12 - 21.
Mann, D. M., Ponieman, D., Leventhal, H., & Halm, E. A. (2009a). Misconceptions about
diabetes and its management among low-income minorities with diabetes. Diabetes Care,
32), 591-593.
Mann, D. M., Ponieman, D., Leventhal, H., & Halm, E. A. (2009b). Predictors of adherence to
diabetes medications: the role of disease and medication beliefs. Journal of Behavioral
Medicine, 32, 278-284.
McCoubrie, R., Jeffrey, D., Paton, C., & Dawes, L. (2005). Managing diabetes mellitus in
patients with advanced cancer: a case note audit and guidelines. European Journal of
Cancer Care, 14, 244-248.
McDonald-Miszczak, L., & Wister, A. (2005). Predicting self-care behaviors among older adults
coping with arthritis: A cross-sectional and 1 year ongitudinal comparative analysis.
Journal of Aging and Health, 17, 836-857.
Meyerhardt, J., Catalano, P., Haller, D., Mayer, R., Macdonald, J., Benson, A., et al. (2003).
Impact diabetes mellitus on outcomes in patients with colon cancer. Journal of Clinical
Oncology, 21, 433-440.
Miaskowski, C., Dodd, M., West, C., Schumacher, K., Paul, S., Tripathy, D., et al. (2004).
Randomized clinical trial fo the effectiveness of self-care intervention to improve cancer
pain management. Journal of Clinical Oncology, 22, 1713 - 1720.

156

Molasiottis, A., Stricker, C. T., Eaby, B., Velders, L., & Coventry, P. A. (2008). Understanding
the concept of chemotherapy-related nausea: the patient experience. European Journal of
Cancer Care, 17, 444-453.
Morrow, A. S., Haidet, P., Skinner, J., & Naik, A. D. (2008). Integrating diabetes selfmanagement with the health goals of older adults: a qualitative exploration. Patient
Education & Counseling, 72, 418-423.
Mystakidou, K., Tsilika, E., Parpa, E., Katsouda, E., Galanos, A., & Vlahos, L. (2006).
Psychological distress of patients with advanced cancer: Influence and contribution of
pain severity and pain interference. Cancer Nursing, 29, 400 - 405.
Nagelkerk, J., Reick, K., & Meengs, L. (2006). Perceived barriers and effective strategies to
diabetes self-management. Journal of Advanced Nursing, 54, 151 - 158.
Nathan, D., Buse, J., Davidson, M., Heine, R., Holman, R., Sherwin, R., et al. (2006).
Management of hyperglycemia in type 2 diabetes: A consensus algorithm for the
initiation and adjustment of therapy. Diabetes Care, 29, 1963-1972.
Noel, P., Frueh, C., Larme, A., & Pughs, J. (2005). Collaborative care needs and preferences of
primary care patients with multimorbidity. Health Expectations, 8, 54 - 63.
Oberst, M., Hughes, S., Chang, A., & McCubbin, M. (1991). Self-care burden, stress appraisal,
and mood among persons receiving radiotherapy. Cancer Nursing, 14, 71-78.
Olsson, I., Mykletun, A., & Dahl, A. (2005). The hospital anxiety and depression rating scale: A
cross-sectional study of psychometrics and case finding abilities in general practice. BMC
Psychiatry, 5, 46 - 52.
. PASW Statistics (Version 18). (2009) [computer software]. Chicago: SPSS Inc.
Patel, K. L. (2008). Impact of tight glucose control on postoperative infection rates and wound
healing in cardiac surgery patients. Journal of Wound, Ostomy & Continence Nursing,
35, 397-406.
Peyrot, M., Rubin, R., Kruger, D., & Travis, L. (2010). Correlates of insulin injection omission.
Diabetes Care, 33, 240-245.
Piette, J., & Kerr, E. (2006). The impact of comorbid chronic conditions on diabetes care.
Diabetes Care, 29, 725 - 731.
Ponzo, M., Gucciardi, E., Weiland, M., Masi, R., Lee, R., & Grace, S. (2006). Gender,
ethnocultural, and psychosocial barriers to diabetes self-management in Italian women
and men with type 2 diabetes. Behavioral Medicine, 26, 153 - 160.

157

Proctor, E., Hasche, L., Morrow-Howell, N., Shumway, M., & Snell, G. (2008). Perceptions
about competing psychosocial problems and treatment priorities among older adults with
depression. Psychiatric Services, 59, 670 - 675.
Psarakis, H. (2006). Clinical challenges in caring for patients with diabetes and cancer. Diabetes
Spectrum, 19, 157-162.
Rao, A., & Cohen, H. (2004). Symptom management in the elderly cancer patient: Fatigue, pain
and depression. Journal of the National Cancer Institute Monographs, 32, 150-157.
Reiner, A., & Lacasse, C. (2006). Symptom correltes in the gero-oncology population. Seminars
in Oncology Nursing, 22, 20 - 30.
Rhee, M. K., Cook, C. B., El-Kebbi, I., Lyles, R. H., Dunbar, V. G., Panayioto, R. M., et al.
(2005). Barriers to diabetes education in urban patients: perceptions, patterns, and
associated factors. Diabetes Educator, 31, 410-417.
Ripsin, C., Kang, H., & Urban, R. (2009). Management of blood glucose in type 2 diabetes
mellitus. American Family Physician, 79, 29-36.
Scain, S. F., Friedman, R., & Gross, J. L. (2009). A structured educational program improves
metabolic control in patients with type 2 diabetes: a randomized controlled trial. Diabetes
Educator, 35, 603-611.
Schoenberg, N. E., Leach, C., & Edwards, W. (2009). "It's a toss up between my hearing, my
heart, and my hip": prioritizing and accommodating multiple morbidities by vulnerable
older adults. Journal of Health Care for the Poor & Underserved, 20, 134-151.
Shah, B. R., & Booth, G. L. (2009). Predictors and effectiveness of diabetes self-management
education in clinical practice. Patient Education & Counseling, 74, 19-22.
Sigal, R., Kenny, G., Wasserman, D., Castaneda-Sceppa, C., & White, R. (2006). Physical
activity/exercise and type 2 diabetes. Diabetes Care, 29, 1433-1438.
Simon, G. (2001). Treating depression in patients with chronic disease. Western Journal of
Medicine, 175, 292-293.
Smyth, D., & Smyth, T. (2005). The relationship between diabetes and cancer. Journal of
Diabetes Nursing, 9, 269-274.
Snaith, R. (2003). The hospital anxiety and depression scale. Health and Quality of Life
Outcomes, 1, 29.
Sousa, V. D., Hartman, S. W., Miller, E. H., & Carroll, M. A. (2009). New measures of diabetes
self-care agency, diabetes self-efficacy, and diabetes self-management for insulin-treated
individuals with type 2 diabetes. Journal of Clinical Nursing, 18, 1305-1312.
158

Sousa, V. D., Zauszniewski, J., Lea, P., & Davis, S. (2005). Relationships among self-care
agency, self-efficacy, self-care and glycemic control. Research and Theory for Nursing
Practice: An International Journal, 19, 217-228.
Sowattanangoon, N., Kotchabhakdi, N., & Petrie, K. (2009). The influence of thai culture on
diabetes perceptions and management. Diabetes Research & Clinical Practice, 84, 245251.
Srokowski, T. P., Fang, S., Hortobagyi, G. N., & Giordano, S. H. (2009). Impact of diabetes
mellitus on complications and outcomes of adjuvant chemotherapy in older patients with
breast cancer. Journal of Clinical Oncology, 27, 2170-2176.
Stowell, J., Hedges, D., A., G., Key, C., & Bloch, G. (2009). Validation of the symptoms of
illness checklist (SIC) as a tool for health psychology research. Journal of health
Psychology, 14, 68-77.
Tadman, M., & Roberts, D. (Eds.). (2007). Oxford Handbook of Cancer Nursing. New York:
Oxford University Press.
Talbot, F., Nouwen, A., Gingras, J., Gosselin, M., & Audet, J. (1997). The assessment of
diabetes-related cogntive and social factors: The multidimensional diabetes
questionnaire. Journal of Behavioral Medicine, 20, 291-312.
Tang, T. S., Brown, M. B., Funnell, M. M., & Anderson, R. M. (2008). Social support, quality of
life, and self-care behaviors among African Americans with type 2 diabetes. Diabetes
Educator, 34, 266-276.
Terret, C., Zulian, G. B., Naiem, A., & Albrand, G. (2007). Multidisciplinary approach to the
geriatric oncology patient. Journal of Clinical Oncology, 25, 1876-1881.
Toljamo, M., & Hentinen, M. (2001). Adherence to self-care and social support. Journal of
Clinical Nursing, 10, 618-627.
Trief, P. M., Teresi, J. A., Eimicke, J. P., Shea, S., & Weinstock, R. S. (2009). Improvement in
diabetes self-efficacy and glycaemic control using telemedicine in a sample of older,
ethnically diverse individuals who have diabetes: the IDEATel project. Age & Ageing,
38, 219-225.
Utz, S. W., Steeves, R. H., Wenzel, J., Hinton, I., Jones, R. A., Andrews, D., et al. (2006).
"Working hard with it:" self-management of type 2 diabetes by rural African Americans.
Family & Community Health, 29, 195-205.
van der Ven, N., Weinger, K., Yi, J., Pouwer, F., Ader, H., van der Ploeg, H., et al. (2003). The
Confidence in Diabetes Self-Care scale: psychometric properties of a new measure of

159

diabetes-specific self-efficacy in Dutch and U.S. patients with type 1 diabetes. Diabetes
Care, 26, 713-718.
Vijan, S., Stuart, N. S., Fitzgerald, J. T., Ronis, D. L., Hayward, R. A., Slater, S., et al. (2005).
Barriers to following dietary recommendations in Type 2 diabetes. Diabetic Medicine,
22, 32-38.
Visovsky, C., Meyer, R., Roller, J., & Poppas, M. (2008). Evaluation and management of
peripheral neuropathy in diabetic patients with cancer. Clinical Journal of Oncology
Nursing, 12, 243-247.
Wagner, E. (1998). Chronic disease managment: What will it take to improve care for chronic
illness. Effective Clinical Practice, 1, 2-4.
Wallace, A., Seligman, H., Davis, T., Schillinger, D., Arnold, C., Bryant-Shilliday, B., et al.
(2009). Literacy-appropriate educational materials and brief counseling improve diabetes
self-management. Patient Education & Counseling, 75, 328-333.
Washburn, S., & Hornberger, C. (2008). Nurse educator guidelines for the management of heart
failure. The Journal of Continuing Education in Nursing, 39, 263-267.
Wasson, J. H., Johnson, D. J., & Mackenzie, T. (2008). The impact of primary care patients' pain
and emotional problems on their confidence with self-management. Journal of
Ambulatory Care Management, 31, 120-127.
Weiler, D., & Crist, J. (2009). Diabetes self-management in a latino social environment. The
Diabetes Educator, 35, 285-292.
Weinger, K., Butler, H., Welch, G., & La Greca, A. (2005). Measuring diabetes self-care: A
psychometric analysis of the self-care inventory-revised with adults. Diabetes Care, 28,
1346-1352.
Wen, L., Shepherd, M., & Parchman, M. (2004). Family support, diet, and exercise among older
mexican americans with type 2 diabetes. The Diabetes Educator, 30, 980-993.
Whittemore, R., Melkus, G., & Grey, M. (2005). Metabolic control, self-management and
psychosocial adjustment in women with type 2 diabetes. Journal of Clinical Nursing, 14,
195-203.
Williams, K., & Bond, M. (2002). The roles of self-efficacy, outcome expectancies and social
support in the self-care behaviours of diabetics. Psychology, Health and Medicine, 7,
127-141.
Williams, P., Piamjariyakul, U., Ducey, K., Badura, J., Boltz, K., Olberding, K., et al. (2006).
Cancer treatment, symptoms monitoring and self-care in adults. Cancer Nursing, 29, 347355.
160

Wong, A., Stewart, A., & Furler, J. (2009). Development and validation of the diabetes
management orientation scale (DMOS): Assessing culturally related approaches to
diabetes self-management. Diabetes Research & Clinical Practice, 86, 24-30.
Wydra, E. (2001). The effectiveness of a self-care managment interactive multimedia module.
Oncology Nursing Forum, 28, 1399 - 1407.
Xu, Y., Toobert, D., Savage, C., Pan, W., & Whitmer, K. (2008). Factors influencing diabetes
self-management in Chinese people with type 2 diabetes. Research in Nursing & Health,
31, 613-625.
Zeber, J., Copeland, L., Hosek, B., Karnad, A., Lawrence, V., & Sanchez-Reilly, S. (2008).
Cancer rates, medical comorbidities, and treament modalities in the oldest patients.
Critical Reviews in Oncology/Hematology, 67, 237-242.

161