Culminating Activity

A ROLE FOR PEER SUPPORT
IN THE
DIABETES EPIDEMIC
A Project
Presented
To the Faculty of
California State University, Chico
_________________________________________________
In Partial Fulfillment
Of the Requirements for the Degree
Master
Of
Public Administration
__________________________________________________
By
Heather Beiden Jacobs
Spring 2016

ii
A ROLE FOR PEER SUPPORT
IN THE
DIABETES EPIDEMIC
A Project By
Heather Beiden Jacobs
Spring 2016
Approved by the Graduate Coordinator
________________________________
Lori M. Weber, Ph.D.
Approved by the Graduate Advisory Committee
_______________________________
Paul Viotti, Ph.D., Chair
________________________________
Lori M. Weber, Ph.D.

iii
TABLE OF CONTENTS
Table of Contents.........................................................................................................................................iii
List of Figures............................................................................................................................................... v
List of Tables ...............................................................................................................................................vi
Dedication...................................................................................................................................................vii
Acknowledgments......................................................................................................................................viii
Abstract........................................................................................................................................................ix
Chapter I. Introduction.................................................................................................................................. 1
What is Peer Support................................................................................................................................. 2
Chapter II. Literature Review ....................................................................................................................... 3
Diabetes and the Epidemic........................................................................................................................ 3
The Role of diabetes Self-Management ................................................................................................... 4
Public Health Crisis .................................................................................................................................. 6
Review of Peer Case Studies .................................................................................................................... 7
Results of Peer Case Studies..................................................................................................................... 9
Chapter III. Methodology ........................................................................................................................... 11
Survey Background................................................................................................................................. 11
Research Question and Hypothesis ........................................................................................................ 11
Survey Methodology .............................................................................................................................. 12
Data Set and Measures ........................................................................................................................... 15
Chapter IV. Survey Results......................................................................................................................... 18
Chapter V. Discussion ................................................................................................................................ 29
Limitations.............................................................................................................................................. 29

iv
Role of Peer Support............................................................................................................................... 30
Potential Challenges and Policy Solutions.............................................................................................. 32
Policy Recommendations........................................................................................................................ 34
Chapter VI. Conclusion .............................................................................................................................. 35
References................................................................................................................................................... 36
Appendix..................................................................................................................................................... 41
Appendix A – The Peer Support Survey................................................................................................. 41

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LIST OF FIGURES
Figure 1. HbA1C ........................................................................................................................................ 22
Figure 2. Diabetes Type ............................................................................................................................. 23
Figure 3. SBGM Frequency ....................................................................................................................... 24
Figure 4. In Charge of Diabetes ................................................................................................................. 25
Figure 5. Diabetes Optimism ..................................................................................................................... 26
Figure 6. Diabetes Self-Rating ................................................................................................................... 27

vi
LIST OF TABLES
Table 1. Demographic and Behavior Characteristics ................................................................................. 19
Table 2. Linear Regression HbA1C ........................................................................................................... 27
Table 3. Linear Regression HbA1C ........................................................................................................... 28

vii
THIS PROJECT IS DEDICATED
To my peers living with diabetes
for their example, support, and understanding.
Thank you.

viii
ACKNOWLEDGEMENTS
I would like to express my tremendous gratitude for my Graduate Advisory Committee,
my beloved spouse and family, and some indispensable individuals for their guidance, support,
and inspiration for this project. This endeavor would not have been possible without the
contributions of these people.
I would like to thank Dr. Paul Viotti for his guidance, support, and encouragement
throughout this prolonged effort. His facilitation of the survey research, development, and
analysis as an Independent Study project allowed me the time and means to pursue this effort to
the fullest. I would like to thank Dr. Lori Weber for sharing her genuine and personal interest in
this project, and for teaching the research methods and statistical models that I used in the
analysis of the data. I would also like to thank David Philhour for contributing his expertise in
data dissection employing both Stata and SPSS, and for always bringing a ray of sunshine to my
day.
I would like to acknowledge the incredible contributions of the medical professionals,
and my diabetes peers whose insights and experience were crucial to the development of this
survey. In particular, I would like to express my appreciation for Chesney Hoagland-Fuchs,
Anjali Asrani, and Dr. Kate Lorig for their willingness to provide pertinent feedback about the
survey and for disseminating the survey link.
Last but not least, I would like to convey my enormous gratitude for Blaine, my friends,
and family for believing in me and supporting me throughout my life. I would like to especially
thank Blaine for his unconditional love and for ruining my plan to die young.

ix
ABSTRACT
Diabetes is a national epidemic that is increasing at alarming rates. Initiatives to address it have
focused predominantly on managing it with medical-centric efforts. Despite these measures,
there is much more to do change our collective course. This study investigates whether
involving people living with diabetes in the public health efforts help alleviate some of the
challenges associated with diabetes self-management (DSM) adherence levels in the diabetes
population.
The role of peer support is recognized as a beneficial component of diabetes
management; however, public health efforts dealing with this epidemic have devoted most
energies and resources to research and clinical efforts, largely ignoring the peer and community
aspects of self-care. While the former are clearly essential, they are insufficient to mitigate the
enormous human and financial costs of diabetes. The latter provide additional, underutilized,
and much-needed resources in reducing the burden of diabetes. The term “peer” refers to a
person living with diabetes.
Previous studies looking at the influences of peer support on diabetes control have shown
peers are at least equally effective as health care practitioners in improving study outcomes,
specifically blood glucose management. The peer support survey for this research was designed
to assess the effects of support from people with diabetes on diabetes self-management (DSM).
It was premised on existing surveys and recommendations of diabetes professionals.
The survey was delivered online and made available for six weeks during the spring of
2015. Survey results showed that of the two hundred seventy nine participants, 57.3% received
no peer support and 42.7% had at least one peer with diabetes. Linear ordinary least squares
regression models predict those with peer support monitor their blood glucose more closely, have

x
better blood glucose averages, and rated their DSM more positively. These results were
statistically significant.
These findings are clinically significant because peer support lies outside the current
medical treatment-focused model of diabetes management. This implies that public health
measures will benefit from expanding the current model to include mechanisms for peer support.
Possible explanations for peer support’s beneficial effects include the sense of belonging to a
community that reduces a sense of isolation, the sharing of best practices, and the modeling of
healthy behaviors. Establishing and funding public health efforts to engage and train people with
diabetes to serve as peer support systems is essential to overcoming the myriad of challenges
associated with the diabetes epidemic.

1
INTRODUCTION:
The diabetes epidemic poses unique challenges that necessitate the adaptation of public health
policies and the development of innovative public health interventions to mitigate against
burgeoning human and financial costs. To do so would mean moving beyond the current
paradigm that focuses predominantly on the medical management of diabetes to include other
social factors that influence self-care, which are additionally necessary to more effectively
address this public health crisis. The medical community has theoretically understood how to
prevent the microvascular (small-vessel) complications of diabetes since the early 1990s and has
reduced the percentage of people with diabetes developing complications; however, there is
greater work to do. Despite these improvements, the trends of complications are continuing to
rise; this is because the increasing number of people diagnosed with diabetes. What is necessary
is the transformation of theoretical knowledge into sustainable action for individuals with living
diabetes. Peer mentors provide a potential conduit to bridge these sides.
Public health efforts to address the diabetes epidemic will benefit from diversifying
mechanisms to incorporate peer support as a key component for resolving this complex public
health crisis. Despite the considerable efforts to manage this population health issue through a
medical paradigm, the disease is rampant and the costs are high, in terms of both human
suffering and finances. The goals of public health efforts to reduce the incidence of people being
diagnosed and prevent potential complications in those who have it require sustained individual
adherence to healthy lifestyle choices. Psychosocial factors and other social determinants
present as common obstacles to attaining these public health objectives. Psychosocial refers to
the influence of social factors on an individual’s mind or behavior, and to the interrelation of

2
behavioral and social factors. Engaging people living with diabetes in peer mentoring programs
provides compelling solutions to such barriers.
Public health programs designed to engage people with diabetes in support roles will
likely benefit from augmenting the tremendous efforts of clinical providers with greater and
better-coordinated mechanisms to address the psychosocial aspects of chronic illness. The
research question for this study is whether involving people living with diabetes in the public
health efforts help alleviate some of the persistent challenges associated with DSM adherence
levels in the diabetes population. The term “peer,” in the context of this paper, refers to a person
who lives with diabetes. The paper presents a brief review of diabetes and its diverse challenges,
moves to an exploration of pertinent case studies, presents the methods and data set for the
survey, analyses the survey results, and discusses the implications of findings.
What is Peer Support?
First, here is a condensed background about the conception of peer support. Peer support is a
mutually beneficial relationship between people who share similar experiences. The concept of
peer support is “based on the belief that people who have faced, endured, and overcome
adversity can offer useful support, encouragement, hope, and perhaps mentorship to others
facing similar situations (Davidson 1999, 1).” The origins of peer support date back to the
eighteenth century, at Bicêtre Hospital, a psychiatric hospital in France. Jean Baptiste Pussin,
the governor of the facility, recognized and acknowledged the benefits of employing mental
health patients in recovery as support staff. The advent of peer support resurfaced in the 1965
when Robert R. Carkhuff and Charles B. Truax wrote “Lay mental health counseling: The effects
of lay group counseling.” Their research demonstrated the benefits provided by laypersons who
were trained to provide support to individuals with mental health issues in a clinical setting

3
(Carkhuff 1965). The mental health consumer/survivor movement of the 1970s was instrumental
in bringing forward the benefits of peer support. Despite this research, many medical health
professionals did not actively employ these techniques until a re-engagement of this notion in the
1980s. By the 1990s, the concepts of peer support and mentoring began to take hold beyond the
mental health arena to other chronic conditions (Davidson 1999).
LITERATURE REVIEW
Diabetes and the Epidemic
Diabetes is a national epidemic, in addition to a worldwide pandemic, and requires more diverse
approaches to successfully manage this public health crisis. According to a 2014 report by the
Centers for Disease Control and Prevention (CDC), 29.1 million Americans have diabetes (CDC
2014a). If current trends continue, it is estimated that one in five American adults will have
diabetes by 2025. In addition, children who were born in 2000 have a one in three chance of
developing diabetes during their lifetime; however, if they are Black, Latino, Pacific Islander or
Native American, their chances are one in two (CDC 2007). The vast majority, ninety to ninety-
five percent, of adults living with diabetes has type 2 (CDC 2014a); however type 1 diabetes
rates are also increasing (Gale 2002).
Type 2 diabetes most commonly results from the body developing a resistance to insulin,
which requires the pancreas to produce more insulin to maintain normal glycemia. Once the
body loses the capacity to compensate, hyperglycemia (high blood sugar) occurs and this is the
diagnostic criteria for type 2 (Kahn 2003). Type 2 diabetes had been called adult-onset, as it was
most commonly seen in adults. This, however, is no longer the case, as younger and younger
people are being diagnosed with type 2 (ADA 2000). Type 1 diabetes, which was previously
known as juvenile-onset, is an autoimmune disorder that destroys the insulin-producing beta

4
cells of the pancreas (HHS 2014). The phrase “diabetes epidemic” generally refers to type 2
diabetes, but the prevalence of type 1 diabetes is increasing as well, particularly in high and
middle-income countries (Tuomilehto 2013).
In 2012, the total national financial costs associated with diabetes were $245 billion
(ADA 2013). This figure includes both the direct medical and indirect medical costs, such as
lost productivity and work absenteeism. The direct medical costs are predominately utilized for
hospitalizations and for the treatment and management of the complications associated with
poorly managed diabetes, such as heart disease, blindness, amputations, and end-stage kidney
failure. Despite recent improvements in the percentage of people having microvascular
complications, as more and more people develop diabetes, incidence and costs will rise despite
these improvements. Diabetes-related healthcare costs contribute to a significant portion of the
overall healthcare costs in the U.S. In 2012, one in every five healthcare dollars was spent on
diabetes (ADA 2013).
Role of Diabetes Self-Management
Diabetes self-management (DSM) is elemental to limiting the potential for such complications
and provides one of the greatest opportunities to manage the proliferating costs associated with
diabetes. DSM involves extensive and ongoing education about the condition and methods to
best manage it, with the primary objective of facilitating optimum glucose control. These
methods include efforts such as regular checking of blood glucose levels, engaging in regular
physical activity, eating a healthy diet, conducting foot screens for sores, and adhering to a
medication regimen (Funnell 2009). Certified diabetes educators (CDEs), registered dieticians
(RDs), nurses, and physicians generally teach diabetes self-management education (DSME).
However, people with diabetes typically spend less than six hours per year with a medical

5
professional. This means that a person with diabetes spends over 8,754 hours per year without
professional diabetes guidance (Peeples 2007). It is in these prolonged absences that people
often confront the daily struggles and challenges of living with a chronic illness that peers can
provide essential guidance, support, and education. Since diabetes outcomes rely predominantly
on the individual’s ability and willingness to adhere to medical recommendations, the
availability of support mechanisms to assist with maneuvering the challenges of self-care seem
prudent.
The vast majority of diabetes management depends upon the individual and her or his
ability to provide DSM. Lack of adherence to prescribed DSM is a major obstacle to address, as
fifty percent of patients do not follow treatment recommendations (Delemater 2006). Factors
that influence a patient’s adherence to DSM include demographics, psychosocial issues, the
healthcare provider(s), healthcare coverage, and availability of resources (Schechter 2002).
Another challenge is that while medical professionals tend to have extensive knowledge about
the condition and have much to offer to patients, very few understand how to sustainably
integrate behavior changes effectively into one’s life. This is largely due to the fact that it is
extremely difficult to understand how to integrate behavior changes into one’s life without first
doing so oneself. Trial and error is one common method employed by people with diabetes to
change their behavior and can improve DSM; however, this method is often a frustrating process,
which severely limits its potential benefits. This, in addition to healthcare provider shortages, is
another reason why people with diabetes, who have overcome obstacles of DSM adherence, may
be appropriate to fill this vacuum and facilitate the integration of DSM into the lives of those not
currently engaging in self-care.

6
Public Health Crisis
Because diabetes is a public health crisis, it requires making the best use of all the resources
available. Diabetes education is an essential component of facilitating DSM, but this poses a
challenge given the epidemic proportions of people with diabetes coupled with shortages of
healthcare providers (HCPs). Providing intensive medical care for people with diabetes requires
extensive labor, which makes it cost-prohibited except for high-risk patients (Heisler 2009). The
shortfall of HCPs creates opportunities to utilize people living with diabetes to assist with filling
the gaps (Baksi 2008). This is the case with community health workers (CHWs) who are
increasingly being used in such capacities. CHW certificate programs prepare and train
individuals for positions in community-oriented health and social services agencies and
programs, provide health education, information and referrals, and client advocacy in both clinic
and community settings. Incorporating peers to work in these capacities may prove to be highly
advantageous, as people with diabetes are an abundant resource, provide unique skillsets beyond
HCPs and CHWs, and the act of providing support benefits the peer’s own personal DSM.
Engaging people with diabetes to serve as peer support provides numerous benefits.
Studies show that people with chronic conditions such as diabetes who participate in peer
support have improved health, outlook, and self-management (Barlow 2005). Peer support is
unique from other forms of social support in that those who engage in it share common
fundamental experiences associated with living with diabetes. These shared connections can be
helpful by creating opportunities for sharing best practices, encouragement, and modeling of
healthy lifestyles. The peer support relationship is often beneficial to both the supporter and the

7
supported. Riessman noted the person who provides support and guidance is often helped more
than the person receiving it (Riessman 1965).
The Chronic Care Model (CCM) provides a theoretical basis for dealing with the diabetes
epidemic but falls short of its potential. The CCM is a population-based, organizational
approach to chronic disease management (Shumaker 2008), which the CDC utilizes for its
diabetes management and prevention efforts (Albright 2009). This model attempts to engage
patients in their care by creating collaboration and communication between the patients and their
medical care teams (Stellefson 2013). The facet of community is one of the six essential
elements of the model; however, the implementation of community engagement is sorely lacking
(Jenkins 2010) and does not go far enough. The role of community is widely documented, as a
key influence for population behaviors. For people with diabetes, there is also an emerging
Diabetes Online Community (DOC) yet to be actively included in managing the epidemic.
Making concerted efforts in public health to integrate and expand the DOC provides an under-
realized and under-utilized resource in combating the epidemic. Engaging this community to
work in collaboration with the healthcare community is vital to slowing the soaring numbers of
people developing diabetes and the rising costs in both human life and economics.
Review of Peer Case Studies
Studies on the effects of peer support on diabetes management are limited. While there are
numerous studies looking at the role of social support and its effects on DSM, only ten studies
measured the effects of peers and met the inclusion criteria for this research. The goal of this
literature search was to identify clinical studies that assess peer-support interventions in diabetes.
Studies were included if they met the following criteria: focus on diabetes self-management
interventions; report clinical, psychosocial, and/or behavioral outcomes; use a random control

8
trials (RCT) study design or pilot study using pretest/posttest design; and include peer support
from a person with diabetes rather than family or general peers. Ten studies met the inclusion
criteria and were included in this review (Baksi et al. 2008; Cade et al. 2009; and Dale et al.
2009; Heisler et al. 2005; Heisler et al.2010; Lorig et al. 2008; Lorig et al. 2009a; Lorig et al.
2010; Philis-Tsimikas et al., 2011; Thom et al. 2013).
Sample size of these studies varied widely from 40 participants (Heisler 2005) to 761
(Lorig 2010) with a mean of 309. Study durations ranged from six-weeks (Heisler 2005) to
eighteen months (Lorig 2010) with a mean length of nine months. All of these studies were
conducted with people living with type 2 diabetes; however not all the studies used exclusively
people living with diabetes in peer support roles. The majority of the studies included only
people with type 2 in peer roles (Baksi et al. 2008; Cade et al. 2009; and Dale et al. 2009; Heisler
et al. 2005; Heisler et al.201; Philis-Tsimikas et al., 2011; Thom et al. 2013). The remaining
three studies utilized people without diabetes in peer roles, but in each the majority had type 2
(Lorig et al. 2008; Lorig et al. 2009a; Lorig et al. 2010).
The formats of interaction between peers ranged from in-person, in-person and online, in-
person and telephone, to phone-based. Primary and secondary outcomes ranged from
psychological aspects to lab values. All but one study (Heisler 2005) used the glycosylated
hemoglobin A1C (HbA1C), a lab value that corresponds to average blood glucose levels over the
previous three-months, as an outcome. Baksi et al., looked at participants’ diabetes knowledge
levels before and after receiving DSME from either a peer adviser with diabetes (PAD) or a
healthcare professional (Baksi 2008).
While training for peers was included in all of the studies, there was much variability to
the amount and type of training provided. In both studies conducted by Heisler et al., peers

9
served as laypersons and correspondingly had the least amount of training for peer subjects. In
these studies, peers received a three-hour training (Heisler 2005; Heisler 2010), plus a DVD and
workbook (Heisler 2010).
The remaining studies utilized people with diabetes to serve in roles as leaders and
provided significantly more training. Dale et al., included a two-day training for peer supporters
that covered communication and listening practices, disease state information, and facilitating
behavior change (Dale 2009). The study conducted by Thom et al., provided “peer-coaches” a
thirty-six hour training over eight weeks. It included DSME training, listening skills, non-
judgmental communication, and role-playing (Thom 2012). Philis-Tsimikas et al., offered “peer-
educators” a forty-hour training, which entailed DSME, mediation techniques, and behavior
modification methods (Philis-Tsimikas 2011). The trainings of Cade et al., were based on the
United Kingdom’s Expert Patient Programme (EPP). The peer educator training for the study
included eighteen - ninety minute weekly sessions on DSME and communication skills, in
addition to thirty-three sessions led by healthcare practitioners (Cade 2009). Baksi et al.,
provided “peer advisers” a four-day residential program based on the model devised by Lorig
and colleagues for Stanford’s Chronic Disease Self-Management Program (Baksi 2008). Lorig
et al., offered a four-day training for “peer leaders” which included DSM protocols, role-playing,
presenting lectures, and managing group dynamics (Lorig 2008; Lorig 2009a; Lorig 2010).
Results of Peer Case Studies
The outcomes of these studies varied. Five of the studies showed positive outcomes for peer
support in terms of psychosocial benefits (Heisler 2005; Heisler 2010; Lorig 2008; Lorig 2009a;
Lorig 2010). HbA1C outcomes improved for peer groups in six of the ten studies (Heisler 2010;
Lorig 2008; Lorig 2009a; Lorig 2010; Philis-Tsimikas et al., 2011; Thom et al. 2013). Philis-

10
Tsimikas et al., showed those in the peer-led group had statistically significant improvements to
blood pressure and lipid levels, in addition to HbA1C levels (Philis-Tsimikas 2011). Of the four
of the studies that compared groups led by peer-advisers to groups led by HCPs, one found peers
less effective (Dale 2009); and three found peers equally as effective as HCPs (Baksi 2008; Cade
2009; and Heisler 2010); but in one study the outcomes did not improve for either the peer-led
group or the group led by HCPs (Cade 2009). The study by Dale et al. was a peer telephone
intervention study, which may partly explain the why it falls outside the norm of the studies
included in this research.
Other keys findings of these studies show there are benefits to utilizing peers in support
capacities. Notably, one study showed patients in the reciprocal peer support group were more
likely to initiate insulin usage compared to those with nurse case managers. This result was
statistically significant (Heisler 2010). Initiating insulin use for people with type 2 diabetes is a
major challenge in healthcare due to a variety of reasons including a patient’s fear of needles,
believing it causes complications, and seeing oneself as a failure (Funnell 2007). These patient
barriers are further compounded by the lack of time HCPs have in office visits to address these
issues in addition to teach how to manage an insulin regimen. Philis-Tsimikas et al., noted that
the participants in the peer-led group had dose-dependent improvements to HbA1C levels
specifically, the more classes a participant attended, the greater reduction of HbA1C. The
authors hypothesize this may be the result of the subsequent support groups and what they call
the “high-touch effect” associated with classes (Philis-Tsimikas 2011).
Another finding that deserves attention is peer support’s effect on psychosocial
components. Four studies show that people who received support from peers improved in self-
assessments of social support and depression (Cade 2009, Heisler 2005; Heisler 2010; and Lorig

11
2009a). While this perceived improvement of support did not translate into improvements
beyond those seen with the group led by HCPs in health markers such as HbA1C (Cade 2009),
there be may more to this than was ascertained in these relatively short studies.
The benefits of improved outlook may not immediately demonstrate improved surrogate
markers, such as the HbA1C; however, there may be great benefit in the long-term, which is
important for chronic life-long illnesses like diabetes. Studies of people with diabetes show an
inverse relationship between social support and depression (Sacco 2006). This association is
compelling given the connection between depression and poor diabetes outcomes. Katon et al.,
found depression in persons with type 2 diabetes was associated with increased mortality (Katon
2005). Similar findings are true for people with type 1 diabetes as well. Lustman et al., found
correlation using univariate analysis exists between depression, poor diabetes self-care, and
hyperglycemia (Lustman 2005). Coulse et al., looked at the effects of depression over a ten-year
period and found that it contributed to significantly more rapid development of coronary heart
disease in both type 1s and type 2s (Coulse 2003). Hence, it may be possible to show benefits if
the studies examined longer time scales in assessing the potential effects of peers on
improvements of self-assessments of social support and depression.
METHODOLOGY
Survey Background:
Research Question and Hypothesis:
The purpose of this survey is to examine the effect of peer support on diabetes self-management.
The survey is available in appendix A. The research question this survey attempts to answer is,
whether involving people living with diabetes in the public health efforts help alleviate some of
the persistent challenges associated with DSM adherence levels in the diabetes population. This

12
analysis will drill down to look at those who receive support from peers living with diabetes
compared to those who do not receive support people with diabetes. Peer support systems
typically involve facilitating mutual support, participating in groups run by peers, and utilizing
peers to provide services and support (Davidson 1999). Each of these aspects may prove to be
highly advantageous for finding means to address some of the challenges of adhering to diabetes
self-management. The hypothesis is that diabetics with peer support are more likely to have
better health indicators for disease-related outcomes than those without peer support. The key
health indicator used in this study is the glycosylated hemoglobin A1C (HbA1C), which is the
gold standard of assessing glucose control and predicting long-term microvascular complications
and outcomes. The methodology for the diabetes peer support survey is detailed below.
Survey Methodology:
The methodology for the survey development included the research of existing surveys, the
selection of questions, the integration of suggestions from diabetes health professionals and
academics, the selection of potential subjects, the process of maintaining the anonymity of
survey takers, the mode of survey delivery, and the means of circulation. Existing surveys
provided the basis for the development of the diabetes peer support survey. The National Health
and Nutrition Examination Survey (NHANES) (CDC 2014b), the National Health Interview
Survey (NHIS) (CDC 2015), and the California Health Interview Survey (CHIS) (UCLA 2014)
were used as examples for health-specific questions. The Census (DOC 2015) and General
Social Survey (GSS) (NORC 2014) provided bases for questions about demographics. The
Diabetes Social Support Questionnaire (DSSQ) (La Greca 2002), the Problem Areas in Diabetes
(PAID) (Polanski 1995), and a diabetes survey from the Stanford Patient Education Research
Center (Lorig 2009b) served as models for more sensitive and specific questions pertaining to

13
diabetes. The remaining questions were developed with suggestions from diabetes medical
professionals and people living with diabetes. The survey in its entirety may be found in
Appendix A.
In efforts to maintain rigorous academic standards for this survey research, questions
regarding attitudes were phrased using a five point Likert scale and were randomized. While
there are fifty-five questions total to the survey, the respondents answered only follow up
questions pertaining to their circumstance. For example, since type 1s are not prescribed oral
diabetes medications, the question regarding oral medications was only asked of people with
type 2. People with type 1 were asked to answer between thirty-three and forty-six questions.
People with type 2 diabetes were asked between thirty-six and fifty questions. The questions
were randomized per the limitations of the web server, Surveymonkey.com. Essential questions
were excluded from randomization including the first, diabetes diagnosis page and follow-up
question pages, in addition to the last, demographics page. The remaining questions were
grouped with follow up questions. This resulted in twelve groupings that were randomized using
the following website: http://www.randomizer.org/form.htm.
The participants for this survey research were people living with diabetes. While minors
were not the intended focus of this survey research and were not being recruited, it was possible
that minors may access the survey. This was because the survey link may have potentially been
listed on online sites that have minors in their membership. In order to preclude minors from
participating, the survey began with a consent form requiring respondents to confirm they are at
least eighteen years of age.
The survey was anonymous. The majority of questions were derived from other
established surveys and the others were developed with the recommendations from diabetes

14
medical professionals and people living with diabetes. The most sensitive questions inquired
about how one feels about her or his diabetes and the individual’s lab results. These questions,
as with the majority, provided responses of “I don’t know” and “prefer to not answer” to further
minimize any potential stress.
This survey was designed to be conducted online, but in situations where the potential to
gather responses without using the internet arose, a paper copy of the survey was made available.
None of the paper copies were returned, and therefore all of the data included was gathered
online. The implementation plan for dissemination involved working with diabetes online
community sites to promote the survey to their memberships, encouraging diabetes professionals
to share with their diabetes populations, and spreading the word via link sharing. The closing
page of the survey asked the respondent to please share the survey link with others living with
diabetes.
The primary means of disseminating this survey was through the researcher’s network of
peers with diabetes or professionals working in the field of diabetes. The survey link was also
posted on some diabetes support groups on Facebook and other diabetes online community sites,
including www.diabeticconnect.com. In an effort to increase responses, emails were sent to
various organizations working in diabetes. Very few of these emails received responses, but
some did produce results, such as the Diabetes Self-Management Program at Stanford. While
the director of the program, Dr. Lorig, was unable to provide the link to the program’s
participants, she sent the link to the program’s Master Trainers, many of whom forwarded it to
their networks.
A Spanish version of the survey was also developed and disseminated. The Latino
population experiences a disproportionate rate of type 2 diabetes and this data will provide

15
pertinent information specific to this population. There were some challenges with acquiring
data from the Spanish-speaking population including the survey being available only online, and
the researcher’s very limited access to connections within the community. The complete survey
and link were emailed to various organizations addressing health in the Spanish-speaking
community. Despite the efforts, no responses were gathered from the Spanish version of the
peer support survey.
Given the fact that the pool of respondents were connected through the researcher’s
network, many of whom shared it with their networks, the data are most likely skewed. The
researcher is a Caucasian college-educated female with type 1 diabetes. Therefore, it is expected
that the data will show higher levels Caucasian college-educated females with type 1 diabetes.
The survey was available online via Survey Monkey for approximately two months. The
decision to extend the survey time was considered. Given the U.S. diabetes population is 29.1
million, at least 2401 responses are required to attain a ninety-five percent confidence level. The
responses began to wane after a month and once it was clear that this number of responses was
unattainable, the choice was made to close the survey.
Data Set and Measures:
The analysis of this data was conducted with Stata 12.1 software. The data set for this research
includes all of the participants who answered the survey question about receiving support for
their diabetes. The unit of analysis is the individual. Two hundred seventy nine responded,
“Yes,” “No,” or “Don’t need support?” to the following questions, “Does anyone provide social
support for you regarding your diabetes? For example, do you have anyone available to
encourage you or to listen to concerns about your diabetes care?” The follow up question asks
the respondent to select the sources of support. These sources include spouse or significant

16
other, friend(s), family member(s), someone with diabetes (in-person), someone with diabetes
(online), medical professional, and other.
The independent variable for the crosstabulations of this research is peer support. Peer
support was recoded as a dichotomous variable. It was derived by grouping participants into two
groups in response to the following question: “Who do you receive support from for your
diabetes?” The “peer” group, coded as 1, includes those who selected either “Someone with
diabetes (in-person),” “Someone with diabetes (online),” or both. The “no peer” grouping,
coded as 0, includes those who selected neither of these two choices. It also includes the
participants who answered either “No” or “Don’t need support.” The “peer” group includes one
hundred nineteen participants, and the “no peer” grouping contains one hundred sixty. Of the
two hundred seventy nine participants, 57.3% received no peer support and 42.7% had at least
one peer. Of the one hundred nineteen in the “peer” group, twenty-six participants selected in-
person peer, fifty-eight chose online peer, and thirty-five answered both.
The dependent variables of the crosstabs include HbA1C levels, feeling in-charge of
one’s diabetes, level of optimism about one’s diabetes, type of diabetes, and self-blood glucose
monitoring (SBGM) frequency. Type of diabetes was recoded as a dummy variable, where type
1 was coded 1 and type 2 was coded 0. Those who selected “prediabetes” were excluded from
the analysis. Prediabetes is a condition where a person’s blood glucose level is above normal,
but not high enough to diagnose as type 2 (Mayo Clinic 2015). The prediabetes option was
included in the survey to minimize people with prediabetes from selecting type 2, which may
have skewed the data, but was excluded in the analysis so that the data set only includes those
clinically diagnosed with diabetes. The dependent categorical variables HbA1C, feeling in

17
charge of one’s diabetes, level of optimism about one’s diabetes, and SBGM frequency were not
recoded.
In linear regression models, the dependent variables are HbA1C, SBGM, and DSM self-
rating in order to evaluate the effect of peer support on these aspects of DSM. HbA1C also
serves as an independent variable in the crosstabulations. It is a nominal variable that ranges in
one percent increments from less than 5.5% to over 12.5% and includes “I don’t know” as not
knowing one’s HbA1C is worth noting. This variable provides a litmus test for assessing the
participant’s DSM, as the HbA1C is the standard for estimating diabetes management
(Delamater 2006a). In effort in quantify the participant’s outlook about their diabetes, questions
were asked to assess their level of optimism and feeling in charge.
The dependent variables seek to assess the effects of traditional and nontraditional factors
on HbA1C. The traditional factors include frequency of SBGM, type of diabetes, and
demographic variables. The nontraditional variables, include the items not typically measured in
medical studies such as peer support, feeling in-charge of one’s diabetes, DSM self-rating, and
level of optimism about one’s diabetes. In the regression models, peer support serves as an
dependent variable.
The dependent variable level of optimism about one’s diabetes is in response to the
following question, “During the past month, have you felt optimistic about your diabetes?” The
respondents were given the choices of “No, I felt it has ruined my life,” “I felt generally quite
discouraged,” “I have lots of ups and downs about it,” “I have felt optimistic for the most part,
occasionally discouraged,” “Very optimistic, rarely discouraged,” “I don't know,” and “Prefer to
not answer.” This variable is an ordinal variable.

18
The dependent variable that evaluates the degree of feeling in charge of one’s diabetes
was derived for the following question, “Over the past month, how much have you felt
personally in charge of your diabetes?” The participant was asked to select “I felt that I played
no part in managing my diabetes,” “I felt that I played a small, unimportant part in managing my
diabetes,” “I felt that I played a small, but important part in managing my diabetes,” “I felt that I
played a major part in managing my diabetes,” “I felt that I was completely in charge of
managing my diabetes,” “I don't know,” or “Prefer to not answer.”
The final dependent variable assesses the participant’s self-rating of her or his DSM.
This was posed by the following question, “How would you rate your diabetes self-
management?” The five point Likert Scale responses included “Poor,” “Fair,” “Good,” “Very
good,” “Excellent,” “I don't know,” and “Prefer to not answer.”
SURVEY RESULTS
The mean duration of diabetes for all respondents was twenty years, with a standard deviation of
fifteen years. Those in the “peer” group had diabetes on average two years longer than the “no
peer” group. The mean age for the entire survey was twenty-eight, with a standard deviation of
nineteen years. Those in the “peer” group were on average nine years younger than those in the
“no peer” group. The mean age for the “peer” group was twenty-three, with a standard deviation
of seventeen years, while the mean age for the “no peer” group was thirty-two, with a standard
deviation of twenty years.
The data is heavily weighted towards certain groups. The demographic and behavioral
characteristics are detailed in table 1. The majority of the total participants, 79.4% were female.
The “no peer” group was 75.4% female and 84.7% of the “peer” group was female. This result
was not quite statistically significant with a p value of 0.068. A preponderance of the

19
participants had type 1 diabetes, and this result was statistically significant. Most of the total
participants, 53.3% had at least a bachelor’s degree, but was not statistically significant (p =
0.973). The participant pool was also predominantly Caucasian/White, with 87.5% of the total
and 92.5% of the “peer” group selecting this category. The race/ethnicity category was
statistically significant with a p value of 0.014.
Table 1 - Demographic and Behavior Characteristics
Variables
No Peer,
(n=160)
Peer,
(n=119)
All,
(n=279)
Age at Diagnosis
Mean (sd) 32 (20) 23 (17) 28 (19)
Min 1 1 1
Max 70 68 70
Year of Diagnosis
Mean (sd) 1996 (15) 1994 (14) 1995 (15)
Min 1945 1951 1945
Max 2015 2014 2015
Diabetes Type (n=279, df = 1, p = .000)
Type 1 48.8% 77.3% 60.9%
Type 2 51.2% 22.7% 39.1%
HbA1C (n=261, df = 8, p = .208)
Less than 5.5% 5.5% 13.0% 8.8%
5.6-6.5% 28.8% 32.2% 30.3%
6.6-7.5% 32.9% 35.7% 34.1%
7.6-8.5% 17.8% 13.0% 15.7%
8.6-9.5% 8.2% 3.5% 6.1%
9.6-10.5% 2.7% 1.7% 2.3%
11.6-12.5% 0.7% 0.0% 0.4%
More than 12.5% 0.7% 0.0% 0.4%
I don't know 2.7% 0.9% 1.9%
In Charge of Diabetes (n=268, df = 6, p = .072)
I played no part in managing my diabetes 1.3% 0.0% 0.7%
I played a small, unimportant part in managing my
diabetes 4.0% 0.8% 2.6%
I played a small, but important part in managing
my diabetes 9.3% 8.5% 9.0%
I played a major part in managing my diabetes 44.0% 44.1% 44.0%
I was completely in charge of managing my
diabetes 35.3% 45.8% 39.9%

20
I don't know 4.7% 0.0% 2.6%
Prefer to not answer 1.3% 0.8% 1.1%
Diabetes Optimism (n=267, df = 6, p = .8)
No, I felt it has ruined my life 1.3% 1.7% 1.5%
I felt generally quite discouraged 7.4% 4.2% 6.0%
I have lots of ups and downs about it 23.5% 23.7% 23.6%
I have felt optimistic for the most part,
occasionally discouraged 41.6% 44.9% 43.1%
Very optimistic, rarely discouraged 23.5% 24.6% 24.0%
I don't know 1.3% 0.0% 0.7%
Prefer to not answer 1.3% 0.8% 1.1%
DSM Rating (n=266, df = , p = .028)
Poor 8.10% 2.50% 5.60%
Fair 16.90% 10.20% 13.90%
Good 37.20% 32.20% 35.00%
Very good 26.40% 35.60% 30.50%
Excellent 11.50% 19.50% 15.00%
SBGM Frequency (n=266, df = 4, p = .007)
Much less than directed 10.8% 0.8% 6.4%
Less than directed 20.3% 15.3% 18.0%
As directed 25.0% 24.6% 24.8%
More than directed 29.1% 39.8% 33.8%
Much more than directed 14.9% 19.5% 16.9%
Gender (n=253, df = 1, p = .068)
Females 75.4% 84.7% 79.4%
Males 24.6% 15.3% 20.6%
Level of Education (n=255, df = 5, p = .973)
No H.S. Diploma 0.7% 1.8% 1.2%
H.S. Diploma 7.6% 7.2% 7.5%
Some College 25.7% 25.2% 25.5%
Associate Degree 12.5% 12.6% 12.5%
Bachelor Degree 29.2% 27.0% 28.2%
Master Degree or higher 24.3% 26.1% 25.1%
Annual Income (n=242, df = 10, p = .381)
$0-$24,999 15.1% 16.5% 15.7%
$25,000-$49,999 16.5% 15.5% 16.1%
$50,000-$74,999 20.1% 20.4% 20.2%
$75,000-$99,999 18.7% 21.4% 19.8%
$100,000-$124,999 9.4% 10.7% 9.9%
$125,000-$149,999 10.8% 4.9% 8.3%
$150,000-$174,999 3.6% 2.9% 3.3%
$175,000-$199,999 0.0% 3.9% 1.7%
$200,000-$224,999 2.2% 0.0% 1.2%
$225,000-$249,999 0.7% 1.0% 0.8%
$250,000 or more 2.9% 2.9% 2.9%
Race/Ethnicity (n=256, df = 5, p = .014)
Multiple ethnicity / Other 2.8% 0.0% 1.6%

21
American Indian or Alaskan Native 1.4% 2.7% 2.0%
Asian / Pacific Islander 0.0% 1.8% 0.8%
Black or African American 6.9% 0.9% 4.3%
Hispanic American 5.5% 1.8% 3.9%
White / Caucasian 83.4% 92.8% 87.5%
Community Type (n=255, df = 2, p = .285)
Rural 25.7% 19.8% 23.1%
Urban 32.6% 28.8% 31.0%
Suburban 41.7% 51.4% 45.9%
The independent variables include HbA1C levels, feeling in charge of one’s diabetes,
level of optimism about one’s diabetes, type of diabetes, gender, and SBGM frequency. The
breakdown of HbA1C is displayed in figure 1. The desired target for HbA1C for most people
with diabetes is less than 7% (NIDDK 2014). Given the breakdown of this nominal variable
does not permit using this as a threshold; the researcher chose the closest value of above or
below 7.5%. 31.0 % of those in the “no peer” group had an HbA1C of greater than 7.5%,
compared to 18.4% of the “peer” group. While the majorities of both groups were at or below
7.5%, the “peer” group had 81.6% and the “no peer” had 69.0%. The HbA1C result was
statistically significant with a p value of 0.022.

22
Figure 1: HbA1C
People with type 1 diabetes represented 60.9% (p = 0.000) of the total participants;
however, there were far greater people with type 1 in the “peer” group than in the “no peer”
group. The “peer” group contains predominantly people with type 1, accounting for 77.3%. The
“no peer” group was more evenly split, with only 48.8% having type 1. All three groups are
summarized by type of diabetes in figure 2.
5.5%
28.8%
32.9%
17.8%
8.2%
2.7%
0.7%
0.7%
2.7%
13.0%
32.2%
35.7%
13.0%
3.5%
1.7%
0.0%
0.0%
0.9%
8.8%
30.3%
34.1%
15.7%
6.1%
2.3%
0.4%
0.4%
1.9%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
Less than 5.5%
5.6-6.5%
6.6-7.5%
7.6-8.5%
8.6-9.5%
9.6-10.5%
11.6-12.5%
More than 12.5%
I don't know
HbA1C
Total Peer No Peer

23
Figure 2: Diabetes Type
The frequency of SBGM varies from individual to individual and depends upon
numerous factors, including if the participant administers insulin. In order to better assess
SBGM, the participants were asked to compare their frequency of SBGM compared to the
recommendations of their medical providers. Roughly one in four in both the “peer” and “no
peer” groups monitored “as directed”. 31.1% of the “no peer” group monitored less than or
much less than directed compared to 16.3% of the “peer” group. The reverse was true for more
than and much more than directed, with 59.3% of the “peer” compared to 45% of the “no peer”
group (see figure 3). This result was statistically significant with a p value of 0.007.
48.8%
77.3%
60.9%
51.2%
22.7%
39.1%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%
No Peer
Peer
Total
Diabetes Type
Type 2 Type 1

24
Figure 3: SBGM Frequency
The dependent variables of feeling in charge of one’s diabetes and level of optimism
about one’s diabetes were used to gauge some of the psychosocial components associated with
living with the condition. A study by Rose, et al. that evaluated 625 people with diabetes found
better coping skills correlated with better glucose management (Rose 2002). In efforts to
understand how the participants assessed their ability to employ diabetes self-management, the
following question was asked: “Over the past month, how much have you felt personally in
charge of your diabetes?” Roughly ten percent more of those in the “peer” group felt they were
completely in charge of their diabetes. 45.8% of the “peer” group chose this response compared
to 35.3% of the “no peer” group (p=0.072). Roughly 44% of each of the groups answered, they
felt played a major part in managing their diabetes (figure 4).
10.8%
20.3%
25.0%
29.1%
14.9%
0.8%
15.3%
24.6%
39.8%
19.5%
6.4%
18.0%
24.8%
33.8%
16.9%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0%
Much less than directed
Less than directed
As directed
More than directed
Much more than directed
SBGM Frequency
Total Peer No Peer

25
Figure 4: In Charge of Diabetes
The next dependent variable is level of diabetes optimism, which serves to measure
psychosocial components. The level of diabetes optimism was calculated by the following
question, “During the past month, have you felt optimistic about your diabetes?” The responses
were similar for all of the groupings (figure 5). Roughly three percent more of the “peer” group
answered, “I felt optimistic for the most part, occasionally discouraged compared to the “no
peer” group. The opposite was true for, “I felt generally quite discouraged,” where 3.2% more of
the “no peer” group chose this response. This result was not statistically significant (p = 0.800).
1.3%
4.0%
9.3%
44.0%
35.3%
4.7%
1.3%
0.8%
8.5%
44.1%
45.8%
0.8%
0.7%
2.6%
9.0%
44.0%
39.9%
2.6%
1.1%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0%
I played no part in managing my diabetes
I played a small, unimportant part in
managing my diabetes
I played a small, but important part in
managing my diabetes
I played a major part in managing my
diabetes
I was completely in charge of managing my
diabetes
I don't know
Prefer to not answer
In Charge of Diabetes
Total Peer No Peer

26
Figure 5: Diabetes Optimism
The final dependent variable is DSM self-rating. The level of DSM self-rating was
estimated by the following question, “How would you rate your diabetes self-management?”
Both groups had similar levels of “good” ratings. 25% of the “no peer” group selected either
“fair” or “poor”, compared to 12.70% of the “peer” group. A majority of 55.10% from the
“peer” group chose either “very good” or “excellent,” while 37.90% of the “no peer” group made
selected these categories. These results were statistically significant (p =0.028).
1.3%
7.4%
23.5%
41.6%
23.5%
1.3%
1.3%
1.7%
4.2%
23.7%
44.9%
24.6%
0.8%
1.5%
6.0%
23.6%
43.1%
24.0%
0.7%
1.1%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0%
No, I felt it has ruined my life
I felt generally quite discouraged
I have lots of ups and downs about it
I have felt optimistic for the most part,
occasionally discouraged
Very optimistic, rarely discouraged
I don't know
Prefer to not answer
Level of Diabetes Optimism
Total Peer No Peer

27
Figure 6: DSM Self-Rating
Ordinary least squares (OLS) regression models were applied to delve deeper into the
data. The independent variable for the two linear regression models is HbA1C. There are
arguments both in favor (Allan 1976; Kim 1975; and O’Brien 1979), and against (Hawkes 1971;
Morris 1970; and Reynolds 1973) treating ordinal variables as continuous variables. Since there
is no consensus, the researcher chose to employ ordinary least square regression models on the
ordinal variable HbA1C. In an effort to surmise the effects on glucose control, the outcome
HbA1C was regressed on the predictor values of peer support, feeling in charge of diabetes and
diabetes optimism. A second OLS regression model analyzes the outcome of HbA1C regressed
on these same predictor values, in addition to SBGM, type of diabetes, gender, income level, and
education level. The first model looks solely at the nonclinical items of interest for this study.
The second looks beyond to include other demographic and clinical factors that influence blood
glucose management.
8.10%
16.90%
37.20%
26.40%
11.50%
2.50%
10.20%
32.20%
35.60%
19.50%
5.60%
13.90%
35.00%
30.50%
15.00%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00%
Poor
Fair
Good
Very good
Excellent
DSM Self-Rating
Total Peer No Peer

28
Table 2: Linear (OLS) Regression for HbA1C
Variables Coefficient SE
Statistical
Significance
Peer Support -0.550 0.183 .003
Diabetes Optimism -0.355 0.099 .000
Feeling In Charge of Diabetes -0.316 0.110 .005
Adjusted R-Squared 0.124
OLS regression highlighted several factors associated with HbA1C levels. The
multilinear regression model in Table 2 predicts the presence of peer support will reduce the
dependent variable (HbA1C) by 0.55 units, holding all other independent variables constant (p =
0.003). Holding the other independent variables constant, the model forecasts that a one-unit
increase in diabetes optimism, the dependent variable (HbA1C) will reduce by 0.335 units (p =
0.000). The model also predicts a one-unit increase in feeling in charge of diabetes, the
dependent variable (HbA1C) will reduce by 0.316 units, holding the other independent variables
constant (p = 0.005). All of the dependent variables in this model were statistically significant
and are non-collinear. This model demonstrates that the variables of peer support and outlook
exert independent effect and may leveraged to improve HbA1C levels and diabetes outcomes.
Table 3: Linear (OLS) Regression for HbA1C
Variables Coefficient SE
Statistical
Significance
Peer Support -0.568 0.200 0.005
Diabetes Optimism -0.354 0.099 0.000
Feeling In Charge of Diabetes -0.305 0.112 0.007
SBGM -0.271 0.086 0.002
Type 1 0.317 0.207 0.126
Education -0.165 0.072 0.023
Income -0.057 0.043 0.182
Female 0.157 0.229 0.494
Adjusted R-Squared 0.150

29
Table 3 shows the results of a more extensive multilinear regression model. The majority
of dependent variables in this model, except for type 1 and female, were associated with
reductions in HbA1C levels. According to this model, both females and type 1s are expected to
have slightly higher HbA1Cs, but these results were not statistically significant. This model
forecasts a unit increase of income a modest will decrease of HbA1C by 0.054, holding the other
independent variables constant. This result was not statistically significant (p = 0.202). In this
expanded model, peer support has the greatest effect on lowering HbA1C. It predicts the
presence of peer support will reduce HbA1C by 0.565 units holding the other dependent
variables constant (p = 0.005). The dependent variables diabetes optimism, feeling in charge of
diabetes, SBGM and education, holding the other dependent variables constant, were associated
with reductions in HbA1C and were statistically significant. These results indicate a non-
collinear relationship between peer support, diabetes optimism, feeling in charge of diabetes,
SBGM, and education that suggest each variable is exerting an independent effect on blood
glucose control.
DISCUSSION:
The results from this survey predict people who know someone with diabetes are more likely to
have lower HbA1C levels, to monitor their blood glucose more frequently, and to rate their DSM
more favorably. It is one of the few studies to survey people with diabetes about peer support
and DSM.
Limitations:
Several limitations should be recognized. First is the lack of diversity of the survey population.
Despite tremendous efforts to reach beyond, the majority participants were linked via the
researchers online networks, which is reflected in the prevalence of college-educated, type 1,

30
Caucasian, females. Second is the questions were based on a self-rating system, not a definitive
source. Lastly, is the small sample size; however, many of the results have statistical
significance. Drawing conclusions beyond these limitations are problematic. Despite these
limitations, this study has several important implications for future diabetes research and policy
interventions for the diabetes epidemic.
Role of Peer Support:
Similar to the case studies presented, the survey findings show that the presence of peer support
has beneficial effects on some components of DSM and blood glucose management. The survey
participants who received support from someone with diabetes were more likely to have lower
HbA1C test results, monitor their blood glucose levels more frequently, and rate their DSM more
favorably. While the cross tabulations did not show statistical significance for the effects of peer
support on HbA1C, the linear regression models predict peer support will have a positive effect
on this variable. These results reveal that interactions with people who understand many of the
nuances one experiences living with diabetes has a favorable effect on DSM. These results
verify previous studies that show diabetes self-management support improve DSM and quality of
life. This is compelling, as managing one’s diabetes is a daily, life-long effort that requires
persistent motivation to sustain behavioral modifications.
Regression models show that nonclinical factors have a beneficial effect on DSM, as
assessed by HbA1C. Despite growing evidence, the standard medical model and chronic care
model (CCM) do not optimize the utilization of the factors identified in this research, specifically
peer support, and outlook, as measured by the optimism and feeling in-charge variables. While
these factors are included in the Standards of Medical Care for Diabetes (ADA 2015) and the

31
CCM, they are difficult to effectively address by medical professionals. This is an area where
peers may be employed to supplement the efforts of the medical community.
Numerous factors may explain the additive benefits of peer support in DSM. First are the
shared experiences that those who live with diabetes have in common. These shared experiences
likely minimize the feeling of being alone in facing the obstacles to living well with diabetes.
Having people in one’s life who intimately understand “what it is like” can improve an
individual’s acceptance of and adherence to behavioral modifications. These peer connections
can foster a sense of responsibility and accountability for one’s DSM that differs from HCPs.
Peers provide a unique external accountability that has the potential to influence those who do
not yet possess internal accountability mechanisms and are not persuaded by the advice
bestowed by medical professionals.
Second is the sharing of best practices in DSM. Best practices of DSM tend to come to
those who implement daily self-management routines. These best practices often include lessons
that cannot be gleaned without practical experience for example, how to treat a low blood sugar
discretely on a first date, or how and when to tell a prospective employer about one’s diabetes, or
how to cope with the stigmas associated with diabetes. These practices come from people who
encounter the variety of obstacles to DSM in their daily lives and are therefore less likely to
originate from those who do not personally participate in DSM, such as medical professionals;
however, HCPs can disseminate the best practices of successful patients. Public health efforts
would benefit from the development of greater mechanisms to collect and disseminate this
information to the diabetes community.
Third is the modeling of behavior. This is an area where significant benefits lie for peers
to supplement the efforts made by the medical community and public health systems. While

32
many people with diabetes are provided DSME and instructed about behavioral modifications,
far too many have no example of how to incorporate these into one’s life. Adherence to self-
management regimens, which is crucial for chronic disease management, requires more than
knowledge; it necessitates the practical application of DSM, and this is an area of great challenge
for the medical community and public health systems.
It is, theoretically speaking, possible to prevent type 2 diabetes and the complications
associated with diabetes mismanagement; however, despite this theoretical knowledge, it has not
translated in disease outcomes. This is because there is a chasm between theory and practical
application, which has not been bridged. The outcomes of a chronic illness, such as diabetes,
necessitate the person living with it to accept and become willing to change, to learn and
incorporate health behavior changes, and sustain these for the duration of her or his lifetime.
Peers provide at least one mechanism to bridge this pervasive divide.
Peers can provide unique assistance in the facilitation of sustainable lifestyle adherence
for people with diabetes. An awareness of the human animal is imperative in trying to improve
adherence rates to diabetes-friendly lifestyles. Humans are a eusocial species who tend to learn
behaviors by observing and emulating more so than by listening alone. This was seen in the
study where participants in the peer arm were more likely to initiate insulin usage compared to
those in the nurse case-manager arm (Heisler 2010). Integrating peers as role models presents
additional and much-needed means to address this major obstacle to improving diabetes
outcomes; however, this requires concerted efforts to by public health systems.
Potential Challenges and Policy Solutions:
The results of the survey and case studies have significant public health policy implications. The
data suggest public health efforts that embrace peers as additional mechanisms to promulgate

33
DSM in the diabetes population will likely benefit from a greater diversity among the efforts to
address the myriad of challenges presented in the diabetes public health crisis. Traditionally,
there have been minimal financial resources committed beyond the medical sphere to address
chronic illnesses. While the efforts to manage diabetes from a clinical perspective are clearly
essential, there is more to attend to in public health efforts. Incorporating peer mentors presents
an opportunity to move beyond the current paradigm, which has proven insufficient at managing
the growing epidemic.
It is important to develop policy solutions to some potential challenges of incorporating
peer into health support roles. First is providing peers with adequate training, and second is
making a peer support model sustainable. As seen in the literature review, the studies that
provided the greatest training (Lorig, et al., Philis-Tsimikas et al., Thom, et al., ), tended to have
more favorable outcomes where the peer support arms saw greater reductions in HbA1C levels
compared to traditional care arms. The training models developed by Lorig, et al. and others
may serve as examples for programs to prepare people living diabetes to serve as peer mentors.
The importance of diverse and appropriate training for peers must not be underestimated. Public
health funds must be allocated to facilitate the training of peer mentors to make the best use of
the peer resources of the diabetes community. The upfront expenses of efficiently and
effectively training people with diabetes to serve as mentors will be significant; however, the
potential long-term savings in both economic and human costs are immense and virtually
unattainable without the active participation of the population.
Finally, if peers are to be a sustainable component in addressing the epidemic, it is
advantageous that these mentors are able to receive compensation for their time and services.
The ACA provides a potential channel to employ peers, as it contains section 5313, which

34
stipulates provisions for integrating community health workers (CHWs) into the public health
workforce. This provision might be expanded to specifically include peers, define their roles and
capacities, create training requirements, and solidify the peer mentor job class as an element of
the medical model for chronic disease care. The greater the coordination of peer mentors to
provide adjunct assistance to the medical community the more potential for making optimum use
of this resource.
It is time to declare a state of emergency for the diabetes health crisis, which would allow
greater coordinated and concerted national, state, and local efforts to tackle an epidemic that
shows no sign of stopping. According to the Federal Emergency Management Agency (FEMA),
declaring a state of emergency is “based upon a finding that the situation is beyond the capability
of the State and affected local governments or Indian tribal government and that supplemental
federal emergency assistance is necessary to save lives and protect property, public health and
safety, or to lessen or avert the threat of a disaster (FEMA 2015).” The diabetes epidemic
exemplifies this definition and would be well-served to be treated accordingly.
POLICY RECOMMENDATIONS
This research has important implications for policy interventions to improve the diabetes public
health crisis. First is to move beyond the medical-management paradigm to a more diverse and
holistic approach to diabetes management by devoting funding for a peer mentor pilot program.
Public funding for diabetes are primarily allocated in the areas of scientific research, awareness,
and prevention. While these investments have and continue to produce meaningful results,
diversifying spending to include the area of psychosocial support is crucial. A pilot program will
allow the development of an effective, scalable, replicable system and will provide essential

35
quantifiable data about the effects of peer mentors on DSM, which are essential to demonstrate
benefits of this underutilized resource.
Second is to increase the sustainability and viability of this resource by solidifying and
formalizing the position of peer mentor as a job class. In order for peer mentors to be utilized
effectively in the public health efforts, this position must be able to bill for services. Community
health care workers (CHWs) provide an avenue to incorporate peer mentors into the paradigm of
diabetes management. However, while CHWs have a standard occupational classification, only
a few states including Arkansas, Minnesota, Oregon, Washington, and West Virginia require
reimbursement for CHW services. The Centers for Medicare and Medicaid Services (CMS), by
deeming peer mentors and CHWs medically necessary, can expedite the acceptance and
utilization of this essential adjunct to the medical model.
CONCLUSION
It is time to treat the diabetes epidemic like a state-of-emergency, given its proliferating financial
and human costs. Turing the tide on this public health crisis requires an all-hands-on-deck
mentality. This means including all available resources to address it. People with diabetes are
one such resource. The diabetes community provides additional mechanisms to supplement and
expand the immense efforts of the medical and public health domains. Peers offer a unique
avenue to bridge the abyss between sides of theoretical understanding and the practical
sustainable application of this knowledge. It is time to maximize this resource

36
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42
APPENDIX A
Welcome to the Diabetes Peer Support Survey!
The purpose of this research project is to assess the real-life experiences of people living with
diabetes. There are no right or wrong answers to these questions. You are invited to participate
in this research project because you live with diabetes.
Your participation in this research study is voluntary. You may choose not to participate. If you
decide to participate in this research survey, you may withdraw at any time with no risk to you.
The procedure involves completing an online survey that will take approximately 10 minutes.
Your responses are anonymous. The survey does not contain information that will personally
identify you such as your name, email address, or IP address. All data is stored in a password
protected electronic format. The results of this study will be used for scholarly purposes only
and may be shared with others conducting research in diabetes.
If you have any questions about the research study, please contact Heather Beiden Jacobs at
hjacobs2@mail.csuchico.edu. This research has been reviewed according to California State
University IRB procedures for research involving human subjects.
1. ELECTRONIC CONSENT: Please select your choice below.
Clicking on the "agree" button below indicates that:
• you have read the above information
• you voluntarily agree to participate
• you are at least 18 years of age
If you do not wish to participate in the research study, please decline participation by checking
the "disagree"
button.
o Agree
o Disagree
2. What year were you diagnosed with diabetes? (enter 4-digit year; for example, 1976)
3. How old were you when you were diagnosed?
* 4. What type of diabetes do you have?
o Type 1
o Type 2
5. Do you use diet and exercise to manage your diabetes?
o Yes
o No
o I don't know
o Prefer to not answer
6. Do you use injectables, other than insulin (examples: Exenatide, Victoza, Symlin)?
o Yes

43
o No
o I don't know
* 7. Do you use oral agents to treat your diabetes?
o Yes
o No
o I don't know
8. How many different oral agents do you take per day for your diabetes?
* 9. Do you take insulin?
o Yes
o No
o I don't know
10. On average, how many shots of insulin do you take per day?
11. How do you administer your insulin?
o Multiple daily injections
o Pump
12. On average, how many shots do you take per day?
* 13. Does anyone provide social support for you regarding your diabetes? For example, do you
have anyone available to encourage you or to listen to concerns about your diabetes care?
o Yes
o No
o Don't need support
o I don't know
14. Who do you get support from for your diabetes? (Please check all that apply)
o Spouse or significant other
o Friend(s)
o Family member(s)
o Someone with diabetes (in-person)
o Someone with diabetes (online)
o Medical professional(s)
o Other (please specify)
15. During the past month, have you felt optimistic about your diabetes?
o No, I felt it has ruined my life

44
o I felt generally quite discouraged
o I have lots of ups and downs about it
o I have felt optimistic for the most part, occasionally discouraged
o Very optimistic, rarely discouraged
o I don't know
16. People living with diabetes do not always feel the same about their diabetes. Currently, are
your feelings about your diabetes better or worse than how you usually feel about your diabetes?
o Much better
o Somewhat better
o About the same
o Somewhat worse
o Much worse
o I don't know
17. Over the past month, how much have you felt personally in charge of your diabetes?
o I felt that I played no part in managing my diabetes
o I felt that I played a small, unimportant part in managing my diabetes
o I felt that I played a small, but important part in managing my diabetes
o I felt that I played a major part in managing my diabetes
o I felt that I was completely in charge of managing my diabetes
o I don't know
18. How would you rate your overall health?
o Poor
o Fair
o Good
o Very good
o Excellent
o I don't know
19. How would you rate your diabetes self-management?
o Poor
o Fair
o Good
o Very good
o Excellent
o I don't know
20. Over the past month, did you check your blood sugar?

45
o Much less than directed by your medical professional
o Less than directed by your medical professional
o As directed by your medical professional
o More than directed by your medical professional
o Much more than directed by your medical professional
21. In a typical week, how many days do you get of aerobic activity (examples: walking,
swimming, biking)?
o I don't regularly get aerobic activity
o Once a week
o 2 to 4 days a week
o I don't know
22. When you get aerobic activity, how long do you usually go?
o Less than 15 minutes
o 15-30 minutes
o 30-60 minutes
o More than 60 minutes
o I don't know
23. When was your last LDL cholesterol test?
o Past month
o Past 3 months
o Past 6 months
o Past 12 months
o More than 1 year
o Never
o I don't know
24. What was your last LDL cholesterol test result?
o Less than 60 mg/dL
o 61-90 mg/dL
o 91-120 mg/dL
o 121-150 mg/dL
o 151-180 mg/dL
o 181-210 mg/dL
o More than 211 mg/dL
o I don't know
* 25. How often do you check your blood sugar?

46
o Daily
o Weekly
o Monthly
o Yearly
o Continuous glucose monitor (CGM)
o I don't know
o I don't check my blood sugar
26. On average, how many times per day do you check your blood sugar?
27. On average, how many times per week do you check your blood sugar?
28. On average, how many times per month do you check your blood sugar?
29. On average, how many times per year do you check your blood sugar?
30. When was your last diabetes (dilated pupil) eye exam?
o Past month
o Past 3 months
o Past 6 months
o Past 12 months
o More than 1 year
o Never
o I don't know
31. Do you currently smoke tobacco?
o Yes
o No, I quit
o No, I never smoked
32. In the past 12 months, have you gone to the emergency room for a diabetes-related issue?
o Yes
o No
o I don't know
33. In the past 12 months, how many times have you gone to the emergency room for a diabetes-
related issue?
34. When was your last HbA1C (glycosylated hemoglobin A1C) test?
o Past month
o Past 3 months

47
o Past 6 months
o Past 12 months
o More than 1 year
o Never
o I don't know
35. What was your last HbA1C result?
o Less than 5.5%
o 5.6-6.5%
o 6.6-7.5%
o 7.6-8.5%
o 8.6-9.5%
o 9.6-10.5%
o 10.6-11.5%
o 11.6-12.5%
o More than 12.5%
o I don't know
36. Do you belong to any diabetes online community sites?
o Yes
o No
o I don't know
37. On average, how often do you go to diabetes online community sites?
o Daily
o A few times per week
o Once a week
o A few times per month
o Once a month
o Rarely
o Never
o I don't know
38. What is the primary purpose of the diabetes sites you attend regularly?
o Education
o Support
o Socializing
o Advocacy
o Empowerment

48
39. How would you rank the following reasons for going to diabetes online communities sites
(most important to least important)?
o Most important, Important, Moderately important, Slightly important, Least important
o Learning about diabetes management
o Giving support
o Receiving support
o Socializing with others who have diabetes
o Advocating for the diabetes community
40. What is your level of engagement diabetes online community sites?
o Highly engaged
o Moderately engaged
o Somewhat engaged
o Little engaged
o Not at all engaged
41. In a typical week, how many days do you get of strengthening or stretching activity (range of
motion, weights, yoga)?
o I don't regularly do strengthening or stretching activities
o Once a week
o I don't know
42. When you are doing strengthening or stretching activities, how long do you usually go?
o Less than 15 minutes
o 15-30 minutes
o 30-60 minutes
o More than 60 minutes
o I don't now
43. Have you taken diabetes self-management education classes?
o Yes
o No
o I don't know
44. What was the format of the diabetes education classes?
o Group
o 1 on 1 with a diabetes educator
o Both
o I don't know

49
45. Have you ever attended an in-person diabetes support group?
o Yes
o No
o I don't know
46. When was the last time you attended an in-person diabetes support group?
o Past Month
o Past 3 months
o Past 6 months
o Past year
o More than 1 year ago
o I don't know
47. In the past 12 months, how many in-person diabetes support groups have you attended?
48. What is your gender?
o Female
o Male
49. In what year were you born? (enter 4-digit birth year; for example, 1976)
50. What is the highest level of school you have completed?
51. Which race/ethnicity best describes you? (Please choose only one.)
o American Indian or Alaskan Native
o Asian / Pacific Islander
o Black or African American
o Hispanic American
o White / Caucasian
o Multiple ethnicity / Other (please specify)
52. What is you approximate average household income?
53. How many people currently live in your household?
54. In what country do you currently live?
o United States
55. In what state or U.S. territory do you live?
56. Do you live in a rural, urban, or suburban area?

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