DetailsBased on the summary of research findings identified fro.docx

Details:
Based on the summary of research findings identified from the
Evidence-Based Project—Paper on Diabetes that describes a
new diagnostic tool or intervention for the treatment of diabetes
in adults or children, complete the following components of this
assignment:
Develop a PowerPoint presentation (a title slide, 6-12 slides,
and a reference slide; no larger than 2 MB) that includes the
following:
1. A brief summary of the research conducted in the Evidence-
Based Project – Paper on Diabetes.
2. A descriptive and reflective discussion of how the new tool
or intervention may be integrated into practice that is supported
by sound research.
While APA format is not required for the body of this
assignment, solid academic writing is expected, and in-text
citations and references should be presented using APA
documentation guidelines, which can be found in the APA Style
Guide, located in the Student Success Center.
You are not required to submit this assignment to Turnitin,
unless otherwise directed by your instructor. If so directed,
refer to the Student Success Center for directions. Only Word
documents can be submitted to Turnitin.
Summary of Article (Includes Discussion of Research
Performed and Clinical Findings)
Content does not fulfill any of the requirements stated in the
assignment criteria.
Some of the requirements stated in the assignment criteria are
present. Findings and/ or methods are described but flawed,
unrealistic, irrelevant, and/or inaccurate.
Content is complete, but somewhat inaccurate and/or irrelevant.
Research lacks relevance, quality, and/or innovation.
Content is comprehensive and accurate, and definitions are
clearly stated. Research is adequate, current, and relevant, and

addresses all of the issues stated in the assignment criteria.
Content is comprehensive and presents ideas and information
beyond those presented through the course. Research is
thorough, current, and relevant, and addresses all of the issues
stated in assignment criteria.
60.0 %Proposed Integration of the New Tool or Intervention
Into Practice, and Explanation of the Impact of the New Tool or
Intervention on nursing practice.
Neither the integration nor the impact of the new tool or
intervention is stated.
The integration into and the impact of the new tool or
intervention is stated but not thoroughly explained. Presentation
does not demonstrate critical thinking and analysis.
The integration and impact of the new tool or intervention on
the nursing practice are stated and explained. Presentation
satisfactorily demonstrates understanding and analysis of the
basic principles.
Statement is descriptive and reflective. Shows some planning
and attention to how various components fit together, but
essential elements are not present.
Statement is thorough, descriptive, reflective, and supported
with practical and sound research. Shows careful planning and
attention to how disparate elements fit together to impact the
nursing profession.
30.0 %Organization and Effectiveness
10.0 %Layout
The layout is cluttered, confusing, and does not use spacing,
headings, and subheadings to enhance the readability. The text
is extremely difficult to read with long blocks of text, small
point size for fonts, and inappropriate contrasting colors. Poor
use of headings, subheadings, indentations, or bold formatting
is evident.
The layout shows some structure, but appears cluttered and busy
or distracting with large gaps of white space or a distracting
background. Overall readability is difficult due to lengthy

paragraphs, too many different fonts, dark or busy background,
overuse of bold, or lack of appropriate indentations of text.
The layout uses horizontal and vertical white space
appropriately. Sometimes the fonts are easy to read, but in a few
places the use of fonts, italics, bold, long paragraphs, color, or
busy background detracts and does not enhance readability.
The layout background and text complement each other and
enable the content to be easily read. The fonts are easy to read
and point size varies appropriately for headings and text.
The layout is visually pleasing and contributes to the overall
message with appropriate use of headings, subheadings, and
white space. Text is appropriate in length for the target
audience and to the point. The background and colors enhance
the readability of the text.
10.0 %Language Use and Audience Awareness (includes
sentence construction, word choice, etc.)
Inappropriate word choice and lack of variety in language use
are evident. Writer appears to be unaware of audience. Use of
?primer prose? indicates writer either does not apply figures of
speech or uses them inappropriately.
Some distracting inconsistencies in language choice (register)
and/or word choice are present. The writer exhibits some lack of
control in using figures of speech appropriately.
Language is appropriate to the targeted audience for the most
part.
The writer is clearly aware of audience, uses a variety of
appropriate vocabulary for the targeted audience, and uses
figures of speech to communicate clearly.
The writer uses a variety of sentence constructions, figures of
speech, and word choice in distinctive and creative ways that
are appropriate to purpose, discipline, and scope.
5.0 %Mechanics of Writing (includes spelling, punctuation,
grammar, language use)
Slide errors are pervasive enough that they impede
communication of meaning.
Frequent and repetitive mechanical errors distract the reader.

Some mechanical errors or typos are present, but are not overly
distracting to the reader.
Slides are largely free of mechanical errors, although a few may
be present.
Writer is clearly in control of standard, written academic
English.
5.0 %Evaluating and Documenting Sources (in-text citations for
paraphrasing and direct quotes, references page listing and
formatting, as appropriate to assignment and style)
Contains no title slide, no references section, and no correctly
cited references within the body of the presentation.
Title slide is incomplete or inaccurate. References section
includes sources, but many citation errors. Citations are
included within the body of the presentation but with many
errors.
Title slide has minor errors. References section includes
sources, but they are not consistently cited correctly. Citations
are included within the body of the presentation but with some
errors.
Title slide is complete. References section includes correctly
cited sources with minimal errors. Correct citations are included
within the body of the presentation.
Title slide is complete. References section includes correctly
cited sources. Correct citations are included within the body of
the presentation.
100 %Total Weightage
Details:
Identify a research or evidence-based article that focuses
comprehensively on a specific intervention or new diagnostic
tool for the treatment of diabetes in adults or children.
In a paper of 750-1,000 words, summarize the main idea of the
research findings for a specific patient population. Research
must include clinical findings that are current, thorough, and
relevant to diabetes and the nursing practice.

Prepare this assignment according to the APA guidelines found
in the APA Style Guide, located in the Student Success Center.
An abstract is not required.
This assignment uses a grading rubric. Instructors will be using
the rubric to grade the assignment; therefore, students should
review the rubric prior to beginning the assignment to become
familiar with the assignment criteria and expectations for
successful completion of the assignment.
You are required to submit this assignment to Turnitin. Refer to
the directions in the Student Success Center. Only Word
documents can be submitted to Turnitin.
Rubric
30.0 %Research or Evidence-Based Article Identified. Article
Focuses on a Specific Diabetic Intervention or New Diagnostic
Tool.
Research or evidence-based article not identified.
Research or evidence-based article identified but does not
address a specific diabetic intervention or diagnostic tool.
Research or evidence-based article identified that focuses on a
specific diabetic intervention or diagnostic tool in general.
specific diabetic intervention and a diagnostic tool.
specific diabetic intervention or diagnostic tool in a
comprehensive manner, allowing all criteria of assignment to be
fully addressed.
50.0 %Summary of Article Includes the Following Content:
Discussion of Research Performed Clinical Findings, and
Significance to Nursing Practice.
Content is incomplete or omits most of the requirements stated
in the assignment criteria. Does not demonstrate an
understanding of the basic principles. Does not demonstrate
critical thinking and analysis of the overall program subject.
Content is incomplete or omits some requirements stated in the
assignment criteria. Demonstrates shallow understanding of the

basic principles only a surface level of evaluation is offered,
methods are described but flawed or unrealistic and strategies
are discussed, but incomplete.
Content is complete, but somewhat inaccurate and/or irrelevant.
Demonstrates adequate understanding of the basic principles.
Reasonable but limited inferences and conclusions are drawn
but lack development. Supporting research is inadequate in
relevance, quality, and/or currentness.
Content is comprehensive and accurate, and definitions are
clearly stated. Sections form a cohesive logical and justified
whole. Shows careful planning and attention to details and
illuminates relationships. Research is adequate, current, and
relevant, and addresses all of the issues stated in the assignment
criteria.
Content is comprehensive. Presents ideas and information
beyond that presented through the course, and substantiates
their validity through solid, academic research where
appropriate. Research is thorough, current, and relevant, and
addresses all of the issues stated in assignment criteria. Final
paper exhibits the process of creative thinking and development
of proposal. Applies framework of knowledge, practice and
sound research. Shows careful planning and attention to how
disparate elements fit together.
15.0 %Organization and Effectiveness
5.0 %Thesis Development and Purpose
Paper lacks any discernible overall purpose or organizing claim.
Thesis and/or main claim are insufficiently developed and/or
vague; purpose is not clear.
Thesis and/or main claim are apparent and appropriate to
purpose.
Thesis and/or main claim are clear and forecast the development
of the paper. It is descriptive and reflective of the arguments
and appropriate to the purpose.
Thesis and/or main claim are comprehensive; contained within
the thesis is the essence of the paper. Thesis statement makes

the purpose of the paper clear.
5.0 %Paragraph Development and Transitions
Paragraphs and transitions consistently lack unity and
coherence. No apparent connections between paragraphs are
established. Transitions are inappropriate to purpose and scope.
Organization is disjointed.
Some paragraphs and transitions may lack logical progression of
ideas, unity, coherence, and/or cohesiveness. Some degree of
organization is evident.
Paragraphs are generally competent, but ideas may show some
inconsistency in organization and/or in their relationships to
each other.
A logical progression of ideas between paragraphs is apparent.
Paragraphs exhibit a unity, coherence, and cohesiveness. Topic
sentences and concluding remarks are appropriate to purpose.
There is a sophisticated construction of paragraphs and
transitions. Ideas progress and relate to each other. Paragraph
and transition construction guide the reader. Paragraph structure
is seamless.
5.0 %Mechanics of Writing (includes spelling, punctuation,
grammar, language use)
Surface errors are pervasive enough that they impede
communication of meaning. Inappropriate word choice and/or
sentence construction are used.
Frequent and repetitive mechanical errors distract the reader.
Inconsistencies in language choice (register), sentence
structure, and/or word choice are present.
Some mechanical errors or typos are present, but are not overly
distracting to the reader. Correct sentence structure and
audience-appropriate language are used.
Prose is largely free of mechanical errors, although a few may
be present. A variety of sentence structures and effective
figures of speech are used.
Writer is clearly in command of standard, written, academic

English.
5.0 %Format
3.0 %Language Use and Audience Awareness (includes sentence
construction, word choice, etc.)
Inappropriate word choice and lack of variety in language use
are evident. Writer appears to be unaware of audience. Use of
'primer prose' indicates writer either does not apply figures of
speech or uses them inappropriately.
Some distracting inconsistencies in language choice (register)
and/or word choice are present. The writer exhibits some lack of
control in using figures of speech appropriately.
Language is appropriate to the targeted audience for the most
part.
The writer is clearly aware of audience, uses a variety of
appropriate vocabulary for the targeted audience, and uses
figures of speech to communicate clearly.
The writer uses a variety of sentence constructions, figures of
speech, and word choice in distinctive and creative ways that
are appropriate to purpose, discipline, and scope.
2.0 %Research Citations (In-text citations for paraphrasing and
direct quotes, and reference page listing and formatting, as
appropriate to assignment)
No reference page is included. No citations are used.
Reference page is present. Citations are inconsistently used.
Reference page is present. Citations are inconsistently used.
Reference page is present and fully inclusive of all cited
sources. Documentation is appropriate and GCU style is usually
correct.
In-text citations and a reference page are complete. The
documentation of cited sources is free of error.
100 %Total Weightage

New Technologies to Advance
Self-Management Support in Diabetes
Not just a bunch of cool apps!
T
he article by Walker et al. (1) in this
issue of Diabetes Care highlights
how new applications of existing
modes of communication and the use of
new technologies can improve the deliv-
ery of care for patients with diabetes. In
this case, care was successfully brought to
a poorly controlled patient group using
the medium of live telephone calls. New
types of technology-based interventions
focus on the use of the telephone, Internet,
mobile communicators, pagers, web-based
programs, and email to assess and monitor
patient health status, address symptoms
and behaviors, and foster changes in vari-
ous aspects of disease management—in this
case better medication adherence and im-
proved glycemic control.
The purpose of these emerging sys-
tems is to expand programs of self-
management support (SMS), a generic
term applied to strategies that provide pa-
tients with the information, tools, and
support they need to take care of their

health problems (2,3). The development
and application of a spectrum of interven-
tions that involve new media expand
evidence-based methods of patient
monitoring and intervention that tradi-
tionally have taken place within the clin-
ical setting to real-world environments in
ways that are congruent with patient life-
style, age, and cultural setting (3,4). The
underlying assumption is that such pro-
grams can be more successful, sustain-
able, and cost- and time-efficient than
traditional approaches and that they have
the potential to reach high risk patient
populations that generally do not come to
their doctor’s office for regular care (3,5).
These SMS programs vary in complexity,
from simple telephone-based programs of
symptom monitoring to highly elaborate,
web-based programs with complex
branching algorithms that assist patients
with improving self-care over time (6).
The increased popularity of these pro-
grams, coupled with real-world questions
about their practical usability in clinical
care, cost, sustainability, and effective-
ness, cause us to reflect on how such pro-
grams should be translated for general use
and integrated into care for patients with
diabetes. Now that the field has matured
by the variety of available evidence-based
technological SMS programs currently
available, the need to establish criteria for
translation into the real-world of clinical
care is apparent (4). Sadly, too often such

programs are supported by time-limited
external resources, and even if proven ef-
fective, they are shelved when the project
is completed because they cannot easily
be integrated and supported by ongoing
care systems.
In this editorial, we provide a per-
spective for surveying this expanding
field by reviewing a template of five inter-
related questions that summarize the ma-
jor translational issues for SMS programs
based on new technologies (Table 1).
Without such consideration, we risk not
taking full advantage of the unique
strengths and not avoiding the potential
drawbacks of these programs to improve
outcomes for patients with diabetes.
What is the specific purpose of the
SMS program?
What is often missing in the selection and
use of SMS systems in clinical care is the
clear targeting of specific goals: what ex-
actly is the target of the program and how
will it be achieved? Are there multiple
SMS goals to be addressed or is the focus
more narrowly drawn? For example, the
telephone-based intervention designed
by Walker et al. was concisely focused on
reducing A1C, as are many SMS systems
for patients with diabetes. In this case,
however, a specific proximal mechanism
of change was designed into the interven-
tion: enhancing medication adherence.
Many existing SMS programs are devel-

oped without this kind of clear outcome
targeting, meaning that a specific behav-
ioral domain and a sequencing of behav-
ioral change goals are not identified at the
outset. SMS programs are often devel-
oped to address only the distal goal of
improved glycemic control by using a
proximal goal of weight reduction, in-
creased physical activity, or medication
adherence. Proximal change goals, how-
ever, are rarely assessed comprehensively
or seen as major outcomes in their own
right, because the primary aim is change
in A1C, the distal goal. This is an impor-
tant consideration, because it is entirely
possible that the proximal goal, in this
case medication adherence, could have
been achieved without being followed by
achievement of the distal goal, in this case
a reduction in A1C. When this occurs, the
intervention is often deemed a failure,
even though the actual behavioral change
targeted was achieved. Many factors affect
glycemic control, and changes in one po-
tential influence may be insufficient to
significantly affect A1C, especially over
the course of time involved in clinical tri-
als. As a general rule, it is crucial to judge
the effectiveness and utility of a specific
SMS intervention by observing changes to
both proximal and distal targets, and not
to rely solely on changes to a distal out-
come that can be influenced by multiple
factors, such as A1C.

A related issue is time frame: can the
change targeted be achieved by a defined
but time-limited intervention, or will it
require a longer-lasting program that in-
cludes a greater investment in patient en-
gagement, intervention, and support? For
example, Walker et al. found that changes
in medication adherence were achievable
within the limited time frame of the study.
In contrast, major reductions in body
weight, the target of many SMS interven-
tions with new technologies, require
much longer time periods, supplemented
by ongoing programs to maintain weight
loss over time. Many SMS programs using
new technologies demonstrate initial suc-
cess and then stop, with a subsequent re-
turn to preintervention levels because
they did not consider that achieving and
maintaining behavioral change is not a
one-time experience. Both diabetes and
disease management are chronic condi-
tions requiring ongoing support and in-
tervention, which can be a major strength
of new technologies if addressed and in-
E d i t o r i a l s
E D I T O R I A L ( S E E W A L K E R E T A L . , P . 2 )
240 DIABETES CARE, VOLUME 34, NUMBER 1, JANUARY
2011 care.diabetesjournals.org
corporated into the SMS program at the
outset. In the current example, it remains

unclear if the documented improvement
in medication adherence can be sustained
and whether the SMS program will need
to be altered to assist with maintenance of
medication adherence over time.
Which patients are to be targeted?
The greater the effort devoted to defining
the patient population of interest, the
better the outcomes, the higher the accep-
tance rate, and the lower the attrition (7).
Tailoring SMS interventions for specific
patient groups increases the probability of
obtaining positive results. For example,
simply making a web-based program
available to everyone may be beneficial
because it is inclusive, but the lack of tai-
loring will often exclude many of the most
needy or high risk patients who could
profit most from the intervention. The cri-
teria identified for inclusion in the pro-
gram reported by Walker et al. were
specific: these were patients who were
poorly controlled, rarely came for care,
identified with a respected institution
(their Union), and were generally from a
single ethnic group. Much effort was de-
voted to using cultural cues to engage
these patients, and the Union was used as
a common, trusted institution to enhance
reach. The success of these efforts was re-
vealed by high patient uptake and rela-
tively low attrition over time. Even the
choice of media was carefully considered:
this was a sample that might best be en-

gaged via live contact through the tele-
phone rather than, for example, through
interactive voice recognition (IVR) tech-
nologies or web-based/email programs,
even though they are far less expensive to
deliver. A comparable program for pa-
tients from another ethnic group or from
another education level might best be de-
signed quite differently. One-size-fits-all
SMS programs may be relatively inexpen-
sive for an entire patient panel, but they
are often highly inefficient with respect to
high risk patients who need them the
most. At minimum, we suggest that the
following patient characteristics should
be considered when making use of new
technologies: age, sex, ethnicity, educa-
tion, severity of disorder or symptoms,
level of risk, and experience with and
preference for different media.
Which media and media
characteristics will be utilized?
Many currently available SMS systems us-
ing new technologies are complex and so-
phisticated in terms of available options
and ability to be customized both within
and across different media. Some empha-
size programs to engage patients in the
process and to assist in the maintenance
of gains over time (8). Still others allow
for “stepped interventions,” for different
levels of intervention intensity based on
patient need or preference, or for multiple
interventions, with patients choosing the

specific goals and subprograms (9). Al-
though this sophistication is attractive,
much of it will be often underused when
the program is translated into clinical
practice with a large, diverse panel of pa-
tients. Most patients access only a limited
number of features in complex SMS pro-
grams (10,11). Although many programs
are elegant, the usability, accessibility,
and targeting of specific goals for both pa-
tients and care teams may be best (12).
Not all media are equally effective for
all types of SMS programs, and careful
consideration needs to be directed at de-
ciding which medium is best for which
intervention and for which patients. For
example, IVR technology has been used
successfully for symptom monitoring and
for relatively short and simple communi-
cations between patient and care team,
such as clinical status reporting (13).
However, IVR programs can be tedious
and repetitious for more complex tasks,
where web-based audio and visual cues
can be more helpful and patients can re-
main engaged for longer time periods.
Consequently, there needs to be a careful
match between each specific SMS pro-
gram target, the clinical population iden-
tified, and the medium selected for use. A
single intervention program cannot easily
be translated across different media with
the assumption that one can be substi-
tuted for another. And not all patients
with diabetes will be equally attracted to

the same program.
Another important issue concerns the
amount of live versus automated patient
contact included in the program. Al-
though costs are generally higher with
Table 1—SMS Questions
1. What is the specific purpose of the SMS program?
a. What are the proximal and distal goals? Is the focus behavior
change, clinical status, or
symptom monitoring?
b. Can behavior change and maintenance be achieved in a time-
limited way, or will it be
gradual, requiring ongoing support?
c. Is the SMS goal comprehensive or highly targeted (intensive
vs. extensive intervention)?
2. Which patients will be targeted?
a. Demographics: age, ethnicity, gender.
b. Severity of disorder or symptoms.
c. Level of risk.
d. Level of media experience and preference (personal and
cultural).
3. Which media and media characteristics will be utilized?
a. Media (web, email, telephone, etc.)?
b. How many bells and whistles (level of program complexity).
c. Is this a stepped intervention, does one program fit all?
d. What is the ratio of human to technological contact
(balancing the cost of human
contact)?

e. How much emphasis is there on patient engagement and
maintenance?
4. Will the SMS program be integrated into the patient’s
ongoing clinical care?
a. Where does the program come from (the practice, health plan,
employer, or stand-alone
source)?
b. Too what degree is the program built around relationships
between patients and HCPs
or staff? Or is this a carved-out, stand-alone program?
c. How and by whom (clinicians, staff, patients) will the
information generated by the
program be used?
d. How will the program be framed for patients, clinicians, and
practice staff?
5. Costs.
a. Who will support the program financially, e.g., clinical
practice, health plan, patient,
medical group?
b. What are the development costs?
c. What are the initial costs for customizing and implementing
the program in each setting?
d. What are the ongoing costs of use over time (information
technology staff to maintain
the program, clinical staff to make use of the information, etc.)?
e. Are there patient costs?
Fisher and Dickinson

care.diabetesjournals.org DIABETES CARE, VOLUME 34,
NUMBER 1, JANUARY 2011 241
more live than automated patient contact,
deciding to utilize less live contact with
some patient groups may not be cost-
efficient. The ideal balance of live to tech-
nological contact is often based on the
patient’s cultural context, level of risk,
age, and life context. To reduce cost, per-
sonal contact can be utilized initially and
then decreased over time, depending on
patient need, once a relationship with the
live program representative has been es-
tablished. And patient preference can be
utilized in tailoring a program—some pa-
tients actually prefer fully automated sys-
tems, whereas others firmly do not. For
example, one highly experienced com-
puter user surprisingly rejected a web-
based program: “I spend my entire day
working on the computer and when I get
home I don’t even want to look at my PC.”
Although a totally automated program
may be effective for a subset of the popu-
lation, a well-balanced, flexible, patient-
tailored level of live contact generally
appeals to a wider audience and may
prove to be most effective in terms of cost
and clinical outcome.
How will the SMS program be
integrated into the patient’s ongoing
clinical care?

How integrated and seamless is the SMS
system with respect to the broader deliv-
ery of the patient’s diabetes care? Most
currently available SMS programs are
free-standing; they are not easily inte-
grated within office-based electronic
health records and other automated clin-
ical care systems, or patients seek them
out on their own, which leaves the SMS
activity completely separated from the
care team. Still others are offered by an
employer or a health insurance plan with
no direct linkage to the diabetes care
team. In the study reported by Walker, et
al., the SMS program was designed to op-
erate independently of the patient’s regu-
lar care team. When translated into the
real-world of clinical care, how would
health care practitioners (HCPs) know
about such a system and make effective
use of it when caring for individual
patients?
The growing interest in integrated
systems of care, modeled after the Patient-
Centered Medical Home (14 –16), sug-
gests a need to provide more coordinated
and informed services in collaboration
with patient need and preference. As
these new models of primary care have
gained acceptance and traction, practices
are showing increased interest in integrat-
ing SMS programs of various types into
their care. Free-standing or carved-out
SMS programs stand in sharp contrast to

these recent developments and raise con-
cerns about the pitfalls of fragmented
care, especially for high risk patients with
diabetes. In general, patients are far more
likely to continue with an SMS program
over time when it is based on a positive
relationship with their HCP and when it is
viewed as an extension of their care (17).
We argue that SMS programs for diabetes
need to be linked to the broader system of
patient care so that both patients and pro-
viders are fully informed about SMS activ-
ities and care can be fully coordinated and
integrated.
Deciding upon the adoption of an
SMS system that uses new technologies
also requires careful consideration of
work flow— how the information derived
from the system will be collected, summa-
rized, and used in the clinical setting.
Who in the office will monitor patient
SMS activities recorded by the program?
How will the information be documented
in the medical record? How will it be
practically summarized for use during the
next clinical encounter or red-flagged for
a more immediate response? SMS infor-
mation adds to already high staff de-
mands to integrate a great deal of clinical
information needed for good diabetes
care (18).
What is the development and
implementation cost and how will
the SMS program be supported

long-term?
The intervention reported by Walker et al.
was funded by grants from National Insti-
tutes of Health and the Union pension
fund. From a translation perspective, no
information is provided about the overall
costs of program development, imple-
mentation, and long-term use. In the real
world of clinical care, it often remains un-
clear who will shoulder the various
substantial costs of SMS programs: the
clinic, the medical group, the health care
plan, the employer, the patient? And how
will the program be framed for the pa-
tient—is this their doctor’s program, their
health plan’s program, their employer’s
program? This framing, based in part on
who designs and pays for it, can have sub-
stantial effects on patient follow-through
and outcome. It is important that SMS
outcome studies track and report infor-
mation regarding the cost of develop-
ment, implementation, and maintenance
to inform decisions regarding the optimal
deployment of the program on an ongo-
ing basis.
CONCLUSIONS — SMS programs
for patients with diabetes make use of the
full range of electronic media, from sim-
ple automated telephone monitoring to
more complex, web-based lifestyle
change programs. Most of these programs
have evolved outside of the traditional
care system and others were developed

internally but often lack the resources to
be maintained over time. Care systems
have been relatively slow to adopt SMS
programs and to integrate them into more
comprehensive care for patients with dia-
betes. The pressing need to deliver cost-
effective, population-based care that
reaches out to the individual needs of pa-
tients with diabetes calls for practitioners
to become proactive in selecting and cus-
tomizing efficient, semi- and fully auto-
mated SMS programs. The program
described in the paper by Walker, et al.
serves as an excellent model, but it also
raises important questions. Using a vari-
ety of different media, these programs
have the potential for expanding care
from traditionally delivered office-based
encounters to care that reaches into the
patient’s home and community. These
programs require that clinicians engage in
active and systematic consideration of the
patients who might use it, the specific
SMS behaviors that are targeted for
change, the type of media that will be
used, the time frame of the program, who
will pay for it, and how the program will
be integrated into a broader program of
comprehensive diabetes care.
LAWRENCE FISHER, PHD, ABPP1
W. PERRY DICKINSON, MD2
From the 1Department of Family and Community
Medicine, University of California, San Fran-

cisco, San Francisco, California, and the 2Depart-
ment of Family Medicine, University of Colorado
Health Sciences Center, Aurora, Colorado.
Corresponding author: Lawrence Fisher, [email protected]
fcm.ucsf.edu.
DOI: 10.2337/dc10-1830
© 2011 by the American Diabetes Association.
Readers may use this article as long as the work is
properly cited, the use is educational and not for
profit, and the work is not altered. See http://
creativecommons.org/licenses/by-nc-nd/3.0/ for
details.
Acknowledgments — L.F. is a consultant to
Roche Diagnostics and Eli Lilly. No other po-
tential conflicts of interest relevant to this ar-
ticle were reported.
New technologies in self-management support
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References
1. Walker E, Shmukler C, Ullman R, Blanco
E, Scollan-Koliopoulus M, Cohen HW.
Results of a successful telephonic inter-
vention to improve diabetes control in ur-

ban adults: a randomized trial. Diabetes
Care 2011;34:2–7
2. Glasgow RE, Funnell MM, Bonomi AE,
Davis C, Beckham V, Wagner EH. Self-
management aspects of the improving
chronic illness care breakthrough series:
implementation with diabetes and heart
failure teams. Ann Behav Med 2002;24:
80 – 87
3. Goldstein DE. E-Healthcare: Harness the
Power of Internet e-Commerce and e-Care.
Gaithersburg, MD, Aspen Publishers,
2000
4. Bennett GG, Glasgow RE. The delivery of
public health interventions via the Inter-
net: actualizing their potential. Annu Rev
Public Health 2009;30:273–292
5. Lorig KR, Sobel DS, Stewart AL, Brown
BW Jr, Bandura A, Ritter P, Gonzalez VM,
Laurent DD, Holman HR. Evidence sug-
gesting that a chronic disease self-man-
agement program can improve health
status while reducing hospitalization: a ran-
domized trial. Med Care 1999;37:5–14
6. Norris SL, Engelgau MM, Narayan KM.
Effectiveness of self-management training
in type 2 diabetes: a systematic review of
randomized controlled trials. Diabetes
Care 2001;24:561–587
7. Carroll KM. Enhancing retention in clini-

cal trials of psychosocial treatments: prac-
tical strategies. NIDA Res Monogr 1997;
165:4 –24
8. Eakin EG, Reeves MM, Marshall AL, Dun-
stan DW, Graves N, Healy GN, Bleier J,
Barnett AG, O’Moore-Sullivan T, Russell
A, Wilkie K. Living well with diabetes: a
randomized controlled trial of a tele-
phone-delivered intervention for mainte-
nance of weight loss, physical activity and
glycemic control in adults with type 2 di-
abetes. BMC Public Health 2010;10:452
9. Lauver DR, Ward SE, Heidrich SM, Keller
ML, Bowers BJ, Brennan PF, Kirchhoff
KT, Wells TJ. Patient-centered interven-
tions. Res Nurs Health 2002;25:246 –255
10. Gerber BS, Brodsky IG, Lawless KA, Smolin
LI, Arozullah AM, Smith EV, Berbaum ML,
Heckerling PS, Eiser AR: Implementation
and evaluation of a low-literacy diabetes ed-
ucation computer multimedia application.
Diabetes Care 2005;28:1574 –1580
11. Glasgow RE, Boles SM, McKay HG, Feil
EG, Barrera M Jr. The D-Net diabetes self-
management program: long-term imple-
mentation, outcomes and generalization
results. Prev Med 2003;36:410 – 419
12. García-Lizana F, Sarría-Santamera A.
New technologies for chronic disease
management and control: a systematic re-

view. J Telemed Telecare 2007;13:62– 68
13. Goldberg LR, Piette JD, Walsh MN, Frank
TA, Jaski BE, Smith AL, Rodriguez R,
Mancini DM, Hopton LA, Orav EJ, Loh E,
WHARF Investigators. Randomized trial
of a daily electronic home monitoring sys-
tem in patients with advanced heart fail-
ure: the Weight Monitoring in Heart
Failure (WHARF) trial. Am Heart J 2003;
146:705–712
14. Arrow K, Auerbach A, Bertko J, Brownlee
S, Casalino LP, Cooper J, Crosson FJ, En-
thoven A, Falcone E, Feldman RC, Fuchs
VR, Garber AM, Gold MR, Goldman D,
Hadfield GK, Hall MA, Horwitz RI,
Hooven M, Jacobson PD, Jost TS, Kot-
likoff LJ, Levin J, Levine S, Levy R, Lin-
scott K, Luft HS, Mashal R, McFadden D,
Mechanic D, Meltzer D, Newhouse JP,
Noll RG, Pietzsch JB, Pizzo P, Reischauer
RD, Rosenbaum S, Sage W, Schaeffer LD,
Sheen E, Silber BM, Skinner J, Shortell
SM, Thier SO, Tunis S, Wulsin L Jr, Yock
P, Nun GB, Bryan S, Luxenburg O, van de
Ven WP. Toward a 21st-century health
care system: recommendations for health
care reform. Ann Int Med 2009;150:493–
495
15. Berenson RA, Hammons T, Gans DN,
Zuckerman S, Merrell K, Underwood WS,
Williams AF. A house is not a home: keep-
ing patients at the center of practice rede-

sign. Health Affairs 2008;27:1219 –1230
16. Davis K, Schoenbaum SC, Audet AM. A
2020 vision of patient-centered primary
are. J Gen Int Med 2005;20:953–957
17. Parchman ML, Pugh JA, Noël PH, Larme
AC. Continuity of care, self-management
behaviors, and glucose control in patients
with type 2 diabetes. Med Care 2002;40:
137–144
18. Pincus HA. The future of behavioral
health and primary care: drowning in the
mainstream or left on the bank? Psycho-
somatics 2003;44:1–11
Fisher and Dickinson
Results of a Successful Telephonic
Intervention to Improve Diabetes Control
in Urban Adults
A randomized trial
ELIZABETH A. WALKER, PHD, RN1
CELIA SHMUKLER, MD2
RALPH ULLMAN, MBA, MS2

EMELINDA BLANCO, MED3
MELISSA SCOLLAN-KOLIOPOULUS, EDD, ANP4
HILLEL W. COHEN, DRPH, MPH3
OBJECTIVE — To compare the effectiveness of a telephonic
and a print intervention over 1
year to improve diabetes control in low-income urban adults.
RESEARCH DESIGN AND METHODS — A randomized trial
in Spanish and English
comparing a telephonic intervention implemented by health
educators with a print intervention.
Participants (N � 526) had an A1C �7.5% and were prescribed
one or more oral agents. All were
members of a union/employer jointly sponsored health benefit
plan. Health coverage included
medications. Primary outcomes were A1C and pharmacy claims
data; secondary outcomes
included self-report of two medication adherence measures and
other self-care behaviors.
RESULTS — Participants were 62% black and 23% Hispanic;
77% were foreign born, and
42% had annual family incomes �$30 thousand. Baseline
median A1C was 8.6% (interquartile
range 8.0 –10.0). Insulin was also prescribed for 24% of
participants. The telephone group had
mean � SE decline in A1C of 0.23 � 0.11% over 1 year
compared with a rise of 0.13 � 0.13%
for the print group (P � 0.04). After adjusting for baseline A1C,
sex, age, and insulin use, the
difference in A1C was 0.40% (95% CI 0.10 – 0.70, P � 0.009).
Change in medication adherence
measured by claims data, but not by self-report measures, was

significantly associated with
change in A1C (P � 0.01). Improvement in medication
adherence was associated (P � 0.005)
with the telephonic intervention, but only among those not
taking insulin. No diabetes self-care
activities were significantly correlated with the change in A1C.
CONCLUSIONS — A 1-year tailored telephonic intervention
implemented by health edu-
cators was successful in significantly, albeit modestly,
improving diabetes control compared with
a print intervention in a low-income, insured, minority
population.
Diabetes Care 34:2–7, 2011
I
mproving glycemic control in type 2
diabetes significantly decreases the risk
of serious chronic complications such
as retinopathy, neuropathy, and ne-
phropathy, as shown by large-scale clini-
cal trials from the last 2 decades (1,2).
These studies, along with smaller trials,
set the stage for evidence-based medical
management of diabetes (3). Although ef-
fective therapies for management have
been developed, treatment goals are often
not reached— especially in lower income
and minority populations (4)—and many
individuals find it challenging to perform
routine self-management (5). Critical re-
views of the scientific literature on inter-
ventions to improve glycemic control

show promising results for improved pro-
cesses of care, such as screening for com-
plications and laboratory tests, as well as
for behavioral interventions and self-
management training (6,7).
Evidence is emerging for the use of
telephonic interventions to improve dia-
betes self-care and health outcomes; stud-
ies include use of automated calls with
nurse follow-up (8) or calls implemented
by individuals with graduate degrees (9).
Telephonic interventions may enhance
self-care adherence (10) by offering the
opportunity to customize information to
individuals under real-world conditions.
Nonetheless, the efficacy of telephonic in-
terventions in all populations and settings
has not been established, and improve-
ments in health outcomes for patients re-
main challenging even with many new
pharmaceutical agents becoming avail-
able and combinations of type 2 diabetes
medications becoming a standard of care.
As an adjunct to diabetes self-
management education and medical care,
a telephonic intervention by health edu-
cators may provide the coaching and mo-
tivation needed for individuals to perform
diabetes self-management activities over
time, especially medication adherence.
The Improving Diabetes Outcomes
(I DO) study aimed to evaluate the incre-
mental effect of a tailored telephone inter-

vention, in English and Spanish, on the
mean A1C levels and medication adher-
ence beyond that achieved with the mail-
ing of print self-management materials.
The population is insured, lower-income,
mostly minority individuals who had
health care and medication benefits cov-
ered in full by their labor union/employer
plan. However, the study protocol al-
lowed only telephonic and print contact
with participants so that individuals who
might not have agreed to participate in
more conventional in-person studies
could take part. The main study outcomes
were changes in A1C and medication ad-
herence. The study also sought to deter-
mine what demographic and behavioral
factors might mediate the effect of the in-
terventions. We now report the main re-
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
From the 1Department of Medicine and Endocrinology, Albert
Einstein College of Medicine, Bronx, New
York; 21199SEIU Benefit and Pension Funds, New York, New
York; the 3Department of Epidemiology
and Population Health, Albert Einstein College of Medicine,
Bronx, New York; and the 4Department of
Medicine, University of Medicine and Dentistry of New Jersey,
Newark, New Jersey.
Corresponding author: Elizabeth A. Walker, [email protected]
Received 26 May 2010 and accepted 23 September 2010.
DOI: 10.2337/dc10-1005. Clinical trial reg. no. NCT00179374,
clinicaltrials.gov.

© 2011 by the American Diabetes Association. Readers may use
this article as long as the work is properly
cited, the use is educational and not for profit, and the work is
not altered. See http://creativecommons.
org/licenses/by-nc-nd/3.0/ for details.
The costs of publication of this article were defrayed in part by
the payment of page charges. This article must therefore be
hereby
marked “advertisement” in accordance with 18 U.S.C. Section
1734 solely to indicate this fact.
See accompanying editorial, p. 240.
C l i n i c a l C a r e / E d u c a t i o n / N u t r i t i o n / P s y c
h o s o c i a l R e s e a r c h
O R I G I N A L A R T I C L E
sults of this translational randomized
controlled behavioral trial, including self-
care data to more fully explain the results.
RESEARCH DESIGN AND
METHODS — The I DO study is a ran-
domized controlled behavioral interven-
tion study comparing the effectiveness of
a telephonic intervention with a print (ac-
tive control) intervention. It was devel-
oped at the Einstein Diabetes Research
and Training Center in collaboration with

a union/employer jointly sponsored
health benefit plan (1199SEIU Benefit
and Pension Funds). As previously de-
scribed (11), eligible participants were
adult (�30 years of age) members of the
health care worker union Fund based in
New York City. These Fund members in-
clude current full-time health workers or
their spouses. The majority of members
are service and clerical workers in nursing
homes or hospitals, and others work as
home health attendants. The Fund pro-
vides full coverage of prescription medi-
cations, medical visits, hospitalizations,
and laboratory tests. Eligible participants
had to read and speak English or Spanish,
with no evidence of cognitive impair-
ment. Eligibility also included the pre-
scription of at least one oral glucose-
lowering agent (OGLA) in the year prior
to enrollment. The eligible A1C was
�7.5%, which is above the usual manage-
ment goal of �7% (3), but would provide
a margin for lowering the A1C in a tele-
phonic intervention with no in-person
contact without raising safety concerns.
The study protocol aimed to evaluate
interventions among individuals who
might face challenges in completing
in-person diabetes self-management edu-
cation programs. It did not include any
face-to-face interactions. Oral informed
consent and Health Insurance Portability
and Accountability Act (HIPAA) authori-
zation were obtained by telephone with

approval of the institutional review board
o f t h e A l b e r t E i n s t e i n C o l l e g e o f
Medicine.
There was a two-step recruitment
process. The Fund database was used to
identify members who might be eligible,
and they were telephoned by study staff.
If a person seemed eligible and completed
a screening questionnaire, oral informed
consent was documented. The second
step was the mailing and completion of an
A1C capillary blood test kit. Individuals
with lab results of A1C �7.5% were en-
rolled and randomized using a computer-
ized randomization scheme to either the
telephone or the print intervention group.
Interventions
All telephone participants could receive up
to 10 calls at 4- to 6-week intervals from
their health educator over the 1-year inter-
vention. Health educators were trained and
supervised by a certified diabetes educator
nurse. Calls were tailored to the participant-
reported needs but focused primarily on
diabetes medication adherence and
secondarily on lifestyle changes through
healthy eating and physical activity. Prob-
lem solving (12), goal setting (13), commu-
nication skills, and preplanning for medical
visits were important elements in the inter-
vention. The protocol was based on im-
proving empowerment and self-efficacy
(14) using social-ecological approaches

(15). Health educators used a manual to
guide the telephone call content, but partic-
ipants were encouraged to choose topics for
each call. See the online appendix supple-
mentary Table A1 (available at http://care.
diabetesjournals.org/cgi/content/full/dc10-
1005/DC1) for an example of a call log that
both guided and documented implementa-
tion of the intervention. All participants
received selected high-quality self-man-
agement materials by mail after random-
ization. Only telephone participants were
prompted by health educators to use
these materials.
Measures
The primary outcome was change in A1C,
measured only at baseline and postinter-
vention using mail-in kits with “filter
paper” methodology (also called “dry-
dot”) from a laboratory vendor, Home
Healthcare Laboratory of America (“Lab-
in-an-Envelope”) (16). This A1C test pro-
cessed with a Roche analyzer had been
approved by the National Glycosylation
Standardization Program (17). Partici-
pants were asked to call the health educa-
tor to guide them through the blood
sampling while using a spring-loaded lan-
cet to draw blood from their fingertips
and fill in one to three circles (1.2 cm
diameter) on a special filter paper card.
This card was then mailed directly to the
laboratory in a prepaid envelope for anal-
ysis. A1C values from the filter paper
method have been reported to corre-

spond to those obtained by conventional
venous whole-blood samples (18,19). If
insufficient blood was obtained for a valid
result, another test kit was sent to
participants.
Medication adherence measures
Pharmacy claims (i.e., administrative)
data from the Fund, including each
OGLA prescription filled, its class, the
date, number of pills dispensed, and
number of pills per day, were used to cal-
culate a medication possession ratio
(MPR) for each participant. This type of
measure of medication adherence has
been used in many studies (20,21). For
each class of OGLA taken by a participant
within the previous year, the number of
pill-days available from each filled pre-
scription was calculated. For each partic-
ipant, MPRs (number of days’ supply of
pills dispensed in 1 year/365) for the 1
year prior to randomization (baseline)
and 1 year post randomization (follow-
up) were calculated (range 0 –1) for each
OGLA class, and then an average of the
class MPRs was used to denote separately
the participant’s pre- and postinterven-
tion MPR. The methods and rationale for
this approach have been previously de-
scribed (11). A recording of insulin use
during the study year was categorized
“ever” or “never” on the basis of prescrip-
tion orders for any insulin product.
Other diabetes self-management be-

haviors were collected by telephone at
baseline and end of study. The four-item
Morisky Self-Reported Medication-
Taking Scale (22) was administered, and
scores �2 were considered poor adher-
ence to diabetes medications. The Sum-
mary of Diabetes Self Care Activities
(SDSCA) (23) scale was also adminis-
tered, including a single medication ad-
herence item: How many days in the most
recent week were diabetes pills taken as
prescribed? This was treated as a nonpara-
metric continuous variable (0 –7 days)
and categorized as adherent (7 days) or
not. Other SDSCA survey items addressed
healthy eating and physical activity and
were analyzed similarly. Hours of TV
watching per day were recorded in cate-
gories (0, 1, 2, 3, 4, �4 h) and dichoto-
mized as �2 or �2 h per day. Self-
reported demographics including sex,
age, race/ethnicity, work status, marital
status, income, education, and birthplace
were collected, as were other characteris-
tics including self-reported height and
weight for calculating BMI, years since di-
abetes diagnosis, and insulin use in the
previous year.
Statistical analysis
The study outcomes, change in A1C
(�A1C) and change in MPR (�MPR),
were calculated as follow-up minus base-
Walker and Associates

line values (negative values represent a
decline) and were assessed for normality
assumptions. �MPR was also dichoto-
mized as �20 percentage points (e.g., go-
ing from 60 to 80%) because very small
changes were not expected to have a
meaningful impact on A1C. Changes in
SDSCA during follow-up were also calcu-
lated both as continuous variables (days)
and categorized as improved, worsened,
or remained the same. Tests of bivariate
associations with study arm were per-
formed similarly to the comparison of
baseline characteristics. Analyses of
�A1C and �MPR outcomes were always
adjusted for baseline levels. Adjustments
for potential confounders or mediators
were performed using linear regression
models for continuous outcomes and bi-
nary logistic models for dichotomous out-
comes. To test potential mediation,
baseline MPR and �MPR �20% were
added to the model predicting �A1C. The
number of educator calls received by par-
ticipants in the telephone group was used
as a proxy for intensity of the interven-
tion. Among those in the telephone
group, the number of calls completed
during the intervention (range 0 –10) was
categorized as 0 –5, 6 – 8, and 9 –10, and
these were entered into regression models

as dummy variables with print group al-
location as reference. A test for trend of
the association of these call categories
with �A1C was also performed. Baseline
values of the outcome variables were
available as an inclusion criterion prior to
randomization, but not all participants
provided follow-up data. Outcome anal-
yses were performed for those with com-
plete data with sensitivity analyses using
two alternate imputation methods to sim-
ulate intention-to-treat analyses. Imputa-
tion for missing outcome data were
carried out with STATA (version 11) mul-
tiple imputation procedure based on a
Bayesian paradigm pooling 100 repeated
imputations taking into account baseline
A1C, age, sex, insulin use, and baseline
MPR. An alternate imputation used base-
line A1C values for missing follow-up that
in this study was the same as a last obser-
vation carried forward (LOCF) approach
(24). Those with missing outcome data
were compared by study arm to assess as-
sumptions of missing at random. Residu-
als-based regression diagnostics were
performed to check linear regression
model assumptions, and first-order inter-
actions of covariates with study arm were
tested with interaction product terms
while simultaneously adjusting for main
effects terms. Hosmer-Lemeshow test for
goodness-of-fit was performed for binary
logistic models and first-order interac-
tions were assessed.

RESULTS — The study flow diagram
is in online appendix as supplementary
Figure A1; it shows the database recruit-
ment pool of 8,083 adults with diabetes
taking OGLAs. Of the 4,548 individuals
assessed for eligibility, 4,021 were ex-
cluded (ineligible 55%, refused 45%),
and 527 individuals were randomized,
with intention-to-treat analysis of 526
cases. Description of baseline characteris-
tics by group and total is found in Table 1.
Participants were mainly minority in
terms of race/ethnicity, and were lower-
income, middle-aged, and foreign born.
The median baseline A1C was 8.6% (in-
terquartile range 8.0 –10).
Primary outcomes
Among the 444 participants (84.4%) with
follow-up A1C, the 228 in the telephone
group exhibited a mean � SE decline in
A1C of 0.23 � 0.11% over the study year
compared with a rise of 0.13 � 0.13% for
the 216 in the print group (P � 0.04).
After adjusting for baseline A1C, sex, age,
and insulin use, the difference in �A1C
between telephone and print groups was
0.40% (95% CI 0.10 – 0.70, P � 0.009).
There was no strong evidence for media-
Table 1—Participant characteristics at baseline
Telephone group Print group Total

n 262 264 526
Female (%) 68.3 65.9 67.1
Race/ethnicity (%)
Black 61.5 61.7 61.6
Hispanic 24.8 20.5 22.6
White 5.7 6.1 5.9
Other 8.0 11.7 9.9
Age (years) 55.7 � 7.4 55.4 � 7.2 55.5 � 7.3
Married (%) 59.2 63.6 61.4
Foreign born* (%) 75.2 78.4 76.8
Spanish preferred (%) 18.7 12.9 15.8
Duration of diabetes (years) 8.8 � 6.8 9.5 � 6.4 9.2 � 6.6
Duration of diabetes (%)
�6 years 37.0 30.7 33.8
6–10 years 33.2 34.8 34.0
�10 years 29.8 34.5 32.1
Employed full time (%) 73.3 74.6 74.0
Household income (%)
�$20,000 17.2 14.4 15.8
$20–29,000 26.7 26.5 26.6
$30–39,000 29.0 29.2 29.1
$40–49,000 10.7 9.1 9.9
�$50,000 16.4 20.8 18.6
Education (%)
�8th grade 16.4 16.7 16.5
9–11th grade 11.8 10.2 11.0
HS or GED 36.3 28.8 32.5
Some college 22.1 26.1 24.1
�College 13.4 18.2 15.8

Self-reported insulin use (%) 21.0 25.0 23.0
Insulin Rx in last year (%) 23.3 24.6 24.0
�2 diabetes pill classes (%) 68.7 68.2 68.4
BMI (kg/m2) 31.8 � 6.2 30.7 � 6.0 31.2 � 6.1
A1C (%) 8.6 (8.0–9.6) 8.7 (8.0–10.2) 8.6 (8.0–10.0)
Morisky scale �2 (%) 35.1 38.6 36.9
Report taking diabetes pills
�7 days per week (%) 27.9 25.4 26.6
Data are means � SD or median (interquartile range). *Foreign
born does not include those born in Puerto
Rico. GED, high school equivalency; HS, high school; Rx,
prescription.
Improving diabetes outcomes study
tion of the �A1C by �MPR. With regard
to the �A1C outcome, no statistically sig-
nificant first order interactions with inter-
vention group were observed.
When �MPR was assessed as an out-
come variable, whether as a continuous
variable or as �20% improvement, statis-
tically significant (P � 0.04 and 0.01, re-
spectively) interactions of intervention
with insulin use (n � 141, 26.8%) during
the 12-month study period were ob-
served. �MPR as a continuous variable
was not significantly associated with the

telephone intervention either among
those taking (P � 0.23) or not taking
(P � 0.39) insulin, whereas �MPR
�20% was significantly associated (P �
0.005) with the telephone intervention
after adjusting for baseline MPR, age,
and sex among those not taking insulin,
but not among those taking insulin (P �
0.28). Among those not taking insulin,
there was a significant (P � 0.001) lin-
ear trend with �MPR �20% for the
numbers of intervention calls received.
Significant associations with interven-
tion calls compared with print were
only observed for those receiving at
least six telephone calls (Table 2).
Secondary outcomes
Attempts were made to complete 10
phone calls over 12 months to telephone
participants (mean � SD number of com-
pleted calls was 7.9 � 2.1). Fewer phone
calls resulted from participants being un-
reachable or refusing the telephone call.
Only 3% (n � 7) of participants had no
phone calls even after much staff effort.
Mean length of each call was 14.1 � 4.6
min. Having at least six completed phone
calls was associated with significant im-
provement in A1C (Fig. 1).
Table 2 highlights the differences be-
tween those who took insulin and at least
one OGLA and those who took only an
OGLA. The telephone intervention was
not associated with a change in medica-

tion adherence (�20% MPR) if the regi-
men included insulin. Despite the lack of
a statistically significant association of
�MPR with the intervention among the
minority taking insulin and an OGLA, for
the group as a whole the multivariable
model provided evidence that the im-
provement of MPR was a mediator of the
intervention association with improved
glycemic control.
From the SDSCA survey, two items
showed significant improvement associ-
ated with the telephone intervention:
number of days per week following a
healthy eating plan and number of days
with �30 min of exercise. The other
items, as well as hours of TV watched
per day, showed a direction toward im-
provement associated with the telephone
intervention, but not significantly so.
However, none of the changes in SDSCA
or TV watching were significantly corre-
lated with �A1C. Although �MPR de-
rived from pharmacy claims data were
significantly (P � 0.01) associated with
�A1C in the adjusted model, changes in
the two self-report medication adherence
measures (number of days taking medica-
tion as prescribed item from the SDSCA
and the Morisky score) were not signifi-
cantly associated with �A1C.
Missing values and analysis with

imputation
Of the 526 randomized participants, fol-
low-up A1C values were not available for
15.6% (18.2% telephone, 13.0% print,
P � 0.10). Of the 82 with missing values,
5 (2 deaths and 3 withdrawals) also had
missing values for the follow-up MPR. For
Figure 1—Decline in A1C, expressed as median (interquartile
range), per category of telephone
intervention intensity (number of calls) compared with print
group (no calls), estimated in a
multiple linear regression model adjusting for baseline A1C,
age, sex, insulin use, and improve-
ment in MPR �20%.
Table 2—Adjusted odds ratios for change in MPR >20%
stratified by insulin use during study
No insulin use (n � 385) Insulin use (n � 141)
OR (95% CI)* P OR (95% CI)* P
Print Reference Reference
0–5 Calls† 1.0 (0.4–2.8) 0.98 0.3 (0.0–2.8) 0.29
6–8 Calls 1.9 (1.0–3.5) 0.04 0.6 (0.2–2.1) 0.41
9–10 Calls 2.6 (1.4–4.6) 0.002 0.4 (0.3–2.2) 0.61
Call linear trend 0.001 0.88
Baseline MPR 0.04 (0.02–1.3) �0.001 0.01 (0.001–0.10)
�0.001
Age (years) 1.0 (1.0–1.0) 0.81 0.9 (0.9–1.0) 0.03
Male 0.9 (0.6–1.6) 0.81 1.0 (0.4–2.6) �0.99
Telephone 2.0 (1.2–3.2) 0.005 0.6 (0.3–1.5) 0.28
*Odds ratio (OR) (95% CI) estimated with binary logistic

regression models. †Call categories for the telephone
intervention with print as reference. Linear trend is across the
categories. Telephone gives the overall odds ratio
(irrespective of number of calls) with print as reference,
estimated in separate adjusted models.
all the baseline characteristics in Table 1,
there were no statistically significant asso-
ciations with those missing a follow-up
A1C among the print group; there was a
single significant association in the tele-
phone group, with those missing an A1C
being (mean � SE) 3.3 � 1.3 years
younger than those not missing an A1C.
Median baseline A1C was 0.6% higher for
those missing in the print group (P �
0.07), and in the telephone group the dif-
ference in median was 0.2% (P � 0.54).
Using the multiple imputation approach,
being in the telephone group compared
with print group was significantly associ-
ated with greater decline in A1C, whether
adjusting only for baseline A1C or also
adjusting for age, sex, insulin use, and
�MPR (both P � 0.03). These significant
associations were also seen (both P �
0.01) when the LOCF imputation ap-
proach was used.

CONCLUSIONS — A tailored tele-
phonic behavioral intervention imple-
mented by health educators under the
supervision of a certified diabetes educa-
tor nurse was successful in significantly,
albeit modestly, improving A1C com-
pared with a print intervention. Greater
intensity of the intervention (�6 calls)
was associated with greater improvement
in A1C.
A possible explanation for the re-
ported differences in intervention effec-
tiveness for medication adherence related
to insulin use (Table 2) may be that being
prescribed insulin in combination with an
OGLA is a regimen complexity that re-
duces adherence to the OGLAs. An alter-
n a t i v e e x p l a n a t i o n m a y b e t h a t
nonclinical health educators, though su-
pervised by a nurse certified diabetes ed-
ucator, may not have been as effective in
medication adherence counseling for par-
ticipants also on various insulin regimens
as they were with those on OGLAs alone.
Only a few self-care activity changes
on the SDSCA were significantly associ-
ated with the intervention. It is possible,
however, that there was an overall cumu-
lative effect on glycemic control of small
improvements in multiple self-care activ-
ities, even if they were individually too
small to show significant associations
with the intervention.

Limitations
The dry-dot methodology for the A1C
measure completed by the participant
and mailed to the laboratory had its own
limitations, which were imposed by the
nature of the protocol to not require sub-
ject visits to a lab or research center. This
A1C methodology may contribute to
greater measurement variability. In this
randomized trial, it would not be ex-
pected to introduce a differential bias; and
if a nondifferential bias were introduced
for the change in A1C, it would be more
likely toward the null. Not all patients
completed the end of protocol survey or
final A1C assessment. Although the final
A1C was unobtainable for 15.6% of par-
ticipants, this may be expected because
we had no in-person contact with them.
However, this did not impact the �MPR
outcomes that were available administra-
tively for all but five participants. Further,
we used two alternate methods of impu-
tation for an intention-to-treat analysis
and both were consistent with results for
those with complete data. A modest num-
ber of participants in the intervention
group had fewer than six calls. The obser-
vation that only those with �6 calls over 1
year had statistically significant though
modest improvements in glycemic con-
trol adds to our confidence that the mech-
anism of the calls, and not type 1 error,
was responsible for the difference be-
tween the telephone and print group out-

comes. However, those accepting more
calls may be more amenable to change,
which could possibly confound these
results.
Strengths
This study explored the comparative ef-
fectiveness of two interventions in a low-
er-income, urban population that was
racially and ethnically diverse; the major-
ity were lower-income immigrants work-
ing in support of health-care systems.
They were homogeneous, however, in
that they did not have economic barriers
to securing medications or medical visits
b e c a u s e o f t h e i r u n i o n / e m p l o y e r -
sponsored health benefits. The sample
was drawn from those with evidence of
difficulty managing their diabetes; they
were individuals who often, because of
life circumstances, are unlikely to volun-
teer for a study requiring them to visit a
research center. Therefore, a strength of
this study is that we may have avoided
selection bias.
Evidence supports diabetes self-
management education having greater
success in health outcomes when it is
maintained over a longer period of time
(25). A telephone intervention may be a
convenient and feasible intervention to
support those who have difficulty access-
ing diabetes self-management education.
This intervention could be more success-

ful in improving A1C if embedded in ei-
ther provider or payer models, especially
if synergistic with other targeted quality
improvement initiatives. In the context of
current related literature (5–9), this study
provides a successful model of an inter-
vention delivering self-management sup-
port at lower cost than studies using
licensed health professionals or more in-
tensive interventions, such as in-person
or those having greater frequency of con-
tact. This study extends previous research
because it focused on a population with
known health disparities. Health educa-
tors trained and supervised by a certified
diabetes educator may promote and
maintain self-management skills and pro-
vide crucial support needed by individu-
als managing their diabetes.
Acknowledgments — This study was sup-
ported by NIH grants R18 DK62038 and
DK020541.
No potential conflicts of interest relevant to
this article were reported.
E.A.W. and H.W.C. contributed to every
aspect of this article. C.S., M.S.-K., and R.U.
contributed to study design, discussion, re-
search data, and editing of the manuscript.
E.B. contributed to the research data, dis-
cussion, and review and editing of the
manuscript.
Parts of this study were presented in oral

form at the 69th Scientific Sessions of the
American Diabetes Association, New Orleans,
Louisiana, 5–9 June 2009.
We gratefully acknowledge the data man-
agement contributions of Fionnuala King of
the 1199SEIU Benefit and Pension Funds; our
talented health educators, including Giovanna
DiFrancesca, Kathleen McCabe, Gisela Mojica,
Jennifer Case, Tara DeWitt, Gabriel Ferreira,
Marlene Taveras, Samara Lipsky, Dr. Hollie
Jones, and Hector Cariello; data management
support from Maria Kalten and Jennifer Lukin,
all staff from the Albert Einstein College of
Medicine; and especially, the participants in
New York City who volunteered for our study.
References
1. Intensive blood-glucose control with sul-
phonylureas or insulin compared with
conventional treatment and risk of com-
plications in patients with type 2 diabetes
(UKPDS 33). UK Prospective Diabetes
Study (UKPDS) Group. Lancet 1998;352:
837– 853
2. ADVANCE Collaborative Group, Patel A,
MacMahon S, Chalmers J, Neal B, Billot L,
Woodward M, Marre M, Cooper M, Glas-
ziou P, Grobbee D, Hamet P, Harrap S,
Heller S, Liu L, Mancia G, Mogensen CE,
Improving diabetes outcomes study

Pan C, Poulter N, Rodgers A, Williams B,
Bompoint S, de Galan BE, Joshi R, Travert
F. Intensive blood glucose control and
vascular outcomes in patients with type 2
diabetes. N Engl J Med 2008;358:2560 –
2572
3. American Diabetes Association. Stan-
dards of medical care in diabetes–2010.
Diabetes Care 2010;33:S11–S61
4. Cheung BM, Ong KL, Cherny SS, Sham
PC, Tso AW, Lam KS. Diabetes preva-
lence and therapeutic target achievement
in the United States, 1999 to 2006. Am J
Med 2009;122:443– 453
5. Norris SL, Engelgau MM, Narayan KM.
Effectiveness of self-management training
in type 2 diabetes: a systematic review of
randomized controlled trials. Diabetes
Care 2001;24:561–587
6. Vermeire E, Wens J, Van Royen P, Biot Y,
Hearnshaw H, Lindenmeyer A. Interven-
tions for improving adherence to treat-
ment recommendations in people with
type 2 diabetes mellitus. Cochrane Data-
base Syst Rev 2005;2:CD003638
7. Clark M. Diabetes self-management edu-
cation: a review of published studies. Pri-

mary Care Diabetes 2008;2:113–120
8. Piette JD, Weinberger M, Kraemer FB,
McPhee SJ. Impact of automated calls
with nurse follow-up on diabetes treat-
ment outcomes in a Department of Veter-
ans Affairs Health Care System: a
randomized controlled trial. Diabetes
Care 2001;24:202–208
9. Wolever RQ, Dreusicke M, Fikkan J,
Hawkins TV, Yeung S, Wakefield J, Duda
L, Flowers P, Cook C, Skinner E. Integra-
tive health coaching for patients with type
2 diabetes: a randomized clinical trial. Di-
abetes Educ 2010;36:629 – 639
10. Walker EA, Schechter CB, Caban A, Basch
CE. Telephone intervention to promote
diabetic retinopathy screening among the
urban poor. Am J Prev Med 2008;
34:185–191
11. Cohen HW, Shmukler C, Ullman R,
Rivera CM, Walker EA. Measurements of
medication adherence in diabetic patients
with poorly controlled HbA(1c). Diabet
Med 2010;27:210 –216
12. Hill-Briggs F. Problem solving in diabetes
self-management: a model of chronic ill-
ness self-management behavior. Ann Be-
hav Med 2003;25:182–193
13. Bodenheimer T, Handley MA. Goal-set-

ting for behavior change in primary care:
an exploration and status report. Patient
Educ Couns 2009;76:174 –180
14. Bandura A. Self-Efficacy: The Exercise of
Control. New York, W.H. Freeman and
Company, 1997
15. Stokols D. Translating social ecological
theory into guidelines for community
health promotion. Am J Health Promot
1996;10:282–298
16. Lab in an Envelope [Internet]. Home Health-
care Laboratories of America. Availabe from
http://www.hhla.com/products_services_
lab_in_an_envelope.html. Accessed 5
May 2010
17. National Glycosylation Standardization
Program. Available from http://www.
ngsp.org. Accessed 5 May 2010
18. Jeppsson JO, Jerntorp P, Almër LO, Pers-
son R, Ekberg G, Sundkvist G. Capillary
blood on filter paper for determination of
HbA1c by ion exchange chromatography.
Diabetes Care 1996;19:142–145
19. Gay EC, Cruickshanks KJ, Chase HP,
Klingensmith G, Hamman RF. Accuracy
of a filter paper method for measuring gly-
cosylated hemoglobin. Diabetes Care
1992;15:108 –110

20. Pladevall M, Williams LK, Potts LA, Di-
vine G, Xi H, Lafata JE. Clinical outcomes
and adherence to medications measured
by claims data in patients with diabetes.
Diabetes Care 2004;27:2800 –2805
21. Karve S, Cleves MA, Helm M, Hudson TJ,
West DS, Martin BC. An empirical basis
for standardizing adherence measures de-
rived from administrative claims data
among diabetic patients. Medical Care
2008;46:1125–1133
22. Morisky DE, Green LW, Levine DM. Con-
current and predictive validity of a self-
reported measure of medication adherence.
Med Care 1986;24:67–74
23. Toobert DJ, Hampson SE, Glasgow RE.
The summary of diabetes self-care activi-
ties measure: results from 7 studies and a
revised scale. Diabetes Care 2000;23:
943–950
24. StataCorp. Multiple-Imputation Reference
Manual. Release 11. College Station,
Texas, Stata Press, 2009
25. Funnell MM, Brown TL, Childs BP, Haas
LB, Hosey GM, Jensen B, Maryniuk M,
Peyrot M, Piette JD, Reader D, Siminerio
LM, Weinger K, Weiss MA. National stan-
dards for diabetes self-management edu-
cation. Diabetes Care 2007;30:1630 –
1637

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