Randomized Effectiveness Trial of a Computer-Assisted Intervention to Improve Diabetes Care

Article (PDF Available)inDiabetes Care 28(1):33-9 · February 2005with41 Reads
DOI: 10.2337/diacare.28.1.33 · Source: PubMed
Abstract
There is a well-documented gap between diabetes care guidelines and the services received by patients in most health care settings. This report presents 12-month follow-up results from a computer-assisted, patient-centered intervention to improve the level of recommended services patients received from a variety of primary care settings. A total of 886 patients with type 2 diabetes under the care of 52 primary care physicians participated in the Diabetes Priority Program. Physicians were stratified and randomized to intervention or control conditions and evaluated on two primary outcomes: number of recommended laboratory screenings and recommended patient-centered care activities completed from the National Committee on Quality Assurance/American Diabetes Association Provider Recognition Program (PRP). Secondary outcomes were evaluated using the Problem Areas in Diabetes 2 quality of life scale, lipid and HbA1c levels, and the Patient Health Questionnaire-9 depression scale. The program was well implemented and significantly improved both the number of laboratory assays and patient-centered aspects of diabetes care patients received compared with those in the control condition. There was overall improvement on secondary outcomes of lipids, HbA1c, quality of life, and depression scores; between-condition differences were not significant. Staff in small, mixed-payer primary care offices can consistently implement a patient-centered intervention to improve PRP measures of quality of diabetes care. Alternative explanations for why these process improvements did not lead to improved outcomes, and suggested directions for future research are discussed.
Randomized Effectiveness Trial of a
Computer-Assisted Intervention to
Improve Diabetes Care
RUSSELL E. GLASGOW,
PHD
1
PAUL A. NUTTING,
MD, MSPH
2
DIANE K. KING,
MS, OTR
1
CANDACE C. NELSON,
MA
1
GARY CUTTER,
PHD
3
BRIDGET GAGLIO,
MPH
1
ALANNA KULCHAK RAHM,
MS
1
HOLLY WHITESIDES,
BS
1
OBJECTIVE There is a well-documented gap between diabetes care guidelines and the
services received by patients in most health care settings. This report presents 12-month fol-
low-up results from a computer-assisted, patient-centered intervention to improve the level of
recommended services patients received from a variety of primary care settings.
RESEARCH DESIGN AND METHODS A total of 886 patients with type 2 diabetes
under the care of 52 primary care physicians participated in the Diabetes Priority Program.
Physicians were stratified and randomized to intervention or control conditions and evaluated on
two primary outcomes: number of recommended laboratory screenings and recommended
patient-centered care activities completed from the National Committee on Quality Assurance/
American Diabetes Association Provider Recognition Program (PRP). Secondary outcomes were
evaluated using the Problem Areas in Diabetes 2 quality of life scale, lipid and HbA
1c
levels, and
the Patient Health Questionnaire-9 depression scale.
RESULTS The program was well implemented and significantly improved both the num-
ber of laboratory assays and patient-centered aspects of diabetes care patients received compared
with those in the control condition. There was overall improvement on secondary outcomes of
lipids, HbA
1c
, quality of life, and depression scores; between-condition differences were not
significant.
CONCLUSIONS Staff in small, mixed-payer primary care offices can consistently imple-
ment a patient-centered intervention to improve PRP measures of quality of diabetes care.
Alternative explanations for why these process improvements did not lead to improved out-
comes, and suggested directions for future research are discussed.
Diabetes Care 28:33–39, 2005
T
he chasm between what is known to
improve outcomes and current pri-
mary care practices has been well
described for diabetes and other chronic
illness (1– 4). Well-designed studies have
demonstrated improved delivery of rec-
ommended services, but their strategies
have not been broadly applied (5,6). Bar-
riers to success include the many compet-
ing demands of primary care and the
limited time available (6,7). We believe
that sustained adoption of best practices
for chronic care requires interventions
that are brief, fit into the flow of patient
visits, do not increase demands on physi-
cian time, and inform the patient-
provider interaction (8–10).
We report a “practical clinical trial”
(10) to improve diabetes care, character-
ized by a clinically relevant intervention, a
diverse sample of patients recruited from
heterogeneous practices, and end point
data on a broad range of outcomes
(10,11). Our purpose is to describe out-
comes important to patients, clinicians,
and policy makers derived from typical
community settings. The intervention
used interactive computer technology to
assist both patients and clinicians in em-
phasizing patient-centered communica-
tion and improved quality of care and to
provide immediate feedback of person-
ally tailored information and recommen-
dations (11–13). Our CD-ROM–assisted
Diabetes Priority Program was evaluated
against a stringent randomized control
condition for its effectiveness in im-
proving both laboratory assay and more
patient-centered aspects of care recom-
mended by the National Committee on
Quality Assurance/American Diabetes As-
sociation Provider Recognition Program
(PRP) (14,15). Secondary end points
evaluated the impact of the Diabetes Pri-
ority Program on quality of life, biologic
outcomes (lipids and HbA
1c
levels), and
depressive symptoms. This report builds
on an initial description of 6-month pro-
cess of care measures (16) and extends
those findings by focusing on longer-term
effects and including biologic and quality-
of-life outcomes not available for the ear-
lier paper.
RESEARCH DESIGN AND
METHODS The Diabetes Priority
Program was a collaboration between our
research team and the Copic Insurance
Company, which provides malpractice
insurance to 95% of the independent
primary care physicians in Colorado. De-
tails of physician and patient recruitment
have been reported previously (16,17)
and are summarized herein. An initial
survey was sent to all 1,258 family physi-
cians and general internists insured by
Copic Insurance Company in Colorado;
1,059 (84%) returned a useable survey
and received a project fact sheet and
follow-up letter inviting their participa-
tion. After a physician agreed to partic-
ipate, a standard protocol was used to
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
From
1
Kaiser Permanente Colorado, Denver, Colorado; the
2
Department of Family Medicine University of
Colorado Health Sciences Center, Center for Research Strategies, Denver, Colorado; and the
3
Cooper Insti
-
tute, Denver, Colorado.
Address correspondence and reprint requests to Russell E. Glasgow, PhD, Kaiser Permanente Colorado,
335 Road Runner Ln., Penrose, CO 81240. E-mail: russg@ris.net.
Received for publication 14 July 2004 and accepted in revised form 22 September 2004.
Abbreviations: PHQ, Patient Health Questionnaire; PRP, Provider Recognition Program.
© 2005 by the American Diabetes Association.
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.
Epidemiology/Health Service/Psychosocial Research
ORIGINAL ARTICLE
DIABETES CARE, VOLUME 28, NUMBER 1, JANUARY 2005 33
generate lists of patients with diabetes,
detailing review of patient billing data for
the previous year, diagnostic codes, and the
need to search all diagnoses for each visit.
All adult patients identified as having
diabetes were sent a letter signed by their
primary care physician inviting them to
participate, a brochure describing the
project, and an opt-out postcard to return
if they did not want to participate (Fig. 1).
To make the project as broadly applicable
as possible, the only inclusion criteria
were age 25 years, ability to read En-
glish, and type 2 diabetes, confirmed
using the Welborn criteria (18). All pro-
cedures were approved by relevant Insti-
tutional Review Boards, and patients were
recruited during 2001–2002.
Design and analyses
We used a two-group, cluster, random-
ized design. Participating physicians were
stratified by size of practice and urban/
rural setting, because these factors were
judged likely to impact results, and then
randomized. To avoid contamination, all
physicians within a given clinic were as-
signed to the same condition. Randomi-
zation was conducted by the project
statistician, who then notified research
staff of condition assignment. To account
for clustering of patients within physi-
cian, which could induce a correlation
among the responses due to patterns of
treatment associated with the clinician, a
generalized regression model using a ran-
dom effect for the physician (a mixed
model) was fitted using the methods of
Laird and Ware (19), adjusting for base-
line score on the dependent variable. A
required sample size of 32 physicians and
774 patients was determined using calcu-
lations to have 90% power (␣⫽0.05, two
tailed) to detect a moderate effect, assum-
ing an intraclass correlation as large as
0.05, and allowing for 20% attrition.
Differences between participants and
nonparticipants, and between conditions
at baseline, were conducted using mixed
model linear regression analyses. Out-
comes were evaluated using mixed model
regression analyses (to account for clus-
tering) and controlling for baseline scores
on the dependent variable and any other
potential confounding variables.
Interventions
Participants assigned to the Diabetes Pri-
ority Program were asked to come 30 min
early to two diabetes-related visits, sched-
uled 6 months apart, to complete the
computerized touch screen assessment
and action-planning procedure. The first
part of the interactive computer program
focused on the medical care participants
were receiving for diabetes. Participants
were asked to recall when they had last
received each of the 11 items contained in
the PRP measures (14) (http://www.ncqa.
org). Responses were multiple choice,
using 6-month intervals (e.g., within last
6 months, 7–12 months ago). Seven of
these items involved procedures per-
formed or ordered by the physician (e.g.,
blood pressure check, measurement of
cholesterol levels, foot examination, mea-
surement of microalbumin levels, dilated
eye examination). Lipids and HbA
1c
as
-
sessments were performed in all partici-
pants as outcome measures and thus were
not eligible for inclusion as care process
measures. The remaining five laboratory
assessment procedures shown in Table 1
were summarized to produce a summary
score of number of laboratory assess-
ments meeting PRP criteria (15). The four
“patient-centered” items that involved
counseling for the patient on lifestyle as-
pects of the PRP measures (i.e., setting a
self-management goal, receiving nutrition
education or therapy, self-monitoring of
blood glucose, and meeting patient satis-
faction items) were summarized into a
patient-centered composite (Table 1).
The second part of the computerized
program focused on development of a
self-management action plan. Patients an-
swered questions on their dietary, physi-
cal activity, and smoking behaviors and
received feedback on each of these. They
next selected a behavior change goal in
the area of smoking, diet, or exercise. The
program guided participants through an
interactive session that included selecting
specific activities to support the goal area
they chose, identifying barriers, and
Figure 1—Modified CONSORT figure.
Improving diabetes care
34 DIABETES CARE, VOLUME 28, NUMBER 1, JANUARY 2005
choosing strategies to help them over-
come these barriers.
Three printouts were generated: an
action plan for the patient, which in-
cluded a summary of assays/checks the
patient might be due for and a copy of the
self-management plan; a summary of the
patient’s needed assessments and self-
management goals for the physician, in-
cluding prominent notation of areas the
patient wished to discuss; and a detailed
printout to be used by the office’s desig-
nated “care manager.” The care manager
was an existing clinic staff member (usu-
ally a nurse or medical assistant) who con-
ducted a brief counseling session with the
patient. Care managers were trained to
use a patient-centered self-management
approach (9,20) that included review of
the medical care needs and self-care goals
that the patient identified and brain-
storming additional strategies that pa-
tients could use to overcome barriers to
their goals. This took an average of 8–10
min. The care manager also attempted a
brief follow-up call after each visit to re-
view progress and to reinforce strategies
developed. These procedures were repeat-
ed at the patient’s next visit (at 6 months).
The intervention was designed to be con-
sistent with recommendations from the
Chronic Care Model for self-management
support (2,21,22) but feasible to imple-
ment during primary care visits (17).
Control condition. Patients of physicians
in the control condition completed a
touch screen computer assessment proce-
dure involving the PRP measures de-
scribed above, as well as general health
risk appraisal items (e.g., use of seatbelts,
cancer screening). They had the same
number of visits as intervention patients
and received a printout, but one that fo-
cused on general health risks and risk re-
duction that did not address the PRP
measures. They did not meet with or re-
ceive calls from a care manager.
Measures
The 52 physicians had different medical
record formats, very few had an estab-
lished diabetes registry, and almost none
reliably recorded most of these PRP activ-
ities. Therefore, we used patient reports of
having received these services as our pri-
mary outcome measure. The scales de-
scribed above were used in two previous
studies with very similar patients (3,22)
and were found to be reliable and to agree
well with medical records in a health care
system that had an electronic diabetes
registry. Secondary outcomes included
the following: 1) The revised Problem
Areas in Diabetes 2 (PAID-2) scale, a re-
vision of the original scale, assessed dia-
betes-specific quality of life (23). The
earlier version has been demonstrated
to be reliable and sensitive to change
(23,24). In this study, the PAID-2 had
an internal consistency of ␣⫽0.93.
2) HbA
1c
assays were conducted at the
University of Colorado Health Sciences
Center using a National Glycohemoglo-
bin Standardization Program certified
Bio-Rad Variant 2 analyzer (Bio-Rad,
Richmond, CA), correlated to an index of
glycemic control established during the
Diabetes Control and Complications
Trial. Its reference range was 4.1– 6.5%.
3) Total cholesterol was assayed using an
enzymatic test with high-performance
liquid chromatography methods,
achieved by microbial esterase, to ensure
virtually complete hydrolysis (99.5%)
of all cholesterol esters. This process al-
lows for direct comparability to Centers
for Disease Control and Prevention
(CDC) reference procedures. HDL cho-
lesterol was assayed using the Roche di-
rect HDL cholesterol automated method
(Roche, Montclair, NJ), which meets the
National Institutes of Health/National
Cholesterol Education Program goals for
acceptable performance. To avoid colin-
earity and to reduce the number of depen-
dent variables, we used the ratio of total to
HDL cholesterol as our lipid outcome.
4) The Patient Health Questionnaire
(PHQ) is a self-administered instrument
that has been validated as a depression
diagnostic and severity measure (25). The
PHQ-9 scores each of the nine Diagnostic
and Statistical Manual, 4th edition de-
pression criteria on a scale of 0 (not at all)
to 3 (nearly every day). A score of 10 has
been documented to have a sensitivity of
88% and a specificity of 88% for major
depression (25). In the present study, the
scale exhibited good internal consistency
(␣⫽0.86).
Table 1—Physician and patient characteristics
Intervention Control
Significance
(P value)
Physicians (n 52)
Single provider office 29.2% 17.9% 0.344
Rural 66.7% 67.9% 0.929
Family practice 62.5% 53.6% 0.525
Sex (% female) 25.0% 25.0% 0.999
Years out of training 15.0 6.1 12.8 8.1 0.330
Patients (n 886)*
Age 62 1.4 64 1.3 0.342
Sex (% female) 52.3% 50.0% 0.662
% with 5 comorbid illnesses 6.1% 6.5% 0.844
No. comorbid illnesses 2.0 0.11 2.2 0.11 0.167
Ethnicity 0.199
White/non-Hispanic 83.5% 77.9%
Black 1.7% 2.7%
Hispanic 11.3% 14.1%
Other 3.4% 5.4%
Education 0.866
Less than high school 13.0% 14.4%
High school graduate 27.1% 25.4%
College 1–3 years 32.0% 32.8%
College/graduate school 27.9% 27.4%
Annual income 0.536
$10,000 12.3% 10.0%
$10,000–$29,999 26.4% 33.9%
$30,000–$49,999 28.0% 23.9%
$50,000 33.3% 32.1%
Data are means SD for physicians and means SE for patients unless otherwise indicated. *Reported
means, SDs, and P values are adjusted for clustering by physician.
Glasgow and Associates
DIABETES CARE, VOLUME 28, NUMBER 1, JANUARY 2005 35
RESULTS
Preliminary analyses
A total of 52 physicians participated, con-
sisting of 22 internal medicine and 30
family practice physicians from 30 clinics
throughout Colorado. Based on an initial
survey of physician characteristics, partic-
ipating physicians did not differ from the
total sample of 1,059 primary care physi-
cians insured by Copic on age or sex of
physician, years in practice, size of prac-
tice, or use of any of a series of several
quality improvement processes for diabe-
tes. Characteristics of participating physi-
cians did not differ between conditions
(Table 2). The number of patients partic-
ipating per practice ranged from 13 to 61
(median 28).
Participants’ characteristics matched
those of a random sample of Colorado di-
abetic patients, as detailed elsewhere by
Glasgow et al. (17). Initial analyses failed
to show baseline differences between con-
ditions on any sociodemographic or base-
line measures (Table 2).
Attrition rates were approximately
equivalent (19% in intervention and 15%
in control), modest across the two condi-
tions at the 12-month follow-up (Fig. 1),
and not due to any consistent reasons.
There were no differences between the
two conditions in the characteristics of
patients who dropped out. Therefore,
analyses were conducted on complete
cases. Analyses using intent-to-treat pro-
cedures (and assuming those lost to fol-
low-up at 12 months were performing at
their most recently collected levels) pro-
duced identical conclusions.
Primary outcomes
Patients were receiving high levels of care
at baseline, especially for the laboratory
assay measures (Table 1). A total of 58
99% of patients were already receiving
recommended services, which is substan-
tially more than in two previous studies of
similar samples using this measure (3,21).
Despite this high initial level of care, pa-
tients in intervention practices showed
significantly greater improvement on
both laboratory assays (F 11.6, P
0.001) and patient-centered (F 39.5,
P 0.001) subsets of the PRP measures.
Subsequent to overall significant ef-
fects, analyses were conducted using only
those patients for each measure that did
not meet National Committee for Quality
Assurance/American Diabetes Associa-
tion–recommended levels at baseline to
account for potential ceiling effects (e.g.,
only those not reporting having under-
gone dilated eye examination within the
past year at baseline). On three of the four
measures for which there were more than
100 patients, the intervention condition
produced superior results on the percent-
Table 2 Baseline, 12-month, and adjusted 12-month results by condition
n Baseline
Unadjusted
12 month
Adjusted
12 month*
Signifi-
cance level
(P value)
Lab procedures completed 670 0.001
Intervention 3.92 0.99 4.29 0.86 4.29
Control 3.88 1.06 4.01 1.06 3.97
Blood pressure
Intervention 98.7% 100%
Control 98.6% 99.7%
Dilated eye exam
Intervention 68.2% 77.2%
Control 66.7% 72.4%
Foot exam
Intervention 79.7% 93.6%
Control 77.9% 83.7%
Microalbumin
Intervention 79.7% 91.3%
Control 74.9% 81.4%
HbA
1c
9.5%
Intervention 94.2% 93.9%
Control 95.8% 97.4%
Patient-centered activities
completed
658 0.001
Intervention 3.04 0.99 3.74 0.57 3.73
Control 2.93 1.03 3.31 0.86 3.32
Self-management: goal setting
Intervention 67.5% 97.1%
Control 62.7% 80.7%
Medical nutrition treatment
Intervention 60.0% 91.9%
Control 56.7% 71.4%
Self-monitor blood glucose
Intervention 88.7% 91.6%
Control 84.5% 89.1%
Patient satisfaction
Intervention 97.6% 98.6%
Control 96.9% 97.8%
Biological outcomes
HbA
1c
(%)
560 0.571
Intervention 7.33 1.34 7.14 1.38 7.11
Control 7.30 1.22 7.13 1.06 7.17
Ratio of total cholesterol to
HDL cholesterol
540 0.733
Intervention 4.32 1.19 4.17 1.18 4.11
Control 4.38 1.16 4.14 1.16 4.15
Other outcomes
Quality of life (PAID-2)† 686 0.964
Intervention 30.3 4.2 29.7 4.9 27.4
Control 28.5 5.0 26.8 4.4 27.5
Percent with major depression
(10 or higher on PHQ-9)‡
683 0.238
Intervention 19.2% 12.2% 12.3%
Control 16.1% 13.6% 13.9%
Data are means SE unless otherwise indicated. ‡Adjusted 12-month values are adjusted for baseline values
on the dependent variable. †Lower scores indicate better quality of life. ‡Tests for significance were run on
continuous PHQ scores.
Improving diabetes care
36 DIABETES CARE, VOLUME 28, NUMBER 1, JANUARY 2005
age of patients meeting criteria at 12
months (P 0.05). All of the PRP mea-
sures having 30 or more patients showed
trends favoring the intervention condi-
tion. These patients received rates of care
averaging 17% higher than control sub-
jects (range 5– 40% higher). The greatest
differences in improvement between con-
ditions were on medical nutrition therapy,
self-management goal setting, dilated eye
examination, and foot examinations.
These were also the areas in which perfor-
mance was lowest at baseline.
Secondary outcomes
Both conditions improved on measures of
lipids, HbA
1c
, quality of life, and depres
-
sive symptoms, but there was not a signif-
icant difference between conditions
(Table 1). On HbA
1c
levels, patients dis
-
played relatively good levels of baseline
control (mean 7.3%). Therefore, suba-
nalyses were conducted only on those pa-
tients exceeding HbA
1c
of 8.0 at baseline
and produced similar results.
Patients demonstrated a moderate
level of baseline mood disturbance; 16
19% of patients scored at levels on the
PHQ-9 that would be considered clinical
depression. Both conditions improved
significantly over time on these outcomes,
but between-condition improvements
were not differential.
Intervention patients only also estab-
lished goals and developed action plans
for behavior change in the areas of healthy
eating, physical activity, or smoking ces-
sation. Repeated-measures ANOVA to
evaluate change showed that participants
generally were successful in making im-
provements in these challenging lifestyle
areas. Significant (P 0.002) and mean-
ingful improvements were seen in all ar-
eas except for smoking cessation. A total
of 12% of the 35 baseline smokers who set
cessation goals quit, but the sample size
precluded significance.
Implementation
The protocol was consistently imple-
mented across the heterogeneous set-
tings. At the 6-month intervention visit,
93% of patients received the computer-
based interactive assessment procedure,
73% discussed the printout with the phy-
sician, 77% met with the care manager to
discuss lifestyle goals, and 67% received
at least one follow-up phone call. These
results are lower than at the initial inter-
vention session (99, 92, 99, and 86%,
respectively) but still good for an effec-
tiveness study implemented by clinical
staff with many competing demands.
CONCLUSIONS There are few
“practical clinical trials” upon which to
base clinical and policy decisions (8 –10).
We included several components to make
this study more generalizable than the
typical efficacy study. The intervention
was delivered by regular clinical staff in a
variety of settings, and few exclusion cri-
teria were used (e.g., patients having co-
morbid conditions including depression
were included); the program was con-
ducted during usual medical care visits
rather than special research appoint-
ments. Also, the touch screen computer
was designed to be user friendly and us-
able by low-literacy patients (questions
and information were presented aloud as
well as on the screen), and the interven-
tion was designed to fit into the flow of
usual care. These actions were generally
successful in making the intervention
practical yet effective. Our assessment plan
also included multiple outcome measures,
including those important to clinicians,
patients, and policy makers (10,26).
Overall, intervention effectiveness
was moderate compared with the strin-
gent control condition. The magnitude of
effect was likely attenuated by good base-
line levels of care and glycemic control, by
limited variability on some measures, and
because intervention was delivered by
clinical staff. Still, the intervention was
successful in increasing both care proce-
dures and patient-centered lifestyle coun-
seling—especially on measures in which
there was the greatest room for improve-
ment. These improvements were seen
across different measures across a variety
of primary care practices and types of pa-
tients. Some of the improvement in qual-
ity of care observed may have been due to
patients who were new to a practice, since
at baseline they would not have had a full
year to receive the various services. We
do not have information on how long pa-
tients had been with their provider, but
this should have affected both conditions
equally.
As reported in several other trials,
these care process improvements did not
translate into significant effects on bio-
logic or quality-of-life improvements over
the time period studied (27–29). Al-
though the program did not enhance
quality of life or reduce depression levels
more than the control condition, both
conditions showed improvement on
these outcomes. Intervention patients
and providers were dealing with more
regimen, lifestyle, and guidelines issues
without a reduction in quality of life or
other apparent adverse consequences.
Some of the improvement in both condi-
tions may have been due to the increased
attention to the care issues assessed.
The finding that reported improve-
ments in care processes did not translate
into improved outcomes has alternative
explanations. The linkage between rec-
ommended care activities and outcomes
may not be strong enough to override nu-
merous potential confounding variables
in real-world research, given the limited
person-years of observation in this study.
Alternatively, it may be that baseline lev-
els of outcome measures were sufficiently
good that it was not possible to detect im-
provement (e.g., to be significant on
HbA
1c
, intervention would have had to
reduce average levels 7.0).
Regular primary care office staff deliv-
ered the intervention consistently despite
competing demands. Almost all patients
received the touch screen computer inter-
vention, and even more staff-intensive as-
pects of the protocol such as meeting with
a care manager and follow-up calls were
completed at relatively high levels for an
effectiveness study. We think this was due
to the specificity the protocol provided,
the integration of the computer into usual
care, and the operational support our staff
provided. We conclude that it is feasible
to deliver an intervention such as the
Diabetes Priority Program in small, in-
dependent, mixed-payer primary care
practices.
This study has both methodological
strengths and weaknesses as well as im-
plications for practice, policy, and future
research (8,10). Strengths include the
scope of the study, the cluster random-
ized and appropriately analyzed design
(30), the stringent control condition that
also received computer assessment and
feedback, the breadth of outcomes and
patient-centered measures included,
analyses that evaluated potential for
translation, and the “practical clinical
trial” focus (8,10). Limitations include the
absence of “gold standard” registry or
electronic medical records data (very few
of the practices had diabetes registries).
Recent reports have documented that
Glasgow and Associates
DIABETES CARE, VOLUME 28, NUMBER 1, JANUARY 2005 37
nonelectronic medical records in most
primary care clinics do not routinely doc-
ument information on recommended
preventive services and that patient self-
report of diabetes care received is gener-
ally accurate (3,22,31).
Future research is needed to investi-
gate characteristics of medical practices
that are associated with outcomes, includ-
ing reach, effectiveness, implementation,
and maintenance (8,32). Interactive com-
puter technology will be used increasing-
ly in future research and practice (12,33).
It is possible that greater benefit could be
obtained through using interactive technol-
ogy to address lifestyle change issues such
as physical activity and nutrition coun-
seling that are not often dealt with suffi-
ciently in primary care (11,12,33–35).
Acknowledgments This work was sup-
ported by the Agency for Health, Research and
Quality, grant HS-10123.
We thank the multimedia teams at Klein
Buendle and InterVision Media; the collab-
oration of Copic Insurance Company and
our collaborating primary care partners
without whom this research would not have
been possible; Physician Recruiter Cecelia
Holland, Patient Recruiter Roxane Smith,
Biostatistician Monika Baier, and Data Man-
ager Wendy Gehring, of the Cooper Institute;
and Barbara McCray for her ongoing adminis-
trative assistance.
References
1. Grant RW, Hamrick HE, Sullivan CM,
Dubey AK, Chueh HC, Cagliero E, Meigs
JB: Impact of population management
with direct physician feedback on care of
patients with type 2 diabetes. Diabetes
Care 26:2275–2280, 2003
2. Bodenheimer TS, Wagner EH, Grumbach
K: Improving primary care for patients
with chronic illness. JAMA 288:1775–
1779, 2002
3. Glasgow RE, Strycker LA: Level of preven-
tive practices for diabetes management:
patient, physician, and office correlates in
two primary care samples. Am J Prev Med
19:9–14, 2000
4. Institute of Medicine, Committee on
Quality of Health Care in America: Cross-
ing the Quality Chasm: A New Health System
for the 21st Century. Washington, D.C.,
National Academy Press, 2001
5. Solberg LI, Kottke TE, Brekke ML, Man-
gan S, Davidson G, Calomeni CA, Conn
SA, Amundson GM, Nelson AF: Failure of
a continuous quality improvement inter-
vention to increase the delivery of preven-
tive services: a randomized trial. Eff Clin
Pract 3:105–115, 2000
6. Nutting P, Rost K, Smith J, Werner JJ,
Elliott C: Competing demands from
physical problems: effect on initiating
and completing depression care over 6
months. Arch Fam Med 9:1059 –1064,
2000
7. Stange KC, Woolf SH, Gjeltema K: One
minute for prevention: the power of lever-
aging to fulfill the promise of health be-
havior counseling. Am J Prev Med 22:
320–323, 2002
8. Glasgow RE: Translating research to prac-
tice: lessons learned, areas for improve-
ment, and future directions. Diabetes Care
26:2451–2456, 2003
9. Holman H, Lorig K: Patients as partners in
managing chronic disease; Partnership is
a prerequisite for effective and efficient
health care. BMJ 320:526 –527, 2000
10. Tunis SR, Stryer DB, Clancey CM: Prac-
tical clinical trials: increasing the value
of clinical research for decision making
in clinical and health policy. JAMA 290:
1624–1632, 2003
11. Glasgow RE, Bull SS: Making a difference
with interactive technology: considera-
tions in using and evaluating computer-
ized aids for diabetes self-management
education. Diabetes Spectrum 14:99 –106,
2001
12. Glasgow RE, Bull SS, Piette JD, Steiner J:
Interactive behavior change technology:
a partial solution to the competing de-
mands of primary care. Am J Prev Med 27:
8087, 2004
13. Street RL Jr, Gold WR, Manning TE:
Health Promotion and Interactive Technol-
ogy: Theoretical Applications and Future
Directions. London, Lawrence Erlbaum
Associates, 1997
14. Joyner L, McNeeley S, Kahn R: ADA’s pro-
vider recognition program. HMO Practice
11:168–170, 1997
15. NCQA and American Diabetes Associa-
tion, Diabetes Physician Recognition
Program [article online]. Available from
http://www.ncqa.org/dprp/dprpmain.
htm. Accessed 23 November 2003
16. Amthauer H, Gaglio B, Glasgow RE, King
DK: Strategies and lessons learned in
patient recruitment during a diabetes
self-management program conducted in a
primary care setting. Diabetes Educ 29:
673–681, 2003
17. Glasgow RE, Nutting PA, King DK, Nel-
son CC, Cutter G, Gaglio B, Kulchak
Rahm A, Whitesides H, Amthauer H: Re-
sults from a practical randomized trial of
the Diabetes Priority Program: improve-
ments in care and patient outcomes us-
ing RE-AIM criteria. J Gen Intern Med.In
Press
18. Welborn TA, Garcia-Webb P, Bonser A,
McCann V, Constable I: Clinical criteria
that reflect C-peptide status in idiopathic
diabetes. Diabetes Care 6:315–316, 1983
19. Laird NM, Ware JH: Randon effects mod-
els for longitudinal data. Biometrics 38:
963–974, 1982
20. 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 24:80 87,
2002
21. Von Korff M, Glasgow RE, Sharpe M: Or-
ganizing care for chronic illness. BMJ 325:
92–94, 2002
22. Glasgow RE, Boles SM, Calder D, Dreyer
L, Bagdade J: Diabetes care practices in
primary care: results from two samples
and three performance indices. Diabetes
Educ 25:755–763, 1999
23. Polonsky WH, Anderson BJ, Lohrer PA,
Welch G, Jacobson AM, Aponte JE,
Schwartz CE: Assessment of diabetes-re-
lated distress. Diabetes Care 18:754 –760,
1995
24. Polonsky W: Emotional and quality-of-
life aspects of diabetes management. Cur-
rent Diabetes Reports 2:153–159, 2002
25. Kroenke K, Spitzer RL, Williams JB: The
PHQ-9: validity of a brief depression se-
verity measure. J Gen Intern Med 16:606
613, 2001
26. Glasgow RE, Magid DJ, Beck A, Ritzwoller
D, Estabrooks PA: Practical clinical trials
for translating research to practice: de-
sign and measurement recommenda-
tions. Med Care. In press
27. O’Connor PJ: Electronic medical records
and diabetes care improvement. Diabetes
Care 26:1–3, 2003
28. Montori VM, Dinneen SF, Gorman CA,
Zimmerman BR, Rizza RA, Bjornsen SS,
Green EM, Bryant SC, Smith SA, the
Translation Project Investigator Group:
The impact of planned care and a dia-
betes electronic management system on
community-based diabetes care: the Mayo
Health System Diabetes Translation Proj-
ect. Diabetes Care 25:1952–1957, 2002
29. Reiber GE, Mcdonnell M, Au D, Fihn SD:
Diabetes quality improvement in Depart-
ment of Veterans Affairs ambulatory
care clinics. Diabetes Care 27 (Suppl. 2):
B61–B68, 2004
30. Murray D: Design and Analysis of Group-
Randomized Trials. New York, Oxford
University Press, 1998
31. Heisler M, Smith DM, Hayward RA, Krein
SL, Kerr EA: How well do patients’ assess-
ment of their diabetes self-management
correlate with actual glycemic control
and receipt of recommended diabetes
services? Diabetes Care 26:738 –743,
2003
32. Glasgow RE, Lichtenstein E, Marcus AC:
Why don’t we see more translation of
Improving diabetes care
38 DIABETES CARE, VOLUME 28, NUMBER 1, JANUARY 2005
health promotion research to practice?
Rethinking the efficacy to effectiveness
transition. Am J Public Health 93:1261–
1267, 2003
33. Bodenheimer TS, Grumbach K: Electronic
technology: a spark to revolutionize pri-
mary care? JAMA 290:259 –264, 2003
34. Knowler WC, Barrett-Connor E, Fowler
SE, Hamman RF, Lachin JM, Walker EA,
Nathan DM: Reduction in the incidence
of type 2 diabetes with lifestyle interven-
tion or metformin. N Engl J Med 346:393–
403, 2002
35. McGlynn EA, Asch SM, Adams J, Keesey J,
Hicks J, DeCristofaro A, Kerr EA: The
quality of health care delivered to adults
in the United States. N Engl J Med 348:
2635–2645, 2003
Glasgow and Associates
DIABETES CARE, VOLUME 28, NUMBER 1, JANUARY 2005 39
    • "In addition, several studies reported trials that included follow-up periods that were too short [17, 19, 21, 23] . Other limitations described by the authors included self-report measures for behavior change [17, 23], small sample sizes [17, 21], inadequate training of study nurses [7], and the absence of a gold standard registry and electronic medical records data [21]. One limitation of this review is that only two databases were used for research However, this issue was mitigated since the included bases represent the largest and most important in health area. "
    [Show abstract] [Hide abstract] ABSTRACT: The chronic care model (CCM) uses a systematic approach to restructure health care systems. The aim of this systematic review was to examine studies that evaluated different elements of the CCM in patients with type 2 diabetes mellitus (T2DM) and to assess the influence of the CCM on different clinical outcomes. There view was performed in the Medline and Cochrane Library electronic databases. The search was limited to randomized controlled trials conducted with T2DM patients. Studies were eligible for inclusion if they compared usual care with interventions that use done or more elements of the CCM and assessed the impact on clinical outcomes. After applying the eligibility criteria, 12 studies were included for data extraction. Of these, six showed evidence of effectiveness of the CCM for T2DM management in primary care as well as significant improvements in clinical outcomes. In the other six studies, no improvements regarding clinical outcomes were observed when comparing the intervention and control groups. Some limitations, such as a short follow-up period and a low number of patients, were observed. Some studies showed that the reorganization of health systems can improveT2DM care. However, it is possible that greater benefits could be obtained through combing all 6 elements of CCM.
    Full-text · Article · Dec 2016
    • "However, mixed results have been reported with regard to glycosylated hemoglobin (HbA1c) reduction . In a systematic review that compared the computerized decision support systems (CDSS) with conventional care [4], CDSS with feedback on the patient's performance and case management by health care providers reduced HbA1c [7][8][9][10][11][12], whereas CDSS without feedback or case management demonstrated no effect [13][14][15][16][17][18][19][20][21] . The HbA1c-lowering effect of the various computer-based interventions, including clinic-, Internet-, and mobile phone-based systems, was as small as –0.2% (95% confidence interval [CI] , –0.4 to –0.1) compared with control inter- ventions [5] . "
    [Show abstract] [Hide abstract] ABSTRACT: Background We developed a patient-centered, smartphone-based, diabetes care system (PSDCS). This study aims to test the feasibility of glycosylated hemoglobin (HbA1c) reduction with the PSDCS. Methods This study was a single-arm pilot study. The participants with type 2 diabetes mellitus were instructed to use the PSDCS, which integrates a Bluetooth-connected glucometer, digital food diary, and wearable physical activity monitoring device. The primary end point was the change in HbA1c from baseline after a 12-week intervention. Results Twenty-nine patients aged 53.9±9.1 years completed the study. HbA1c and fasting plasma glucose levels decreased significantly from baseline (7.7%±0.7% to 7.1%±0.6%, P<0.0001; 140.9±39.1 to 120.1±31.0 mg/dL, P=0.0088, respectively). The frequency of glucose monitoring correlated with the magnitude of HbA1c reduction (r=–0.57, P=0.0013). The components of the diabetes self-care activities, including diet, exercise, and glucose monitoring, were significantly improved, particularly in the upper tertile of HbA1c reduction. There were no severe adverse events during the intervention. Conclusion A 12-week application of the PSDCS to patients with inadequately controlled type 2 diabetes resulted in a significant HbA1c reduction with tolerable safety profiles; these findings require confirmation in a future randomized controlled trial.
    Full-text · Article · Jun 2016
    • "Nine articles were classified in this group. Six studies contained 5 RCTs and 1 observational study with control indicated significant positive changes on patient's physical activity status and other 3 RCT studies demonstrated no changes [15, 16, 18, 25, 32, 45, 52, 69]. As noted in these studies, telephone coaching (n = 2, 50 % positive effect) had a positive effect on physical activity changes only in type II diabetic population. "
    [Show abstract] [Hide abstract] ABSTRACT: To review published evidences about using information technology interventions in diabetes care and determine their effects on managing diabetes. Systematic review of information technology based interventions. MEDLINE®/PubMed were electronically searched for articles published between 2004/07/01 and 2014/07/01. A comprehensive, electronic search strategy was used to identify eligible articles. Inclusion criteria were defined based on type of study and effect of information technology based intervention in relation to glucose control and other clinical outcomes in diabetic patients. Studies must have used a controlled design to evaluate an information technology based intervention. A total of 3613 articles were identified based on the searches conducted in MEDLINE from PubMed. After excluding duplicates (n = 6), we screened titles and abstracts of 3607 articles based on inclusion criteria. The remaining articles matched with inclusion criteria (n = 277) were reviewed in full text, and 210 articles were excluded based on exclusion criteria. Finally, 67 articles complied with our eligibility criteria and were included in this study. In this study, the effect of various information technology based interventions on clinical outcomes in diabetic patients extracted and measured from selected articles is described and compared to each other. Information technology based interventions combined with the usual care are associated with improved glycemic control with different efficacy on various clinical outcomes in diabetic patients.
    Full-text · Article · Mar 2015
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