Group Title: Health and Quality of Life Outcomes 2008, 6:87
Title: Evaluating the reliability, validity and minimally important difference of the Taiwanese version of the diabetes quality of life (DQOL)
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Title: Evaluating the reliability, validity and minimally important difference of the Taiwanese version of the diabetes quality of life (DQOL)
Series Title: Health and Quality of Life Outcomes 2008, 6:87
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Creator: Huang IC
Liu JH
Wu AW
Wu MY
Leite W
Hwang CC
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Health and Qality of Life Otcomes ioed
Health and Quality of Life Outcomes ...I.eC


Research


Evaluating the reliability, validity and minimally important
difference of the Taiwanese version of the diabetes quality of life
(DQOL) measurement
I-Chan Huang* 1, Jung-Hua Liu1, Albert W Wu2,3, Ming-Yen Wu4,
Walter Leite5 and Chyng-Chuang Hwang4


Address: 'Department of Epidemiology and Health Policy Research, College of Medicine, University of Florida, Gainesville, FL, USA, 2Department
of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA, 3Department of Medicine,
School of Medicine, Johns Hopkins University, Baltimore, MD, USA, 4Tainan Hospital, Department of Health, Tainan, Taiwan and 5Department
of Educational Psychology, College of Education, University of Florida, Gainesville, FL, USA
Email: I-Chan Huang* ichuang@ufl.edu; Jung-Hua Liu jhliul 115@hotmail.com; Albert W Wu awu@jhsph.edu; Ming-
Yen Wu mingyenwu@ymail.com; Walter Leite Walter.Leite@coe.ufl.edu; Chyng-Chuang Hwang chyngc.hwang@msa.hinet.netail.com
* Corresponding author



Published: 28 October 2008 Received: 20 March 2008
Health and Quality of Life Outcomes 2008, 6:87 doi:10.1 186/1477-7525-6-87 Accepted: 28 October 2008
This article is available from: http://www.hqlo.com/content/6/1/87
2008 Huang et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Abstract
Background: Few diabetes HRQOL instruments are available in Chinese language. We tested
psychometric properties of a Diabetes Quality of Life (DQOL) in Chinese language for diabetes
patients in Taiwan and estimated its minimally important differences (MIDs).
Methods: Data were collected from 337 patients treated in diabetes clinics of a Taiwan teaching
hospital. Pearson's correlations among domain scores of the DQOL (satisfaction, impact, and
worry), the D-39S (a diabetes-specific instrument, including domains of diabetes control, energy
and mobility, social burden and anxiety and worry, and sexual functioning) and the RAND-12 (a
generic instrument, including physical health composite (PHC) and mental health composite
(MHC)) were estimated to determine convergent/discriminant validity. Known-groups validity was
examined using 2-hour postprandial plasma glucose (2 h PPG), hemoglobin Alc (HbAlc)) and
presence of complications retinopathyy, neuropathy, and diabetic foot complications rather than
the known groups of cardiovascular and cerebrovascular complications). We used a combined
anchor- and distribution-based approach to establish MIDs.
Results: The DQOL scores were more strongly correlated with the physical domains of the D-
39S (diabetes control and energy and mobility) and RAND-12 PHC than psychological domains of
the D-39S (social burden, anxiety and worry, and sexual functioning) and RAND-12 MHC. The
DQOL showed satisfactory discriminative ability for the known groups of 2 h PPG and HbAlc
(effect size (ES) 2 0.2) and retinopathy, neuropathy, and diabetic foot complications (ES 2 0.3), but
less satisfactory for the known groups of cardiovascular and cerebrovascular complications. MIDs
for the DQOL domains were 3-5 points for satisfaction, 4-5 points for impact, 6-8 points for
worry, and 3-4 points for overall HRQOL.
Conclusion: We validated a DQOL in Chinese language for diabetes patients in Taiwan and
provided MIDs to facilitate the measure of diabetes HRQOL.



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Health and Quality of Life Outcomes 2008, 6:87


Background
Diabetes mellitus (DM) is associated with long-term dam-
age of multiple organ systems and increased age-adjusted
mortality rates. Conventional assessment for diabetic
patients relies on clinical measures, e.g., glycemic control
and diabetes complications. However, the use of clinical
measures alone for diabetes management is limited
because clinical measures can not fully capture patient's
health outcomes, especially psychological impact [1].
Health-related quality of life (HRQOL) measures, which
emphasize daily functioning and well-being, are useful
adjuncts to clinical indicators for assessing diabetic health
outcomes.

Several instruments are available for assessing diabetes
HRQOL, including generic and diabetes-specific instru-
ments. Generic instruments measure HRQOL domains
which are universally important across diseases, while
diabetes-specific instruments measure specific impacts of
diabetes on functioning and well-being. Specific instru-
ments may be more sensitive to patients' score changes
over time [2,3].

There is a great need to develop and validate diabetes
HRQOL instruments for Chinese populations, which
comprise the largest group of people with diabetes [4].
Although more than a dozen of diabetes HRQOL instru-
ments have been developed [5,6], only three instruments
are available in Chinese language (including a translated
Diabetes-39 (D-39) [7] and a translated Diabetes Impact
Measurement Scales (DIMS) [8] for Chinese people in Tai-
wan, and a translated Diabetes Quality of Life (DQOL) [9]
for Chinese people in Canada). Each instrument, how-
ever, may measure somewhat different concepts of
HRQOL. For example, the D-39 measures the concepts of
physical functioning and psychosocial well-being associ-
ated with diabetes including the domains of energy and
mobility, diabetes control, anxiety and worry, social bur-
den, and sexual functioning [10], whereas DQOL meas-
ures the burden associated with diabetes treatment and
glycemic control including the domains of satisfaction
with treatment, impact of treatment, and worry about
future effect of diabetes [11]. Therefore, it is important to
validate and compare different instruments within the
same population and to test whether one instrument may
be used combined with another to better capture compre-
hensive diabetes HRQOL.

In testing the usefulness of diabetes HRQOL instruments,
the selection of psychometric methods and clinical varia-
bles can influence the success of instrument validation
[7]. Hemoglobin Alc (HbAlc) a measure reflecting a
longer-term glycemic control is commonly used as an
external variable to validate instruments, but the associa-
tion between HbAlc and HRQOL is weak [12]. Validation


might be improved by further including other laboratory
indicators (e.g., fasting plasma glucose (FPG) and post-
prandial plasma glucose (PPG)) to better account for the
impact of fluctuations and acute increase of glycemia
(hyperglycemic spikes) on health [13,14]. Additionally,
hyperglycemic symptoms and diabetic complications are
major determinants of HRQOL [15,16]. The use of labo-
ratory indicators (FPG, PPG and HbAlc) together with
diabetes complications would be helpful for validating
diabetes HRQOL instruments.

An issue limiting the use of diabetes HRQOL measures is
that little guidance is available to interpret HRQOL scores,
especially clinical meaning in score difference among
treatment groups or score change within individuals over
times [5,17]. Conventionally, the interpretation of score
changes/differences relies on tests of statistical signifi-
cance. Yet, statistical significance is not equivalent to clin-
ical significance because the former does not directly link
to clinical sensibility and is partially determined by sam-
ple size [3]. Clinicians are interested in interpreting score
differences, especially minimally important difference
(MID) which can serve as the lowest benchmark to deter-
mine clinical meaning of HRQOL scores [3,18].

Two methods are commonly used to determine MID: dis-
tribution-based and anchor-based approaches [3,19]. Dis-
tribution-based approaches rely on statistical properties
of the sample (e.g., variation of score distribution) or the
instrument (e.g., measurement precision of scale) to
establish clinically meaningful change [19]. Anchor-based
approaches assess the extent to which changes in measure-
ment instruments correspond to a minimally important
change defined by external indicators. These indicators
may include clinical variables (e.g., laboratory and physi-
ological measures and clinical ratings) and patient-
reported outcomes (PRO) (e.g., global change in health)
[20].

To date, there is no consensus on the best approach to
evaluate MID [3,19]. Studies have recommended that
MID estimations should apply anchored-based
approaches using clinical and/or PRO indicators com-
bined with supportive information from the distribution-
based estimates to generate a small range of values for
MID [20-23]. The strength of using multiple approaches
to establish a range of MID is to demonstrate variability
among estimates.

The main purpose of this study was to validate and inter-
pret a Taiwanese version of the DQOL [24]. We evaluated
the psychometric properties of the DQOL using several
clinical variables: 1) laboratory indicators: fasting plasma
glucose, 2-hour postprandial plasma glucose, and HbAlc,
and 2) complications of diabetes: retinopathy, neuropa-


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Health and Quality of Life Outcomes 2008, 6:87


thy, diabetic foot disorder, cardiovascular, and cerebrov-
ascular diseases. To better interpret the DQOL, we
estimated the MID using a combined anchor-based and
distribution-based approach.

Methods
Participants and data collection
Data were collected from the Taiwan Diabetes Health Sur-
vey, an initiative to systematically develop HRQOL instru-
ments for diabetes patients. In the second-year of the
project, we focused on the DQOL and assessed its psycho-
metric properties. Face-to-face interviews were conducted
by two trained research associates for type-1 and type-2
diabetes patients who utilized outpatient services in the
Tainan Hospital a Taiwan's Department of Health
(DOH) affiliated teaching hospital between 07/2006-
10/2006. In total, data from 337 diabetes patients were
collected for the statistical analysis. This study was
approved by the Institutional Review Board of the Tainan
Hospital and received informed consent from each
patient.

Data on laboratory measure, clinical diagnosis and
HRQOL assessment were collected at the same time from
individual patients and tested using the same methods for
all patients. Laboratory indicators include fasting plasma
glucose (FPG), 2-hour postprandial plasma glucose (2 h
PPG), and HbAlc. Diabetes complications were
abstracted from medical records, including retinopathy
(none vs. background, proliferative, or decreased vision),
neuropathy (none vs. present), diabetic foot disorders
(none vs. foot ulceration, sepsis, or amputation), cardio-
vascular complications (none vs. angina, or previous
myocardial infarction or congestive heart failure), and cer-
ebrovascular complications (none vs. transient ischemic
attack, or stroke).

Background of developing the DQOL
The DQOL was originally developed to assess HRQOL for
type-1 diabetes [11] and has been adapted for type-2 dia-
betes [25-27]. The original DQOL consists of 46 items
measuring the domains of satisfaction with treatment,
impact of treatment, worry about future effects of diabe-
tes, and worry about social/vocational issues [11]. The
DQOL has been translated to Chinese language for people
in Canada [9], with a modification of the original instru-
ment (i.e., adding and replacing some items) to capture
culture-sensitive issues such as eating and sexual activities.
These modifications are necessary because eating style and
joyfulness are essential components of Chinese culture
where family gathering and social activities are centered
on meals. By contrast, sexual activity is a taboo subject in
Chinese culture especially among elderly people who are
less willing to reported sexual functioning. Our previous
study suggests that measuring sexual functioning by dia-


betes elderly people is less reliable and less valid com-
pared to other diabetes HRQOL domains [7].

The extant Chinese version developed for Chinese people
in Canada can not be directly applied to our study popu-
lation because different spoken dialects and syntax (i.e.,
using different rules and principles to govern the sentence
structure) are used by Chinese people in Canada and Tai-
wan. The extant DQOL in Chinese language was devel-
oped based on the dialect of Cantonese [9], where people
in Taiwan use Mandarin Chinese. To address this issue, we
included all items of the extant Chinese version form Can-
ada [9], but explicitly modified syntax of individual item.
For example, we replaced an item


of the extant Chinese version from Canada by the item
1..** iA',l AAjR0j, for Taiwanese. After
item modification and replacement, we translated our
Taiwanese version of the DQOL back to an English ver-
sion [25] and compared the semantics of the translated
English version to the original English version. We also
invited seven diabetes patients (four males and three
males; age range 60-80 years) from the same hospital and
applied cognitive debriefing tests to assess the level of
comprehension and cognitive equivalence of the items.
The finding from cognitive debriefing tests suggests a
minor revision in the wordings for some items.

This Taiwanese version includes the same items as those
in a Chinese version developed in Canada [9]. Compared
to the original DQOL, for satisfaction domain, we
dropped one item asking about sexual life (How satisfied
are you with your sex life?), and replaced it with a new
item for diabetes control (How satisfied are you with your
control over your diabetes?). For impact domain, we
dropped two items asking about interference with sexual
life (How often does your diabetes interfere with your sex
life?) and insulin reactions (How often do you hide from
others the fact that you are having an insulin reaction?).
We replaced them with two new items on eating out (How
often does your diabetes interfere with your eating out?)
and traveling/vacation (How often do you avoid a vaca-
tion or trip because of your diabetes?). For worry domain,
consistent with a Chinese version from Canada we
dropped seven items asking about social/vocational worry
associated with marriage, children, education, job, and
insurance because these items are appropriate for younger
adults. We, however, added three items relevant to worry
about requiring insulin in the future (How often do you
worry about requiring insulin in the future?), death (How
often do you worry about death due to diabetes?), and
eating food (How often do you worry about eating the


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Health and Quality of Life Outcomes 2008, 6:87


wrong food?). The resulting version of the DQOL con-
sisted of 42 items measuring three domains: 15 items for
the satisfaction with treatment domain, 20 items for the
impact of treatment domain, and 7 items for the worry
about future effect of diabetes domain. Our factor analysis
suggests a goodness-of-fit for the factorial structures of the
translated DQOL [24]. The detailed process of developing
a Taiwanese version of the DQOL has been described in
our previous study [24].

All items are scored on a five-point Likert scale, ranging
from 1 (very satisfied) to 5 (very dissatisfied) in satisfac-
tion domain, and from 1 (never) to 5 (all the time) in
impact and worry domains. Domain scores were calcu-
lated by summing responses of all items in the corre-
sponding domains, and lineally transforming them to a
1-100 scale with higher scores representing poorer
HRQOL. A summary score (overall HRQOL) is further
derived by summing three domain scores and lineally
transforming to a 1-100 scale.

Other HRQOL measures: the D-39S and the RAND- 12
We collected other HRQOL measures to validate the
DQOL, including the D-39S and the RAND-12. The D-39S
is a short-form (23 items) of the D-39, which is a diabetes-
specific HRQOL instrument designed for patients with
type-1 and type-2 diabetes [10]. The D-39 has been trans-
lated to Chinese language by our research team and dem-
onstrates good psychometric properties [7]. We shortened
the D-39 using the Ant Colony Algorithm and structural
equation modeling, which specifically retained items
showing best correlation with clinical variables and good-
ness-of-fit for the construct of interest [28]. The D-39S
covers the same domains as the D-39: energy and mobil-
ity, diabetes control, anxiety and worry, social burden,
and sexual functioning. Items are administered using
seven response categories with score ranging from 1 (not
affected at all) to 7 (extremely affected). Domain scores
are calculated by summing all items in the same domain,
and linearly transformed them to 1-100, with high scores
representing poor HRQOL.

The RAND-12, a generic HRQOL instrument, is a short-
form of the RAND-36 [29]. The RAND-12 uses 12 items to
capture two underlying constructs: physical and mental
health. We calculated two summary scores, a physical
health composite (PHC) and mental health composite
(MHC), which are norm-based standardized scores with a
mean 50 and a standard deviation 10. Higher scores in
PHC and MHC represent better HRQOL. We used the
RAND-12 PHC and MHC instead of the SF-12 physical
component score (PCS) and mental component score
(MCS) because evidence suggests that the SF-12 might be
less sensitive to detect important difference in HRQOL
between the known groups [30].


Psychometric analyses for the DQOL
Psychometric properties of the DQOL were examined
using internal consistency (reliability), convergent/discri-
minant validity, and known-groups validity.

Internal consistency of each domain was estimated using
Cronbach's alpha coefficient. An alpha of> 0.7 is consid-
ered to be acceptable for the purpose of group compari-
sons [31]. Convergent and discriminant validity was
assessed through a multi-trait multi-method (MTMM)
which compares Pearson's correlation coefficients among
domains of the DQOL with the D-39S and the RAND-12.
As described in the Introduction, because the DQOL
essentially measures satisfaction and impact of diabetes
treatment, whereas the D-39S measures physical function-
ing and psychological well-being associated with diabetes,
we hypothesized that the two DQOL domains (satisfac-
tion with treatment and impact of treatment) would be
more strongly associated with physical domains of the D-
39S (diabetes control and energy and mobility) compared
to with psychosocial domains of the D-39S (social bur-
den, anxiety and worry, and sexual functioning). We also
hypothesized that the worry domain of the DQOL which
focuses more on physical aspects (such as worry about
complication, change of physical appearance and death)
would be strongly associated with physical domains of
the D-39S (diabetes control and energy and mobility)
compared to with psychosocial domains of the D-39S
(social burden, anxiety and worry, and sexual function-
ing). With respect to the association between the DQOL
and the RAND-12, we assumed that the DQOL domains
would be more strongly associated with PHC compared to
with MHC. A magnitude of Pearson's correlation coeffi-
cient 0-0.39, 0.4-0.69, and > 0.7 is classified as weak,
moderate, and strong, respectively [31].

Known-groups validity of the DQOL was examined by the
extent to which the DQOL can discriminate between clin-
ically well-defined patient groups, including laboratory
diagnosis and diabetic complication groups. Laboratory
diagnosis known groups are for those patients whose val-
ues of laboratory measures were below vs. above the
accepted cut-off points: 110 mg/dL for FPG, 140 mg/dL
for 2 h PPG, and 7.0% for HbAlc [32]. Diabetes compli-
cation known groups are for patients who were diagnosed
with vs. without complications of retinopathy, neuropa-
thy, diabetic foot diseases, cardiovascular, and cerebrovas-
cular diseases, respectively. We calculated Cohen's effect
size (ES) to indicate the magnitude of known-groups
validity (unit: standard deviation [SD]) [31], defined as
the differences in domain scores between known groups
(e.g., HbAlc below vs. above 7.0%) divided by the pooled
standard deviation of both groups. A magnitude of effect
size < 0.2 SD, 0.2-0.49 SD, 0.5-0.79 SD, and 2 0.8 SD is
classified as negligible, small, moderate, and large, respec-


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Health and Quality of Life Outcomes 2008, 6:87


tively. We hypothesized that, compared to the RAND-12
the DQOL and the D-39S would discriminate better
between laboratory known groups (with a ES > 0.2)
because these two instruments center on burden of diabe-
tes treatment and symptoms of glycemic control. We also
hypothesized that, compared to the RAND-12 the DQOL
and the D-39S would discriminate better between the
known groups of retinopathy, neuropathy, and diabetic
foot complications rather than the known groups of cardi-
ovascular and cerebrovascular complications. This is
because these three complications are closely associated
with diabetes treatment and control, and their impact
could be directly captured by the domains included in
both diabetes-specific instruments.

We also compared scores of the DQOL domains by treat-
ment regimens, including 1) lifestyle modification alone
or lifestyle modification plus oral agent (L/LO) and 2)
lifestyle modification plus insulin or lifestyle modifica-
tion plus oral agent and insulin (LI/LOI). We hypothe-
sized that patients who were treated with L/LO regimen
would demonstrate better HRQOL compared to patients
who were treated with LI/LOI regimen.

Establishment of minimally important difference
We used a cross-sectional method to determine MID
which compares HRQOL scores in patients who were clas-
sified by level of health-relevant criteria [3,19]. Because
there is no consensus on the superiority of a anchor- vs.
distribution-based approach to determine MID (also see
Introduction), we specifically combined the findings
using a anchor-based approach (differences between
health distinguishable groups) with a distribution-based
approach [21,23].

Three-single items measuring patient's self-reported dia-
betes severity, general health status, and global quality of
life were considered as anchors. Items of diabetes severity
and global quality of life were rated by a seven categories,
with scores ranging from 1 to 7 (from most severe/very
dissatisfied to least severe/very satisfied). The item of gen-
eral health status was rated by a five categories, with score
ranging from 1 to 5 (poor, fair, good, very good, and
excellent). We estimated differences in average HRQOL
scores across adjacent categories of a specific anchor
[21,23,33]. We considered the MID to be the difference in
average scores corresponding to the effect size between 0.2
and 0.5 [23,34].

For a distribution-based approach, we estimated a stand-
ard error of measurement (SEM) which accounts for relia-
bility of the DQOL and standard deviation of patients
under the investigation. SEM was estimated by a standard
deviation of the DQOL scores multiplied by a square root
of one minus internal consistency of the DQOL scores.


Based on evidence supported by Wyrwich and colleagues,
we adopted a one-SEM criterion to reflect MID [35,36].
We finally used the findings derived from three anchors
and a SEM to generate a range of MID values for individ-
ual DQOL domain.

In this study, all of the analyses were performed using the
STATA 9.02 [37].

Results
Patient characteristics
Table 1 shows patients' characteristics (N = 337). Briefly,
mean age was 61.6 years (SD: 10.9) and 51% were male.
For laboratory indicators, mean FPG was 151 mg/dL (SD:
48), mean 2 h PPG was 204 mg/dL (SD: 78), and mean
HbAlc was 7.9% (SD: 2.0). For diabetes complications,
16% had retinopathy, 14% had cardiovascular disease,
13% had diabetic foot disorder, 13% had neuropathy,
and 5% had cerebrovascular disease. The majority of the
subjects (88%) were treated with lifestyle modification or
lifestyle modification plus oral agent.

Internal consistency
Internal consistency was > 0.7 for all domains of the
DQOL (0.90, 0.89, and 0.83 for impact, satisfaction, and
worry domains, respectively).

Convergentldiscriminant validity
Table 2 shows convergent and discriminant validity of the
DQOL against the D-39S and the RAND-12. In general,
domain scores of the DQOL were moderately correlated
with the D-39S (except sexual functioning) and the
RAND-12 PHC and MHC, with Pearson's correlation coef-
ficients > 0.4. Magnitudes in the correlations of all DQOL
domains with the diabetes control and energy/mobility of
the D-39S were slightly larger than with the other D-39S
domains (social burden, anxiety and worry, and sexual
functioning). For example, Pearson's correlation coeffi-
cients of the satisfaction domain of the DQOL with diabe-
tes control and energy/mobility of the D-39S were all
0.57, which were larger than with the other D-39 domains
(0.30 through 0.46). Magnitudes in the correlations of all
DQOL domains with the RAND-12 PHC were slightly
larger than with the RAND-12 MHC.

Known-groups validity
Table 3 shows the known-groups validity tested using lab-
oratory indicators and diabetes complications. After
adjusting for age, gender, education background, and dia-
betes duration, the impact, worry, and overall HRQOL
domains of the DQOL demonstrated discernible discrim-
inative ability for 2 h PPG groups (effect sizes in score dif-
ference 2 0.2), but the satisfaction domain did not (effect
size <0.2). For HbAlc groups, the satisfaction, worry, and
overall HRQOL domains of the DQOL demonstrated dis-


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Health and Quality of Life Outcomes 2008, 6:87




Table I: Patients characteristics (N = 337)


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Mean (SD) or %


Demographic variables
Age in years, mean (SD)
<55, %
55-59.9,%
60-64.9
65-69.9
70-74.9
> 75
Gender, %
Male
Female
Education, %
No formal education
Primary and junior high schools
Senior high school
College
Graduate
Employment status, %
Yes
No

Laboratory tests
Fasting plasma glucose (FPG), mg/dL
2-hour postprandial plasma glucose (2 h PPG), mg/dL
Hemoglobin Al c (HbAl c), %

Diabetes complications
Retinopathy, %
Neuropathy, %
Diabetic foot complications, %
Cardiovascular complications, %
Cerebrovascular complications, %

Number of comorbid conditions, mean (SD)
Duration of DM in years, mean (SD)
Type of treatment, %
Lifestyle modification alone or lifestyle modification plus oral agent
Lifestyle modification plus insulin or lifestyle modification plus oral agent and insulin
Type of diabetes, %
Type-I (age<30 years old and BMI<23)
Type-2 (either age > 30 years old or BMI > 23, or both)

t Classification of type of diabetes [49,50]


cemable discriminative ability, but the impact domain
did not. Discriminative ability of the DQOL and the D-
39S by 2 h PPG and HbAlc known groups was compro-
mised, depending on specific domains. Compared to the
RAND-12, both diabetes-specific HRQOL instruments
showed slightly better discrimination by using laboratory
indicators. No specific domains of the DQOL, the D-39S,
and the RAND-12 showed discernible discriminative abil-
ity for FPG groups.

For the diabetes complication known groups, after adjust-
ing for age, gender, education background, and diabetes
duration, the impact, worry, and overall HRQOL domains


of the DQOL demonstrated better discrimination than the
satisfaction domain. This is especially evident for the
known groups of retinopathy, neuropathy, and diabetic
foot complications. Taking neuropathy as an example, the
effect sizes in score differences of the impact, worry, and
overall HRQOL domains were 0.44, 0.46, and 0.45,
respectively, which were larger than the satisfaction
domain (0.24). The discriminative ability of the DQOL
and the D-39S by the known groups of retinopathy, neu-
ropathy, and diabetic foot complications was compro-
mised. Compared to the RAND-12, both diabetes-specific
HRQOL instruments showed slightly better discrimina-
tion by laboratory indicators. By contrast, compared to



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61.6 (10.9)
25.2
14.0
19.3
18.4
10.4
12.8

50.7
49.3

7.1
52.5
21.4
17.8
1.2

40.4
59.6


150.7 (47.8)
204.0 (78.0)
7.9 (2.0)


16.1
12.5
12.5
14.0
5.3

1.8 (1.2)
9.2 (6.3)

87.9
12.1

1.2
98.8








Health and Quality of Life Outcomes 2008, 6:87


Table 2: Convergent/discriminant validity the DQOLt worry, and overall HRQOL domains were all above 0.2
DQOL DQOL DQOL DQOL (i.e., 0.20, 0.48, 0.29, and 0.39, respectively), indicating
SATD IMPL WORL ALLQ clinically important difference.

D-39 Minimally important differences
Diabetes control -0.57 -0.66 -0.54 -0.70 Table 4 shows MID for individual DQOL domain esti-
Energy and mobility -0.57 -0.68 -0.54 -0.71 mated using the anchor- and distribution-based
Social burden -0.46 -0.65 -0.47 -0.64 approaches. Because case numbers were small for catego-
Anxiety and worry -0.51 -0.59 -0.46 -0.62 ries 1-3 in the self-reported diabetes severity and global
Sexual functioning -0.30 -0.37 -0.29 -0.38 quality of life anchors, these three categories were col
RAND-12 quality lie anchors, these three categoes were col-
PHC# -0.53 -0.72 -0.57 -0.73 lapsed. For the self-reported diabetes severity anchor,
MHC# -0.50 -0.68 -0.53 -0.69 MIDs (estimated by averaging differences in values
between adjacent categories with corresponding effect size
t Values in the cells are Pearson's correlation coefficients: weak (0- 0.2-0.5) were 2.6, 4.1, 6.5, and 3.1 points for the domains
0.39), moderate (0.40-0.69), and strong (> 0.7) of satisfaction, impact, worry, and overall HRQOL,
t SAT: satisfaction; IMP: impact; WOR: worry; ALL: overall respectively. For the general health status anchor, the esti-
Negative value in Pearson's correlation coefficients is due to better
HRQOL indicated by lower scores for the DQOL and the D-39S and mated MIDs were 4.9, 3.6, 5.6, and 3.7 points for the
higher scores for the RAND-12 domains of satisfaction, impact, worry, and overall
# PHC: physical health composite; MHC: mental health composite HRQOL, respectively. For the global quality of life anchor,
the estimated MIDs were 3.0, 4.5, 7.0, and 4.1 points for
the RAND-12 the discriminative ability of the DQOL and the domains of satisfaction, impact, worry, and overall
the D-39S was compromised by cardiovascular complica- HRQOL, respectively. SEM of the distribution-based
tion known groups, but less satisfied by cerebrovascular approach shows the MIDs were 3.7, 4.6, 8.0, and 3.0
complication known groups. points for the domains of satisfaction, impact, worry, and
overall HRQOL, respectively.
Treatment effect
Figure 1 shows associations of HRQOL and types of dia- Combining the findings from anchor- and distribution-
betes treatments 1) lifestyle modification alone or life- based approaches, the range of MIDs were 3-5 points for
style modification plus oral agent (L/LO) and 2) lifestyle the satisfaction domain, 4-5 points for the impact
modification plus insulin or lifestyle modification plus domain, 6-8 points for the worry domain, and 3-4 points
oral agent and insulin (LI/LOI). As we hypothesized, after for the overall HRQOL.
adjusting for age, gender, education, and diabetes dura-
tion patients treated by LI/LOI regimen were associated Discussion
with more impaired HRQOL in all domains than patients Although the DQOL has been widely used in many stud-
treated by L/LO regimen. The effect sizes in the score dif- ies [25-27], rigorous psychometric assessments for a Chi-
ferences between two regimens for satisfaction, impact, nese language version are still limited. In this study, we


Table 3: Known-groups validity the DQOLf


DQOL DQOL DQOL DQOL D39 D39 D39 D39
SAT$ IMPt WORt ALL$ DC$ EM$ SB AW$


RAND-12 RAND-12
PHC$ MHC$


Laboratory indicators
Fasting plasma glucose
2-hour postprandial plasma glucose
Hemoglobin Alc

Diabetes complications
Retinopathy
Neuropathy
Diabetic foot complications
Cardiovascular complications
Cerebrovascular complications


0.04 0.06 0.12 0.08 0.08 0.07 0.05
0.17 0.24 0.34 0.28 0.39 0.40 0.26
0.23 0.17 0.22 0.23 0.19 0.20 0.20


0.48 0.41
0.57 0.64
0.60 0.59
0.32 0.15
0.30 0.41


t Covariate adjustment: age, gender, education background, and duration
SAT: satisfaction; IMP: impact; WOR: worry; ALL: overall; DC: diabetes control; EM: energy and mobility; SB: social burden; AW: anxiety and
worry; SF: sexual functioning; PHC: physical health composite; MHC: mental health composite
Values in the cells are effect size: negligible (< 0.2), small (0.2-0.49), moderate (0.5-0.79), and large (> 0.8)


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0 -0.19
0.21 0.43
0.14 -0.04


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Health and Quality of Life Outcomes 2008, 6:87


45


43 -


35 -


30-


o 25 -

S20-

-JM

15 -


10 -


5 -


0 4-


ES=0.20


ES=0.48


Saisfaction


ES=0.29


Wony


ES=039


Overall


DQOL domiu


Figure I
Relationship between DQOL score and type of treatment. ES: effect size, which is the difference in HRQOL scores
(unit: SD) of the DQOL between two treatment categories.


tested psychometric properties of the DQOL in Chinese
language for diabetes patients treated in Taiwan. Our ver-
sion was harmonized with a previous version developed
for Chinese people in Canada [9], which may facilitate the
use in Chinese populations worldwide.

Our findings indicate that scores of the DQOL were mod-
erately correlated with the D-39S (except sexual function-
ing) and the RAND-12. Magnitudes in the correlations of
all DQOL domains with the physical relevant domains of
the D-39S (diabetes control and energy/mobility) were
slightly larger than with other domains of the D-39S
(social burden, anxiety and worry, and sexual function-
ing). Additionally, magnitudes in the correlations of all
DQOL domains with the RAND-12 PHC were slightly
larger than with MHC. A DQOL validation study by
Yildirim and colleagues reported that domains scores of
the DQOL were more strongly correlated with the physi-
cal domains (e.g., mobility, vision, hearing, breathing,
and so on) of the 15D (a generic HRQOL measure) than


with the psychological domains (e.g., mental function,
depression, distress, and so on) [38]. Another DQOL val-
idation study by Jacobson and colleagues also reported
that domains scores of the DQOL were more strongly cor-
related with the physical domains (e.g., role physical func-
tioning and general health) of the SF-36 than with the
psychosocial domains (e.g., social functioning) [25].
Taken together, these findings might suggest that the con-
cepts captured by the DQOL, such as satisfaction with
treatment, impact of treatment, and worry about future
diabetes effects (complications, change of physical
appearance and death), are more physical than psychoso-
cial relevant. Therefore, the HRQOL constructs included
in the DQOL and the D-39S are not completely equiva-
lent.

For known-groups validity, we found that, compared to
the RAND-12 both diabetes-specific HRQOL instruments
demonstrated slightly better discrimination by known
groups of 2 h PPG and HbAlc. Additionally, compared to


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D Lfestyke modification alone or
liestyBe modification phs oral
agent
SLifestyle edification phs
insulin or lifestyle dification
plis oral agent and insulin


http://www.hqlo.com/content/6/1/87


Tpn .n








Health and Quality of Life Outcomes 2008, 6:87




Table 4: Minimally important differences of the DQOL


http://www.hqlo.com/content/6/1/87


Anchor I:
Self-reported diabetes severit

DQOL Category Mean Difference
in meant


SAT#






IMP#


WOR# 1-3
4
5
6
7


ALL#


39.42
36.33
33.66
31.28
24.52

32.17
22.17
19.42
15.36
10.28

33.93
26.90
25.46
19.78
10.02

35.05
28.02
25.51
21.78
15.32


3.12#
2.32#
2.41 :
7.54


10.26
2.88:
4.0 1
5.29:


7.28:
1.46
5.79:
10.93


7.22
2.44:
3.73#
7.03


Anchor 2:
General health status


MID Category Mean Difference
in mean


2.62 I
2
3
4
5

4.06 I
2
3
4
5

6.53 I
2
3
4
5

3.09 I
2
3
4
5


44.33
36.58
32.00
26.26
14.38

35.58
22.17
19.05
10.61
7.81

32.50
27.34
25.98
10.63
9.38

38.19
28.18
24.83
16.20
10.42


13.24
5.1 I1
4.68:
8.48


3.59:
7.94
3.64:
13.74


4.58:
14.02
1.89
6.67:


7.21
7.94
3.72#
10.68


Anchor 3:
global quality of life

MID Category Mean Difference
in mean


4.90 1-3
4
5
6
7

3.60 1-3
4
5
6
7

5.62 1-3
4
5
6
7

3.72 1-3
4
5
6
7


42.21
36.36
33.23
32.05
23.24

28.82
22.58
20.30
15.99
12.33

26.74
29.41
23.19
21.93
12.84

33.25
28.64
25.40
22.72
16.31


5.87
2.99:
1.38
8.50


5.70:
2.51
4.95:
2.9 I1


-3.26
6.66:
2.30
7.27:


4.27:
3.37:
3.23#
5.63#


t Covariate adjustment: age, gender, education background, and duration
: Differences in mean HRQOL scores across adjacent categories with corresponding effect size 0.2-0.5
MID: minimally important difference, which is the average of the differences in mean HRQOL scores across adjacent categories with effect size
0.2-0.5
# SAT: satisfaction; IMP: impact; WOR: worry; ALL: overall


the RAND-12 both diabetes HRQOL instruments (DQOL
and D-39S) discriminated better between the known
groups of retinopathy, neuropathy, and diabetic foot
complications than the known groups of cardiovascular
and cerebrovascular complications. This finding may be
in part due to the fact that the indicators of 2 h PPG,
HbAlc, retinopathy, neuropathy, and diabetic foot com-
plications are closely associated with diabetes treatment
and diabetes control, and their impact might be directly
captured by the domains included in both diabetes-spe-
cific instruments. In contrast to retinopathy, neuropathy,
and diabetic foot complications, the impact of cardiovas-
cular and cerebrovascular complications (especially for
stroke as an example) on daily functioning is more signif-
icant and might not be directly attributed to glycemic con-
trol, which could be better captured by the RAND-12. This
finding suggests that it might be an ideal approach to use
diabetes-specific HRQOL instruments combined with
generic HRQOL instruments to fully measure HRQOL
burden for diabetes patients [7,39,40].


Our study extends conventional methods used to validate
diabetes HRQOL instruments. Previous studies have often
used HbAlc as a glycemic control indicator to validate
HRQOL instruments. However, evidence is mixed regard-
ing the association between HbAlc and HRQOL [1,41-
43]. Our results suggest that 2 h PPG may be a more sen-
sitive laboratory indicator to validate HRQOL in patients
with diabetes. Rather than averaging blood glucose levels
from the preceding 2-3 months, 2 h PPG captures short
term fluctuations in metabolic control. Epidemiologic
studies have reported that patients with normal HbAlc,
but abnormal 2 h PPG, are more prone to postprandial
hyperglycemia, leading to substantially an increased risk
of death from macrovascular diseases [13,44]. In most
cases, PPG levels increase before and faster than FPG [45].
The usefulness of 2 h PPG over HbAlc and FPG has been
demonstrated in our previous study to test validity of dia-
betes HRQOL measures using the D-39 [7].

A previous DQOL study suggests that patients with diabe-
tes complications tended to report more impaired DQOL
scores compared to their counterparts [9]. In this study,


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SEM


MID


2.99 3.71






4.52 4.58


6.97 7.97






4.13 3.02







Health and Quality of Life Outcomes 2008, 6:87


we support the value of using several individual complica-
tions as known groups to validate diabetes HRQOL
instruments. We found that the effect sizes in the impact,
worry, and overall HRQOL domains were greater than 0.2
for the known groups of retinopathy, neuropathy, and
diabetic foot complications, suggesting clinically mean-
ingful difference. Interestingly, the effect sizes in HRQOL
scores between levels of laboratory indicators were gener-
ally smaller than for the presence and absence of diabetes
complications. This may be due, in part, to the fact that
clinical symptoms (e.g., hypoglycemia) and events (e.g.,
diabetic foot) are more evident to patients than laboratory
abnormalities, leading to significant impairment on well-
being.

Our study suggests that patients who received more inten-
sive treatment (lifestyle modification plus insulin or life-
style modification plus oral agent and insulin) was
associated with the more impaired HRQOL in all
domains compared to patients who received less intensive
treatment (lifestyle modification alone or lifestyle modifi-
cation plus oral agent). These comparisons were inde-
pendent of the influence of age, gender, education, and
diabetes duration. Johnson et al reported that, using the
SF-12 patients on oral medication plus insulin had signif-
icantly lower physical and mental health than patients on
oral medication, followed by lifestyle modification alone
[30]. Similarly findings were also reported by Saito et al
using the SF-36 [46]. However, some longitudinal studies
reported that HRQOL was not significantly changed after
patients taking insulin therapy [26,43]. This discrepancy
may be due to the fact that some factors, e.g., increased
patient education, family support, and decreased hyperg-
lycemic symptoms, may offset the discomfort and prob-
lems related to insulin therapy.

To facilitate interpretation of the DQOL scores, we esti-
mated the minimally important difference (MID). MID
has been defined as the smallest difference in a HRQOL
measure that is perceived by patients as being clinically
meaningful [3,47]. Importantly, the choice of anchors
might influence the MID estimation. Guyatt et al sug-
gested that a useful anchor for MID should be interpreta-
ble and moderately correlated with target instruments
[3,48]. Revicki et al recommended that anchors derived
from patient's perspective should be given the most
weight because they reflect the intuitive interpretation for
the change in patient-reported outcomes [20]. In this
study, three anchors we used (i.e., patient's self-reported
diabetes severity, general health status, and global quality
of life) were all based on patient's viewpoint. We specifi-
cally found that when the levels of external indicators
indicated impairment, HRQOL measured by the DQOL
showed impairment. Furthermore, these indicators were
moderately correlated with the DOQL scores (Pearson's


correlation coefficients > 0.4). As a result, we consider
these indicators to be legitimate anchors and potentially
be useful by other studies to interpret diabetes HRQOL
measures.

Because neither anchor-based nor distribution-based
approaches are superior to one another, we estimated
MIDs based on several anchors and combined these esti-
mates with a distribution-based estimate (i.e., standard
error of measurement) [21-23]. We found that the range
of MIDs for the DQOL were 3-5 points for satisfaction
domain, 4-5 points for impact domain, 6-8 points for
worry domain, and 3-4 points for overall HRQOL. The
estimation of MIDs can be helpful for calculating sample
size when HRQOL is used as an end point in clinical
investigations. Interestingly, the findings from this and
earlier studies [23] suggest that the combined use of
anchor-based and distribution-based approaches tend to
expand the range of MIDs compared to using either
approach. The MID derived from the distribution-based
approach is more likely to be on the opposite end of the
MID range compared to the anchor-based approach. More
studies using different anchor and distribution-based
approaches need to be conducted to confirm these find-
ings.

There are a number of limitations to this study. First, the
generalizability of our results may be limited because our
samples were collected from a single center in Taiwan.
Second, although the DQOL was designed for measuring
patients with type-1 and type-2 diabetes, only 4 patients
in our sample had type-1 diabetes. Therefore, the psycho-
metric properties of DQOL are based largely on type-2
diabetes. Further studies are needed to replicate our find-
ing in patients with type-1 diabetes. Third, we estimated
MIDs using a cross-sectional rather than a longitudinal
design. We calculated differences in average HRQOL
scores across adjacent categories of an anchor for MID
estimations [21,23,33]. However, the resulting differences
between adjacent groups by a cross-sectional design may
not accurately reflect longitudinal changes within the
same group. This latter method is known as the minimally
important change (MIC) or responsiveness [3,19]. A lon-
gitudinal design would be preferable approach to exam-
ine changes in HRQOL.

Conclusion
There is a great need to develop and validate diabetes
HRQOL instruments for Chinese populations. In this
study, we validated a Taiwanese version of the DQOL in
Chinese language for diabetes patients in Taiwan. We
used different psychometric methods together with differ-
ent laboratory indicators and diabetes complications to
validate the DQOL. In addition to providing a useful
questionnaire, we also used a combined anchor-based


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Health and Quality of Life Outcomes 2008, 6:87


and distribution-based method to interpret the DQOL
scores. Further evaluation and improvement are indi-
cated, especially to estimate responsiveness.


Competing interests
The authors declare that they have no competing interests.


Authors' contributions
IH, JL, and CH conceived the study and its design. MW
and CH collected the data in Taiwan. IH, JH, and WL ana-
lyzed the data. IH, JL, and CH interpreted the data. IH
drafted the manuscript. IH, JL, AW, MW, WL, and CH
revised critically for important intellectual content.


Acknowledgements
We thank Dr. Alan M.Jacobson for providing original DQOL and Dr. Alice
Y. Chang for assistance in the translation of Chinese version of the DQOL.
This study was supported by a grant from the Taiwan Department of
Health (DOH), under contract # DOH-9508.

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