International Translation of Health-Related Assessment

The Reliability and Validity of the Diabetes Care Profile for Chinese Populations

Evaluation & the Health Professions 2015, Vol. 38(2) 200-218 ª The Author(s) 2014 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0163278714525628 ehp.sagepub.com

Jing Li1, Zheng Li1, Weigang Zhao2, Hui Pan2, and Edward J. Halloran3

Abstract This study aimed to translate into Chinese the Diabetes Care Profile (DCP), a measure of psychosocial factors and diabetes treatment, and to test the reliability and validity of the instrument within a Chinese population. The English version of the DCP was translated into Chinese following the standard translation methodology with consideration to cultural adaptation. The questionnaire was administered to 313 people with type 2 diabetes in an urban community in Beijing, China. Cronbach’s a coefficient was used to calculate reliabilities, which ranged from .55 to .86 on DCP subscales. Mean values on the DCP differed by diabetes treatment as expected and supports the construct validity of the DCP. The overall score

1

School of Nursing, Peking Union Medical College, Beijing, P.R. China Department of endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China 3 School of Nursing, University of North Carolina at Chapel Hill, NC, USA 2

Corresponding Author: Zheng Li, School of Nursing, Peking Union Medical College, No.9 Dongdan 3 Alley, Dongcheng District, Beijing 100730, China. Email: [email protected]

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on the DCP correlated well both with blood glucose levels and with a validated measure of a Chinese version of the Diabetes Specific Quality of Life scale, thus supporting the DCP’s criterion validity. The DCP is an acceptable measure of the psychosocial factors related to diabetes and its treatment in a Chinese population with type 2 diabetes. Keywords type 2 diabetes, reliability, validity, measure, Chinese

Introduction Diabetes mellitus is a major and growing global health problem (Colagiuri, 2010). The prevalence of diabetes for all age groups worldwide was estimated to be 2.8% in 2000 and is projected to increase to 4.4% in 2030, while the total number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030 (Wild, Roglic, Green, Sicree, & King, 2004). Type 2 diabetes mellitus (T2DM) affects about 85–95% of the people with diabetes in developed countries and an even higher percentage in low- and middle-income countries (World Health Organization, [WHO], 1994). In China, the prevalence of diabetes is relatively high and continues to increase (Gu et al., 2003; Pan, Yang, Li, & Liu, 1997; Xu et al., 2010). Yang et al. (2010) reported that 9.7% of the population—or 92.4 million adults—in China have diabetes (men: 10.6%; women: 8.8%). The prevalence of diabetes increased with age: 3.2%, 11.5%, and 20.4% among persons who were 20 to 39, 40 to 59, and 60 years of age, respectively. The prevalence of diabetes was higher among urban residents than among rural residents (11.4% vs. 8.2%). Diabetic care in China is a major public health challenge. People and their families living with T2DM also face a challenge that requires considerable dedication and commitment to a lifelong regimen imposed by this chronic disease (Funnell et al., 2010). Self-care strategies—including changing eating habits, maintaining healthy body weight, exercising regularly, self-monitoring blood sugar and undertaking foot care—can normalize blood glucose levels and help reduce diabetesrelated mortality and morbidity. Achieving these self-care strategies, however, can be difficult for many patients (Chang, Chiou, Lin, Lin, & Tai, 2005; The Diabetes Control and Complications Trial Research Group, 1993). For example, in the United States, the percentage of patients performing daily self-monitoring of blood glucose (SMBG) has increased

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dramatically over time; still, 36.4% of patients report that they do not perform SMBG daily (Centers for Disease Control and Prevention [CDC], 2013). In contrast, a multicenter diabetic self-care study of 1,524 T2DM patients in China (Liu, Fu, Luan, & Zhan, 2009), revealed that slightly more than half (56.6%) of patients monitored their fasting blood glucose (FBG); 12.2% of patients monitored their postprandial blood glucose (PBG); and only 4% of patients took FBG and PBG monitoring both. Another recent multicenter cross-sectional study in China showed similar results with less than one third of T2DM patients testing their hemoglobin A1c (HbA1c) 2 or more times per year (Fu et al., 2012). Thus, insufficient or poor self-care— resulting in poor glycemic control—has been confirmed, and the current status of glycemic control of T2DM offers a depressing picture for the morbidity and mortality associated with T2DM in China (Fu et al., 2012; Liu et al., 2009). According to the World Health Organization (WHO, 2013), in China, the mortality of cardiovascular diseases and diabetes is 312 per 100,000 for men and 260 per 100,000 for women (compared to 190 for men and 122 for women per 100,000 in the United States). Strategies and possible solutions must be considered and implemented immediately to improve the self-care of diabetes and therefore to improve glycemic control. The American Diabetes Association (ADA, 2012) recommends that assessing the patient’s psychosocial situation should constitute an ongoing part of the medical management of diabetes. Psychosocial factors influence the individual’s and their family’s ability and willingness to embrace diabetes self-care, which in turn affects subsequent behavior change and improved health status (Cunningham et al., 2005). Psychosocial screening and follow-up interventions may include, but are not limited to, attitudes toward diabetes and self-care, expectations for medical management and outcomes, self-efficacy for self-care, general and diabetes-related quality of life, as well as financial, social, and emotional resources (ADA, 2012). Because self-care behaviors related to diabetes treatment are complex, patients may face difficulties in implementing their therapeutic regimen. Inasmuch as psychosocial factors add further complexity to diabetes self-care, a more holistic approach towards the patient and their family needs to be adopted. The Diabetes Care Profile (DCP) is an instrument designed to measure comprehensively these important factors related to diabetes and its treatment (Fitzgerald et al., 1996). The DCP evolved from an educational needs assessment instrument called the diabetes educational profile (DEP), which was itself based on the health belief model. Six constructs labeled ‘‘Control Problems,’’ ‘‘Psychosocial Impact,’’ ‘‘Barriers to Compliance,’’ ‘‘Benefits

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of Regimen,’’ ‘‘Regimen Complexity,’’ and ‘‘Risk of Complications’’— combined with demographic and clinical information—were included in the DEP to evaluate the patient’s attitudes, beliefs, behaviors, and knowledge related to diabetes (Hess, Davis, & Harrison, 1986). Adapted from the DEP to incorporate factors assessing social support and adherence to an individual’s prescribed regimen, the DCP comprises 16 subscales assessing the patients’ diabetes attitudes (Positive Attitude, Negative Attitude and Support Attitude), diabetes beliefs (Importance of Care and Long-term Care Benefits), adherence to diabetes self-care (Self-care Adherence and Diet Adherence), the difficulties of diabetes self-care (Medical Barriers, Exercise Barriers and Monitoring Barriers), the complexity of the treatment (Control Problem and Understanding Management of Practice), psychosocial impact (Social and Personal Factors), self-efficacy (Self-care Ability), and social support (Support Needs and Support). Previous studies showed that several subscales of the DCP could discriminate different levels of disease severity and that these subscales were correlated with HbA1c levels (Cunningham et al., 2005; Fitzgerald et al., 1996, 1998). Moreover, the DCP acted as a measure of DiabetesSpecific Quality of Life domains (perceptions of control, personal, social, and emotional functioning; Anderson, Fitzgerald, Wisdom, Davis, & Hiss, 1997). Ethnicity had no impact on the DCP scale scores, and the DCP was reliable for Hispanic, non-Hispanic White veterans, African American, and Caucasians with T2DM (Cunningham et al., 2005; Fitzgerald et al., 1998). It can be applied to patients treated in community clinics, academic medical centers, or endocrinology clinics (Fitzgerald et al., 1998, 1996). While the DCP can be considered a reliable and valid instrument among multiple ethnicities and sites, however, there are no reports of the DCP being translated for non-English-speaking populations, nor have reliability and validity issues been examined in a Chinese population. To improve the self-care of T2DM in China, the patient, family, and health professionals need to understand the psychosocial and treatment factors that have a negative impact on self-care in Chinese patients. Furthering this understanding would form an initial step in developing strategies to improve the adherence of self-care. Investigator-developed questionnaires were used in two recent studies (Jia, Wang, & Liu, 2005; Liu, & Wang, 2009) in China to evaluate the relationship between the self-care of diabetes and the impact of factors resulting in healthcare professionals’ limited ability to compare findings across diverse settings and populations. When researchers in China (Wang, Lan, et al., 2012; Wang, Zhang, et al., 2012) investigated the relationship between self-care and related factors, they

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downplayed psychosocial factors and focused instead on demographic and biological factors, over which health providers can have no influence (Sigurdardottir, 2005). One concern is that, as a result of this emphasis in previous studies, healthcare professionals may not consider factors related to patients’ self-care behavior, ability, and willingness. Using a validated and reliable instrument in samples across China should help produce more actionable results, and the DCP promises to be such a vehicle for producing more accurate findings, which will in turn inform future clinical practice and research in China. Therefore, the aim of this study is to translate the DCP into Chinese and to use the translation to test the reliability and validity of the instrument among a representative sample of Chinese people with T2DM. Specifically, the study examines (1) whether the Chinese version of the DCP differs for insulin-using and noninsulin-using type 2 diabetics, (2) whether the scale scores are associated with HbA1c levels, and (3) whether scale scores correlate in expected directions with the validated Diabetes Specific Quality of Life (DSQL-C) instrument (Fang, 2001).

Method Sample The study was conducted in a community diabetes service center in Beijing, China, from January 2011 to January 2012. Male and female patients were eligible if they met the following inclusion criteria: (a) they had received a diagnosis of T2DM more than 1 year previously, (b) they were ethnically Han Chinese, and (3) they were aged 18 years or older. Of an estimated 1,100 patients with diabetes visiting the primary care setting of the community diabetes service center during the data collection period, 340 patients were approached by research staff, 332 were eligible, and 313 agreed to participate and completed the study. The Ethics Committee of the School of Nursing of Peking Union Medical College reviewed and approved the study, and all of the patients signed the informed consent document.

Instrumentation The DCP. The DCP is a self-administered questionnaire specific to diabetes. It contains 234 items and 16 scales encompassing social and psychological factors related to diabetes and its treatment and questions concerning demographic information and self-care practices. These scales assess the patients’ diabetes attitudes, diabetes beliefs, adherence to diabetes self-care,

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and the difficulties of diabetes self-care. The reliabilities range from .60 to .95 (Fitzgerald et al., 1996) or .54 to .97 (Cunningham et al., 2005). Two bilingual investigators translated the DCP into Chinese with consideration to patients’ cultural background. An expert panel consisting of two endocrinologists, one dietitian, and two nurses compared the two translated versions (Chinese to English and English to Chinese) for content equivalence. Back-translation into Chinese was conducted by two nursing education experts who are fluent in both Chinese and English, in order to check the adequacy and accuracy of the first English to Chinese translation. The expert panel finalized the Chinese version of the DCP after comparing it with the original English version. Differences in the linguistic structure of English and Chinese were considered during the translation process. For example, in all of the subscales stems we used ‘‘Please circle one answer that suits you best for each line’’ instead of ‘‘circle one answer for each line.’’ This wording was considered more respectful and polite. Five experts (two endocrinologists and three diabetes Nurse Specialists) were then asked to judge the content validity index (CVI) of the Chinese version of the DCP. The experts rated each item based on two criteria: relevance and clarity (Yaghmaie, 2003). A 4-point Likert-type scale was used to evaluate the CVI. The components ranged from 1 ¼ not relevant to 4 ¼ very relevant for rating relevance and 1 ¼ not clear to 4 ¼ very clear for clarity. CVI is the percentage of those items rated by the experts as either 3 or 4. Grant and Davis (1997) suggested the minimum acceptable agreement score should range from .7 to .8. To assess the language quality of the Chinese version of the DCP, a pilot test was performed with 20 patients from a diabetes outpatient clinic in a community hospital. All participants stated that they had no difficulties in understanding the items and agreed to complete all items, taking about 30–40 min to do so. DSQL-C. In this study, the diabetes-specific quality-of-life—Chinese version (DSQL-C; Fang, 2001) was used to assess and predict patients’ quality of life. The DSQL-C is a 24-item self-report questionnaire comprising four subscales that assess physical, emotional, social, and control functioning. It rates responses on a 5-point Likert-type scale (1 ¼ never, 2 ¼ infrequently, 3 ¼ sometimes, 4 ¼ frequently, 5 ¼ always). The constituent items are summed to calculate the total score (120 points). A lower score is indicative of a decreased impact from the disorder and a higher quality of life. The internal consistency estimate, Cronbach’s a ¼ .95, was very

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acceptable, and it was found to correlate significantly to the SF-36 (.62; Fang, 2001).

Data Analysis Statistical analyses were carried out using Statistical Package for the Social Sciences (SPSS) version 12.0 (SPSS, Chicago, IL, USA). Independent t-tests were used to evaluate the differences between insulin-treated and noninsulin-treated patients for age, duration of diabetes, systolic blood pressure, diastolic blood pressure, body mass index (BMI [kg/m2]), and HbA1c levels. To compare the difference of gender between the two subgroups, a w2 test was used. Internal consistency measures were determined by Cronbach’s a for each subscale of the DCP and DSQL-C. An independent t-test was used to compare the mean differences for each of the 16 DCP subscales between insulin-treated and noninsulin-treated patients. Partial correlation coefficients were calculated to examine the relationship of the DCP scales to HbA1c levels. Duration of diabetes was set as the controlling variable because this factor was directly affected by diabetes self-management (Xu, Toobert, Savage, Pan, & Whitmer, 2008). Pearson correlation coefficients were also used to determine the relationship between the DCP scales with patient responses and the external, validated instruments. Correlations of .30 were considered supportive of construct validity and indicative of criterion validity.

Results Characteristics of Subjects The final sample comprised 313 T2DM patients aged 19 to 83 years old, with an average age of 64.4 + 10.3 years old. Most patients were women (67.73%), and 175 (56%) had attended high school or higher. The average duration of diabetes was 8.1 + 6.3 years, and 121 used insulin alone or both insulin and oral hypoglycemic agents to control their diabetes (38.7%: the insulin-treated subgroup), while 192 used oral agents alone or controlled their diabetes with diet (61.3%: the noninsulin-treated subgroup). The average HbA1c was 7.4 + 1.5% (range: 5.4–13.7%). There were significant differences in age (t ¼ 4.38, p ⬍ .01), gender (w2 ¼ 3.99, p ⬍ .05), duration of diabetes (t ¼ 11.21, p ⬍ .01) and BMI (t ¼ 2.41, p ⬍ .05) between the two subgroups. There was no significant difference in systolic pressure, diastolic pressure and HbA1c between the subgroups (Table 1).

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Table 1. Characteristics of Participants. All Patients Mean + SD or N (%) (n ¼ 313)

Insulin treated Mean + SD or N (%) (n ¼ 121)

Noninsulin treated Mean + SD or N (%) (n ¼ 192)

t/2

Age (years) 64.4 + 10.27 67.51 + 10.37 62.44 + 9.73 4.38** Gender Men 101 (32.27) 31 (25.60) 70 (36.5) 3.99* Women 212 (67.73) 90 (74.40) 122 (63.5) 175 (55.91) 56 (46.28) 119 (61.98) High school education or higher Duration of diabetes 8.05 + 6.32 12.58 + 6.40 5.19 + 4.28 11.21** (years) Systolic blood 126.32 + 6.79 pressure 140 mmHg 27 (8.74) 14 (4.53) 13 (4.21) 2.00 ⬍140 mmHg 282 (91.26) 107 (34.63) 175 (56.63) Diastolic blood 79.53 + 5.90 pressure 90 mmHg 36 (11.67) 16 (5.19) 20 (6.49) .799 ⬍90 mmHg 272 (88.31) 100 (32.47) 172 (55.84) BMI 25.37 + 2.60 25.81 + 2.76 25.09 + 2.46 2.41* HbA1c (%) 7.40 + 1.44 7.30 + 1.07 7.47 + 1.62 1.08 Note. n ¼ 313. BMI ¼ body mass index; HbA1c ¼ hemoglobin A1c. *p ⬍ .05. **p ⬍ .01.

Reliability The CVI of the Chinese version of DCP was .89 for the total scale. Alpha reliability estimates for each subscale varied (Table 2), and ranged from a low of .49 (Control Problems) to a high of .86 (Understanding Management Practice). Using the ‘a if item deleted’ function in SPSS, for the Control Problem subscale, item 4 was deleted because its variance was zero. Furthermore, the item to total correlation of Control Problems subscale ranged from .06 to .64. When items Q5, c) and Q5, f) of the Control Problems subscale were deleted, the Cronbach’s a was improved from .49 to .55. Eight subscales were found to be lower than the expected minimum internal consistency reliability level of .70. The DSQL-C, the previously validated scale, was also reliable with a Cronbach’s a of .80.

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Table 2. Subscale Internal Consistency of Diabetes Care Profile and Correlations Between Diabetes Care Profile Scales and HbA1c Level. Scale Control problems Social and personal factors Positive attitude Negative attitude Self-care ability Importance of care Self-care adherence Diet adherence Medical barriers Exercise barriers Monitoring barriers Understanding mgt. practice Long-term care benefits Support needs Support Support attitudes

Reliability (n)

Correlation (n)

0.49 (312) 0.71 (313) 0.76 (313) 0.70 (313) 0.70 (313) 0.56 (313) 0.61 (313) 0.75 (285) 0.69 (94) 0.54 (312) 0.69 (213) 0.86 (148) 0.67 (313) 0.61 (313) 0.73 (312) 0.77 (313)

0.08 (312) 0.01 (313) 0.31** (313) 0.23** (313) 0.33** (313) 0.22** (313) 0.47** (313) 0.31** (285) 0.14* (287) 0.19* (312) 0.12 (213) 0.28** (148) 0.29** (313) 0.26** (313) 0.25** (312) 0.19** (313)

Note. HbA1c ¼ glycosylated hemoglobin. *p ⬍ .05. **p ⬍ .01.

Scale Differences Among Treatment Groups The six subscales of the DCP were compared for the insulin- and noninsulin-using groups (Table 3). Patients using insulin reported more control problems, a lower positive attitude, more difficulty following their insulin regimen (Medical Barriers) and monitoring their blood or urine glucose (Monitoring Barriers), and a better understanding of their self-care (Understanding Management Practice). Diabetes had less impact on the social and personal life of patients not using insulin (Social and Personal Factors).

Correlations Between DCP Scales and HbA1c Levels Four subscales were significantly correlated (r  .30) with HbA1c levels, including Positive Attitude, Self-care Ability, Self-care Adherence, and Diet Adherence. Patients who reported a better attitude toward diabetes, greater ability of self-care, and a better adherence of self-care and diet had lower HbA1c levels (Table 2). Negative Attitude, Importance of Care, Medical Barriers, Exercise Barriers, Understanding Management Practice, Long-term

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Table 3. Scales Means Differences Among Treatment Groups With Type 2 Diabetes. Scale Control problems Social and personal factors Positive attitude Negative attitude Self-care ability Importance of care Self-care adherence Diet adherence Medical barriers Exercise barriers Monitoring barriers Understanding mgt. practice Long-term care benefits Support needs Support Support attitudes

Insulin treated Mean + SD (n) 2.12 2.68 3.20 2.79 3.74 4.29 3.73 3.52 1.77 2.13 1.95 2.64 3.47 2.84 2.89 4.15

+ 0.31 (121) + 0.48 (121) + 0.61 (121) + 0.67 (121) + 0.56 (121) + 0.46 (121) + 0.52 (121) + 0.76 (111) + 0.64 (118) + 0.73 (120) + 0.48 (97) + 0.57 (75) + 0.69 (121) + 0.88 (121) + 0.95 (121) + 0.54 (121)

Noninsulin treated Mean + SD (n) 1.88 + 0.39 2.51 + 0.46 3.33 + 0.43 2.94 + 0.68 3.85 + 0.54 4.29 + 0.36 3.74 + 0.52 3.37 + 0.74 1.61 + 0.40 2.08 + 0.72 1.67 + 0.52 2.40 + 0.52 3.43 + 0.61 2.69 + 0.67 2.78 + 0.75 4.23 + 0.39

(191) (192) (192) (192) (192) (192) (192) (174) (169) (192) (116) (73) (192) (192) (191) (192)

t 5.99** 3.10** 1.99* 1.91 1.73 0.04 0.33 1.67 2.39* 0.61 4.05** 2.69** 0.58 1.58 1.13 1.47

Note. t ¼ Diabetes Care Profile subscale means compared between Chinese patients using insulin and not using insulin. *p ⬍ .05. **p ⬍ .01.

Care Benefits, Support Needs, Support, and Support Attitude tended to correlate with HbA1c levels but did not meet the criteria of r  .30.

Correlations Between DCP Scales and External Measures To examine the criterion validity of the Chinese version of the DCP, it was compared to the DSQL-C, which was considered to offer standard measures of physical, emotional, social, control functioning, and global quality of life. Correlations between the 16 DCP subscales and the 4 dimensions DSQL-C were calculated (Table 4). The total score of the DSQL-C represents the global quality of life of patients with T2DM. A higher score represents a lower quality of life. The physical functioning subscale of DSQL-C was hypothesized to correlate positively with Control Problems and negatively with Self-care Ability and Long-term Care Benefits. Only Long-term Care Benefits did not meet the criteria of r  .30, though the correlation was statistically

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Table 4. Correlations of Diabetes Care Profile Scales With External Measures. Diabetes specific quality of life (DSQL-C) Scale Control problems Social and personal factors Positive attitude Negative attitude Self-care ability Importance of care Self-care adherence Diet adherence Medical barriers Exercise barriers Monitoring barriers Understanding mgt. practice Long-term care benefits Support needs Support Support attitudes

Physical Emotional Social Control functioning functioning functioning functioning

Total scores

0.34** 0.06

0.21** 0.37**

0.11* 0.35**

0.44** 0.26**

0.37** 0.27**

0.32** 0.04 0.30** 0.13* 0.31** 0.16** 0.16** 0.15** 0.31** 0.05

0.43** 0.40** 0.37** 0.06 0.38** 0.12* 0.17** 0.15** 0.28** 0.07

0.25** 0.35** 0.02 0.10 0.04 0.06 0.13** 0.05 0.00 0.01

0.25** 0.05 0.26** 0.14* 0.08 0.10** 0.18** 0.13** 0.12 0.14

0.45** 0.24** 0.37** 0.11 0.37** 0.18** 0.21* 0.18** 0.33** 0.02

0.21**

0.10

0.01

0.10

0.07 0.08 0.14*

0.06 0.13* 0.29**

0.16** 0.10 0.15**

0.06 0.13* 0.13*

0.16** 0.10 0.10 0.24**

*p ⬍ .05. **p ⬍ .01.

significant. The correlation between the two others were significant (r  .03 and p  .01) and in the expected direction. Physical functioning was also significantly correlated in a negative direction with Positive Attitude and Self-care Adherence and in a positive direction with Monitoring Barriers. The emotional functioning subscale of DSQL-C was hypothesized to correlate positively with Control Problems, Social and Personal Factors and Negative Attitude. Negative correlations were expected between emotional functioning and Positive Attitude, Self-care Ability and Support Attitude. All of the correlations were significant and in the expected direction, except that of Control Problems and Support Attitudes, which failed to meet the criteria of r  .30. Self-care Adherence was also found to correlate negatively with emotional functioning. The social functioning subscale of the DSQL-C was expected to correlate positively with Social and Personal Factors and negatively with Support

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Attitudes. Both of the correlations were found to be significant and in the expected direction, while Support Attitudes failed to meet the criteria of r  .30. Negative Attitude was also found to correlate significantly and positively with social functioning. The control functioning subscale of the DSQL-C was expected to correlate positively with Control Problems, Medical Barriers, Exercise Barriers and Monitoring Barriers; and negatively with Self-care Ability, Diet Adherence, and Long-term Care Benefits. Only one of the expected correlations was significant, as control functioning had positive correlations with Control Problems. Other DCP subscales did not meet the standard of a .30 level or higher. Control Problems, Positive Attitude, Self-care Ability, Self-care Adherence, and Monitoring Barriers correlated significantly with the total score in the correct directions as expected, but Social and Personal Factors, Negative Attitude, Medical Barriers, Long-term Care Benefits, and Support Attitudes did not, although they did correlate in the expected direction.

Discussion This study provides the first evidence of its kind of the reliability and validity of the Chinese version of the DCP. The first step of the study was to translate and modify the language of the DCP in accordance with Chinese culture. The use of Western instruments translated from English into other languages has been a frequent feature and trend of scientific studies around the world. Cultural and environment characteristics need to be taken into account during the translation of such instruments (Vivienne et al., 2008). Previously, it was demonstrated that ethnicity had no impact on the DCP scale when the scale was compared between African Americans and Whites, and between Hispanics and non-Hispanic Whites (Cunningham et al., 2005; Fitzgerald et al., 1998). The recommended criteria—having two or more translators independently develop target versions for translation and back-translation simultaneously—was adopted (Jones, Lee, Phillips, Zhang, & Jaceldo, 2001). This standard approach strengthened the integrity of the translation process with consideration of the cultural background. Interestingly, age, gender, duration of diabetes, and BMI significantly differed between the two subgroups, yet previous studies indicated that age, gender, and BMI had no impact on diabetes self-care (Huang, Courtney, Edwards, & McDowell, 2010; Sousa, Zauszniewski, Musil, McDonald, & Milligan, 2004; Vincze, Barner & Lopez, 2004), while duration of diabetes

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did affect diabetes self-management (Xu et al., 2008). Increased duration is associated with increased adherence to self-management, so duration of diabetes was set as the controlling variable during the data analysis. In general, the DCP proved a reliable instrument for Chinese people with T2DM, although the reliabilities were lower than those in previous studies (Cunningham et al., 2005; Fitzgerald et al., 1998, 1996). Cronbach’s a coefficients were above .7 for all but 8 of the 16 DCP scales. Two items in Q5 (c and f) were problematic, warranting further exploration. There are three reasons to be concerned about the lower Cronbach’s a coefficients. First, the use of instruments that are developed in other countries and cultures, and then implemented in a Chinese sample, is a potential threat to internal validity. For example, all of the patients chose ‘‘0 days’’ for question 4 in the Control Problems subscale: ‘‘How many days in the last month have you had ketone in your urine?’’ According to the China Guideline for T2DM (Chinese Diabetes Society, 2012), urine ketone monitoring is one of the most important parts of daily self-care for Type 1 and gestational or pregestational diabetes. Patients with any type of diabetes under stress, accompanied with other diseases or having high blood glucose over 16.7 mmol/L, should take urine ketone as routine monitoring. For T2DM, urine ketone monitoring is not the same as blood glucose self-monitoring as a routine behavior of self-care in patients’ homes, but it will often be done under a doctor’s guidelines in hospital. Second, the sample of the current study was less diverse, or more homogeneous than those in previous studies. Only Han Chinese T2DM patients were enrolled in the current study, as opposed to comparing both type 1 and type 2 diabetes in the previous study, which also possibly had more heterogeneous subcultures involved (Fitzgerald et al., 1996). Third, the reliability of the results obtained through the questionnaires can be compromised by the use of ambiguous questions, or by a questionnaire that is overly long (United Kingdom Medicines Information, 2012). Taking 30–40 min to complete a questionnaire can cause patients to lose patience or become bored, which in turn could affect the completion of some questions. The importance of completing the questionnaire in a reasonably short time should be highlighted (Stahl et al., 2003), and future psychometric studies should examine ways to reduce the size of the instrument without compromising reliability or validity. According to previous studies, the DCP can differ according to treatment (Fitzgerald et al., 1996). The current study supports the DCP’s validity and the comparisons of the DCP scales with those in other studies, support that confirms the hypothesized differences in previous studies. Insulin use had a significant main effect on Control Problems, Social and Personal Factors,

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Positive Attitude, Medical Barriers, Monitoring Barriers, and Understanding Management Practice. The results indicate that, compared to noninsulin-treated T2DM patients, insulin-treated T2DM patients have more problems controlling their disease; greater life impact from diabetes: lower positive attitude toward diabetes; more barriers both to medicine and to blood or urine sugar monitoring; and more knowledge about disease management. These results were similar to those reported in previous studies (Fitzgerald et al., 1996, 1998). A longer duration of diabetes was found to be significant in insulin-treated patients compared to noninsulin-treated patients, such that patients using insulin had a better understanding of management practice. Significant correlations were found between four subscales (Positive Attitude, Self-care Ability, Self-care Adherence, and Diet Adherence) of the DCP and HbA1c levels; these correlations support the convergent validity of the DCP. Patients with a more positive attitude, better selfcare ability, better self-care, and diet adherence tend to have lower HbA1c levels. Further evidence of convergent validity is the fact that the DCP scale scores were found to be correlated with HbA1c levels when the DCP was tested in previous studies (Fitzgerald et al., 1998, 1996). Only T2DM patients were used in this sample; therefore, the correlations of DCP and HbA1c between Chinese and other ethnicities cannot be compared. Obtaining a larger and more diverse sample, across the various ethnicities in China, needs to be considered in the future. Validity is analogous to accuracy (Davidshofer & Murphy, 2005), so convergent validity was generated from correlations between two different tools measuring the same trait (Polit & Beck, 2004) to examine the accuracy of the new instrument. The DCP comprehensively covered the social and psychological aspects of diabetes and its treatment (Fitzgerald et al., 1996). Six subscales of the DCP measured diabetes-specific quality of life domains, including perceptions of control as well as personal, social, and emotional functioning (Anderson et al., 1997). These aspects coincide with those assessed by the DSQL-C. The hypotheses were found to be supported and in the expected direction when DSQL-C and DCP were compared. Physical functioning and emotional functioning both correlated with Selfcare Adherence, in addition to the hypothesized correlations. Five subscales of DCP were found to have low to moderate correlation with global quality of life (r ¼ .37, .45, .37, .37, .33, p ⬍ .001). Patients with more control problems and monitoring barriers lived a lower quality of life; patients with a more positive attitude, better self-care ability, and better self-care lived a higher quality of life. These findings confirm the convergent validity

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findings of the DCP when they were tested in a previous study (Fitzgerald et al., 1996). Because the translation method and cultural modification might influence convergent validity, further studies need to be conducted to support or challenge these findings. The sample size of this study is similar to that of previous studies (Cunningham et al., 2005; Fitzgerald et al., 1996). While patients in this study were recruited solely from the city of Beijing, their demographic and clinical characteristics are similar to those of patients found in a nationwide survey (Pan, Yang, Jia, Weng, & Tian, 2009). Further, diabetes prevalence and control rates are similar among the populations of major cities in China. On the other hand, although the findings of this study may extend to other urban populations, they do not necessarily apply to rural populations. In China, there are significant differences in health and economic status between rural and urban populations. (Chinese Center for Disease Control and Prevention, 2012).

Implications for Health Professionals The complexity of the treatment of diabetes was one of the most important determinants of patient nonadherence in self-care. Individual-tailored patient education and intervention promise increased success in administering self-care by enhancing the cooperation of well-informed, trained, and motivated patients. The DCP provides health care professionals with a thorough holistic diabetes assessment tool for the foundation of effective patient education and intervention. Therefore, health care professionals in China can give patients tailored and individualized interventions, enhanced by the use of the DCP, and thereby improve adherence to self-care and reduce disease-related complication and mortality. Furthermore, health care professionals can comprehensively and thoroughly improve their understanding by using a reliable and valid instrument to compare the evaluation and intervention results achieved. The ultimate goal is to implement better strategies for developing better blood glucose control in China.

Limitations The results of this study were limited to participants from a community diabetes service center. Participants may not be representative of people who do not go to this center or people in other communities. This convenience sample may work against the desired representativeness of samples. Moreover, the 16 subscale DCP takes a relatively long time to complete. An

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adequate explanation of the aim of the questionnaire and a quiet and comfortable setting should be provided. Although a few people showed impatience when they took more than 30 min, they were able to complete the entire measure. Using the DCP across cultures and languages may, however, feature some potential threats to the accuracy and precision of T2DM because of its length and complexity. For this reason, a future study should examine the psychometric properties of the DCP for Chinese patients with a view to reducing the size and improving the user-friendliness of the instrument.

Conclusions This study has presented the first evidence of its kind for the use of the newly validated and reliable DCP for Chinese patients, the largest and most rapidly growing T2DM population in the world. Also, this is the first known study to test the reliability and validity of DCP in a foreign language. Though the psychometric properties in this Chinese sample were acceptable and appropriate, further replication is recommended with regard to different types of diabetes, treatments for the disease, or regions of China. Acknowledgment The authors wish to acknowledge the assistance of the Diabetes Center staff of Beijing Chaoyang Second Hospital and all of the participants who took part in this study. Thanks to Dr. David G. Arthur, PhD, Dr. James T. Fitzgerald, PhD, and Dr. Nancy Glass, PhD, for their suggestions.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This research was supported by a grant (Peking Union Medical College Youth Found) from the Peking Union Medical College, Beijing, P. R. China.

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