PEDIATRICS Vol. 121 No. 4 April 2008, pp. e912-e919 (doi:10.1542/peds.2007-1484)
ARTICLE |
Self-Management Behaviors, Racial Disparities, and Glycemic Control Among Adolescents With Type 2 Diabetes
a Center for Health Services Research
b Diabetes Research and Training Center
c School of Nursing
d Department of Pediatrics
e Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
 |
ABSTRACT |
|---|
 TOP ABSTRACT METHODSRESULTSDISCUSSIONREFERENCES |
|---|
OBJECTIVE. Type 2 diabetes is a growing problem among adolescents, but little is known about self-management behaviors in this population. Our aim was to examine self-management behaviors and glycemic control among adolescents with type 2 diabetes.
METHODS. From 2003 to 2005, a telephone survey of adolescents with type 2 diabetes was performed. Chart review obtained most recent glycated hemoglobin and clinical characteristics. Analyses compared patient characteristics and self-management behaviors to recent glycated hemoglobin levels.
RESULTS. Of 139 patients contacted, 103 (74%) completed the study. The mean age was 15.4 years: 69% were girls, 47% were white, and 46% were black. Mean glycated hemoglobin was 7.7%, and the average duration of diabetes was 2.0 years. More than 80% of patients reported 
75% medication compliance, and 59% monitored blood glucose >2 times daily. However, patients reported frequent episodes of overeating, drinking sugary drinks, and eating fast food. More than 70% of patients reported exercising 2 times a week, but 68% reported watching 2 hours of television daily. Nonwhite patients had higher glycated hemoglobin and hospitalizations per year compared with white patients. In multivariable analyses, nonwhite race remained significantly associated with higher glycated hemoglobin even after adjusting for age, gender, BMI, insurance status, and other factors. Nonwhite patients were more likely to watch 2 hours of television per day (78% vs 56%), to report exercising 
1 time per week (35% vs 21%), and to drink 1 sugary drink daily (27% vs 13%).
CONCLUSION. Although patients reported good medication and monitoring adherence, they also reported poor diet and exercise habits and multiple barriers. Nonwhite race was significantly associated with poorer glycemic control even after adjusting for covariates. This may, in part, be related to disparities in lifestyle behaviors. Additional studies are indicated to further assess self-management behaviors and potential racial disparities in adolescents with type 2 diabetes.
Key Words: type 2 diabetes • adolescents • self-management • disparities
Abbreviations: T2DM—type 2 diabetes • T1DM—type 1 diabetes • CDP—Children's Diabetes Program • A1C—glycated hemoglobin
Type 2 diabetes (T2DM) is a growing problem among adolescents. Although T2DM previously represented <5% of new diabetes diagnoses in pediatrics, it now accounts for as much as 20% to 50% of new diagnoses.1–7 Similar to adults, T2DM in children disproportionally affects minority populations, including blacks, Hispanics, American Indians, and Asian/Pacific Islanders.1–7 One recent national study suggests that the current prevalence of type 2 diabetes is 0.44 per 1000 youth (age: 10–19 years), with higher rates in minority populations.8 The rapid rise in T2DM in both adults and adolescents has been linked to several factors, but much of this rise is thought to be related to the dramatic increase in obesity and sedentary lifestyles.1–7,9–11 There is concern that diabetes in children may continue to rise, because >30% of adolescents, particularly minority children, are now considered overweight or are at risk for overweight.12,13
Initial studies suggest that adolescents with T2DM have similar diabetes-related characteristics as their adult counterparts, putting them at high risk for diabetes-related complications. Children with T2DM are more likely to have lipid disorders, hypertension, obesity, hyperinsulinemia, and other risk factors for the development of cardiovascular disease, renal disease, and other complications.5,14,15 Improved self-management behaviors could help to improve glycemic control, obesity, and other diabetes-related risks to help reduce the development of diabetes-related complications. Unfortunately, 
50% of adolescents with chronic problems do not comply with care recommendations.16
Although there has been some research to examine the role of self-management behaviors and barriers in adolescents with type 1 diabetes (T1DM),16–24 there has been very little research to examine these issues in adolescents with T2DM. One recent study of predominantly minority adolescents and young adults with diabetes found poor rates of diabetes self-management and difficulties obtaining adequate health care follow-up, but unfortunately this study was not able to accurately discern which patients had T1DM or T2DM.25 Adolescents with T2DM are a unique population with different physical, socioeconomic, and psychosocial dynamics than adolescents with T1DM. As opposed to adolescents with T1DM who have often been diagnosed with diabetes at a younger age, adolescents with T2DM are often faced with the new diagnosis of a chronic disease at the exact same time they are transitioning from dependent relationships to autonomy. Adolescents with T2DM also may be dealing with the comorbid problem of obesity and are more likely to have other family members who are obese or have diabetes. Finally, adolescents with T2DM often come from minority families with specific cultural, ethnic, and socioeconomic barriers to care. The goal of this study was to perform a comprehensive survey to examine self-management behaviors, perceived barriers to these behaviors, and the relationship to glycemic control in adolescents with T2DM.
|
METHODS |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
|---|
Design and Setting
A cross-sectional survey was conducted of all of the adolescents with a clinical diagnosis of T2DM who were cared for at the Children's Diabetes Program (CDP) at the Vanderbilt Eskind Diabetes Clinic. The CDP currently provides care for >1600 children with T1DM and
200 adolescents with T2DM. In the past year, >20% of patients referred to the clinic have been diagnosed with T2DM. Patients were referred by primary care providers throughout Tennessee, Indiana, Illinois, Kentucky, Louisiana, and Alabama. Patients were eligible for the study if they were ages 12 to 21 years with a clinical diagnosis of T2DM on the basis of evaluation by a pediatric endocrinologist in the CDP. Patients were typically diagnosed with T2DM on the basis of clinical characteristics (eg, elevated BMI, presence of acanthosis nigricans, and metabolic syndrome), strong family history, laboratory values (increased insulin or c-peptide levels and absence of autoantibodies), and clinical course over time. Families of all of the eligible patients were contacted via letter, and informed consent and assent were obtained. Consenting subjects participated in a telephone survey as outlined below. A medical chart review was also performed to collect the most recent glycated hemoglobin (A1C) and other clinical data. A modest financial incentive ($15 each to guardian and child) was provided. The Vanderbilt Institutional Review Board approved the study.
Measures
From March 2003 to April 2005, parents or guardians were contacted by telephone and consented verbally. Once consent was obtained, assent was obtained from the adolescent. For the adolescent interview, parents or guardians were asked to leave the room to provide the adolescent with privacy, and they were generally very supportive of this request. To further promote privacy, parents were typically consented and interviewed first, and adolescents were assented and interviewed on a separate occasion. The adolescent was encouraged to answer all of the questions as honestly as possible. All of the responses were kept confidential to promote accurate responses.
The telephone survey consisted of 85 questions related to diabetes self-management behaviors and perceived barriers to adherence and took 20 to 40 minutes to administer. Questions included all of the domains of diabetes self-management, including glucose monitoring, diet and exercise behaviors, and medication usage. Questions were selected and adapted from previously validated surveys of healthy and chronically ill adolescents, adolescents with T1DM, and adults with diabetes.26–34 The majority of questions were close ended, but some open-ended questions were included to better ascertain adolescents' perceptions about barriers to self-care behaviors. A series of open-ended questions asked adolescents what they thought was the hardest thing about checking blood sugars, performing exercise, following a diet, and other behaviors. In addition, as part of the survey, a subscale of perceived barriers to eating healthy (healthy eating barriers scale) was created and consisted of 11 items. A subscale of perceived barriers to exercise (exercise barriers scale) was created in a similar manner and consisted of 10 items. These subscale items consisted of statements about perceived barriers, such as, "I eat unhealthy because I am eating away from home with friends" (see Tables 2 and 3 for the items). Responses to these items included "never (0)," "sometimes (1)," or "always (2)." These 2 subscales were derived from a previously validated scale of perceived barriers to diet and exercise in adults with T2DM35 and from 2 other validated scales of perceived barriers in adolescents and adults with diabetes.28,29 The internal reliability of the healthy eating barriers scale and exercise barriers scale in the current study was good (Cronbach's 
of .78, and .77, respectively).
|
|
Consent was also obtained to abstract medical charts. Charts were abstracted from paper records at the CDP and from the electronic medical charts at Vanderbilt University Medical Center. This included the 4 most recent A1C values, BMI, medications, and use of clinical services. The most recent A1C value abstracted from the chart was collected a median of 1.4 months before the survey interview (interquartile range: 0–4 months). The number of visits in the diabetes clinic and a clinic show rate were calculated. Patients typically were seen in the clinic for 3 to 6 visits after initial diagnosis or referral to obtain adequate diabetes education and management. After this, patients were typically scheduled to be seen every 3 months.
Analysis
Patients' characteristics and responses were described using means with SD for continuous variables and percentages for categorical variables. All of the open-ended questions were coded on the basis of common patient-reported responses by 2 independent reviewers, and any discrepancies were resolved by a third reviewer. Bivariate analyses were then performed to compare the most recent A1C value with patient characteristics and survey responses by using parametric tests (t tests, one-way analysis of variance or Pearson correlations) or nonparametric statistics (Wilcoxon rank-sum and Kruskal-Wallis for categorical variables and Spearman correlations for continuous variables), depending on the distribution of the variable.
Multivariable analyses were performed to further explore the relationship between patient characteristics and A1C. In the first multivariable analysis, we examined the relationship between patient characteristics and the most recent A1C value by using an ordinary least-square regression method. In a second analysis, we examined the relationship between patient characteristics and the rate of change in the 4 most recent A1C values. For this analysis, we applied an ordinary least-square regression method with correction for intrasubject correlation among repeated measures of A1C with a bootstrap estimation method.36,37 We performed this analysis by including time ordering (1, 2, 3, and 4) of the most recent A1C values measured in the model. We repeated the analysis using actual time (days) between the A1C values and obtained similar results. We assessed for interaction between time and race by including a cross-product in the model to assess whether rates of change in A1C differed between whites and nonwhites.
For all of the multivariable models, regression residuals were examined graphically, and sensitivity analyses were conducted by using Box-Cox transformations of outcome variables to achieve normality of the residuals. Results were generally similar with untransformed or transformed analyses, and results from untransformed models are reported. Covariates included in all of the multivariable models were chosen a priori and included age, race, gender, insurance status, BMI percentile, clinic show rate, duration of diabetes, and family history of diabetes. Bivariate correlation analysis and the multivariable variance inflation factor were assessed to examine collinearity among the variables included in all of the regression models, and no collinearity was identified. All of the regression models were validated via bootstrap model validation.36
After the initial analyses suggested disparities in glycemic control by race, we posthoc performed additional bivariate analyses to examine other potential differences by race. Patients were dichotomized as white or nonwhite (primarily black) for these analyses. All of the analyses were performed by using Stata 8.0 (Stata Corp, College Park, TX), and R 2.3.1 (www.r-project.org).
|
RESULTS |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
|---|
A total of 182 potential patients with T2DM were initially identified. Of these, 20 had incorrect contact information, 21 were ineligible (age <12 or >21 years, or uncertainty about T2DM diagnosis), and 2 were autistic and could not participate in the survey. Of the 139 patients contacted, 103 (74%) consented and completed the study, 18 refused, and 18 did not respond to repeated telephone calls. Patient characteristics are outlined in Table 1. Mean age was 15.4 years, 69% were female, and the majority was white or black. Only 2% of the population reported that they were of Hispanic or Latino origin. Mean A1C was 7.7%, and the average duration of diabetes was 2.0 years. There was a strong family history of diabetes, and most patients were at risk for overweight (BMI
85% for age and gender) or overweight (BMI 95% for age and gender).
|
More than 37% of patients perceived that following diet and exercise recommendations was the most challenging aspect of diabetes care, whereas 31% reported that it was medication adherence (see Table 2). More than 80% of patients reported
75% medication compliance, and 59% monitored blood glucose >2 times a day. Common reasons reported for nonadherence to medication usage or glucose monitoring included lack of motivation and distraction by competing interests (eg, school and friends).
Exercise and diet behaviors and perceived barriers are reported in Tables 3 and 4. Patients reported frequent episodes of overeating, drinking sugary drinks, and eating fast food. More than 70% of patients reported exercising 2 times a week, but 68% reported watching 2 hours of television daily. When subjects were asked their perception of the hardest thing about following a good diet, the most common response was dealing with cravings or temptations. Common barriers to eating healthy included feeling stressed or sad, dealing with temptations or cravings, and eating outside the home. When subjects were asked their perception of the hardest thing about following an exercise plan, the most common response was lack of motivation. Patients endorsed many barriers to exercise including feeling too stressed, feeling bored or sad, being too busy, or not having access to proper equipment.
|
In bivariate analyses, higher A1C was significantly associated with older age, nonwhite race, longer duration of diabetes, and being on insulin (see Table 5). Nonwhites had a mean A1C of 8.4 compared with 7.0 for whites (P < .01). A1C was not significantly associated with gender, insurance status, family history of diabetes, or other factors. A1C was also not significantly associated with patients' report of self-management behaviors or perceived barriers to self-management. In multivariate analysis, nonwhite race and duration of diabetes remained significantly associated with the most recent A1C value even after adjustment for age, insurance, clinic show rate, and other factors (see Table 6). An examination of the relationship between patient characteristics and the rate of change of the 4 most recent A1C values also demonstrated a racial disparity in glycemic control (see Fig 1); in a multivariate model, nonwhite race was significantly (P < .001) associated with higher A1C values over time even after adjusting for age, gender, insurance, family history of diabetes, and other factors.
|
|
|
Nonwhite patients had higher hospitalizations per year (0.43 vs 0.17; P < .05) compared with white patients. Nonwhite patients were significantly more likely to be on insulin therapy than whites (56% vs 33%; P < .05). Nonwhite patients were more likely to watch
2 hours of television per day (78% vs 56%; P < .05). There was also a trend for nonwhite patients to be more likely to report exercising 1 time per week (35% vs 21%; P = .12), and to drink 1 sugary drink daily (27% vs 13%; P = .06). There were no significant racial differences for age, gender, duration of diabetes, BMI, or for other self-management behaviors or perceived barriers to self-management. |
DISCUSSION |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
|---|
This study represents one of the most comprehensive examinations of self-management behaviors and perceived barriers to care of adolescents with T2DM to date. Although these adolescents reported good medication and monitoring behaviors, they also reported many poor diet and exercise habits and multiple barriers to self-management. Nonwhite race was significantly associated with poorer glycemic control even after adjusting for covariates. This may, in part, be related to disparities in lifestyle behaviors but requires additional investigation.
Of concern, our study reveals that these adolescents report many concerning self-management behaviors and many barriers to performing diabetes-related self-management. Unlike patients with T1DM who can have immediate negative consequences if they do not take care of their diabetes, patients with T2DM may not have this incentive to be more adherent. In our study, poor diet and exercise behaviors and perceived barriers to these behaviors were particularly common. Although we were unable to demonstrate a significant relationship between patient-reported behaviors or barriers and glycemic control, this is not surprising given the limitations of self-report, our small sample size, and the fact that glycemic control is influenced by many other factors, including genetics, provider recommendations, and medications. Nonetheless, improving patient self-management behaviors and reducing barriers could be important steps to improve diabetes care for these patients. Although some of these reported barriers, such as insufficient access to adequate food or exercise equipment, may be readily amenable to interventions, other barriers, such as dealing with competing interests and emotional factors, may require more detailed examination and more intensive approaches. Providers need to be cognizant that these adolescents endorse many barriers related to stress, anxiety, depressed mood, and other emotional factors that may make it particularly difficult for them to improve their behaviors. Addressing these barriers may represent an important opportunity to develop long-term behavioral changes that could result in sustained improvement in clinical outcomes.
Multiple different analyses in our study demonstrated a disparity in glycemic control between whites and nonwhites, suggesting the robustness of this finding. The results are also consistent with studies in adults with diabetes that have demonstrated racial disparities in diabetes-related measures.38–44 Disparities in self-management behaviors could be a plausible explanation for some of the difference in glycemic control, because previous studies have shown that minority children may not have the family support, financial resources, or access needed to participate in rigorous exercise programs or to eat healthily.45–47 However, the etiology for the racial disparity in glycemic control in our study remains unclear. Although some of the difference in glycemic control might be related to differences in self-management behaviors, our survey only uncovered modest racial differences in these behaviors and was not able to directly link these behaviors with worse glycemic control. A previous study by Lipton et al25 of primarily black and Hispanic children and young adults with T1DM or T2DM also found many poor self-management behaviors but lacked a sufficient non-Hispanic white population to assess potential racial disparities. Another possible explanation for disparities in glycemic control could relate to physiologic differences, because previous studies have suggested that nonwhites may have higher insulin resistance, which may contribute to their worse glycemic control.1 The higher rate of insulin use among nonwhites may have been related to greater insulin resistance. Alternatively, the insulin may have been added as a consequence of poorer glycemic control. Other unmeasured or inadequately measured confounders may have also contributed to our findings.
A recent study found that adolescents with T2DM often have family members with diabetes or obesity, and these family members often have poor lifestyle behaviors and poor glycemic control, which may contribute to worse control for the child.48 In our study, we did not find a strong relationship with having a family history of diabetes and worse glycemic control. Our finding is supported by subsequent focus groups with the parents and guardians, which found that, whereas some family members with diabetes may act as negative role models, others can be very positive.49 Involving the entire family in any interventions may be an important opportunity for improving outcomes in these children. One small comparison study has suggested that active family participation can have a positive effect on glycemic control in adolescents with T2DM, even in minority families with socioeconomic barriers.50
There are several limitations to our study. As with all cross-sectional studies, our analyses can only identify associations and not causation. Although this study represents 1 of the largest samples of adolescents with T2DM to date, our sample size was small, which limits our ability to demonstrate significant associations or to perform additional analyses to rule out residual confounding. We did not make any adjustments for multiple comparisons in this exploratory analysis. Measurement of self-reported behaviors in adolescents is always challenging, and our measure of behaviors and perceived barriers may not always reflect actual behaviors or barriers. Finally, we recruited our patients from a single clinic site. Although this site serves a diverse population from a large geographic area, we followed relatively few Latino patients, and our results may not be generalizable to all adolescents with diabetes.
T2DM is a significant health problem in this country. The growing rate of T2DM in adolescents will have a substantial public health impact in the future if these patients are not able to attain good control of their diabetes. Unfortunately, poor self-management behaviors and barriers to care were common among these adolescents with T2DM. Although these findings are very concerning, they may also represent important opportunities to improve care for these patients. In particular, addressing perceived barriers to self-management may be an excellent approach for changing behaviors to create long-term improvements in diabetes care and outcomes. Our results also suggest possible racial disparities, and the role of race in adolescents with T2DM needs to be further evaluated in larger studies.
|
ACKNOWLEDGMENTS |
|---|
This study was performed with support from the Vanderbilt Diabetes Research and Training Center (National Institutes of Health grant P60 DK020593-28), and an National Institute of Diabetes and Digestive and Kidney Diseases career award for Dr Rothman (National Institutes of Health grant K23 DK065294).
|
FOOTNOTES |
|---|
Accepted Sep 14, 2007.
Address correspondence to Russell L. Rothman, MD, MPP, Internal Medicine and Pediatrics, Vanderbilt Center for Health Services Research, Suite 6000 Medical Center East, Vanderbilt University Medical Center, Nashville, TN 37232-8300. E-mail: russell.rothman{at}vanderbilt.edu
The authors have indicated they have no financial relationships relevant to this article to disclose.
The funding source played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Dr Rothman had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
| What's Known on This Subject Type 2 diabetes is a growing problem in adolescents. Adolescents with type 2 diabetes are at high risk for developing complications early in life. Improved self-management could help to improve glycemic control, obesity, and other factors to help prevent diabetes complications.
|
\
| What This Study Adds This study represents the most comprehensive examination of self-management behaviors and perceived barriers to care of adolescents with type 2 diabetes to date. It demonstrates many poor self-management behaviors, perceived barriers to self-management, and possible racial disparities.
|
|
REFERENCES |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
|---|
1. American Diabetes Association. Type 2 diabetes in children and adolescents. Pediatrics. 2000;105 (3 pt 1):671 –680
2. Pinhas-Hamiel O, Zeitler P. Type 2 diabetes in adolescents, no longer rare.
Pediatr Rev. 1998;19
(12):434
–435
3. Kaufman FR. Type 2 diabetes mellitus in children and youth: a new epidemic. J Pediatr Endocrinol Metab. 2002;15 (suppl 2):737 –744[Web of Science]
4. Fagot-Campagna A, Narayan KM, Imperatore G. Type 2 diabetes in children.
BMJ. 2001;322
(7283):377
–378
5. Fagot-Campagna A, Pettitt DJ, Engelgau MM, et al. Type 2 diabetes among North American children and adolescents: an epidemiologic review and a public health perspective. J Pediatr. 2000;136 (5):664 –672[CrossRef][Web of Science][Medline]
6. Arslanian SA. Type 2 diabetes mellitus in children: pathophysiology and risk factors. J Pediatr Endocrinol Metab. 2000;13 (suppl 6):1385 –1394[Web of Science]
7. Ludwig DS, Ebbeling CB. Type 2 Diabetes mellitus in children: primary care and public health considerations.
JAMA. 2001;286
(12):1427
–1430
8. Liese AD, D'Agostino RB Jr, Hamman RF, et al. The burden of diabetes mellitus among US youth: prevalence estimates from the SEARCH for Diabetes in Youth Study.
Pediatrics. 2006;118
(4):1510
–1518
9. Pinhas-Hamiel O, Dolan LM, Daniels SR, Standiford D, Khoury PR, Zeitler P. Increased incidence of non-insulin-dependent diabetes mellitus among adolescents. J Pediatr. 1996;128 (5 pt 1):608 –615[CrossRef][Web of Science][Medline]
10. Kaufman FR. Diabetes in children and adolescents: areas of controversy. Med Clin North Am. 1998;82 (4):721 –738[CrossRef][Web of Science][Medline]
11. Dietz WH. Overweight and precursors of type 2 diabetes mellitus in children and adolescents. J Pediatr. 2001;138 (4):453 –454[CrossRef][Web of Science][Medline]
12. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002.
JAMA. 2004;291
(23):2847
–2850
13. Ogden CL, Flegal KM, Carroll MD, Johnson CL. Prevalence and trends in overweight among US children and adolescents, 1999–2000.
JAMA. 2002;288
(14):1728
–1732
14. Taha D. Hyperlipidemia in children with type 2 diabetes mellitus. J Pediatr Endocrinol Metab. 2002;15 (suppl 1):505 –507[Web of Science]
15. Pavkov ME, Bennett PH, Knowler WC, Krakoff J, Sievers ML, Nelson RG. Effect of youth-onset type 2 diabetes mellitus on incidence of end-stage renal disease and mortality in young and middle-aged Pima Indians.
JAMA. 2006;296
(4):421
–426
16. Kyngäs HA, Kroll T, Duffy ME. Compliance in adolescents with chronic diseases: a review. J Adolesc Health. 2000;26 (6):379 –388[CrossRef][Web of Science][Medline]
17. Kyngäs H. A theoretical model of compliance in young diabetics. J Clin Nurs. 1999;8 (1):73 –80[CrossRef][Web of Science][Medline]
18. Kyngäs H. Compliance of adolescents with chronic disease. J Clin Nurs. 2000;9 (4):549 –556[CrossRef][Web of Science][Medline]
19. Kyngäs HA, Skaar-Chandler CA, Duffy ME. The development of an instrument to measure the compliance of adolescents with a chronic disease. J Adv Nurs. 2000;32 (6):1499 –1506[CrossRef][Web of Science][Medline]
20. Bryden KS, Peveler RC, Stein A, Neil A, Mayou RA, Dunger DB. Clinical and psychological course of diabetes from adolescence to young adulthood: a longitudinal cohort study.
Diabetes Care. 2001;24
(9):1536
–1540
21. Rosilio M, Cotton JB, Wieliczko MC, et al. Factors associated with glycemic control–a cross-sectional nationwide study in 2579 French children with type 1 diabetes: the French Pediatric Diabetes Group. Diabetes Care. 1998;21 (7):1146 –1153[Abstract]
22. Delamater AM, Jacobson AM, Anderson B, et al. Psychosocial therapies in diabetes: report of the Psychosocial Therapies Working Group.
Diabetes Care. 2001;24
(7):1286
–1292
23. Anderson BJ. Family conflict and diabetes management in youth: clinical lessons from child development and diabetes research.
Diabetes Spectr. 2004;17
(1):22
–26
24. Rewers A, Chase HP, Mackenzie T, et al. Predictors of acute complications in children with type 1 diabetes.
JAMA. 2002;287
(19):2511
–2518
25. Lipton R, Drum M, Burnet D, Mencarini M, Cooper A, Rich B. Self-reported social class, self-management behaviors, and the effect of diabetes mellitus in urban, minority young people and their families.
Arch Pediatr Adolesc Med. 2003;157
(9):919
–925
26. Schlundt DG. Computerized behavioral assessment of dietary compliance in IDDM patients.
Diabetes Educ. 1988;14
(6):567
–570
27. Schlundt DG, Zimering RT. The dieter's inventory of eating temptations: a measure of weight control competence. Addict Behav. 1988;13 (2):151 –164[CrossRef][Web of Science][Medline]
28. Schlundt DG, Rea MR, Kline SS, Pichert JW. Situational obstacles to dietary adherence for adults with diabetes. J Am Diet Assoc. 1994;94 (8):874 –876[CrossRef][Web of Science][Medline]
29. Schlundt DG, Pichert JW, Rea MR, Puryear W, Penha ML, Kline SS. Situational obstacles to adherence for adolescents with diabetes.
Diabetes Educ. 1994;20
(3):207
–211
30. Gilmer MJ, Speck BJ, Bradley C, Harrell JS, Belyea M. The Youth Health Survey: reliability and validity of an instrument for assessing cardiovascular health habits in adolescents. J Sch Health. 1996;66 (3):106 –111[Web of Science][Medline]
31. Speck BJ, Bradley CB, Harrell JS, Belyea MJ. A food frequency questionnaire for youth: psychometric analysis and summary of eating habits in adolescents. J Adolesc Health. 2001;28 (1):16 –25[CrossRef][Web of Science][Medline]
32. Brener ND, Kann L, Kinchen SA, et al. Methodology of the youth risk behavior surveillance system. MMWR Recomm Rep. 2004;53 (RR-12):1 –13[Medline]
33. Perks SM, Roemmich JN, Sandow-Pajewski M, et al. Alterations in growth and body composition during puberty. IV. Energy intake estimated by the youth-adolescent food-frequency questionnaire: validation by the doubly labeled water method.
Am J Clin Nutr. 2000;72
(6):1455
–1460
34. Toobert DJ, Hampson SE, Glasgow RE. The summary of diabetes self-care activities measure: results from 7 studies and a revised scale. Diabetes Care. 2000;23 (7):943 –950[Abstract]
35. Schlundt DG, Ribble RL, Stetson BA. The personal diabetes questionnaire: a tool for assessment of diabetes self-management. Ann Behav Med. 2001;(23 , suppl):S17
36. Harrell FEJ. Regression Modeling Strategies. New York, NY: Springer; 2001
37. Feng Z, Mclerran D, Grizzle J. A comparison of statistical methods for clustered data analysis with Gaussian error. Stat Med. 1996;15 (16):1793 –1806[CrossRef][Web of Science][Medline]
38. National Diabetes Data Group. Diabetes in America. 2nd ed. Bethesda, MD: National Institutes of Health; 1995
39. Jiang HJ, Stryer D, Friedman B, Andrews R. Multiple hospitalizations for patients with diabetes.
Diabetes Care. 2003;26
(5):1421
–1426
40. National Diabetes Information Clearinghouse. National diabetes statistics: diabetes in select U.S. populations. Available at: http://diabetes.niddk.nih.gov/populations/index.htm. Accessed April 9, 2007
41. The California Medi-Cal Type 2 Diabetes Study Group. Closing the gap: effect of diabetes case management on glycemic control among low-income ethnic minority populations.
Diabetes Care. 2004;27
(1):95
–103
42. Agency for Healthcare Research and Quality. National Healthcare Disparities Report, 2005. Rockville, MD. Available at: www.ahrq.gov/qual/nhdr05/nhdr05.htm. Accessed April 9, 2007.
43. Harris MI, Eastman RC, Cowie CC, Flegal KM, Eberhardt MS. Racial and ethnic differences in glycemic control of adults with type 2 diabetes. Diabetes Care. 1999;22 (3):403 –408[Abstract]
44. Kirk JK, D'Agostino RB Jr, Bell RA, et al. Disparities in HbA1c levels between African-American and non-Hispanic white adults with diabetes: a meta-analysis.
Diabetes Care. 2006;29
(9):2130
–2136
45. Sallis JF, Prochaska JJ, Taylor WC. A review of correlates of physical activity of children and adolescents. Med Sci Sports Exerc. 2000;32 (5):963 –975
46. Gordon-Larsen P, McMurray RG, Popkin BM. Determinants of adolescent physical activity and inactivity patterns. Pediatrics. 2000;105 (6). Available at: www.pediatrics.org/cgi/content/full/105/6/e83
47. Fitzgibbon ML, Stolley MR. Environmental changes may be needed for prevention of overweight in minority children. Pediatr Ann. 2004;33 (1):45 –49[Web of Science][Medline]
48. Pinhas-Hamiel O, Standiford D, Hamiel D, Dolan LM, Cohen R, Zeitler PS. The type 2 family: a setting for development and treatment of adolescent type 2 diabetes mellitus.
Arch Pediatr Adolesc Med. 1999;153
(10):1063
–1067
49. Mulvaney SA, Schlundt DG, Mudasiru E, et al. Parent perceptions of caring for adolescents with type 2 diabetes.
Diabetes Care. 2006;29
(5):993
–997
50. Bradshaw B. The role of the family in managing therapy in minority children with type 2 diabetes mellitus. J Pediatr Endocrinol Metab. 2002;15 (suppl 1):547 –551[Web of Science]
PEDIATRICS (ISSN 1098-4275). ©2008 by the American Academy of Pediatrics
CiteULike 
Connotea 
Del.icio.us 
Digg 
Facebook 
Reddit 
Technorati 
Twitter What's this?
This article has been cited by other articles:
|
|
 |
|
  A. A. Kaissi and M. Parchman Organizational Factors Associated With Self-management Behaviors in Diabetes Primary Care Clinics The Diabetes Educator, September 1, 2009; 35(5): 843 - 850. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
