Type 1 diabetes is a chronic disease affecting ∼3 million Americans (1). The disease is characterized by autoimmune destruction of insulin-producing cells; affected individuals depend on insulin therapy for survival. Although type 1 diabetes is the third most common chronic disease in childhood, 85% of individuals with type 1 diabetes are adults (2).

Optimal management of type 1 diabetes comes at the expense of heavy burden for those with the disease. Although major advances have been made both in the pharmacology of insulin analogs and technological tools to assist in glucose management, only 21% of individuals with type 1 diabetes are able to meet their glycemic targets (3). These struggles are more apparent during adolescence and young adulthood, a developmental period during which adherence levels are low and glycemic control is poorest. In fact, only 17% of youth achieve glycemic targets (3).

Type 1 diabetes increases morbidity and mortality from microvascular and macrovascular disease. The Diabetes Control and Complications Trial and its Epidemiology of Diabetes Interventions and Complications follow-up study examined the effect of intensive blood glucose control on the risk of future complications in patients >13 years of age with type 1 diabetes. These studies demonstrated that intensive blood glucose control profoundly reduces the risk of future microvascular complications (46).

Despite the many technological advances during the past several years, A1C has worsened rather than improving in the adolescent years, and the period from late adolescence through early adulthood remains a challenging time for youth with type 1 diabetes. The unrelenting day-to-day management of type 1 diabetes becomes particularly difficult (7) at this time of many transitions, as these emerging adults make important decisions about their education, career, and health care. Some move away from their home support to attend college, whereas others face the demands of work with or without college-related stress. Each scenario makes diabetes management challenging. Also, the transition from pediatric to adult health care providers can be a stressful process for patients and families.

High self-efficacy has been shown to improve the likelihood of achieving target glycemic control in adolescents and adults with diabetes (8,9). The Global TEENs Study showed that the diabetes health-related quality of life (D-HRQOL) demonstrated a strong association with glycemic control, with lower A1C correlating with higher D-HRQOL (10). In adults with type 1 diabetes, a diagnosis of depression increases the risk of severe hypoglycemic and hyperglycemic symptoms, and dysglycemia increases the risk of depression (11). Other factors that have been shown to affect glycemic control in adolescents include school, living situation, balancing of competing demands, and fitting into social circles (12). These observations highlight domains that may impact diabetes control in emerging adults with type 1 diabetes.

Whereas previous studies examined the effect of these factors independently in relation to glycemic control, we added participants’ living and work situations to gain a broader understanding of the interplay among multiple factors that have the potential to influence diabetes control. In this cross-sectional study, we quantified diabetes self-efficacy, QOL, and depression while exploring future educational and vocational plans for patients in the transition age-group.

Our goal was to evaluate self-efficacy, QOL, and depressive symptoms in adolescents and young adults in relation to diabetes control. Although technological advances have not translated into improved metabolic control, it is possible that they may create the premises for changes in the domains listed above. We hypothesized that 1) Individuals with higher self-efficacy and QOL and few or no depressive symptoms have better glycemic control and 2) living situation/parental support and work circumstances influence glycemic control independent of the reported self-efficacy assessment.

Study Population

Individuals with type 1 diabetes who met inclusion criteria were approached sequentially during their routine follow-up visit at the Diabetes Center at Women and Children’s Hospital of Buffalo (currently Oishei Children’s Hospital) over a period of 8 months (June 2016 to February 2017). We included individuals 16–21 years of age who managed their diabetes with multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). Participants’ use of continuous glucose monitoring (CGM) systems was not recorded, although at that time >35% of patients in our clinic were CGM users. We categorized study participants into two groups: 1) teens aged 16–17 years who were in high school (11th or 12th grade) and 2) young adults aged 18–21 years who were high school graduates (up to 2 years post–high school graduation). The two groups were separated based on high school graduation because this time period is beset by significant life changes and challenges as patients move from their homes to attend college and/or work. We excluded participants with intellectual impairment that would preclude answering survey questions, non–English speaking participants, and those residing in a residential long-term care or mental health facility. There were no specific exclusion criteria regarding diabetes duration, A1C at diagnosis, or duration since diagnosis of type 1 diabetes.

This study was approved by the University at Buffalo institutional review board and was conducted in accordance with the Helsinki Declaration. Participants >18 years of age provided informed consent, whereas younger participants provided assent and their parent/guardian provided parental permission. Participants received $10 as compensation for their time to complete the surveys that were not part of standard of care.

Participant Parameters

Date of birth, date of type 1 diabetes diagnosis, and insulin management regimen were obtained from the electronic medical record. For each participant, point-of-care A1C was measured on the day the surveys were completed using a calibrated Alere Afinion Analyzer, a method certified by the National Glycohemoglobin Standardization Program for point-of-care A1C testing (13).

Study Surveys

Participants completed a brief questionnaire on paper that gathered information on current living situation, composition of household, and current and future educational and/or vocational plans. They were also queried about their current (for high school graduates) or future (for pre–high school graduates) plans regarding education, work, and living environment. These questions are regularly asked at follow-up visits as part of standard care.

Additionally, all participants completed three validated paper questionnaires: a depression screening (the Patient Health Questionnaire Modified for Teens (PHQ-9 Modified), the Stanford Self-Efficacy for Diabetes Scale (DSES) and a diabetes QOL measure (the Diabetes Quality of Life Brief Clinical Inventory (DQOL-BCI).

The PHQ-9 Modified is a depression screen that is routinely used in the adolescent population, including in those with chronic diseases such as type 1 diabetes. The PHQ-9 Modified (Cronbach α >0.8) (14) includes nine Likert scale questions to which participants answer from 0 = not at all to 3 = nearly every day. Additionally, there are questions that ask how a participant has been feeling over the past year as well as probe thoughts of suicidality in the past month or ever in life. Participants answered all the questions on the PHQ-9 Modified, including those assessing suicidality. Individuals with scores corresponding to the moderate or severe depressive symptom range (score >10) were referred to a clinical psychologist and/or social worker for psychological counseling.

The DSES is a reliable (α = 0.83), eight-question survey that asks participants to rate their confidence in performing activities related to diabetes care such as mealtimes, exercise, and hypoglycemia management (15). Responses range from 1 (not confident at all) to 10 (totally confident) that the individual could carry out a type 1 diabetes self-management activity. For this tool, higher scores indicate higher perceived self-efficacy. Lorig et al. (16) reported a mean score of 6.87 ± 1.76 in 186 participants.

The DQOL-BCI is a 15-item questionnaire that asks participants to rate such domains as satisfaction with their current diabetes treatment, the amount of time it takes to manage their diabetes, and how well they adhere to diabetes self-management activities if they are socializing with people who are not aware of their diabetes. Participants were advised that they could skip any survey question they did not feel comfortable answering. Although this survey has been validated and determined to be reliable (α = 0.85) in adults and not in adolescents, the questionnaire includes key discussion items that are part of standard care for youth with type 1 diabetes. Responses range from “very satisfied” to “very dissatisfied” for eight questions and from “never” to “all the time” for seven questions (17). Because the DQOL-BCI does not have a standardized scoring system, we adopted the scoring system of the Pediatric Quality of Life Inventory (18) to measure diabetes-specific QOL. This process entailed first scoring the answers based on a five-point Likert scale ranging from 1 = best DQOL (very satisfied) to 5 = worst DQOL (very dissatisfied), followed by a transformation to a scale ranging from 0 to 100, with 0 indicating lowest DQOL and 100 indicating highest DQOL. Higher scores indicated higher diabetes-related QOL.

Data Analysis

Data were expressed as mean ± SD and percentages. Mann Whitney U tests were used to assess differences between participants who were still in high school (HS) versus those who had graduated high school within the past 2 years (HSG). Spearman’s correlations were used to compute associations between DSES, DQOL, PHQ9, A1C, and time since diagnosis. Associations between DSES and A1C, as well as DQOL and A1C, were further tested using general linear models. Additionally, the estimated marginal means of A1C, adjusted for DSES, were computed after the addition of living arrangements (participants living with parents vs. separately) and vocational/work environment (participants not working vs. working part time or full time). The prevalence of mild, moderate, or severe depression among adolescents and young adults was calculated from the PHQ-9 Modified survey. Associations between PHQ-9 Modified score and A1C were tested in our participants. IBM SPSS Statistics v. 26 software was used for data analysis.

Participant Characteristics

A total of 159 patients followed by a multidisciplinary team at the Oishei Children’s Hospital Diabetes Center were sequentially approached by research personnel, and 125 (78.6%) enrolled in the study. There was an even distribution of females and males. Eighty-six percent of participants identified as non-Hispanic White, whereas 14% identified as African American, Hispanic/Latino, American Indian, or Asian/Pacific Islander. The mean age was 17.7 ± 1.4 years, and time since type 1 diabetes diagnosis was 7.2 ± 4.3 years. Fifty of the 125 participants (40%) were HSG, and 49.7% of participants used CSII.

Glycemic Control and Survey Results

At the time data were collected, the American Diabetes Association (ADA) recommended goal for A1C was <7.5% for children (<19 years) and <7.0% for adults. Only 10.4% (13/125) met the ADA-recommended A1C goals. All participants received their diabetes care from a multidisciplinary team led by pediatric endocrinologists at a tertiary center.

Results of the questionnaires of the entire cohort, as well as for the HS and HSG groups separately, are summarized in Table 1. All 125 participants completed the three validated surveys, and their responses were included in data analysis. A total of 120 participants answered questions regarding living arrangements and vocational plans; three participants in the HS group and two in the HSG group did not complete the entire questionnaire. The five questionnaires that were incomplete were excluded from data analysis.

TABLE 1

Survey Results

HS (n = 75)HSG (n = 50)Total (n = 125)
DSES 8.3 ± 1.4 8.2 ± 1.1 8.2 ± 1.3 
DQOL-BCI 74.3 ± 13.8 74.9 ± 11.1 74.3 ± 12.9 
PHQ-9 Modified 2.7 ± 4.2 3.0 ± 4.1 2.9 ± 4.1 
A1C, % 9.3 ± 1.8 9.1 ± 2.0 9.2 ± 1.9 
 (n = 72) (n = 48) (n = 120) 
Living arrangements, %a    
 Living with parents 33 43 41 
 Not living with parents 67b 57 58 
Vocational plans, %a    
 Attending college 83 77 81 
 Not attending college 17c 23 19 
 Attending college + workingd 97 81 91 
HS (n = 75)HSG (n = 50)Total (n = 125)
DSES 8.3 ± 1.4 8.2 ± 1.1 8.2 ± 1.3 
DQOL-BCI 74.3 ± 13.8 74.9 ± 11.1 74.3 ± 12.9 
PHQ-9 Modified 2.7 ± 4.2 3.0 ± 4.1 2.9 ± 4.1 
A1C, % 9.3 ± 1.8 9.1 ± 2.0 9.2 ± 1.9 
 (n = 72) (n = 48) (n = 120) 
Living arrangements, %a    
 Living with parents 33 43 41 
 Not living with parents 67b 57 58 
Vocational plans, %a    
 Attending college 83 77 81 
 Not attending college 17c 23 19 
 Attending college + workingd 97 81 91 

Data are mean ± SD unless otherwise indicated.

a

Questions about living arrangements and vocational plans were not asked of three HS and two HSG participants. For the HS group, living arrangements and vocational plans indicated their future plans after graduating from high school.

b

Indicates those planning to not to live with parents or unsure about living arrangements.

c

Indicates those planning to not attend college or unsure about college plans.

d

Indicates the percentage of participants who work (HSG) or plan to work (HS) in addition to attending college.

The PHQ-9 Modified scores for the cohort were as follows: 16% had a score of 5–9, corresponding to mild depression; 6% had a score 10–14, corresponding to moderate depression; 0.8% had a score of 15–19, corresponding to moderately severe depression, and 0.8% had a score of 20–27, corresponding to severe depression. In the overall cohort, there was no association between PHQ-9 Modified scores and A1C. However, we did find a negative association between PHQ-9 modified score and DSES and DQOL, as reported in the literature (Table 2).

TABLE 2

Correlation Matrix

DSES, Total ScoreA1C, %DQOL, Mean ScorePHQ-9 Modified, Mean Score
DSES, total score Correlation coefficient 1.00 −0.23* 0.51** −0.31** 
Significance (two-tailed) — 0.01 0.000 0.00 
n 125 123 125 125 
A1C, % Correlation coefficient −0.23* 1.00 −0.23* 0.04 
Significance (two-tailed) 0.01 — 0.01 0.64 
n 123 123 123 123 
DQOL, mean score Correlation coefficient 0.51** −0.23* 1.00 −0.41** 
Significance (two-tailed) 0.00 0.01 — 0.00 
n 125 123 125 125 
PHQ-9 Modified, mean score Correlation coefficient −0.31** 0.04 −0.41** 1.00 
Significance (two-tailed) 0.00 0.64 0.00 — 
n 125 123 125 125 
Time since diagnosis, years Correlation coefficient −0.09 0.23* −0.05 −0.09 
Significance (two-tailed) 0.29 0.01 0.57 0.33 
n 125 123 125 125 
DSES, Total ScoreA1C, %DQOL, Mean ScorePHQ-9 Modified, Mean Score
DSES, total score Correlation coefficient 1.00 −0.23* 0.51** −0.31** 
Significance (two-tailed) — 0.01 0.000 0.00 
n 125 123 125 125 
A1C, % Correlation coefficient −0.23* 1.00 −0.23* 0.04 
Significance (two-tailed) 0.01 — 0.01 0.64 
n 123 123 123 123 
DQOL, mean score Correlation coefficient 0.51** −0.23* 1.00 −0.41** 
Significance (two-tailed) 0.00 0.01 — 0.00 
n 125 123 125 125 
PHQ-9 Modified, mean score Correlation coefficient −0.31** 0.04 −0.41** 1.00 
Significance (two-tailed) 0.00 0.64 0.00 — 
n 125 123 125 125 
Time since diagnosis, years Correlation coefficient −0.09 0.23* −0.05 −0.09 
Significance (two-tailed) 0.29 0.01 0.57 0.33 
n 125 123 125 125 
*

Correlation is significant at the 0.05 level (two-tailed).

**

Correlation is significant at the 0.01 level (two-tailed).

Perceived DSES for the overall cohort was 8.2 ± 1.30. The average DQOL-BCI score was 74.3 ± 12.9. There were no significant differences in DSES score, DQOL-BCI score, depressive symptoms, or A1C values between the HS and HSG groups (P >0.05). The 13 participants who met ADA-recommended A1C goals had significantly higher mean DSES (9 ± 0.7, P <0.05) and DQOL-BCI (82.8 ± 9.6, P <0.05) scores and lower A1C concentrations (6.6 ± 0.5%, P <0.05) than the rest of the cohort. However, they did not differ significantly in mean depressive symptoms.

The mean DSES score for the entire cohort (8.2 ± 1.3) correlated negatively with A1C (R −0.23, P = 0.01). There was also a negative correlation between DQOL-BCI score (74.3 ± 12.9) and A1C (R −0.23, P = 0.01).

Exploratory Analyses

The exploratory analyses included two additional constructs: work/vocational plans and living situation. For the overall cohort, 81% either attended (HSG) or planned to attend (HS) college. The majority of participants (91%) who were attending or planning to attend college reported working or planning to work at the same time. Additionally, 58% of participants reported current or future living arrangements away from home. Among the HSG group, 77% were attending college, and 81% of these youth reported working either part time or full time. Fifty-seven percent of HSG participants reported that they were not living with parents. In the HS group, 83% had plans to attend college, whereas the remainder either did not plan to attend college or were not sure of their future educational plans. With regard to vocational plans for the future, 97% of the HS participants who planned to attend college also planned to work, and only 33% planned to live at home.

The association between DSES and A1C was further tested using general linear models. Living situation and work were added to the models, first as separate constructs and then as a combined moderation term accounting for participants’ living situation and work. With each of these additions, the trend between DSES and A1C was lost (Table 3). After adjustment for DSES, there was trend toward higher A1C among participants who worked and did not live with parents (Figure 1). Additionally, with the addition of living situation and work, the association between DQOL and A1C was no longer significant (data not shown). There was a significant positive correlation between DSES and DQOL (r = 0.5, P = 0.00).

TABLE 3

Association Between Diabetes Self Efficacy and Living and Work Arrangements and A1C

SourceType III Sum of SquaresDfMean SquareFSignificancePartial η2
Corrected model 18.05a 4.51 1.24 0.30 0.05 
Intercept 287.40 287.40 78.69 0.00 0.46 
DSES total 13.35 13.35 3.66 0.05 0.04 
Living with parents 0.59 0.59 0.16 0.69 0.00 
Work 5.13 5.13 1.40 0.24 0.02 
Living with parents * work 0.15 0.15 0.04 0.84 0.00 
SourceType III Sum of SquaresDfMean SquareFSignificancePartial η2
Corrected model 18.05a 4.51 1.24 0.30 0.05 
Intercept 287.40 287.40 78.69 0.00 0.46 
DSES total 13.35 13.35 3.66 0.05 0.04 
Living with parents 0.59 0.59 0.16 0.69 0.00 
Work 5.13 5.13 1.40 0.24 0.02 
Living with parents * work 0.15 0.15 0.04 0.84 0.00 

Dependent variable: A1C (%).

a

R2 = 0.05 (adjusted R2 = 0.01).

FIGURE 1

The solid line represents participants not living with their parents and shows the changes in the estimated marginal means of A1C (%) in those not working versus those working full time or part time. There is a trend toward higher A1C in participants who are working (P >0.05). The dotted line represents participants living with their parents and shows the changes in the estimated marginal means of A1C (%) in those not working versus those working full time or part time. There is a trend toward higher A1C in participants who are working (P >0.05). These trend lines were adjusted for DSES at the mean value (8.2%) for the entire cohort.

FIGURE 1

The solid line represents participants not living with their parents and shows the changes in the estimated marginal means of A1C (%) in those not working versus those working full time or part time. There is a trend toward higher A1C in participants who are working (P >0.05). The dotted line represents participants living with their parents and shows the changes in the estimated marginal means of A1C (%) in those not working versus those working full time or part time. There is a trend toward higher A1C in participants who are working (P >0.05). These trend lines were adjusted for DSES at the mean value (8.2%) for the entire cohort.

Close modal

In this study, we evaluated patient perceptions of diabetes care self-management, QOL, and mood in a population of emerging adults with type 1 diabetes in relation to HSG. This cohort of patients with type 1 diabetes had high DSES and above average DQOL scores, and few reported depressive symptoms. However, diabetes control was poor, likely related to multiple lifestyle demands competing with type 1 diabetes self-management. The mean A1C in our cohort (9.2%) was similar to that reported by the T1D Exchange Clinic NEtwork (19), whereas the percentage of participants meeting age-appropriate ADA-recommended A1C goals (10.4%) appears to be lower (14–17%) (3,19).

Although higher scores on the DSES indicate higher perceived self-efficacy (15), these scores did not appear to translate to participants’ achievement of age-appropriate ADA-recommended A1C goals. A1C concentrations were inversely associated with DSES scores and diabetes QOL, but the associations were not strong. Nevertheless, these correlations highlight that perceived self-efficacy and above-average satisfaction with overall diabetes care per se do not always equate with optimal glycemic control. Furthermore, it is important to note that the DSES questionnaire assessed participants’ perceived self-efficacy in diabetes management, and participants were not required to demonstrate their diabetes knowledge as part of this study. We were therefore unable to evaluate any gaps in perceived versus actual knowledge.

Depression rates in our sample were similar to rates previously reported in youth with type 1 diabetes. The SEARCH for Diabetes in Youth study (20) found that 14% of youth with diabetes had mild depression, whereas 8.6% had moderate or severe depression, and Hood et al. (21) found rates of 12.8% for mild depression and 6.6% for moderate or severe depression among youth with type 1 diabetes. Although depression is associated with poor diabetes self-care, it is unlikely that depressive symptoms are the cause for poor glycemic control in our cohort because the prevalence of moderate to severe depression was very low. This finding may explain the lack of association between depression and glycemic control in our population, although we did find negative associations between DSES, DQOL, and PHQ-9 Modified scores. Other factors, including multiple lifestyle demands and/or stressors during these critical transition years, may have played a significant role in competing with diabetes self-management tasks (22,23).

In our cohort, a large percentage of participants indicated ongoing or future plans for college education along with work, while also reporting a plan to live away from home. These demographic situations could create lifestyle stressors that compete with diabetes self-care. Accordingly, we did see a trend toward higher A1C in participants who were working part time or full time and living separately from their parents. Furthermore, the inverse association between DSES and A1C could no longer be demonstrated in our participants when living arrangements and work situation were added to the equation. The current study did not evaluate diabetes distress in youth, which remains an important avenue for future research.

We recognize that our results may be limited in part by the homogeneous racial makeup of the group. At the same time, this cohort is representative of the population of patients followed at our tertiary multidisciplinary diabetes center and mirrors nationally reported data in youth with type 1 diabetes aged 18–25 years and in the SEARCH study (19,24). Of note is that the percentage of participants using an insulin pump (49.7%) for insulin delivery in our sample was slightly lower than that previously reported in this age-group (55%) (19). However, this study was not designed to examine the potential effect of modality of insulin delivery (CSII vs. MDI) on DSES, DQOL, and PHQ-9 Modified scores or A1C.

These findings underscore the ongoing need to identify the barriers between knowledge of diabetes care and implementation of such knowledge in this population. This finding is not limited to type 1 diabetes, as it has been observed in patients with obesity, prediabetes, and type 2 diabetes, for whom self-management education requires focused professional support (25). Our findings highlight an area of further study to implement transition plans by pediatric diabetes teams as they prepare older teenagers and young adults for the rigors of independence in diabetes care (2629). Pediatric diabetes teams recognize the ADA and Endocrine Society recommendations that individuals in this age-group should receive additional diabetes knowledge and self-management education (30,31). However, despite these efforts, moving the needle toward improved glycemic control in this population remains challenging (27,32).

In conclusion, our data suggest that our cohort of young people with type 1 diabetes report good diabetes self-efficacy in comparison with mean self-efficacy scores reported by Lorig et al. (16). However, based on the lack of achievement of optimal diabetes control, high DSES scores alone may not be a positive prognostic indicator of optimal diabetes control. We recognize that a prospective design may shed additional light on the value of the parameters we measured and that the cross-sectional design of our study limits the generalization of our findings to other youth with type 1 diabetes. Future structured interventions are necessary to assist this vulnerable population as they transition into adulthood while attending college and/or entering the work force, both of which pose demands that may compete with diabetes self-management.

Acknowledgments

The authors thank all of the participants of the study and our research study team members: H. Elsinghorst, A. House, A. Marrone, S. Michalovic, B. Shine, and E. Tabaczynski. L.D.M. receives research support from AstraZeneca, JDRF, Novo Nordisk, Sanofi Aventis. T.Q. receives support for clinical trials from Ascendis, Janssen, Opko, and Provention Bio. I.M. receives support for clinical trials from Rhythm.

Funding

Departmental funds were used to compensate study participants for their time.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

All authors contributed to study design. T.S. and L.D.M. performed the literature review. T.S., L.D.M. and T.Q. planned and executed the study. T.S. collected data and performed data entry. I.M. and T.S. analyzed the data. All authors interpreted the data, participated in manuscript preparation and revisions, and approved the final manuscript: T.Q. was the principal investigator of the study and played a key role in assessing the need for the study and defining study objectives. T.Q. is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

1.
Centers for Disease Control and Preventgion
.
National diabetes statistics report, 2020
.
Atlanta, GA
,
Centers for Disease Control and Prevention, US Department of Health and Human Services
,
2020
.
2.
Pettitt
DJ
,
Talton
J
,
Dabelea
D
, et al.;
SEARCH for Diabetes in Youth Study Group
.
Prevalence of diabetes in U.S. youth in 2009: the SEARCH for diabetes in youth study
.
Diabetes Care
2014
;
37
:
402
408
3.
Foster
NC
,
Beck
RW
,
Miller
KM
, et al
.
State of type 1 diabetes management and outcomes from the T1D Exchange in 2016–2018
.
Diabetes Technol Ther
2019
;
21
:
66
72
4.
Kilpatrick
ES
,
Rigby
AS
,
Atkin
SL
.
The Diabetes Control and Complications Trial: the gift that keeps giving
.
Nat Rev Endocrinol
2009
;
5
:
537
545
5.
Nathan
DM
,
Genuth
S
,
Lachin
J
, et al.;
Diabetes Control and Complications Trial Research Group
.
The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus
.
N Engl J Med
1993
;
329
:
977
986
6.
Nathan
DM
,
Cleary
PA
,
Backlund
JY
, et al.;
Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group
.
Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes
.
N Engl J Med
2005
;
353
:
2643
2653
7.
Borus
JS
,
Laffel
L
.
Adherence challenges in the management of type 1 diabetes in adolescents: prevention and intervention
.
Curr Opin Pediatr
2010
;
22
:
405
411
8.
Beckerle
CM
,
Lavin
MA
.
Association of self-efficacy and self-care with glycemic control in diabetes
.
Diabetes Spectr
2013
;
26
:
172
178
9.
Guo
J
,
Yang
J
,
Wiley
J
,
Ou
X
,
Zhou
Z
,
Whittemore
R
.
Perceived stress and self-efficacy are associated with diabetes self-management among adolescents with type 1 diabetes: a moderated mediation analysis
.
J Adv Nurs
2019
;
75
:
3544
3553
10.
Anderson
BJ
,
Laffel
LM
,
Domenger
C
, et al
.
Factors associated with diabetes-specific health-related quality of life in youth with type 1 diabetes: the Global TEENs Study
.
Diabetes Care
2017
;
40
:
1002
1009
11.
Gilsanz
P
,
Karter
AJ
,
Beeri
MS
,
Quesenberry
CP
 Jr
,
Whitmer
RA
.
The bidirectional association between depression and severe hypoglycemic and hyperglycemic events in type 1 diabetes
.
Diabetes Care
2018
;
41
:
446
452
12.
Chao
AM
,
Minges
KE
,
Park
C
, et al
.
General life and diabetes-related stressors in early adolescents with type 1 diabetes
.
J Pediatr Health Care
2016
;
30
:
133
142
13.
National Glycohemoglobin Standardization Program
.
Certified methods and laboratories
.
Available from http://www.ngsp.org/certified.asp. Accessed 15 May 2020
14.
Nandakumar
AL
,
Vande Voort
JL
,
Nakonezny
PA
, et al
.
Psychometric properties of the Patient Health Questionnaire-9 modified for major depressive disorder in adolescents
.
J Child Adolesc Psychopharmacol
2019
;
29
:
34
40
15.
Self-Management Resource Center
.
Self-efficacy for diabetes
.
16.
Lorig
K
,
Ritter
PL
,
Villa
FJ
,
Armas
J
.
Community-based peer-led diabetes self-management: a randomized trial
.
Diabetes Educ
2009
;
35
:
641
651
17.
Burroughs
TE
,
Desikan
R
,
Waterman
BM
,
Gilin
D
,
McGill
J
.
Development and validation of the Diabetes Quality of Life Brief Clinical Inventory
.
Diabetes Spectr
2004
;
17
:
41
49
18.
Varni
JW
,
Burwinkle
TM
,
Jacobs
JR
,
Gottschalk
M
,
Kaufman
F
,
Jones
KL
.
The PedsQL in type 1 and type 2 diabetes: reliability and validity of the Pediatric Quality of Life Inventory generic core scales and type 1 diabetes module
.
Diabetes Care
2003
;
26
:
631
637
19.
Miller
KM
,
Foster
NC
,
Beck
RW
, et al.;
T1D Exchange Clinic Network
.
Current state of type 1 diabetes treatment in the U.S.: updated data from the T1D Exchange clinic registry
.
Diabetes Care
2015
;
38
:
971
978
20.
Lawrence
JM
,
Standiford
DA
,
Loots
B
, et al.;
SEARCH for Diabetes in Youth Study
.
Prevalence and correlates of depressed mood among youth with diabetes: the SEARCH for Diabetes in Youth study
.
Pediatrics
2006
;
117
:
1348
1358
21.
Hood
KK
,
Lawrence
JM
,
Anderson
A
, et al.;
SEARCH for Diabetes in Youth Study Group
.
Metabolic and inflammatory links to depression in youth with diabetes
.
Diabetes Care
2012
;
35
:
2443
2446
22.
Davidson
M
,
Penney
ED
,
Muller
B
,
Grey
M
.
Stressors and self-care challenges faced by adolescents living with type 1 diabetes
.
Appl Nurs Res
2004
;
17
:
72
80
23.
Ersig
AL
,
Tsalikian
E
,
Coffey
J
,
Williams
JK
.
Stressors in teens with type 1 diabetes and their parents: immediate and long-term implications for transition to self-management
.
J Pediatr Nurs
2016
;
31
:
390
396
24.
Bell
RA
,
Mayer-Davis
EJ
,
Beyer
JW
, et al.;
SEARCH for Diabetes in Youth Study Group
.
Diabetes in non-Hispanic white youth: prevalence, incidence, and clinical characteristics: the SEARCH for Diabetes in Youth Study
.
Diabetes Care
2009
;
32
(
Suppl. 2
):
S102
S111
25.
Funnell
MM
,
Tang
TS
,
Anderson
RM
.
From DSME to DSMS: developing empowerment-based diabetes self-management support
.
Diabetes Spectr
2007
;
20
:
221
226
26.
Weissberg-Benchell
J
,
Wolpert
H
,
Anderson
BJ
.
Transitioning from pediatric to adult care: a new approach to the post-adolescent young person with type 1 diabetes
.
Diabetes Care
2007
;
30
:
2441
2446
27.
Garvey
KC
,
Telo
GH
,
Needleman
JS
,
Forbes
P
,
Finkelstein
JA
,
Laffel
LM
.
Health care transition in young adults with type 1 diabetes: perspectives of adult endocrinologists in the U.S
.
Diabetes Care
2016
;
39
:
190
197
28.
Buschur
EO
,
Glick
B
,
Kamboj
MK
.
Transition of care for patients with type 1 diabetes mellitus from pediatric to adult health care systems
.
Transl Pediatr
2017
;
6
:
373
382
29.
Peters
A
,
Laffel
L
;
American Diabetes Association Transitions Working Group
.
Diabetes care for emerging adults: recommendations for transition from pediatric to adult diabetes care systems: a position statement of the American Diabetes Association, with representation by the American College of Osteopathic Family Physicians, the American Academy of Pediatrics, the American Association of Clinical Endocrinologists, the American Osteopathic Association, the Centers for Disease Control and Prevention, Children with Diabetes, The Endocrine Society, the International Society for Pediatric and Adolescent Diabetes, Juvenile Diabetes Research Foundation International, the National Diabetes Education Program, and the Pediatric Endocrine Society (formerly Lawson Wilkins Pediatric Endocrine Society)
.
Diabetes Care
2011
;
34
:
2477
2485
30.
American Diabetes Association
.
Structured care program offers youth with type 1 diabetes improved transition from pediatric to adult care
.
31.
Endocrine Society
.
Transitions of care: a successful approach to managing pediatric to adult transitions of care
.
32.
Garvey
KC
,
Foster
NC
,
Agarwal
S
, et al
.
Health care transition preparation and experiences in a U.S. national sample of young adults with type 1 diabetes
.
Diabetes Care
2017
;
40
:
317
324
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