For older adolescents and young adults (AYAs) with type 1 diabetes, successful transition from pediatric to adult diabetes care requires ongoing planning and support. Yet, the transition to adult care is not always smooth. Some AYAs struggle to leave pediatric care or experience significant gaps between pediatric and adult diabetes care. The use of diabetes-specific transition readiness assessments can inform transition planning and support successful preparation for adult care. This study evaluated transition readiness in a diverse sample of AYAs nearing transition to adult diabetes care. Findings suggest that AYAs may benefit from additional preparation and education related to sexual health, tobacco use, and diabetes complications.
Older adolescents and young adults (AYAs) with type 1 diabetes experience a number of changes as they age into adulthood. This developmental period also is associated with increased mental health challenges and engagement in risky behaviors such as disordered eating and substance use, with potentially serious complications for AYAs with type 1 diabetes (1). In addition to typical developmental tasks of young adulthood, including pursuing higher education, employment, and financial independence, AYAs with type 1 diabetes often assume increased responsibility for diabetes self-management and must transfer their diabetes care from a pediatric-focused to an adult-focused clinician (1). Unfortunately, the transition process may not be a smooth one for all AYAs with type 1 diabetes. AYAs are at increased risk for out-of-range glycemic control, and gaps in diabetes care are prevalent (2). It is recommended that AYAs engage in specialty diabetes care visits at least three times per year. Yet, research suggests that up to 30% of individuals with type 1 diabetes in this age-group do not have follow-up care with an endocrinologist for at least 1 year during this transition period (3,4).
Clinical guidelines highlight best practices to support AYAs during the pediatric-to-adult-care transition process, with the provision of more frequent and targeted planning and support at least 1 year before the transfer to adult diabetes care (4,5). A planned transition from pediatric- to adult-focused medical care is associated with greater satisfaction with medical care, more effective self-management post-transfer, and improved clinical outcomes (6–8).
Unfortunately, the incorporation of transition preparation into pediatric diabetes care has been inconsistent and challenging to implement (9,10). In a large national study with AYAs with type 1 diabetes, only 50% of pediatric patients reported discussing transfer with a pediatric provider, and up to 34% did not feel prepared to transition to adult-focused diabetes care (9). There also may be significant gaps in diabetes education and diabetes self-management skills if initial diabetes education at the time of diagnosis of younger children or teens was targeted more to adult caregivers without additional education targeted to AYAs themselves as they assume more responsibility for their care (11,12).
Thus, assessing transition readiness is a crucial step in the transition process. Transition readiness refers to the capacity, knowledge, and skills of AYAs and related supportive partners (e.g., parents or guardians) to initiate and progress through the transition process. It is recommended that transition readiness be routinely assessed among AYAs with type 1 diabetes starting early in adolescence (13). There are a number of transition readiness tools (e.g., the Transition Readiness Assessment Questionnaire [14] and the Self-Management and Transition to Adulthood with Rx = Treatment Questionnaire [15]) that assess broad transition-related topics associated with taking medication, keeping appointments, tracking health issues, talking with clinicians, and managing daily activities. Higher general transition readiness has been associated with higher self-reported diabetes management (16). Yet, a recent study of Canadian adolescents (mean age 17 years) found that <50% of patients nearing transfer indicated readiness for transition, suggesting that patients immediately facing the transfer to adult diabetes care may require additional support (17).
The majority of previous studies with AYAs with type 1 diabetes have used general transition readiness measures instead of disease-specific tools (12,16,17). The use of diabetes-specific transition readiness assessments may assist clinicians in identifying key gaps in diabetes skills and knowledge and developing targeted strategies to improve these skills. Furthermore, differences in diabetes-specific transition readiness also may be associated with demographic and clinical characteristics such as race/ethnicity, third-party payer type (e.g., public or private insurance), glycemic control, and diabetes technology use (18–20), but these factors have not been adequately explored in AYAs from diverse backgrounds.
This study evaluated diabetes-specific transition readiness in a sample of AYAs who were nearing transition to adult diabetes care, including identifying transition tasks that AYAs felt most and least confident in completing and evaluating associations among transition readiness, sociodemographic and clinical characteristics, and diabetes self-management. It was hypothesized that older age, in-range glycemic control, and higher self-reported diabetes self-management would be associated with higher self-reported transition readiness.
Research Design and Methods
Participants
Participants included 52 AYAs with type 1 diabetes recruited from a pediatric medical center serving a diverse population of children and AYAs with type 1 diabetes. AYA participants enrolled in a behavioral randomized clinical trial evaluating a health communication and transition readiness intervention. Inclusion criteria for the study were age 17–23 years at study enrollment, preexisting diagnosis of type 1 diabetes for >12 months, planning to transition from pediatric- to adult-focused diabetes care within the next 12 months, and having no other significant medical illness or developmental disability. As part of the larger project, 187 recruitment letters were sent to potential participants. Of these, 76 AYAs were reached and eligible to participate, and 52 AYAs enrolled. Primary reasons for not enrolling included lack of interest and time.
Measures
AYA participants provided written consent (for those aged ≥18 years) or assent (for those <18 years of age with parent/guardian consent) to participate in the longitudinal study in accordance with institutional review board protocol and completed surveys at baseline. Demographic characteristics were self-reported, including identified gender, race, ethnicity, education, employment, and subjective socioeconomic status (SES). Subjective SES was measured using the MacArthur Scale of Subjective Social Status, a single item that assessed participants’ perceived social status rank relative to their peers using a scale of 0 (worst off) to 100 (best off) (21). Type 1 diabetes life interference was assessed based on AYAs’ reports of the number of school/work days missed because of diabetes in the past year. Medical information, including A1C and diabetes management regimen, was extracted from the electronic health record.
Diabetes-specific transition readiness was assessed using the Readiness for Emerging Adults With Diabetes Diagnosed in Youth (READDY), v. 1.1, tool (22). Each item on the READDY tool begins with “I am able to …” followed by a diabetes management skill (e.g., “state my target A1C” or “call the office for treatment advice”). Participants rated their confidence level when performing each specific skill using a 5-point Likert scale coded as 1 = “Haven’t thought about it,” 2 = “I plan to start,” 3 = “No, I still need lots of practice,” 4 = “Somewhat, but I need a little practice,” and 5 = “Yes, I can do this.” READDY items are grouped into four subscales: Diabetes Knowledge (Cronbach’s α = 0.76), Health System Navigation (Cronbach’s α = 0.83), Health Behaviors (Cronbach’s α = 0.88), and Insulin Self-Management (Cronbach’s α = 0.88). Subscale scores represent the mean item score. Of note, the Insulin Self-Management subscale includes a subset of items regarding insulin pump use, and this item subset was only completed by participants using insulin pumps at the time of baseline questionnaire completion (n = 24). Higher READDY subscale scores indicate higher levels of transition readiness.
The Diabetes Management Questionnaire (DMQ) assessed participants’ frequency of engagement in diabetes self-management tasks over the past month (23). The 20-item survey includes items related to insulin management, diet/physical activity, and blood glucose monitoring. Participants rated each item on a 5-point Likert scale on which 1 = “almost never” and 5 = “almost always.” Higher scores indicate higher frequency of engagement in diabetes self-management tasks. This scale is highly reliable (published Cronbach’s α = 0.79; current study Cronbach’s α = 0.80) (23).
Statistical Analyses
Data were analyzed using SPSS, v. 27, statistical software. Evaluation of READDY mean item scores identified diabetes transition tasks that AYA participants felt most confident in completing and least confident in completing. Nonparametric tests, including Spearman correlations and Mann-Whitney U tests, evaluated associations among skewed variables. Independent t tests and Pearson correlations evaluated associations among normally distributed variables. Categorical variables were dichotomized (Table 1).
Sociodemographic Characteristics
| AYA Characteristics . | Mean ± SD or % (n) . |
|---|---|
| Age, years | 20.63 ± 1.12 |
| Type 1 diabetes duration, years | 10.94 ± 4.72 |
| A1C, % | 8.24 ± 1.79 |
| Subjective SES score | 64.50 ± 21.6 |
| Gender identity* Male Female Nonbinary | 44.23 (23) 51.92 (27) 3.85 (2) |
| Race/ethnicity† Non-Hispanic white AYAs of color Black/African American Asian American Biracial Another race Hispanic/Latina/o/x | 48.08 (25) 51.92 (27) 34.62 (18) 3.85 (2) 1.95 (1) 1.95 (1) 9.62 (5) |
| Financial independence Yes No Declined to answer | 19.23 (10) 75.00 (39) 5.77 (3) |
| Third-party payer type Private/other Public | 71.15 (37) 28.85 (15) |
| CGM use Yes No | 38.46 (20) 61.54 (32) |
| Pump use Yes No | 46.15 (24) 53.85 (28) |
| School enrollment Yes No | 80.77 (42) 19.23 (10) |
| Employment status Yes No | 66.67 (34) 33.33 (17) |
| Type 1 diabetes life interference None ≥1 missed day | 71.15 (37) 28.85 (15) |
| AYA Characteristics . | Mean ± SD or % (n) . |
|---|---|
| Age, years | 20.63 ± 1.12 |
| Type 1 diabetes duration, years | 10.94 ± 4.72 |
| A1C, % | 8.24 ± 1.79 |
| Subjective SES score | 64.50 ± 21.6 |
| Gender identity* Male Female Nonbinary | 44.23 (23) 51.92 (27) 3.85 (2) |
| Race/ethnicity† Non-Hispanic white AYAs of color Black/African American Asian American Biracial Another race Hispanic/Latina/o/x | 48.08 (25) 51.92 (27) 34.62 (18) 3.85 (2) 1.95 (1) 1.95 (1) 9.62 (5) |
| Financial independence Yes No Declined to answer | 19.23 (10) 75.00 (39) 5.77 (3) |
| Third-party payer type Private/other Public | 71.15 (37) 28.85 (15) |
| CGM use Yes No | 38.46 (20) 61.54 (32) |
| Pump use Yes No | 46.15 (24) 53.85 (28) |
| School enrollment Yes No | 80.77 (42) 19.23 (10) |
| Employment status Yes No | 66.67 (34) 33.33 (17) |
| Type 1 diabetes life interference None ≥1 missed day | 71.15 (37) 28.85 (15) |
Dichotomized male/female.
Dichotomized non-Hispanic White/AYAs of color.
Results
Participants included 52 AYAs nearing planned transfer to adult diabetes care. The AYAs had a mean duration of diabetes of 10.9 ± 4.7 years and a mean A1C of 8.2%. Twelve AYAs (23.1%) had an A1C <7.0%. Approximately 38.5% of AYAs used continuous glucose monitoring (CGM), and 46.2% used an insulin pump. Table 1 describes the total sample demographics. There were no missing data for READDY items; three items were answered as “decline to answer” (two responses related to the impact of glucose control before and during pregnancy and one related to answering questions about family history).
Overall, AYAs reported high confidence in completing tasks associated with transition readiness, with the highest READDY subscale scores for Health Behaviors and Insulin Self-Management. AYAs rated the highest confidence in completing insulin administration and dosing, testing blood glucose levels before meals, and performing diabetes care in front of others; they rated the lowest confidence in explaining the impact of diabetes on sexual health, explaining the impact of tobacco on heart health, and listing examples of tests done in routine visits to identify or prevent complications of diabetes (Table 2). The two items most frequently rated as “I plan to start” concerned insurance, including calling an insurance company to ask about coverage for supplies and having medical insurance or speaking to a social worker/financial counselor about getting coverage. For the sample who completed items related to insulin pump use, all items were rated with high confidence, with the top three being changing infusion sets and filling insulin reservoirs (mean 5.00 ± 0), programming basal rates and bolus dose information (mean 4.91 ± 0.42), and using the dose calculator in the pump (mean 4.83 ± 0.83).
Lowest and Highest Confidence in READDY Items
| READDY Item (subscale) . | Mean ± SD . |
|---|---|
| Highest confidence* | |
| Give my own insulin with a syringe, pen, or pump (Insulin Self-Management) | 4.81 ± 0.63 |
| Determine my insulin dose according to my blood glucose (Insulin Self-Management) | 4.81 ± 0.53 |
| Test blood glucose before each meal and when having symptoms of low glucose values (Health Behaviors) | 4.79 ± 0.58 |
| Perform diabetes care (take insulin, check blood glucose) in front of peers, friends, coworkers, or in public when necessary (Health Behaviors) | 4.77 ± 0.58 |
| Lowest confidence | |
| Explain the impact of diabetes on sexual health/function (Diabetes Knowledge) | 2.90 ± 1.54 |
| Explain the long-term impact of tobacco on heart health in people with diabetes (Diabetes Knowledge) | 3.19 ± 1.52 |
| List examples of tests done in routine visits to identify or prevent complications of diabetes (Diabetes Knowledge) | 3.85 ± 1.29 |
| READDY Item (subscale) . | Mean ± SD . |
|---|---|
| Highest confidence* | |
| Give my own insulin with a syringe, pen, or pump (Insulin Self-Management) | 4.81 ± 0.63 |
| Determine my insulin dose according to my blood glucose (Insulin Self-Management) | 4.81 ± 0.53 |
| Test blood glucose before each meal and when having symptoms of low glucose values (Health Behaviors) | 4.79 ± 0.58 |
| Perform diabetes care (take insulin, check blood glucose) in front of peers, friends, coworkers, or in public when necessary (Health Behaviors) | 4.77 ± 0.58 |
| Lowest confidence | |
| Explain the impact of diabetes on sexual health/function (Diabetes Knowledge) | 2.90 ± 1.54 |
| Explain the long-term impact of tobacco on heart health in people with diabetes (Diabetes Knowledge) | 3.19 ± 1.52 |
| List examples of tests done in routine visits to identify or prevent complications of diabetes (Diabetes Knowledge) | 3.85 ± 1.29 |
The overall highest scoring items were insulin pump–related items. Because insulin pump items were only completed by insulin pump users (46% of the total sample), they are not included in this table.
Table 3 presents associations among AYA demographic and clinical characteristics and transition readiness. Non-Hispanic White race/ethnicity and CGM/pump use were associated with higher scores in Health System Navigation and Health Behaviors (P <0.05 for both); older age also was associated with higher scores in Health System Navigation. Private insurance type and being employed (e.g., having a paid job) were associated with higher Health Behaviors scores (P <0.05 for both). A1C was negatively correlated with Insulin Self-Management (P <0.05). More type 1 diabetes life interference (i.e., missing days of work or school because of diabetes) was associated with higher scores in Diabetes Knowledge and Health System Navigation (P <0.05). Gender identity, school enrollment, financial independence status, and type 1 diabetes duration were not associated with any READDY subscales. Diabetes Knowledge, Health Behaviors, and Insulin Self-Management subscale scores were positively correlated with DMQ scores (P <0.05 for all).
Associations Between Demographic and Clinical Characteristics and READDY Subscales
| Demographic and Clinical Characteristics . | READDY Subscales . | |||
|---|---|---|---|---|
| Diabetes Knowledge (mean 4.13 ± 0.56) . | Health System Navigation (mean 4.33 ± 0.66) . | Health Behaviors (mean 4.57 ± 0.62) . | Insulin Self-Management (mean 4.68 ± 0.58) . | |
| Age | rs = −0.04 (P = 0.75) | rs = 0.27 (P = 0.05) | rs = 0.25 (P = 0.07) | rs = 0.19 (P = 0.18) |
| Type 1 diabetes duration | r = −0.11 (P = 0.45) | rs = −0.16 (P = 0.27) | rs = 0.16 (P = 0.27) | rs = 0.17 (P = 0.24) |
| A1C | rs = −0.20 (P = 0.18) | rs = −0.21 (P = 0.15) | rs = −0.20 (P = 0.16) | rs = −0.42 (P = 0.01) |
| Subjective SES | r = 0.19 (P = 0.18) | rs = 0.27 (P = 0.06) | rs = 0.23 (P = 0.10) | rs = 0.13 (P = 0.36) |
| Gender identity | t(48) = −0.43 (P = 0.67) | U = 282.00 (P = 0.58) | U = 247.50 (P = 0.21) | U = 288.00 (P = 0.63) |
| Race/ethnicity | t(50) = 1.06 (P = 0.29) | U = 194.50 (P = 0.01) | U = 216.00 (P = 0.02) | U = 230.00 (P = 0.03) |
| Financial independence | t(47) = −0.35 (P = 0.73) | U = 188.50 (P = 0.87) | U = 165.50 (P = 0.46) | U = 153.50 (P = 0.27) |
| Third-party payer type | t(50) = −0.91 (P = 0.37) | U = 210.00 (P = 0.18) | U = 163.00 (P = 0.02) | U = 208.00 (P = 0.13) |
| CGM use | t(50) = 2.09 (P = 0.04) | U = 158.00 (P = 0.01) | U = 175.50 (P = 0.01) | U = 306.00 (P = 0.78) |
| Pump use | t(45) = 0.73 (P = 0.47) | U = 161.50 (P = 0.01) | U = 144.00 (P <0.01) | U = 215.00 (P = 0.15) |
| School enrollment | t(50) = −0.07 (P = 0.95) | U = 161.50 (P = 0.26) | U = 196.50 (P = 0.75) | U = 135.00 (P = 0.06) |
| Employment status | t(49) = 0.73 (P = 0.47) | U = 228.50 (P = 0.23) | U = 177.50 (P = 0.02) | U = 287.50 (P = 0.97) |
| Type 1 diabetes life interference | t(50) = 2.51 (P = 0.02) | U = 166.50 (P = 0.03) | U = 193.50 (P = 0.08) | U = 206.50 (P = 0.12) |
| DMQ (mean 65.70 ± 15.44) | r = 0.33 (P = 0.02) | rs = 0.26 (P = 0.06) | rs = 0.44 (P <0.01) | rs = 0.31 (P = 0.03) |
| Demographic and Clinical Characteristics . | READDY Subscales . | |||
|---|---|---|---|---|
| Diabetes Knowledge (mean 4.13 ± 0.56) . | Health System Navigation (mean 4.33 ± 0.66) . | Health Behaviors (mean 4.57 ± 0.62) . | Insulin Self-Management (mean 4.68 ± 0.58) . | |
| Age | rs = −0.04 (P = 0.75) | rs = 0.27 (P = 0.05) | rs = 0.25 (P = 0.07) | rs = 0.19 (P = 0.18) |
| Type 1 diabetes duration | r = −0.11 (P = 0.45) | rs = −0.16 (P = 0.27) | rs = 0.16 (P = 0.27) | rs = 0.17 (P = 0.24) |
| A1C | rs = −0.20 (P = 0.18) | rs = −0.21 (P = 0.15) | rs = −0.20 (P = 0.16) | rs = −0.42 (P = 0.01) |
| Subjective SES | r = 0.19 (P = 0.18) | rs = 0.27 (P = 0.06) | rs = 0.23 (P = 0.10) | rs = 0.13 (P = 0.36) |
| Gender identity | t(48) = −0.43 (P = 0.67) | U = 282.00 (P = 0.58) | U = 247.50 (P = 0.21) | U = 288.00 (P = 0.63) |
| Race/ethnicity | t(50) = 1.06 (P = 0.29) | U = 194.50 (P = 0.01) | U = 216.00 (P = 0.02) | U = 230.00 (P = 0.03) |
| Financial independence | t(47) = −0.35 (P = 0.73) | U = 188.50 (P = 0.87) | U = 165.50 (P = 0.46) | U = 153.50 (P = 0.27) |
| Third-party payer type | t(50) = −0.91 (P = 0.37) | U = 210.00 (P = 0.18) | U = 163.00 (P = 0.02) | U = 208.00 (P = 0.13) |
| CGM use | t(50) = 2.09 (P = 0.04) | U = 158.00 (P = 0.01) | U = 175.50 (P = 0.01) | U = 306.00 (P = 0.78) |
| Pump use | t(45) = 0.73 (P = 0.47) | U = 161.50 (P = 0.01) | U = 144.00 (P <0.01) | U = 215.00 (P = 0.15) |
| School enrollment | t(50) = −0.07 (P = 0.95) | U = 161.50 (P = 0.26) | U = 196.50 (P = 0.75) | U = 135.00 (P = 0.06) |
| Employment status | t(49) = 0.73 (P = 0.47) | U = 228.50 (P = 0.23) | U = 177.50 (P = 0.02) | U = 287.50 (P = 0.97) |
| Type 1 diabetes life interference | t(50) = 2.51 (P = 0.02) | U = 166.50 (P = 0.03) | U = 193.50 (P = 0.08) | U = 206.50 (P = 0.12) |
| DMQ (mean 65.70 ± 15.44) | r = 0.33 (P = 0.02) | rs = 0.26 (P = 0.06) | rs = 0.44 (P <0.01) | rs = 0.31 (P = 0.03) |
Bold type indicates statistical significance.
Discussion
This study examines transition readiness behaviors in a diverse sample of AYAs nearing transfer to adult diabetes care. Results suggest that AYAs feel confident in administering insulin, calculating insulin doses, and monitoring glucose levels. However, they may benefit from additional preparation and education related to reproductive health, tobacco use, and diabetes complications and from direct assistance related to health insurance. Additionally, racial disparities that are evident in glycemic control and other diabetes-related outcomes (24,25) may also be evident in transition readiness behaviors. Specifically, AYAs of color and AYAs who were not using diabetes technology reported lower confidence related to transition readiness skills in the areas of Health System Navigation and Health Behaviors.
Using the READDY tool, AYA participants in this sample reported relatively high overall confidence in transition readiness skills. Mean subscale scores and areas of lower confidence (e.g., sexual health and knowledge about tobacco) were similar to other samples of AYAs using this tool (26,27). However, in our sample of diverse AYAs nearing transition, Diabetes Knowledge was the lowest-scored subscale, and scores were relatively low compared with published mean scores (e.g., Diabetes Knowledge mean = 4.40 [26] or mean = 4.61 [27]). This finding may indicate an important area of assessment, particularly given that all AYAs in the sample were nearing transition to adult diabetes care. The READDY measure is intended to serve as a clinical tool to support transition planning, and identification of topics with lower reported confidence can be used to identify priority areas for assessment, education, and intervention over time (22).
Demographic and clinical characteristics were associated with select READDY domains. Older age, private insurance, and employment were associated with higher confidence in some indices of transition readiness. Interestingly, none of these demographic and clinical characteristics were associated with READDY Diabetes Knowledge subscale score. AYAs of color and AYAs who were not currently using diabetes technology reported lower confidence in indices of transition readiness compared with non-Hispanic White AYAs and AYAs using diabetes technology. Research has consistently demonstrated that AYAs of color often do not have access to diabetes technology at the same rates as non-Hispanic White youth (20). AYAs of color also often experience racism in the quality and delivery of diabetes medical care (28). Chronic experiences of racism in health care may explain the findings that AYAs of color report lower confidence in Health System Navigation and Health Behaviors on the READDY measure compared with AYAs who identify as non-Hispanic White. Differences in confidence between AYAs of color and non-Hispanic White AYAs may also reflect broader systemic factors, including barriers to accessing health care and other resources and clinicians’ implicit or explicit biases related to care delivery and technology use, which are all rooted in ongoing inequity and racial oppression (20,28).
In the current study, glycemic control (A1C) was only significantly associated with the Insulin Self-Management subscale score. This is consistent with published research (26) and highlights the complexity of behaviors related to diabetes self-management and readiness for transition. Health care clinicians are encouraged to use disease-specific tools that assess key behaviors for transition readiness rather than relying solely on glycemic control as a proxy for diabetes self-care. Furthermore, missing ≥1 day of school or work because of diabetes was associated with higher Diabetes Knowledge and Health System Navigation scores; this finding potentially represents AYAs who are missing school or work to attend medical appointments and are staying engaged with the health care system during a busy developmental period.
Strengths and Limitations
Strengths of our study include a diverse sample of AYAs nearing transition to adult diabetes care and assessment of relevant sociodemographic and clinical characteristics unique to an AYA sample. For example, subjective SES was assessed instead of household income because measures of household or individual income might not fully represent AYAs’ social and financial status during this transitional period.
Our study also had limitations, including recruitment of participants from a single site and a relatively small sample size. However, AYAs have many competing priorities and, given the multiple transitions occurring during this period and the added challenges of the coronavirus disease 2019 pandemic, enrolling in research may be challenging. Given the sample size, some variables were dichotomized (e.g., race/ethnicity and gender), and AYAs identifying as nonbinary could not be analyzed as an independent group. There is a crucial need for more diverse cohorts of participants in diabetes research (29). The READDY tool is a diabetes-specific measure, and some challenges common among AYAs such as substance use and disordered eating are not explicitly assessed (22). Additionally, psychosocial functioning, including diabetes distress and mood, may inform transition needs and influence transition-related outcomes (17). As highlighted in conceptual and clinical models of transition readiness (7,26,30,31), future research should track READDY scores over time and use transition readiness and psychosocial indicators to predict outcomes in adult-focused diabetes care.
Conclusion
This study used the READDY measure to evaluate transition readiness in a diverse sample of AYAs with type 1 diabetes nearing transfer to adult diabetes care. Its findings revealed overall high transition readiness, with lowest confidence in Diabetes Knowledge. AYAs of color and AYAs who were not using diabetes technology reported relatively lower confidence in Health Behaviors and Health System Navigation. Our findings emphasize the need to 1) provide additional education related to sexual health, tobacco use, and diabetes complications and 2) use disease-specific transition readiness screeners to support transition planning. Further studies are needed to determine whether transition readiness assessment in pediatric diabetes care can predict diabetes self-management outcomes after transfer to adult diabetes care.
Article Information
Funding
This work was supported by a Pathway to Stop Diabetes Accelerator Award from the American Diabetes Association (1-18-ACE-27), awarded to M.M.
Duality of Interest
No potential conflicts of interest relevant to this article were reported.
Author Contributions
B.L.B. and M.M. wrote the manuscript. B.L.B. and C.H.W. analyzed the data. R.S. provided critical feedback on the manuscript. All authors edited, reviewed, and approved the manuscript. M.M. 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.
M.M. is currently affiliated with the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (NIH/NIDDK), Bethesda, MD. This work was conducted prior to her employment with NIH/NIDDK.
B.L.B. is currently affiliated with Loyola University Chicago, Chicago, IL.
