The Type of Patient Training Does Not Impact Outcomes in the First 90 Days of Automated Insulin Delivery Use

Abstract

Background:

Youth starting Omnipod 5 (OP5) can onboard with a diabetes educator or self-start with support from online, industry-provided educational modules. We compared glycemic control and pump interaction by training type among youth initiating OP5.

Methods:

This retrospective review included 297 youth with type 1 diabetes (T1D) aged <22 years initiating OP5. We analyzed baseline continuous glucose monitor (CGM) data and pump and CGM data from the first 90 days of OP5 use. Multilevel mixed-effects regression assessed for changes in time in range (TIR) from baseline to 90 days by training type.

Results:

Of youth initiating OP5, 42.4% trained with a diabetes educator and 57.6% self-started. At baseline, self-starters had a longer T1D duration (5.0 (2.6,7.9) vs. 2.5 (1.3, 5.5) years, P = 0.001), more time <54 mg/dL (0.3% (0.1,1) vs. 0.15% (0,1), P = 0.01), and a higher coefficient of variation (40.2% (37, 44.4) vs. 38.7% (34.4, 42.4), P = 0.004). After 90 days of OP5 use, groups did not differ in time in automated mode or boluses per day. In a longitudinal model, after adjusting for baseline TIR and T1D duration, 90-day TIR was 10.5%-points higher (CI: 9.2–11.8, P < 0.0001), positively associated with baseline TIR (β = 0.82, CI: 0.78–0.85, P < 0.0001), and 1.1%-points greater among self-starters (CI: 0.06−2.2; P = 0.04).

Conclusions:

After 90 days of OP5 use, glycemic control and pump interactions were minimally different between youth who self-started and those who trained with a diabetes educator. For youth at a tertiary care center previously using an Omnipod system, online educational modules offered by industry provide sufficient training for use.

Introduction

Diabetes technology has been exponentially increasing since 2016 when the first automated insulin delivery (AID) system became commercially available. AID systems have been shown in both clinical trial and real-world data to lower hemoglobin A1c (HbA1c) and increase time in range (TIR) in youth with type 1 diabetes (T1D).^1

–5 High-quality education is essential for successful use of diabetes technology.⁶ Increases in digital device use in day-to-day life have paralleled advances in AID systems and digital platforms now offer a second option for AID training beyond traditional in-person methods. During the COVID-19 pandemic, telemedicine diabetes education conducted with an educator via video expanded and proved to be an effective and well-accepted tool among youth and young adults with T1D, especially when providing education on insulin pumps and continuous glucose monitors (CGMs).^7
–9 Digital education may provide an innovative way to improve the efficacy of diabetes education.^6,9

Industry-sponsored online, self-start education is a relatively new option for diabetes device training. In this training modality, online modules created by the device company are accessed by the device user. The online modules guide the user through device setup and insertion, with the end goal of the user being connected to the AID system and ready to use it for diabetes self-management upon completion of the training modules. Currently, no published data are available on the impact of online, self-start training modules on glycemic outcomes for youth with T1D starting AID therapy.

Omnipod 5 (OP5), a tubeless AID system, was FDA approved for use in youth ≥6 years of age on January 27, 2022, and a full commercial launch began on August 1, 2022. OP5 was subsequently approved for ages ≥2years on August 19, 2022. At the Children’s Hospital of Philadelphia (CHOP), youth with T1D who were already using an Omnipod insulin pump could choose to complete individualized education with a Certified Diabetes Care and Education Specialist (CDCES) when starting OP5 or to self-start with support from online, industry-provided educational modules. We hypothesized that youth training with a CDCES would have superior glycemic control and AID interaction during the first 90 days of OP5 use compared with youth who self-started.

Methods

This retrospective chart review included youth with T1D followed at the Diabetes Center at CHOP who initiated OP5. Glycemic and education data were collected from youth who started OP5 on or before November 1, 2022. The research was deemed exempt by the CHOP Institutional Review Board (IRB) and granted a waiver of consent.

Inclusion and exclusion criteria

Youth with T1D followed at the Diabetes Center at CHOP are enrolled in a comprehensive diabetes education program at the time of diagnosis. Before pump-specific onboarding, families are educated on basic concepts of insulin pump therapy. Youth meeting the following criteria were included: aged 2–21 years, clinical diagnosis of T1D, verifiable data in Glooko and/or Dexcom Clarity. Youth who reported using OP5 but who did not have verifiable baseline data in either Dexcom Clarity or Glooko could not be included in the study (n = 39). A final sample size of 297 youth was included.

Data collection

Demographic data, including self-reported race and ethnicity, sex, insurance type, and clinical diabetes data, were collected from the electronic health record. OP5 start date was determined based on Glooko reports and verified by documentation in the electronic health record. Glycemic data collection included CGM data from the 14 days before OP5 initiation (baseline) and CGM and insulin pump data from the first 90 days of OP5 use. Baseline CGM data were recorded from Glooko preferentially and Dexcom Clarity if unavailable in Glooko. CGM metrics collected included the following: glucose management indicator (GMI), average glucose, coefficient of variation (CV), time above range (TAR) >250 mg/dL and >180 mg/dL, TIR 70–180 mg/dl, and time below range (TBR) <70 mg/dL and <54mg/dl. CGM and insulin pump data for first 90 days of OP5 use were extracted from Glooko. Insulin pump delivery data were recorded, including percent basal insulin, percent bolus insulin, percent bolus overrides, average number of boluses per day, time in automated mode, automated mode: limited, and time in activity mode. All data were collected and managed using REDCap electronic data capture tools hosted at CHOP.^10,11

Outcomes measured

The primary outcome was TIR after 90 days of OP5 for those who completed training with CDCES versus those who completed industry-provided self-start virtual education. Secondary outcomes included additional CGM metrics (TAR, TBR, GMI, and CV) and insulin pump interaction metrics (time in automated mode, automated mode: limited, and activity mode; boluses per day, percent bolus overrides, percent bolus insulin, and percent basal insulin).

Statistical analysis

A priori power analysis calculations were performed to determine the sample size needed to detect a ≥5%-point difference in TIR between the two groups with 80% power and a type-1 two-tailed error rate of 5%. Assuming 5% missing data, a standard deviation of 13.4% for TIR after 90 days of OP5 use, and 79.1% within-subjects correlation between baseline and final TIR,⁵ a sample size of 214 was required. Despite this power calculation, all eligible youth were included, increasing the power to 90%.

Summary data are reported as frequency and percentage for categorical data, and as mean and standard deviation for continuous data. Chi-square tests compared differences in self-start versus educator onboarding for categorical variables, and t-tests compared between-group differences for continuous variables. Multilevel mixed-effects linear regression models controlling for baseline glycemic control and age at OP5 start were fit for glycemic outcomes. Multivariable regression models adjusting for baseline TIR and age at OP5 initiation were fit for pump interaction outcomes. Stata statistical software v.18 (StataCorp, College Station, TX, 2023) was used to complete all analyses.

Results

Sample demographics

In total, 336 youth with T1D started OP5 on or before November 1, 2022, and 297 with verifiable Glooko data are included in our analyses. Median age at OP5 start for all youth was 12.3 (9.4, 15.4) years, 44.8% identified as female, and 81.8% as Non-Hispanic white (NHW). Most youth had private insurance (85.9%) and were prior Omnipod users (84.8%). The median T1D duration at OP5 start was 3.9 (2.0, 6.6) years. Among the youth starting OP5, 42.4% completed training with a CDCES, while 57.6% utilized the self-start, online curriculum.

Baseline demographics and glycemic control by onboarding group

Table 1 displays between-group comparisons by onboarding type. Groups did not differ in sex, race and ethnicity, or insurance type. Youth who completed education with a CDCES tended to be younger than those who self-started (11.8 (8.9, 15.3) vs. 12.7 (9.7, 15.5) years, P = 0.08) and had a shorter T1D duration (2.5 (1.3, 5.5) vs. 5.0 (2.6, 7.9) years, P = 0.001). More youth who trained with a CDCES had a T1D duration <1 year (20.6% vs. 4.1%, P < 0.0001). Despite instructions to onboard with a CDCES, three youth on multiple daily injections (MDIs) completed self-start education. All youth transitioning from another brand of insulin pump to OP5 completed training with a CDCES.

Table 1.

Characteristics of Youth Starting Omnipod 5 Based on Onboarding Type

	CDCES trained (n = 126)	Self-Starters(n = 171)	P value
Age at OP5 start (years)	11.8 (8.9, 15.3)	12.7 (9.7, 15.5)	P = 0.076^a
T1D duration at OP5 start (years)	2.5 (1.3, 5.5)	5.0 (2.6, 7.9)	P = 0.001 ^a
Female sex	41.3% (n = 52)	47.4% (n = 81)	P = 0.30^b
Publicly insured	15.9% (n = 20)	12.8% (n = 22)	P = 0.46^b
Race/ethnicity
NHW	80.2% (n = 101)	82.6% (n = 142)
NHB	6.4% (n = 8)	3.5% (n = 6)	P = 0.74^b
Other	13.5% (n = 17)	14.0% (n = 24)
Previous insulin regimen
Omnipod	66.7% (n = 84)	98.3% (n = 168)
Other insulin pumps	4.8% (n = 6)	0	P < 0.001 ^b
Multiple daily injections	28.6% (n = 36)	7.7% (n = 3)
Baseline glycemic control
CGM active time (%)	97.7 (93.4, 98.7)	97.2 (93.5, 98.6)	P = 0.56
GMI (%)	7.5 (6.9, 8.3)	7.7 (7.1, 8.2)	P = 0.30
Average glucose (mg/dL)	172 (149, 207.5)	184 (157, 205)	P = 0.30
CV (%)	38.7 (34.4, 42.2)	40.2 (37, 44.4)	P = 0.004
TIR (%) (70–180 mg/dL)	59 (38, 72.5)	49 (39, 61)	P = 0.04
TAR (%) (>250 mg/dL)	13 (5, 30.5)	18 (9, 29)	P = 0.15
TAR (%) (>180 mg/dL)	28 (25, 57.5)	46 (34, 58)	P = 0.11
TBR (%) (<70 mg/dL)	2 (1, 3.4)	2 (1, 4.6)	P = 0.12
TBR (%) (<54 mg/dL)	0.15 (0, 1)	0.3 (0.1, 1)	P = 0.01

Continuous data reported as median (IQR). Bolded P-values indicate statistical significance.

Wilcoxon signed-rank test.

Chi-square test.

CDCES, Certified Diabetes Care and Education Specialist; OP5, Omnipod 5; T1D, type 1 diabetes; NHW, non-Hispanic white; NHB, non-Hispanic black; GMI, glucose management indicator; CV, coefficient of variation; TIR, time in range; TAR, time above range; TBR, time below range.

Of youth starting OP5, 252 (84.8%) were prior Omnipod users. Among prior Omnipod users, those who chose to train with a CDCES were younger (11.4 (8.7, 15.2) vs. 12.7 (9.7, 15.5); P = 0.009) and had a shorter duration of T1D (3.1 (1.7, 5.7) vs. 4.9 (2.6, 7.6); P = 0.0001), but did not have differences in CGM active time, average glucose, GMI, TIR, TAR, or TBR at baseline.

Baseline TIR was higher in those who trained with a CDCES (59% (38, 72.5) vs. 49% (39, 61), P = 0.04) although the GMI did not differ (7.5% (6.9, 8.3) vs. 7.7% (7.1, 8.2%), P = 0.30). Self-starters had greater glycemic variability at baseline as evidenced by a higher CV (40.2% (37, 44.4) vs. 38.7% (34.4, 42.2), P = 0.004) and more TBR (<54 mg/dL, 0.3% (0.1, 1) vs. 0.15% (0, 1), P = 0.01) than those who trained with a CDCES.

Regression analysis of glycemic control and pump interactions by onboarding type

CGM active time during the first 90 days of OP5 use did not differ between self-starters and the CDCES-trained group (92.5% (85.7, 95.6) vs. 92.7% (86.4, 95.8), P = 0.71). Multilevel mixed-effects linear regression analysis was conducted to assess for differences between onboarding type while controlling for baseline TIR, baseline insulin regimen, and T1D duration at OP5 start (Table 2). TIR during the first 90 days of OP5 use increased by 10.5%-points (CI: 9.2–11.8, P < 0.0001) and was positively associated with baseline TIR (β = 0.82, CI: 0.78–0.85, P < 0.0001). A 1.1%-point greater increase in TIR was observed in youth who self-started relative to those who trained with a CDCES (β = 1.12, CI: 0.78–0.85, P = 0.04) (Fig. 1). The impact of onboarding type on 90-day TIR did not vary by baseline TIR. Youth previously managed with the MDI regimen saw a 2.1%-point greater improvement in TIR compared with youth previously using an insulin pump. Multilevel mixed-effects regression showed similar effects for GMI, average glucose, and TAR with greater improvements in youth who self-started (Table 2). Although youth who trained with a CDCES had TBR <54 mg/dL and a lower CV, no between-group differences for TBR <70 mg/dL were found.

FIG. 1.

Changes in time in range (70–180 mg/dL) from baseline to the first 90 days of OP5 use by Omnipod 5 education type.

Table 2.

Glycemic Data by Onboarding Type Calculated Using Multivariable, Mixed-Effects Regression Analyses That Include Baseline TIR, Duration of T1D, and Previous Insulin Regimen as Covariates

	CDCES training (n = 126)	Self-Starters (n = 171)	Coefficient (95% CI)
90-Day glycemic control
GMI (%)	7.6 (7.5, 7.6)	7.5 (7.4, 7.5)	−0.1 (−0.2, −0.02)
Average glucose (mg/dL)	178 (176, 181)	174 (172, 176)	−4.4 (−7.7, −1.1)
CV (%)	38.4 (37.4, 39.4)	39.7 (38.9, 40.4)	−0.05 (−0.09, −0.00005)
TIR 70–180 mg/dL (%)	57.0 (56.2, 57.9)	58.1 (57.5, 58.8)	1.1 (0.06, 2.2)
TAR (%) (>250 mg/dL)	18.2 (17.0, 19.3)	16.6 (15.7, 17.4)	−1.6 (−3.1, −0.1)
TAR (%) (>180 mg/dL)	40.5 (39.6, 41.5)	38.9 (38.1, 39.6)	−1.6 (−2.9, −0.4)
TBR (%) (<70 mg/dL)	2.3 (1.9, 2.7)	2.8 (2.4, 3.2)	0.5 (−0.1, 1.1)
TBR (%) (<54 mg/dL)	0.4 (0.3, 0.5)	0.6 (0.5, 0.8)	0.2 (0.0009, 0.4)
90-Day pump data
Time in auto mode (%)	88.2 (84.7, 91.8)	87.2 (84.6, 89.9)	−1.0 (−5.5, 3.5)
Time in activity (%)	1.8 (1.2, 2.3)	1.7 (1.2, 2.2)	−0.05 (−0.8, 0.7)
Basal insulin (%)	53.4 (51.9, 55.0)	50.9 (49.7, 52.2)	−2.5 (−4.6, −0.4)
Boluses per day	5.6 (5.0, 6.1)	5.6 (5.1, 6.1)	0.03 (−0.8, 0.8)
Bolus overrides (%)	13.2 (9.9, 16.6)	13.9 (10.8, 17.1)	0.7 (−4.2, 5.6)

Bolded P-values indicate statistical significance.

In multilevel mixed-effects regression models that included baseline TIR, prior insulin regimen, and T1D duration as covariates (Fig. 1), groups did not differ in insulin pump interactions and insulin delivery parameters, as defined by time in auto mode, time in activity mode, boluses per day, and percent bolus overrides. Youth who trained with a CDCES received 2.5%-points more of their total daily insulin dose as basal insulin than youth who self-started (CI: −4.6, −0.4; P = 0.02). Baseline TIR (β = 0.2, CI: 0.07, 0.3, P = 0.002) and duration of T1D at OP5 start (β = 0.7, CI: 0.2, 1.2; P = 0.004) were both positively associated with time in auto mode. Baseline TIR (β = 0.02, CI: 0.01, 0.02; P < 0.0001) was positively associated with boluses per day, while T1D duration (β = −0.06, CI: −0.1, −0.02; P = 0.002) and prior MDI therapy (β = −1.1, CI: −2.0, −0.2; P = 0.01) were negatively associated with boluses per day. Youth with greater baseline TIR were more likely to override recommended boluses (β = 0.06, CI: 0.01, 0.1; P = 0.01).

Discussion

Despite differences in baseline glycemic control and T1D duration, we found minimal differences in glycemic control and insulin pump interactions after 90 days of OP5 use between youth who completed training with a CDCES versus online, industry-provided education. It is also notable that there were no differences in the chosen onboarding type based on race/ethnicity or insurance type. Diabetes education is a pillar of T1D management.^6,12 Youth are recommended to attend quarterly clinic appointments, with more frequent visits required when initiating CGM and AID systems. Although essential for T1D care, these frequent visits contribute to direct health care costs, lost productivity, and financial burdens for those with T1D and their families.^13
–15 Access to virtual diabetes education has the potential to reduce burden, cost, and improve quality of life for people with T1D,^16
–18 particularly in light of the nationwide shortage of diabetes care team members^19
–21 and challenges accessing care for those outside urban areas.²⁰ Effective implementation of online, self-directed AID training for those with sound diabetes knowledge coupled with ongoing support from the diabetes care team has the potential to better support access to diabetes technology.

The benefits of online AID education must be carefully considered in the context of a nationwide shortage of pediatric endocrinologists^19
–21 and diabetes educators.²² As this workforce ages, enrollment in training programs decreases,²¹ and the incidence of T1D continues to increase,²³ there are not enough pediatric endocrinologists and diabetes educators to meet the needs of the growing population of people with T1D.²¹ Furthermore, access to T1D care is not equally distributed, with 96.8% of pediatric endocrinologists located in urbanized areas. One-third of youth must travel more than 20 miles to access a pediatric endocrinologist and only 20% of youth residing in rural areas have access within this distance.²⁰ With approximately 35,000 CDCESs in the United States to care for the over 37 million Americans with known diabetes, there is approximately one diabetes educator for every 10,000 people with diabetes.²² Developing innovative approaches to diabetes care and education has the potential to both improve access to care for youth with T1D and their families while also reducing the strain on endocrinologists and CDCESs.^22,24

Professional guidelines recommend quarterly follow-up clinic visits and regular visits with diabetes educators.^6,25 While these visits are the cornerstone of diabetes self-management, they can be costly for families who must travel to the appointments and spend time away from work and school. The cost of diabetes care in the United States is significant. A person with T1D will incur approximately $1000 in out-of-pocket costs per year and nearly half a million dollars in direct health care costs and lost income in their lifetime.¹⁵ Diabetes care, particularly for those using AID systems that allow for remote data sharing with the clinical diabetes team, is uniquely well suited to telemedicine and has been shown to overcome barriers to care such as time and travel.^26,27 Virtual diabetes education visits were implemented as a matter of necessity during the COVID-19 pandemic and in postpandemic times, people with T1D and their families prefer having the option of virtual care and education.¹⁷ The use of a comprehensive diabetes care model, including diabetes education, delivered via telemedicine, has been shown to be an effective way to reach many patients while simultaneously improving glycemic control and facilitating technology adoption^8,18,28,29 and reducing inequities in use.³⁰ These successes with care team member-delivered virtual diabetes care and education suggest the benefits of exploring additional online educational modalities.

Furthermore, the use of well-designed mobile applications has been shown to provide a convenient learning modality for people with diabetes while also improving glycemic control.³¹ Online, self-directed education also has the potential to educate people beyond youth and families and has already been shown to provide effective diabetes education for school personnel.^7,32 Given the amount of time that youth spend in the care of people outside of their immediate family, school nurses, childcare providers, and others may also benefit from a self-guided online curriculum on AID systems. An online, self-directed diabetes technology curriculum has also been shown to be effective in addressing unmet educational needs education for pediatric endocrinology fellows.^33,34 An online educational tool that can be easily distributed, accessed by many, and provide quality education would provide additional support for youth and families starting AID systems, particularly in areas where access to CDCESs is limited.

Despite the benefits of virtual education, CDCESs remain essential members of the diabetes care team. At CHOP, our team-based diabetes care model focuses on CDCES-led intensive diabetes education and management in the first year.³⁵ Close communication and support from CDCESs in the first year of diagnosis provide our T1D population with a strong foundation in diabetes self-management skills that is essential for success with AID systems. Insulin pump initiation requires hands-on demonstration of physical skills, including insulin cartridge fills and pump-site applications, that are difficult to replace with virtual education, which does not allow for direct assessment and feedback from a CDCES. Although those new to insulin pump therapy and AID likely benefit from in-person education with a CDCES, for those with prior experience with these hands-on aspects of pump use, online education may be equally effective. Although prior OP5 users could choose the type of onboarding training, in our cohort, 33.3% of previous Omnipod users opted to train with a CDCES. Of these youth, those who chose to self-start had a longer duration of T1D, indicating that more experience with T1D and education from the diabetes care team contribute to increased confidence in self-starting a new AID system. Despite the promise of online education, this educational modality is a supplement to, and not a replacement for, the care provided by highly skilled CDCESs.

There are several limitations to our study. The retrospective study design did not allow for assessment of youth and family satisfaction with training. Our sample initiated OP5 during the first 6 months of the full marked release and included many early adopters of technology, who may have had more confidence to self-start with support from online industry education. Youth and their families who were previously using Omnipod were free to choose from online, industry-provided education or training with a CDCES. It is likely that these families are aware of their personal learning preferences and chose the learning modality that met their needs best. Online AID education is unlikely to be the best choice for all youth and their families. Furthermore, CHOP has a robust, predominantly in-person diabetes education program that starts at diagnosis.³⁵ Having benefited from a strong foundation in diabetes education from diagnosis, particularly with the physical aspects of starting an insulin pump, youth and families in this cohort may have been more empowered to consider online education. Despite these limitations, these data provide evidence that self-guided education is a viable alternative to in-person CDCES education for youth and families. Careful screening of eligible families may be necessary to ensure success with this type of training.

Conclusions

In the face of rapidly evolving AID technology and nationwide shortages of pediatric endocrinologists and CDCESs, innovative approaches to diabetes technology education are needed to promote equitable access to care, reduce the burden of T1D, and improve clinical outcomes. Youth and families with a solid foundation in diabetes self-management education provided by highly trained CDCESs and prior experience with an insulin pump should be given the option to self-start OP5 using online education. Supplementary online educational materials focused on advanced aspects of AID use, including sick-day management and exercise mode, may provide additional education needed to support youth and families in attaining recommended glycemic targets. Future studies addressing online education should examine youth and family experience with online education and satisfaction with training experiences in addition to glycemic outcomes.

Footnotes

Authors’ Contributions

B.E.M. and S.M. formulated the clinical question. J.D., A.R., S.M., and R.S. collected data. B.E.M. and S.M. wrote the article with contributions from J.D. B.E.M. and A.K. completed the statistical analyses and created the figures and tables. J.D., A.R., and R.S. made critical contributions to the article. All authors edited, reviewed, and approved the article.

Author Disclosure Statement

S.M. has received a speaker honorarium from Dexcom. B.E.M. is supported by the National Institutes of Health (NIH; PI: K23DK129827) and has received investigator-initiated research support from Tandem Diabetes Care, Inc. (TDC20210226) and the Cystic Fibrosis Foundation, industry-sponsored research support from Medtronic, and research supplies from Dexcom, Inc. and Digostics. The other authors have no conflicts of interest to disclose.

Funding Information

No funding was received for this article.

References

Brown

, Forlenza

, Bode

, et al. Multicenter trial of a tubeless, on-body automated insulin delivery system with customizable glycemic targets in pediatric and adult participants with type 1 diabetes. Diabetes Care, 2021; 44(7):1630–1640.

Breton

, Kanapka

, Beck

, et al. A randomized trial of closed-loop control in children with type 1 diabetes. N Engl J Med, 2020; 383(9):836–845.

Garg

, Grunberger

, Weinstock

, et al. Improved glycemia with hybrid closed-loop versus continuous subcutaneous insulin infusion therapy: Results from a randomized controlled trial. Diabetes Technol Ther, 2023; 25(1):1–12.

Ekhlaspour

, Town

, Raghinaru

, et al. Glycemic outcomes in baseline hemoglobin A1C subgroups in the international diabetes closed-loop trial. Diabetes Technol Ther, 2022; 24(8):588–591.

Marks

, Meighan

, Zehra

, et al. Real-world glycemic outcomes with early omnipod 5 use in youth with type 1 diabetes. Diabetes Technol Ther, 2023; 25(11):782–789.

Lindholm Olinder

, DeAbreu

, Greene

, et al. ISPAD clinical practice consensus guidelines 2022: Diabetes education in children and adolescents. Pediatr Diabetes, 2022; 23(8):1229–1242.

Bassi

, Strati

, Parodi

, et al. Patient satisfaction of telemedicine in pediatric and young adult type 1 diabetes patients during COVID-19 pandemic. Front Public Health, 2022; 10:857561.

Cobry

, Reznick-Lipina

, Pyle

, et al. Diabetes technology use in remote pediatric patients with type 1 diabetes using clinic-to-clinic telemedicine. Diabetes Technol Ther, 2022; 24(1):67–74.

Hermanns

, Ehrmann

, Finke-Groene

, Kulzer

. Trends in diabetes self-management education: Where are we coming from and where are we going? A narrative review. Diabet Med, 2020; 37(3):436–447.

10.

Harris

, Taylor

, Thielke

, et al. Research electronic data capture (REDCap)–A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform, 2009; 42(2):377–381.

11.

Harris

, Taylor

, Minor

, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform, 2019; 95:103208.

12.

American Diabetes Association Professional Practice C. 14. Children and Adolescents: Standards of Care in Diabetes-2024. Diabetes Care, 2024; 47(Suppl 1):S258–S281.

13.

Blanchette

, Toly

, Wood

. Financial stress in emerging adults with type 1 diabetes in the United States. Pediatr Diabetes, 2021; 22(5):807–815.

14.

Vitale

, Wentzell

, Laffel

LMB

. “Fear that one day I may not be able to afford insulin”: The emotional burden of diabetes costs during emerging adulthood. Diabetes Technol Ther, 2022; 24(12):915–919.

15.

Sussman

, Benner

, Haller

, et al. Estimated lifetime economic burden of type 1 diabetes. Diabetes Technol Ther, 2020; 22(2):121–130.

16.

Leksell

, Toft

, Rosman

, et al. Virtual clinic for young people with type 1 diabetes: A randomised wait-list controlled study. BMC Endocr Disord, 2023; 23(1):255.

17.

Tanenbaum

, Zaharieva

, Addala

, et al. ‘Much more convenient, just as effective’: Experiences of starting continuous glucose monitoring remotely following Type 1 diabetes diagnosis. Diabet Med, 2022; 39(11):e14923.

18.

Aleppo

, Gal

, Raghinaru

, et al. Comprehensive telehealth model to support diabetes self-management. JAMA Netw Open, 2023; 6(10):e2336876.

19.

Vigersky

, Fish

, Hogan

, et al. The clinical endocrinology workforce: Current status and future projections of supply and demand. J Clin Endocrinol Metab, 2014; 99(9):3112–3121.

20.

, Holt

, Cheng

, et al. Population-based geographic access to endocrinologists in the United States, 2012. BMC Health Serv Res, 2015; 15:541.

21.

Allen

, Aye

, Boney

, et al. Sustaining the Pediatric endocrinology workforce: Recommendations from the pediatric endocrine society workforce task force. J Pediatr, 2021; 233:4–7.

22.

Kavookjian

, Bzowyckyj

, DiNardo

, et al. Current and emerging trends in diabetes care and education: 2021 national practice and workforce survey. Sci Diabetes Self Manag Care, 2022; 48(5):307–323.

23.

Mayer-Davis

, Lawrence

, Dabelea

, et al. Incidence trends of type 1 and type 2 diabetes among youths, 2002–2012. N Engl J Med, 2017; 376(15):1419–1429.

24.

Gabbay

, Barrett

. Endocrinologist burnout: We need to tackle it and bring joy to work. J Clin Endocrinol Metab, 2020(7):105.

25.

Limbert

, Tinti

, Malik

, et al. ISPAD clinical practice consensus guidelines 2022: The delivery of ambulatory diabetes care to children and adolescents with diabetes. Pediatr Diabetes, 2022; 23(8):1243–1269.

26.

Frielitz

F-S

, Dördelmann

, Lemke

, et al. Assessing the benefits and challenges of video consultations for the treatment of children with type 1 diabetes—A qualitative study among diabetes professionals. Exp Clin Endocrinol Diabetes, 2021; 129(11):831–836.

27.

von Sengbusch

, Doerdelmann

, Lemke

, et al. Parental expectations before and after 12-month experience with video consultations combined with regular outpatient care for children with type 1 diabetes: A qualitative study. Diabet Med, 2021; 38(6):e14410.

28.

Predieri

, Leo

, Candia

, et al. Glycemic control improvement in Italian children and adolescents with type 1 diabetes followed through telemedicine during lockdown due to the COVID-19 pandemic. Front Endocrinol (Lausanne), 2020; 11:595735.

29.

Lee

, Carlson

, Albanese-O’Neill

, et al. Adoption of telemedicine for Type 1 diabetes care during the COVID-19 pandemic. Diabetes Technol Ther, 2021; 23(9):642–651.

30.

Ebekozien

, Mungmode

, Sanchez

, et al. Longitudinal trends in glycemic outcomes and technology use for over 48,000 people with type 1 diabetes (2016–2022) from the type 1 diabetes exchange quality improvement collaborative. Diabetes Technol Ther, 2023; 25(11):765–773.

31.

Lee

, Kim

. Evaluation of mobile applications for patients with diabetes mellitus: A scoping review. Healthcare (Basel), 2024; 12(3).

32.

Gutierrez

. Improving the care of students with diabetes in rural schools utilizing an online diabetes education program for school personnel. Rural Remote Health, 2020; 20(1):5596.

33.

Marks

, Waldman

, Reardon

, et al. Improving pediatric endocrinology trainees’ knowledge about insulin pumps and continuous glucose monitors with online spaced education: Technology knowledge optimization in T1D (TeKnO T1D). Pediatr Diabetes, 2020; 21(5):814–823.

34.

Marks

, Wolfsdorf

, Waldman

, et al. Pediatric endocrinology trainees’ education and knowledge about insulin pumps and continuous glucose monitors. Diabetes Technol Ther, 2019; 21(3):105–109.

35.

Hawkes

, Willi

, Murphy

. A structured 1-year education program for children with newly diagnosed type 1 diabetes improves early glycemic control. Pediatr Diabetes, 2019; 20(4):460–467.