Practical Considerations and Implementation of Automated Insulin Delivery Systems *

Abstract

The technological progress to date with automated insulin delivery (AID) has ushered in a new era of challenges and opportunities for people with diabetes (PWD), spotlighting implementation considerations. Beyond physiologic and technologic variation, cost, access, and health care professional (HCP) endorsement/experience lead to uneven uptake of AID technologies and attenuate universal ease of use. For AID to be broadly implemented, we must prioritize the lived experience for PWD and consider how to alleviate burden to promote physical/functional health, psychological well-being, and social well-being. Expectations and education help HCPs and PWD navigate the similarities and differences between AID devices, and help find common ties: users need to give the system time to work, learn to trust it, and not try to “trick” the system. Despite these learnings, disparities in uptake exist, both in clinical trials and in routine clinical care. Strategies to proactively address AID disparities must be enacted at multiple levels, including recognizing HCP biases, using clinic-based benchmarking efforts, and addressing insurance and policy barriers, all of which increase in importance as AID becomes more common for people with type 2 diabetes. Furthermore, broader implementation will require comprehensive health care system integration efforts, including new data solutions. Overall, the success of AID requires ongoing transformation of clinical paradigms, with lockstep alignment between PWD and their families, health care professionals, researchers, funders, policy makers, and industry partners.

Keywords

automated insulin delivery human machine interaction disparities diabetes education

This article is part of a collection of publications to commemorate the 75th Anniversary of the NIDDK.

The best automated insulin delivery (AID) system is the one that an individual chooses and can use day after day. While simple in principle, the implementation challenges are complex. Different AID systems provide unique challenges for different people. Beyond physiologic and technologic variation, cost, access, and health care professional (HCP) endorsement/experience lead to uneven uptake of AID technologies, and attenuate universal ease of use.

Research supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)/National Institutes of Health (NIH) and other funders has been key to developing many currently available, transformational technologies for treatment of diabetes. Forums like the Fifth Artificial Pancreas workshop hosted by the NIDDK/JDRF/Helmsley Charitable Trust in May 2023 have facilitated these collaborative advancements, while additionally promoting forward facing conversations essential to the next era of AID.¹ Equally important to the technological revolution are the considerations that impact the broader ecosystem of diabetes care. How do we get AID technology into the hands of everyone with diabetes? How do we prepare them for success with realistic expectations? This chapter explores the factors necessary to advance AID at the individual, health care, institutional and payor/systems level.

Human-Machine Interaction and the Lived Experience with AID

Automated insulin delivery technology has necessarily evolved with consideration to how people with diabetes (PWD) and HCPs will use the device. The ideal AID system would not require input from the user apart from placement and removal. As we work toward this goal, broader concepts of how humans interact with machines are adapted for the AID paradigm (Figure 1).² Humans are active participants in AID system functions, with the user responsible for perceiving diabetes and device state and determining if action is needed. The AID system is responsible for the intake of information from various sources (including the PWD) and responding accordingly. Between the AID system and the PWD lies the “interface zone.”

Figure 1.

Human-machine interaction model for AID (adapted from Redmill and Rajan 2)

Professional disciplines have tackled different aspects of the human-machine interaction, with important work continuing in the “interface zone.” For example, human factors work helps determine ways to maximize safety and usability of AID systems, evaluating workflows, device interface, and comprehensive aspects of how the user interacts with the system.² Similarly, an entire body of research has been focused on the holistic user experience, which extends beyond the literal human-machine interface.^3-7 This allows the ability to overlay modern terminology and new concepts into human-machine interaction paradigms; for example, form-factor, cost-benefit trade-off, user burden, and problem-solving when issues arise, and the potential for de-skilling of basic diabetes management.

For AID to be broadly implemented, we must consider the physical, psychological, and social well-being of its users, and ideally alleviate burden in each of these areas. However, in reality, trade-offs are required to achieve an acceptable balance of burden and benefit. In addition to the large body of literature showing glycemic improvement with AID, there is considerable evidence that AID systems are associated with improved quality of life (QoL) for many users with regard to safety, reduced anxiety around diabetes self-management and hypoglycemia, improved sleep, and lower diabetes distress.^8,9 Challenges persist, however, intrinsic to the human-machine interaction. For example, AID systems increase visibility of diabetes, which has been reported as a barrier to device uptake in both adults and adolescents with diabetes; and intrusive alarms in social situations can cause embarrassment, distress, and serve as a nuisance.^4,7

Ongoing research with AID must prioritize the lived experience for PWD. This will continue to be assessed and addressed with a broad range of methodologies. Quantitative, survey-based objective measurements offer broad signals for exploration, while subjective measurements, such as interviews, focus groups, and therapeutic writing offer more nuanced exploration of individual experiences with AID. By utilizing these mixed methodologies, it is possible to gather a deeper understanding of important aspects that influence continuation or discontinuation of systems, satisfaction or dissatisfaction, and impact on QoL and functional health status.

Wide-ranging domains of inquiry address different aspects of health status and QoL, including indicators of physical/functional health status, psychological well-being, and social well-being as overarching categories.^3,10 Within these, there are subcategories that affect individuals to a greater or lesser extent depending on their personal circumstances, including depression and anxiety, diabetes distress, fear of hypoglycemia, treatment satisfaction, sleep quality/quantity, self-efficacy, sexual/reproductive health, and the impact of devices and technologies on what it is to be human. Overall, cross-disciplinary research must continue to discover optimal ways to enhance the lived experience of PWD using AID systems.

Expectations and Education for AID Systems

“It knows you eat pizza for breakfast, and will dose accordingly”—said the endocrinologist, circa 2017, when the first commercial hybrid closed-loop system became available in the United States. At the time, it was unclear how true or untrue this was. The relationship between a PWD and diabetes technology starts with their expectations of the system.

Right-sized technology expectations began with CGM, which fundamentally shifted understanding of glycemic excursions. Where people previously interpreted four to eight glucose readings each day, suddenly 288 data points were available and ready for consumption. PWD needed to learn to live with CGM, to recognize the benefits and the drawbacks, and to avoid becoming overwhelmed by data.^11,12 Early guidance on insulin adjustments based on real-time information, such as trend arrows, and retrospective data analysis grew from the NIH–funded Diabetes Research in Children Network (DirecNet),^13,14 and JDRF CGM trials,¹⁵ as well as early clinical practice,^16-18 ultimately culminating in an international guideline.¹⁹ CGM education set the stage for future success with AID.

Once CGM transformed the landscape of diabetes, AID changed how to traverse it. AID required new thinking at ecological scale: regulatory, payor, endocrinology practice, clinic-level, and individual/family engagement—all to accept CGM-informed insulin delivery with reduced user-intervention. With every new AID system, the diabetes community would ride the familiar “hype cycle” of inflated expectations, discouragement, and plateau of reality.²⁰ Trust became a core requirement for both PWD and clinicians, and was gained in a stepwise fashion, applying first to predictive low glucose suspend, and thereafter to hybrid closed-loop systems. Research showed that trust was context-dependent (eg, easier at night compared with post-prandial), impacted by previous experience with manufacturers, and reliant on perceptions of accuracy and safety.²¹ The reality is remarkably consistent across devices: users need to give the system time to work, learn to trust it, and don’t try to “trick” the system. Often PWD must pace their intervention rather than be overly aggressive with insulin dosing while waiting for the system to respond.

While expectations and trust capitalize on similarities between systems, education deciphers the differences. The CARES framework evolved out of NIDDK-funded AID research (Table 1), lending structure to understanding how systems Calculate insulin delivery, settings that are Adjustable, when to Revert to open-loop, necessary Education, and Sensor/Share characteristics.^22,23 Clinicians require knowledge of which “levers to pull” for optimal treatment outcomes in different systems^24-26 and how to help PWD choose the best system for them. As AID evolves, differences in algorithm specifications may become less relevant, though pillars of structured education for PWD and clinicians will remain paramount, including education around insulin pharmacodynamics.

Table 1.

CARES Paradigm for Differentiating AID Systems.

C: Calculate	• How does the algorithm calculate insulin delivery? • Which components of insulin delivery are automated (eg, basal suspensions, basal modulation, high glucose corrections, food boluses, etc)?
A: Adjust	• How can the user adjust insulin delivery? • Which parameters can be adjusted to influence insulin delivery during automation (eg, carbohydrate ratios, insulin action time, basal rates, sensitivity factors)? • Which parameters are fixed?
R: Revert	• When should the user choose to revert to open-loop/no automation? • When will the system default to open-loop/no automation?
E: Educate	• What are the key education points for the advanced diabetes device (eg, essential training, tips and tricks, best practices, etc)?
S: Sensor/Share	• What are the specific characteristics of the sensor used in the AID system (duration of use, accuracy, interference, lag)? • How is sensor glucose and insulin administration information able to be shared to remote users and health care professionals?

Practical performance will inevitably continue to chase user and clinician expectations, and education will remain critical to achieving optimal glycemic and user-reported outcomes. While current systems may not know whether a person will eat pizza for breakfast, future systems will better handle the challenge when they do.

Technology Use Disparities and Psycho-Social Determinants of Health

There has been growing awareness of disparities in AID use, both in the development of the technology, and in the integration into routine diabetes management. In clinical practice, disparities are evident within the first year following diagnosis of type 1 diabetes.²⁷ Racial and ethnic minority groups have been significantly underrepresented in the large-scale clinical trials that led to FDA approval/clearance of AID systems.^28,29 This lack of representation in clinical trials leads to device development that does not consider the needs and preferences of minority groups. It also precludes identification of important barriers to AID use that can inform education for PWD and support programs for onboarding, and subsequently impacting overall outcomes.^30,31 The American Diabetes Association (ADA) is acutely aware of these disparities, calling explicit attention to AID as an agent of equalization in the 2025 Standards of Care in Diabetes, recommending “AID systems should be the preferred insulin delivery method to improve glycemic outcomes and reduce hypoglycemia and disparities in youth and adults with type 1 diabetes.”³²

Two recent large-scale studies confirm the major disparities in the use of AID across racial/ethnic groups (Table 2), with the lowest use found in Non-Hispanic black populations.^33,34 In a review of >48 000 medical records from 15 adult and pediatric type 1 diabetes (T1D) centers, Ebekozien et al found trends of increased AID use and lower A1c levels over past few years, but that these gains were not equivalent across race/ethnic groups. A second review of over 5000 records found similar results; however, disparities in AID use were even greater in black children compared with black adults.³³ These racial/ethnic disparities persist even after controlling for other contributors to health inequities such as type of insurance (eg, public vs private). This indicates that it is important to investigate additional contributors, such as implicit provider bias³⁵ and the technology divide,³⁶ among other factors.

Table 2.

Percents of AID Use Across Race/Ethnic Groups.

	Setting, sample	Non-Hispanic white (%)	Hispanic (%)	Non-Hispanic (%) black	Other (%)
Conway et al³³	Single center, n=4071 Adults	56.6	35.3	20.4	46.3
Conway et al³³	Single center, n=1468 Pediatric	45.2	30	25.9	37.6
Ebekozien et al³⁴	Registry, N=>48 000 Adult and pediatric	36.0	23.0	15.0	n/a

It is critical to address disparities because of their impact on medical outcomes. While incidence rate of T1D is increasing, this rise is occurring more rapidly among racial/ethnic minority groups compared with non-Hispanic whites.³⁷ Furthermore, as AID use proves to benefit people with type 2 diabetes (T2D), the impact of these disparities will increase exponentially since, in the United States, more than 80% of individuals with T2D belong to a minority group.³⁸ In addition to racial and ethnic disparities, gaps in AID access and use across insurance types and socioeconomic (SES) levels have yet to receive the warranted scientific attention, although early research is documenting significantly less technology use and suboptimal glycemic outcomes in lower SES groups.³⁹

Strategies to address AID disparities must be enacted at multiple levels. HCPs as individuals are responsible for ensuring all PWD are offered AID as standard of care, identifying and reducing their own biases in prescribing AID systems, and promoting shared decision making with PWD.⁴⁰ Clinic-based strategies need to include promotion of benchmarking to reduce the impact of bias,^41,42 quality improvement methods,^43-45 and screening for and addressing social determinants of health.^46,47 Insurance barriers must be addressed; transformational policy changes for AID access will require robust engagement of all relevant stakeholders.^48,49 Future studies should test these and other strategies in rigorous clinical trials specifically designed to improve AID equity.

Implementation Challenges and Health System Transformation

It is challenging for HCPs to keep pace with the technological advances of AID: knowing how to recommend AID systems, navigating insurance coverage, ordering and onboarding sufficiently, and then competently working with multiple systems. There have been efforts over the past decade to identify needs and help equip HCPs with the resources needed to understand current systems and provide education and support to PWD.^50,51 DiabetesWise (https://diabeteswise.org/) is a free online resource funded by the Helmsley Charitable Trust, which provides educational vignettes from PWD and their care partners as well as training resources for PWD and HCPs.⁵² The Panther Program (https://www.pantherprogram.org/) offers device-specific overviews, comparison charts, and clinical care worksheets to support HCPs in prescribing and using new technologies. The educational model for this program follows the CARES framework, providing HCPs with concise information on how each device operates.^22,53 While these efforts aim to provide unbiased access to device specifics, ongoing work needs to educate, re-educate with system updates, and support the HCP community as technology transforms.

Broader adoption of AID systems has brought to light the need for new data solutions at the clinic and hospital level. Current CGM and AID systems record a large volume of glucose and insulin dosing data. However, questions regarding ownership, governance, availability, and standardization of access abound.⁵⁴ Data are provided through over half a dozen online platforms, each requiring different account management and some requiring subscription plans by the health system. This leads to challenges scaling any data paradigm beyond individual clinics, let alone health care systems and networks. Accessible data varies by manufacturer, ranging from only summary statistics for some and minute-by-minute presentation of algorithm actions for others. HCPs and PWD need to download and review these data to adjust AID system settings or suggest behavioral modifications to optimize glycemia.

Several projects have aimed to streamline these data processes and allow for direct data transfer from a commercial industry partner into a clinical electronic health record (EHR).^55,56 These efforts so far have been limited, allowing for transfer from one commercial entity to one hospital system’s EHR without real-time access. PWD should be afforded access to their own data and permit others, including HCPs, to have unbridled access. In the United States, the 21st Century Cures Act mandates laboratory results be released to individuals through an EHR, unless certain circumstances exist.⁵⁷ Thus, precedence exists regarding release of data through EHR, which provides intriguing opportunities for when CGM and AID data are more seamlessly integrated. Eliminating these barriers may prompt more HCPs and innovators to embrace diabetes technologies, thereby improving care and autonomy for PWD. Broad availability of data does raise additional considerations however, including ensuring adequate reimbursement by payers for HCP time required to review AID data, and avoiding unrealistic expectations about how often this can occur.

Within the realm of in-hospital care, there is a need for both real-time device integration and updated ordering around dynamic insulin dosing. At some institutions, PWDs admitted to the hospital for diabetes or non-diabetes-based reasons may continue to use their CGM and AID system, whereas in others, AID and even CGM may not be allowed “by policy.” To permit more permanent adoption, optimization of workflows will require both real-time CGM-based alerts and EHR entry of AID system-based insulin dosing, which is currently far outside the realm of conventional hospital ordering and dosing. Updating communication between the devices and the hospital systems is a necessary step to further integrate AID within hospital-based care. The time is now to ensure present day tools are leveraged in all necessary care settings and creating the framework needed to achieve these goals remains a critical area of investigation.

Conclusion

While we have come a long way with diabetes technology advances, taking stock of where we are today helps us move forward for the future. The success of AID will be measured not just by time-in-glycemic-target ranges, but in worldwide access, use, and equity. It is only possible to change clinical paradigms with lockstep alignment between PWD and their families, health care professionals, researchers, funders, policy makers, and industry partners.

Footnotes

Acknowledgements

The author group would like to thank the NIDDK for 75 years of supporting research to improve the lives of people living with diabetes.

Abbreviations

ADA, American Diabetes Association; AID, automated insulin delivery; DirecNet, Diabetes Research in Children Network; EHR, electronic health record; HCP, health care professional; NIDDK, National Institute of Diabetes and Digestive and Kidney Diseases; NIH, National Institutes of Health; PWD, people with diabetes; QoL, quality of life; SES, socioeconomic status; T1D, type 1 diabetes.

Author Contributions

LHM: Conceptualization, Investigation, Writing (original, review and editing), Project Administration; GPF: Conceptualization, Investigating, Writing (original, review and editing); LG-F: Conceptualization, Investigating, Writing (original, review and editing); KH: Conceptualization, Investigating, Writing (original, review and editing); OE: Investigating, Writing (original, review and editing); KB-K: Investigating, Writing (original, review and editing); LML: Investigating, Writing (original, review and editing); JLS: Investigating, Writing (original, review and editing); RL: Investigating, Writing (original, review and editing); and SAW: Investigating, Writing (original, review and editing).

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: LHM is an employee and shareholder of Tandem Diabetes Care. GPF conducts research sponsored by Medtronic, Dexcom, Abbott, Tandem, Insulet, and Beta Bionics and has been a speaker/consultant/ad board member for Medtronic, Dexcom, Abbott, Tandem, Insulet, Beta Bionics, Lilly, and Sequel. LG-F has received consulting fees/research support from Dexcom, Abbott Diabetes Care, Johnson and Johnson, Merck, and Astrazeneca, and is a partner in HFS-Global LLC which in partnership with the University of Virginia licenses use of the Hypoglycemia Fear Survey for clinical trials conducted by for-profit pharmaceutical and medical device corporations, with part of these funds supporting ongoing research and education on the problem of hypoglycemia. KH has received consulting fees from Sanofi, MannKind, and Havas Health; and a research grant from Embecta. OE have received research grants through his organization T1D Exchange from Medtronic, Lexicon, Vertex, Lilly, and Sanofi. KB-K is founder and CEO of Spotlight-AQ Ltd; speaker honoraria from Roche, Tandem, Novo Nordisk, and Sanofi. Research funding from Dexcom, Breakthrough T1D, NIH, and Roche. LML serves as consultant or on advisory boards for Boehringer Ingelheim, Medtronic, Tandem Diabetes, Dexcom, Arbor Biotech, Sanofi, Sequel, Mannkind, and Vertex. JLS has conducted clinical trials for Abbott Diabetes Care, Dexcom, JDRF/Breakthrough T1D, Insulet, Medtronic, and Provention Bio and has received in-kind support for research studies from Dexcom and Medtronic. She has consulted for Abbott Diabetes, Insulet, Medscape, Medtronic, Vertex, and Ypsomed. She has been a member of advisory boards for Cecelia Health, Insulet, Mannkind, Medtronic Diabetes, StartUp Health T1D Moonshot, and Vertex. RL has received consulting fees from Abbott Diabetes Care, Adaptyx Biosciences, Biolinq, Capillary Biomedical, Deep Valley Labs, Gluroo, PhysioLogic Devices, Portal Insulin, Sanofi, and Tidepool. He has served on advisory boards for Provention Bio and Lilly. He receives research support from his institution from Insulet, Medtronic, Sinocare, and Tandem. SAW has conducted clinical trials for Abbott Diabetes, JDRF/Breakthrough T1D, Medtronic, and Tandem; has received honoraria for consulting or speaking engagements from Abbott, Dexcom, Insulet, Medtronic, and Tandem; and has served on advisory boards for Zealand. He currently receives research support (to his institution) from Abbott Diabetes.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Laurel H. Messer

Gregory P. Forlenza

Korey Hood

Osagie Ebekozien

Katharine Barnard-Kelly

Lori M. Laffel

Jennifer L. Sherr

Rayhan Lal

References

Aaron

Tian

Yeung

, et al. NIH Fifth Artificial Pancreas Workshop 2023: meeting report: the Fifth Artificial Pancreas Workshop: enabling fully automation, access, and adoption. J Diabetes Sci Technol. 2024;18(1):215-239.

Redmill

Rajan

Human Factors in Safety-Critical Systems. 1st ed. London: Routledge; 1997.

Kadiyala

Hovorka

Boughton

CK.

Closed-loop systems: recent advancements and lived experiences. Expert Rev Med Devices. 2024;21(10):927-941.

Messer

Tanenbaum

Cook

, et al. Cost, hassle, and on-body experience: barriers to diabetes device use in adolescents and potential intervention targets. Diabetes Technol Ther. 2020;22(10):760-767.

Tanenbaum

Iturralde

Hanes

, et al. Trust in hybrid closed loop among people with diabetes: perspectives of experienced system users. J Health Psychol. 2020;25(4):429-438.

Wong

Barley

Hanes

, et al. Parental perspectives: identifying profiles of parental attitudes and barriers related to diabetes device use. Diabetes Technol Ther. 2020; 22(9):674-680.

Tanenbaum

Hanes

Miller

Naranjo

Bensen

Hood

KK.

Diabetes device use in adults with Type 1 diabetes: barriers to uptake and potential intervention targets. Diabetes Care. 2017;40(2):181-187.

Roos

Hermanns

Groß

Kulzer

Haak

Ehrmann

Effect of automated insulin delivery systems on person-reported outcomes in people with diabetes: a systematic review and meta-analysis. eClinicalMedicine. 2024;76:102852.

Godoi

Marques

Padrão

HME

, et al. Glucose control and psychosocial outcomes with use of automated insulin delivery for 12 to 96 weeks in type 1 diabetes: a meta-analysis of randomised controlled trials. Diabetol Metab Syndr. 2023;15(1):190.

10.

Speight

Choudhary

Wilmot

, et al. Impact of glycaemic technologies on quality of life and related outcomes in adults with type 1 diabetes: a narrative review. Diabet Med. 2023;40(1):e14944.

11.

Pickup

Ford Holloway

Samsi

Real-time continuous glucose monitoring in type 1 diabetes: a qualitative framework analysis of patient narratives. Diabetes Care. 2015;38(4):544-550.

12.

Hirsch

IB.

Clinical review: realistic expectations and practical use of continuous glucose monitoring for the endocrinologist. J Clin Endocrinol Metab. 2009;94(7):2232-2238.

13.

Diabetes Research in Children Network (DirecNet) Study Group. Use of the DirecNet Applied Treatment Algorithm (DATA) for diabetes management with a real-time continuous glucose monitor (the FreeStyle Navigator). Pediatr Diabetes. 2008;9(2):142-147.

14.

Messer

Ruedy

Xing

, et al. Educating families on real time continuous glucose monitoring: the DirecNet navigator pilot study experience. Diabetes Educ. 2009;35(1):124-135.

15.

Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group. Effectiveness of continuous glucose monitoring in a clinical care environment: evidence from the Juvenile Diabetes Research Foundation continuous glucose monitoring (JDRF-CGM) trial. Diabetes Care. 2010;33(1):17-22.

16.

Evert

Trence

Catton

Huynh

Continuous glucose monitoring technology for personal use: an educational program that educates and supports the patient. Diabetes Educ. 2009;35(4):565-7571.

17.

Laffel

Aleppo

Buckingham

, et al. A practical approach to using trend arrows on the Dexcom G5 CGM system to manage children and adolescents with diabetes. J Endocr Soc. 2017;1(12):1461-1476.

18.

Aleppo

Laffel

Ahmann

, et al. A practical approach to using trend arrows on the Dexcom G5 CGM System for the management of adults with diabetes. J Endocr Soc. 2017;1(12):1445-1460.

19.

Battelino

Danne

Bergenstal

, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42(8):1593-1603.

20.

Messer

Berget

Hybrid closed-loop systems to date: hype versus reality. Diabetes Technol Ther. 2023; 25(1):91-94.

21.

Iturralde

Tanenbaum

Hanes

, et al. Expectations and attitudes of individuals with Type 1 diabetes after using a hybrid closed loop system. Diabetes Educ. 2017;43(2):223-232.

22.

Messer

Berget

Forlenza

GP.

A clinical guide to advanced diabetes devices and closed-loop systems using the CARES paradigm. Diabetes Technol Ther. 2019;21(8):462-469.

23.

Messer

Forlenza

Wadwa

, et al. The dawn of automated insulin delivery: a new clinical framework to conceptualize insulin administration. Pediatr Diabetes. 2018;19(1):14-17.

24.

Messer

Forlenza

Sherr

, et al. Optimizing hybrid closed-loop therapy in adolescents and emerging adults using the MiniMed 670G system. Diabetes Care. 2018;41(4):789-796.

25.

Castañeda

Mathieu

Aanstoot

, et al. Predictors of time in target glucose range in real-world users of the MiniMed 780G system. Diabetes Obes Metab. 2022;24(11):2212-2221.

26.

Messer

Breton

MD.

Therapy settings associated with optimal outcomes for t: slim X2 with Control-IQ technology in real world clinical care. Diabetes Technol Ther. 2023;25(12): 877-882.

27.

Tremblay

Liu

Laffel

LM.

Health disparities likely emerge early in the course of Type-1 diabetes in youth. J Diabetes Sci Technol. 2022;16(4):929-933.

28.

Akturk

Agarwal

Hoffecker

Shah

VN.

Inequity in racial-ethnic representation in randomized controlled trials of diabetes technologies in Type 1 diabetes: critical need for new standards. Diabetes Care. 2021;44(6):e121-e123.

29.

García-Tirado

Villard

Hall

Bisio

Gonder-Frederick

Under-representation of diverse populations and glycemic outcomes in major clinical trials of automated insulin delivery. Diabetes Technol Ther. 2023;25(10):752-754.

30.

Pemberton

Collins

Drummond

, et al. Enhancing equity in access to automated insulin delivery systems in an ethnically and socioeconomically diverse group of children with type 1 diabetes. BMJ Open Diabetes Res Care. 2024;12(3):e004045.

31.

Noor

Kamboj

Triolo

, et al. Hybrid closed-loop systems and glycemic outcomes in children and adults with Type 1 diabetes: real-world evidence from a U.S.-based multicenter collaborative. Diabetes Care. 2022;45(8):e118-e9.

32.

Elsayed

Mccoy

Aleppo

, et al. Diabetes technology: standards of care in diabetes—2025. Diabetes Care. 2025;48(suppl 1):S146-S66.

33.

Conway

Gerard Gonzalez

Shah

, et al. Racial disparities in diabetes technology adoption and their association with HbA1c and diabetic ketoacidosis. Diabetes Metab Syndr Obes. 2023;16:2295-2310.

34.

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 T1D exchange quality improvement collaborative. Diabetes Technol Ther. 2023;25(11):765-773.

35.

Odugbesan

Addala

Nelson

, et al. Implicit racial-ethnic and insurance-mediated bias to recommending diabetes technology: insights from T1D exchange multicenter pediatric and adult diabetes provider cohort. Diabetes Technol Ther. 2022;24(9):619-627.

36.

Ebekozien

Fantasia

Farrokhi

Sabharwal

Kerr

Technology and health inequities in diabetes care: how do we widen access to underserved populations and utilize technology to improve outcomes for all. Diabetes Obes Metab. 2024;26(suppl 1):3-13.

37.

Mayer-Davis

Dabelea

Lawrence

JM.

Incidence trends of Type 1 and Type 2 diabetes among youths, 2002-2012. N Engl J Med. 2017;377(3):301.

38.

Cheng

Kanaya

Araneta

GRM

, et al. Prevalence of diabetes by race and ethnicity in the United States, 2011-2016. JAMA. 2019;322(24):2389.

39.

Addala

Auzanneau

Miller

, et al. A decade of disparities in diabetes technology use and HbA. Diabetes Care. 2021;44(1):133-140.

40.

Friedman

Odugbesan

Ebekozien

Aleppo

Increasing access to connected insulin pens by promoting shared decision-making through collaborative quality improvement. Clin Diabetes. 2024;42(4):470-473.

41.

Prahalad

Hardison

Odugbesan

, et al. Benchmarking diabetes technology use among 21 U.S. Clin Diabetes. 2024;42(1):27-33.

42.

Mungmode

Noor

Weinstock

, et al. Making diabetes electronic medical record data actionable: promoting benchmarking and population health improvement using the T1D exchange quality improvement portal. Clin Diabetes. 2023;41(1):45-55.

43.

Ebekozien

Mungmode

Buckingham

, et al. Achieving equity in diabetes research: borrowing from the field of quality improvement using a practical framework and improvement tools. Diabetes Spectr. 2022;35(3):304-312.

44.

Steenkamp

Brouillard

Aia

, et al. Reducing inequity in the use of automated insulin delivery systems by adults with Type 1 diabetes: key learnings from a safety net diabetes clinic program. Endocr Pract. 2024;30(6):558-563.

45.

Odugbesan

Mungmode

Rioles

, et al. Increasing continuous glucose monitoring use for Non-Hispanic Black and Hispanic people with Type 1 diabetes: results from the T1D exchange quality improvement collaborative equity study. Clin Diabetes. 2024;42(1):40-48.

46.

Odugbesan

Wright

Jones

, et al. Increasing social determinants of health screening rates among six endocrinology centers across the United States: results from the T1D exchange quality improvement collaborative. Clin Diabetes. 2024;42(1):49-55.

47.

Addala

Mungmode

Ospelt

, et al. Current practices in operationalizing and addressing racial equity in the provision of Type 1 Diabetes care: insights from the Type 1 Diabetes exchange quality improvement collaborative health equity advancement lab. Endocr Pract. 2024;30(1):41-48.

48.

Ebekozien

Roadmap to achieving continuous glucose monitoring equity: insights from the T1D exchange quality improvement collaborative. Diabetes Spectr. 2023;36(4):320-326.

49.

Ebekozien

Mungmode

Odugbesan

, et al. Addressing type 1 diabetes health inequities in the United States: approaches from the T1D exchange QI collaborative. J Diabetes. 2022;14(1):79-82.

50.

Messer

Vigers

Akturk

, et al. Increasing use of diabetes devices: what do healthcare professionals need? Clin Diabetes. 2023;41(3):386-398.

51.

Levine

Dalton

, et al. Enhancing resources for healthcare professionals caring for people on intensive insulin therapy: summary from a national workshop. Diabetes Res Clin Pract. 2020;164:108169.

52.

Wong

Addala

Hanes

, et al. DiabetesWise: an innovative approach to promoting diabetes device awareness. J Diabetes. 2023;15(7):597-606.

53.

Messer

Berget

Centi

Mcnair

Forlenza

GP.

Evaluation of a new clinical tool to enhance clinical care of control-IQ users. J Diabetes Sci Technol. 2023:17(6):1602-1609.

54.

Jendle

Adolfsson

Choudhary

, et al. A narrative commentary about interoperability in medical devices and data used in diabetes therapy from an academic EU/UK/US perspective. Diabetologia. 2024;67(2):236-245.

55.

Espinoza

Shah

Raymond

Integrating continuous glucose monitor data directly into the electronic health record: proof of concept. Diabetes Technol Ther. 2020;22(8):570-576.

56.

Aleppo

Chmiel

Zurn

, et al. Integration of continuous glucose monitoring data into an electronic health record system: single-center implementation. J Diabetes Sci Technol. 2023;19(2):426-430.

57.

21st Century Cures Act, Pub. L. No. Public Law No:114-255(2016).