Meals No Longer Need to be Math Problems: Shifting from Precise Carbohydrate Counting to a Continuum of Carbohydrate Awareness as Automated Insulin Delivery Advances

Abstract

Counting carbohydrates to determine the right amount of insulin to dose for meals turns each meal into a math problem. While advanced carbohydrate counting is most effective for traditional pump therapy, algorithms incorporated in automated insulin delivery systems consider sensor glucose, sensor trend, rate of glucose change, and active insulin to continuously adjust insulin delivery, thus working with less accurate carbohydrate entry or simple qualitative meal announcements. A continuum of carbohydrate awareness is a framework for using carbohydrate information to inform insulin dosing. Since ongoing adjustment of insulin therapy settings is not required with fully autonomous insulin delivery, the diabetes care and education specialist (DCES) can serve an expanded role providing education regarding dosing behaviors that influence functioning of the adaptive closed-loop algorithm. The dietitian/DCES shifts from teaching advanced carbohydrate counting and math skills to person-centered conversations focusing on overall health and can lead in expanding technology access to a broader population.

Background

Even with the advent of automated insulin dose calculators as part of smart insulin delivery systems, counting carbohydrates and determining the right amount to dose for meals turnseach meal into a math problem. Approximately 48 million U.S. adults are reported to have low English literacy skills, and 69 million adults have low numeracy skills. ¹ Diabetes numeracy, the ability to apply health numeracy skills to solve problems and perform self-management tasks specific to diabetes care, is crucial in the management of a numbers-rich condition such as diabetes, particularly for those on complex, intensive insulin therapy. ²

Research conducted in a specialty endocrinology clinic found that deficiencies in diabetes numeracy skills are present in patients on insulin pump therapy, suggesting that some patients with diabetes on insulin pump therapy cannot complete numeracy-related self-management tasks such as counting carbohydrates or determining appropriate insulin dosing. ² In the study, participants with lower diabetes numeracy scores had higher A1C levels, were older, were more likely to describe their diabetes self-care as poor, and had less confidence in their ability to use the features of their insulin pump. Further research in larger, more diverse populations is needed to determine diabetes numeracy skills in individuals on insulin pump therapy and to investigate how advancing technologies such as fully autonomous insulin dosing requiring only qualitative meal announcements influence the impact of reduced diabetes numeracy skills on clinical outcomes.

Traditionally, insulin pump therapy was limited to people who could perform advanced or precise carbohydrate counting as taught in the Diabetes Control and Complications Trial.^3,4 Despite being a key strategy to optimize insulin pump therapy, many with diabetes struggle to accurately determine the carbohydrate content of foods consumed. ⁵ In an online survey of people with type 1 diabetes, 63% of the participants reported they would either strongly or extremely like to be liberated from counting carbohydrates. Furthermore, 62% reported they find it difficult to calculate the right amount of insulin for complex high protein and/or high fat meals. ⁶ Complex high protein and/or high fat meals are common in the standard American diet, including many restaurant meals that have become a mainstay of consumer diets. ⁷

Automation Advances

Advances in automated insulin delivery (AID) systems are addressing this concern. A randomized controlled trial of 31 adolescent subjects with type 1 diabetes using AID found that both precise carbohydrate and relative meal-size bolus dosing resulted in achieving similar glycemic outcomes, suggesting that AID systems can provide optimal glycemia (improved time-in-range without compromising time-below-range) for nonexpert carbohydrate counters. ⁸ While advanced (precise) carbohydrate counting is most effective for traditional pump therapy, algorithms incorporated in AID systems consider sensor glucose, the sensor trend, rate of glucose change, and active insulin to continuously adjust insulin delivery and thus work with less accurate carbohydrate entry or simple qualitative meal announcements. These systems can also somewhat compensate for missed or late boluses.^8–10

One AID system, now available, referred to hereafter in this article as the bionic pancreas (BP), provides adaptive closed-loop algorithms enabling fully autonomous insulin delivery automatically titrating all therapeutic insulin including basal, correction, and prandial insulin. Instead of precise carbohydrate counting, the system requires qualitative meal announcements to autonomously determine prandial insulin. The user’s body weight is the only input needed to initiate the AID system as the system perpetually learns and automatically refines dosing no longer requiring manually calculating, programming, and adjusting system settings. ¹⁰ This significantly reduces the cognitive (mental) burden on users, making it accessible to a broader population, including those who may not be as well-versed in diabetes management or those with a history of high A1C levels. Fully autonomous systems such as the BP may now be suitable for individuals who previously struggled with manual management due to the complexity involved. Moreover, the BP’s ability to maintain target glycemia with less user input could make it an option for those who experience diabetes burnout or those who have difficulty maintaining intensive insulin management plans. The BP exemplifies a shift toward simplifying diabetes management, reducing the need for high-level expertise, and potentially changing the traditional criteria for insulin pump therapy candidacy.^10,11

Carbohydrate Awareness

Some understanding of carbohydrate counting or carbohydrate awareness is still important for AID users, especially when the goal is to maximize time-in-range. A continuum of carbohydrate awareness (illustrated in Fig. 1) is a framework for using carbohydrate information to inform insulin dosing and to improve glycemia starting with the ability to identify foods containing carbohydrate to being able to quantify carbohydrate foods as servings or assign meal size (simplified carbohydrate counting) to determining grams of carbohydrate in a food/serving (advanced or precise carbohydrate counting). It is recognized that as the complexity of carbohydrate awareness increases, the potential burden increases. ¹² According to the Consensus Recommendations for the use of Automated Insulin Delivery Technologies in Clinical Practice, bolusing insulin or announcing a meal before eating carbohydrates is vital to achieve optimal postprandial performance of the various AID systems. ¹³ With advancing automation, the dietitian/diabetes care and education specialist (DCES) can shift from teaching advanced carbohydrate counting and math skills to true person-centered conversations focusing on healthy food choices including nutrient dense carbohydrates with adequate fiber and healthy protein and fat choices, as part of an overall healthy lifestyle. This frees up time for the dietitian/DCES to focus on the person’s individualized needs and fosters a healthy relationship with food.

FIG. 1.

Continuum of carbohydrate awareness to determine insulin doses or announce meals. (Adapted from Figure 8.3 in MacLeod et al. ¹² )

As AID systems continue to advance, the need for meal announcements may be eliminated. Current makers of AID systems, university researchers, and some members of the diabetes community are testing and refining next-generation systems that allow the user to skip meal announcements. The algorithms used in fully closed loop systems vary, using a range of different methods to identify and counter postprandial glucose excursions. Some fully closed loop systems identify the rate of acceleration of the post-meal rise, and the ratio determining the bolus can be adjusted depending on how the user typically responds. Some systems use an initial, small dose of insulin (bolus priming), followed by larger doses to respond to the rise in post-meal blood sugar. A small amount of insulin will open the capillaries and make the next dose of insulin much more efficient, which lets the algorithm get ahead of the blood glucose rise.^14,15 In addition, novel ultra-rapid-acting insulin analogs with a more physiological time-action profile are being investigated for use in AID devices, to further improve postprandial glycemia.^16,17

Redefining Successful Diabetes Management and Expanding Technology Access

With current AID systems, particularly fully autonomous insulin dosing, and with the continued rapid advances expected with AID technology, it may be time to redefine or reframe success in insulin management recognizing the need for a balance between achieving an optimal level of glycemia with the effort required to achieve it. The individual perceptions of device benefits and burdens (both cognitive and physical burden) are key in continued device use. ¹⁸ It will be important to continue to study the quality-of-life impact of AID systems as they advance, potentially bringing important insights for addressing cognitive (mental), emotional, or behavioral barriers toward optimal use. ¹⁹ Critical are efforts to maximize benefits and minimize burdens of AID therapy and with sensitivity to the personal trade-off between both. ¹⁸

Behaviors that contribute to optimal glycemia in partially automated AID can be challenging in the context of fully autonomous insulin delivery, requiring significant adjustments in learning to rely on a technology device or algorithm rather than oneself to effectively and safely manage diabetes. There can be a sense of lack of control due to the limited opportunity for user input and interaction in treatment decisions.^11,20,21 Distrust in the functioning of the AID system may lead users to take certain actions to retain some level of personal control. These actions may include efforts to override or “trick” the system into delivering more or less insulin or using work-arounds such as entering carbohydrates that the user does not intend to consume or announcing “more” or “less” than usual meals to influence the amount of insulin delivered. ²¹ Discussing the consequences of these behaviors and their impact on management goals can help in optimizing device use. ¹⁸ The clinical team can discuss any points of trepidation a user may have in converting to a fully autonomous system so that the clinical team can target support to those areas whether its fear of hypoglycemia or discomfort with relinquishing control to the algorithm.

Expanding AID access and adoption will necessitate addressing barriers related to social determinants of health, finances, and an expansion of the number and type of clinician prescribing AID systems. ²² It is important to note that the BP pivotal trial cohort, while not perfectly representative, was more reflective of the general U.S. population with type 1 diabetes than previous pivotal studies of semi-automated systems in terms of race/ethnicity, socioeconomic status, range of baseline A1C, and the distribution of baseline therapies (injection therapy, insulin pump, and semi-automated insulin delivery systems); thus, the significant improvements in glycemia observed may be more generalizable. ¹⁰ With less complex onboarding and ongoing insulin therapy settings adjustments required from the clinical team with fully autonomous systems, this potentially opens the door to advanced AID system integration in the primary care setting, thus serving to further expand technology access. ²⁰

The Expanded Role of the Dietitian/DCES

Since ongoing adjustment of insulin therapy settings is not required with the BP system, the DCES can play an expanded role in the ongoing management of the system in partnership with the user. Prescriptive authority is not required for providing the education regarding dosing behaviors that influence the functioning of the adaptive closed-loop algorithm. The dietitian/DCES can shift from teaching advanced carbohydrate counting and math skills to true person-centered conversations focusing more on healthy food choices as part of an overall healthy lifestyle. While precise or advanced carbohydrate counting currently provides improved glycemic outcomes, less rigorous approaches to determine the carbohydrate amount of foods make it possible to see improved glycemia with AID systems for a broader range of individuals with type 1 diabetes. A reduced focus on carbohydrate counting could also potentially lessen the risk of eating disorders. ²³

Additional individualized attention provided to each client can focus on interactions between nutrition, physical activity/performance, and timing as opposed to teaching math skills. Individualized approaches including ongoing education, training, and support are essential to achieving the best glycemic outcomes while reducing the self-management burden, making room for the mental space to problem-solve other aspects of diabetes management. This individualization may help long-term insulin pump users to “let go” of habits that may be limiting the newer systems from being optimized. The DCES can also ensure that actionable backup plans are in place and regularly reviewed. ¹³ The DCES can also serve as a valuable resource as technology integration expands into the primary care setting.

Conclusions

The future is bright with promising developments underway to significantly improve clinical outcomes and quality of life while reducing the burden of care for people living with diabetes. Expanding diabetes technology access to the primary care setting is possible with these advanced technologies particularly when supported by a skilled DCES. The dietitian/DCES can lead in expanding diabetes technology access and adoption and enable data-informed care for a broader population of people living with type 1 diabetes, potentially leading to an overall improvement in the health of the populations served.