Carb counting is an essential daily task for people with type 1 diabetes (T1D) that helps them to determine how much insulin they need at mealtimes based on their insulin-to-carb ratio (e.g., if the ratio is 1 unit for 10 g of carbs, a meal with 50 g of carbs will require 5 units of insulin). This ratio is unique to each person and can vary with each meal.
In addition to helping you determine more accurately the insulin dose required for a meal, carb counting helps you to stay within target blood sugar levels and have more dietary flexibility.
A complex task
The downside is that carb counting is a tedious task that requires precision and in-depth knowledge of carb-containing foods, nutrition fact labels and the impact of certain nutrients on blood sugar levels. According to a study conducted in 2012, the margin of error in the carb calculations of people with T1D was approximately 21%. And some situations can further complicate things, for instance, when nutritional values are not available or when eating out.
Even though carb counting has long been one of the most complex and demanding tasks of living with T1D, there are technological advances and recent studies that hint to the possibility of alleviating this burden.
Facilitating carb counting through technology and digital tools
Carb-counting mobile applications are among the different technologies and digital tools that help to simplify carb calculations. These applications give you access to comprehensive food databases and can quickly estimate the amount of carbs in the food you log in. Some applications (e.g., SNAQ) even provide advanced functionalities such as image recognition to facilitate food identification and carb counting. However, you should keep in mind that the data may be more or less reliable, and that apps from foreign countries may display nutritional values that are different from those of products sold in Canada. But these apps are still convenient and available at all times on your phone.
In addition, technologies such as continuous glucose monitors, or CGMs (Guardian, Dexcom, FreeStyle Libre) track blood glucose in real time and provide useful information for understanding the impact of different foods on blood sugar levels. Some of these devices can even be paired with insulin pumps in systems called hybrid closed loop systems or “artificial pancreases.” These systems, which include the Tandem Control-IQ and Medtronic 780G systems, use an algorithm to adjust insulin administration throughout the day based on blood sugar levels in order to minimize hyperglycemia (high blood sugar) and hypoglycemia (low blood sugar).
Even though these technologies don’t directly simplify carb counting, they help users better understand how their meals impact their blood sugar levels, and they automatically adjust insulin delivery to facilitate glycemic management.
Looking into a simplified carb counting method
While artificial pancreases have shown their benefits for improving blood sugar management and quality of life, managing post-meal blood sugar levels still remains a challenge because these systems depend on the ability of users to accurately count and enter the amount of carbs they eat.
Many studies have been conducted over the years in order to simplify carb counting and/or improve post-meal blood sugar management (e.g., by adding amylin injections). Some have attempted to let the pump automatically adjust insulin delivery during a meal based only on measurements from the CGM sensor, without the user entering their mealtime or the content of their meal. However, this approach led to significant post-meal hyperglycemia episodes and proved to be ineffective.
A study published in 2023 focused on another simplified method for counting carbs, called meal-size estimation. This method had already been examined before, but this study was the first one to compare its effectiveness with “traditional” carb counting among 30 participants with T1D who used an artificial pancreas.
This alternative approach simply consists of estimating whether the carb content of a meal is low (less than 30 g), medium (30 to 60 g), high (60 to 90 g) or very high (more than 90 g). The insulin pump then determines the bolus to administer based on the estimation.
The study measured time spent in range at different times of the day (night vs day), as well as time spent in hypoglycemia and hyperglycemia. The results show that time spent within, over and under range was similar with both approaches, but with slightly more elevated blood sugar levels with the simplified method. These results are encouraging because they suggest that a simpler carb counting approach could be considered with the use of artificial pancreases. This would provide a more convenient and less burdensome solution for people with T1D have access to these technologies.
In summary, technological advances such as mobile applications and hybrid closed loop systems provide new perspectives for simplifying T1D managing and improving the quality of life of people living with this condition.
- Brazeau, A-S. et al. (2013). Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes. Diabetes research and clinical practice vol. 99(1): 19-23. doi:10.1016/j.diabres.2012.10.024
- Gingras, V. et al. (2018). The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes. Diabetes, obesity & metabolism vol. 20(2): 245-256. doi:10.1111/dom.13052
- El-Khatib FH, et al. (2010). A bihormonal closed-loop artificial pancreas for type 1 diabetes. Sci Transl Med,2(27):ra27. doi: 10.1126/scitranslmed.3000619
- Dovc K. et al. (2020). Faster compared with standard insulin aspart during day- and-night fully closed-loop insulin therapy in type 1 diabetes: a double-blind randomized crossover trial. Diabetes Care, 43:29–36
- Haidar, Ahmad et al. (2023). A Randomized Crossover Trial to Compare Automated Insulin Delivery (the Artificial Pancreas) With Carbohydrate Counting or Simplified Qualitative Meal-Size Estimation in Type 1 Diabetes. Diabetes care, dc222297. doi:10.2337/dc22-2297
Écrit par : Sarah Haag, R.N. BSc.
- Rémi Rabasa-Lhoret, M.D., Ph.D.
- Nathalie Kinnard, scientific writter and research assistant
- Claude Laforest, Marie-Christine Payette, Andréanne Vanasse, patient partners of the BETTER project
Linguistic revision by: Marie-Christine Payette