Results: We collected a large range of postprandial glucose and insulin dynamics for 53 common food products and mixed meals. Currently available glycemic measures were found to be inadequate to describe the heterogeneity in postprandial dynamics. By estimating model parameters from glucose and insulin data, the physiology-based dynamic model accurately describes the measured data whilst adhering to physiological constraints. Conclusions: The physiology-based dynamic model provides a systematic framework to analyze postprandial glucose and insulin profiles. By changing parameter values the model can be adjusted to simulate impaired glucose tolerance and insulin resistance.

Diabetes is a serious and life-threatening condition that reduces the quality of life of the patient and is also costly, both in medical costs and in lost work-hours. 1 The incidence and severity of the complications of diabetes can considerably be reduced if patients develop a lifestyle that leads to good glycemic control. 2,3 Research has shown that diabetes education can reduce HbA1c over a longer period, 4,5 resulting in a lower risk of complications. 6,7 Education is therefore a fundamental part of diabetes care. It is currently provided in several 1-on-1 or group sessions with a physician, diabetes nurse, dietician, or podiatrist. This is time-consuming and costly.A major part of diabetes education is learning how to adjust insulin injections based on carbohydrate intake, exercise and factors like stress or illness. However, possibilities for the patient to safely practice with this newly acquired knowledge are limited to trying different strategies on his own body. This gives a considerable risk of hypo-or hyperglycemia. Here, we describe the development and verification of the physiological model for healthy subjects that forms the basis of the Eindhoven Diabetes Education Simulator (E-DES). E-DES shall provide diabetes patients with an individualized virtual practice environment incorporating the main factors that influence glycemic control: food, exercise, and medication.

The Eindhoven Diabetes Education Simulator project was initiated to develop an educational solution that helps diabetes patients understand and learn more about their diabetes. This article describes the identification of user preferences for the development of such solutions. Young seniors (aged 50–65 years) with type 2 diabetes were chosen as the target group because they are likely to have more affinity with digital devices than older people and because 88% of the Dutch diabetes population is >50 years of age. Data about the target group were gathered through literature research and interviews. The literature research covered data about their device use and education preferences. To gain insight into the daily life of diabetes patients and current diabetes education processes, 20 diabetes patients and 10 medical experts were interviewed. The interviews were analyzed using affinity diagrams. Those diagrams, together with the literature data, formed the basis for two personas and corresponding customer journey maps. Literature showed that diabetes prevalence is inversely correlated to educational level. Computer and device use is relatively low within the target group, but is growing. The interviews showed that young seniors like to play board, card, and computer games, with others or alone. Family and loved ones play an important role in their lives. Medical experts are crucial in the diabetes education of young senior diabetes patients. These findings are translated into a list of design aspects that can be used for creating educational solutions.

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