More exciting research results to be published soon!
Clinical Trials (blood sugar)
Figure 1 presents simulations and measurements which were performed on a diabetic subject. The solid line represents the simulation output for the entire day, while the dots represent the blood glucose measurements.

Figure 2 and Figure 3 respectively shows the predictive quality of the AIDA software(the only competitor) and our software.

Figure 1 Our accurate blood sugar
predictions (type 1)

Clinical Trials (insulin)
Let us investigate the quality of predictions for insulin requirements using the well-known CHO mass or GI methods. The results for the CHO and GI experiments for one test subject are shown in Figure 4 and Figure 5 respectively. Pearson’s R²- values were calculated for linearized trend fits through the plotted data.

The R²-values for the CHO and the GI methods were 0.603 and 0.558 respectively for the example test subject. For the CHO method the worst spread was found at 50 g of CHO, namely a factor of 12, while for a GI at 65 the spread factor was close to 3.

From the figures the need for a better insulin prediction method according to ingested carbohydrates is obvious. Therefore, our method is proposed as an alternative. The method is theoretically derived from energy principals and is very easy to use. A theoretical approach is preferred to an empirical one since theory often leads to better insight.

The quality of insulin predictions according to the method is investigated by using the Lee and Wolever measurements for the same test subject as in Figure 4 and Figure 5. The results are given in Figure 6.

The linear trend line for the method yields an R²-value of 0.929 for this test subject. The value is significantly higher than those calculated for the other two methods and therefore indicates that the predictor is better than both CHO counting and the GI methods.

Figure 2 AIDA simulations

Figure 3 Our simulations

Figure 4 Current insulin predictions
using CHO counting

Figure 5 Current insulin predictions using a GI based method

Figure 6 Our insulin predictions