  Objective: Numerous glucose prediction algorithm have been proposed to
empower type 1 diabetes (T1D) management. Most of these algorithms only account
for input such as glucose, insulin and carbohydrate, which limits their
performance. Here, we present a novel glucose prediction algorithm which, in
addition to standard inputs, accounts for meal absorption and physical exercise
information to enhance prediction accuracy. Methods: a compartmental model of
glucose-insulin dynamics combined with a deconvolution technique for state
estimation is employed for glucose prediction. In silico data corresponding
from the 10 adult subjects of UVa-Padova simulator, and clinical data from 10
adults with T1D were used. Finally, a comparison against a validated glucose
prediction algorithm based on a latent variable with exogenous input (LVX)
model is provided. Results: For a prediction horizon of 60 minutes, accounting
for meal absorption and physical exercise improved glucose forecasting
accuracy. In particular, root mean square error (mg/dL) went from 26.68 to
23.89, p<0.001 (in silico data); and from 37.02 to 35.96, p<0.001 (clinical
data - only meal information). Such improvement in accuracy was translated into
significant improvements on hypoglycaemia and hyperglycaemia prediction.
Finally, the performance of the proposed algorithm is statistically superior to
that of the LVX algorithm (26.68 vs. 32.80, p<0.001 (in silico data); 37.02 vs.
49.17, p<0.01 (clinical data). Conclusion: Taking into account meal absorption
and physical exercise information improves glucose prediction accuracy.
