## 3 Monitoring Campaign
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Population Feature** & \multicolumn{2}{c}{**Value**} \\ \hline Subjects (number) & \multicolumn{2}{c}{25} \\ Sex & \multicolumn{2}{c}{14 men—11 women} \\ Occupation & \multicolumn{2}{c}{16 students—9 office workers} \\ \hline
**Population Feature** & **Median** & **Min** & **Max** \\ \hline Age (years) & 24.51 & 18 & 56 \\ Body Mass Index (BMI, kg/m\({}^{2}\)) & 22.20 & 19.42 & 24.80 \\ Duration of diabetes (years) & 9 & 5 & 29 \\ Fingersticks per day & 7 & 5 & 12 \\ Insulin units per day & 47 & 36 & 59 \\ (fast insulin + slow insulin, median) & 6.8 & 6.3 & 7.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data regarding the patients considered in the study. coverage that exceeds 24 h, whereas the fast insulin is to make up for rising glucose levels, caused by eating or hyperglycemia induced by other influencers. In this paper, some of the methods aimed at modeling glycemia oscillations are compared and discussed. At present, most of the previous literature on diabetes management systems have only taken into account glycemia and insulin levels, and sometimes an estimation of meals, but it seems reasonable to incorporate additional variables that could also influence glucose levels as far as it is possible to measure or estimate them. It seems that there is a general agreement in using glycemia, insulin, and meals as remarkable variables [42], and the studies with this set of variables are a majority. There have also been some works in the last years taking into account other variables, mainly exercise, both in silico and in vivo [43], and also consider heart rate and temperature. This way, and only in recent years has there been a general agreement in the need to include new variables, whose influence is out of doubt, that now can be measured [6]. Therefore, the following features collected in our experimental stage, which are variously related to the glucose level of the patient, are taken into account:  
* Glycemia: An array of past measures. * Insulin injections: Past values of fast insulin doses. This hormone, with an exogenous origin for a diabetic patient, is the feature that mainly governs how much the level of glucose in the blood will decrease. * Meal ingestion: Past values, in the same sense as for the insulin. It should be noted that the selected patients had proper experience in managing carbohydrate counting. The food that we all eat is conversed and absorbed as glucose, which is discharged into the bloodstream, increasing glycemia almost instantly. * Exercise: Influential historical data, measured in the number of steps taken. Physical activity raises the muscles' request for glucose. It also improves blood circulation, so insulin's effectiveness is increased as long as exercise makes the cellular barriers more permeable, letting glucose go inside the cells easily. * Heart rate: Current and previous values. An increment of this feature can be due to diverse causes. Obviously, it increases with physical activity. However, such a rise can also be generated by stressful situations or hypo/hyperglycemia situations. * Sleep: The data values collected indicated just \"asleep\" or \"awake\". Hence, it seems reasonable to express sleep as a series of the amount of time spent sleeping in the past. A low quality of sleep at night can lead to both unbalanced glucose dynamics and insulin resistance.