## 5 Discussion  
The purpose of this research was to find a relationship between the intensity of physical activity and the presence of type 1 diabetes among children and develop a non-invasive method of type 1 diabetes detection. Assessment of the physical activity was based on ActiGraph activity monitor measurements. The ActiGraph measurements for health-related research were also carried out in published findings [17; 18; 19; 20].  
Decision tree forests, as well as other computational intelligence methods were applied for the detection of different diseases, e.g., breast cancer and heart disease [50; 51]. Application of decision tree forests, which included five parameters connected with the intensity of physical activity, enabled the prediction of type 1 diabetes presence among children and adolescents between the ages of six and 18 with a high accuracy of 86.09% and specificity of 84.35%. The PNN also showed a high accuracy of 84.35%. Our results were comparable to similar articles, in which neural networks were used for outcome prediction of diabetes presence. For example, an SVM algorithm using the RBF kernel, the same as was used in this paper, was able to predict the presence of elevated blood glucose level via electrochemical measurement of saliva with approximately 85% accuracy [52].  
As the final result of the study, it was concluded that if the number of steps is lower than around 61,000 a week, it is likely that the child is suffering from type 1 diabetes. After dividing this by the seven days of a week, we obtained the average number of steps per day, which was around 9000, but it should be noted that gender and age were not included in the calculation of this result. The updated international literature indicates that we can expect, among children, boys to average 12,000-16,000 steps/day, girls to average 10,000-13,000 steps per day, and adolescents to reach approximately 8000-9000 steps/day [53]. Thus, the obtained result was consistent with the normative international literature.  
Decreased physical activity of ill children compared to healthy peers was the result of the disease. Many children also complained that it was difficult for them to go through a pitch of more than 100 m, that it was difficult for them to run, play sports, exercise, lift something heavy, take a bath, or shower by themselves, and that they felt pain and were tired.  
The results of the research are promising and encourage developing a mobile application for type 1 diabetes diagnosis dedicated to children and adolescents. Although the popularity of using mobile phones applications in various health disorders has reached about 30%, it should be taken into account that young people are more likely to use and more effective at using new mobile phone applications, and the popularity and potential acceptance of mobile health solutions have an increasing tendency [54].  
Conceptualization, A.C., S.C., and D.M.; data curation, S.C.; formal analysis, S.C.; funding acquisition, D.M.; investigation, S.C.; methodology, A.C. and S.C.; project administration, A.C.; software, S.C.; supervision, D.M.; validation, D.M.; visualization, S.C.; writing, original draft, A.C.; writing, review and editing, A.C., S.C., and D.M.  
This project is financed by the Minister of Science and Higher Education of the Republic of Poland within the \"Regional Initiative of Excellence\" program for years 2019-2022; Project Number 027/RID/2018/19; the amount granted: 11,999,900 PLN.  
There are no acknowledgments related to this study.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript: