ABSTRACT
Background: Nowadays, digital games are not just entertainment, but beside routine treatments, they are used in patient care, especially in patients with diabetes. Application of digital games in patient's education can improve self-management of diabetes. The aim of the present study was to evaluate the effect of a mobile game (Amoo) implementation on enhancing dietary information in patients with type 2 diabetes. Methods: A mobile game (called Amoo), which was developed by researchers of this study, was applied to assess the self-education of patients with diabetes. Sixty patients with type 2 diabetes participated in the study. The participants took part in a pre-intervention test to determine their dietary information. The participants were randomly divided into one of two groups, including the intervention group: played the game for 15 minutes daily for 6 weeks, and the control group: did not involve in the game. A post-intervention test was run to show a possible improvement in dietary information. Data were analyzed using paired t test and suitable non-parametric testes including Mann-Whitney and Wilcoxon signed rank tests as well as Spearman and Pearson correlation coefficients via IBM SPSS statistics version 21 (SPSS, v 21.0, IBM, Armonk, NY, USA). A P-value less than 0.05 was considered as a significant level. Results: The results indicated a statistically significant difference between the pre and post test scores in the intervention group (p<0.001). However, there was no significant difference in fasting blood sugar (p=0.125). Conclusion: The mobile game (Amoo) could enhance the knowledge of patients with type 2 diabetes about food calories and glycemic index. This means that mobile games may serve as an educational aid to these patients.
ABSTRACT
INTRODUCTION: Iron deficiency anemia (IDA) and ß-thalassemia trait (ß-TT) are the most common types of microcytic hypochromic anemias. The similarity and the nature of anemia-related symptoms pose a foremost challenge for discriminating between IDA and ß-TT. Currently, advances in technology have gave rise to computer-based decision-making systems. Therefore, advances in artificial intelligence have led to the emergence of intelligent systems and the development of tools that can assist physicians in the diagnosis and decision-making. AIM: The aim of the present study was to develop a neural network based model (Artificial Neural Network) for accurate and timely manner of differential diagnosis of IDA and ß-TT in comparison with traditional methods. METHODS: In this study, an artificial neural network (ANN) model as the first precise intelligent method was developed for differential diagnosis of IDA and ß-TT. Data set was retrieved from Complete Blood Count (CBC) test factors of 268 individuals referred to Padad private clinical laboratory at Ahvaz, Iran in 2018. ANN models with different topologies were developed and CBC indices were examined for diagnosis of IDA and ß-TT. The proposed model was simulated using MATLAB software package version 2018. The results showed the best network architecture based on the advanced multilayer algorithm (4 input factors, 70 neurons with acceptable sensitivity, specificity, and accuracy). Finally, the results obtained from ANN diagnostic model was compared to existing discriminating indexes. RESULT: The results of this model showed that the specificity, sensitivity, and accuracy of the proposed diagnostic system were 92.33%, 93.13%, and 92.5%, respectably; i.e. the model could diagnose frequent occurrence of IDA in patients with ß-TT. CONCLUSION: The results and evaluation of the developed model showed that the proposed neural network model has a proper accuracy and generalizability based on the initial factors of CBC testing compared to existing methods. This model can replace the high-cost methods and discriminating indices to distinguish IDA from ß-TT and assist in accurate and timely manner diagnosis.
