Physical activity and sedentary behaviors (screen time and homework) among overweight or obese adolescents: a cross-sectional observational study in Yazd, Iran.

BACKGROUND: The growing number of adolescents who are overweight or obese (OW / OB) is a public concern. The present study was aimed to evaluate physical activity (PA) and sedentary behaviors (SB) (screen time (ST) and homework time (HT)) among Yazd OW/OB adolescents. METHODS: This cross-sectional study was performed among 510 students aged 12-16 in Yazd, Iran. The general information, PA, and SB (ST and HT) were collected by interview based on the WHO standard questionnaire. Anthropometric data were assessed by precise instruments. Daily energy intake (Energy) was obtained from a 7-day food record. Nutritionist 4 software (version I) was run to estimate the energy. RESULTS: There was a high prevalence of SB > 2h/day (97.6), ST > 2h/day (70.3%), overweight or obesity (40%), abdominal obesity (36.9%), physical inactivity (29.8%) among the students. The younger age (p = 0.014), energy (p < 0.001), no access to the yard (p < 0.001), family size ≤ 2 (p = 0.023), passive transportation, (p = 0.001), the highest school days' HT (p = 0.033) and SB (p = 0.021), and the highest weekends' HT among the students were the risk factors for OW/OB. The highest PA level was associated with a lower risk of OW/OB (p < 0.001). The findings were not the same in both sexes. Compared to the normal weight students, OW / OB spent more time on school days and weekdays for ST (P <0.001), HT (P <0.001, P = 0.005) and SB (P <0.001), respectively. OW/OB students showed a higher weekends' ST (p < 0.001) and lower HT (p = 0.048) than normal-weight students. CONCLUSION: The prevalence of SB, ST, OW/OB, and physical inactivity were common. The school days and weekends' HT, the school days' SB and HT, age, energy, PA, and access to the yard, family size, and passive transportation were related to the greater chances of OW/OB students. Given that the expansion of online education and self-isolation in a new situation with COVID-19, it seems we will meet the worrying results.

Effect of implementing a mobile game on improving dietary information in diabetic patients.

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.

A fuzzy based dietary clinical decision support system for patients with multiple chronic conditions (MCCs).

Due to the multifaceted nature of Multiple Chronic Conditions (MCCs), setting a diet for these patients is complicated and time-consuming. In this study, a clinical decision support system based on fuzzy logic was modeled and evaluated to aid dietitians in adjusting the diet for patients with MCCs. Mamdani fuzzy logic with 1144 rules was applied to design the model for MCCs patients over 18 years who suffer from one or more chronic diseases, including obesity, diabetes, hypertension, hyperlipidemia, and kidney disease. One hundred nutrition records from three nutrition clinics were employed to measure the system's performance. The findings showed that the diet set by nutritionists had no statistically significant difference from the diet recommended by the fuzzy model (p > 0.05), and there was a strong correlation close to one between them. In addition, the results indicated a suitable model performance with an accuracy of about 97%. This system could adjust the diet with high accuracy as well as humans. In addition, it could increase dietitians' confidence, precision, and speed in setting the diet for MCCs patients.

Determining the effective factors in predicting diet adherence using an intelligent model.

Adhering to a healthy diet plays an essential role in preventing many nutrition-related diseases, such as obesity, diabetes, high blood pressure, and other cardiovascular diseases. This study aimed to predict adherence to the prescribed diets using a hybrid model of artificial neural networks (ANNs) and the genetic algorithm (GA). In this study, 26 factors affecting diet adherence were modeled using ANN and GA(ANGA). A dataset of 1528 patients, including 1116 females and 412 males, referred to a private clinic was applied. SPSS Ver.25 and MATLAB toolbox 2017 were employed to make the model and analyze the data. The results showed that the accuracy of the proposed ANN and ANGA models for predicting diet adherence was 93.22% and 93.51%, respectively. Also, the Pearson coefficient showed a significant relationship among the factors. The developed model showed the proper performance for predicting adherence to the diet. Moreover, the most effective factors were selected using GA. Some important factors that affect diet adherence include the duration of the marriage, the reason for referring to the clinic, weight, body mass index (BMI), weight satisfaction, lunch and dinner times, and sleep time. Therefore, applying the proposed model can help dietitians identify people who need more support to adhere to the diet.