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Relationship between body mass index and gestational diabetes mellitus
Mansour Rezaei , Fateme Rajati , Negin Fakhri ,
Volume 77, Issue 4 (July 2019)
Abstract
Background: Gestational diabetes mellitus (GDM) is one of the most common medical complications in pregnancy, which is associated with many serious consequences for mother and her fetus. Body mass index (BMI) in pregnant women is considered as one of most effective factor for the incidence of GDM. The aim of this study was to determine the relationship between BMI at pregnant women in the early months of pregnancy and the incidence of GDM.
Methods: In this retrospective cohort study, the case of six hundred fifty-nine pregnant women who referred to health centers in Kermanshah City from September 2010 to September 2012 by convenience sampling method were selected and investigated. This study was sponsored by Kermanshah University of Medical Sciences. Height and weight were measured for each woman at the beginning of pregnancy and maternal body mass index (BMI) was calculated based on height and weight measurements. Then the pregnant women were divided into four groups based on BMI: thin (BMI less than 18.9 kg/m2), normal (BMI between 19 kg/m2 and 24.9 kg/m2), overweight (BMI between 25 kg/m2 and 29.9 kg/m2) and obese (BMI more than 30 kg/m2). Those women who had diabetes at the beginning of pregnancy were excluded from the study. GDM was considered as fasting blood glucose ≥92 between 26-30 weeks of gestation.
Results: The mean±SD age of pregnant women was 27.7±5.85 year and the mean of BMI was 24.4±4.0 kg/m2. The GDM was shown in 30.7% of women. Association between BMI and GDM were statistically significant (P<0.001). The risk of GDM onset was 1.24 times, for each unit increased in BMI, (P<0.001). The risk of GDM was significantly higher in overweight [OR=2.97, CI (2.01-4.39)] and obese [OR=16.89, CI (8.46-33.70)] women. Being underweight increased the risk of GDM onset up to 1.19 times, but not significant.
Conclusion: There is a significant relationship between maternal BMI in pregnant women at the beginning of pregnancy with GDM onset. Increased BMI is correlated with an increase in the incidence of GDM.

Comparison of gestational diabetes prediction with artificial neural network and decision tree models
Mansour Rezaei, Negin Fakhri , Fateme Rajati , Soodeh Shahsavari ,
Volume 77, Issue 6 (September 2019)
Abstract
Background: Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural network (ANN) and decision tree and also comparing these models in the diagnosis of GDM.
Methods: In this modeling study, among the cases of pregnant women who were monitored by health care centers of Kermanshah City, Iran, from 2010 to 2012, four hundred cases were selected, therefore the information in these cases was analyzed in this study. Demographic information, mother's maternal pregnancy rating, having diabetes at the beginning of pregnancy, fertility parameters and biochemical test results of mothers was collected from their records. Perceptron ANN and decision tree with CART algorithm models were fitted to the data and those performances were compared. According to the accuracy, sensitivity, specificity criteria and surface under the receiver operating characteristic (ROC) curve (AUC), the superior model was introduced.
Results: Following the fitting of an artificial neural network and decision tree models to data set, the following results were obtained. The accuracy, sensitivity, specificity and area under the ROC curve were calculated for both models. All of these values were more in the neural network model than the decision tree model. The accuracy criterion for these models was 0.83, 0.77, the sensitivity 0.62, 0.56 and specificity 0.95, 0.87, respectively. The surface under the ROC curve in ANN model was significantly higher than decision tree (0.79, 0.74, P=0.03).
Conclusion: In predicting and categorizing the presence and absence of gestational diabetes mellitus, the artificial neural network model had a higher accuracy, sensitivity, specificity, and surface under the receiver operating characteristic curve than the decision tree model. It can be concluded that the perceptron artificial neural network model has better predictions and closer to reality than the decision tree model.

Mobile applications as a new way to health: a systematic review
Negar Heidari , Fatemeh Rajati , Mojgan Rajati, Paria Heidari,
Volume 81, Issue 11 (February 2024)
Abstract

                                                                  
Background: Management of chronic diseases, such as hypertension and diabetes, requires a comprehensive long-term care plan. Adherence to self-management behaviours is crucial in improving health outcomes and quality of life for individuals living with these conditions. The research highlighted in this review study aimed to explore the potential of mobile health technology in enhancing primary and secondary prevention of chronic diseases. By providing personalized interventions, mobile applications can play a significant role in supporting individuals in the self-management of their hypertension and diabetes, ultimately leading to better disease control and improved overall well-being.
Methods: The present study is a systematic review of research examining the impact of mobile application interventions on the self-management of hypertension and diabetes. The review analyzes studies published between July 2013 to March 2023, retrieved from the PubMed and Scopus international databases using keywords such as Mobile Health, mHealth, adherence, Hypertension, High Blood Pressure, and Diabetes.
Results: A total of 1398 abstracts were found, of which 12 articles met the inclusion and exclusion criteria for this study. The research indicates that mobile health (mHealth) applications have significant potential to optimize healthcare processes and facilitate improved access to health information. These digital tools can combine various treatment methods with attractive, user-friendly solutions that allow patients to actively monitor a range of health indicators, such as diet, body weight, blood pressure, mood, and sleep patterns. By enabling this type of continuous self-monitoring, mHealth apps can empower individuals to take a more active role in managing their well-being. Additionally, these applications can facilitate greater collaboration between healthcare providers, patients, and their families, thereby enhancing the overall coordination and accessibility of care. As such, mHealth technologies can be effectively leveraged in conjunction with traditional medical services to improve health outcomes and expand access to critical health information.
Conclusion: The present study found a significant increase in mobile health app usage. To understand the real, long-term impact of this technology on health, further longitudinal studies are needed. Comprehensive research is crucial to guide the development of effective digital health interventions that can improve individual and population outcomes over time.


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