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Aim: Type 2 diabetes mellitus (T2DM) is a common disease that imposes a substantial burden on the healthcare system and patients. Lifestyle modification such as sleep hygiene plays a crucial role in glycemic control. Sleep disorders impact many aspects of health. In this study, we aimed to investigate the correlation between sleep quality and glycemic control in T2DM. Method: This cross-sectional study was performed on 163 T2DM patients, attending Shahid Beheshti Hospital in Hamadan, Iran from March 2020 to 2021. Besides recording the demographic data and HbA1c level of participants, they were asked the Pittsburgh Sleep Quality Index questionnaire for evaluating sleep quality. We employed SPSS ver. 21 for data analysis and considered 0.05 as a significant level Results: Among all participants, 62 (38%) were female and 30.7% were illiterate. The mean age was 56.67 ± 12.90 years, and HbA1c was 9.03 ± 1.92 mg/dL. Among sleep metrics, mean waking time was 8.74 ± 1.74 hours, and average sleep time was 12.90 ± 4.90 hours. Overall, 58.2% of the participants had poor glycemic control and 44.8% were suffering from poor sleep quality. We found that patients with poor glycemic control exhibited significantly higher levels of sleep disturbances compared to those with good glycemic control (P < 0.001). Conclusion: Sleep quality is associated with glycemic control in patients with T2DM. Sleep disorders are common among diabetic patients. Thus, healthcare providers need to consider sleep quality improvement in their holistic approach to diabetes management.

Background: Low health literacy (HL) is associated with an extensive range of health outcomes. Objective: The present study was performed to inquire about the relationship between HL and glycemic control in gestational diabetes in order to design interventional future preventing programs. Methods: This cross-sectional study was performed on 104 Iranian pregnant women with gestational diabetes mellitus (GDM) referred from urban and rural areas to endocrinology clinic of Hamadan Beheshti Hospital, in 2017. Iranian Health Literacy Questionnaire (IHLQ) and a sociodemographic checklist were distributed among women. Correlation between HL and glycemic control was examined using SPSS. The significance level was set at P < 0.05. Results: Among women, 48.1% (50) were affected by uncontrolled diabetes and only 22% (11) had an adequate level of HL. An adequate level of HL were 50% and 22% in glycemic controlled and uncontrolled women, respectively. In univariate analysis, there was a significant relationship between diabetes control and adequate HL. So, problematic HL could increase the chance of uncontrolled diabetes more than three times (odds ratio: 3.5; CI: 1.5–8.3; P value: 0.004). Among all related variables, education and being housewife were considered as protective and risk factors for problematic HL, respectively. Conclusion: In conclusion, this study has provided evidence of limited HL and its relationship with low glycemic control in pregnant women with GDM. The problem was more serious in low educated, rural, housekeepers, and older-aged women. This deficit needs to be addressed by health planners and policymakers who are responsible for promoting the health of people and decreasing health inequalities community.

Background: The main goal of this study was to diagnose the two most common thyroid disorders, namely, hyperthyroidism and hypothyroidism, based on multinomial logistic regression and neural network models. In addition, the study evaluated the predictive ability of laboratory tests against the individual clinical symptoms score. Materials and Methods: In this study, the data from patients with thyroid dysfunction who referred to Imam Khomeini Clinic and Shahid Beheshti Hospital in Hamadan were collected. The data contained 310 subjects in one of three classes—euthyroid, hyperthyroidism, and hypothyroidism. Collected variables included demographics and symptoms of hypothyroidism and hyperthyroidism, as well as laboratory tests. To compare the predictive ability of the clinical signs and laboratory tests, different multinomial logistic regression and neural network models were fitted to the data. These models were compared in terms of the mean of the accuracy and area under the curve (AUC). Results: The results showed better performance of neural network model than multinomial logistic regression in all cases. The best predictive performance for logistic regression (with a mean accuracy of 91.4%) and neural network models (with a mean accuracy of 96.3%) was when all variables were included in the model. In addition, the predictive performance of two models based on symptomatic variables was superior to laboratory variables. Conclusions: Both neural network and logistic regression models have a high predictive ability to diagnose thyroid disorder, although neural network performance is better than logistic regression. In addition, as achieving less error prediction model has always been a matter of concern for researchers in the field of disease diagnosis, predictive nonparametric techniques, such as neural networks, provide new opportunities to obtain more accurate predictions in the field of medical research.