No abstract

Level V, cross-sectional descriptive study.

Background: Health information technology helps patients to take better care of themselves and improves health status of patients with chronic diseases, such as diabetes. Objective: This study aimed to identify factors influencing the use of health information technology in diabetes management. Methods: This was a review study conducted in 2019. To obtain the related articles, databases including Scopus, Web of Science, Proquest and PubMed were searched and the time frame was between 2010 and 2018. Initially, 1159 articles were retrieved and after screening 28 articles were selected to be included in the study. Results: Factors influencing the use of health information technology in diabetes management could be divided into the motivational and inhibitory factors and each of them could be categorized into five groups of organizational, technical, economic, individual and ethical/legal factors. The motivational factors included training, system ease of use, economic support, having computer literacy, and maintaining privacy and confidentiality. The inhibitory factors included a lack of long-term planning, technical problems, inadequate financial resources, old age, and concerns over confidentiality issues. Conclusion: Identifying motivational and inhibitory factors can help to make better use of technology for diabetes management. This approach, in turn, can improve the acceptability of the technology and saves cost, reduces long-term complications of diabetes, and improves quality of life in diabetic patients.

Background: Due to widespread of coronavirus disease 2019 (COVID-19) infection, identification of its risk factors and clinical characteristics are important. The aim of the present study was to assess Vitamin D levels in individuals with severe acute respiratory syndrome coronavirus-19 infection and to report on its potential as a predictive marker. Materials and Methods: All patients, diagnosed with COVID-19 infection from February 16 to March 21, 2020, and referred to Firoozgar Hospital, Tehran, Iran, were enrolled in this study. Vitamin D analysis was undertaken on patient serum samples using a commercial kit (Pars Azmoon Co., Tehran, Iran). SPSS v. 22 was used for statistical analysis. Results: Vitamin D serum concentration was analyzed in a total of 317 patients whose mean age ± standard deviation was 62.05 ± 15 years and with 62.5% being male. A significant association of Vitamin D level and death was observed. Higher levels of serum Vitamin D had protection against death (odds ratio = 0.955 [95% confidence interval = 0.923–0.988], P = 0.008). Conclusion: As a preliminary study in the Iranian population who suffered COVID-19 disease, we identified that Vitamin D deficiency was associated with a higher death rate and intensive care unit admission.

Background: Hyperprolactinemia is associated with changes in body composition and metabolic abnormalities. Normalization of prolactin (PRL) has been suggested to reverse these abnormalities. The present study was designed to determine the effect of treatment duration on metabolism as well as metabolic alterations after treatment in comparison with baseline in patients with prolactinoma in Iranian individuals. Methods: In a prospective and longitudinal study, 27 consecutive patients with prolactinoma were assessed during 6 months. Anthropometric data and metabolic variables were studied at baseline and at 3 and 6 months after normalization of PRL. Results: In the present study, there was a statistically significant decrease of metabolic syndrome (Met.S) after 3 months (P = 0.01), with a further decline after 6 months (P < 0.001) of cabergoline therapy. Moreover, a statistically significant decline was seen in total cholesterol (P = 0.007 and P = 0.01 after 3 and 6 months, respectively) and uric acid (P = 0.05 and P = 0.03 after 3 and 6 months, respectively) after normalization of the serum PRL. Conclusions: We found a significant reduction in Met.S after normalization of PRL level in patients with prolactinoma. We suggest that it is important to consider the metabolic profile of patients with prolactinoma. Then, patients may benefit even at 3 months after treatment.