scholarly journals Knowledge, Beliefs and Practices of People diagnosed with Type-1 Diabetes towards Diabetes Mellitus and Diabetic Foot Syndrome

2022 ◽  
Vol 5 (1) ◽  
pp. 01-08
Author(s):  
Shiju Raman Unni ◽  
Hani Naguib ◽  
Mary Mccallum
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Diabetic Foot ◽  
Healthcare Providers ◽  
Diabetic Foot Syndrome ◽  
Cross Sectional ◽  
Beliefs And Practices ◽  
Behaviour Change Counselling

Background: Diabetes Mellitus (DM) is associated with significant morbidity and mortality. Diabetic foot syndrome is one of the most common devastating preventable complications of diabetes mellitus (DM). Objectives: We aimed to evaluate the knowledge, Beliefs and Practices (KBP) among Omani patients with type 1 diabetes mellitus (T1DM) regarding DM and Diabetes foot. Design: A cross sectional descriptive study was used. Settings: A secondary care, polyclinic named Bawshar in Muscat, Oman where patients were seen three days per week. Sample Size:A convenient sample of 100 participants between age group 16 to 30 years were involved. Materials and methods: A validated semi- structured questionnaire was used to assess KBP of T1DM with six domains. During the study period from November 2019 to December 2019. .The data was analysed by using Statistical Package for the Social Sciences (SPSS) Statistics Inc., Chicago, US version 20. Results: There were 50 females, 50 males; 5 % of patients were illiterate and 30% of them were working. 65% were students. Only 50% checked their foot regularly and only 55% check there blood glucose regularly .57% don’t know the cause of diabetes, 25% don’t know the complications of the same while 20% don’t know cause of diabetic foot and 25% don’t know the symptoms of diabetic foot. 20% beliefs checking blood glucose is the responsibility of the doctor and 85% beliefs walking bare foot is high risk factor for DM foot. Conclusions: In reality healthcare providers must be trained to counsel people with DM to plan adequate interventions that enable an understanding of the offered information. A well-structured ,Behaviour change counselling (BCC) like Motivational interviewing (MI)are considered the ideal practices for this patients, to prevent DM complications.

scholarly journals Glycemic control associated factors in type 1 diabetes mellitus patients: a cross sectional study

2015 ◽  
Vol 7 (S1) ◽  
Author(s):  
Patrícia Ramos Guzatti ◽  
Amely PS Balthazar ◽  
Maria Heloisa Busi da Silva Canalli ◽  
Thais Fagnani Machado
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Type 1 Diabetes Mellitus ◽  
Associated Factors ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional

scholarly journals A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

2021 ◽  
Vol 11 (4) ◽  
pp. 1742
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Wai Lok Woo ◽  
Bo Wei ◽  
Domingo-Javier Pardo-Quiles
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Feature Selection ◽  
Blood Glucose ◽  
Type 1 Diabetes Mellitus ◽  
Support Vector ◽  
Chronic Hyperglycemia ◽  
Predictive Algorithms

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

Comparative analysis of different drug delivery methods of injectable hydrogel nanomaterials of insulin biomaterials via multiple daily injections and continuous subcutaneous insulin infusion in the treatment of type 1 diabetes mellitus in children

2021 ◽  
Vol 11 (7) ◽  
pp. 1154-1160
Author(s):  
Yan Sun ◽  
Haoshu Niu ◽  
Zhixia Wang ◽  
Ying Wang ◽  
Xuechun Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Drug Delivery ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Type 1 Diabetes Mellitus ◽  
Continuous Subcutaneous Insulin Infusion ◽  
Insulin Infusion ◽  
Insulin Dosage ◽  
Subcutaneous Insulin

The aim of this study was to investigate the difference between multiple daily injections (MDI) and continuous subcutaneous insulin infusion (CSII) in blood glucose control during the treatment of type 1 diabetes mellitus (T1DM) in children. under the nano-hydrogel delivery carrier. In order to improve the efficiency and therapeutic effect of the experiment, this paper adopts injectable nanomaterial-polymer composite hydrogel as drug delivery system to cooperate with insulin injection to improve the effective utilization of drugs. Eighty children diagnosed with T1DM by the department of Endocrinology, Genetics, and Metabolism of INNER MONGOLIA BAOGANG Hospital from October 2018 to December 2019 were selected as research subjects for this study. The children were randomly divided into MDI group (treated with MDI) and CSII group (treated with CSII), with 40 children in each group. The basic data of the children were compared, and changes in hemoglobin A1c (HbA1c) at admission and 1, 2, and 3 months after treatment were detected. During the detection, the blood glucose level, therapeutic time of blood glucose normalization, and daily insulin dosage were recorded. The HbA1c and fasting blood glucose (FBG) were followed up three months after discharge, and incidences of hypoglycemia in the two groups were observed. The results showed that the mean value of HbA1c in the MDI group was higher than that in the CSII group (P < 0.05). Each patient was assessed for the number of times their blood sugar was allowed to dip below normal levels; patients with less hypoglycemia had a higher rate of blood sugar control. The control rates of blood glucose in the MDI and CSII groups were 19.21% and 23.50%, respectively. The CSII group showed significantly higher blood glucose rates than the MDI group (P < 0.05). The therapeutic time of blood glucose normalization in the MDI group was significantly longer than that in the CSII group (P < 0.05). There was no significant difference in the average daily insulin dosage between the MDI and CSII groups (P > 0.05), which indicated that CSII therapy had significant advantages in reducing blood glucose in children with T1DM.

Programmed Cell Death-1 Monoclonal Antibody Therapy and Type 1 Diabetes Mellitus: A Review of the Literature

2019 ◽  
pp. 089719001985092 ◽  
Author(s):  
Kyle A. Farina ◽  
Michael P. Kane
Keyword(s):  
Diabetes Mellitus ◽  
Cell Death ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Programmed Cell Death ◽  
Type 1 Diabetes Mellitus ◽  
Inhibitor Therapy ◽  
Blood Glucose Monitoring ◽  
Programmed Cell Death 1

Two Food and Drug Administration-approved programmed cell death-1 (PD-1) inhibitors, nivolumab (Opdivo®), and pembrolizumab (Keytruda®), are indicated for treatment-resistant malignancies. Inhibition of PD-1 also inhibits T-cell peripheral tolerance, enhancing autoimmunity. Various autoimmune conditions have been reported with the use of these agents, including type 1 diabetes mellitus (T1DM). This article reviews literature regarding the development of T1DM in patients treated with PD-1 inhibitors and identifies strategies for the appropriate identification, monitoring, and follow-up of these patients. Published cases of T1DM related to PD-1 inhibitor therapy were identified using PubMed. Eighty-three identified publications were reviewed, of which 37 publications involving 42 cases of anti-PD-1 therapy-induced T1DM were identified. The average age of patients at presentation was 62 years and 59.5% were male. The mean number of PD-1 inhibitor doses received was 5, with a mean time to presentation of 11 weeks. Initial presentation of diabetic ketoacidosis was reported in 69% of cases, with an average blood glucose of 660 mg/dL and an average HbA1cof 8.7%. The exact mechanism PD-1 inhibitor therapy-induced T1DM is unknown. Blood glucose monitoring is recommended for all patients receiving anti-PD-1 therapy. Further research is needed to delineate the frequency of this adverse effect, as well as to evaluate potential risk factors and ideal management strategies.

scholarly journals Prevalence of Celiac Disease in Omani Children with Type 1 Diabetes Mellitus: A Cross Sectional Study

2013 ◽  
Vol 28 (4) ◽  
pp. 260-263 ◽  
Author(s):  
Siham Al-Sinani ◽  
Sharef Waadallah Sharef ◽  
Saif Al-Yaarubi ◽  
Ibrahim Al-Zakwani ◽  
Khalid Al-Naamani ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Celiac Disease ◽  
Type 1 Diabetes Mellitus ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional
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