The impact of type 1 diabetes and eating disorders: the perspective of individuals

2008 ◽  
Vol 17 (7b) ◽  
pp. 169-176 ◽  
Author(s):  
Sharon Hillege ◽  
Barbara Beale ◽  
Rose McMaster
Keyword(s):  
Eating Disorders ◽  
Type 1 Diabetes ◽  
The Impact

Low carbohydrate diets in eating disorders and type 1 diabetes

2020 ◽  
pp. 135910452098077
Author(s):  
Melissa Hart ◽  
Kirrilly Pursey ◽  
Carmel Smart
Keyword(s):  
Eating Disorders ◽  
Type 1 Diabetes ◽  
Dietary Intake ◽  
Dietary Restriction ◽  
Eating Behaviours ◽  
Optimal Outcomes ◽  
Low Carbohydrate Diet ◽  
Low Carbohydrate ◽  
The Impact

Dietary intake requires attention in the treatment of both eating disorders and type 1 diabetes (T1D) to achieve optimal outcomes. Nutritional management of both conditions involves encouraging a wide variety of healthful foods in the context of usual cultural and family traditions. In recent times, low carbohydrate diets have seen a rise in popularity, both in T1D and in the general population. Low carbohydrate diets involve dietary restriction, although the extent depends on the level of carbohydrate prescription. Although dietary restriction is a known risk factor for eating disorders, there is limited literature on the impact of following a low carbohydrate diet on the development and maintenance of eating disorders in T1D. The aim of this review is to discuss the impact of dietary restriction on the development and treatment of eating disorders and propose considerations to enable optimum health outcomes in individuals with T1D, an at risk group. In order to achieve this, clarity regarding strategies that allow both flexibility in dietary intake and facilitate healthy eating behaviours, whilst achieving glycaemic targets, are required.

The impact of real-time continuous glucose monitoring on metabolic control in children with type 1 diabetes

2020 ◽  
Author(s):  
Ruxandra Calapod Ioana ◽  
Irina Bojoga ◽  
Duta Simona Gabriela ◽  
Ana-Maria Stancu ◽  
Amalia Arhire ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Metabolic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
The Impact

Model estimates of the results of diabetes patients using modern glucometers with tri-color technology for displaying test results

2019 ◽  
pp. 81-89
Author(s):  
Larisa Dmitrievna Popovich ◽  
Svetlana Valentinovna Svetlichnaya ◽  
Aleksandr Alekseevich Moiseev
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Economic Benefits ◽  
Test Results ◽  
Self Monitoring ◽  
Patients With Diabetes ◽  
The Impact

Diabetes – a disease in which the effect of the treatment substantially depends on the patient. Known a study showed that the use of glucometers with the technology of three-color display of test results facilitates self-monitoring of blood sugar and leads to a decrease in glycated hemoglobin (HbAlc). Purpose of the study: to modeling the impact of using of a glucometer with a color-coded display on the clinical outcomes of diabetes mellitus and calculating, the potential economic benefits of reducing the hospitalization rate of patients with diabetes. Material and methods. Based on data from two studies (O. Schnell et al. and M. Baxter et al.) simulation of the reduction in the number of complications with the use of a glucometer with a color indication. In a study by O. Schnell et al. a decrease of HbA1c by 0.69 percent is shown when using the considered type of glucometers, which was the basis of the model. Results. In the model, the use of a glucometer with a color-coded display for type 1 diabetes led to a decrease in the total number of complications by 9.2 thousand over 5 years per a cohort of 40 thousand patients with different initial levels of HbA1c. In a cohort of 40 thousand patients with type 2 diabetes, the simulated number of prevented complications was 1.7 thousand over 5 years. When extrapolating these data to all patients with diabetes included in the federal register of diabetes mellitus (FRD), the number of prevented complications was 55.4 thousand cases for type 1 diabetes and 67.1 thousand cases for type 2 diabetes. The possible economic effect from the use of the device by all patients with a diagnosis of diabetes, which are included in the FRD, estimated at 1.5 billion rubles for a cohort of patients with type 1 diabetes and 5.3 billion rubles for patients with type 2 diabetes. Conclusion. Improving the effectiveness of self-monitoring, which is the result of the use of glucometers with color indicators, can potentially significantly reduce the incidence of complications in diabetes and thereby provide significant economic benefits to society.

Identifying and managing eating disorders in type 1 diabetes mellitus: An all-Wales study

2021 ◽  
pp. 135910452110095
Author(s):  
Jacinta O A Tan ◽  
Imogen Spector-Hill
Keyword(s):  
Diabetes Mellitus ◽  
Primary Care ◽  
Eating Disorders ◽  
Type 1 Diabetes ◽  
Eating Disorder ◽  
Type 1 Diabetes Mellitus ◽  
Healthcare Professionals ◽  
Health And Wellbeing ◽  
Level Of Knowledge

Background: Co-morbid diabetes and eating disorders have a particularly high mortality, significant in numbers and highly dangerous in terms of impact on health and wellbeing. However, not much is known about the level of awareness, knowledge and confidence amongst healthcare professionals regarding co-morbid Type 1 Diabetes Mellitus (T1DM) and eating disorders. Aim: To understand the level of knowledge and confidence amongst healthcare professionals in Wales regarding co-morbid T1DM and eating disorder presentations, identification and treatment. Results: We conducted a survey of 102 Welsh clinicians in primary care, diabetes services and eating disorder services. 60.8% expressed low confidence in identification of co-morbid T1DM and eating disorders. Respondents reported fewer cases seen than would be expected. There was poor understanding of co-morbid T1DM and eating disorders: 44.6% identified weight loss as a main symptom, 78.4% used no screening instruments, and 80.3% consulted no relevant guidance. The respondents expressed an awareness of their lack of knowledge and the majority expressed willingness to accept training and education. Conclusion: We suggest that priority must be given to education and training of all healthcare professionals in primary care, diabetes services and mental health services who may see patients with co-morbid T1DM and eating disorders.

Care of children with type 1 diabetes mellitus in school – An interventional study

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Umamaheswari Gurunathan ◽  
Hemchand Krishna Prasad ◽  
Sherline White ◽  
Bala Prasanna ◽  
Thangavelu Sangaralingam
Keyword(s):  
Type 1 Diabetes ◽  
Education Program ◽  
School Teachers ◽  
Post Intervention ◽  
Community Based ◽  
Structured Education ◽  
Type 1 Dm ◽  
The Impact ◽  
Determining Factor

AbstractObjectivesPaucity of data from India on care of children with Type 1 diabetes in schools. Aims: To study assess the knowledge, attitude, practices and fear of Type 1 diabetes in school teachers and to assess the impact of an educational model on the fear of teachers and care of children in Type 1 DM at school hours.MethodsA community based study, involving school teachers and the intervention being educating them about diabetes conducted. Data pertaining to basic demography, attitude of teachers towards diabetic children, Hypoglycemia fear factor survey- parent version with worries domain and preparedness of school was collected. An education program was conducted on diabetes care in children. Immediately and after three months, the proforma details and HFSP-W scores reassessed.ResultsForty two teachers (mean age: 38.7±5.4; M:F ratio 2:40) participated in the study. Post intervention, a higher willingness to have the diabetic child in class (100 vs. 57.1%; p>0.05), better support in daily care (100 vs. 92%; p>0.05), participation in sports activities (100 vs. 7.1%; p<0.05) observed. HFSP-W scores were 38.8±4.5 (pre-intervention), 22.5±4.3 (immediate post intervention) and 29.5±3.2 (at 3months) (p<0.05). To study the determinants of improvement in HFSP-W a regression analysis was performed: presence of glucometer the most likely determining factor (T=1.999, p=0.05).ConclusionThere is a significant element of fear in the minds of teachers towards hypoglycemia which improves with a structured education program.

scholarly journals Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an in silico Trial

2021 ◽  
pp. 193229682110123
Author(s):  
Chiara Roversi ◽  
Martina Vettoretti ◽  
Simone Del Favero ◽  
Andrea Facchinetti ◽  
Pratik Choudhary ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
Open Loop ◽  
Random Errors ◽  
Linear Regression Models ◽  
Reference Case ◽  
Counting Error ◽  
The Impact

Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

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