Cabaran Psikologi bagi Remaja yang Menghidap Diabetes Jenis 1

Managing a chronic disease is very challenging and requires effective coping strategies to overcome difficulties for maintaining the health and stability of quality of life. Type 1 diabetes is one of the most common chronic diseases in children and adolescents that requires complex treatment and care. Type 1 diabetes management aspects include insulin intake, checking blood glucose levels, monitoring risk and treatment of disease complications, dietary intake, and physical activity. In addition, the lack of understanding of diabetes among family members and friends also makes them difficult to adapt to the disease and can cause them to experience psychological problems and stress. Hence, this paper aims to identify the psychological challenges faced by children and adolescents with type 1 diabetes. The study is conducted by review the literature on 9 published articles from 2010 to 2017 obtained from the Science Direct, SAGE, PubMed and NIH Public Access. The findings have identified several aspects of psychology often experienced by children and adolescents with type 1 diabetes such as depression, anxiety, stress, and distress. This psychological condition can have a negative effect on life skills, glucose control and ability to deal with diabetes management. It is hoped that proper management in encountering psychological challenges will lead to better results.

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A Review of Type 1 Diabetes (T1D)

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch002 ◽

2020 ◽

pp. 13-49

Author(s):

Eileen O'Donnell ◽

Liam O'Donnell

Keyword(s):

Type 1 Diabetes ◽

Blood Glucose ◽

Glucose Levels ◽

Blood Glucose Levels ◽

Acting Insulin ◽

Short Period ◽

Sporting Activities ◽

To Come

The diagnosis of Type 1 Diabetes (T1D) will come as an unwelcome surprise to most people. Within a short period of time, the person will have to come to understand and manage this chronic illness. The terminology associated with the T1D condition will also be totally new to the person: diabetes mellitus, pancreas, hyperglycaemia (hyper), hypoglycaemia (hypo), bolus (fast acting insulin), basal (slow acting insulin), ketones and blood glucose levels. The purpose of this article is to assist newly diagnosed patients' understanding of T1D, people who are already living with T1D, carers of people with T1D, partners and family members of someone with T1D, work colleagues, and friends who participate in the same sporting activities or go on holiday with a person who has T1D. In addition, this article reviews how people living with T1D can still enjoy exercise and maintain the best quality of life possible; whilst controlling the blood glucose levels in their body for the rest of their lives to prevent the onset of complications associated with diabetes.

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A Review of Type 1 Diabetes (T1D)

International Journal of User-Driven Healthcare ◽

10.4018/ijudh.2018070102 ◽

2018 ◽

Vol 8 (2) ◽

pp. 14-45

Author(s):

Eileen O'Donnell ◽

Liam O'Donnell

Keyword(s):

Type 1 Diabetes ◽

Blood Glucose ◽

Glucose Levels ◽

Blood Glucose Levels ◽

Acting Insulin ◽

Short Period ◽

Sporting Activities ◽

To Come

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Carbohydrate counting with a bolus calculator improves post-prandial blood glucose levels in children and adolescents with type 1 diabetes using insulin pumps

Pediatric Diabetes ◽

10.1111/j.1399-5448.2012.00883.x ◽

2012 ◽

Vol 13 (7) ◽

pp. 545-551 ◽

Cited By ~ 49

Author(s):

Rebecka Enander ◽

Christer Gundevall ◽

Agneta Strömgren ◽

John Chaplin ◽

Ragnar Hanas

Keyword(s):

Type 1 Diabetes ◽

Blood Glucose ◽

Children And Adolescents ◽

Insulin Pumps ◽

Carbohydrate Counting ◽

Glucose Levels ◽

Blood Glucose Levels

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Physical Activity, Dietary Patterns, and Glycemic Management in Active Individuals with Type 1 Diabetes: An Online Survey

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18179332 ◽

2021 ◽

Vol 18 (17) ◽

pp. 9332

Author(s):

Sheri R. Colberg ◽

Jihan Kannane ◽

Norou Diawara

Keyword(s):

Physical Activity ◽

Type 1 Diabetes ◽

Online Survey ◽

Diabetes Management ◽

Physically Active ◽

Glucose Levels ◽

Blood Glucose Levels ◽

And Performance ◽

Glycemic Management

Individuals with type 1 diabetes (T1D) are able to balance their blood glucose levels while engaging in a wide variety of physical activities and sports. However, insulin use forces them to contend with many daily training and performance challenges involved with fine-tuning medication dosing, physical activity levels, and dietary patterns to optimize their participation and performance. The aim of this study was to ascertain which variables related to the diabetes management of physically active individuals with T1D have the greatest impact on overall blood glucose levels (reported as A1C) in a real-world setting. A total of 220 individuals with T1D completed an online survey to self-report information about their glycemic management, physical activity patterns, carbohydrate and dietary intake, use of diabetes technologies, and other variables that impact diabetes management and health. In analyzing many variables affecting glycemic management, the primary significant finding was that A1C values in lower, recommended ranges (<7%) were significantly predicted by a very-low carbohydrate intake dietary pattern, whereas the use of continuous glucose monitoring (CGM) devices had the greatest predictive ability when A1C was above recommended (≥7%). Various aspects of physical activity participation (including type, weekly time, frequency, and intensity) were not significantly associated with A1C for participants in this survey. In conclusion, when individuals with T1D are already physically active, dietary changes and more frequent monitoring of glucose may be most capable of further enhancing glycemic management.

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The Development of an Exercise Advisor App for Type 1 Diabetes: Digitization Facilitates More Individualized Guidance

Journal of Diabetes Science and Technology ◽

10.1177/1932296820979811 ◽

2020 ◽

pp. 193229682097981

Author(s):

Sarah M. McGaugh ◽

Stephanie Edwards ◽

Howard Wolpert ◽

Dessi P. Zaharieva ◽

Nany Gulati ◽

...

Keyword(s):

Type 1 Diabetes ◽

Diabetes Management ◽

Management Strategies ◽

Target Range ◽

Feeding Strategies ◽

Insulin Regimen ◽

Glucose Levels ◽

Blood Glucose Levels ◽

Carbohydrate Feeding

Maintaining blood glucose levels in the target range during exercise can be onerous for people with type 1 diabetes (T1D). Using evidence-based research and consensus guidelines, we developed an exercise advisor app to reduce some of the burden associated with diabetes management during exercise. The app will guide the user on carbohydrate feeding strategies and insulin management strategies before, during, and after exercise and provide targeted and individualized recommendations. As a basis for the recommendations, the decision trees for the app use various factors including the type of insulin regimen, time of activity, previous insulin boluses, and current glucose level. The app is designed to meet the various needs of people with T1D for different activities to promote safe exercise practices.

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LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management

Sensors ◽

10.3390/s21093303 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3303

Author(s):

Jeremy Beauchamp ◽

Razvan Bunescu ◽

Cindy Marling ◽

Zhongen Li ◽

Chang Liu

Keyword(s):

Type 1 Diabetes ◽

Blood Glucose ◽

Diabetes Management ◽

Artificial Pancreas ◽

Real Patient ◽

Glucose Levels ◽

Blood Glucose Levels ◽

Novel Approach ◽

The 1960S

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.

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