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Current exercise guidelines for individuals with type 1 diabetes (T1D) do not consider the impact that high altitude may have on blood glucose levels (BGL) during exercise.
The number of obese children with insulin resistance and type 2 diabetes is increasing, but the best management strategy is not clear.
The aim of this study was to investigate the relationship between a child's weight and a broad range of family and maternal factors.
To analyze the incidence of type 1 diabetes in 0- to 14-year olds in Western Australia, from 1985 to 2002, by region and socioeconomic status.
If the gut becomes damaged it may not be able to process the foods that we eat as well as it used to. This may also affect how we look after diabetes.
This study is looking for the causes of type 1 diabetes, so that we can find ways to prevent it. We will follow many women around Australia during pregnancy until early childhood, looking at the child's birth, environment and genes.
Diabetes is the name for a number of different metabolic disorders in which the body's healthy levels of blood sugar (glucose) can't be maintained.Diabetes can have a significant impact on quality of life should complications develop. Diabetes can affect the individual's entire body.
Automated insulin delivery (AID) improves glycemia in people with type 1 diabetes (T1D). However, concern remains about early worsening of diabetic retinopathy (EWDR) following rapid and large glycemic improvements. This study evaluated diabetic retinopathy (DR) outcomes in adolescents and young adults with T1D (aged 10-30 years) following AID initiation.
Digital interventions have emerged as promising tools to support mental well-being in diabetes. This review aimed to evaluate the effectiveness of digital health interventions in improving mental health outcomes among adults with diabetes, as well as assess the methodological quality of relevant studies and provide a commentary on research gaps and future directions.
To map and systematise existing research on the use of artificial intelligence (AI) in mental health-based diabetes care contexts, identify trends and potential gaps in the literature, examine methodological limitations and highlight future research directions.