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Background: The incidence rates of childhood onset type 1 diabetes are almost universally increasing across the globe but the aetiology of the disease...
The objective of this study was to determine whether real-time continuous glucose monitoring (CGM) with preset alarms at specific glucose levels would prove...
The objective of this study was to examine whether setting the low glucose alarm of a Guardian® REAL-Time continuous glucose monitoring system (CGMS) to 80 mg/d
Current continuous glucose monitoring (CGM) systems measure glucose levels in the interstitial fluid to estimate blood glucose concentration.
Aveni Liz Haynes Davis BA (Hons), MBBChir, MA (Cantab), PhD MBBS FRACP PhD Principal Research Fellow Co-director of Children’s Diabetes Centre
A community-led, trauma-informed psychosocial intervention to improve health outcomes of children and young people with Type-1 diabetes.
School time represents a significant component of overall glycaemia for children with type 1 diabetes (T1D), and glucose levels during instructional time may be important for optimising academic progress. There is, however, limited literature regarding glycaemia during school hours. This study aimed to evaluate glucose levels during school in primary school-aged children with T1D in Western Australia (WA) and to compare these with non-school days.
Adolescence is a period of rapid transformation when meeting targets for optimal diabetes care is often challenging due to competing life demands. For more than two decades a diabetes transition clinic in Sydney, Australia, has sustained positive outcomes and demonstrated aspects of resilience in the care of individuals living with type 1 diabetes (T1D) who have transitioned from paediatric to adult care. Many studies have focused on resilience in acute care setting showever, studies that examine the factors that support resilience in settings that care for individuals with long-term, chronic conditions such as T1D are lacking.
Type 1 diabetes and diabetic ketoacidosis (DKA) have a significant impact on individuals and society across a wide spectrum. Our objective was to utilize machine learning techniques to predict DKA and HbA1c>7 %.
Given limited data regarding the involvement of disadvantaged groups in paediatric diabetes clinical trials, this study aimed to evaluate the socioeconomic representativeness of participants recruited into a multinational clinical trial in relation to regional and national type 1 diabetes reference populations.