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Tim Jones

Co-head, Diabetes and Obesity Research

Asha Parkinson

Research Assistant

Smooth sailing for Drina thanks to burden-breaking technology

Recent diabetes technology is helping 12-year-old Drina keep on top of her condition and be independent, while significantly easing the disease burden on her family.

Directing immune development to curb sky-rocketing disease

Once upon a time it was infectious diseases like polio, measles or tuberculosis that most worried parents. With these threats now largely under control, parents face a new challenge – sky-rocketing rates of non-infectious diseases such as asthma, allergies and autism.

What life is like living with type 1 diabetes

Every decision a child with type 1 diabetes makes can impact on their blood glucose levels.

The effect of oral insulin on subcutaneous insulin requirements and glycaemia in T1DM

Liz Tim Davis Jones MBBS FRACP PhD MBBS DCH FRACP MD Co-director of Children’s Diabetes Centre Co-head, Diabetes and Obesity Research Co-director of

Using continuous glucose monitoring to detect early dysglycaemia in children participating in the ENDIA study (Sub Protocol)

Aveni Liz Haynes Davis BA (Hons), MBBChir, MA (Cantab), PhD MBBS FRACP PhD Principal Research Fellow Co-director of Children’s Diabetes Centre

Temporal Eating Patterns and Ultra-Processed Food Consumption Assessed from Mobile Food Records of Australian Adults

Temporal eating patterns and ultra-processed food (UPF) consumption have independently been associated with obesity and non-communicable diseases. Little is known about the temporal patterns of UPF consumption, as data is challenging to collect. Temporal data can be extracted from mobile food records (mFRs). The aim of this study was to identify the temporal eating patterns of those consuming UPFs using an mFR. 

Evaluating Type 1 Diabetes Resources to Improve Awareness and Knowledge of Type 1 Diabetes Within Community Sport Settings

A main challenge identified by youth during exercise and sport is the lack of knowledge and awareness around type 1 diabetes (T1D) particularly in community sport settings. Working with youth living with T1D, parents and community sport coaches, our team has developed resources for the T1D and sporting community. This study was to evaluate the acceptability and usability of the resources.

Machine learning techniques to predict diabetic ketoacidosis and HbA1c above 7% among individuals with type 1 diabetes — A large multi-centre study in Australia and New Zealand

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 %.